THE INFLUENCE OF EARLY PARENT INVOLVEMENT AND DEPRESSION ON
ACADEMIC ACHIEVEMENT, PSYCHOSOCIAL BEHAVIORS, AND MOTIVATION IN
CHILDREN WITH LEARNING DISABILITIES ACROSS ELEMENTARY SCHOOL
By
Seema Mahdavi

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
School Psychology – Doctor of Philosophy
2017

ABSTRACT
THE INFLUENCE OF EARLY PARENT INVOLVEMENT AND DEPRESSION ON
ACADEMIC ACHIEVEMENT, PSYCHOSOCIAL BEHAVIORS, AND MOTIVATION IN
CHILDREN WITH LEARNING DISABILITIES ACROSS ELEMENTARY SCHOOL
By
Seema Mahdavi
Children with learning disabilities represent the largest category of students served within
special education systems in schools, and are at increased risk for academic and psychosocial
problems in comparison to peers without learning disabilities. While much of clinical practice
and research focus has been on academic interventions, understanding other risk and protective
factors that hinder or promote academic and psychosocial development in children with and
without learning disabilities is critical to inform other points of intervention. More specifically,
parenting behavior in early childhood including, parent depression and parent school
involvement, may hinder or promote outcomes for children with learning disabilities. The
purpose of this study was to examine whether parent depression, a risk factor, and parent school
involvement, a protective factor, predicted academic and psychosocial outcomes in children with
subtypes of learning disabilities (i.e., reading disability, math disability, co-morbid reading and
math disability) differentially as compared to children without learning disabilities at two time
points in elementary school, kindergarten and fifth grade. Primary analyses used multi-sample
Structural Equation Modeling (SEM) to evaluate the effects of parent depression and parent
school involvement on math and reading academic achievement and psychosocial behavior at
kindergarten, as well as on psychosocial behavior in fifth grade. Additionally, the long-term
effects of academic achievement and psychosocial behavior in kindergarten on these outcomes in
fifth grade were also examined. Data was drawn from the Early Childhood Longitudinal Study –

Kindergarten Cohort of 1998-1999 (ECLS-K) kindergarten and fifth grade data collection waves.
The final study sample consisted of approximately 10,630 children. Results indicated that parent
depression did not significantly predict parent school involvement, however direct effects of
parent depression on kindergarten reading achievement and direct effects of parent school
involvement on kindergarten outcomes were noted for children with and without learning
disabilities. Additionally, complex relations between kindergarten and fifth grade academic and
psychosocial outcomes arose. Finally, effects of gender, age, race, and socio-economic status
were examined and significantly influenced both kindergarten and fifth grade outcomes.
Findings point to the importance of promoting parent school involvement as a means to improve
outcomes for children with and without learning disabilities, as well as the importance of
promoting early academic achievement and psychosocial development.

Copyright by
SEEMA MAHDAVI
2017

ACKNOWLEDGEMENTS

Graduate school, and the dissertation project specifically, is a long and at times, arduous
journey. My graduate school journey would not have been possible without the resources and
supports from several entities. I would like to acknowledge the National Center for Education
Statistics for granting me access to a fantastically rich dataset and the many researchers and
participants whom contributed so significantly to educational and psychological science in their
various roles that culminated in the Early Childhood Longitudinal Study – Kindergarten Cohort
of 1998-1999. I would also like to thank the Michigan State University School Psychology
Program for fostering a diverse and bright academic environment.
First, I am forever grateful to Dr. Jodene G. Fine, my dissertation chair, academic
advisor, and wise mentor. Her years of guidance, support, and patience in all academic matters
have helped shape, deepen, and refine my research and clinical skills. She has left a permanent
impression on my being, pushing me to think more critically and modeling a type of caring and
warmth that cannot be adequately expressed in words. I am lucky to have her in my circle.
Second, I would like to thank the members of my dissertation committee, Drs. Ryan
Bowles, Carol Sue Englert, and Evelyn Oka, for their interest, constructive feedback, and time.
Dr. Bowles’s generosity in sharing his statistical expertise and his readiness to impart his
knowledge about early child development was instrumental to completing this project. I am
appreciative of Dr. Englert’s willingness to share her thoughtful analysis and interpretation
through the eyes of a special education and literacy expert. I am thankful for Dr. Oka’s keen
ability to guide my understanding of complex psychosocial behaviors and mold my discussion of
equally complex results.

v

Third, to the friends and comrades who supported me through this journey, thank you! To
my cohort at Michigan State University and Nationwide Children’s Hospital, especially Kara
Constantine, Samanta Boddapati De Anda, Taylor Hicks-Hoste, and Mohammed Palejwala,
thank you for providing a listening ear, endless cheerleading, thoughtful guidance, and comedic
relief. To Hannah Sahin and Michelle Baker, thank you for being a part of another milestone in
my life and for your constant support and love.
Finally, this dissertation is dedicated to my parents, Anu and Ali, who have made my
successes possible and who have supported me through all life’s challenges. Their tireless
sacrifices, boundless encouragement, and unwavering love have allowed me to pursue my
professional passions. My graduate degree is as much theirs as it is mine. To Shireen, your
optimism, love, and silliness lifted me so many times throughout graduate school. There is no
better sister than you. To Lee Ann, Roger, Hannah, Ivy, and Michael, thank you for always
sharing your enthusiasm for my work.
And Thomas, love of my life, without you I would not have reached this point with an
ounce of sanity. Your devotion, patience, support, and humor have helped me tackle the hills and
valleys of graduate school.

vi

TABLE OF CONTENTS

LIST OF TABLES...................................................................................................................... x
LIST OF FIGURES..................................................................................................................... xii
CHAPTER 1................................................................................................................................ 1
INTRODUCTION....................................................................................................................... 1
CHAPTER 2............................................................................................................................... 4
LITERATURE REVIEW...................... .................................................................................... 4
Learning Disabilities and Academic Achievement..................................................................... 4
Definitions of Learning Disability.............................................................................................. 6
Mathematics Disability....................................................................................... 9
Mathematics disability subtypes......................................................................... 15
Summary............................................................................................................. 16
Reading Disability.............................................................................................. 17
Co-morbid Mathematics and Reading Disability............................................... 22
Psychosocial Development in Children with Learning Disabilities........................................... 23
Externalizing Problems....................................................................................... 26
Internalizing Problems........................................................................................ 29
Interpersonal Problems....................................................................................... 33
Motivational Factors in Children with Learning Disabilities..................................................... 35
Self-Regulation................................................................................................... 35
Ability Beliefs..................................................................................................... 37
Intrinsic Motivation............................................................................................ 39
Summary............................................................................................................. 40
Parent Influences on Psychosocial Development, Motivation, and Academic Achievement.... 41
Parental Depression............................................................................................ 41
Parent School Involvement................................................................................. 44
Purpose of Present Study............................................................................................................ 48
Research Questions and Hypotheses.......................................................................................... 50
CHAPTER 3...............................................................................................................................
METHOD...................................................................................................................................
Study Model................................................................................................................................
Study Design...............................................................................................................................
Data Collection.................................................................................................
ECLS-K Original Sample...........................................................................................................
Missing Data.....................................................................................................
Final Sample for Current Study..................................................................................................
Exclusion Criteria...............................................................................................
Learning Disabled Sample.................................................................................
Sampling Weights..............................................................................................

vii

54
54
54
57
57
61
62
71
72
73
75

Variables and Measures.............................................................................................................. 80
Parent Depression................................................................................................ 81
Early Parent School Involvement....................................................................... 83
Academic Achievement...................................................................................... 85
Early academic achievement............................................................................... 85
Later academic achievement............................................................................... 86
Psychosocial Behaviors....................................................................................... 88
Kindergarten psychosocial behavior................................................................... 89
Fifth grade psychosocial behavior...................................................................... 91
Motivation........................................................................................................... 93
Covariates............................................................................................................ 95
Age at assessment................................................................................................ 95
Gender................................................................................................................. 96
Race-ethnicity..................................................................................................... 96
Socioeconomic status.......................................................................................... 97
Data Analyses............................................................................................................................. 98
Preliminary Analyses.......................................................................................... 99
Structural Equation Analyses.............................................................................. 100
Model Fit............................................................................................................. 104
CHAPTER 4............................................................................................................................... 122
RESULTS................................................................................................................................... 122
Preliminary Analyses.................................................................................................................. 122
Differences between LD and non-LD Groups.................................................... 122
Differences between Categories of LD............................................................... 124
Parent depression group differences................................................................... 125
Parent school involvement group differences..................................................... 125
Academic achievement group differences.......................................................... 126
Psychosocial behavior group differences............................................................ 127
Motivation group differences.............................................................................. 128
Analyzing the Structural Model.................................................................................................. 141
Establishing Factors by Testing the Measurement Model.................................. 141
Measurement Invariance.................................................................................... 142
Testing the Structural Latent Model................................................................... 142
Research Question One: Influence of Parent Depression and Parent Involvement on Outcomes
for All Children........................................................................................................................... 147
Mediation Analysis............................................................................................. 147
Research question 1a.......................................................................................... 148
Research question 1c........................................................................................... 149
Research question 1d........................................................................................... 150
Influence of covariates........................................................................................ 150
Moderation Analysis........................................................................................... 156
Research question 1b........................................................................................... 156
Research Question Two: Comparison Between Children with LD and Children without LD... 157
Testing the Model Separately for LD and non-LD Samples............................... 158
Multi-group model............................................................................................... 158

viii

Procedural steps.................................................................................................. 158
Research question 1a for multi-group sample.................................................... 159
Research question 1c for multi-group sample..................................................... 159
Research question 1d for multi-group sample..................................................... 160
Influence of covariates......................................................................................... 161
Multi-group Moderation Analysis....................................................................... 164
Research Question Three: Group Comparison Between Subtypes of LD................................... 165
Testing the Mediation Model Separately for LD Subtype Samples.................... 165
Testing the Moderation Model Separately for LD Subtype Samples.................. 166
CHAPTER 5................................................................................................................................ 167
DISCUSSION.............................................................................................................................. 167
Parent Depression and School Involvement as Predictors of Kindergarten Outcomes............... 170
Kindergarten Achievement and Psychosocial Behavior as Predictors of Fifth Grade
Outcomes..................................................................................................................................... 177
Kindergarten Academic Achievement................................................................. 177
Kindergarten Psychosocial Behavior.................................................................. 181
Fifth Grade Psychosocial Behavior and Motivation as Predictors of Academic Achievement . 184
Conclusions and Clinical Implications........................................................................................ 187
Limitations and Future Research................................................................................................. 188
APPENDIX.................................................................................................................................. 190
REFERENCES............................................................................................................................ 192

ix

LIST OF TABLES

Table 1

Overview of Original ECLS-K Data Collection Sample ……………...............

Table 2

Comparison of Demographic Information Between Included and Excluded
Samples……………………………………………………................................ 65

Table 3

Comparison of Covariates and Predictor Variables for Included and Excluded
Samples………………………………………………........................................ 66

Table 4

Comparison of Covariates and Predictor Variables for Included and Excluded
Samples (Ordinal)………………………………………...................................

68

Table 5

Overview of Data Collection for Final Sample……………………...................

77

Table 6

Demographic Characteristics of Final Sample (n=10,630).................................

78

Table 7

Summary of ECLS-K Variables Corresponding to Constructs and
Covariates…………………………………………………………....................

80

Confirmatory Factor Analysis for a One-Factor Model of Parent
Depression……………………………………………………….......................

83

Confirmatory Factor Analysis for a One-Factor Model of Parent School
Involvement……………………………………………………………............

85

Confirmatory Factor Analysis for a One-Factor Model of Kindergarten
Psychosocial Behavior………………………………………………................

93

Table 8

Table 9

Table 10

60

Table 11

Confirmatory Factor Analysis for a One-Factor Model of Fifth Grade
Psychosocial Behavior………………………………………………................. 93

Table 12

Summary of Research Questions, Variables, and Analyses……………............

103

Table 13

Descriptive Statistics for Full Sample………………………………….............

105

Table 14

Unweighted Descriptive Statistics between LD and Non-LD Samples..............

107

Table 15

Unweighted Descriptive Statistics between LD and Non-LD Samples
(Categorical Variables)…………………………………………………............ 109

Table 16

Weighted Descriptive Statistics between LD and Non-LD Samples…..............

Table 17

Weighted Descriptive Statistics between LD and Non-LD Samples

x

112

(Categorical Variables)………………………………………………................ 114
Table 18

Correlation Matrix for LD (upper) and Non-LD (lower) Sample Part 1……..... 117

Table 19

Correlation Matrix for LD (upper) and Non-LD (lower) Sample Part 2………. 119

Table 20

Unweighted Subpopulation Descriptive Statistics………………………..........

Table 21

Unweighted Subpopulation Descriptive Statistics (Categorical
Variables)……………………………………………………………................. 131

Table 22

Weighted Subpopulation Descriptive Statistics…………………………..........

Table 23

Weighted Subpopulation Descriptive Statistics (Categorical
Variables)……………………………………………………………................. 137

Table 24

Final Confirmatory Factor Analysis for a Four-Factor Model…………............

Table 25

Parameter Estimates for a Four-Factor Structural Regression Model
(Mediation Model)............................................................................................... 154

Table 26

Parameter Estimates for a Four-Factor Structural Regression Multi-group
Model Across Children with and without Learning Disability….......................

xi

129

135

152

162

LIST OF FIGURES

Figure 1.

Simplified Conceptual Model…………………………………………............... 53

Figure 2.

Detailed Conceptual Model (Reading)………………………………….............

55

Figure 3.

Detailed Conceptual Model (Math)……………………………………..............

56

Figure 4.

Modified Structural Model (Mediation Paths in red)……………………...........

145

Figure 5.

Modified Structural Model (Moderation Paths in green)……………………....

146

xii

CHAPTER 1
INTRODUCTION
Currently, students with a learning disability (LD) represent the largest category of
disability in schools (Cortiella & Horowitz, 2014). By definition, children with LD experience
more academic difficulties than their typically developing peers due to underlying neurological
and psychological deficits that interfere with learning (APA, 2013; IDEA, 2004). Research also
suggests that children with LD are at increased risk of experiencing social-emotional and
behavioral problems (Willcutt et al., 2013), and diminished motivation in school (Bender &
Wall, 1994; Graham & Harris, 2003; Grolnick & Ryan, 1990). Moreover, children with LD are a
heterogeneous group. Research indicates that children with various subtypes of LD experience
different academic and psychosocial problems, and children with multiple LD’s experience
worse prognoses compared to children with one LD or no LD (Martinez & Semrud-Clikeman,
2004; Willcutt et al., 2013). Studies of long-term outcomes indicate that children with LD are
more likely to drop out of grade school, have lower levels of post-secondary education, remain
unemployed or receive fewer promotions, have low self-esteem, and have increased likelihood of
mental health problems, behavioral problems, and juvenile delinquency (Boetsch, Green, &
Pennington, 1996; Daniel et al., 2006; Goldston et al., 2007; McGee, Prior, Williams, Smart, &
Sanson, 2002; Willcutt et al., 2013).
Despite the multiple points of intervention, much of the extant research and practical
efforts for children with LD have been focused on academic interventions in schools to target the
primary academic problem. More recently, with the passage of special education legislation that
reflects the need for earlier intervention and inclusion of parents in their children’s educational
planning, educators and researchers have begun to pay more attention to early preventative

1

efforts that may mitigate the long-term academic problems for children with LD, including
consideration of other psychological, motivational, and social-emotional factors. In order to
develop interventions designed to target academic and non-academic factors, several areas must
be investigated further. First, gaining an understanding of how academic and psychosocial
factors develop and influence one another in children with LD over the course of time is
important. It has been established that early academic performance is related to later academic
performance, and that early psychosocial problems are related to later psychosocial problems
(Morgan, Farkas, Tufis, & Sperling, 2008; Morgan, Farkas, & Wu, 2012). However, evidence
regarding how academic achievement and psychosocial behaviors influence one another is
mixed; some studies indicate that early psychosocial problems do not influence later academic
achievement (Morgan, Farkas, Tufis, & Sperling, 2008), whereas others do (Hinshaw, 1992;
Lepola, Poskiparta, Laakkonen, & Niemi, 2005; Trzesniewski, Moffitt, Caspi, Taylor, &
Maughan, 2006). Finally, there is evidence that children experiencing academic problems
demonstrate lower motivation to learn (Tabassam & Grainger, 2002). However, much of the
research examining these relations in children with LD is confounded by methodological factors
including various definitions of LD, no differentiation of subgroups of LD, small sample sizes,
and lack of control groups. Thus, relatively little is known about whether these relationships hold
for children with LD compared to typical populations, and more specifically for children with
various subtypes of LD.
Second, understanding the early risk and protective factors that exist in relation to
developing an LD and associated problems, and how those risk and protective factors influence
one another may lead to more targeted preventative efforts. Various risk factors for developing
an LD have been researched including, genetic predisposition, prenatal and postnatal biological

2

insults, and environmental factors such as poverty (Aarnoudse-Moens et al., 2009; Bhate &
Wilkinson, 2006; Cortiella & Horowitz, 2014; Gaddes, 2013; Piper et al., 2012). Additional risk
factors such as parent depression can exacerbate academic and social-emotional problems
(National Research Council and Institute of Medicine, 2009). On the other hand, protective
factors such as increased parent involvement have been related to more positive academic and
behavioral outcomes, social-emotional well-being, and motivation to learn (Christenson, 2000;
Epstein, 1994; Gonzalez-DeHass, Willems, & Holbein, 2005; McWayne et al., 2004; Pianta,
Rimm-Kaufman, & Cox, 1999).
Overall, the purpose of this study is to examine whether parent depression, a risk factor,
and parent involvement, a protective factor, predict academic, psychosocial, and motivational
outcomes in children with subtypes of LD compared to children without LD over the course of
elementary school. The current study will test a hypothesized latent variable model to examine
the relations between parent depression, parent involvement, early academic and psychosocial
outcomes, and later academic, psychosocial, and motivation outcomes across elementary school
in a sample of children with reading disability, math disability, reading-math disability, and
without LD. By determining the magnitude by which parent variables influence child outcomes,
conclusions about whether parent depression and parent involvement serve as risk and protective
factors differentially for children with and without LD may be drawn. This study takes advantage
of a large, nationally representative dataset in order to address methodological problems in other
studies including small sample sizes and lack of control groups. This model also accounts for
various environmental factors including, socioeconomic status and race-ethnicity that are
associated with LD and various academic and psychosocial outcomes (Cortiella & Horowitz,
2014; Coutinho, Oswald, & Best, 2002; O’Connor & Fernandez, 2006).

3

CHAPTER 2
LITERATURE REVIEW
Learning Disabilities and Academic Achievement
Learning disability (LD) is an umbrella term used to describe a range of neurologicallybased disorders that affect a person’s ability to process, think about, and perform verbal and/or
non-verbal tasks. Over the past several decades, researchers have identified specific
psychological and neurological processes that characterize specific types of LD (e.g., dyslexia,
dyscalculia). Additionally, researchers have also examined the genetic and environmental factors
that influence the development of learning disabilities, including prenatal and postnatal risk
factors. For example, maternal illness, malnutrition, and exposure to teratogens in utero, low
birth weight, premature birth, postnatal traumatic injuries, exposure to environmental toxins, and
nutritional or environmental deprivation are risk-factors for developing an LD (AarnoudseMoens et al., 2009; Bhate & Wilkinson, 2006; Cortiella & Horowitz, 2014; Gaddes, 2013; Piper
et al., 2012). Environmental factors, including poverty, have also been shown to affect the
likelihood of developing an LD. For example, children from low-income families are overidentified with LD’s in schools (Cortiella & Horowitz, 2014). The effects of poverty (e.g.,
malnutrition, lower levels of early academic enrichment, greater likelihood of exposure to toxins
such as lead) may account for the fact that there is a higher incidence of LD’s for children living
in poverty (Cortiella & Horowitz, 2014). Similarly, children from African-American and
Hispanic backgrounds tend to be diagnosed with LD’s at a disproportionate rate. Research
suggests that differences in diagnostic practices, and racial biases or stereotypes may play a part
in over-identification of minority children with an LD (Harris-Murri, King, & Rostenberg, 2006).

4

One way to conceptualize how genetic and environmental factors influence development
of an LD and outcomes for children with LD is through a risk-resiliency model. Zimmerman and
Arunkumar (1994) described a risk-resiliency model that posits that positive child outcomes can
occur despite risk factors through a process by which the interaction between protective factors
and risk factors alters the influence of risk factors and decreases the likelihood of negative
outcomes. Following this model, if a child has multiple genetic and environmental risk factors
for developing an LD (e.g., familial history of LD, cognitive deficits), protective factors
including parent involvement, access to education, and positive parent mental health, may
improve the prognosis of the child who does develop an LD. Understanding the nature of LD, as
well as the risk and protective factors that may influence the development of an LD and
associated academic and psychosocial problems is imperative to designing assessment and
intervention practices that target more than just the symptoms of the LD (e.g., academic
interventions).
In general, LD’s are persistent and permanent (Cortiella & Horowitz, 2014). According
to the National Center for Learning Disabilities (Cortiella & Horowitz, 2014), students with a
learning disability account for approximately 42% of all children with disabilities served by the
special education system in public schools across the country (Cortiella & Horowitz, 2014). It is
estimated that an additional 15% of students struggle academically due to unidentified learning
disabilities or lack of intervention (Cortiella & Horowitz, 2014). Thus, children with learning
disabilities represent the largest group of students with a disability in schools. Children with
LD’s are at-risk for poorer prognosis in life including, low academic achievement, higher rates of
high school drop-out, lower level of higher education attainment and future employment,
increased behavioral problems and juvenile delinquency, poorer social skills and self-esteem,

5

and increased likelihood of mental health problems including depression and anxiety (Boetsch,
Green, & Pennington, 1996; Daniel et al., 2006; Goldston et al., 2007; McGee, Prior, Williams,
Smart, & Sanson, 2002; Reschly & Christenson, 2006; Robinson & Oppenheim, 1998; Willcutt
et al., 2013). The following sections will provide an overview of how LD’s are defined and
diagnosed, the prominent features associated with LD, and the extant research on academic
outcomes for children with LD.
Definitions of Learning Disability
There are two widely accepted definitions of and guides to diagnosing an LD. First, the
Individuals with Disabilities Education Act (IDEA, 2004) states that a specific learning disability
(SLD), another term for LD, is
“a disorder in one or more of the basic psychological processes involved in
understanding or in using language, spoken or written, that may manifest itself in
the imperfect ability to listen, think, speak, read, write, spell, or do mathematical
calculations, including conditions such as perceptual disabilities, brain injury,
minimal brain dysfunction, dyslexia, and developmental aphasia. Such term does
not include a learning problem that is primarily the result of visual, hearing, or
motor disabilities, of mental retardation, of emotional disturbance, or of
environmental, cultural, or economic disadvantage (pg. 118).”
According to IDEA, there are eight categories of SLD: oral expression, listening
comprehension, written expression, basic reading skills, reading fluency skills, reading
comprehension, mathematics calculation, and mathematics problem solving. IDEA stipulates two
main ways to diagnose an SLD. The first method is based on a pattern of strengths and
weaknesses in the student’s academic performance and/or intellectual ability in relation to their

6

age, intellectual development, or state age- or grade-level standards, and consideration of quality
of instruction received. If a student exhibits significant weaknesses in one of the eight areas and
strengths in other areas, they may be diagnosed with an LD. The second method is based on the
student’s progress towards state age- or grade-level standards after receiving evidence-based
interventions (EBI), typically through a process known as Response-to-Intervention (RtI). RtI
involves a systematic process of assessing the student’s deficits, applying EBI’s, and monitoring
the student’s progress or growth following intervention. If a student does not make adequate
progress after a certain amount of time receiving intervention, the student may be diagnosed with
an LD.
The second definition of LD is from the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5; APA, 2013). The DSM-5 defines a specific learning disorder, another term
for LD, as a
“persistent difficulty in reading, writing, arithmetic, or mathematical reasoning
skills during formal years of schooling. Symptoms may include inaccurate or slow
and effortful reading, poor written expression that lacks clarity, difficulties
remembering number facts, or inaccurate mathematical reasoning. Current
academic skills must be well below the average range of scores in culturally and
linguistically appropriate tests of reading, writing, or mathematics. The
individual’s difficulties must not be better explained by developmental,
neurological, sensory (vision or hearing), or motor disorders and must
significantly interfere with academic achievement, occupational performance, or
activities of daily living (APA, 2013b, pg. 1).”

7

According to the DSM-5, a diagnosis should be based on a review of a person’s
developmental, medical, educational, and familial history, previous academic reports,
observations from teachers, and a student’s progress after academic interventions have been
conducted.
Despite the definitions and guidelines provided by the IDEA and DSM-5, there is still
considerable variability in diagnosis of LD in both research and practice. Individual school
districts and practitioners may set more specific criteria to determine whether a student is making
“adequate” progress or “demonstrates a pattern of strengths and weaknesses.” For example, a
school might implement an intervention for 6 weeks, whereas another school may implement an
intervention for 8 weeks before determining whether a student’s progress (or lack thereof) is
indicative of an LD. Likewise, a school may stipulate that a student must show at least two or
three areas of strength in comparison to a weakness. In research, studies may use more liberal
(e.g., performing at or below the 30th percentile) or more conservative (e.g., performing at or
below the 10th percentile) criteria to determine whether a student demonstrates an LD (Murphy,
Mazzocco, & Hanich, 2007).
Another source of variability stems from the changing definitions of LD. Currently, the
DSM-5 and IDEA models of identification both highlight the importance of considering
student’s academic growth based on the application of EBI’s; however this is a fairly new
consideration. Previous models of LD identification recommended diagnosis of LD based on a
severe discrepancy between a student’s achievement scores and their intellectual ability (i.e., IQ
score). Research has demonstrated that a severe discrepancy model may be biased in
identification of students with LD (Fletcher, Coulter, Reschly, & Vaughn, 2004). Thus, previous
research may inaccurately identify students with an LD.

8

Finally, previous research has often studied children with LD as a homogeneous group,
irrespective of the specific types of academic struggles that they may have (e.g., reading, math).
Rourke and Fuerst (1992) noted that there is “an almost lack of sensitivity to the notion that there
may be subtypes of children with LD for whom various kinds of academic and social learning
may be more or less difficult to achieve.” (p. 362). Thus, greater specificity and attention to LD
subtypes is warranted in both clinical and research efforts aimed at understanding the risk and
protective factors that influence important outcomes and providing the most effective and
appropriate intervention. More recent research has begun to explore the differences and
similarities among subtypes of LD, although more work is needed in this area. The remainder of
this section will focus on math disability and reading disability, two of the most common
subtypes of learning disability.
Mathematics Disability. Historically, research on academic achievement has centered
on reading disabilities; however the number of children who show deficits in math is reportedly
equal to, if not greater, than those with reading problems (Mazzocco, 2001). Mathematics
learning disability (MD) refers to a number of neuropsychological deficits that interfere with
learning of concepts and/or procedures in one or more domains of math, such as algebra or
geometry (Geary, 2004; Geary, Hoard, Nugent, & Bailey, 2012; Mazzocco, 2007). Estimates of
the prevalence of MD vary; some report between 5%-8% of children have an MD, which is
comparable to the number of children with reading disabilities (Geary, Hoard, Nugent, & Bailey,
2012; Judge & Watson, 2011). Berch and Mazzocco (2007) report that up to 10% of children
have persistent math difficulties, indicating that there may be more children who are low
achieving in math or not identified with an LD. As with reading disabilities, research indicates
both genetic and environmental factors contribute to the development of MD (Light & DeFries,

9

1995; Shalev & Gross-Tsur, 2001). In one study, parents and siblings of children with
arithmetic-based subtypes of MD were ten times more likely to be diagnosed with MD compared
to the general population (Shalev & Gross-Tsur, 2001).
A major barrier to understanding the true prevalence of MD is the lack of consensus on
the definition of MD across research studies and clinical practice, as well as few specific
screening or diagnostic protocols to evaluate a student for a MD (Geary, 2004; Gersten, Jordan,
& Flojo, 2005). According to some researchers, children who perform at or below the 35 th
percentile on a standardized assessment of math are characterized as having math difficulties
(Clements & Sarama, 2009). Researchers argue that this more liberal definition allows for
identification of children who are performing in the low average range and may be at greater risk
for developing more serious math difficulties or MD in the future to receive earlier intervention
(Gersten, Jordan, & Flojo, 2005). However, liberal criterion may conflate research on students
with MD with low achievement in general. Other researchers have used a cut-point of
performance below the 25th percentile (1st quartile) on a standardized assessment (Geary,
Hamson, & Hoard, 2000; Gross-Tsur, Manor, & Shalev, 1996; Morgan, Farkas, Hillemeier, &
Maczuga, 2014; Murphy et al, 2007). However, Geary (2004) points out that a cut-point at the
25th percentile (or higher) does not align with the estimation of 5%-8% of students having a true
MD. The most conservative criterion of performance that has been used by researchers to study
children with MD is performance at or below the 10th percentile (Mazzocco & Myers, 2003).
Overall, the standardized achievement tests used to gauge performance capture a wide range of
mathematical procedures and concepts, whereas children with MD often have more severe
deficits in some areas and not others (Geary, 2004). Thus, scores on standardized tests may over
or underestimate the student’s competencies in specific procedures and domains of mathematics.

10

Researchers indicate that true MD is generally persistent, and reliance on one test score at one
point in time does not indicate a MD in and of itself. Consideration of academic history and
response to evidence-based interventions is an important component of diagnosis (Geary, 2004).
For example, some research indicates that children who do not respond to intensive intervention
in fact retrieval may have more complex cognitive and memory deficits, and the persistence of
retrieval problems despite intervention is a strong indicator of MD (Howell, Sidorenko, & Jurica,
1987). Overall, when examining the research on MD, it is important to consider how the
disability is defined. Throughout this literature review, children with MD will refer to children
who demonstrate low achievement (e.g., below 35th percentile) to very low achievement (e.g.,
below the 10th percentile) in mathematics.
Children with mathematics learning disability do not follow a typical developmental
trajectory when learning math. A typical developmental pattern for most children begins with
numerical knowledge and builds into conceptual knowledge of mathematics, and finally to
computational mechanisms involved in solving more complex problems. In infancy, children
begin to develop an understanding of numbers and quantity. Investigation of infant ability using
dishabituation paradigms indicates that infants discriminate small quantities when presented with
arrays of dots or objects (Starkey & Cooper, 1980). Mathematics research in infancy suggests
that there is some inherent drive to understand basic numerical principles and counting (Geary,
2006), although there is little doubt that experience and observational learning account for much
of mathematical development. For children with MD, research suggests that the ability to
identify numbers and discriminate quantities is generally intact, although these skills are often
delayed (Badian, 1983; Geary, Hoard, & Hamson, 1999; Gross-Tsur, Manor, & Shalev, 1996).

11

As children begin preschool and kindergarten they are exposed to more formal numberrelated activities. During this time, foundational skills of math, including counting knowledge
and strategy development, are reinforced. Counting knowledge begins as children learn to make
associations between written numbers, number names, and mental representations of quantities.
By around age four, most typically developing children have the ability to sequence numbers
from one to ten (Geary, 2006). There are several principles of counting knowledge that continue
to develop during early childhood, and with application and error correction become more
automatized and accurate. These principles include learning one-to-one correspondence (only
one word for each object), stable order (the order of number words remains the same despite the
set), cardinality (the final number word represents the total quantity of objects), abstraction (any
object(s) can be grouped and counted), order-irrelevance (items can be counted in any order),
and ordinality (successive number words signify sequentially larger quantities) (Geary, 2004).
These principles form the basis for understanding of arithmetic operations.
In regards to counting knowledge, studies indicate that children with MD are able to
accurately identify some counting principles, but not all. In a study by Gelman and Meck (1983),
first grade children were asked to watch a puppet count objects and identify if there were errors
in counting. Children with MD and reading disability (RD) accurately identified one-to-one
correspondence errors when they occurred on the last item (i.e., double counting last item), but
not when they occurred on the first item, indicating difficulties with working memory. Also,
these children identified violations of non-essential counting principles, such as counting
adjacent items consecutively, as errors. These findings were replicated in a subsequent study,
which also found that children with MD and RD-MD persist with these same errors in second
grade, and only children with reading disabilities did not show these same counting errors

12

(Geary, Bow-Thomas, & Yao, 1992). Thus, many children with MD show difficulty
understanding counting principles, particularly principles of order irrelevance. These difficulties
likely delay their ability to learn and complete higher-order arithmetic problems (Ohlsson &
Rees, 1991).
Typical development of arithmetic concepts, including addition, subtraction,
multiplication, and division, develop through several mechanisms. Applying counting
knowledge, use of manipulatives to aid counting, and successively more complex strategy use
evolves with practice. For example, children typically begin to solve addition problems using a
“count all” strategy in which they count all the objects in a given problem set starting with “one.”
As children develop memory skills and fluidity with counting, they may begin with the larger
addend and count on, also known as the min strategy (e.g. to solve 5 + 3, a child would begin
with “five” and count up three numbers until they reach “eight”) (Geary, 2006). As children
amass repeated practice with solving basic operations, they develop associations between the
problem and answer. These associations are stored in long-term memory and can be retrieved
relatively effortlessly and quickly. Siegler (1989) noted that children use multiple strategies to
solve math problems at any point in time and that strategy use depends on the accuracy and
speed associated with using a given strategy and overall efficiency. Retrieval is the most efficient
process, thus math fluency is characterized by accurate and quick retrieval of basic math facts.
Development of math calculation skills involves not only fluency of basic math facts but
also deep conceptual knowledge. Deep conceptual knowledge, often taught and practiced in
elementary and secondary school, may include but is not limited to learning properties of basic
operations such as the associative property (order of addition or multiplication of multiple
numbers is irrelevant), understanding ratios (parts of a whole), and estimation. Many of these

13

concepts are learned through physical manipulation of objects, applications to other concepts,
and continual maturation of memory, attention, and other executive functioning capacities
(Geary, 2004; Geary 2006).
For children with MD, research suggests that the types of math strategies used and
change in strategy use over time is less developed (Geary, Hoard, & Hamson, 1999; Hanich,
Jordan, Kaplan, & Dick, 2001; Jordan & Montani, 1997; Gross-Tsur, Manor, & Shalev, 1996). In
a study by Jordan and colleagues (2003), children with MD used finger counting in second and
third grade, whereas their peers without MD used primarily verbal counting and automatic
retrieval of math facts. Children with RD and typically achieving children showed maturation in
strategy use between first and second grade, from finger counting to verbal counting and
automatic retrieval. Some studies indicate that by the end of elementary school children with MD
do become more proficient at verbal counting strategies and demonstrate fewer counting errors
(Geary & Brown, 1991). In terms of using the most advanced strategy, retrieval, to solve
arithmetic and word problems, children with MD and RD-MD demonstrate significantly more
errors and variable response times compared to typically achieving peers, and do not show the
same improvements in fact retrieval over time, as with verbal counting (Barrouillet, Fayol, &
Lathuliere, 1997; Fayol, Barrouillet, & Marinthe, 1998; Geary & Brown, 1991; Gersten, Jordan,
& Flojo, 2005; Rasanen & Ahonen, 1995). Similar deficits in fact retrieval have also been noted
in children who have a lesion to the left hemisphere and associated subcortical regions early in
life (Ashcraft, Yamashita, & Aram, 1992). Another study found that children with poor
automaticity of fact retrieval performed similarly to children who are fluent in fact retrieval on
untimed story problems, but performed significantly worse when the problems were timed
(Jordan, Hanich, & Kaplan, 2003). Thus, cognitive processes including, working memory and

14

long-term storage and retrieval, as well as development of more efficient strategies differ in
children with MD compared to typically achieving peers or peers with RD only. The use of
inefficient strategies uses additional attention and working memory resources, and further
hinders the development of automaticity of fact retrieval and understanding of more abstract
concepts.
Mathematics disability subtypes. As previously mentioned, children with MD are a
heterogeneous group, and may show strengths and weaknesses in different mathematical
domains influenced by their developmental trajectory. There is evidence to support several
subtypes of MD including, procedural, semantic, and visual-spatial subtypes. Characteristics of
children with procedural subtype of MD include more counting errors, immature strategy use
(e.g., finger counting, counting all), misalignment of numbers, and errors with regrouping (e.g.,
carrying or borrowing). These features may be related to working memory and attention deficits
that make it difficult for children to hold and manipulate number words and counting procedures
mentally (Hitch & McAuley, 1991). Additionally, problems with the phonetic-articulatory
system may also hinder a child’s ability to process, hold, and attend to verbal representations of
numbers (Geary, 1993; McLean & Hitch, 1999). Characteristics of children with semantic
subtype of MD include difficulty retrieving basic facts from long-term memory, more errors with
fact retrieval, and inconsistent reaction time patterns with fact retrieval. It is hypothesized that
problems with the language system responsible for encoding long-term memories representations
of phonetic (the number word) and semantic (the quantity of the number) properties associated
with arithmetic problems are at play (Geary, 2004). Another possibility is that difficulties
inhibiting irrelevant associations lead to difficulties with fact retrieval (Barrouillet, Fayol, &
Lathuiere, 1997; Hanich et al., 2001). For example, in a study by Geary and colleagues (2000)

15

children with MD and children with RD made more counting-string errors (6 + 2 = 7 or 3,
because 7 comes after 6, and 3 comes after 2), indicating that they had difficulty inhibiting the
irrelevant information in working memory related to the problem addends. Finally,
characteristics of children with visual-spatial subtype of MD include difficulties with estimation
tasks and misunderstanding or errors in representing numerical information spatially (e.g., on a
number line or graph).
Summary. Given the developmental nature of mathematics learning, it would not be
unreasonable to assume that early math difficulties subsume later math difficulties, and that in
general MD is a persistent disability. Several studies indicate that math difficulties do persist
across elementary school grades (Morgan, Farkas, Hillemeier, & Maczuga, 2014; Morgan,
Farkas, & Wu, 2009). In one study, students performing below the 10 th percentile in fall and
spring of kindergarten had the slowest growth rates in math from first through fifth grade, and
approximately 65%-70% of students with MD in kindergarten continued to experience MD in
first, third, or fifth grade (Morgan, Farskas, & Wu, 2009). Morgan, Faskas, Hillemeier, and
Maczuga (2014) found that children with who were performing in the lowest 25 th percentile in
mathematics at the end of kindergarten were seventeen times more likely to have math disability
throughout elementary and middle school. These studies not only demonstrate the stability of
MD, but also highlight the importance of early academic skills in predicting later academic
problems. Early math skills have been shown to predict reading, math, and science achievement
from kindergarten through eighth grade, including for students who enter school with lower math
skills (Claessens & Engle, 2013). Additionally, general math achievement in kindergarten entry
has been shown to be highly predictive of math achievement in third grade (Claessens, Duncan,
& Engel, 2009; Duncan et al., 2007; Jordan, Kaplan, Ramineni, & Locuniak, 2009), and studies

16

indicate that the gap between children with and without math difficulties tends to remain stable
or increase over time (Shin, Davison, Long, Chan, & Heistad, 2013).
In sum, children with MD represent a heterogeneous group of students with
neuropsychological deficits that interfere with learning and performance of procedures and
concepts in various mathematical domains. These children often show a different developmental
trajectory that is characterized by poor counting knowledge, problems with fact retrieval, and
inefficient strategy use. Although researchers have begun to examine the defining features, and
neuropsychological and biological correlates of subtypes of MD, there is a lack of consensus on
the diagnostic criteria for MD and various definitions of MD complicate the interpretation of
many research findings. Nevertheless, it is well established that children with MD have lower
academic achievement compared to peers, greater social and emotional problems, and face
poorer prognosis in later life for employment and social-emotional adjustment in adulthood
(Reschly & Christenson, 2006; Martinez & Semrud-Clikeman, 2004).
Reading Disability. Literacy is one of the most important skills in today’s society,
required for success in school, the work force, and daily living. However, many children and
adults struggle to read. In fact, the majority of students with an LD have a RD (Lerner & Johns,
2009; Shaywitz & Shaywitz, 2003). Reading disability (RD) is characterized by
neuropsychological deficits that lead to difficulty reading, not otherwise attributable to low
intellectual functioning or environmental factors such as cultural considerations, poverty, socialemotional problems, or lack of instruction or exposure to language and text (Shaywitz &
Shaywitz, 2005). The predominant features of RD include inaccurate or dysfluent word
recognition, poor decoding skills, and poor spelling (Judge & Watson, 2011; Pennington, 2009).
RD is typically diagnosed based on a persistent pattern of difficulty in reading despite

17

intervention and performance in the lowest 10th percentile on standardized assessment of reading.
The prevalence of RD in children is estimated between 5% and 17.5% (Shaywitz & Shaywitz,
2005). Reading disabilities are equally prevalent among boys and girls, although some studies
indicate that boys are more likely to be identified with LD in reading in school and receive
special education services (Shaywitz, 2003). This phenomenon may be due to different
behavioral manifestations of learning problems (e.g., more hyperactivity, behavioral problems)
between boys and girls that leads to differential attention from teachers regarding the severity of
the learning problems. Etiological studies indicate that RD is highly heritable; 23% to 65% of
children with RD have a parent with RD (Scarborough, 1990), and approximately 40% of their
siblings have RD (Pennington & Gilger, 1996).
For typically developing children, there are two general routes (dual-route model) by
which they learn to read: the phonological and orthographic routes (Ehri, 2015; Woollams,
2015). Infants and toddlers pick up on language sounds from their environment (Hart & Risley,
1995). As children begin formal instruction in reading, they learn to associate orthographic
representations (print) of letters with their respective language sounds, also known as phonemic
awareness (Ehri, 2015). Through the process of decoding, or segmenting words into their
individual phonemes and stringing phonemes together, children use the phonetic code (lettersound associations) to read words. Meaning is assigned to the word once the child decodes the
word. This process is known as the phonological route (Ehri, 2015). The phonological route is an
indirect method of reading, used by beginning readers and by advanced readers when faced with
a complex, unfamiliar word. It is a slow, deliberate, and effortful process that requires attention,
working memory, and visual, auditory, and language-processing. On the other hand, the
orthographic route (also known as the word-form route) is a more direct method of reading used

18

by skilled readers (Ehri, 2015). The orthographic route involves automatic recognition of whole
words associated with their semantic meaning, and is the most efficient and fastest method of
reading. When a child uses the phonological route to read words multiple times, they commit
words and their associated meanings to memory. As a child progresses from using the
phonological route to the orthographic route to read, they develop greater reading fluency, or the
ability to read accurately, quickly, and with little effort. As children develop reading fluency,
they begin to read to learn, or in other words, read to comprehend new information. Reading
comprehension is the ability to understand, remember, and think about the text that is being read.
Reading fluency and comprehension influence one another, such that children who are fluent
readers are better able to understand what they read and children who have strong
comprehension skills become more fluent readers (Klauda, & Guthrie, 2008).
There is considerable research that supports the dual-route model of reading. Functional
neuroimaging research indicates that three main areas of the brain are involved in reading
(Shaywitz, 2003; Pugh et al., 2000). Two posterior systems in the left hemisphere, the dorsal
temporo-parietal circuit and the ventral occipito-temporal circuit, are involved in automatic word
recognition. The temporo-parietal region (also known as the word analysis system) contains
Wernicke’s area, and is implicated in phonological awareness (Pugh et al., 2000). The occipitotemporal circuit (also known as the word form system) is involved in rapid recognition of whole
words (Pugh et al., 2000). Finally, the inferior frontal gyrus in the anterior portion of the brain,
which contains Broca’s area, is involved in articulation and has been associated with silent
reading (Pugh et al., 2000). Thus, beginning readers rely on the temporo-parietal circuit to
decode words via the phonological route, but as they practice decoding the word multiple times it

19

becomes stored in the occipito-temporal word form area and is retrieved via the orthographic
route.
Children with RD have core deficits in phonological processing that impact their
decoding skills, reading fluency, and/or reading comprehension (Brady, 1991; Bus & Ijzendoorn,
1999; Cain, Oakhill, & Bryant, 2000; Olson, Forsberg, & Wise, 1994; Pennington, 2009;
Snowling, 2001; Torgesen, 2000; Wagner, Torgesen, & Rashotte, 1994; Yuill & Oakhill, 1991).
Although several models of RD point to deficits in auditory perceptual processing and naming
speed (e.g., Tallal, 1980; Wolf & Bowers, 1999; Wolf, Bally, & Morris, 1986), a substantial
amount of research supports the hypothesis that RD is related to core problems with
phonological processing. Pennington (2009) found that individuals with RD showed
underactivation in the temporo-parietal regions of the brain associated with phonological
awareness and decoding. Consequently, underactiviation in the occipito-temporal word form
system was also found given that storage of known words is dependent on accurate decoding
(Pennington, 2009). Several studies have also indicated individuals with RD demonstrate
weaknesses in other neuropsychological and cognitive domains after controlling for performance
on phonological measures such as, verbal comprehension and speech and language processing
(Pennington & Bishop, 2009), slower verbal naming speed and general processing speed
(Compton, DeFries, & Olson, 2001; Landerl, Fussenegger, Moll, & Willburger, 2009), and
impairments in executive functions including verbal working memory, response inhibition, and
attention (Ebejer et al., 2010; Purvis & Tannock, 2000; Roodenrys, Koloski, & Grainger, 2001;
Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005).
Similar to MD, studies have indicated that RD is persistent over time, and that student’s
early reading achievement and skills are predictive of later reading achievement (Algozzine,

20

Wang, & Violette, 2011; Froiland, Powell, Diamond, & Son, 2013; Judge & Bell, 2010; Lin,
Morgan, Hillemeier, Cook, Maczuga, & Farkas, 2013). For example, McNamara, Scissons, and
Dahleu (2005) found that measures of phonemic awareness and alphabetic principle in
kindergarten predicted first grade reading achievement. The results of this study also revealed
that children who were poor readers in kindergarten were more likely to have reading difficulties
in first grade. This trend is true in later grades as well; Lin and colleagues (2013) found that
children with RD in third grade (performing in the lowest 10% on reading achievement test)
were almost twenty times more likely to still have an RD in fifth grade. Similarly, Judge and Bell
(2010) found that children with reading disabilities made less growth in reading across
kindergarten through fifth grade compared to children without LD. Despite the evidence
indicating that RD is stable over time, and children with RD typically demonstrate problems
early on, a study by Lipka and colleagues (2006) found a small subset of children who did not
demonstrate reading or phonological deficits until third and fourth grade. Thus, RD is a
heterogeneous category, and there may be some children who develop reading problems later in
life, particularly in regards to problems with reading comprehension but not word reading.
Overall, children with RD have marked impairments in their ability to decode, read
fluently, and comprehend text. These features are based in core deficits in phonological
processing, and are associated with individual neurological differences and neuropsychological
impairments such as verbal working memory, processing speed, and attention deficits. Similar to
children with other disabilities, children with RD are at-risk for poor academic achievement,
higher levels of academic frustration, school dropout, low self-esteem, and poorer prognosis in
adulthood (e.g., unemployment) (Boetsch, Green, & Pennington, 1996; Daniel et al., 2006;
Goldston et al., 2007; McGee, Prior, Williams, Smart, & Sanson, 2002; Willcutt et al., 2013).

21

Co-morbid Mathematics and Reading Disability. Research indicates that math and
reading disabilities co-occur (RD-MD) in approximately 30% to 70% of individuals with either
disorder (Kovas et al., 2007; Landerl & Moll, 2010; Willcutt et al., 2013). One proposed model
of co-morbidity with empirical support is the correlated liabilities model (Willcutt et al., 2013).
This model suggests that RD and MD co-occur more frequently than expected by chance because
they share etiological factors, whereas additional etiological influences specific to RD or MD
lead to distinct disabilities occurring alone. Twin analyses indicate that there are shared genetic
influences between RD and MD, as well as separate genetic and environmental influences that
uniquely influence the development of RD or MD alone (Knopik, Alarcon, & DeFries, 1997;
Kovas et al., 2007; Willcutt et al., 2010). In concordance with the correlated liabilities model,
several research studies suggest that children with RD-MD think and perform differently from
children with RD or MD alone (Jordan, Hanich, & Kaplan, 2003b; Jordan & Montani, 1997;
Martinez & Semrud-Clikeman; 2004; Willcutt et al., 2013).
In a comprehensive study examining academic, neuropsychological, and psychosocial
functioning of children with RD, MD, RD-MD, and children without LD, results indicated that
children with RD-MD demonstrated significantly more impairments than children with one LD
or without LD, and that children with LD demonstrated more impairments than children without
LD (Willcutt et al., 2013). Children with RD-MD demonstrated greater inattention symptoms,
higher internalizing symptoms, lower grades in school, lower global functioning in daily
responsibilities, greater social isolation, and greater neuropsychological impairments in verbal
comprehension, phoneme awareness, working memory, naming speed, processing speed,
vigilance, and response variability compared to children with one LD. Further analyses also
revealed that verbal comprehension, working memory, and processing speed were associated

22

with both math and reading deficits, whereas phoneme awareness and naming speed were
associated independently with reading deficits and set shifting was associated with math deficits.
These findings remained after controlling for performance IQ, ADHD symptoms, and comorbidity. Overall, this study suggests that RD-MD is characterized by multiple
neuropsychological deficits that are distinct, but related to RD and MD.
Other studies have also found that children with RD-MD perform differently from
children with one LD. For example, on math calculation tasks, children with RD-MD
demonstrated more errors with finger counting and performed worse overall than children with
MD only (Jordan, Hanich, & Kaplan, 2003b). Additionally, children with RD-MD showed more
stability in math and reading problems throughout second and third grade compared to children
with MD only. Interestingly, in a study by Jordan, Kaplan, and Hanich (2002) children with RD
performed similarly to children with MD in mathematics at the end of third grade. This finding
may be indicative of a negative influence of reading difficulties on mathematics performance,
and possibly, more globally across multiple academic subjects (Grimm, 2008; Jordan, Kaplan, &
Hanich, 2002). Overall, children with multiple LD’s tend to have greater academic and
psychosocial problems and neuropsychological deficits compared to children with one LD and
children without LD.
Psychosocial Development in Children with Learning Disabilities
Psychosocial development describes the interactions between social factors (e.g.,
interpersonal relationships, school environment) and psychological factors (e.g., cognitions,
behaviors, and affect) that influence an individual’s daily functioning. Although the main focus
of attention in regards to identification, intervention, and research of children with LD’s is the
neuropsychological and academic profile, attention to non-academic outcomes, such as

23

psychosocial development, is equally important (Moore, Lippman, & Ryberg, 2015). Research
indicates that although academic outcomes are strong predictors of occupation, health, income,
and other important life success indices, non-academic outcomes such as social competence
(e.g., interpersonal skills), emotional well-being (e.g., internalizing problems), and maladaptive
behaviors (e.g., externalizing problems) all serve important roles in supporting or hindering
academic achievement, and child development broadly (Moore, Lippman, & Ryberg, 2015). For
children who are already at-risk for academic underachievement, such as children with LD,
understanding how psychosocial factors may serve as protective factors may inform
interventions that improve their overall success in school.
Research suggests that early academic problems influence children’s behavior and mood
(Morgan, Farkas, & Wu, 2012). Experiencing academic problems in reading and/or math early
on may lead to increased feelings of frustration, anxiety, depressed mood, and negative behaviors
(e.g., interpersonal problems, aggression), triggering a cycle of reciprocal influences between
poor academic performance and negative mood and behavior (Fleming, Harachi, Cortes, Abbott,
& Catalano, 2004; Lane, Beebe-Frankenberger, Lambros, & Pierson, 2001; Morgan, Farkas, &
Wu, 2012; Wehby, Falk, Barton-Arwood, Lane, & Cooley, 2003). Furthermore, as children
experience academic failure in reading and/or math, they are more likely to actively avoid
reading or math activities at home or school thereby reducing the opportunities they have for
academic practice and growth (Griffiths & Snowling, 2002; Guthrie, Schafer, & Huang, 2001;
Senechal, LeFevre, Hudson, & Lawson, 1996). Given that children with LD are, by definition,
more likely to experience school failure, it is not surprising that studies have found that children
with LD are more vulnerable to psychosocial problems (Gadeyne, Ghesquiere, & Onghena,
2004), even in comparison to generally low-achieving children (La Greca & Stone, 1990;

24

Maughan, Pickles, Hagell, Rutter, & Yule, 1996; Ochoa & Palmer, 1995; Roeser, Eccles, &
Strobel, 1998).
Although a link between academic achievement and psychosocial factors has been
established, the exact mechanisms by which problems develop are unclear. Some researchers
have posited that increased psychosocial problems in children with academic difficulties may be
related to a “common cause” variable, such as inattention, which leads to problems in academics,
behavior, and social-emotional well-being (Morgan, Farkas, Tufis, & Sperling, 2008). Deficits in
higher-order executive functioning skills such as, attention and inhibition, are common features
in children with LD, as well as children with behavior and social-emotional problems. Another
possibility is that academic and behavioral problems are reciprocally causative over time,
indicating that interventions targeting both academics and psychosocial factors are necessary.
Hinshaw (1992) examined seventeen studies that suggested an overall bidirectional causal model
between reading and behavior problems (see also Lepola, Poskiparta, Laakkonen, & Niemi,
2005; Onatsu-Arvilommi & Nurmi, 2000; Trzesniewski, Moffitt, Caspi, Taylor, & Maughan,
2006), however several of these studies do not control for earlier reading problems as predictors.
Although the current study is unable to address whether academic problems lead to psychosocial
adjustment issues, whether psychosocial adjustment issues lead to academic failure, or whether
academic problems and psychosocial adjustment issues are reciprocally causative, this study
aims to understand the patterns in academic achievement and psychosocial behaviors across
kindergarten to fifth grade and may be able to shed light on whether children with academic
problems and/or psychosocial adjustment issues in kindergarten continue on a negative trajectory
in one or both areas.

25

Overall, more studies are needed to determine how early academic and psychosocial
problems influence the academic and psychosocial trajectory of children with LD’s. Much of the
research on psychosocial problems in children with LD’s has focused on behaviors, cognitions,
and emotions related to academic task such as, attitudes towards reading (Chapman, Tunmer, &
Prochnow, 2000; Lepola, Salonen, & Vauras, 2000; Viljaranta, Lerkkanen, Poikkeus, Aunola, &
Nurmi, 2009), in comparison to research on more general internalizing, externalizing, and
interpersonal problems. The following sections will review the current literature on externalizing
problems, internalizing problems, and interpersonal problems in children with LD.
Externalizing Problems. Externalizing problems describe a range of maladaptive
behaviors including, aggression, disruptiveness, and poor emotional control (e.g., anger), as well
as inattention, hyperactivity, and impulsivity. McIntosh (2005) hypothesized that a “coercive
cycle of education failure” leads students to view academic demands as aversive and engage in
maladaptive behaviors that are maintained by escaping academic tasks such as, receiving a timeout or being sent to the principal’s office. The loss of instruction time increases the academic gap
between what is expected in the classroom and what the student can do, which leads to increased
aversion and escape-maintained problem behaviors. Several studies have demonstrated a
relationship between low academic achievement and behavior problems in children with LD
(Algozzine, Wang, & Violette, 2011; Algozzine, Horner, & Putnam, 2008; Beitchman & Young,
1997; Bender & Smith, 1990; Konstantareas & Homatidis, 1989). Other studies indicate that
externalizing problems tend to be stable and have negative long-term impacts (Bub, McCartney,
& Willett, 2007; Kwon, Kim, & Sheridan, 2012). For example, children with high levels of
externalizing problems in kindergarten were more at-risk for continued externalizing problems in
third and fifth grade (Morgan, Farkas, & Wu, 2009).

26

Some studies estimate that nearly half of individuals with RD meet criteria for at least
one behavioral or emotional disorder (Goldston et al, 2007; Maughan, Rowe, Loeber, &
Stouthamer-Loeber, 2003). Attention deficit hyperactivity disorder (ADHD) is one of the most
frequent co-occurring externalizing problems, particularly inattentive and combined subtypes
that are characterized by significant inattention (Willcutt et al., 2010). Individuals with RD are
also at a higher risk for externalizing disorders such as, ODD and CD, but several studies suggest
that associations between RD and antisocial behavior may be restricted to the subset of
individuals with comorbid ADHD (Frick et al., 1991; Maughan et al., 2003; Willcutt &
Pennington, 2000). Other studies have found that poor readers are more likely to act out, be
aggressive and argumentative, and have higher rates of externalizing pathology than children
without reading difficulties (Fleming et al., 2004; Hinshaw, 1992; Lin, Morgan, Hillemeier,
Cook, Maczuga, & Farkas, 2013; Morgan, Farkas, & Wu, 2009; Trzesniewski, Moffitt, Caspi,
Taylor, & Maughan, 2006).
Academic problems have been found to be long-term predictors of externalizing
pathology. Morgan, Farkas, and Wu (2012) found that students who identified as poor readers in
third grade were twice as likely to identify themselves as angry in fifth grade, compared to those
who were not identified as poor readers. Another study found that children in first grade with RD
(bottom 10% on standardized reading test) were more likely to have externalizing and
internalizing problems in third grade than children without RD. Interestingly, children with poor
task engagement in first grade were more likely to experience reading problems (bottom 10% on
standardized reading test) in third grade, however this was not true for children who
demonstrated severe externalizing behaviors in first grade. Analyses included an autoregressor

27

variable for reading or behavioral problems in first grade to account for the influence of existing
problems (Morgan, Farkas, Tufis, & Sperling, 2008).
In regards to children with MD, there are no previous studies that have reported the
prevalence of co-morbid behavioral or emotional disorders (Willcutt et al., 2013); however some
studies have found elevated attentional problems, and internalizing and externalizing symptoms
in children with MD compared to children without LD (Auerbach, Gross-Tsur, Manor, & Shalev,
2008; Cirino, Fletcher, Ewing-Cobbs, Barnes, & Fuchs, 2007; Lin, Morgan, Hillemeier, Cook,
Maczuga, & Farkas, 2013; Shalev, Auerbach, & Gross-Tsur, 1995; Willcutt et al., 2011).
Scarborough and Parker (2003) found that children with MD and MD+RD had more behavioral
problems in second grade compared to children with RD and without LD. Additionally, some
studies have found that elevated inattention and externalizing symptoms are more prevalent in
children with RD+MD compared to children with MD only, indicating that multiple learning
challenges increase the prevalence of externalizing psychopathology (Badian, 1999; Shalev,
Auerbach, & Gross-Tsur, 1995; White, Moffitt, & Silva, 1992). Greenham (1999) conducted a
review of psychosocial adjustment in children with LD and concluded “the literature regarding
externalizing behavior problems in individuals with LD is fairly consistent,” (p.179) with the
majority of studies indicating increased aggression, disruptive behaviors, and/or hyperactiveinattentive behaviors in children with LD compared to typically developing children, as rated by
parents and/or teachers.
Overall, there is strong evidence to suggest that children with reading challenges are atrisk for greater externalizing psychopathology concurrently and in the future compared to
children without LD. Levels of externalizing problems in children with LD tend to be at the
subclinical level, but are still more problematic compared to children without LD (Greenham,

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1999). Although there is less research on children with MD, the preliminary results also point to
increased risk of externalizing pathology in elementary and middle school grades. In addition to
externalizing behaviors, children with LD also seem to be challenged with internalizing disorders
disproportionate to their non-LD peers.
Internalizing Problems. Internalizing problems describe a range of emotional
difficulties including, anxiety and depression. Several studies suggest that children with LD
experience more internalizing problems than their peers without LD (Arthur, 2003; Margalit,
1998). Earlier studies have found that middle school students with LD are more likely to report
loneliness, victimization, less social satisfaction (Bender & Wall, 1994; Sabornie, 1994), and
chronic depression and anxiety (Cohen, 1986; Goldstein, Paul, & Sanfilippo-Cohn, 1985),
compared to their non-LD peers. These findings suggest that internalizing problems in children
with LD may be even more prevalent in later elementary and middle school grades. Studies also
indicate that higher levels of stress and anxiety in older students with LD are related to lower
academic self-efficacy, self-confidence, and increased self-doubt and self-criticism, compared to
students without LD (Sparks & Lovett, 2009).
Studies examining children with RD have found that elevated levels of anxiety and
depression (Arnold et al., 2005; Carroll, Maughan, Goodman, & Meltzer, 2005) was related to
increased suicidal ideation or suicide attempts in adolescents with RD compared to their
neurotypical peers (Daniel et al., 2006). These findings remain even after controlling for other
symptoms of psychopathology including, ADHD and conduct disorders (Daniel et al., 2006,
Goldston et al., 2007; Maughan, Rowe, Loeber, & Stouthamer-Loeber, 2003; Willcutt &
Pennington, 2000; Willcutt et al., 2013). Some researchers have found that as children with RD
become more aware of their learning challenges in comparison to their typically achieving peers,

29

they report feeling more depression (Maughan, Rowe, Loeber, & Stouthamer-Loeber, 2003).
Children with RD and RD+MD have also self-reported more impairment on the Sense of
Inadequacy scale on the Behavior Assessment System for Children, 2nd Edition (BASC-2;
Reynolds & Kamphaus, 2006) compared to typically achieving children (Martinez & SemrudClikeman, 2004). As with externalizing problems, there appears to be a longitudinal effect of
academic problems on later internalizing psychopathology. For example, students with RD in
third grade were twice as likely to identify themselves as sad, lonely, and distractible in fifth
grade (Morgan, Farkas, & Wu, 2012).
There are very few studies that examine internalizing problems in children with MD. One
study examined the differences in emotional adjustment and school functioning in a group of 120
middle school (6th to 8th grade) students with RD, MD, RD-MD, and students who were typically
achieving. They found that students with RD-MD reported the poorest functioning on the School
Maladjustment, Clinical Maladjustment, and Emotional Symptoms Index on the BASC-2 selfreport, compared to the typically achieving students. Additionally, the MD group reported poorer
functioning on the School Maladjustment Composite compared to typically achieving children.
Further analysis revealed that the RD-MD group showed greater impairment on Atypicality scale
(e.g., odd and immature social behaviors) and Depression scale compared to the typically
achieving group (Martinez & Semrud-Clikeman, 2004). Literature on children with nonverbal
learning disorder (NVLD), a disorder characterized by weak math skills, intact word decoding,
and deficits in motor coordination, spatial organization, and social competence, indicates higher
levels of social isolation, elevated symptoms of withdrawal from social engagement, and more
internalizing problems (Greenham, 1999; Osman, 2000; Rourke, 1989; White, Moffitt, & Silva,
1992). These studies may provide additional support for the hypothesis that children with MD

30

and associated social deficits are also at-risk for greater internalizing psychopathology; although
more research to clarify the psychosocial correlates of MD with and without RD is needed.
Despite findings of increased internalizing problems in children with one or more LD,
some studies have found that children with one LD do not show a discernable increase in
internalizing psychopathology (Barkauskiene, 2009; Jorm, Share, Matthews, & McLean, 1986;
Miller, Hynd, & Miller, 2005; Sideridis, Mouzaki, Simos, & Protopapas, 2006). For example,
Barkaukiene (2009) examined 204 children in second to fourth grade, of which 102 children
were diagnosed with one or more LD through a school psycho-educational evaluation. Results
indicated that only children with multiple LD demonstrated higher internalizing problems, in
comparison to children with single LD or no LD. This finding extended the findings of a study
conducted by Miller, Hynd, and Miller (2005) indicating that children with RD did not show
significant internalizing psychopathology based on parent, teacher, and self-report of
internalizing symptoms on the Behavioral Assessment System for Children (BASC; Reynolds &
Kamphaus, 2002) and Children’s Depression Inventory (CDI; Kovacs, 1985). It is important to
note that this study classified children as RD based on two models: a 20-point discrepancy
between a reading achievement score and their IQ, or based on a reading achievement standard
score below 85. Thus, findings may be conflated based on inclusion of children with low average
reading skills or systematic biases or errors involved in using a severe discrepancy model to
diagnose a true LD.
Along with differences in diagnostic criteria, the mixed findings in the literature may be
due to differences in rater’s perceptions (e.g., parent, teacher, or self). For example, one study
found that children with RD did not self-report higher internalizing problems, although parents
and teachers did (Heiervang, Stevenson, Lund, & Hugdahl, 2001). A recent meta-analysis

31

indicated that approximately 70% of children with LD have higher levels of anxiety compared to
children without LD (Nelson & Harwood, 2011), however, levels of anxiety in the LD
population were not clinically significant and several important factors moderated the
relationship between LD status and anxiety level including, type of rater and setting of LD
diagnosis (e.g., clinic/hospital or school). This study found that there was low agreement
between types of raters (parents, teachers, child), and that children diagnosed in clinic/hospital
settings were regarded as having higher internalizing symptomology.
Overall, the research is inconclusive regarding whether children with LD, and more
specifically certain subtypes of LD, experience increased internalizing symptoms compared to
children without LD. It seems that children with LD tend to experience more internalizing
symptoms dependent on the type of informant and diagnostic criteria. However, this relationship
may not always be clinically significant and may hold true for children with multiple LD
compared to children with one LD. As with externalizing problems, children with high levels of
internalizing problems in kindergarten are more likely to have persistent internalizing problems
in third and fifth grade (Morgan, Farkas, & Wu, 2009). Greater risk for internalizing problems in
school is likely influenced by academic failure and peer comparisons. This study will add to the
current literature by exploring whether children with LD experience more internalizing
symptomology compared to children without LD, based on teacher-reports of LD within a
school-diagnosed sample. Additionally, differences between sub-groups of children will be
explored, potentially adding more clarity to this issue. Finally, given that internalizing and
externalizing problems often negatively influence children’s social competence, with some
research suggests that children with LD demonstrate more deficits in interpersonal skills, this

32

study will explore the relations between internalizing and externalizing problems and
interpersonal skills.
Interpersonal Problems. Interpersonal problems involve difficulties getting along with
others, making or maintaining friendships, and experiencing social isolation or rejection. Across
several studies, children with LD consistently demonstrate more interpersonal problems. For
example, studies have reported increased social isolation (Mishna, 1996), peer rejection (Kuhne
& Weiner, 2000; Ochoa & Olivarez, 1995), and difficulties with social integration (Gadeyne,
Ghesquiere, & Onghena, 2004) in children with LD from first grade through adolescence. More
specifically, children with RD tend to experience more peer rejection and lower social status
(Lopes, Cruz, & Rutherford, 2002). Estimates from meta-analyses and review studies indicate
that approximately 25% to 30% of children with LD are socially rejected compared to 8% to
16% in the typical population (Greenham, 1999). However, several studies have reported that
40% to 70% of children with LD are rated within the average range in terms of acceptance by
peers (Conderman, 1995; Ochoa & Palmer, 1991). Thus, there is evidence to support that
children with LD tend to be rejected by peers more than children without LD but, similar
proportions of children with and without LD are rated within the average range for peer
acceptance.
Regarding social skills, the extant literature reveals a similar trend to those findings on
peer acceptance/rejection. Namely, although children with LD are typically rated has having
poorer social skills in comparison to children without LD, a similar proportion of children with
and without LD are rated as having average social skills (Greenham, 1999). For example, a metaanalysis examining 152 studies, found that approximately 75% of children with LD demonstrate
social skills deficits, and that these deficits differentiated children with LD from children without

33

LD (Kavale & Forness, 1996). However, other researchers have found that children with LD and
low-achieving children without LD received similar, low ratings on social skills and
demonstrated fewer social interactions in comparison to typically-achieving children (Tur-Kaspa
& Bryan, 1995; Vaughn & Haager, 1994). Thus, the research suggests that children with LD tend
to experience more social skills problems; however there may be a common factor of academic
underachievement that underlies interpersonal skill deficits.
In terms of subtypes of LD, several studies report small, but significant, weaknesses in
social functioning for children with RD (Goldston et al., 2007; Willcutt, Pennington, Olson, &
DeFries, 2007; Willcutt et al., 2011). Children with RD in third grade are twice as likely to selfreport as unpopular in fifth grade compared to children without RD (Morgan, Farkas, & Wu,
2012). For children with MD, research indicates similar findings, including more problems with
social awareness, social anxiety, social isolation, and social withdrawal compared to children
without MD (Auerbach, Gross-Tsur, Manor, & Shalev, 2008; Shalev, Auerbach, & Gross-Tsur,
1995; Shalev, Auerbach, Manor, & Gross-Tsur, 2000; Willcutt et al., 2011). In general, research
supports the notion that children with LD tend to experience more interpersonal problems than
children without LD, although there appears to be equivalent numbers of children with and
without LD who possess average social skills. Given that interpersonal problems are often
associated with other externalizing and internalizing problems this pattern is not unexpected.
However, there is limited research that examines the relation between externalizing, internalizing
and interpersonal problems in children with LD. Research examining the protective and risk
factors related to development of psychosocial problems in children with LD, particularly
subtypes of LD, is warranted and will better inform the types of interventions that may be
implemented both at home and in the classroom.

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Motivational Factors in Children with Learning Disabilities
Motivation can be defined as the desire and decision to engage in goal-oriented
behaviors. This broad definition encompasses multiple constructs of motivation including, an
individual’s ability to demonstrate self-regulated behavior, show effort, persistence, and interest,
and evaluate thoughts about their behavior. Compared to the literature on typically achieving
children, there is relatively little research on the motivational profile of children with LD,
especially for subtypes of LD, and for motivational constructs including intrinsic motivation. The
following sections review the extant literature on three aspects of motivation in children with
LD: self-regulated learning, ability beliefs, and intrinsic motivation.
Self-Regulation. Self-regulation, or self-regulated learning, is a cornerstone of goaldirected behavior, and is an important component of academic performance (Pintrich, 2000;
Pintrich & De Groot, 1990; Pintrich & Zusho, 2002). Self-regulation involves an individual’s
ability to engage in a recursive process of self-monitoring their thoughts, behaviors, and
emotions (e.g., What am I doing? What can I do? How do I feel about this?), self-instructing or
guiding their behavior (e.g., What do I have to do? How do I do it?), and self-reinforcing their
behavior based on performance (e.g., How did I do? What do I need to do the same or
differently?) (Bandura, 1991). Self-regulated learning requires three components. First, a student
must possess metacognitive skills to monitor, plan (e.g., set goals), evaluate, and change their
thoughts and behaviors. Second, a student must persist and engage in a task in order to evaluate
their performance, use feedback, and make modifications. Finally, a student must possess
strategies to engage cognitively with the task (e.g., memory and comprehension strategies). This
model of self-regulated behavior is rooted in social cognitive theory (Bandura, 1991), which

35

posits that behavior is influenced by one’s own perceptions of their abilities, thoughts, behaviors,
and emotions within their social context (e.g., social comparisons to peer groups).
Several studies have found that children with lower academic achievement demonstrate
greater difficulty with self-regulated learning tasks. For example, children with academic
difficulties are more impulsive, set lower academic goals, have difficulty evaluating their
performance accurately, and are more critical of themselves (Borkowski & Thorpe, 1994). These
problems with self-regulated learning have negative effects on their psychosocial development
including greater levels of anxiety, lower self-esteem, and lower levels of intrinsic motivation
(Sideridis, Morgan, Botas, Padeliadu, & Fuchs, 2006; Zimmerman & Risemberg, 1997).
Children with identified LD’s demonstrate less academic self-regulated learning and are less
motivated to engage or persist in on-task behaviors in class (Bender & Wall, 1994; Graham &
Harris, 2003; Grolnick & Ryan, 1990; Wilson & David, 1994; Wong, Harris, Graham, & Butler,
2003). Using the ECLS-K, 1998-1999 Kindergarten Cohort data, Morgan, Farkas, Tufis, and
Sperling (2008) investigated trends in task engagement and LD across first through third grade,
and found that children with RD in first grade were more likely to demonstrate poor task
engagement in school in third grade. Several components of self-regulated learning including
goal-directed behavior and persistent task engagement have been associated with increased
academic achievement in reading and math (Bodovski & Youn, 2011; DiPerna, Lei, & Reid,
2007; Fantuzzo et al, 2007; McClelland, Acock, & Morrison, 2006; McClelland, Morrison, &
Holmes, 2000; McWayne, Fantuzzo, & McDermott, 2004).
Researchers have hypothesized why children with LD exhibit lower levels of selfregulation. First, students with LD often struggle with self-awareness and self-assessment
(Butler, 1998, 1999), which may impede their ability to evaluate their performance accurately

36

and make use of feedback to employ more efficient cognitive strategies and persist on tasks.
Second, self-regulated learning relies on several executive functioning abilities, including
attention, working memory, planning, organization, and the ability to work independently (i.e.,
initiation). Given that children with LD demonstrate deficits in executive functioning including,
working memory and attention regulation (Fuchs, Compton, Fuchs, Bryant, Hamlett, & Lambert,
2012; Fuchs et al., 2006; Geary, Hoard, Byrd-Craven, Nugent, Numtree, 2007; Geary, Hoard, &
Hamson, 1999; Goldston et al., 2007; Passolunghi & Siegel, 2001, 2004), it is plausible that core
executive functioning deficits impede self-regulated learning in children with LD. Issues with
self-regulated learning, including executive functioning and self-evaluation, are also likely
related to one’s beliefs about their skillset and ability.
Ability Beliefs. Ability beliefs (also known as perceived competence), includes a
students’ belief about their ability to perform a given task. In other words, ability beliefs
encompass a student’s answer to the question, “Can I do this task?” More positive ability beliefs
have been associated with increased task persistence, use of more advanced cognitive strategies,
better metacognitive skills, lower levels of anxiety, increased intrinsic motivation, mastery
learning goals, and increased achievement (Bouffard-Bouchard, Parent, & Larivee, 1991;
Fincham & Cain, 1986; Paris & Oka, 1986; Pintrich & De Groot, 1990; Schunk, 1985; Wong,
Wiest, & Cusick, 2002).
Several studies have shown that students with LD possess more negative ability beliefs.
In one study of elementary school children with school-identified LD, students with LD reported
lower academic ability beliefs and academic self-concept compared to their typically achieving
peers (Tabassam & Grainger, 2002). In a more recent study, sixth through twelfth grade students
with and without LD were assessed on measures of academic self-efficacy, theories of

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intelligence, academic goal preferences, and attributions for task persistence (Baird, Scott,
Dearing, & Hamill, 2009). Students with LD were more likely to report low academic selfefficacy, and attributed more effort as a sign of low academic ability. Mediational models
suggested that these findings were influenced by the tendency of students with LD to view
intelligence as fixed (i.e., entity view instead of malleable). Negative ability beliefs may arise in
children with LD, in part, due to attribution of failure to internal causes such as their ability or
their effort, and attribution of academic successes to external causes such as help from the
teacher or good luck (Bender & Wall, 1994; Butkowsky & Willows, 1980; Cooley & Ayres,
1988; Fincham & Cain, 1986; Kistner, Osborne, & LeVerrier, 1988; Lewis & LawrencePatterson, 1989; Pearl, 1982, 1992; Rogers & Saklofske, 1985). However, negative ability beliefs
seem to be restricted to the academic domain for children with LD; non-academic self-concept
has not been found to differ from typically achieving peers (Chapman, 1988; Harter, Whitesell,
& Junkin, 1998; Kloomok & Cosden, 1994). Chapman (1988) argued that negative school
attitudes develop earlier in the lives of children who experience academic failure and persist
through high school.
Motivational constructs are often highly related to one another. For example, ability
beliefs play a role in self-regulatory mechanisms that influence a student’s behavior, cognitions
(Eccles & Wigfield, 2002; Pajares, 1996; Wigfield & Eccles, 2000) and affect, such as, anxiety
that can hinder achievement (Bandura, Caprara, Barbaranelli, Gerbino, & Pastorelli, 2003;
Cervone, Kopp, Schaumann, & Scott, 1994). Several studies suggest that more positive ability
beliefs are related to self-regulated learning including, increased persistence and use of cognitive
strategies (Chapman, 1988). Meltzer and colleagues (2004) found that students with LD who
held positive academic ability beliefs were more likely to engage and persist in academic tasks,

38

and to use cognitive strategies. Additionally, academic ability beliefs are related to intrinsic
motivation; as children feel more academically competent they become more intrinsically
motivated to learn and vice-versa (Guay, Boggiano, & Vallerand, 2001; Ryan & Deci, 2000).
Intrinsic Motivation. Intrinsic motivation encompasses a student’s desire and value
towards a goal and/or tasks to reach a goal. Research indicates that students who are interested in
a task and value the task for its importance and for the purposes of learning and challenging
themselves are more likely to engage in self-regulated learning and have improved academic
performance (Ames & Archer, 1988; Deci & Ryan, 1985; Dweck & Elliott, 1983; Greene,
Miller, Crowson, Duke, Akey, 2004; Meece, Blumenfeld, & Hoyle, 1988; Nolen, 1988; Pintrich
& De Groot, 1990; Paris & Oka, 1986). Froiland and Oros (2014) found that student’s self-rating
of interest in reading in fifth grade positively predicted 8th grade reading achievement, even after
controlling for SES, ethnicity, gender, and prior reading achievement. Thus, when students enjoy
and value a task, they are more likely to be engaged in school work which increases the
likelihood of academic achievement (Lepper, Corpus, & Iyengar, 2005; Unrau & Schlackman,
2006).
Several studies have also shown that intrinsic motivation is related to academic
achievement in children with LD (Adelman, 1978; Adelman & Taylor, 1986; Deci & Chandler,
1986; Deci, Hodges, Pierson, & Tomassone, 1992; Switzky & Schultz, 1988) However, studies
examining levels of intrinsic motivation in children with LD are mixed. Some studies have found
that children with LD demonstrate less intrinsic motivation than students without LD. In a study
of fifth and sixth grade students in Greece, children with LD demonstrated lower intrinsic
motivation and academic ability beliefs compared to children without LD (Zisimopoulos &
Galanaki, 2009). In a study of 89 students with LD in fourth through eighth grade, children with

39

LD had lower academic intrinsic motivation, as well as more negative self-perceptions of their
academic ability in comparison to children without LD (Wilson & David, 1994). However, in a
study by Pintrich, Anderman, and Klobucar (1994), fifth grade children with LD did not differ on
measures of intrinsic motivation or ability beliefs from children without LD, but did have lower
levels of metacognitive skills. Inconsistent findings may be due to study confounds including
grouping children with different subtypes of LD and different conceptualizations of motivational
constructs. Further research is needed to examine how these motivational constructs develop and
influence academic achievement.
Despite the mixed results regarding levels of intrinsic motivation in children with LD,
studies do indicate that intrinsic motivation fosters persistence on difficult tasks, such as a
challenging reading passage (Guthrie et al., 2007). Similar to findings regarding attribution style
and academic ability beliefs, children with LD who attribute academic success to their effort
instead of to other external factors are more likely to show greater levels of academic intrinsic
motivation (Adelman, 1978; Black, 1974). Intrinsic motivation is associated with several positive
academic and social-emotional outcomes including lower rates of school dropout (Deci & Ryan,
2008; Grant, 2008; Hardre & Reeve, 2003).
Summary. Overall, research suggests that children with LD may have more difficulty
with self-regulated learning, lower academic ability beliefs, and lower academic intrinsic
motivation. Some research suggests that deficits in self-awareness and executive functioning
such as, attention, planning, and organization, may contribute to these motivational problems.
Additionally, social comparisons with peers and academic failure in school also contribute to
poorer academic ability beliefs which in turn affect intrinsic motivation. However, it is still
unclear whether motivation differs among children with different subtypes of LD or multiple

40

LDs. Additionally, it is unclear how psychosocial problems, particularly internalizing and
interpersonal problems, may influence motivation in children with LD.
Parent Influences on Psychosocial Development, Motivation, and Academic Achievement
It is well-established that parenting effects children’s development. Several parenting
variables have been studied including, parent psychological well-being, parenting style, family
structure, and parent involvement in their child’s education and development. Within the
literature, more attention has been paid to the relation between parenting variables and child
outcomes in early infancy and toddlerhood or in adolescence, with relatively fewer studies
examining outcomes in elementary school-age children (Bodovski & Youn, 2010). This section
will review the extant literature on two dimensions of parenting, parent depression and parent
school involvement, in relation to children’s academic and psychosocial development, with
particular attention to research on elementary school-age children with and without LD.
Parental Depression. According to the DSM-5, depression in adults is characterized by
episodic or persistent symptoms of depressed mood signified by feelings or observations of
sadness, emptiness, and hopelessness, loss of interest or pleasure in daily activities, maladaptive
changes in sleeping and eating patterns, fatigue, restlessness or psychomotor retardation, feelings
of worthlessness or excessive guilt, difficulty thinking or concentrating, and/or suicidal ideation
or attempts (APA, 2013). These symptoms impede an individual’s ability to function in daily
life, and are not due to a medical condition or the use of substances. Parent depression,
particularly maternal depression, has been widely studied in relation to child outcomes
(Goodman, 2007; Goodman et al., 2011; National Research Council and Institute of Medicine,
2009). Many studies indicate that high levels of parent depression are associated with increased
behavioral problems in toddlers (Coyne, Low, Miller, Seifer, & Dickstein, 2007; Dietz, Jennings,

41

Kelley, & Marshal, 2009; Gross, Shaw, Burwell, & Nagin, 2009). Elementary school-age
children with highly depressed mothers have been shown to have more internalizing and
externalizing problems and lower reading skills in kindergarten and first grade (Ashman,
Dawson, & Panagiotides, 2008; Kurstjens & Wolke, 2001; Sohr-Preston & Scaramella, 2006).
Maternal depression has also been linked to increased risk-taking behavior (e.g., substance use
and sexual risk behavior), increased likelihood of internalizing problems including depression,
increased problem behaviors, social impairments, and lower academic achievement in
adolescents (Champion et al., 2009; Cortes et al., 2009; Goosby, 2007; Gross, Shaw, &
Moilanen, 2008; Gross, Shaw, Moilanen, Dishion, & Wilson, 2008; Martins & Gaffan, 2000;
Mazza et al., 2009; Rice, Lifford, Thomas, & Thapar, 2007).
Researchers have hypothesized about the mechanisms by which parental depression
negatively influences child development. Several researchers have found that higher parental
depression is associated with decreased parent engagement in their children’s development and
education (McLoyd, 1998; National Research Council and Institute of Medicine, 2009). Some
researchers have noted that parental depression is associated with fewer positive interactions
between parent and child, more disengaged parenting practices, and more inconsistent and
punitive parenting. For example, Bodovski and Youn (2010) found that parental depression was
associated with decreased parental warmth and increased use of physical punishment, and those
children with more depressed parents in kindergarten had lower reading and math achievement
in fifth grade using the Early Childhood Longitudinal Study-Kindergarten dataset. Other
researchers have found that mothers living in poverty are more likely to experience depression,
and that maternal depression mediates the negative effects of poverty on cognitive, behavioral,
and social-emotional outcomes for children (Belle, 1990; Carlson & Corcoran, 2001; Johnson &

42

Flake, 2007; McLoyd, 1990; Pachter et al., 2006). Moreover, some research suggests that
maternal depression is associated with increased behavioral problems across racial-ethnic
groups. For example, Pachter and colleagues (2006) found that maternal depression is directly
related to increased behavioral problems in African American children age six to nine, and this
effect was partially mediated by parenting practices in Caucasian and Latino children. Thus,
parental depression may influence children’s development via direct and indirect pathways.
Despite research examining the influence of parent depression on child outcomes, no
studies have examined the influence on parental depression in children with LD. Several studies
have examined other aspects of parent internalizing psychopathology including, anxiety and
parenting stress, in relation to child outcomes for children with other chronic conditions. One
study examined symptoms of anxiety in mothers of children in middle school with specific LD,
which included children with dyslexia, dysgraphia, and dyscalculia (83% of children in the
sample were diagnosed with all three LD’s) (Karande, Kumbhare, Kulkarni, & Shah, 2009).
They found that mothers of children with LD had high levels of anxiety, with concerns mainly
related to their child’s academic performance, behavior and prognosis in life. Despite a lack of
control group, this study provides some evidence that parents of children with LD experience
increased internalizing psychopathology. Another study examining parenting stress found that
mothers of children with LD reported higher levels of parenting stress compared to mothers of
children without LD (Antshel & Joseph, 2006). In this study, mothers of children with RD had
the highest level of stress. High parenting stress has been associated with increased maternal
depression (Hassiotis, 1997; Lipman, Boyle, Dooley, & Offord, 2002; Moes, Koegel,
Schreibman, & Loos, 1992), thus, parenting stress may mediate the effects of maternal
depression on child outcomes. Overall, research suggests that parental depression and stress are

43

higher among parents of children with chronic conditions, such as ADHD or other health
problems (National Research Council and Institute of Medicine, 2009), however little is known
about the influence of parent depression on children with LD.
In sum, research suggests parental depression is related to a variety of negative outcomes
in children from infancy through adolescence, including lower academic achievement, increased
internalizing and externalizing problems, and social impairments. Research also suggests that
parental depression is related to socioeconomic status, and that the negative effects of parental
depression on child development may operate differentially across racial-ethnic groups. Finally,
research suggests that parental depression is associated with parenting practices, including lower
levels of parental warmth, increased punitive punishment, and lower levels of parent
involvement in children’s development and learning. Further research is warranted to investigate
how parent depression influences the academic and psychosocial outcomes in children with LD.
Parent School Involvement. Parent school involvement is a broad term that
encompasses parent behaviors and perceptions that are related to their child’s development,
cognitively, educationally, behaviorally, socially, and emotionally. Parent involvement has been
conceptualized in several ways including, parents’ discussion about schooling and their
aspirations for their child (Bloom, 1980), parents’ participation in school and educational
activities (Stevenson & Baker, 1987), and parents’ rules and monitoring at home related to
schooling and education (Marjoribanks, 1983).
Given the wide range of behaviors that constitute parent involvement, it is important for
studies to define the parameters. In this study, parent involvement is defined as parents’
participation in school activities such as, contact with teachers and attending school events. It is
important to recognize that this type of parent school involvement may not represent all parents

44

equally. For example, Hill and colleagues (2004) found that parent school involvement, as
broadly defined as attending school events, knowing about their child’s school performance, and
having communication with their child’s teacher, was related to lower rates of behavioral
problems for students whose parents achieved higher educational levels (a proxy for socioeconomic status). Additionally, Grolnick and colleagues (1997) found family SES was a strong
predictor of parent’s involvement in school and at home, however it was not associated with
personal involvement such as sharing aspirations for their child’s education were not
significantly related to SES. Regarding ethnic differences, studies have also found that parent
school involvement was related to improved achievement for African-American students (Hill et
al., 2004). Overall, studies have indicated that effects of parent school involvement, as defined
by school-based activities in comparison to home-based activities, on academic and psychosocial
outcomes vary. Relations may differ by socio-demographic variables including SES and race
(Hill & Craft, 2003), and differences may be mediated by the student’s level of psychosocial
competence and academic skills.
Parental involvement has been associated with a variety of positive child outcomes. Early
on, parent involvement helps children transition from home to formal schooling in preschool or
kindergarten, and connections between parents and school help children develop early skills vital
to later school success (Christenson, 2000; Epstein, 1994; Pianta, Rimm-Kaufman, & Cox,
1999). Studies have found that parent involvement is positively related to early literacy and math
skills (Dickinson & DeTemple, 1998; Gauvain, Savage, & McCollum, 2000). In a study by
McWayne and colleagues (2004), children in kindergarten whose parents showed higher levels
of involvement (e.g., providing enrichment at home) had higher social skills, self-regulation
skills, and were more cooperative in school compared to children whose parents were less

45

involved. Several studies have also suggested that parental involvement is positively related to
children’s academic achievement across grades and within families of varying socioeconomic
statuses and racial-ethnic backgrounds (Christenson, Rounds, & Gorney, 1992; Fan & Chen,
2001; Fehrmann, Keith, & Reimers, 1987; Izzo, Weissberg, Kasprow, & Fendrich, 1999; Jeynes
2005, 2007; Keith, Keith, Quirk, Cohen-Rosenthal, & Franzese, 1996; Keith et al., 1998; Miedel
& Reynolds, 2000; Shaver & Walls, 1998; Stevenson & Baker, 1987). In contrast, some studies
have not found any significant relations between parent involvement and children’s academic
achievement (Sheldon, 2002; Watkins, 1997). Mixed results may be due to varying definitions of
parental involvement, as well as grouping heterogeneous populations.
Parental involvement has also been linked to children’s motivation in school (GonzalezDeHass, Willems, & Holbein, 2005; Grolnick & Slowiaczek, 1994; Sanders, 1998). For
example, Ames and colleagues (1993) found that when second through fourth grade children
perceived their parents are more involved in their education (e.g., knowledge of class and
teacher), they had more positive academic competency beliefs and intrinsic motivation. In
another study, Izzo and colleagues (1999) found that parent participation in school activities was
positively related to children’s engagement in school in kindergarten through third grade;
however increased parent-teacher contact was related to lower school engagement because the
nature of parent-teacher contacts was primarily due to behavioral concerns. On the other hand,
some studies indicate that early parent involvement, including contact with teachers, may
actually serve as a protective factor because parents are more aware of their child’s academic
difficulty early on which may lead to early intervention (Miedel & Reynolds, 2000).
The extant literature on the influence of parent involvement on outcomes for children
with LD specifically is relatively sparse in comparison to the literature examining parent

46

involvement in typically developing samples or children with other risk factors such as, low
socio-economic status. One of the first articles to examine parental involvement in relation to
children with LD highlighted the importance of interactions between parents and educational
staff, stating “when parents are actively involved with professionals, three processes occur: the
parents and professional exchange information, the parents are encouraged to grow in their role,
and a trusting, productive relationship between parents and teachers is built.” (McLoughlin,
Edge, & Strenecky, 1978, p.291-292). This article also identified several stages and ways in
which parents may be involved in promoting the academic success of their child with an LD. For
example, monitoring and being aware of early signs of difficulty, participating in school, home,
and community activities that promote development of children’s skills, and advocating for early
intervention. The importance of parental involvement in the academic and behavioral
development of children with disabilities is now reflected in special education law which
encourages and specifies the rights of parents to be involved in their children’s educational
planning (IDEA, 2004).
Given that parental involvement increases school readiness, and school readiness has
been found to positively influence later academic achievement, it is reasonable to suggest that
early parental involvement may help set children on a more positive academic and psychosocial
trajectory. In a more recent study using the ECLS-K data, parents’ engagement in home literacy
activities in kindergarten positively predicted academic achievement in eighth grade indirectly
via children’s kindergarten achievement (Froiland, Peterson, & Davison, 2012). Children who
develop LD’s may have a variety of early risk factors including, neurobiological and
environmental vulnerabilities (e.g., genetic predisposition, poverty, parental depression). Parental
involvement, especially early on in a child’s education, may serve a protective factor in children

47

with LD that alters the influence of risk factors and promotes better psychosocial and academic
outcomes. Thus, research suggests that parental involvement leads to improved academic, socialemotional, behavioral, and motivational outcomes in children; however, it is unclear how these
mechanisms operate in children with LD and what factors may hinder or promote parent
involvement in school-based activities.
Purpose of Present Study
There is a plethora of evidence suggesting that children with LD experience more
academic difficulties and are more likely to experience academic failure and be at risk for school
dropout (Reschly & Christenson, 2006). Additionally, there is strong research suggesting that
children with LD experience more psychosocial problems, including internalizing and
externalizing problems and poor interpersonal skills (Willcutt et al., 2013). These problems tend
to persist, and put children with LD at greater risk for increased mental health problems in
adulthood and juvenile delinquency (Cortiella & Horowitz, 2014). However, there are several
gaps and methodological issues in the existing literature. First, many studies use small samples
that are not representative of the population at large (La Greca & Stone, 1990; Martinez &
Semrud-Clikeman, 2004; Maughan, Rowe, Loeber, & Stouthamer-Loeber, 2003). Second,
several studies do not use control samples thereby limiting the conclusions that can be drawn in
relation to children with LD (Karande, Kumbhare, Kulkarni, & Shah, 2009; Trzesniewski,
Moffitt, Caspi, Taylor, & Maughan, 2006). Third, several studies group children with various
subtypes of LD into one category thereby ignoring the differences among these heterogeneous
groups and limiting the ability to understand how children defined by different academic
problems function (Gadeyne, Ghesquiere, & Onghena, 2004; Rourke & Fuerst, 1992). This study
aims to address these issues by using a large, nationally representative dataset, and examining

48

group differences among children with subtypes of LD including, math disability, reading
disability, co-morbid math and reading disability, and children without LD.
These same methodological issues plague the small number of studies that have
examined motivational factors including, self-regulated learning, ability beliefs, and intrinsic
motivation in children with LD. Some studies have demonstrated that children with LD have
more difficulty with self-regulated learning, more negative ability beliefs, and lower intrinsic
motivation for learning, but results are far from conclusive. Moreover, despite research
indicating the relation between low academic performance, increased psychosocial problems,
and lower motivation for learning, it is unclear how these three factors influence one another in
children with subtypes of LD.
Very little research has examined the specific role of parent involvement as a protective
factor and parent depression as a risk factor in children with LD. Research suggests that
increased parent involvement in beneficial for children with LD, but much of the research is
theoretical in nature. Likewise, research examining the effects of parental depression on child
outcomes mainly focus on typically developing children and does not specifically examine
children who develop learning disabilities. Finally, some of the existing research studies do not
account for the effects of socio-economic status and race-ethnicity on academic and psychosocial
outcomes for children with LD. This study uses statistical methods that can account for these
potential influences within a complex model.
The purpose of the current study is to test a structural equation model examining the
relations between parent depression, parent involvement, early academic achievement and
psychosocial behaviors in kindergarten, and later psychosocial behaviors, motivation, and
academic achievement in fifth grade among a sample of children with and without LD. Using

49

special education eligibility in fifth grade to identify students who have reading disability, math
disability, co-morbid reading and math disability, and no LD, the current study examines
whether parent depression served as a risk factor and parent involvement served as a protective
factor, and differentially predicted improved outcomes for children with and without LD.
Structural equation modeling (SEM) will be used test the proposed model (see Figure 2 and
Figure 3).
Research Questions and Hypotheses
The following research questions were proposed. A simplified conceptual model is
presented in Figure 1. The full conceptual model is explained in detail in the following chapter
(colored lines represent pathways examined in different research questions). Research question
one focuses on specifying relations between constructs in the conceptual model. Research
question two and three focus on testing whether the specified relations are the same or different
for children with and without LD.
Research Question 1. What are the relations between parent depression and parent school
involvement on early child academic achievement and psychosocial in kindergarten, and
motivation, psychosocial behaviors, and academic achievement outcomes in fifth grade?
1a. Does parent depression indirectly predict early academic achievement and
psychosocial behaviors in kindergarten via parent school involvement? This question is depicted
by red paths in Figure 2 and Figure 3.
Hypothesis 1a. Lower levels of parent depression will indirectly predict higher levels of
academic achievement and more positive psychosocial behaviors in kindergarten via parent
school involvement. Lower levels of parent depression will be directly related to higher levels of

50

parent school involvement, which in turn, will predict higher levels of academic achievement
and more positive psychosocial behaviors. Parent school involvement is a mediating variable.
1b. Does parent school involvement in kindergarten predict children’s motivation and
psychosocial behaviors in fifth grade given their level of academic achievement and
psychosocial behaviors in kindergarten? This question is depicted by green paths in Figure 2 and
Figure 3.
Hypothesis 1b. As the level of academic achievement and psychosocial behaviors in
kindergarten increases (e.g., more positive performance) higher levels of parent school
involvement will predict higher levels of motivation and more positive psychosocial behaviors in
fifth grade. Early academic achievement and psychosocial behaviors are moderating variables.
1c. Does early academic achievement and psychosocial behaviors in kindergarten predict
academic achievement in fifth grade? This question is depicted by blue paths in Figure 2 and
Figure 3.
Hypothesis 1c. Higher levels of academic achievement and more positive prosocial
behaviors in kindergarten will predict higher levels of academic achievement in fifth grade.
1d. Does motivation and psychosocial behaviors in fifth grade predict academic
achievement in fifth grade? This question is depicted by yellow paths in Figure 2 and Figure 3.
Hypothesis 1d. Higher levels of motivation and more positive prosocial behaviors in fifth
grade will predict higher levels of academic achievement in fifth grade. Previous academic and
psychosocial performance will be controlled given that the model is run with all variables
simultaneously.
Research Question 2. Do the relations between parent depression and parent school
involvement on early child academic achievement and psychosocial in kindergarten, and

51

motivation, psychosocial behaviors, and academic achievement outcomes in fifth grade differ for
children identified with learning disabilities in 5th grade and children without learning
disabilities?
Hypothesis 2. The relations between parenting variables and academic, psychosocial, and
motivation outcomes hypothesized in research question 1 will be stronger for high-risk children
with LD compared to children without LD. In other words, the magnitude of the relations will be
greater for children with LD compared to children without LD. Thus, parent depression will
serve as a greater risk factor, parent school involvement will serve as a greater protective factor,
and the relations between academic achievement, motivation, and psychosocial behavior will be
greater for children with LD compared to children without LD.
Research Question 3. Does type of learning disability (math disability, reading disability,
math-reading disability) moderate the relations between parent depression and parent school
involvement on early child academic achievement and psychosocial in kindergarten, and
motivation, psychosocial behaviors, and academic achievement outcomes in fifth grade?
Hypothesis 3. The relations between the parenting variables and academic, psychosocial,
and motivation outcomes hypothesized in research question 1 will be strongest for children with
the highest level of risk, children with RD-MD, then for children with one LD (MD or RD), then
for children with no LD. In other words, the magnitude of the relations will be the least for
children without LD, greater for children with one LD, and the greatest for children with comorbid RD-MD.

52

Figure 1. Simplified Conceptual Model
Parent Mental
Health and
Parenting
Behavior

Early Academic
Achievement
and
Psychosocial
Behaviors

Covariates:
• Gender
• Race
• SES
• Age at Assessment

Motivation and
Psychosocial
Behaviors

Academic
Achievement

53

CHAPTER 3
METHOD
Study Model
The current study aimed to test a structural equation model examining the relations
between early parenting variables, namely parent depression and parent school involvement, and
outcomes in kindergarten and fifth grade. Outcomes of interest included early academic
achievement and psychosocial behaviors in kindergarten, and motivation, academic achievement,
and psychosocial behaviors in fifth grade among a sample of children with and without LD. A
structural equation model specified four latent constructs and five observed variables. The latent
variables included parent depression, parent school involvement, early psychosocial behaviors
(kindergarten), and psychological behaviors in fifth grade. The observed variables included
standard scores on assessments of reading and math in kindergarten and fifth grade, and a mean
score on a measure of motivation. Covariates included the child’s gender, race-ethnicity, SES,
and age at kindergarten assessment. Structural equation modeling (SEM) was used to test the
proposed model (see Figure 2 and Figure 3). The model in Figure 2 depicts relations for
academic achievement in reading and the model in Figure 3 depicts relations for academic
achievement in mathematics. The models are presented separately for simplicity; however
academic achievement will be run simultaneously.

54

Figure 2. Detailed Conceptual Model (Reading)

Parent
Depression

Motivation

Early
Academic
Achievement:
Reading

Academic
Achievement:
Reading

Early Parent
School
Involvement

Early
Psychosocial
Behavior

Psychosocial
Behavior

Note: Latent variables are shown in circles. The dashed lines represent the interaction
between the endogenous variable (parent school involvement) and the moderator
variables.
Covariates:
• Gender
• Race
• SES
• Age at Assessment

55

Figure 3. Detailed Conceptual Model (Math)

Parent
Depression

Motivation

Early
Academic
Achievement:
Math

Academic
Achievement:
Math

Early Parent
School
Involvement

Early
Psychosocial
Behavior

Psychosocial
Behavior

Note: Latent variables are shown in circles. The dashed lines represent the interaction
between the endogenous variable (parent school involvement) and the moderator
variables.
Covariates:
• Gender
• Race
• SES
• Age at Assessment

56

Study Design
Data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999
(ECLS-K) kindergarten and fifth grade data collection waves were used to examine the
hypothesized relation between parental depression and early parent school involvement, early
academic achievement and psychosocial behaviors, and motivation, psychosocial behaviors, and
academic achievement outcomes in children with and without learning disabilities in
mathematics and/or reading. The U.S. Department of Education’s (DOE) National Center for
Education Statistics (NCES) within the Institute of Education Science (IES) conducted the
ECLS-K by following a nationally representative sample of children entering kindergarten in the
fall of 1998 through the eighth grade. This study took advantage of data at two time points to
understand the influence of early parenting variables on child motivation, psychosocial
behaviors, and academic achievement within a large sample of children identified with and
without learning disabilities.
Data Collection. Data were collected using a multi-informant and multi-method design,
allowing for in-depth analysis of child, family, school, and other environmental variables related
to child development and education. Data were collected at multiple time points: the fall and
spring of kindergarten (also known as base year; 1998-1999), the fall and spring of first grade
(1999-2000), the spring of third grade (2002), the spring of fifth grade (2004), and the spring of
eighth grade (2007) (see Table 1). Children were sampled in the fall of 1998 if they were
entering kindergarten. Approximately 81% of children sampled in the fall of kindergarten
attended kindergarten for the first time, approximately 4% of children repeated kindergarten, and
approximately 15% of children were missing data on kindergarten entry status.

57

Several sampling procedures were used in subsequent rounds of data collection to address
issues of representativeness of the sample to the population and attrition. In the spring of 1999
(kindergarten spring), an additional 1,430a children were sampled for the study via efforts to
convert schools that had originally refused to participate in the fall. In the fall of 1999 (first grade
fall), data were collected for children who had been sampled in kindergarten and stayed in the
same school for first grade (i.e., non-movers), and for a random 50% of students who were
sampled in kindergarten but transferred to another school for first grade (i.e., movers). However,
given that many children do change schools between kindergarten and first grade schools, a high
number of students moving to the same first grade location were treated as non-movers. This
procedure was done to limit costs associated with data collection. Finally, a half-open interval
sampling procedure was implemented in the same 50 percent subsample of schools used in the
fall first grade data collection time frame to “freshen” the spring 2000 first grade sample. This
was done to include first grade students who had not been enrolled in kindergarten in 1998-1999,
and thus, would have not had a chance to be included in the base year sample. A total of 170a
students were added to the first grade sample via the freshening procedures. For this study,
children added during the first grade freshening procedures were not included because they are
missing data collected during the spring of kindergarten (see Missing Data and Exclusion
Criteria sections below).
The primary methods of data collection involved interviews, questionnaires, and direct
child assessments. Computer-assisted telephone or in-person interviewing was used to conduct
parent interviews and questionnaires. The parent interview lasted approximately 65 minutes in
the spring kindergarten round. Participating parents were sent a thank you letter and educational
activity booklet upon completion of the interview. Computer-assisted interviewing and one-on-

58

one untimed administrations of hard-copy instruments were used to conduct direct child
assessments. Self-administered questionnaires were distributed to teachers and school
administrators to complete.
Field staff received training prior to conducting assessments. Assessors were selected
based on previous experience with other large research studies (e.g., NAEP, TIMSS), and
received intensive training on interviewing techniques and assessments via home training
modules and in-person training sessions. Spanish-bilingual assessors also received additional
training to practice Spanish versions of assessments and interviewing techniques. For the data
collection periods used in this study assessors attended five days of training and field supervisors
attended an additional two days of training prior to the fall kindergarten data collection. Prior to
spring kindergarten data collection assessors attended an additional review of procedures over a
four-week period. Prior to the fifth grade data collection, assessors attended at least eight hours
of home-study training and five to eight days of in-person training. During the fifth grade data
collection period, assessors also completed a certification process involving written exercises and
performing a live, observed assessment. Overall, 99% of assessors scored above an 85% on the
certification procedures (considered passing), and 1% were offered remedial training and
completed a second observed assessment before being cleared. The primary investigator was
granted permission to use the restricted-use ECLS-K data for this project under a restricted-data
license agreement provided to her dissertation faculty chair. A waiver from the Michigan State
University Institutional Review Board was obtained for completion of this project (see Appendix
A).

59

Table 1
Overview of Original ECLS-K Data Collection Sample
Data Collection
Unweighted
Unweighted
1,a
Wave
Sample Size
Number of
Respondents2,a

Age of Study Child
(months)

Data Collection
Period

Kindergarten F

21,390

19,680

Mean
65.79

SD
14.80

Fall 1998

Kindergarten S

22,8103

20,580

74.63

4.87

Spring 1999

First Grade F

6,5104

5,420

79.94

4.97

Fall 1999

First Grade S

21,3605

17,320

86.94

4.84

Spring 2000

Third Grade S

21,360

15,300

111.06

4.77

Spring 2002

Fifth Grade S

16,1406

11,820

134.63

4.93

Spring 2004

Eighth Grade S

12,130

9,720

171.25

6.69

Spring 2007

Note. F = Fall; S = Spring; a All unweighted sample sizes were rounded to the nearest 10 due to
restrictions set forth via the licensing agreement; 1 Sample size represents total number of
children sampled for the study. Some children were non-responders or ineligible; 2 Represents
total number of children with child and/or parent data; 3 approximately 1,430 children were
sampled from schools that were converted from refusals in the fall; 4 Data were collected from
only 30% of sampled schools containing about 27% of base year students; 5 approximately
21,190 children remained eligible after the base year, and 165 children were sample via the
sample freshening procedure; 6 approximately 5,210 children were excluded due to ineligibility
(e.g., moved, hard refusals, no first or third grade data) in fifth grade.

60

ECLS-K Original Sample
The ECLS-K used a multistage, stratified, clustered, equal probability systematic
sampling design. The following information is obtained from the publicly available U.S.
Department of Education National Center of Education Statistics Early Childhood Longitudinal
Study, Kindergarten Class of 1998 –99: User’s manual for the ECLS-K base year public-use data
files and electronic codebook (2001). The primary sampling units (PSU) consisted of geographic
areas based on groups of counties. The 1990 county-level population data and 1994 population
estimates of five-year-olds by race-ethnicity were used to determine a total of 1,335 PSUs (U.S.
DOE NCES, 2001). One hundred PSUs were selected for the ECLS-K based on census region,
race-ethnicity, and 1988 per capita income. Within each PSU, public schools with a minimum of
24 kindergarteners and private schools with a minimum of 12 kindergarteners were selected
(schools with fewer students were clustered together) (U.S. DOE NCES, 2001). The target
number of schools sampled per PSU was proportional to the weighted measure of size of each
PSU, with a minimum of one school per PSU for any PSU that was so small that it would not
otherwise have been allocated a school. Of the school districts with sampled schools, 75% of
public school districts and 93% of private school districts cooperated with the study. Overall, a
total of 1,280 schools (934 public) were selected for the ECLS-K (U.S. DOE NCES, 2001).
Children that identified as Asian or Pacific Islander (API) were the only groups that were
oversampled to meet the nationally representative sample size criteria. Two independent
sampling strata were created within each school, one for API students and another for the
remaining students. Within each stratum, students were selected using equal probability
systematic sampling. Twins were sampled as a unit (both included), rather than separate. Once
the sample was finalized, parental information was obtained from the schools, and parental

61

consent for each child was obtained. The ECLS-K study followed the consent procedures
specified by participating school districts. Implicit consent was obtained from about half of the
schools via a form sent to each of the selected student’s home. The first wave of data collection
in the fall of 1998 and spring of 1999 (kindergarten year) included a total of 21,260 children
across the country. Within the base year sample, children were White, non-Hispanic (55.2%),
Black or African-American (15.1%), Hispanic, Race Specified (8.3%), Hispanic, Race not
Specified (9.4%), Asian (6.4%), Native Hawaiian or Other Pacific Islander (1.0%), Native
American or Alaskan Native (1.8%), and Multi-racial (2.4%); 51.1% of children were male
(n=10,866); the average age of children was 65.8 months (SD = 14.8). In the spring of 2004
(fifth grade), a total of 11,820 children participated in the study. Demographic characteristics of
the fifth grade sample remained roughly the same: White, non-Hispanic (57.0%), Black or
African-American (11.4%), Hispanic, Race Specified (9.3%), Hispanic, Race not Specified
(9.8%), Asian (6.9%), Native Hawaiian or Other Pacific Islander (1.3%), Native American or
Alaskan Native (1.9%), and Multi-racial (2.4%); 50.7% of children were male (n= 5,987); the
average age of children was 134.6 months (SD = 4.9).
Missing Data. Given that longitudinal research studies often have missing data due to
attrition (e.g., move school, decline participation, death), the data were analyzed to determine the
number of children missing data at kindergarten and fifth grade time points (approximately 9,620
children did not have any data at either the kindergarten spring or fifth grade spring time point;
after exclusionary criteria were applied approximately 10,720 children did not have any data at
one of the time points of interest). To better understand the nature of the missing data and the
exclusionary factors, comparison tests were calculated to determine if there were significant
differences on demographic variables and predictor variables between children with and without

62

missing data (see Table 2, Table 3, and Table 4). Given that large sample sizes often yield a
higher rate of significant group differences, effect sizes and odds ratios were examined as well.
Although several significant differences emerged between the two groups, only a few yielded
medium to large effect sizes suggesting a more meaningful statistically significant difference
across the groups. There were very small to small effect sizes (Cohen’s d ranged from .02 to .31)
for differences in SES, age at fifth grade, teacher-rated early psychosocial behaviors in
kindergarten, and student-rated motivation in fifth grade. There were medium effect sizes for
differences in academic achievement (Cohen’s d ranged from .42 to .44), with the included
group having higher scores, on average of 4 T-score points, on reading and math achievement at
both time points. Medium to large effect sizes were noted for fifth grade psychosocial behavior
(Cohen’s d ranged from .55 to .81), with the approaches to learning scale having the most
significant difference across groups. On average, students included in the study were rated has
having higher approaches to learning behavior by approximately .5 points. Differences in the
academic achievement and fifth grade psychosocial behavior between the included and excluded
study group is a limitation of the study. However, these findings are not entirely unexpected as
students with more pervasive disabilities and impairments (e.g., cognitive disability, autism
spectrum disorder) were excluded from the study and have a higher likelihood of lower academic
and psychosocial skills as they may mature at a slower rate than their typical peers as the
demands of school increase over time. Also, the excluded sample had far fewer children with
learning disabilities; students with learning disability were 3.11 times more likely to be included
in the study. However, the inclusion of children with learning disabilities is important for the aim
of this study and the sample of children without learning disabilities is similar in both groups.
Instead of imputing a large amount of missing data, sample weights were applied to all analyses

63

to account for non-response biases and maintain generalizability of the final results to the
population. Robust weighted least square and maximum likelihood estimation options within the
MPlus program were used to test the factor structure of each latent variable and the full structural
model. The final sample used in the structural model consisted of 10,630 children.

64

Table 2
Comparison of Demographic Information Between Included and Excluded Samples
Characteristics
Included Sample
Excluded Sample
Chi-Square (df)
Na (%)
Na (%)
Odds Ratio (95% CI)
Total
10,630
10,720
Child Gender
50.67 (1), p<.001***
1.22 (1.15-1.28)
Male
5,180 (48.7%)
5,690 (53.1%)
Female
5,450 (51.3%)
4,930 (46.0%)
Child
297.60 (8), p<.001***
Race/Ethnicity
1.04 (1.03-1.06)
White/Non5,930 (55.7%)
5810 (54.2%)
Hispanic
Black/African1,220 (11.5%)
1,980 (18.5%)
American
Hispanic, Race
960 (9.0%)
800 (7.5%)
Specified
Hispanic, No
1,120 (10.5%)
890 (8.3%)
Race Specified
Asian, Non800 (7.5%)
570 (5.3%)
Hispanic
Native Hawaiian
140 (1.3%)
80 (0.7%)
or Other Pacific
Islander
Native
210 (2.0%)
170 (1.6%)
American or
Alaskan Native
More Than One
230 (2.2%)
280 (2.7%)
Race
Learning
144.12 (1), p<.001***
Disability Status
3.11 (2.56-3.78)
With Learning
410 (3.9%)
140 (1.3%)
Disability
Without
10,220 (96.1%)
10,580 (98.7%)
Learning
Disability
Note: a All unweighted sample sizes were rounded to the nearest 10 due to restrictions set
forth via the licensing agreement.

65

Table 3
Comparison of Covariates and Predictor Variables for Included and Excluded Samples
Variables
Included
Excluded
Independent Samples tCohen’s d
Sample
Sample
test (df)
Mean (SD)
Mean (SD)
Kindergarten SES
.04 (.80)
-.03 (.80)
-6.96 (20098), p<.001***
.09
Age at
68.41 (4.32)
68.51 (4.66)
1.52 (18894), p=.13
.02
Assessment (K,
1999)
Age at
134.54 (4.38)
135.81(5.75)
7.11 (1229), p<.001***
.25
Assessment
(5th grade, 2004)
Kindergarten
Achievement
Direct Math
51.63 (9.69)
47.64 (9.78)
-12.64 (1270), p<.001***
.41
Assessment
.42
Direct
51.56 (9.58)
47.50(9.92)
-12.60 (1251), p<.001***
Reading
Assessment
Fifth Grade
Achievement
Direct Math
51.54 (9.52)
47.40 (10.31) -12.68 (1292), p<.001***
.42
Assessment
Direct
51.46 (9.56)
47.20 (10.01) -13.27 (1300), p<.001***
.44
Reading
Assessment
Early
Psychosocial
Behaviors
3.20 (.65)
2.99 (.71)
-21.18 (18141),
.31
Teacher-Rated
p<.001***
.29
Approaches to
.27
Learning
3.25 (.59)
3.07 (.66)
Teacher-Rated
-19.28 (17916),
.31
Self-Control
3.19 (.61)
3.02 (.66)
p<.001***
.23
Teacher-Rated
Interpersonal
-18.07 (17983),
Skills
1.58 (.58)
1.78 (.70)
p<.001***
Teacher-Rated
Externalizing
1.52 (.48)
1.64 (.55)
Teacher-Rated
21.79 (17209), p<.001***
Internalizing
15.87 (17735), p<.001***
Fifth Grade
Psychosocial
Behavior

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Table 3 (cont’d)
Teacher-Rated
3.11 (.66)
2.58 (.65)
Approaches to
Learning
Teacher-Rated
3.27 (.58)
2.90 (.66)
Self-Control
Teacher-Rated
3.12 (.62)
2.71 (.67)
Interpersonal
Skills
Teacher-Rated
1.61 (.56)
1.96 (.70)
Externalizing
Teacher-Rated
1.60 (.51)
1.98 (.64)
Internalizing
Motivation
Student-Rated
2.75 (.63)
2.56 (.69)
School
Note: Equal variance not assumed for all variables.

67

-25.65 (1317), p<.001***

.81

-17.40 (1226), p<.001***

.60

-18.71 (1245), p<.001***

.64

16.11 (1209), p<.001***

.55

18.59 (1200), p<.001***

.66

-8.67 (1291), p<.001***

.29

Table 4
Comparison of Covariates and Predictor Variables for Included and Excluded Samples
(Ordinal)
Variables
Included Sample
Excluded
Chi-Square (df)
Na (%)
Sample
Odds Ratio (95% CI)
Na (%)
Parent Depression
Felt bothered
76.75(3), p<.001***
Never
5260 (49.5%)
4390 (41.0%)
.96 (.92-1.00)
Some Time
3490 (32.8%)
3140 (29.3%)
Moderate Amount
770 (7.2%)
770 (7.2%)
Most of Time
260 (2.4%)
420 (3.9%)
Poor appetite
102.21(3), p<.001***
Never
7090 (66.7%)
5850 (54.6%)
.89 (.86-.93)
Some Time
2010 (18.9%)
1980 (18.4%)
Moderate Amount
390 (3.7%)
450 (4.2%)
Most of Time
300 (2.8%)
470 (4.3%)
Felt the “blues”
69.91(3), p<.001***
Never
7880 (74.1%)
6660 (62.2%)
.99 (.93-1.05)
Some Time
1460 (13.8%)
1510 (14.1%)
Moderate Amount
250 (2.3%)
280 (2.6%)
Most of Time
170 (1.6%)
270 (2.5%)
Trouble focusing
56.43(3), p<.001***
Never
5750 (54.1%)
4790 (44.7%)
.98 (.93-1.03)
Some Time
3300 (31.0%)
3070 (28.7%)
Moderate Amount
450 (4.2%)
490 (4.6%)
Most of Time
270 (2.6%)
380 (3.5%)
Felt depressed
76.30(3), p<.001***
Never
7210 (67.8%)
5960 (55.6%)
.97 (.91-1.04)
Some Time
2090 (19.6%)
2170 (20.3%)
Moderate Amount
250 (2.3%)
300 (2.8%)
Most of Time
230 (2.1%)
300 (2.8%)
Felt things were effortful
86.06(3), p<.001***
Never
5590 (52.6%)
4530 (42.2%)
.93 (.90-.96)
Some Time
2960 (27.9%)
2760 (25.7%)
Moderate Amount
500 (4.7%)
530 (5.0%)
Most of Time
700 (6.6%)
900 (8.4%)
Felt fearful
15.04(3), p=.03**
Never
8320 (78.3%)
7270 (67.8%)
1.04 (.98-1.10)
Some Time
1160 (10.9%)
1130 (10.5%)
Moderate Amount
160 (1.5%)
190 (1.7%)
Most of Time
130 (1.2%)
140 (1.3%)
Difficulty sleeping
75.14(3), p<.001***
Never
4870 (45.8%)
4020 (37.5%)
.95 (.92-.99)
Some Time
3420 (32.2%)
3020 (28.1%)
Moderate Amount
750 (7.1%)
750 (7.0%)

68

Table 4 (cont’d)
Most of Time
Talked less
Never
Some Time
Moderate Amount
Most of Time
Felt lonely
Never
Some Time
Moderate Amount
Most of Time
Felt sad
Never
Some Time
Moderate Amount
Most of Time
Difficulty “to get going”
Never
Some Time
Moderate Amount
Most of Time
Parent School Involvement
Contact school
Yes
No
Attended open house
Yes
No
Attended PTO
Yes
No
Attended parent group
Yes
No
Attended teacher
conference
Yes
No
Attended school event
Yes
No
Volunteered at school
Yes
No

730 (6.9%)

940 (8.8%)

7930 (74.6%)
1440 (13.5%)
260 (2.5%)
150 (1.4%)

6820 (63.6%)
1450 (13.6%)
250 (2.4%)
210 (2.0%)

7910 (74.4%)
1480 (13.9%)
210 (2.0%)
170 (1.6%)

6680 (62.3%)
1510 (14.1%)
250 (2.4%)
300 (2.8%)

6130 (57.7%)
3130 (29.4%)
330 (3.1%)
190 (1.7%)

5110 (47.7%)
2960 (27.6%)
400 (3.7%)
270 (2.5%)

5830 (54.9%)
3210 (30.2%)
440 (4.2%)
280 (2.7%)

4900 (45.7%)
2980 (27.8%)
460 (4.3%)
400 (3.7%)

5160 (48.6)
4800 (45.2)

5190 (48.4)
3800 (35.4)

7520 (70.7)
2430 (22.9)

6310 (58.9)
2650 (24.7)

3680 (34.7)
6270 (59.0)

2790 (26.1)
6170 (57.6)

920 (8.6)
9040 (85.0)

700 (6.5)
8270 (77.2)

33.98(3), p<.001***
1.06 (1.00-1.12)

83.35(3), p<.001***
.89 (.84-.95)

60.88(3), p<.001***
1.00 (.94-1.06)

51.49(3), p<.001***
1.00 (.95-1.04)

66.56(1), p<.001***
1.27 (1.20-1.35)
63.33(1), p<.001***
.77 (.72-.82)
71.74(1), p<.001***
.77 (.73-.82)
12.75(1), p<.001***
.83 (.75-.92)
27.64(1), p<.001***
.81 (.75-.88)

8570 (80.6)
1400 (13.1)

7470 (69.7)
1500 (14.0)

6870 (64.6)
3090 (29.0)

5750 (53.7)
3220 (30.0)

5140 (48.4)
4820 (45.3)

4020 (37.5)
4950 (46.2)

69

50.18(1), p<.001***
.80 (.76-.85)
87.65(1), p<.001***
.76 (.72-.81)

Table 4 (cont’d)
Participated in fundraiser
43.87(1), p<.001***
Yes
6140 (57.8)
5110 (47.6)
.82 (.78-.87)
No
3810 (35.9)
3860 (36.0)
Note: a All unweighted sample sizes were rounded to the nearest 10 due to restrictions set
forth via the licensing agreement.

70

Final Sample for Current Study
The final sample for the current study included children from the ECLS-K data set who
had parent and teacher reports of psychosocial behavior, direct assessment of math and reading
skills in kindergarten and fifth grade, and self-report of motivation in fifth grade. School staff,
namely the primary special education teacher, also completed a questionnaire for each child with
an IEP which indicated their primary disability in the spring of fifth grade. This information was
used to determine students identified with a primary learning disability in math, reading, or both
math and reading in fifth grade. All unweighted sample sizes reported in this document were
rounded to the nearest 10 due to restrictions set forth via the licensing agreement.
Participants assigned the longitudinal sample weight C2_6FP0 were retained for the
current study. The longitudinal sample weight C2_6FP0 was assigned to children who had parent
interview data across four rounds of data collection (spring kindergarten, spring first grade,
spring third grade, and spring fifth grade) in conjunction with child assessment, school, teacher
or classroom data. This sample weight is more restrictive than a weight that would account for
just spring kindergarten and spring fifth grade data, but it is the weight that best fits the analysis
based on the available weights published in the ECLS-K restricted dataset. Using a more
restrictive weight still adjusts for the current studies intended analyses, and will allow for
generalizability to the kindergarten class of 1998-1999.
The final sample consisted of 10,630 children. Mean child age at each time point is
reported in Table 5. Children in the final sample were approximately 56% White, non-Hispanic,
11% Black or African-American, 9% Hispanic, race specified, 10% Hispanic, no race specified,
7% Asian, 1% Native Hawaiian or Other Pacific Islander, 2% Native American or Alaskan
Native, and 2% Multi-racial; 5,712 were male (50.3%). The majority of children entered

71

kindergarten in the fall of 1998 for the first time (n=8,900; 83.7%). Approximately 3% of
children repeated kindergarten and 13% of children did not have data on kindergarten entry
status. By the fifth grade data collection period (spring 2004), a small percentage of students had
been retained and were in a grade below fifth grade (n=910; 8.6%) and an even smaller
percentage of students had been promoted to a grade above fifth grade (n=20; 0.2%). Within this
study, children’s age at assessment was used as a covariate to control for effects due to
maturation (see Covariates section below for further discussion on retention). See Table 6 for
child demographic characteristics.
Exclusion Criteria. Given the interest in the relations between parent involvement,
parental depression, motivation, psychosocial behaviors, and academic achievement in children
with learning disabilities, children with intellectual disability, neurological conditions (e.g.,
traumatic brain injury, cerebral palsy), neurodevelopmental disorders (e.g., pervasive
developmental disorder, autism spectrum disorder, attention-deficit and hyperactivity disorder),
and mental health issues were excluded from the sample so as not to confound results. Exclusion
was based on parent and/or school report of a diagnosis of developmental delay, intellectual
disability, cerebral palsy, pervasive developmental disorder, autism spectrum disorder, visual or
hearing impairment, attention-deficit and hyperactivity disorder, anxiety or depression disorder,
or “other disability” during kindergarten and/or fifth grade; 1,100 children met these exclusion
criteria. Participants who are missing data entirely across one of the two waves of data collection
of interest (kindergarten or fifth grade) were also excluded (9,620 children did not have any data
at either the kindergarten spring or fifth grade spring time point). No other exclusionary criteria
were applied.

72

Learning Disabled Sample. Overall, a total of 410 children with a learning disability
were included in the current study, of which 180 children had a reading disability, 20 children
had a math disability, and 220 children had a reading and math disability. Field supervisors were
provided the name of all fifth grade students who received special education services and their
primary special education teacher (e.g., teacher who managed the child’s Individualized
Education Plan (IEP) or spent the most time providing services to the child). Identification of
learning disability was based on special education records and report of the child’s main
disability category by the child’s fifth grade primary special education teacher. Thus, in this
study diagnosis of a learning disability is presumed to follow IDEA definition of specific
learning disability. Schools likely used a pattern of strengths or weaknesses model, response-tointervention, or discrepancy paradigm to identify a learning disability. Direct information about
the child’s learning disability category (e.g., reading, math, or both) was unavailable, so LD type
was determined based on the special education teacher’s report of the child’s primary IEP goals.
If the child had primary reading goals, it was assumed that they demonstrated academic
difficulty in reading and thus, were categorized as LD-Reading. If the child had primary math
goals, it was assumed that they demonstrated academic difficulties in math, and were categorized
as LD-math. If the child had primary goals in both reading and math, it was assumed that they
had academic difficulties in both subjects and were categorized as LD-Co-morbid Math and
Reading. Of the 410 children identified with LD, less than 10 children did not have primary
reading or math academic goals specified in their IEP, thus they were not included in the
categorized analysis.
This study examined how early parenting variables influenced motivation, psychosocial
behaviors, and academic achievement in children diagnosed with learning disabilities in fifth

73

grade, rather than children diagnosed with learning disabilities in kindergarten or children who
were diagnosed with learning disabilities at any point in time between kindergarten and fourth
grade for two primary reasons. First, research indicates that identification of learning disabilities
in very young children (i.e., preschool/early kindergarten) is often unreliable and inaccurate
(Rathvon, 2004; Scarborough, 1998). Instead of having a true learning disability, children in
kindergarten may be struggling academically because they have not received formal education
prior to kindergarten (e.g., instruction in basic reading principles in preschool or at home),
children may be adapting to classroom expectations and behavioral or motivational issues may
be more salient in screening/testing, and because there is a wide range of natural variation in the
rate at which children begin to learn to read or do math (e.g., some children in kindergarten may
be “fast learners” compared to other children who take a longer time to grasp reading or math
principles).
Second, by examining children who were identified with a learning disability in fifth
grade, it is presumed that these children have either had persistent academic problems throughout
elementary school and continue to qualify for special education services or they have been
recently diagnosed with a learning disability. Moreover, the stability (e.g., persistence and
permanence) of learning disabilities is a cornerstone of the diagnosis. Children who have been
diagnosed with a learning disability prior to fifth grade, and no longer qualified for special
education as a child with a learning disability in fifth grade, presumably were no longer
experiencing impairment in academic functioning, and may have received support earlier in
elementary school. Given that this study aims to answer questions about how early parenting
variables influence outcomes for children with and without persistent academic problems it is

74

rational to examine students who are identified or continue to be identified as a child with a
learning disability at the point at which outcomes are measured (fifth grade).
Overall, 3.9% of the study sample had an LD, 1.7% had an RD, 0.2% had an MD, and
2.1% had a RD-MD. This represents a lower prevalence of children with LD in the study sample
as compared to general population estimates. Extant literature indicates 5-10% of the population
has a MD (Berch & Mazzocco, 2007; Geary, Hoard, Nugent, & Bailey, 2012; Judge & Watson,
2011), 5% and 17.5% of the population has a reading disability (Shaywitz & Shaywitz, 2005),
and approximately 30% to 70% of individuals with either disorder have a co-morbid RD or MD
(Kovas et al., 2007; Landerl & Moll, 2010; Willcutt et al., 2013). There are several reasons that
the study sample may have a lower LD sample size including, overlap in the presence of LD in
children who were excluded from the study, particularly children with identified ADHD and
social-emotional and behavior problems. Researchers suggest that approximately 24% to 38% of
children with RD and/or MD have co-morbid ADHD (Capano, Minden, Chen, Schachar, &
Ickowicz, 2008; DuPaul, Gormley, & Laracy, 2013; Langberg, Vaughn, Brinkman, Froehlich, &
Epstein, 2010), and between 18%-19% of children with oppositional-defiant disorder, anxiety,
and/or depression have an LD (Mayes & Calhoun, 2006). Additionally, students with LD were
identified based on report of LD status from the child’s IEP, thus children who may receive a
clinical diagnosis of LD and who are not yet being served in the school and children who may
have an LD but are being served via undocumented accommodations are not able to be
accounted for. The relatively lower prevalence of LD compared to the general population is a
limitation of the study and conclusions were interpreted with caution.
Sampling Weights. Descriptive analyses on the final sample include unweighted and
weighted means. Unweighted descriptive statistics represent the ECLS-K population where each

75

case was counted equally. Weighted descriptive statistics represents the entire population of
children attending kindergarten in 1998, and accounts for those children who were not sampled.
The weighted sample reflects population estimates from the 1994 U.S. Census. Full sample
weights and nesting variables (based on the Taylor Series method for computing standard errors)
were applied to structural equation analyses to adjust for differential selection probabilities,
reduce non-response bias and standard error, and allow for results that are generalizable to the
population.

76

Table 5
Overview of Data Collection for Final Sample
Data Collection Wave
Age of Study Child (months)

Data Collection Period

Kindergarten Spring

Mean
68.41

SD
4.32

Spring 1999

Fifth Grade Spring

134.54

4.38

Spring 2004

77

Table 6
Demographic Characteristics of Final Sample (n = 10,630)
Characteristic
Sample Na
Sample %

Weighted N

Weighted
%

Child Gender
Male

5,180

48.7%

1,644,099

49.0%

Female

5,450

51.3%

1,709,317

51.0%

White/NonHispanic

5,930

55.7%

1,880,891

56.1%

Black/AfricanAmerican

1,220

11.5%

553,007

16.5%

Hispanic, Race
Specified

960

9.0%

306,944

9.2%

1,120

10.5%

352,309

10.5%

Asian

800

7.5%

100,708

3.0%

Native Hawaiian or
Other Pacific
Islander

140

1.3%

27,480

0.8%

Native American or
Alaskan Native

210

2.0%

61,841

1.8%

More Than One
Race

230

2.2%

68,676

2.0%

410

3.9%

123,453

3.7%

10,220

96.1%

3,229,963

96.3%

Child Race/Ethnicity

Hispanic, No Race
Specified

Learning Disability
Status
Dichotomized
With Learning
Disability
Without Learning
Disability
Categorized

78

Table 6 (cont’d)
Math Disability

20

0.2%

9,052

0.3%

Reading Disability

180

1.7%

40,126

1.2%

Co-morbid Math
and Reading
Disability

220

2.0%

72,492

2.2%

Note: a All unweighted sample sizes were rounded to the nearest 10 due to restrictions set
forth via the licensing agreement.

79

Variables and Measures
A description of the measures for each of the predictor and outcome variables are
described below in relation to the conceptual model, including parent depression, early parent
school involvement, academic achievement, psychosocial behaviors, and motivation. Refer to
Table 7 for a list of latent constructs and variables.
Table 7
Summary of ECLS-K Variables Corresponding to Constructs and Covariates
Construct
ECLS-K Variables
ECLS-K Data
Source
1. Parent Depression*

a. Parent report of symptoms of depression
(Kindergarten Spring)

a. Parent Interview
Questionnaire

2. Parent School
Involvement*

a. Parent report of school involvement
(Kindergarten Spring)

a. Parent Interview
Questionnaire

3. Early Psychosocial
Behavior*

a. Teacher report on Externalizing Problem
Behaviors Scale (Kindergarten Spring)
b. Teacher report on Internalizing Problem
Behaviors Scale (Kindergarten Spring)
c. Teacher report on Interpersonal Scale
(Kindergarten Spring)
d. Teacher report on Self-Control Scale
(Kindergarten Spring)
e. Teacher report on Approaches to
Learning Scale (Kindergarten Spring)

a/b/c/d/e. Teacher
SRS

4. Psychosocial
Behavior*

a. Teacher report on Externalizing Problem a/b/c/d/e. Teacher
Behaviors Scale (5th Grade)
SRS
b. Teacher report on Internalizing Problem
Behaviors Scale (5th Grade)
c. Teacher report on Interpersonal Scale
(5th Grade)
d. Teacher report on Self-Control Scale (5th
Grade)
e. Teacher report on Approaches to
Learning Scale (5th Grade)

5. Motivation

a. Child report SDQ School scale (5th
grade)

80

a. Child SDQ.
Teacher SRS

Table 7 (cont’d)
6. Early Academic
Achievement

a. Math T-Score (Kindergarten Spring)
b. Reading T-Score (Kindergarten Spring)

a/b. Direct Child
Cognitive
Assessment

7. Later Academic
Achievement

a. Math T-Score (5th Grade)
b. Reading T-Score (5th Grade)

a/b. Direct Child
Cognitive
Assessment
a/b/c/d. Parent
Interview
Questionnaire

8. Covariates

a. Gender
b. Race
c. Age (Kindergarten Spring)
d. SES
Note: * indicates a latent construct. Confirmatory Factor Analysis was conducted on
individual items to identify constructs.

Parent Depression. Parent symptoms of depression were measured using information
obtained from the parent interview during the spring of kindergarten. The respondent was
typically the child’s mother, although if the mother was unavailable, the respondent was another
parent, guardian, or household member (in that order of preference). In the current sample, the
majority of the parent interviews were completed by biological mothers (n=7,030; 66%) and
female guardians such as adoptive mothers, step-mothers, or foster mothers (n= 100; 1%). In
each wave of data collection, attempts were made to collect information from the same
respondent (e.g., the mother at each data collection point), however in some cases this was not
possible and another parent, guardian, or household member served as the respondent.
Parents answered the following twelve items about their own psychological well-being
using a four-point Likert scale (1=Never, 2=Some of the time, 3=A moderate amount of the time,
4=Most of the time). These items map closely to the criteria for depression listed in the DSM-5
(APA, 2013).
-

How often during the past week have you felt that you were bothered by things that don’t
usually bother you?

81

-

How often during the past week have you felt that you did not feel like eating, that your
appetite was poor?

-

How often during the past week have you felt that you could not shake off the blues even
with help from your family?

-

How often during the past week have you felt that you had trouble keeping your mind on
what you were doing?

-

How often during the past week have you felt depressed?

-

How often during the past week have you felt that everything you did was effortful?

-

How often during the past week have you felt fearful?

-

How often during the past week have you felt that your sleep was restless?

-

How often during the past week have you felt that you talked less than usual?

-

How often during the past week have you felt lonely?

-

How often during the past week have you felt sad?

-

How often during the past week have you felt that you could not get going?
Parent responses on these items were used as indicators of their psychological well-being.

Since these individual items were provided in the ECLS-K data set and no composites were
created, factor analysis was conducted in order to combine items to represent the specific
construct of interest. Confirmatory Factor Analysis (CFA) was conducted using weighted least
square parameter estimates (estimator WLSMV) specifying all twelve items representing the
construct of parent depression using MPlus student version 7.4 (Muthén & Muthén, 2012) with
the current sample of approximately 10,630 participants. CFA identifies sets of variables that
reliably measure a hypothesized construct (Kline, 2011). All the items loaded substantially on
the hypothesized factor of parent depression. The model had a close fit with the data. See Table 8

82

for the final factor loadings and their standardized and unstandardized coefficients. Fit indices
included a Root Mean Square Error of Approximation (RMSEA) equal to .031 (CI = .028-.034),
CFI =.957, and TLI=.947. Higher scores on this latent variable are indicative of higher rates of
depressive symptomology.
Table 8
Confirmatory Factor Analysis for a One-Factor Model of Parent Depression
Items
Unstandardized
Standard Error (SE)
Felt bothered
1.000
0.000
Poor appetite
1.002
0.040
Felt the “blues”
1.361
0.037
Trouble focusing
1.080
0.031
Felt depressed
1.360
0.034
Felt things were effortful
1.061
0.036
Felt fearful
0.960
0.050
Difficulty sleeping
0.970
0.038
Talked less
1.164
0.041
Felt lonely
1.206
0.039
Felt sad
1.311
0.032
Difficulty “to get going”
1.058
0.035
Note: All factor loadings were significant (p<.001).

Standardized
0.612
0.613
0.832
0.661
0.832
0.649
0.587
0.593
0.712
0.737
0.802
0.647

Early Parent School Involvement. Parent school involvement was measured using the
parent interview data from the spring of kindergarten. Parents responded to the following eight
questions about their involvement in their child’s schooling. Parents responded “1=Yes” or
“2=No” to the following items:
-

During this school year, have you or another adult in your household taken it upon
yourself to contact your child’s teacher or school for any reason having to do with your
child?

-

Since the beginning of this school year, have you or the other adults in your household
attended an open house or a back-to-school night?

83

-

Since the beginning of this school year, have you or the other adults in your household
attended a meeting of a PTA, PTO, or Parent-Teacher Student Organization?

-

Since the beginning of this school year, have you gone to a meeting of a parent advisory
group or policy counsel?

-

Since the beginning of this school year, have you or another adult in your household gone
to a regularly-scheduled parent-teacher conference with your child’s teacher or meeting
with your child’s teacher?

-

Since the beginning of this school year, have you or another adult in your household
attended a school or class event, such as a play, sports event, or science fair?

-

Since the beginning of this school year, have you or another adult in your household
acted as a volunteer at the school or served on a committee?

-

Since the beginning of this school year, have you or another adult in your household
participated in fundraising for your child’s school?
Parent responses on these eight items were used as indicators of early parent involvement

in children’s schooling and psychosocial development. Since these individual items were
provided in the ECLS-K data set and no composites were created, factor analyses were
conducted in order to combine items to represent specific constructs of interest. CFA using
weighted least square parameter estimates (estimator WLSMV) specifying all eight items
representing the construct of interest using MPlus student version 7.4 (Muthén & Muthén, 2012)
was conducted with the current sample of approximately 10,630. All the items loaded onto the
hypothesized construct of parent school involvement. The model fit the data closely. Fit indices
included a RMSEA equal to .017 (CI = .013-.022), CFI =.962, and TLI=.947. However, one
item, “Contact School,” had a low standardized factor loading of 0.235 and communality below

84

0.1, thus it was removed from the final analysis as it did not significantly contribute to the
overall construct. See Table 9 for the final factor loadings and their standardized and
unstandardized coefficients. All the items loaded onto the hypothesized construct of parent
school involvement. The model fit the data closely. Fit indices included a RMSEA equal to .020
(CI = .015-.025), CFI =.964, and TLI=.946. Higher scores on this latent variable are indicative of
less school involvement.
Table 9
Confirmatory Factor Analysis for a One-Factor Model of Parent School Involvement
Items
Unstandardized Standard Error (SE) Standardized
Attend Open House
1.000
0.000
0.659
Attend PTA/PTO
0.747
0.067
0.492
Attend Parent Advisory Group
0.618
0.060
0.407
Attend Parent-Teacher Conference
0.613
0.072
0.404
Attend School Event
0.872
0.053
0.574
Volunteer at School
1.104
0.060
0.727
Participate in School Fundraising
0.792
0.072
0.522
Note: All factor loadings were significant (p<.001).
Academic Achievement. For the current study, early academic achievement is defined as
the child’s performance on direct cognitive assessments of mathematics and reading skills in the
spring of their kindergarten year. Later academic achievement is defined as the child’s
performance on direct cognitive assessments of mathematics and reading skills in the spring of
5th grade.
Early academic achievement. In the spring of kindergarten, the full ECLS-K direct
child cognitive assessment took approximately 50 to 70 minutes to administer per child in a oneon-one setting. Using a two-stage assessment design, children first completed a routing section,
consisting of 12 to 20 items of varying difficulty, to determine the appropriate form for each of
the subject areas (reading, mathematics, and general knowledge). The assessment included
multiple choice and open-ended items. All items were answered with pointing or verbal

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responses. The reading assessment included questions to measure print familiarity, letter
recognition, beginning and ending sounds, rhyming sounds, word recognition, receptive
vocabulary, listening comprehension, and comprehension of words in context. The mathematics
assessment included questions to measure number sense, number properties, basic operations,
and geometry and spatial sense. Manipulatives and paper and pencil were available from some
questions. Children with disabilities with documented Individualized Education Plans (IEP),
Individualized Family Service Plans (IFSP), or 504 Plans on file with the school were permitted
accommodations during direct assessment. Permitted accommodations included modified setting
(e.g., special lighting, quiet room, adaptive chair or table), scheduling testing during a particular
time of day or for shorter periods at a time, and assistive devices to improve access to the
assessments (e.g., hearing aid, cane, brace, voice synthesizer).
Item-response theory (IRT) standardized scores (T-scores) were calculated for each
subject area. For this study, the standardized scores for math and reading will be used in the
structural model separately (i.e., not combined average or latent factors). Standardized scores
have a mean of 50 and a standard deviation of 10. IRT standardized scores allow for comparison
of child performance to the population and direct comparisons of academic achievement across
time points. The range of IRT standardized scores on the reading assessment and math
assessment were 0.0 to 90.0. The reliability for standardized scores was 0.95 for the reading
assessment and 0.94 for the math assessment.
Later academic achievement. Children were assessed using the ECLS-K direct
cognitive assessment covering three subject areas (mathematics, reading, and science). The
assessment was created with input from child development, elementary education, and content
area experts, and validity of the assessment was established via convergent validity with the

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Woodcock-McGrew-Werder Mini-Battery of Achievement (MBA) (alpha = 0.73 for reading
assessment and 0.80 for math assessment). The content of the assessments extended upon the
scales developed for assessments in prior years. For example, in the kindergarten reading
assessment, children were asked to read short sentences, whereas in the fifth grade assessment,
children were asked to read passages that were more complex. The assessment took
approximately 96 minutes to administer and was administered using hard-copy assessments and
computer-assisted interviewing. Similar to the kindergarten assessment, a two-stage assessment
approach was used to determine which form the student would complete. The routing test
included 18 to 25 items, in each subject area.
The reading assessment included questions that measured children’s ability to make
literal inferences from text (using key words in the text to answer comprehension questions),
extrapolate from cues in text to make inferences, use background knowledge and context cues in
a sentence to understand homonyms, evaluate connections between problems in narrative and
real-life problems, and comprehension of nonfiction text (e.g., biographical and expository text)
through questions requiring students to identify the “tone” of the remark, infer the author’s intent
behind a character or selection in the text, and identify evidence to support or refute ideas
presented in the text. The math assessment included questions that measured conceptual,
procedural, and problem-solving knowledge in relation to number sense, numerical operations,
measurement, geometry and spatial sense, data analysis, statistics, probability, patterns, algebra,
and functions. Item-response theory (IRT) standardized scores (T-scores) were calculated for
each subject area. Similar to the early academic achievement variable, the standardized scores
for math and reading will be used in the structural model separately (i.e., not combined average
or latent factors). The range of IRT standardized scores on the reading assessment and math

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assessment were 0.0 to 96.0. The reliability for standardized scores was 0.93 for the reading
assessment and 0.94 for the math assessment.
Psychosocial Behaviors. For the current study, early psychosocial behaviors were
measured using teacher’s ratings on a self-administered social rating scale (SRS) in the spring of
kindergarten. Later psychosocial behaviors were measured using teacher’s ratings on the SRS in
the spring of fifth grade. The SRS is a modified version of the Social Skills Rating System
(Graham & Elliott, 1990). The SSRS has been shown to discriminate between students with and
without disabilities (Demaray et al., 1995; Bramlett, Smith, & Edmonds, 1994). For each child,
their primary teacher in kindergarten and their reading teacher in fifth grade rated the frequency
of specific behaviors using a Likert scale from one (Never) to four (Very Often). The SRS
included five scales total. The Approaches to Learning (ATL) Scale consisted of six items in the
spring of kindergarten and seven items in the spring of fifth grade. The kindergarten items
measured the child’s attentiveness, task persistence, eagerness to learn, learning independence,
flexibility, and organization. In the spring of fifth grade, a seventh item regarding the child’s
ability to follow classroom rules was added. The Self-Control (SC) scale consisted of four items
relating to the child’s ability to respect the property rights of others, control their temper, accept
peer ideas for group activities, and respond appropriately to peer pressure. The Interpersonal
Skills (IP) scale consisted of five items related to the child’s skills in forming and maintaining
friendships, getting along with people who are different, comforting or helping other children,
expressing feelings, expressing ideas and opinions (and feelings in the kindergarten survey) in a
positive way, and showing sensitivity to the feelings of others. The Externalizing Problem
Behaviors (EB) scale consisted of five items in the spring of kindergarten and six items in the
spring of fifth grade related to the frequency with which a child displayed problem behaviors

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including, arguing, fighting, getting angry, acting impulsively, and disturbing ongoing activities.
In the spring of fifth grade a sixth item regarding the frequency with which a child talks during
quiet study was added. Finally, the Internalizing Problem Behavior (IB) scale consisted of four
items related to the presence of symptoms of anxiety, loneliness, low self-esteem, and sadness.
Mean scores for each scale were reported on a scale from one to four.
Higher scores on the ATL, SC, and IP scales indicated more positive behavior, whereas
lower scores on the EB and IB scales indicated less problem behavior. Thus, the EB and IB
scales were reverse coded, so that higher scores were indicative of less problem behavior and
more positive functioning. Thus, higher scores on these latent variables are indicative of more
positive psychosocial behavior. The split-half reliability for teacher SRS scale scores in the
spring of kindergarten was 0.89 for ATL, 0.80 for SC, 0.89 for IP, 0.90 for EB, and 0.78 for IB.
In the spring of fifth grade, split-half reliability for teacher SRS scale scores was 0.91 for ATL,
0.79 for SC, 0.88 for IP, 0.89 for EB, and 0.77 for IB, indicating moderate to high reliability.
Kindergarten psychosocial behavior. Although composites were created, factor
analyses were conducted in order to combine items to represent the specific construct of interest,
psychosocial behaviors. First, Confirmatory Factor Analysis (CFA) using maximum likelihood
estimation with robust standard errors (MLR) specifying all five mean scores for the spring of
kindergarten representing the construct of interest was conducted with the current sample using
MPlus student version 7.4 (Muthén & Muthén, 2012). The model did not fit the data adequately.
Results of an Exploratory Factor Analysis (EFA) specifying the five mean scores indicated that
the score for the Self-Control scale and the Externalizing Behaviors scale loaded on a secondfactor. Based on the factor loadings from the EFA and theoretical considerations, two distinct
constructs emerged: learning and social-emotional skills, and maladaptive behaviors. The first

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construct, learning and social-emotional skills, consisted of the Approaches to Learning,
Interpersonal Skills, and Internalizing Behaviors scales. These scales address self-regulated
learning (i.e., attentiveness, task persistence, eagerness to learn, learning independence,
flexibility, organization) and social-emotional characteristics including relational skills (i.e.,
forming and maintaining friendships, getting along with people who are different, comforting or
helping other children, expressing feelings, expressing feelings, ideas, and opinions in a positive
way, and showing sensitivity to the feelings of others) and emotional well-being (i.e., presence of
anxiety, loneliness, low self-esteem, and sadness). The second construct, maladaptive behaviors,
consisted of the Self-Control and Externalizing Behaviors scale. These scales address emotional
regulation (e.g., ability to respect the property rights of others, control their temper, accept peer
ideas for group activities, and respond appropriately to peer pressure) and externalizing problems
(e.g., arguing, fighting, getting angry, acting impulsively, and disturbing ongoing activities).
Confirmatory Factor Analysis (CFA) using maximum likelihood estimation with robust
standard errors (MLR) representing the two constructs of interest was conducted with the current
sample using MPlus student version 7.4 (Muthén & Muthén, 2012). All the items loaded
substantially onto the hypothesized factors of psychosocial behavior. The model fit the data
adequately. Fit indices included a RMSEA equal to .044 (CI = .035-.053), CFI =.987, and
TLI=.966. However, the two second-order factors are highly correlated (r =.857). Given the high
correlation between the two-second order factors, and problems associated with identification of
latent constructs with only two or three observed variables, the error variances between the SelfControl and Externalizing Behaviors scales were correlated. This allowed for examination of a
more parsimonious latent variable identified by five observed scores. See Table 10 for the final
factor loadings and their standardized and unstandardized coefficients. All the items loaded

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significantly onto the hypothesized construct of psychosocial behavior. The model fit the data
adequately. Fit indices included a RMSEA equal to .044 (CI = .035-.053), CFI =.987, and
TLI=.966.
Fifth grade psychosocial behavior. Given that there were several different items added
to the fifth grade SRS survey, it was hypothesized that all five scales would load onto the
hypothesized construct of psychosocial behaviors: Approaches to Learning, Self-Control,
Interpersonal Skills, Externalizing Behavior, and Internalizing Behavior. Theoretically, the
Approaches to Learning scale consists of items that may closely align with the construct of
motivation. For example, task persistence, learning independence, eagerness to learn, and
executive functioning skills such as organization, are all related to aspects of self-regulated
learning and intrinsic motivation. Since additional data was available to examine motivation as a
separate construct at the fifth grade time point, the ATL scale score was examined in context of
that construct of motivation, as well as the construct of psychosocial behavior to establish
discriminative validity and ensure that it would be most appropriate to include the ATL variable
within the construct of psychosocial behavior. It should be noted that this procedure was not
done at the kindergarten time point. For the kindergarten construct of psychosocial behavior, the
ATL scale was included. This was done for several reasons. First, unlike the fifth grade data,
there is no self-report data of student’s motivation in kindergarten. Thus, a separate construct for
motivation in kindergarten was not examined within the model. Second, although some of the
items comprising the SRS, and in particular the ATL scale, were similar in kindergarten and fifth
grade, there were additional items in fifth grade. The differential information collected at the two
time points contributes to different conceptualizations and combinations of the scales. Thus, in

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kindergarten, the ATL scale was examined within the broader construct of psychosocial behavior
in order to capture important information related to prosocial and motivation behaviors.
To examine the fit of the ATL scale in fifth grade, correlations were examined first. The
correlations between the ATL scale and six observed motivation variables (see Motivation
section below for more detailed explanation) were compared to the correlations between the
ATL scale and the remaining four observed psychosocial variables (SC, IP, EB, IB). ATL was
more strongly correlated with the four observed psychosocial variables (values of r ranged from
.38 to .72) than with the six observed motivation variables (values of r ranged from .13 to .42).
Next an exploratory factor analysis specifying all 11 variables (five psychosocial variables and
six motivation variables) was conducted. The correlation between two theoretical second-order
factors, psychosocial behavior and motivation, was low (r=.24) indicating a shared variance of
only 5%. Additionally, although the ATL scale loaded significantly (p<.05) on both second-order
factors, ATL loaded much higher on the psychosocial factor (standardized factor loading =
0.768) than on the motivation factor (standardized factor loading = 0.121). Thus, it was
concluded that the ATL scale more appropriately aligns with the construct of psychosocial
behavior rather than motivation.
Confirmatory Factor Analysis (CFA) using maximum likelihood estimation with robust
standard errors (MLR) specifying the five hypothesized SRS mean scores for the spring of fifth
grade representing the construct of interest was conducted with the current sample using MPlus
student version 7.4 (Muthén & Muthén, 2012). See Table 11 for the final factor loadings and
their standardized and unstandardized coefficients. All the items loaded significantly onto the
hypothesized construct of psychosocial behavior. The model fit the data adequately. Fit indices
included a RMSEA equal to .057 (CI = .049 -.065), CFI =.980, and TLI=.959.

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Table 10
Confirmatory Factor Analysis for a One-Factor Model of Kindergarten Psychosocial Behavior
Items
Unstandardized Standard Error (SE) Standardized
Approaches to Learning
1.000
0.000
0.754
Self-Control
1.002
0.023
0.839
Interpersonal Skills
1.134
0.025
0.919
Externalizing Behavior
0.778
0.030
0.653
Internalizing Behavior
0.389
0.026
0.395
Note: All factor loadings were significant (p<.001).

Table 11
Confirmatory Factor Analysis for a One-Factor Model of Fifth Grade Psychosocial Behavior
Items
Unstandardized Standard Error (SE) Standardized
Approaches to Learning
1.000
0.000
0.793
Self-Control
1.009
0.025
0.903
Interpersonal Skills
1.063
0.021
0.898
Externalizing Behavior
0.852
0.026
0.770
Internalizing Behavior
0.421
0.026
0.414
Note: All factor loadings were significant (p<.001).
Motivation. Children’s level of motivation in school was measured in 5th grade using a
self-report scale, Self-Description Questionnaire (SDQ), and a teacher report of self-regulation
and intrinsic motivation from the Social Rating Scale (SRS). For the SDQ, assessors read all
questions to children, and took approximately 5 minutes to administer all items. Children
answered 42 questions, across six scales, about their perceptions of their competence and interest
in peer relations, externalizing behaviors, emotions, and school subjects, including reading and
math. The items on the academic and peer scales of the SDQ were adapted from a validated tool,
the Self-Description Questionnaire II (Marsh, 1992). The internalizing and externalizing scales
were created for the ECLS-K study. The SDQ School scale (reliability α=.83) consists of six
items regarding how well children feel they do in all school subjects, and their enjoyment of all
school subjects. The SDQ Reading scale (reliability α=.90) and Math scale (reliability α=.92)
each consisted of eight items regarding children’s perceptions of their reading grades, difficulty

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of reading work, and interest in and enjoyment of reading and math, respectively. The SDQ Peer
(reliability α=.82) scale consisted of six items regarding children’s perceptions of their
popularity, and ability to make and get along with friends/other children. The
Anger/Distractibility (Externalizing) scale (reliability α=.78) included six items regarding
children’s perceptions of their externalizing problem behaviors including fighting, arguing,
talking and disturbing others, and distractibility. The SDQ Sad/Lonely/Anxious (Internalizing)
scale (reliability α=.79) consisted of eight items regarding children’s perceptions of internalizing
problems including, feeling sad, lonely, ashamed of mistakes, frustrated, and worried about
school and friendships. Children rated each item using a four-point response scale: 1=not at all
true, 2=a little bit true, 3=mostly true, 4=very true. Mean scores for each scale were reported on a
scale from one to four. Higher scores on the School, Reading, Math, and Peer scales indicated
higher levels of motivation, whereas higher scores on the Externalizing and Internalizing scales
indicated perception of more problems and lower levels of motivation. The Externalizing and
Internalizing scales were reverse coded, so that higher scores were indicative of less problems
and higher levels of motivation.
Since this study aims to understand children’s motivation in regards to their academics,
the three academic scales (Math, Reading, School) were analyzed for underlying commonality.
Exploratory Factor Analyses and Confirmatory Factor Analyses were run on the three academic
scales and then on all six scales, however no theoretically meaningful combination of scales
arose as significant and adequately fitting the data (e.g., one factor of the three academic scales,
two factors of the three academic scales and the three psychosocial scales, or all six scales on one
construct of motivation). For the current study, children’s mean score the SDQ School scale
comprised the construct of motivation. The School scale was chosen over the Math and Reading

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scales, because the goal of the study is to examine how early parent variables, academic
performance, and psychosocial behaviors influence a child’s motivation in school broadly, and
the items within the School scale subsume math and reading. Moreover, it is likely that children
who struggle in reading and/or math will have lower motivation in those subject areas, however,
the effects of academic underperformance in math or reading on general school motivation may
be more nuanced.
Covariates. The child’s age at assessment (Spring of Kindergarten), socioeconomic
status (SES), gender, and race were included in the model to control for their differential
influence on parent depression, parent school involvement, child academic achievement,
motivation, and psychosocial behaviors. Grade retention was not used as a control variable for
two reasons. First, the primary goal of this study is to examine relations between early parenting
variables and academic and psychosocial outcomes in children with and without LD, not the
effects of grade retention on these outcomes. Second, research has established that children who
are retained in grade school have a higher likelihood of academic failure and more psychosocial
problems (Cortiella & Horowitz, 2014), thus controlling for retention may remove important
variance in the outcomes for children with academic underachievement including children with
LD. Finally, a small percentage of students (n= 910; 8.6%) in the study sample were ever
retained in grade, thus, the effects of retention are likely minimal within such a large sample.
Age at assessment. A composite of the child’s age at the time of assessment in the fall of
their kindergarten year was included. This composite was derived by determining the number of
days between the child’s assessment date and date of birth, and dividing by 30 to obtain age in
months. Given that children may start kindergarten at different ages (e.g., five or six), it is

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important to control for age in order to remove variance related to age-related maturity or
influences of age on the variables of interest.
Gender. A composite of the child’s gender was included based on information from the
kindergarten fall parent interview. Given that some studies have found gender differences in
academic achievement (Duckworth & Seligman, 2006; Matthews, Ponitz, & Morrison, 2009;
Nowell & Hedges, 1998; Weaver-Hightower, 2003), motivation and learning behaviors in school
(Cvencek, Meltzoff, & Greenwald, 2011; McGeown, Goodwin, Henderson, & Wright, 2011;
Silverman, 2003), externalizing problems and internalizing problems (Chaplin & Aldao, 2013;
Leadbeater, Kuperminc, Blatt, & Hertzog, 1999), and interpersonal skills (Crombie, 1988), it is
important to control for these potential influences within the current study. Additionally, some
studies show that female students are more at-risk for math disabilities than their male peers
(Fryer & Levitt, 2010; Jordan, Kaplan, Nabor Olah, & Locuniak, 2006) whereas other studies
show no gender differences (Lachance & Mazzocco, 2006). Other research indicates that boys
are four to five times more likely to be diagnosed with an LD (Shapiro, Church, & Lewis, 2002).
Therefore, it is important to control for gender effects that may arise due to disproportional
identification of LD.
Race-ethnicity. A composite of the child’s race-ethnicity was included based on
information gathered through the parent interview. This composite included the following
categories: White, non-Hispanic; Black or African-American, non-Hispanic; Hispanic, race
specified; Hispanic, no race specified; Asian; Native Hawaiian or other Pacific Islander; Native
American or Alaskan Native; and more than one race specified, non-Hispanic. Given that some
studies show racial differences in academic achievement (Blackorby & Wagner, 2005; Chatterji,
2006) it is important to control for these potential influences within the study. Additionally,

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research has indicated that race-ethnicity, along with gender and socioeconomic status, influence
the likelihood of developing and being identified as having a learning disability in school (Judge
& Bell, 2010), thus it is important to understand how these factors operate within the proposed
model, and account for these influences.
Socioeconomic status. Socioeconomic status (SES) is a measure of social standing.
Given that research has shown negative effects of poverty on academic achievement (Chatterji,
2006; Denton & West, 2002; Lee, Grigg, & Donahue, 2007; Morgan, Farkas, Hillemeier, &
Maczuga, 2014), psychosocial problems (McLoyd, 1998), and parent school involvement
(Evans, 2004), it is important to consider the influence of SES within the hypothesized model.
The SES variable used in the current study was a composite variable of three indicators, income,
parent education, and parent occupation, and reflects the family’s SES in the spring of
kindergarten. The SES variable is a continuous variable that ranges from -4.75 to 2.75, with
higher values corresponding to higher SES. Only the kindergarten SES variable was used in the
current study, because it was highly correlated with the SES variable in the spring of fifth grade
(unweighted r= .859, weighted r=.839). The specific components of the SES variable include
Father/male guardian’s education, Mother/female guardian’s education, Father/male guardian’s
occupation, Mother/female guardian’s occupation, and household income. Since not all parents
responded to all questions in both rounds, missing values were present for some of the SES
indicators (28.2%). Hot deck imputation was used to impute missing values, which involves
using the value reported for an item by a respondent that is similar to the non-respondent on
other characteristics. A categorical search algorithm, CHAID (Chi-squared Automatic
Interaction Detector), was used to analyze and determine the best predictors for respondents and
non-respondents. Additionally, for households with one parent, SES was computed by averaging

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the available components. Each indicator (income, parent education, and parent occupation) was
standardized to have a mean of 0 and standard deviation of 1. The specific indicators are outlined
below. Household income was collected from the parent interview in the spring of kindergarten.
Income represents the primary caregiver’s annual reported household income. Parent education
was collected from the parent interview during the fall of kindergarten (or spring if the
household did not participate in the fall). The respondent parent reported the highest level of
education attained. Education level was recoded from 1 to 9 (1 = 8th grade or below; 2 = 9th to
12th grade, 3 = high school diploma/equivalent, 4= vocational/technical program, 5 = some
college, 6 = Bachelor’s degree, 7 = graduate/professional school/no degree, 8 = Master’s degree,
9 = Doctorate or professional degree). Parent occupation was collected via the parent interview
in the fall of kindergarten. Parent occupation was recoded to reflect the average of the 1989
General Social Survey (GSS; Nakao & Treas, 1992) prestige score of the occupation. This was
done by averaging the corresponding prestige score of the 1980 Census occupational category
codes covered by the ECLS-K occupation (NCES, 2001). The 22-position scale ranks occupation
according to their relative prestige.
Data Analyses
Statistical Package for the Social Sciences (SPSS) Premium version 24 was used to
prepare data and run descriptive and preliminary analyses. MPlus student version 7.4 (Muthén &
Muthén, 2012) was used to model the data. The sample of interest was selected following the
application of exclusionary criteria, and variables of interest were imported into SPSS to run
preliminary analyses. Analyses were conducted using sample and nesting weights, and compared
to results without sample and nesting weights. All data analyses were conducted on a
freestanding computer in Erickson Hall at Michigan State University, as required by the

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restricted-use license agreement from the IES. Investigators obtained approval for use of the
ECLS-K data set for this study by the Institute of Education Sciences (IES) and Michigan State
University Institutional Review Board (IRB; see Appendix A).
Preliminary Analyses. Prior to testing the structural equation model, preliminary
analyses were conducted. Data screening (e.g., plots) and descriptive statistics (see Table 13)
using all variables of interest were conducted to better understand the ECLS-K population and
constructs of interest. Correlation matrices (see Table 18 and Table 19) for all variables of
interest were run to screen for multicollinearity and the distribution of variables were examined
using graphs to address potential problems with outliers and account for skewed distribution. No
study variables were highly correlated (r >.85), indicating no potential problems with
multicollinearity (Kline, 2011). Outliers were included in the study given that the research
questions aim to examine group differences among children with LD and it was expected that
some parents and children would fall in the extreme ends of the spectrum on measures of
depression, academic performance, and psychosocial behaviors.
Additional preliminary analyses were conducted without latent variable modeling prior to
testing the structural equation model. Means and standard deviations of continuous variables (see
Table 14 and Table 16) and frequencies and percentages of categorical variables (see Table 15
and Table 17) were examined, with and without weights. Independent samples t-test were
conducted to determine whether there were significant differences on mean scores for all
continuous variables of interest between the LD and non-LD groups. Chi-square test of
independence was conducted to determine whether there were significant differences on mean
scores for all categorical variables of interest between the LD and non-LD groups.

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Structural Equation Analyses. Prior to testing the proposed conceptual model, the
proposed latent factor structure was tested using Confirmatory Factor Analysis using MPlus
student version 7.4 (Muthén & Muthén, 2012). Brown (2015) recommends testing the
measurement model for each construct separately prior to testing the full structural model. The
five specified latent variables in the model are described under the variables and measures
section above. Each latent variable is measured by several observed variables or indicators, and
each indicator loaded on one specified factor. The latent factors included parent depression,
parent school involvement, early psychosocial behaviors, and fifth grade psychosocial behaviors
using the full study sample (n=10,630). The four latent factors in conjunction with five additional
observed variables compose the measurement model.
Given that the full structural model will be tested with different groups, group invariance
testing was conducted to determine whether the model measures similar constructs across groups
(e.g., children with LD and children without LD; children with MD, RD, MD-RD, and no LD).
Group invariance was tested by examining the difference between the Comparative Fit Index
(CFI) for configural, metric, and scalar invariance tests for each latent variable. Cheung &
Rensvold (2002) recommend a change in CFI between configural, metric, and scalar invariances
that is equal or less than .01 to indicate adequate fit across groups. For parent depression, results
indicated adequate fit for LD and no-LD groups (CFI=.968 for configural model, CFI=.971 for
metric model, CFI=.980 for scalar model, and CFI=.012). For parent involvement, results
indicated adequate fit for LD and no-LD groups (CFI=.968 for configural model, CFI=.967 for
scalar model, and CFI=-.001; metric model is not available with categorical data). For
kindergarten psychosocial behavior, results indicated adequate fit for LD and no-LD groups
(CFI=.987 for configural model, CFI=.985 for metric model, CFI=.977 for scalar model, and

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CFI=-.01). For fifth grade psychosocial behavior, results indicated adequate fit for LD and noLD groups (CFI=.975 for configural model, CFI=.972 for metric model, CFI=.968 for scalar
model, and CFI=-.007). Overall, all latent variables passed tests for group invariance.
The proposed model was tested using latent variable structural equation modeling (SEM)
techniques that allow for unobserved latent variables in the model to be tested in relation to the
hypotheses developed using the ECLS-K non-experimental data. Latent variable SEM allows for
simultaneous confirmatory factor analysis of all latent variables and the magnitude of the
relations between constructs (Kline, 2011). First, it was hypothesized that lower levels of parent
depression will indirectly predict better early academic achievement and early psychosocial
behavior in kindergarten, via higher parent involvement (research question 1a). Second, it was
hypothesized that higher levels of parent school involvement will predict higher academic
achievement and psychosocial behaviors in fifth grade dependent on levels of early academic
achievement and early psychosocial behaviors in kindergarten (research question 1b). Third, it
was hypothesized that increased early academic achievement and psychosocial behaviors in
kindergarten will positively predict academic achievement in fifth grade, where early academic
achievement and psychosocial behaviors are accounted for (research question 1c). Fourth, it was
hypothesized that higher motivation and psychosocial behaviors in fifth grade will positively
predict academic achievement in fifth grade (research question 1d).
Next, multiple-group SEM was explored to answer the research questions pertaining to
group differences in the specified relationships of the conceptual model. First, children with an
LD were compared to children without an LD by simultaneously fitting the model to both groups
to determine whether the magnitude of relations between parent depression and school
involvement and child academic, psychosocial, and motivational outcomes differ. It was

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hypothesized that parent depression and school involvement in kindergarten would be more
strongly predictive of motivation, psychosocial behaviors, and academic achievement in children
with LD compared to children without LD (research question 2). Thus, low levels of parent
depression and increased parent involvement were hypothesized to serve as greater protective
factors for children who are academically underachieving compared to children who are not.
Finally, in order to determine whether subtype of LD moderates the effects of the
conceptual model, a three group SEM was explored by comparing children with RD, children
with RD-MD, and children without LD. Children with MD cannot be included as an independent
subgroup in this analysis due to inadequate sample size. However, given that children with MD
often experience co-morbid reading problems, regardless of whether they are identified for
services through a formal identification process, this study will group children with MD and
children with RD-MD for the proposed multi-group analysis (Research Question 3). Significant
findings were interpreted with caution, as this was a limitation of the study. Moreover, this
analysis was exploratory in nature and may be under-identified given that the sample sizes
between the categorical groups are uneven (e.g., n=20 MD, n=180 RD, n=220 RD-MD,
n=10,220 non-LD). It was hypothesized that the magnitude of relations between parent
depression and school involvement on academic achievement, psychosocial behaviors, and
motivation outcomes would be strongest for children with combined LD (e.g., RD-MD), then
children with one LD, then children without LD (research question 3); thereby keeping in line
with the hypothesis that low levels of parent depression and high levels of parent school
involvement serve as a greater protective factor for children who are most academically at-risk
(e.g., RD-MD group). In order to compare the groups, it was proposed that cross-group equality
were used to derive unstandardized estimates of the parameters in all groups (Kline, 2011). Then,

102

the parameters would be freely estimated across all groups and a comparison between the
constrained and unconstrained (e.g., freely estimated) model fit would be examined using
absolute and relative model fit statistics (Kline, 2011). This comparison would determine
whether the fit of the model worsened under constrained parameters which would indicate group
differences (e.g., unequal direct effects). Overall, multiple-group SEM was used to determine
whether parent depression and parent involvement have a different effect on outcomes for
children with subtypes of LD (i.e., RD, RD-MD) and without LD. Weighted least squares and
maximum likelihood estimation with robust standard errors were used to estimate model
parameters.
Table 12
Summary of Research Questions, Variables, and Analyses
Research
Analyses
Variables
Question
1. Model Fit

Latent
Variable
Structural
Equation
Modeling

2/3. Model Fit Multiplefor
Group SEM
Subsamples of
Learning
Disabilities

Covariates

Parent Depression*
Early Parent School Involvement*
Early Academic Achievement
Early Psychosocial Behavior*
Motivation
Psychosocial Behavior*
Academic Achievement

Gender
Race
SES
Age

Parent Depression*
Early Parent School Involvement*
Early Academic Achievement
Early Psychosocial Behavior*
Motivation
Psychosocial Behavior*
Academic Achievement

Gender
Race
SES
Age

*Note: See Table 7 for specific indicators corresponding with latent variables.

103

Model Fit. To determine model fit, absolute and relative fit indices were used. The chisquare statistic, an absolute fit index, was examined in analyses comparing mediation paths. A
non-significant chi-square index (p > 0.01) signifies that the null hypothesis is not rejected,
where the null hypothesis is a perfect fit in the population. Thus, a non-significant chi-square
statistic indicates an adequate model fit. However, the chi-square statistic is affected by sample
size, which may lead to rejection of the null hypothesis even when there are differences between
the observed and predicted covariances (Kline, 2011). Thus, additional fit indices were
considered due to the study’s large sample size. Comparative Fit Index (CFI) and Tucker-Lewis
Fit Index (TLI) are relative fit indices which both account for sample size (Kline, 2011). The CFI
and TLI values greater than or equal to 0.95 indicate an adequate fit (Hooper, Coughlan, &
Mullen, 2008). Finally, the Root Mean Square Error of Approximation (RMSEA) was used as an
indicator of absolute fit. The RMSEA provides an estimation of the model with optimal
parameter estimates fits the covariance matrix for the population. An RMSEA value less than or
equal to 0.07 and with a 90% confidence interval with an upper limit less than or equal to 0.08 is
considered an adequate model fit (Hooper, Coughlan, & Mullen, 2008). For multiple-group SEM
analyzing mediation paths, chi-square significance testing was conducted by calculating the chisquare difference statistic between the constrained and unconstrained models. If significant, this
suggested the model parameters are unequal across groups.

104

Table 13
Descriptive Statistics for Full Sample
Variables
Mean
Covariates
Kindergarten SES
.04
Age at Assessment
68.41
(K, 1999)
Age at Assessment
134.54
th
(5 grade, 2004)
Parent Depression
Felt bothered
1.59
Poor appetite
1.38
Felt the “blues”
1.25
Trouble focusing
1.51
Felt depressed
1.33
Felt things were effortful
1.62
Felt fearful
1.19
Difficulty sleeping
1.73
Talked less
1.25
Felt lonely
1.25
Felt sad
1.44
Difficulty “to get going”
1.51
Parent School Involvement
Contact school
1.48
Attended open house
1.24
Attended PTO
1.63
Attended parent group
1.91
Attended teacher conference 1.14
Attended school event
1.31
Volunteered at school
1.48
Participated in fundraiser
1.38
Kindergarten Achievement
Direct Math Assessment
51.63
Direct Reading Assessment
51.56
Fifth Grade Achievement
Direct Math Assessment
51.54
Direct Reading Assessment
51.46
Early Psychosocial Behaviors
Teacher-Rated Approaches
3.20
to Learning
3.25
Teacher-Rated Self-Control
Teacher-Rated Interpersonal 3.19
Skills
1.58
Teacher-Rated
Externalizing

S.D.

Range

Weighted
Mean

Weighted
S.D.

.80
4.32

-4.75-2.75
45.77-96.30

-.03
68.31

.78
4.29

4.38

110.87-153.53

134.53

4.38

.75
.71
.59
.72
.64
.88
.52
.90
.58
.58
.65
.72

1-4
1-4
1-4
1-4
1-4
1-4
1-4
1-4
1-4
1-4
1-4
1-4

1.60
1.42
1.27
1.55
1.36
1.70
1.21
1.78
1.27
1.27
1.47
1.55

.74
.74
.61
.76
.69
.95
.54
.93
.63
.63
.68
.75

.50
.43
.48
.29
.35
.46
.50
.49

1-2
1-2
1-2
1-2
1-2
1-2
1-2
1-2

1.46
1.28
1.66
1.92
1.16
1.34
1.52
1.41

.50
.45
.47
.28
.37
.47
.50
.49

9.69
9.58

14.39-85.84
17.08-87.73

50.85
50.99

9.68
9.70

9.52
9.56

21.90-80.61
16.61-81.02

51.11
51.16

9.61
9.52

.65

1-4

3.15

.67

.59
.61

1-4
1-4

3.22
3.15

.61
.62

.58

1-4

1.62

.60

105

Table 13 (cont’d)
Teacher-Rated Internalizing
Fifth Grade Psychosocial
Behavior
Teacher-Rated Approaches
to Learning
Teacher-Rated Self-Control
Teacher-Rated Interpersonal
Skills
Teacher-Rated
Externalizing
Teacher-Rated Internalizing
Motivation
Student-Rated School

1.52

.48

1-4

1.55

.50

3.12

.66

1-4

3.09

.67

3.27
3.12

.58
.62

1-4
1-4

3.25
3.10

.59
.63

1.61

.56

1-4

1.64

.58

1.60

.51

1-4

1.62

.53

2.75

.63

1-4

2.72

.64

106

Table 14
Unweighted Descriptive Statistics between LD and Non-LD Samples
Variables
Learning
No Learning
Disability
Disability
Independent Samples
t-test (df)
a
a
N
Mean
N
Mean (SD)

Cohen’s
d

(SD)
Covariates
Kindergarten
SES

390

-.32 (.71)

9880

.06 (.80)

10.38(428),
p<.001***

.50

Age at
Assessment
(K, 1999)

380

69.18
(4.94)

9330

68.38
(4.30)

-3.13(404), p=.002**

.17

400

135.23
(5.00)

9790

134.51
(4.35)

-2.85(423), p=.005**

.15

390

40.81
(9.06)

9930

52.06
(9.47)

23.89(418),
p<.001***

1.21

350

40.32
(7.52)

9500

51.98
(9.40)

28.34(394),
p<.001***

400

41.41
(8.86)

9790

51.95
(9.31)

23.29(435),
p<.001***

Age at
Assessment
(5th grade,
2004)
Kindergarten
Achievement
Direct Math
Assessment
Direct
Reading
Assessment
Fifth Grade
Achievement
Direct Math
Assessment
Direct
Reading
Assessment
Early
Psychosocial
Behaviors
TeacherRated
Approaches
to Learning
TeacherRated SelfControl
TeacherRated
Interpersonal
Skills

1.37

1.16
1.46

390

39.17
(8.15)

9790

51.95
(9.28)

30.33(433),
p<.001***

390

2.56 (.67)

9700

3.23 (.64)

19.21(415),
p<.001***

390

3.03 (.64)

9630

3.26 (.59)

7.06(412), p<.001***

.37

380

2.86 (.65)

9610

3.20 (.61)

9.87(407), p<.001***

.54

107

1.02

Table 14 (cont’d)
TeacherRated
380 1.78 (.70) 9660
1.57 (.57)
-5.80(403),
Externalizing
p<.001***
Teacher380 1.71 (.55) 9610
1.51 (.48)
Rated
-6.97(404),
Internalizing
p<.001***
Fifth Grade
Psychosocial
Behavior
Teacher410 2.67 (.61) 9310
3.14 (.65)
15.14(449),
Rated
p<.001***
Approaches
to Learning
Teacher400 3.04 (.63) 9230
3.28 (.58) 7.58(434), p<.001***
Rated SelfControl
Teacher400 2.84 (.66) 9110
3.13 (.62) 8.95(434), p<.001***
Rated
Interpersonal
Skills
Teacher410 1.80 (.61) 9250
1.60 (.55)
-6.28(436),
Rated
p<.001***
Externalizing
Teacher410 1.79 (.54) 9150
1.59 (.51)
-7.29(437),
Rated
p<.001***
Internalizing
Motivation
Student400 2.56 (.69) 9790
2.76 (.62) 5.70(426), p<.001***
Rated School
Note: Equal variance not assumed for all variables. a All unweighted sample sizes were
rounded to the nearest 10 due to restrictions set forth via the licensing agreement.

108

.34
.39

.75

.40

.45

.34

.38

.30

Table 15
Unweighted Descriptive Statistics between LD and Non-LD Samples (Categorical Variables)
Variables
Learning Disability
No Learning
Disability
Chi-Square (df)
Na (%)
Na (%)
Parent Depression
Felt bothered
6.36(3), p=.10
Never
190 (47.1%)
5070 (49.6%)
Some Time
120 (30.2%)
3360 (32.9%)
Moderate Amount
30 (6.3%)
740 (7.3%)
Most of Time
20 (4.1%)
240 (2.4%)
Poor appetite
13.94(3), p=.003**
Never
240 (58.0%)
6850 (67.1%)
Some Time
80 (19.8%)
1920 (18.8%)
Moderate Amount
20 (5.8%)
370 (3.6%)
Most of Time
20 (4.3%)
280 (2.7%)
Felt the “blues”
11.64(3), p=.009**
Never
270 (65.5%)
7610 (74.5%)
Some Time
60 (15.7%)
1400 (13.7%)
Moderate Amount
10 (3.4%)
230 (2.3%)
Most of Time
10 (2.9%)
160 (1.6%)
Trouble focusing
9.69(3), p=.021**
Never
210 (50.2%)
5550 (54.3%)
Some Time
110 (27.3%)
3180 (31.2%)
Moderate Amount
30 (6.3%)
420 (4.2%)
Most of Time
20 (3.9%)
260 (2.5%)
Felt depressed
8.80(3), p=.032**
Never
260 (62.6%)
6950 (68.0%)
Some Time
80 (18.4%)
2010 (19.7%)
Moderate Amount
20 (4.1%)
230 (2.3%)
Most of Time
10 (2.9%)
210 (2.1%)
Felt things were
3.05(3), p=.38
effortful
200 (49.3%)
5390 (52.7%)
Never
100 (24.9%)
2860 (28.0%)
Some Time
20 (5.8%)
480 (4.7%)
Moderate Amount
30 (7.5%)
670 (6.6%)
Most of Time
11.24(3), p=.01**
Felt fearful
310 (74.9%)
8010 (78.4%)
Never
30 (8.0%)
1130 (11.1%)
Some Time
10 (2.4%)
150 (1.5%)
Moderate Amount
10 (2.4%)
120 (1.1%)
Most of Time
7.13(3), p=.07
Difficulty sleeping
170 (40.1%)
4700 (46.0%)
Never
130 (30.4%)
3290 (32.2%)
Some Time
30 (8.2%)
720 (7.0%)
Moderate Amount
40 (9.2%)
690 (6.8%)

109

Table 15 (cont’d)
Most of Time
Talked less
Never
Some Time
Moderate Amount
Most of Time
Felt lonely
Never
Some Time
Moderate Amount
Most of Time
Felt sad
Never
Some Time
Moderate Amount
Most of Time
Difficulty “to get going”
Never
Some Time
Moderate Amount
Most of Time
Parent School
Involvement
Contact school
Yes
No
Attended open house
Yes
No
Attended PTO
Yes
No
Attended parent group
Yes
No
Attended teacher
conference
Yes
No
Attended school event
Yes
No
Volunteered at school
Yes

13.51(3), p=.004**
280 (68.6%)
60 (13.8%)
10 (1.9%)
10 (3.4%)

7640 (74.8%)
1380 (13.5%)
250 (2.5%)
140 (1.4%)
2.66(3), p=.45

290 (69.3%)
60 (13.8%)
10 (2.7%)
10 (2.2%)

7620 (74.6%)
1420 (13.9%)
200 (1.9%)
160 (1.6%)
3.27(3), p=.35

230 (55.1%)
110 (26.3%)
10 (3.4%)
10 (2.7%)

5910 (57.8%)
3020 (29.5%)
310 (3.1%)
180 (1.7%)

210 (51.4%)
120 (28.7%)
20 (4.6%)
10 (2.9%)

5620 (55.0%)
3090 (30.3%)
420 (4.2%)
270 (2.7%)

210 (51.0%)
160 (39.6%)

4950 (48.5%)
4640 (45.4%)

.69(3), p=.88

3.08(1), p=.08

11.40(1), p=.001***
250 (61.6%)
120 (28.7%)

7260 (71.1%)
2310 (22.7%)
10.29(1), p=.001***

110 (26.3%)
260 (64.0%)

3580 (35.0%)
6010 (58.8%)

40 (9.2%)
340 (81.4%)

880 (8.6%)
8700 (85.2%)

.40(1), p=.53

2.56(1), p=.11
330 (80.4%)
40 (10.1%)

8230 (80.6%)
1350 (13.3%)

230 (55.3%)
140 (35.0%)

6640 (65.0%)
2940 (28.8%)

10.97(1), p=.001***

16.94(1), p<.001***
150 (37.2%)

110

4990 (48.8%)

Table 15 (cont’d)
No
220 (53.1%)
4600 (45.0%)
Participated in
17.16(1), p<.001***
fundraiser
Yes
190 (46.4%)
5950 (58.2%)
No
180 (43.7%)
3630 (35.5%)
a
Note: All unweighted sample sizes were rounded to the nearest 10 due to restrictions set
forth via the licensing agreement.

111

Table 16
Weighted Descriptive Statistics between LD and Non-LD Samples
Variables
Learning Disability
No Learning Disability

N
Covariates
Kindergarten
SES

Mean (SD)
-.44

Age at
Assessment
(K, 1999)
Age at
Assessment
(5th grade,
2004)
Kindergarten
Achievement
Direct Math
Assessment

116,267

69.56

117,019

41.00

8.69 3,128,922

51.22

9.51

Direct
Reading
Assessment

107,695

39.86

7.35 2,992,525

51.39

9.53

Direct
Reading
Assessment
Early
Psychosocial
Behaviors
TeacherRated
Approaches
to Learning
TeacherRated SelfControl
TeacherRated

3,229,963

Mean (SD)

123,453

Fifth Grade
Achievement
Direct Math
Assessment

.87

N

5.45 2,995,631

Independent Samples
t-test (df)
Cohen’s d

-.01

.78

68.27

4.23

117,910 135.48 5.44 2,810,864 134.49 4.33

170.59(131,117),
p<.001***
.52
-79.95(121,779),
p<.001***
.26
-61.60(124,247),
p<.001***
.20

393.64(127,748),
p<.001***
1.21
499.98(121,110),
p<.001***
1.35

118,237

40.85

8.71 2,810,503

51.54

9.40

115,974

39.69

7.60 2,804,928

51.63

9.29

412.05(130,101),
p<.001***
1.18
519.08(130,705),
p<.001***
1.41

116,739

2.53

.68

3,019,827

3.17

.66

320.16(125,521),
p<.001***
.96

116,783

3.04

.64

3,003,567

3.22

.60

114,804

2.83

.64

2,991,553

3.16

.62

94.50(125,036),
p<.001***
.29
172.19(123,251),
p<.001***

112

Table 16 (cont’d)
.52
Interpersonal
Skills
TeacherRated
Externalizing
TeacherRated
Internalizing
Fifth Grade
Psychosocial
Behavior
TeacherRated
Approaches
to Learning
TeacherRated SelfControl
TeacherRated
Interpersonal
Skills
TeacherRated
Externalizing
TeacherRated
Internalizing
Motivation
Student-Rated
School

115,625

1.83

.76

3,010,906

1.61

.59

115,557

1.71

.56

2,988,811

1.54

.50

121,416

2.67

.63

2,577,957

3.11

.67

120,939

3.07

.57

2,551,266

3.26

.59

120,671

2.81

.63

2,503,546

3.11

.63

121,233

1.79

.59

2,553,381

1.63

.58

121,416

1.76

.48

2,525,138

1.61

.53

-96.62(121,071),
p<.001***
.32
-99.38(122,667),
p<.001***
.32

238.27(134,658),
p<.001***
.68
116.34(133,427),
p<.001***
.33
160.60(132,447),
p<.001***
.48
-93.33(132,475),
p<.001***
.27
-103.67(135,808),
p<.001***
.30

118,237

2.59

.78

2,810,151

Note: Equal variance not assumed for all variables.

113

2.73

.64

59.60(124,952),
p<.001***
.20

Table 17
Weighted Descriptive Statistics between LD and Non-LD Samples (Categorical Variables)
Variables
Learning Disability No Learning Disability
Chi-Square
N (%)
N (%)
(df)
Parent Depression
Felt bothered
1802.44(3),
Never
67,928 (56.4%)
1,678,074 (52.9%)
p<.001***
Some Time
40,785 (33.8%)
1,149,172 (36.2%)
Moderate Amount
7,557 (6.3%)
270,662 (8.5%)
Most of Time
4227(3.5%)
74,467 (2.3%)
Poor appetite
8182.52(3),
Never
76,606 (63.6%)
2,240,349 (70.5%)
p<.001***
Some Time
26,806 (22.2%)
680,528 (21.4%)
Moderate Amount
11,686 (9.7%)
139,603 (4.4%)
Most of Time
5399 (4.5%)
115,078 (3.6%)
Felt the “blues”
3086.84(3),
Never
89,679 (74.4%)
2,539,143 (80.0%)
p<.001***
Some Time
25,628 (21.3%)
487,456 (15.4%)
Moderate Amount
2959 (2.5%)
82,716 (2.6%)
Most of Time
2230 (1.9%)
64,737 (2.0%)
Trouble focusing
11576.32(3),
Never
62,341 (51.7%)
1,849,482 (58.2%)
p<.001***
Some Time
38,569 (32.0%)
1,046,561 (32.9%)
Moderate Amount
8,629 (7.2%)
171,109 (5.4%)
Most of Time
10,958 (9.1%)
110,021 (3.5%)
Felt depressed
13355.76(3),
Never
85,769 (71.2%)
2,306,057 (72.6%)
p<.001***
Some Time
24,381 (20.2%)
695,835 (21.9%)
Moderate Amount
8,649 (7.2%)
70,261 (2.2%)
Most of Time
1,698 (1.4%)
102,708 (3.2%)
Felt things were
1704.75(3),
effortful
72,607 (60.4%)
1,730,262 (54.6%)
p<.001***
Never
30,606 (25.5%)
961,967 (30.3%)
Some Time
5,950 (4.9%)
166,363 (5.2%)
Moderate Amount
11,058 (9.2%)
312,837 (9.9%)
Most of Time
24168.65(3),
Felt fearful
98,819 (82.0%)
2,677,019 (84.2%)
p<.001***
Never
11,696 (9.7%)
403,675 (12.7%)
Some Time
1,723 (1.4%)
54,854 (1.7%)
Moderate Amount
8,259 (6.9%)
42,146 (1.3%)
Most of Time
Difficulty sleeping
3906.93(3),
Never
55,487 (46.0%)
1,520,760 (47.9%)
p<.001***
Some Time
44,091 (36.6%)
1,109,936 (34.9%)
Moderate Amount
5,821 (4.8%)
269,732 (8.5%)

114

Table 17 (cont’d)
Most of Time
Talked less
Never
Some Time
Moderate Amount
Most of Time
Felt lonely
Never
Some Time
Moderate Amount
Most of Time
Felt sad
Never
Some Time
Moderate Amount
Most of Time
Difficulty “to get going”
Never
Some Time
Moderate Amount
Most of Time
Parent School
Involvement
Contact school
Yes
No
Attended open house
Yes
No

15,097 (12.5%)

277,753 (8.7%)

90,992 (75.5%)
16,565 (13.7%)
4,911 (4.1%)
8,029 (6.7%)

2,550,184 (80.2%)
469,415 (14.8%)
96,061 (3.0%)
63,820 (2.0%)

91,892 (76.3%)
21,557 (17.9%)
5,424 (4.5%)
1,624 (1.3%)

2,549,165 (80.3%)
471,399 (14.8%)
78,789 (2.5%)
76,233 (2.4%)

75,437 (62.8%)
37,218 (31.0%)
5,480 (4.6%)
2,007 (1.7%)

1,950,599 (61.4%)
1,038,623 (32.7%)
115,544 (3.6%)
71,723 (2.3%)

63,205 (52.5%)
47,986 (39.8%)
5,360 (4.4%)
3,946 (3.3%)

1,832,568 (57.7%)
1,073,920 (33.8%)
152,764 (4.8%)
116,180 (3.7%)

69,276 (56.3%)
53,769 (43.7%)
72,137 (59.1%)
49,874 (40.9%)

1,741,317 (53.9%)
1,488,311(46.1%)

12382.25(3),
p<.001***

3352.69(3),
p<.001***

588.51(3),
p<.001***

1865.93(3),
p<.001***

271.29(1),
p<.001***
.91 (.90-.92)

2,348,497 (72.8%)
877,730 (27.2%)

10969.61(1),
p<.001***
1.85 (1.831.87)

Attended PTO
Yes
No

31,396 (25.7%)
90,615 (74.3%)

1,111,307 (34.4%)
2,116,551 (65.6%)

3956.06(1),
p<.001***
1.52 (1.501.54)

Attended parent group
Yes
No

9,884 (8.0%)
113,569 (92.0%)

270,813 (8.4%)
2,955,952 (91.6%)

23.13(1),
p<.001***
1.05 (1.031.08)

Attended teacher
conference
Yes

109,988 (89.1%)

115

2,706,336 (83.8%)

2465.32(1),
p<.001***
.63 (.62-.65)

Table 17 (cont’d)
No
Attended school event
Yes
No
Volunteered at school
Yes
No

Participated in
fundraiser
Yes
No

13,464 (10.9%)

522,551 (16.2%)

86,347 (69.9%)
37,105 (30.1%)

2,138,755 (66.2%)
1,090,376 (33.8%)

47,701 (38.6%)
75,752 (61.4%)

60,403 (48.9%)
63,050 (51.1%)

116

1,559,005 (48.3%)
1,670,182 (51.7%)

1,924,517 (59.7%)
1,301,651 (40.3%)

733.50(1),
p<.001***
.84 (.83-.85)
4427.23(1),
p<.001***
1.48 (1.471.50)
5665.41(1),
p<.001***
1.54 (1.631.56)

Table 18
Correlation Matrix for LD (upper) and Non-LD (lower) Sample Part 1
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

-

-.04

.17

.03

.09

.02

-.03

-.01

.05

.12

.02

.07

-.07

.05

.03

.03

-.09

-.07

.04

-.05

2

.01

-

.16

.09

.15

-.06

-.07

-.15

-.32

-.08

-.08

-.17

-.32

-.32

-.24

-.01

-.13

-.12

-.05

-.12

3

.16

.19

-

.59

.65

-.46

-.31

.02

-.16

.00

.05

-.09

-.09

-.08

-.06

.07

-.00

-.05

-.01

-.00

4

.06

.13

.64

-

.74

-.71

-.25

.02

-.11

.08

-.06

-.10

-.06

-.08

-.01

-.01

-.01

-.00

-.01

.01

5

.07

.15

.68

.79

-

-.54

-.33

-.00

-.17

-.02

-.06

-.08

-.16

-.15

-.13

.11

-.04

-.00

.02

.02

6

-.03

-.09

-.49

-.70

-.57

-

.29

-.06

.15

.05

.06

.09

.04

.10

.01

-.06

-.04

-.10

-.04

-.10

7

-.05

-.09

-.37

-.28

-.34

.26

-

-.12

.18

.04

-.03

.05

.10

.15

.02

-.07

-.04

-.05

-.01

-.04

8

-.01

-.11

.06

.07

.06

-.09

-.07

-

.09

-.02

.06

.06

.22

.08

.12

-.05

.02

.01

-.01

.08

9

-.03

-.25

-.10

-.07

-.09

.06

.05

.08

-

.24

.10

.36

.31

.35

.23

.01

.03

.05

.09

.12

10

.00

-.12

-.10

-.06

-.089

.06

.06

.04

.18

-

.31

.08

.11

.26

.16

.04

-.02

-.03

.00

.05

11

-.02

-.09

-.03

-.02

-.02

.02

.02

.07

.08

.17

-

.09

.10

.17

.08

-.04

.02

-.01

.02

.06

12

.00

-.16

-.03

-.02

-.02

.01

.02

.05

.16

.05

.04

-

.29

.21

.07

.01

.06

.05

-.00

-.04

13

-.04

-.27

-.10

-.07

-.08

.04

.04

.11

.26

.12

.06

.14

-

.32

.22

-.02

.10

.12

-.07

.09

14

-.03

-.35

-.15

-.12

-.14

.09

.08

.11

.26

.21

.12

.16

.30

-

.11

.05

.06

.04

.01

.02

15

-.02

-.26

-.12

-.07

-.10

.06

.06

.08

.20

.19

.07

.09

.21

.28

-

.04

.01

.07

-.01

.06

16

.02

-.05

-.02

-.03

-.02

.04

.02

-.06

.00

.02

.01

.01

.01

.02

.01

-

.34

.41

.38

.37

17

.01

-.17

-.06

-.05

-.05

.05

.06

-.04

.07

.03

.02

.05

.05

.07

.04

.30

-

.39

.33

.31

18

-.01

-.14

-.05

-.06

-.06

.06

.03

-.03

.06

.03

.00

.06

.06

.07

.05

.38

.38

-

.34

.61

19

.03

-.04

-.01

-.03

-.02

.03

.02

-.10

-.01

.01

-.00

.01

-.01

-.01

-.01

.37

.29

.41

-

.41

20

-.00

-.16

-.05

-.04

-.05

.04

.04

-.02

.07

.03

.00

.06

.07

.07

.07

.38

.33

.60

.39

-

21

.01

-.15

-.07

-.07

-.06

.07

.03

-.04

.05

.02

.02

.05

.04

.08

.02

.31

.30

.35

.33

.35

22

.02

-.07

-.01

-.03

-.02

.03

.02

-.03

.03

-.01

-.01

.01

.04

.03

.05

.24

.20

.31

.27

.34

23

.03

-.03

-.02

-.04

-.03

.04

.03

-.08

.00

.01

.01

.00

-.02

-.01

.00

.27

.29

.29

.34

.30

117

Table 18 (cont’d)
24

-.01

-.11

-.04

25

-.01

-.14

-.06

26

.00

-.06

27

.03

28

-.05

-.06

.06

.02

-.03

.04

.02

.01

.05

.06

.06

.05

.31

.35

.40

.32

.41

-.06

-.07

.06

.05

-.04

.06

.02

.00

.05

.05

.07

.07

.28

.27

.45

.32

.50

-.03

-.05

-.05

.05

.03

-.07

.03

-.00

-.02

.02

.00

.01

.00

.37

.30

.50

.38

.58

-.10

-.04

-.04

-.04

.05

.03

-.06

-.00

.01

.01

.03

-.00

.03

.01

.29

.29

.36

.39

.36

.16

.37

.42

.20

.23

-.15

-.17

-.03

-.16

-.11

-.03

-.05

-.15

-.22

-.15

-.04

-.11

-.09

-.03

-.10

29

.23

.42

.42

.20

.23

-.15

-.19

-.05

-.20

-.10

-.05

-.08

-.20

-.25

-.18

-.05

-.12

-.10

-.02

-.12

30

.10

.46

.36

.20

.22

-.15

-.14

-.07

-.20

-.10

-.06

-.10

-.20

-.26

-.20

-.05

-.13

-.11

-.00

-.11

31

.08

.43

.37

.20

.22

-.16

-.17

-.05

-.18

-.09

-.05

-.11

-.16

-.24

-.15

-.05

-.12

-.09

-.02

-.10

32

-.01

.08

.17

.08

.10

-.10

-.07

.02

-.05

-.04

-.01

-.03

-.02

-.05

-.03

-.04

-.04

-.06

-.04

-.05

33

.02

.20

.39

.31

.30

-.33

-.13

.04

-.09

-.07

-.02

-.03

-.07

-.14

-.09

-.04

-.09

-.08

-.04

-.08

34

-.01

.16

.26

.31

.27

-.36

-.08

.05

-.08

-.04

-.03

-.03

-.05

-.10

-.06

-.05

-.08

-.07

-.05

-.06

35

-.01

.17

.27

.30

.29

-.34

-.11

.04

-.08

-.05

-.02

-.02

-.07

-.12

-.07

-.04

-.08

-.07

-.04

-.05

36

.03

-.14

-.27

-.33

-.28

.42

.05

-.07

.05

.03

.02

.02

.04

.09

.06

.03

.06

.08

.05

.06

37

-.01

-.12

-.21

-.14

-.15

.13

.19

-.03

.06

.05

.01

.01

.05

.08

.04

.02

.06

.04

.02

.03

Note. 1=age, 2=SES, 3=K teacher ATL, 4=K teacher SC, 5= K teacher interpersonal, 6=K teacher externalizing, 7= K teacher
internalizing, 8=parent contact, 9=parent open house, 10=parent PTA, 11=parent advisory group, 12=parent-teacher conference,
13=parent school event, 14=parent volunteer, 15=parent fundraising, 16=parent bothered, 17=parent appetite, 18=parent blues,
19=parent trouble focusing, 20=parent depressed, 21=parent things effortful, 22=parent fearful, 23=parent sleep restless, 24=parent
talk less, 25=parent lonely, 26=parent sad, 27=parent “not get going,” 28=K reading t-score, 29=K math t-score, 30=5th grade reading
t-score, 31=5th grade math t-score, 32= motivation school, 33= 5th teacher ATL, 34=5th teacher SC, 35=5th teacher interpersonal,
36=5th teacher externalizing, 37=5th teacher internalizing

118

Table 19
Correlation Matrix for LD (upper) and Non-LD (lower) Sample Part 2
21
22
23
24
25
26
27
28
29
1
.01
.01 -.09 -.07
.06
.01
.17
.31
.06
2
- -.18 -.13 -.03 -.11
.25
.28
.14
.21
.04
3
- -.06 -.09
.01
.01
.34
.39
.03
.03
.03
4
- -.03 -.11 -.04 -.06
.16
.14
.11
.01
.08
5
.01 -.04
.04 -.04
.20
.20
.07
.00
.05
6
.02
.06 -.00
.01 -.00 -.02 -.09
-.05
.05
7
.01
- -.03
.06 -.03 -.05 -.09
-.10
.08
.04
8
.07
.05
.05
.05
.03 -.05 -.12
-.09
.17
9
.10
.16
.10
.03
.07
.01 -.17
-.19
.02
10
.06
- -.03 -.01
.02 -.03 -.06
-.05
.00
.04
11
.07
.06
- -.02
.04 -.05
.01
.02
.01
.01
12
.05
.10
.12
.03 -.01
.06 -.03
-.07
.00

119

30
.08

31
.03

34
.00

38
-.04

39
.01

40
-.00

41
.04

42
-.05

.37

.32

-.02

.03

.07

.07

-.13

.25

.31

.01

.35

.32

.32

.10

.04

.06

.30

.33

.33

.14

.13

-.00

.29

.33

.33

-.01

.00

-.09

-.38

-.43

-.37

.10
.27
.38
.34
.51

.00

-.08

.04

-.09

-.12

-.15

.10

.25

-.09

-.13

.06

-.02

-.10

-.01

-.02

-.22

-.17

.07

-.01

-.08

-.09

.02
.08

.10

-.05

-.06

-.06

-.04

-.04

.00

.10

.03

.01

-.01

-.05

-.06

-.04

.05

-.06

-.16

-.10

-.07

.00

-.07

-.04

.07

-.00

-.18
-.16
-.13
.22

.14

Table 19 (cont’d)
13
.12
.09

.08

.07

.13

.04

.00

-.06

-.18

-.21

-.13

-.02

-.04

-.05

.00

.06

.01

.04

.01

.02

.02

-.15

-.15

-.18

-.19

.01

-.05

-.09

-.08

.10

.12

.00

.08

-.01

-.02

-.10

-.09

-.17

-.13

.01

-.04

-.05

-.04

.01

.32
.36
.47

.32
.31
.60

.42
.32
.57

.36
.28
.38

.05
-.02
-.03

.01
-.07
-.08

-.05
-.03
-.06

-.04
-.02
-.06

-.14
-.06
-.12

-.07
-.05
.02

-.11
-.08
-.02

.03
.00
.07
.03

.03
-.02

-.04
-.03

-.03
.02

-.00
-.04

.02
-.00

-.05
-.02

14

.11

.05

15

.10

16
17
18

.06
.29
.30
.34

.24
.32
.35

.05
.08
.29
.37
.30

19
20

.27
.32

.26
.33

.30
.36

.37
.44

.36
.60

.42
.58

.28
.32

-.01
-.03

.07
-.02
.078
.07
-.06

21
22
23
24
25
26
27
28

.22
.30
.30
.28
.29
.37
.12
.12

.29
.21
.28
.33
.35
.21
.04
.05

.25
.23
.31
.26
.31
.36
.04
.01

.34
.30
.34
.41
.38
.34
-.08

.26
.33
.27
.50
.52
.30
-.10

.32
.39
.32
.43
.50
.32
-.05

.37
.18
.41
.36
.35
.29
-.05

-.06
-.05
-.01
-.06
-.00
.06
.03
-

-.07
-.10
.03
-.05
-.06
.04
.03
.65

-.13
-.14
-.07
-.08
-.01
-.01
.02
.39

-.09
-.11
-.05
-.06
-.00
-.03
.02
.32

-.01
.04
.04
-.03
-.04
-.04
.01
-.04

-.07
-.13
-.06
-.10
-.07
-.12
-.08
.10

-.09
-.10
.00
-.08
-.08
-.08
-.01
.15

-.08
-.16
-.05
-.06
-.06
-.08
-.03
.13

-.08

-.11

-.04

-.04

.73

-

.49

.60

-.20

.11

.15

.14

29

120

.02
.00
.16
.10
.09
.09
.05
.10
.02
.13
.10

.06
.06
-.00
.04
.00
.07
.08
.03
.08
.04
.04
-.02
-.14
-.09

Table 19 (cont’d)
30
.13
.06
31
.12
.04
32
.04
.03
33
.09
.03
34
.09
.02
35
.08
.02
36
.08
.03
37
.04
.01

.01
.02
.04
.04
.02
.02
.02
.02

-.10

-.10

-.02

-.05

.61

.64

-

.59

-.19

.12

.18

.13

-.09

-.10

-.02

-.06

.56

.71

.72

-

-.17

.13

.10

.11

-.05

-.05

-.03

-.05

.15

.13

.17

.18

-

.10

.04

.08

-.06

-.07

-.05

-.07

.29

.29

.37

.35

.29

-

.66

.68

-.05

-.07

-.04

-.06

.17

.16

.22

.21

.16

.68

-

.78

-.05

-.06

-.03

-.06

.18

.18

.24

.21

.20

.71

.80

.06
.02

.07
.04

.05
.02

.06
.04

-.15
-.15

-.13
-.20

-.20
-.18

-.18
-.20

-.15
-.14

-.59
-.38

-.70
-.30

-.61
-.35

.16
.06
.02
.57
.73
.61
.28

-.11
-.10
-.03
-.21
-.30
-.38
.27
-

Note. 1=age, 2=SES, 3=K teacher ATL, 4=K teacher SC, 5= K teacher interpersonal, 6=K teacher externalizing, 7= K teacher
internalizing, 8=parent contact, 9=parent open house, 10=parent PTA, 11=parent advisory group, 12=parent-teacher conference,
13=parent school event, 14=parent volunteer, 15=parent fundraising, 16=parent bothered, 17=parent appetite, 18=parent blues,
19=parent trouble focusing, 20=parent depressed, 21=parent things effortful, 22=parent fearful, 23=parent sleep restless, 24=parent
talk less, 25=parent lonely, 26=parent sad, 27=parent “not get going,” 28=K reading t-score, 29=K math t-score, 30=5th grade reading
t-score, 31=5th grade math t-score, 32= motivation school, 33= 5th teacher ATL, 34=5th teacher SC, 35=5th teacher interpersonal,
36=5th teacher externalizing, 37=5th teacher internalizing

121

CHAPTER 4
RESULTS
Preliminary Analyses
Group means and standard deviations of predictor, covariate, and outcome variables for
the study sample are displayed in Table 14, 15, 16, and 17.
Differences between LD and non-LD Groups. Based on independent samples t-tests,
all predictor, covariate, and outcome variables were significantly different across samples with
and without LD. However, due to a large sample size and unequal sample sizes across groups,
effect sizes (Cohen’s d) and odds ratios were analyzed to determine meaningful statistical
significance between groups. In terms of covariates, a small effect size was noted for age at
kindergarten between the LD and non-LD group, indicating that children with an LD diagnosis
were approximately 24 days older than their peers without LD. A medium effect size was noted
for family SES, indicating that children with LD came from families with lower SES than their
non-LD peers.
In terms of predictor variables, such as parent depression symptoms, children with LD
had a significantly higher likelihood (odds ratio = 1.84) of having a parent endorse having a poor
appetite “most of the time” compared to “never” having a poor appetite than children without
LD. Children with LD were 2.11 times more likely to have a parent endorse feeling the “blues”
“most of the time” compared to “never” feeling the blues than children without LD. Children
with LD had a significantly higher likelihood (odds ratio = 1.65) of having a parent endorse
trouble focusing “most of the time” compared to “never” having trouble focusing as compared to
parents of children without LD. Children with LD had a significantly higher likelihood (odds
ratio = 1.50) of having a parent endorse feeling depressed “most of the time” compared to

122

“never” feeling depressed than parents of children without LD. Children with LD were 2.21
times more likely to have a parent endorse feeling fearful “most of the time” compared to
“never” feeling fearful than children without LD. Children with LD had a significantly higher
likelihood (odds ratio = 2.70) of having a parent report talking less “most of the time” compared
to “never” talking less than children without LD.
In regards to parent school involvement indicators, children with LD were 1.46 times
more likely to have a parent not attend an open house event than children without LD. Children
with LD had a significantly higher likelihood (odds ratio = 1.45) of not having a parent attend a
PTO meeting than children without LD. Children with LD were 1.55 times more likely to have a
parent not volunteer at school than children without LD. Children with LD had a significantly
higher likelihood (odds ratio = 1.54 ) of not having a parent participate in fundraisers at school
than children without LD.
In terms of outcome variables, children with LD had significantly lower academic
achievement scores in reading and math in kindergarten and in fifth grade compared to their
peers without LD. The effect sizes for difference in academic achievement scores was large
(ranging from 1.16 to 1.46). In regards to early psychosocial behavior in kindergarten, children
with LD had lower scores as rated by their teachers on the approaches to learning scale, selfcontrol scale, interpersonal skills scale, and externalizing and internalizing scales. However, the
effect size was large for only the approaches to learning scale, medium for the interpersonal
skills scale, and small for the remainder of the scales. In regards to psychosocial behavior in fifth
grade, children with LD had lower scores as rated by their teachers on all five scales again,
however only the approaches to learning scale had a large effect. Finally, children with LD self-

123

reported lower motivation for all school subjects compared to children without LD, however the
effect size was small.
Differences between Categories of LD. In order to examine the group mean differences
in study variables of interest between samples with subtype of LD, a between-group, one-way
analysis of variance (ANOVA) was conducted for continuous variables: academic achievement,
psychosocial behaviors, and motivation. Since the assumption of homogeneity of variance was
not met with regards to academic achievement, psychosocial behavior, or motivation scores, the
Welch correction (Welch, 1951) was applied to these ANOVA’s and Games-Howell post-hoc
analyses were used. To examine the group mean differences for categorical study variables of
interest, parent depression and parent school involvement, the Kruskal-Wallis H Test, a nonparametric equivalent of an ANOVA, was conducted; Dunn-Bonferroni post-hoc analyses were
used. Unweighted and weighted subpopulation means and standard deviations are found in
Tables 20, 21, 22 and 23. A new variable was created and participants were grouped via learning
disability status. Identification of learning disability was based on special education records and
report of the child’s main disability category by the child’s fifth grade primary special education
teacher. Direct information about the child’s learning disability category (e.g., reading, math, or
both) was unavailable, so LD type was determined based on the special education teacher’s
report of the child’s primary IEP goals. If the child had primary reading goals, it was assumed
that they demonstrated academic difficulty in reading and thus, were categorized as LD-Reading.
If the child had primary math goals, it was assumed that they demonstrated academic difficulties
in math, and were categorized as LD-math. If the child had primary goals in both reading and
math, it was assumed that they had academic difficulties in both subjects and were categorized as
LD-co-morbid Math and Reading. Overall, 410 children had a learning disability of which 180

124

children had a RD, 20 children had a MD, and 220 children had a co-morbid RD-MD. Of the 410
children identified with LD, less than 10 children did not have primary goals specified in their
IEP, thus they were not included in the categorized analysis. Due to the small sample size of the
MD group and the exploratory nature of this study, and given that children with MD often
experience co-morbid reading problems, regardless of whether they are identified and receive
services through a formal identification process, the MD sample was grouped with the RD-MD
sample (n=230).
Parent depression group differences. Significant group differences were found on three
items related to parent depression. Specifically, a Kruskal-Wallis H test showed a statistically
significant difference across groups in parent responses regarding having a poor appetite, χ2(2) =
14.83, p=.001, having difficulty “shaking the blues,” χ2(2) = 10.92, p=.004, and feeling fearful,
χ2(2) = 6.86, p=.032. Dunn-Bonferroni post-hoc analyses indicated children without LD had
parents whom reported significantly less frequency of problems with poor appetite compared to
children with RD-MD (p<.001). Children without LD also had parents who reported
significantly less frequency of difficulty “shaking the blues” compared to children with RD
(p<.05). Finally, children with RD had parents who reported significantly less frequency of
feeling fearful compared to children with RD-MD (p<.05). No other statistically significant
group differences emerged.
Parent school involvement group differences. Significant group differences were found
on five items related to parent school involvement. A Kruskal-Wallis H test showed a
statistically significant difference across groups in parent responses regarding the frequency with
which parent’s report attending school open houses, χ2(2) = 21.22, p<.001, attending PTA
meetings, χ2(2) = 12.01, p=.002, attending school events, χ2(2) = 12.84, p=.002, acting as a

125

school volunteer, χ2(2) = 18.50, p<.001, and participating in school fundraisers, χ2(2) = 16.53,
p<.001. Post-hoc analyses indicated children without LD had parents whom reported attending
school open houses (p<.001), PTA meetings (p<.05), school events (p=.001), acting as a school
volunteer (p<.001), and participating in school fundraisers (p<.05) significantly more frequently
than parents of children with RD-MD. Parents of children without LD also reported participating
in school fundraisers significantly more frequently than parents of children with RD (p<.05).
Additionally, children with RD had parents whom reported attending school open houses
significantly more frequently than parents of children with RD-MD (p<.05). No other
statistically significant group differences emerged.
Academic achievement group differences. Significant group effects on academic
achievement in reading scores in kindergarten, F (2, 9850) = 417.39, p<.001, math scores in
kindergarten, F (2, 10304) = 333.47, p<.001, reading scores in fifth grade, F (2, 10171) = 482.84,
p<.001, and math scores in fifth grade, F (2, 10178) = 393.56, p<.001 were found. Effect sizes,
calculated using eta squared, were .05 for reading scores in kindergarten, .05 for math scores in
kindergarten, .07 for reading scores in fifth grade, and .05 for math scores in fifth grade,
suggesting small effect sizes. Games-Howell post-hoc analyses revealed that children without
LD performed higher than children with RD and children with RD-MD (p<.05) on both reading
and math achievement tests at both time points. Similarly, children with RD performed
significantly higher than children with RD-MD (p<.05) on both academic measures at both time
points. On average, children with RD-MD scored approximately 13 points lower on the
kindergarten reading assessment, 14 points lower on the kindergarten math assessment, 14 points
lower on the fifth-grade reading assessment, and 14 points lower on the kindergarten math
assessment when compared to children without LD. Children with RD only scored, on average, 3

126

points higher on kindergarten reading, 6 points higher on kindergarten math, 3 points higher on
fifth grade reading, and 7 points higher on fifth grade math compared to children with RD-MD.
Psychosocial behavior group differences. Significant group effects on all five scales of
psychosocial behavior in kindergarten and all five scales of psychosocial behavior in fifth grade
were found. In kindergarten, significant group differences emerged for the approaches to
learning scale, F (2, 10080) = 203.42, p<.001, the self-control scale, F (2, 10012) = 25.98,
p<.001, the interpersonal scale, F (2, 9980) = 50.32, p<.001, the externalizing behaviors scales, F
(2, 10039) = 16.63, p<.00, and the internalizing behaviors scale, F (2, 9987) = 25.79, p<.001.
Effect sizes, calculated using eta squared, were .04 for the approaches to learning scale, .01 for
the self-control scale, .01 for the interpersonal scale, .01 for the externalizing behaviors scale,
and .01 for the internalizing behaviors scale, suggesting small effect sizes. Games-Howell posthoc analyses revealed that children without LD received more positive ratings from teachers
indicating better approaches to learning, self-control, and interpersonal skills, and fewer
externalizing behaviors and internalizing behaviors compared to children with RD and children
with RD-MD (p<.05) in kindergarten. Children with RD performed significantly higher than
children with RD-MD (p<.05) on the approaches to learning scale and significantly lower on the
internalizing behaviors scale (p<.05), but no significant differences were found regarding selfcontrol, interpersonal skills, or externalizing behaviors in kindergarten. On average, children
with RD-MD scored approximately .3 to .8 points lower on the approaches to learning, selfcontrol, and interpersonal scales, and .2-.3 points higher on the externalizing behaviors and
internalizing behaviors scales when compared to children without LD in kindergarten. Children
with RD scored, on average, .3 points higher on the approaches to learning scale and .1 points

127

lower on the internalizing behaviors scale when compared to children with RD-MD in
kindergarten.
In fifth grade, significant group differences emerged for the approaches to learning scale,
F (2, 9713) = 114.37, p<.001, the self-control scale, F (2, 9624) = 30.67, p<.001, the
interpersonal scale, F (2, 9506) = 40.28, p<.001, the externalizing behaviors scales, F (2, 9655) =
19.28, p<.00, and the internalizing behaviors scale, F (2, 9546) = 27.55, p<.001. Effect sizes,
calculated using eta squared, were .02 for the approaches to learning scale, .01 for the selfcontrol scale, .01 for the interpersonal scale, .01 for the externalizing behaviors scale, and .01 for
the internalizing behaviors scale, suggesting small effect sizes. Games-Howell post-hoc analyses
revealed that children without LD received more positive ratings from teachers indicating better
approaches to learning, self-control, and interpersonal skills, and fewer externalizing behaviors
and internalizing behaviors compared to children with RD and children with RD-MD (p<.05) in
fifth grade as well. Children with RD did not perform significantly differently than children with
RD-MD (p<.05) on any of the scales. On average, children with RD-MD scored approximately
.3 to .5 points lower on the approaches to learning, self-control, and interpersonal scales, and .2
points higher on the externalizing behaviors and internalizing behaviors scales when compared to
children without LD in fifth grade.
Motivation group differences. Significant group effects on student’s self-reported
motivation across all school subjects, F (2, 10183) = 20.40, p<.001, however with statistically
negligible effect size (eta=0.004). Interestingly, children with RD rated themselves significantly
lower in terms of school motivation than children without LD (p<.05), as well as children with
RD-MD (p<.05). There was no significant mean difference in student’s self-reported school
motivation between children without LD and children with RD-MD.

128

Table 20
Unweighted Subpopulation Descriptive Statistics
Variables

No Learning
Disability

Reading Disability

Reading-Math
Disability
Na

Na

Mean (SD)

Na

Mean (SD)

Kindergarten
SES

9880

.06 (.80)

170

-.23 (.73)

220

-.40 (.68)

Age at
Assessment
(K, 1999)

9330

68.38 (4.30)

170

69.00 (4.63)

210

69.27 (5.21)

Age at
Assessment
(5th grade,
2004)
Kindergarten
Achievement
Direct Math
Assessment
Direct Reading
Assessment
Fifth Grade
Achievement
Direct Math
Assessment
Direct Reading
Assessment
Early
Psychosocial
Behaviors
Teacher-Rated
Approaches to
Learning
Teacher-Rated
Self-Control
Teacher-Rated
Interpersonal
Skills
Teacher-Rated
Externalizing
Teacher-Rated

9790

134.51
(4.35)

180

135.00
(4.73)

220

135.40 (5.22)

9930

52.06 (9.47)

170

44.19 (8.47)

210

38.10 (8.69)

9500

51.98 (9.40)

160

41.60 (6.60)

190

39.07 (7.98)

9790

51.95 (9.31)

189

45.55 (8.43)

220

37.96 (7.68)

9790

51.95 (9.28)

170

40.81 (7.65)

210

37.65 (8.21)

9700

3.23 (.64)

170

2.74 (.65)

210

2.42 (.65)

9630

3.26 (.59)

170

3.08 (.63)

210

2.98 (.63)

9610

3.20 (.61)

170

2.95 (.65)

210

2.80 (.65)

9660

1.57 (.57)

170

1.73 (.66)

210

1.81 (.73)

Mean
(SD)

Covariates

129

Table 20 (cont’d)
Internalizing
9610
1.51 (.48)
160
1.63 (.50)
210
1.77 (.58)
Fifth Grade
Psychosocial
Behavior
Teacher-Rated 9310
3.14 (.65)
180
2.73 (.62)
230
2.63 (.60)
Approaches to
Learning
Teacher-Rated 9230
3.28 (.58)
170
3.11 (.62)
230
2.98 (.63)
Self-Control
Teacher-Rated 9110
3.13 (.62)
170
2.90 (.64)
220
2.79 (.67)
Interpersonal
Skills
Teacher-Rated 9250
1.60 (.55)
180
1.78 (.60)
230
1.80 (.63)
Externalizing
Teacher-Rated 9150
1.59 (.51)
170
1.77 (.58)
230
1.81 (.52)
Internalizing
Motivation
Student-Rated 9790
2.76 (.62)
180
2.46 (.66)
220
2.64 (.71)
School
Note: a All unweighted sample sizes were rounded to the nearest 10 due to restrictions set
forth via the licensing agreement.

130

Table 21
Unweighted Subpopulation Descriptive Statistics (Categorical Variables)
Variables

No Learning
Disability
Na (%)

Parent Depression
Felt bothered
Never
Some Time
Moderate Amount
Most of Time
Poor appetite
Never
Some Time
Moderate Amount
Most of Time
Felt the “blues”
Never
Some Time
Moderate Amount
Most of Time
Trouble focusing
Never
Some Time
Moderate Amount
Most of Time
Felt depressed
Never
Some Time
Moderate Amount
Most of Time

5070
(53.9)
3360
(35.7)
740 (7.9)
240 (2.6)
6850
(72.7)
1920
(20.4)
370 (3.9)
280 (3.0)
7610
(80.9)
1400
(14.9)
230 (2.5)
160 (1.7)
5550
(58.9)
3180
(33.8)
420 (4.5)
260 (2.8)
6950
(73.9)
2010
(21.4)
230 (2.4)
210 (2.3)

Mean
(SD)
1.59
(.74)

Reading Disability

Reading-Math
Disability

Na (%)

Na (%)

90 (59.9)

Mean
(SD)
1.55
(.79)

50 (29.3)
10 (7.0)
10 (3.8)
1.37
(.70)

110 (71.3)

1.44
(.82)

30 (19.1)
10 (3.8)
10 (5.7)
1.25
(.58)

110 (72.4)

1.40
(.77)

30 (19.2)
10 (3.8)
10 (4.5)
1.51
(.71)

100 (63.1)

1.53
(.81)

40 (25.5)
10 (7.0)
10 (4.5)
1.33
(.64)

110 (70.7)
30 (21.7)
10 (4.5)
10 (3.2)

131

1.40
(.72)

100
(48.3)
80
(38.8)
10 (7.5)
10 (4.7)
120
(61.4)
50
(25.2)
20 (8.9)
10 (4.5)
150
(76.1)
30
(17.4)
10 (4.0)
10 (2.5)
100
(52.2)
70
(35.8)
10 (7.5)
10 (4.5)
140
(70.8)
40
(20.8)
10 (5.0)
10 (3.5)

Mean
(SD)
1.70
(.83)

1.56
(.83)

1.33
(.67)

1.64
(.81)

1.41
(.74)

Table 21 (cont’d)
Felt things were
effortful
Never
Some Time
Moderate Amount
Most of Time

1.62
(.88)
5390
(57.3)
2860
(30.4)
480 (5.1)
670 (7.1)

Some Time
Moderate Amount
Most of Time
Difficulty sleeping
Never
Some Time
Moderate Amount
Most of Time
Talked less
Never

Most of Time

140 (1.5)

Some Time
Moderate Amount
Most of Time
Felt sad
Never

1.13
(.44)
140 (90.4)

<10 (1.9)
<10 (.6)

7620
(81.0)
1420
(15.1)
200 (2.1)
160 (1.8)

5910

1.79
(.95)
80 (49.0)

20 (11.5)
10 (8.3)

130 (80.9)

1.30
(.71)

20 (12.1)
10 (3.2)
10 (3.8)
1.25
(.58)

130 (81.5)

1.29
(.71)

20 (11.5)
10 (3.2)
10 (3.8)
1.44
(.65)

132

100 (66.9)

1.89
(1.0)
90
(43.6)
70
(36.1)
20 (7.9)
20
(12.4)

50 (31.2)

1.24
(.57)

1.31
(.75)
160
(70.3)
20
(10.9)
10 (3.5)
10 (4.5)

10 (7.0)

1.72
(.89)

Moderate Amount

Felt lonely
Never

10 (5.7)
20 (10.8)

4700
(50.0)
3290
(35.0)
720 (7.7)
690 (7.4)

1.70
(.89)
100
(52.0)
70
(33.5)
10 (7.5)
10 (7.0)

40 (22.9)

8010
(85.1)
1130
(12.0)
150 (1.6)
120 (1.2)

7640
(81.2)
1380
(14.6)
250 (2.7)

Some Time

100 (60.5)

1.19
(.51)

Felt fearful
Never

1.67
(1.0)

1.43
(.72)

150
(76.2)
40
(18.3)
<10
(1.5)
10 (4.0)

160
(77.2)
40
(18.3)
10 (3.0)
<10
(1.5)
120

1.33
(.70)

1.29
(.60)

1.51
(.72)

Table 21 (cont’d)
Some Time
Moderate Amount
Most of Time
Difficulty “to get
going”
Never
Some Time
Moderate Amount
Most of Time

Parent School
Involvement
Attended open house
Yes
No
Attended PTO
Yes
No
Attended parent group
Yes
No
Attended teacher
conference
Yes
No
Attended school event
Yes
No

(62.8)
3020
(32.1)
310 (3.3)

10 (3.8)

170 (1.9)

10 (3.2)
1.51
(.72)

5620
(59.7)
3090
(32.9)
420 (4.5)
270 (2.9)

7260
(75.8)
2310
(24.2)
3580
(37.3)
6010
(62.7)
880 (9.2)

10 (3.0)
1.54
(.84)

100 (63.1)

10 (5.7)
10 (5.7)

1.24
(.43)

120 (77.0)

1.23
(.42)

40 (23.0)
1.63
(.48)

50 (33.5)

1.66
(.47)

110 (66.5)
1.91
(.29)

10 (9.3)

1.91
(.29)

150 (90.7)
1.14
(.35)

8230
(85.9)
1350
(14.1)

50
(26.0)
150
(74.0)
20
(11.1)
180
(88.9)

110 (60.5)
50 (34.0)

1.38
(.49)

1.74
(.44)

1.89
(.31)

1.13
(.34)
180
(86.5)
30
(13.5)

10 (8.0)

133

130
(62.0)
80
(38.0)

1.08
(.27)
150 (92.0)

1.31
(.46)

1.54
(.66)
110
(54.2)
80
(39.3)
10 (5.0)
<10
(1.5)

40 (25.5)

8700
(90.8)

6640
(69.3)
2940
(30.7)

(59.0)
70
(34.0)
10 (4.0)

40 (26.1)

1.34
(.48)

120
(58.0)
90
(42.0)

1.42
(.50)

Table 21 (cont’d)
Volunteered at school
Yes
No
Participated in fundraiser
Yes

4990
(52.0)
4600
(48.0)

1.48
(.50)

70 (43.8)

1.56
(.50)

90 (56.2)

1.38
(.49)

1.48
(.50)

80
(38.6)
130
(61.4)

1.61
(.49)

1.49
(.50)

5950
90 (52.5)
100
(62.1)
(51.0)
3630
80 (47.5)
100
(37.9)
(49.0)
Note: a All unweighted sample sizes were rounded to the nearest 10 due to restrictions set
forth via the licensing agreement.
No

134

Table 22
Weighted Subpopulation Descriptive Statistics
Variables
No Learning Disability

Reading Disability

N

Mean (SD)

N

Kindergarten
SES

3,353,415

-.03 (.78)

40,126

Age at
Assessment
(K, 1999)

3,111,8989 68.31 (4.30)

38,867

2,928,775

134.53
(4.38)

40,126

3,245,941

50.85 (9.68)

3,100,220

Mean (SD)

Reading-Math
Disability
N

Mean (SD)

-.25 (.75)

81,545

-.54 (.91)

68.58
(4.57)

75,835

70.02 (5.83)

76,003

135.91 (5.81)

39,035

45.04 (7.62) 76,202

38.86 (8.53)

50.99 (9.70)

37,583

41.78 (6.33) 68,330

38.57 (7.52)

2,928,740

51.11 (9.61)

40,126

47.25 (7.35) 76,329

37.42 (7.41)

2,920,902

51.16 (9.52)

40,126

42.44 (6.52) 74,066

38.03 (7.67)

3,136,565

3.15 (.67)

39,542

2.75 (.65)

75,632

2.41 (.66)

3,120,350

3.22 (.61)

39,542

3.15 (.60)

75,676

3.00 (.64)

3,106,357

3.15 (.62)

39,542

3.01 (.58)

73,697

2.74 (.65)

Covariates

Age at
Assessment
(5th grade,
2004)
Kindergarten
Achievement
Direct Math
Assessment
Direct
Reading
Assessment
Fifth Grade
Achievement
Direct Math
Assessment
Direct
Reading
Assessment
Early
Psychosocial
Behaviors
TeacherRated
Approaches
to Learning
TeacherRated SelfControl
TeacherRated
Interpersonal

135

134.63
(4.62)

Table 22 (cont’d)
Skills
TeacherRated
Externalizing
TeacherRated
Internalizing
Fifth Grade
Psychosocial
Behavior
TeacherRated
Approaches
to Learning
TeacherRated SelfControl
TeacherRated
Interpersonal
Skills
TeacherRated
Externalizing
TeacherRated
Internalizing
Motivation
StudentRated School

3,126,532

1.62 (.60)

39,202

1.69 (.67)

74,859

1.89 (.79)

3,104,368

1.55 (.50)

38,835

1.55 (.50)

75,158

1.78 (.58)

2,699,372

3.09 (.67)

38,937

2.78 (.63)

80,696

2.62 (.62)

2,672,205

3.25 (.59)

38,778

3.22 (.57)

80,379

3.00 (.56)

2,624,217

3.10 (.63)

38,469

2.97 (.67)

80,420

2.75 (.60)

2,674,614

1.64 (.58)

38,937

1.73 (.52)

80,513

1.82 (.62)

2,646,345

1.62 (.53)

38,729

1.72 (.52)

80,696

1.78 (.46)

2,928,388

2.72 (.64)

40,126

2.32 (.62)

76,392

2.74 (.82)

136

Table 23
Weighted Subpopulation Descriptive Statistics (Categorical Variables)
Variables

No Learning
Disability
N (%)

Parent Depression
Felt bothered
Never
Some Time
Moderate Amount
Most of Time
Poor appetite
Never
Some Time
Moderate Amount
Most of Time
Felt the “blues”
Never
Some Time
Moderate Amount
Most of Time
Trouble focusing
Never
Some Time
Moderate Amount
Most of Time

1678074
(52.9)
1149172
(36.2)
270662
(8.5)
74467
(2.3)
2240349
(70.5)
680528
(21.4)
139603
(4.4)
115078
(3.6)
2539143
(80.0)
487456
(15.4)
82716
(2.6)
64737
(2.0)
1849482
(58.2)
1046561
(32.9)
171109
(5.4)
110021
(3.5)

Reading Disability

Mean
(SD)

N (%)

1.60
(.74)

21281
(54.2)
12973
(33.1)
3189 (8.1)

Mean
(SD)
1.63
(.82)

1788 (4.5)
1.41
(.74)

27085
(69.0)
7942 (20.2)

1.47
(.83)

2205 (5.6)

1.54
(.75)

27359
(69.7)
9278 (23.6)

45033
(56.7)
27644
(34.8)
4368 (5.5)

1.40
(.72)

47969
(60.4)
18633
(23.4)
9688
(12.2)
3194 (4.0)

1242 (3.2)

60537
(76.2)
16351
(20.6)
1717 (2.2)

1352 (3.4)

879 (1.1)

25503
(65.0)
8685 (22.1)
3329 (8.5)
1714 (4.4)

137

N (%)

Mean
(SD)
1.55
(.74)

2439 (3.1)

1998 (5.1)

1.27
(.61)

Reading-Math
Disability

1.52
(.83)

35286
(44.4)
29654
(37.3)
5300 (6.7)
9244
(11.6)

1.60
(.85)

1.28
(.56)

1.86
(.98)

Table 23 (cont’d)
Felt depressed
Never
Some Time
Moderate Amount
Most of Time

Felt things were
effortful
Never
Some Time
Moderate Amount
Most of Time

2306057
(72.6)
695835
(21.9)
70261
(2.2)
102708
(3.2)

Some Time
Moderate Amount
Most of Time
Difficulty sleeping
Never
Some Time
Moderate Amount
Most of Time
Talked less
Never
Some Time

29681
(75.7)
6397 (16.3)

1730262
(54.6)
961967
(30.3)
166363
(5.2)
312837
(9.9)

2126 (5.4)
1026 (2.6)

672 (0.8)

1.65
(.98)
23877
(60.9)
9232 (23.5)
2047 (5.2)
4074 (10.4)

6983 (8.8)
1.08
(.34)

2677019
(84.2)
403675
(12.7)
54854
(1.7)
42146
(1.3)

2550184
(80.2)
469415
(14.8)

36724
(93.6)
2238 (5.7)

269 (0.7)

19811
(50.5)
14864
(37.9)
2097 (5.3)

7990
(10.1)
1.67
(.84)

2459 (6.3)
1.27
(.61)

30771
(78.4)
4662 (11.9)

138

1.46
(.95)
60313
(75.9)
9458
(11.9)
1723 (2.2)

0 (0)

1.78
(.93)

1.42
(.68)

1.63
(.93)
46948
(57.6)
21374
(27.0)
3903 (4.9)

1.20
(.52)

1520760
(47.9)
1109936
(34.9)
269732
(8.5)
277753
(8.7)

1.35
(.70)

54305
(68.3)
17984
(22.6)
6523 (8.2)

1.70
(.95)

Felt fearful
Never

1.36
(.69)

35397
(44.5)
27723
(34.9)
3725 (4.7)

1.92
(1.06)

12639
(15.9)
1.37
(.81)

58669
(73.8)
11672
(14.7)

1.45
(.88)

Table 23 (cont’d)
Moderate Amount
Most of Time
Felt lonely
Never
Some Time
Moderate Amount
Most of Time
Felt sad
Never
Some Time
Moderate Amount
Most of Time
Difficulty “to get
going”
Never
Some Time
Moderate Amount
Most of Time

Parent School
Involvement
Attended open house
Yes
No
Attended PTO
Yes
No

96061
(3.0)
63820
(2.0)
2549165
(80.3)
471399
(14.8)
78789
(2.5)
76233
(2.4)
1950599
(61.4)
1038623
(32.7)
115544
(3.6)
71723
(2.3)

1.27
(.63)

1.47
(.68)

1561 (3.9)

3351 (4.2)

2236 (5.7)

5793 (7.1)

31445
(80.2)
5264 (13.4)
1765 (4.4)

59062
(74.3)
15895
(20.0)
3659 (4.6)

757 (1.9)

867 (1.1)

27359
(69.7)
8959 (22.8)
2188 (5.6)
724 (1.8)

1283 (1.6)
1.68
(.96)

1832568
(57.7)
1073920
(33.8)
152764
(4.8)
116180
(3.7)

1111307
(34.4)
2116551
(65.6)

1.27
(.45)

1.40
(.68)

46296
(58.5)
28258
(35.7)
3292 (4.2)

1.54
(.75)

2348497
(72.8)
877730
(27.2)

1.28
(.64)

38976
(49.0)
37382
(47.0)
2902 (3.7)

3722 (9.5)

224 (0.3)

1.66
(.48)

15434
(39.9)
23251
(60.1)

139

1.26
(.44)

1.60
(.49)

1.49
(.66)

1.55
(.58)

22447
(57.2)
10604
(27.0)
2458 (6.3)

28699
(74.2)
9985 (25.8)

1.32
(.61)

42382
(52.0)
39162
(48.0)
15744
(19.3)
65800
(80.7)

1.48
(.50)

1.81
(.40)

Table 23 (cont’d)
Attended parent group
Yes
No
Attended teacher
conference
Yes
No
Attended school event
Yes
No
Volunteered at school
Yes
No
Participated in
fundraiser
Yes
No

270813
(8.4)
2955952
(91.6)

1.92
(.28)

3483 (8.7)
36643
(91.3)

1.16
(.37)

1559005
(48.3)
1670182
(51.7)
1924517
(59.7)
1301651
(40.3)

6401 (7.8)

36524
(91.0)
3601 (9.0)

1.34
(.47)

29154
(72.7)
10972
(27.3)

1.52
(.50)

17532
(43.7)
22594
(56.3)

1.40
(.49)

20860
(52.0)
19266
(48.0)

140

1.92
(.27)

75143
(92.2)
1.09
(.29)

2706336
(83.8)
522551
(16.2)

2138755
(66.2)
1090376
(33.8)

1.91
(.28)

1.12
(.32)
71899
(88.2)
9645
(11.8)

1.27
(.45)

1.56
(.50)

1.48
(.50)

56138
(68.8)
25407
(31.2)
28883
(35.4)
52662
(64.6)
38488
(47.2)
43057
(52.8)

1.31
(.46)

1.65
(.48)

1.53
(.50)

Analyzing the Structural Model
Establishing Factors by Testing the Measurement Model. Prior to running the
structural equation model (Figure 2 and Figure 3), Confirmatory Factor Analysis (CFA) was
conducted to test the four-factor measurement model. The first latent factor, parent depression,
was indicated by twelve observed variables. The second latent factor, parent school involvement,
was indicated by seven observed variables. The third latent factor, early psychosocial behavior,
was indicated by five observed variables. The fourth latent factor, fifth grade psychosocial
behavior, was indicated by five observed variables. All factors were assumed to covary with one
another and their associations were specified as unanalyzed in the measurement model (Kline,
2005).
The four-factor CFA fit the data adequately. Absolute fit indices included a RMSEA
equal to .020 (CI = .019 -.021) and relative fit indices, CFI =.934, TLI=.927. The chi-square test
of model fit was not available due to the use of WLSMV estimation and because sample weights
were applied to the analyses (Muthen & Muthen, 2015). Modifications to the model were
examined based on evaluation of factor loadings, modification indices, absolute fit indices, and
theoretical considerations. Examination yielded removal of two variables in the parent school
involvement construct, attending parent advisory group and attending parent-teacher conference.
These variables had low communalities (<.10), and preliminary analyses revealed little variance
in these variables across groups. Modifications improved the model fit. See Table 24 for
unstandardized and standardized estimates of the individual factor loadings for the final
measurement model. The final model fit the data adequately using weighted least square
parameter estimates. Absolute fit indices included a RMSEA equal to .012 (CI = .011 -.013), and
relative fit indices, CFI =.952, TLI=.947.

141

Measurement Invariance. Prior to running multi-group analyses, the final four-factor
measurement model was tested for group invariance. Group invariance was tested by examining
configural, metric, and scalar invariance models and comparing models across groups.
Configural invariance was tested by not specifying any equality constraints across groups. Metric
invariance was tested by constraining the factor loadings, but allowing intercepts to differ across
groups (LD and no LD, and then again for RD, RD-MD, and no LD). Scalar invariance was
tested by constraining the factor loadings and intercepts to be equal across groups. Scalar
invariance implies that constructs are equal across groups, therefore allowing comparisons on
latent variables to be made across groups. Cheung & Rensvold (2002) recommend a change in
CFI between invariance models that is equal or less than .01 to indicate adequate fit across
groups. Results indicated adequate fit for LD and no-LD groups (CFI = .962 for the configural
model, CFI=.965 for the metric model, CFI=.958 for scalar the model, and CFI=.007). Results
also indicated adequate fit for the three-group analysis, RD, RD-MD, and no LD groups, (CFI =
.956 for the configural model, CFI=.962 for the metric model, CFI=.956 for the scalar model,
and CFI=.006). Thus, the measurement model met requirements for scalar invariance thereby
indicating that constructs are equal across groups.
Testing the Structural Latent Model. There are two main methods to analyze
structural models using complex sample data in Mplus (Muthen & Muthen, 2015). The first
method takes into account complex sampling design effects including stratification, clustering,
and sampling weights. Given that the ECLS-K study used a multistage, stratified, clustered,
equal probability systematic sampling design and provided stratification and clustering weights,
in addition to application of sampling weights to account for missing data, this method was
chosen to analyze the structural latent model. However, this method does not specify random

142

effects in a way that allows for analysis of interaction terms, such as in a moderation analysis.
Thus, a second method that specifies random effects was also used to test interactions. However,
there were significant limitations to analyzing both mediation and moderation (interaction) paths
simultaneously using these methods. First, MLR is the given estimator for this analysis, however
bootstrapping confidence intervals to test mediation effects is unavailable with this type of
simultaneous analysis of separate mediation and moderation paths in complex samples (Muthen
& Muthen, 2015). Second, traditional fit statistics such as chi-square, RMSEA, CFI, and TLI, are
unavailable with analysis of moderation effects in complex samples. Instead AIC and BIC,
predictive fit indices, are provided for researchers to compare alternative models to the proposed
model. Models with smaller AIC values are considered more likely to be replicated in the
population from which the sample is drawn. Third, multi-group analysis is unavailable when
testing interactions using numerical integration in a complex sample. Instead, models run for
each subset of the sample may be compared for goodness of fit using AIC/BIC.
The full structural model (Figure 2 and Figure 3) was tested specifying estimation of both
complex sample design features and random slopes with covariates, including gender, child’s
race, SES, and child’s age at kindergarten, allowed to freely predict parent depression, parent
school involvement, academic achievement and psychosocial behaviors at both time points, and
motivation in 5th grade. Observed variables were centered to allow for interpretation of
interaction effects. Bootstrapping of confidence intervals was unavailable with this type of
analysis specified; bootstrapping is not available for complex sample design models with random
slopes estimated using MLR without replicate weights (Muthen & Muthen, 2015). Overall, the
full structural model did not converge when mediation paths, interaction paths, and covariates
were all specified, nor when mediation paths and interaction terms were specified without

143

covariates. Attempts to diagnosis and fix convergence issues were unsuccessful, including
addressing negative residual variances, rescaling the residual variances of four variables
(kindergarten math and reading achievement t-scores and fifth grade math and reading
achievement t-scores), increasing the number of iterations, freeing specific factor loadings and
fixing factor variances, inputting different parameter starting values, and inputting covariates in a
step-wise manner.
This led to a change in the structural equation paths that could be estimated
simultaneously, while accounting for complex sample design and including covariate effects.
Thus, indirect paths (mediation) related to research question 1a examining the effect of parent
depression on early academic achievement and psychosocial behavior in kindergarten via school
parent involvement were analyzed in the model without specifying interaction terms. Interaction
paths (moderation) related to research question 1b examining the effect of children’s academic
achievement and psychosocial behavior as moderators on the relationship between parent school
involvement in kindergarten on children’s motivation and psychosocial behavior in fifth grade
were tested without specifying indirect mediation paths. All other direct paths were kept
constant. Figure 4 and Figure 5 display the final structural models.

144

Figure 4. Modified Structural Model (Mediation Paths in red).

Parent
Depression

Early
Academic
Achievement:
Reading &
Math

Motivation

Academic
Achievement
: Reading &
Math

Early Parent
School
Involvement

Early
Psychosocial
Behavior

Psychosocial
Behavior

Note: Latent variables are shown in circles.
Covariates:
• Gender
• Race
• SES
• Age at Assessment

145

Figure 5. Modified Structural Model (Moderation Paths in green).

Parent
Depression

Early
Academic
Achievement:
Reading &
Math

Motivation

Academic
Achievement
: Reading &
Math

Early Parent
School
Involvement

Early
Psychosocial
Behavior

Psychosocial
Behavior

Note: Latent variables are shown in circles. The dashed lines represent the
interaction between the exogenous variable (parent school involvement) and the
moderator variables.
Covariates:
• Gender
• Race
• SES
• Age at Assessment

146

Research Question One: Influence of Parent Depression and Parent Involvement on
Outcomes for All Children
The first research question sought to explore the relation between parent depression and
parent school involvement on children’s outcomes (academic achievement, psychosocial
behavior, and motivation) in kindergarten, and the relation between kindergarten and fifth grade
outcomes by testing a structural latent variable model using a sample of children with (n=10,220)
and without LD (n=410) with age at kindergarten, gender, race, and SES as covariates in the
model. It was hypothesized that the latent factor measurement model, along with the structural
model, would be empirically supported as indicated by strong data fit indices.
Mediation Analysis. The structural model specifying indirect effects of parent
depression on kindergarten achievement and psychosocial behavior and direct paths from
kindergarten to fifth grade outcomes (Figure 4) was tested using estimator WLSMV. WLSMV is
the preferred estimator when categorical and continuous latent variables are included in a model.
Covariates were allowed to freely predict all variables, however the model did not converge.
Covariates were then added to the model in a step-wise manner with all covariates allowed to
freely predict outcome variables only (fifth grade achievement, fifth grade psychosocial
behavior, fifth grade motivation, kindergarten psychosocial behavior, and kindergarten
achievement). Then, covariates were allowed to freely predict exogenous variables (parent
depression, parent school involvement). All non-significant covariate paths (p<.01) were
removed and the modified structural model included only those covariates that uniquely
contributed to the model. The model fit the data adequately, RMSEA = 0.012 (CI = .011 -.013),
and relative fit indices, CFI =.923, TLI=.914. However, not all structural paths were significant
(p<.01). Specifically, parent depression did not significantly predict parent school involvement

147

(= -.024, p=.400), children’s psychosocial behavior in kindergarten (= -.044, p=.025), or
children’s math achievement in kindergarten (= -0.054, p= .012), parent school involvement did
not significantly predict children’s motivation (= -.009 , p=.797) or psychosocial behavior (=
-.087, p= .011) in fifth grade, children’s psychosocial behavior in kindergarten did not
significantly predict their fifth grade achievement in reading (= .014, p=.544), children’s
reading achievement in kindergarten did not significantly predict their later psychosocial
behavior (= -.045, p=.107) or motivation in fifth grade (=.081 , p=.012), children’s math
achievement in kindergarten did not significantly predict their motivation in fifth grade (=.042 ,
p=.180), and children’s motivation in fifth grade did not significantly predict their fifth grade
reading achievement (= .007, p=.637). Paths involved in the mediation analysis were kept in
the model regardless of significance, as these paths were integral to answering research question
1a. However, all other paths that were not significant at the p<.01 level were removed, and the
structural model was simplified. Table 25 displays the factor loadings and path estimates for the
final structural model. The simplified structural model fit the data adequately, RMSEA = .012
(CI = .011 - .013), and relative fit indices, CFI = .922, TLI= .914. All structural paths were
significant (p<.01) except for those that were intentionally not trimmed from the model to
analyze research question 1a.
Research question 1a. Research question 1a sought to explore whether parent depression
indirectly predicted early academic achievement and psychosocial behaviors in kindergarten via
parent school involvement. Results did not support the hypothesized relationship that lower
levels of parent depression would indirectly predict higher levels of academic achievement and
more positive psychosocial behaviors in kindergarten via parent school involvement. Parent
depression was not significantly related to parent school involvement (= -.031, SE= .027, ns).

148

Thus, since parent school involvement was not related to parent depression, it did not serve as a
mediator for any relationship between parent depression and child academic and psychosocial
outcomes in kindergarten. As would be expected from these results, the indirect effects tested
using biased corrected bootstrapped standard errors was also nonsignificant. The indirect effect
from parent depression to early psychosocial behavior via parent school involvement was nonsignificant (= .008, SE=.009, 95% CI= -.016-.031, ns). The indirect effect from parent
depression to kindergarten reading achievement via parent school involvement was nonsignificant (= .008, SE= .010, 95% CI= -.018-.039, ns). The indirect effect from parent
depression to kindergarten math achievement via parent school involvement was non-significant
(= .008 SE= .010, 95% CI= -.019-.035, ns). These findings do not support the hypothesized
mediational model.
Interestingly, there were significant direct effects from parent school involvement to
kindergarten outcomes. Higher levels of parent school involvement were predictive of more
positive early psychosocial behavior (= -.255, p<.001), higher kindergarten math achievement
(= -0.276, p<.001), and higher kindergarten reading achievement (= -0.257, p<.001).
Additionally, lower levels of parent depression symptomology were directly related to higher
kindergarten reading achievement (= -0.068, p=.001).
Research question 1c. Research question 1c examined the direct effects of early
academic achievement and psychosocial behavior on academic achievement in fifth grade. The
model supported the hypothesis that higher levels of reading achievement in kindergarten were
predictive of higher levels of reading achievement (= .308, SE= .025, p<.001) and math
achievement (= .102, SE= .022, p<.001) in fifth grade. Likewise, higher levels of math
achievement in kindergarten were predictive of higher levels of reading achievement (= .293,

149

SE=.024, p<.001) and math achievement (= .530, SE= .022, p<.001) in fifth grade. However,
there was only partial support for the hypothesis that more positive psychosocial behaviors in
kindergarten were predictive of higher levels of academic achievement in fifth grade.
Specifically, more positive psychosocial behaviors in kindergarten were related to higher math
achievement in fifth grade (= 0.066, SE= .402, p<.001), but psychosocial behavior in
kindergarten was not significantly related to reading achievement in fifth grade (= -.026,
p=.201).
Research question 1d. Research question 1d sought to explore whether student-rated
motivation in school and psychosocial behaviors predicted academic achievement in fifth grade.
The model supported the hypothesis that more positive psychosocial behavior in fifth grade was
related to higher math achievement (= .067, SE= .308, p=.001) and reading achievement (=
.127, SE= .277, p<.001) in fifth grade. However, there was only partial support for the influence
of student-rated motivation. Student-rated motivation was predictive of higher math achievement
(= .089, SE= .238, p<.001), however it was not significantly predictive of reading achievement
(= -.008, p=.590).
Influence of covariates. Covariates, including SES, child’s gender, age, and race, had
effects on many of the study variables. First, there was an effect of family SES of parent
depression, parent school involvement, and academic achievement at both time points. Lower
family SES significantly predicted more depressive symptomology in parents (= -.193, t>1.96)
and lower levels of parent school involvement (= -.523, t>1.96). Higher family SES
significantly predicted higher kindergarten reading achievement (= .218, t>1.96), higher
kindergarten math achievement (= .245, t>1.96), higher fifth grade reading achievement (=
.156, t>1.96), and higher fifth grade math achievement (= .110, t>1.96). Older children at

150

kindergarten performed more positively on teacher-rated kindergarten psychosocial behavior (=
.012, t>1.96), however children’s age at kindergarten did not have a significant effect on other
study variables. Child gender significantly predicted kindergarten psychosocial behavior (=
.234, t>1.96), fifth grade psychosocial behavior (= .185, t>1.96), and fifth grade math
achievement (= -.136, t>1.96). This finding suggested that females were more likely to have
more positive teacher-ratings of psychosocial behavior at both time points, however, males were
more likely to perform higher on fifth grade math achievement. Finally, child race/ethnicity
significantly predicted parent school involvement, psychosocial behavior at both time points, and
reading and math achievement at both time points. These findings suggest children of AfricanAmerican/Black race/ethnicity (= .195, t>1.96) and Hispanic race/ethnicity (= .098, t>1.96)
were more likely to have parents with lower levels of parent school involvement. Children of
African-American/Black race/ethnicity were more likely to have less positive teacher-rated
psychosocial behavior in kindergarten (= -.130, t>1.96), less positive teacher-rated psychosocial
behavior in fifth grade (= -.100, t>1.96), lower kindergarten math achievement score (= -.117, t>1.96), lower fifth grade math achievement score (= -.107, t>1.96), and lower fifth grade
reading achievement scores (= -.082, t>1.96). Children of Asian race/ethnicity were more
likely to have more positive teacher-rated psychosocial behavior in fifth grade (= .072, t>1.96).
Children of Hispanic-not otherwise specified race/ethnicity were more likely to have lower
kindergarten reading achievement scores (= -.074, t>1.96) and lower fifth grade reading
achievement scores (= -.076, t>1.96). Lastly, children of Native American race/ethnicity were
more likely to have lower kindergarten math achievement scores (= -.075, t>1.96).

151

Table 24
Final Confirmatory Factor Analysis for a Four-Factor Model
Items
Unstandardized
Standard Error
Standardized
(SE)
Parent
Depression
Felt the “blues”
Felt depressed
Felt bothered
Poor appetite
Trouble focusing
Felt things were
effortful
Felt fearful
Difficulty
sleeping
Talked less
Felt lonely
Felt sad
Difficulty “to get
going”
Parent School
Involvement
Volunteer at
School
Attend Open
House
Attend PTA/PTO
Attend School
Event
Participate in
School
Fundraising
Early
Psychosocial
Behavior
Self-Control
Interpersonal
Skills
Approaches to
Learning
Internalizing
Behavior
Externalizing
Behavior

R2

1.000
1.120
0.917
0.890
1.030
1.172

0.000
0.031
0.038
0.034
0.043
0.063

0.715
0.712
0.537
0.520
0.590
0.536

0.511
0.506
0.288
0.271
0.348
0.287

0.578
1.105

0.021
0.058

0.467
0.515

0.219
0.266

0.865
0.841
1.068
0.917

0.038
0.030
0.037
0.035

0.602
0.585
0.688
0.532

0.363
0.342
0.473
0.283

1.000

0.000

0.748

0.560

0.872

0.064

0.653

0.426

0.657
0.707

0.056
0.053

0.492
0.529

0.242
0.280

0.722

0.063

0.540

0.292

1.000
1.024

0.000
0.039

0.818
0.812

0.670
0.660

1.056

0.047

0.779

0.607

0.361

0.033

0.359

0.129

0.975

0.031

0.801

0.642

152

Table 24 (cont’d)
Psychosocial
Behavior
Approaches to
1.000
0.000
Learning
Interpersonal
0.917
0.033
Skills
Self-Control
0.836
0.029
Internalizing
0.362
0.024
Behavior
Externalizing
0.800
0.034
Behavior
Note: All factor loadings were significant (p<.001).

153

0.852

0.725

0.833

0.664

0.815
0.391

0.694
0.153

0.793

0.628

Table 25
Parameter Estimates for a Four-Factor Structural Regression Model (Mediation Model)
Items
Unstandardized
Standard Error
Standardized
R2
(SE)
Factor Loadings
Parent Depression
(PD)
Felt the “blues”
Felt depressed
Felt bothered
Poor appetite
Trouble focusing
Felt things were
effortful
Felt fearful
Difficulty sleeping
Talked less
Felt lonely
Felt sad
Difficulty “to get
going”
Parent School
Involvement (PSI)
Volunteer at School
Attend Open House
Attend PTA/PTO
Attend School
Event
Participate in
School Fundraising
Early Psychosocial
Behavior (EPB)
Self-Control
Interpersonal Skills
Approaches to
Learning
Internalizing
Behavior
Externalizing
Behavior
Fifth-grade
Psychosocial
Behavior (FPB)
Approaches to
Learning

1.000
1.120
0.927
0.867
1.070
1.101

0.000
0.028
0.035
0.030
0.037
0.059

0.728
0.722
0.543
0.511
0.611
0.520

0.530
0.521
0.295
0.261
0.374
0.271

0.599
1.084
0.849
0.843
1.104
0.925

0.019
0.059
0.035
0.027
0.032
0.033

0.486
0.508
0.596
0.599
0.707
0.545

0.236
0.258
0.355
0.359
0.500
0.297

1.000
0.939
0.690
0.709

0.000
0.068
0.054
0.056

0.713
0.677
0.518
0.530

0.508
0.459
0.268
0.281

0.701

0.059

0.525

0.276

1.000
1.152
1.313

0.000
0.039
0.056

0.734
0.811
0.886

0.539
0.657
0.785

0.479

0.033

0.421

0.177

0.917

0.030

0.681

0.464

1.000

0.000

0.910

0.828

154

Table 25 (cont’d)
Interpersonal Skills
Self-Control
Internalizing
Behavior
Externalizing
Behavior

0.851
0.737
0.399

0.030
0.025
0.026

0.814
0.757
0.445

0.662
0.573
0.198

0.711

0.030

0.747

0.558

Direct Effects
PD  EPB
PSI  EPB
PD  PSI
EPB  FPB
K Math  FPB
PD  K Reading
PSI  K Reading
PD  K Math
PSI  K Math
EPB  Motivation
FPB  5th Reading
FPB  5th Math
EPB  5th Math
K Reading  5th
Reading
K Math  5th
Reading
K Reading  5th
Math
K Math  5th Math
Motivation  5th
Math

-0.048
-0.143
-0.056
0.520
0.010
-1.480
-3.097
-1.107
-3.243
0.268
2.001
1.063
1.426
0.306

0.020 (ns)
0.018
0.049 (ns)
0.047
0.002
0.422
0.423
0.449 (ns)
0.456
0.040
0.277
0.308*
0.402
0.025

-0.048
-0.255
-0.031
0.384
0.149
-0.068
-0.257
-0.052
-0.276
0.186
0.127
0.067
0.066
0.308

0.298

0.024

0.293

0.102

0.022

0.102

0.542
1.330

0.022
0.238

0.530
0.089

Indirect Effects
0.007 (ns)
0.150 (ns)

PD  PSI  EPB
0.008
0.008
PD  PSI  K
0.174
0.008
Reading
PD  PSI  K
0.182
1.131 (ns)
0.009
Math
Note: (ns) indicates paths were non-significant. * indicates paths were significant at
(p<.01). All other structural paths were significant at (p<.001).

155

Moderation Analysis. The structural model specifying interactions between parent
school involvement and kindergarten academic achievement and psychosocial behavior, along
with direct paths from kindergarten to fifth grade outcomes (Figure 5) was tested using estimator
MLR. MLR is the given estimator when interactions between categorical and continuous latent
variables are included in a complex sample design model (Muthen & Muthen, 2015). As with the
mediation model, covariates were allowed to freely predict all variables, however the model did
not converge. Covariates were then added to the model in a step-wise manner with all covariates
allowed to freely predict outcome variables only (fifth grade achievement, fifth grade
psychosocial behavior, fifth grade motivation, kindergarten psychosocial behavior, and
kindergarten achievement). Then, covariates were allowed to freely predict exogenous variables
(parent depression, parent school involvement). All non-significant covariate paths (p<.01) were
removed and the structural model included only those covariates that uniquely contributed to the
model. Traditional model fit indices were unavailable as this was a non-nested model with MLR
as the estimator (AIC = 476034.751, BIC = 477027.060). All paths that were significant in the
mediation model maintained the same direction and significance.
Research question 1b. Research question 1b sought to explore whether parent school
involvement in kindergarten predicted children’s motivation and psychosocial behaviors in fifth
grade as moderated by children’s level of academic achievement and psychosocial behavior in
kindergarten. It was hypothesized that as the level of academic achievement and psychosocial
behaviors in kindergarten increases (e.g., more positive performance), higher levels of parent
school involvement will predict higher levels of motivation and more positive psychosocial
behaviors in fifth grade. Early academic achievement and psychosocial behaviors were
moderating variables. However, the model did not support this hypothesis. Direct paths from

156

parent school involvement to fifth grade motivation (= .016, SE = .088, p=.851), and parent
school involvement to fifth grade psychosocial behavior (= -.095, SE = .065, p=.143) were nonsignificant. As expected, interactions between parent school involvement and kindergarten
psychosocial behavior (= -.033, SE = .033, p=.312), parent school involvement and
kindergarten reading achievement (= .002, SE = .002, p=.302), and parent school involvement
and kindergarten math achievement (= -.003, SE = .002, p=.090) on fifth grade motivation were
non-significant. Interactions between parent school involvement and kindergarten psychosocial
behavior (= -.015, SE = .021, p=.466), parent school involvement and kindergarten reading
achievement (= .001, SE = .001, p=.268), and parent school involvement and kindergarten math
achievement (= .000, SE = .001, p=.710) on fifth grade psychosocial behavior were also nonsignificant.
Research Question Two: Group Comparison Between Children with LD and Children
Without LD
The second research question sought to explore whether the relation between parent
depression and parent school involvement on academic achievement and psychosocial behavior
in kindergarten, and academic achievement, psychosocial behavior, and motivation in fifth grade
differed between children with LD (n=10,220) and children without LD (n=410) by testing the
structural latent variable model in research question one across groups. Overall, results suggest
the relations between the constructs and factor loadings specified in the model (tested in research
question one) differ across children with and without LD. Specifically, there were several
significant paths between parent school involvement and kindergarten child outcomes,
kindergarten achievement and psychosocial behavior and fifth grade outcomes, and fifth grade
psychosocial behavior on fifth grade achievement for children without LD. For children with

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LD, fewer significant findings arose. Specifically, significant effects of kindergarten math
achievement on fifth grade reading achievement, kindergarten psychosocial behavior on fifth
grade psychosocial behavior, and children’s school motivation in fifth grade on fifth grade math
achievement were found for children with LD.
Testing the Model Separately for LD and non-LD Samples. The same structural
model specifying mediation paths (Figure 4) was tested separately for each group. The same
covariates, SES, gender, child race, that were significant in the model for research question one,
were included in the multi-sample model. Results demonstrated that the model fit the data
adequately with children without LD, RMSEA = .012 (CI = .011 - .013), and relative fit indices,
CFI = .919, TLI= .911. However, the model fit was adequate based on an absolute fit index, but
below adequate for children with LD based on relative fit indices, RMSEA = .026 (CI = .018 .033), and relative fit indices, CFI = .765, TLI= .740.
Multi-group model. The same model tested above was used to specify a multi-sample
model to test for equivalence across samples of children with LD and without LD.
Procedural steps. The structural paths and factor loadings in the multi-sample model
were freely estimated across groups, exercising no constraints on the factor loadings or paths
across groups (see Figure 4). The results of the model where the paths were freely estimated fit
the data adequately, RMSEA = .012 (CI = .011 - .013), and relative fit indices, CFI = .914, TLI=
.908. The results of this freely estimated model were compared to a model in which the factor
loadings and all paths were constrained to be equal across groups. The fit of the model
significantly worsened, based on the chi-square distribution for 40 degrees of freedom (p<.001),
when the factor loadings, covariate estimates, and structural paths were all constrained to be

158

equal across groups, X2 = 452.58, p = 0.0, df = 40, RMSEA = .013 (CI= .012 - .014), CFI = .884,
TLI =.879. See Table 26 for group differences on factor loadings and path estimates.
Research question 1a for multi-group sample. In the freely estimated model, parent
depression did not significantly predict kindergarten psychosocial behavior or academic
achievement via parent school involvement for either group. Parent depression was not
significantly related to parent school involvement for either group. Tests of indirect effects
between parent depression, school involvement, and kindergarten variables were all nonsignificant for children without LD (= -.002, p=.590 for the indirect effect on kindergarten
psychosocial behavior, = -.002, p=.594 for the indirect effect on kindergarten reading
achievement, and = -.002, p=.590 for the indirect effect on kindergarten math achievement).
This finding held for children with LD (= -.002, p=.592 for the indirect effect on kindergarten
psychosocial behavior, = -.003, p=.599 for the indirect effect on kindergarten reading
achievement, and = -.002, p=.592 for the indirect effect on kindergarten math achievement).
Although these findings were not aligned with apriori hypotheses, these findings were not
unexpected given the full sample model results.
Other interesting findings arose regarding the influence of parent variables on
kindergarten outcomes. Parent school involvement significantly predicted early psychosocial
behavior for children without LD (= -0.254, t>1.96), however this relation was non-significant
for children with LD. Likewise, parent school involvement significantly predicted kindergarten
reading achievement (= -.260, t>1.96) and math achievement (= -.275, t>1.96) for children
without LD, but did not significantly predict this relation in children with LD.
Research question 1c for multi-group sample. Paths between kindergarten
achievement and psychosocial behavior to fifth grade achievement were examined within the

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freely estimated model for each group. For children without LD, kindergarten reading
achievement significantly predicted fifth grade achievement in math and reading; this relation
did not hold for children with LD. Similarly, kindergarten math achievement significantly
predicted fifth grade psychosocial behavior for children without LD, but not for children with
LD. Kindergarten math achievement more strongly predicted fifth grade reading achievement for
children with LD (= 0.786, t>1.96) compared to children without LD (= 0.279, t>1.96).
However, the opposite was true for the magnitude of relation between kindergarten math
achievement and fifth grade math achievement; this relation was stronger for children without
LD (= 0.519, t>1.96) as compared to children with LD (= 0.345, t>1.96). More positive
kindergarten psychosocial behavior significantly predicted higher levels of children’s motivation
in fifth grade and higher math achievement in fifth grade for children without LD, however these
findings did not hold for children with LD. Interestingly, more positive kindergarten
psychosocial behavior more strongly predicted more positive fifth grade psychosocial behavior
for children with LD (=.456, t>1.96) as compared to children without LD (=.377, t>1.96).
Research question 1d for multi-group sample. Paths between fifth grade student-rated
motivation and fifth grade teacher-rated psychosocial behavior to fifth grade achievement were
examined within the freely estimated model for each group. Fifth grade psychosocial behavior
significantly predicted reading and math achievement in fifth grade for children without LD, but
not for children with LD. Student-ratings of motivation were significantly predictive of fifth
grade math achievement for children with LD indicating that children with LD were more likely
to rate themselves as highly motivated in school but receive lower math achievement scores (=
-.293, t>1.96). Conversely, for children without LD, higher student-ratings of motivation were
significantly predictive of higher fifth grade math achievement (= .113, t>1.96).

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Influence of covariates. The same covariates, SES, gender, child race, that were
significant in the model for research question one, were included in the multi-sample model.
Several differences between children without LD and children with LD emerged. All covariate
pathways specified in research question one remained significant for children without LD
(p<.001, except the effect of gender on kindergarten psychosocial behavior and the effect of
gender on fifth grade psychosocial behavior was significant at the p<.01 level). However, for
children with LD, five covariate paths were significant. First, children’s gender had a stronger
influence on kindergarten psychosocial behavior for children with LD (= .253, p=.002), than
children without LD (=.228, p<.001). This finding suggests that females with LD were more
likely to have more positive psychosocial behavior than their male peers. Additionally, AfricanAmerican/Black children with LD were more likely to have more negative psychosocial behavior
ratings in kindergarten (= -.431, p=.001) compared to their LD peers. Regarding effects of
covariates on achievement variables, children with LD identified as Hispanic-not otherwise
specified were more likely to have lower kindergarten reading achievement than their LD peers
(=-.407, p<.001), and this relationship was stronger for children with LD identified as Hispanicnot otherwise specified as compared to Hispanic-not otherwise specified children without LD
(=-.073, p=.002). Children with LD identified as Native American were also more likely to
have lower kindergarten math achievement (=-.132, p=.001). Finally, children with LD from
families with higher SES were more likely to have higher fifth grade reading achievement
(=.431, p<.001) compared to children without LD from families with higher SES (=.159,
p<.001).

161

Table 26
Parameter Estimates for a Four-Factor Structural Regression Multi-group Model Across
Children with and without Learning Disability
Items
Unstandardized Standardized
Unstandardized
Standardized
No Learning Disability
Parent Depression
(PD)
Felt the “blues”
Felt depressed
Felt bothered
Poor appetite
Trouble focusing
Felt things were
effortful
Felt fearful
Difficulty sleeping
Talked less
Felt lonely
Felt sad
Difficulty “to get
going”
Parent School
Involvement (PSI)
Volunteer at School
Attend Open House
Attend PTA/PTO
Attend School
Event
Participate in
School Fundraising
Early Psychosocial
Behavior (EPB)
Self-Control
Interpersonal Skills
Approaches to
Learning
Internalizing
Behavior
Externalizing
Behavior
Fifth-grade
Psychosocial
Behavior (FPB)
Approaches to

Learning Disability

1.000
1.124
0.926
0.864
1.062
1.104

0.726
0.722
0.541
0.511
0.611
0.520

1.000
1.124
0.926
0.864
1.062
1.104

0.771
0.762
0.558
0.503
0.531
0.550

0.600
1.080
0.844
0.835
1.102
0.919

0.498
0.506
0.601
0.591
0.703
0.539

0.600
1.080
0.844
0.835
1.102
0.919

0.375
0.516
0.473
0.651
0.781
0.596

1.000
0.924
0.666
0.728

0.718
0.672
0.505
0.547

1.000
0.924
0.666
0.728

0.771
0.809
0.555
0.571

0.693

0.523

0.693

0.279

1.000
1.133
1.244

0.745
0.811
0.866

1.000
1.133
1.244

0.779
0.848
0.912

0.465

0.418

0.465

0.392

0.915

0.695

0.915

0.637

1.000

0.898

1.000

0.887

162

Table 26 (cont’d)
Learning
Interpersonal Skills
Self-Control
Internalizing
Behavior
Externalizing
Behavior

0.874
0.766
0.398

0.820
0.771
0.433

0.874
0.766
0.398

0.763
0.730
0.444

0.736

0.758

0.736

0.705

Direct Effects (No LD)
PD  EPB
PSI  EPB
PD  PSI
EPB  FPB
K Math  FPB
PD  K Reading
PSI  K Reading
PD  K Math
PSI  K Math
EPB  Motivation
FPB  5th Reading
FPB  5th Math
EPB  5th Math
K Reading  5th
Reading
K Math  5th
Reading
K Reading  5th
Math
K Math  5th Math
Motivation  5th
Math

(LD)
-0.031(ns)
-0.143
0.012 (ns)
0.493
0.009
-1.030 (ns)
-3.049
-0.873(ns)
-3.151
0.247
2.088
1.037*
1.348
0.296

Direct Effects

-0.030 (ns)
-0.254
0.006 (ns)
0.377
0.139
-0.048 (ns)
-0.260
-0.042 (ns)
-0.275
0.176
0.133
0.066*
0.065
0.301

-0.031 (ns)
-0.143
0.012 (ns)
0.524
0.014 (ns)
-1.030 (ns)
-3.049
-0.873 (ns)
-3.151
0.351 (ns)
-0.305 (ns)
0.793 (ns)
1.623 (ns)
-0.658 (ns)

-0.030 (ns)
-0.018 (ns)
0.089 (ns)
0.456
0.192
-0.073 (ns)
-0.028 (ns)
-0.052 (ns)
-0.025 (ns)
0.225 (ns)
-0.020 (ns)
0.051 (ns)
0.092 (ns)
-0.515 (ns)

0.281

0.279

0.850

0.786

0.094

0.095

0.317 (ns)

0.247 (ns)

0.528
1.666

0.519
0.113

0.375
-3.320

0.345*
-0.293

Indirect Effects
-.002 (ns)
-.002 (ns)

PD  PSI  EPB
-.002 (ns)
-.002 (ns)
-.002 (ns)
PD  PSI  K
-.035 (ns)
-.035 (ns)
-.003 (ns)
Reading
PD  PSI  K
-.036 (ns)
-.002 (ns)
-.036 (ns)
-.002 (ns)
Math
Note: (ns) indicates paths were non-significant. * indicates paths were significant at (p<.01).
All other structural paths were significant at (p<.001).

163

Multi-group Moderation Analysis. Research question 2b sought to explore whether
early academic achievement and psychosocial behavior moderated the relation between parent
school involvement and children’s motivation and psychosocial behavior in fifth grade
differently for children with LD (n=410) as compared to children without LD (n=10,220) by
testing the structural latent variable model in research question 1b across groups. Overall, results
suggest that kindergarten academic achievement and psychosocial behavior do not moderate
relations between parent school involvement and fifth grade outcomes for children with or
without LD. Direct relations between parent school involvement and fifth grade outcomes,
motivation and psychosocial behavior, were non-significant for both groups. Specifically, parent
school involvement did not significantly predict fifth grade motivation for children with LD (=
-.076, SE = .119, p=.521), or children without LD (= .027, SE = .031, p=.391). Parent school
involvement did not significantly predict fifth grade psychosocial behavior for children with LD
(= -.041, SE = .069, p=.559), or children without LD (= -.027, SE = .021, p=.200).
Additionally, interactions between parent school involvement and kindergarten psychosocial
behavior (LD sample: = .219, SE = .136, p=.108; No LD sample: = -.053, SE = .031, p=.088),
parent school involvement and kindergarten reading achievement (LD sample: = .012, SE =
.013, p=.362; No LD sample: = .000, SE = .001, p=.835), and parent school involvement and
kindergarten math achievement (LD sample: = -.018, SE = .012, p=.138; No LD sample: = .001, SE = .001, p=.398) on fifth grade motivation where non-significant for children with LD
and children without LD. Interactions between parent school involvement and kindergarten
psychosocial behavior (LD sample: = -.049, SE = .090, p=.586; No LD sample: = -.012, SE =
.022, p=.566), parent school involvement and kindergarten reading achievement (LD sample: =
.003, SE = .005, p=.501; No LD sample: = .001, SE = .001, p=.238), and parent school

164

involvement and kindergarten math achievement (LD sample: = -.005, SE = .004, p=.204; No
LD sample: = .000, SE = .001, p=.692) on fifth grade psychosocial behavior where nonsignificant for children with LD and children without LD.
Research Question Three: Group Comparison Between Subtypes of LD
The third research question sought to explore whether the relation between parent
depression and parent school involvement on academic achievement and psychosocial behavior
in kindergarten, and academic achievement, psychosocial behavior, and motivation in fifth grade
by testing a structural latent variable model in research question one differed between children
with RD (n=180), RD-MD (n=230), and children without LD (n=10,220). Overall, results
suggest that the theoretical model outlined and tested in research question 1 does not fit the data
for subtypes of LD including children with RD and children with RD-MD in this study. Multigroup analysis to examine the difference in model fit, and differences in construct factor loadings
and paths was unable to be computed.
Testing the Mediation Model Separately for LD Subtype Samples. The same
structural model specifying mediation paths (Figure 4) was tested separately for each group. The
same covariates, SES, gender, child race, that were significant in the model for research question
one, were included in the multi-sample model. Results demonstrated that the model fit the data
adequately with children without LD, RMSEA = .012 (CI = .011 - .013), and relative fit indices,
CFI = .919, TLI= .911. However, the model did not fit the data well for children with RD,
RMSEA = .034 (CI = .017 - .046), and relative fit indices, CFI = .673, TLI= .638. Lastly, the
model did not converge for children with RD-MD. Attempts to correct convergence issues were
unsuccessful, including addressing negative residual variances, rescaling the residual variances
of variables with a value larger than 10, increasing the number of iterations, and freeing specific

165

factor loadings and fixing factor variances. Given that the model had poor fit for one subtype of
LD sample, and the aim of this study research question was to examine the fit of the same apriori
specified theoretical model across three subtypes of LD, specific changes to the model in
research question 1 (e.g., removing non-significant paths) were not completed.
Testing the Moderation Model Separately for LD Subtype Samples. To explore
whether early academic achievement and psychosocial behavior moderated the relation between
parent school involvement and children’s motivation and psychosocial behavior in fifth grade
differently for children with RD (n=180), children with RD-MD (n=230), and children without
LD (n=10,220) the same structural model specifying moderation paths (Figure 5) was tested
separately for each group. The same covariates, SES, gender, child race, that were significant in
the model for research question 1b, were included in the multi-sample model. Results
demonstrated that the model fit the data for children without LD, as well as for children with RD.
As in the LD and no-LD analysis in research question 2b, neither direct paths between parent
school involvement and fifth grade motivation or psychosocial behavior or hypothesized
moderated paths between parent school involvement, kindergarten achievement, and
kindergarten psychosocial behavior were significant for either group. The model was not
identified for children with RD-MD. Multi-group analysis across subtypes of LD was not
completed due to lack of model fit with all groups and non-significant findings in the full sample
and dichotomous LD-no LD sample.

166

CHAPTER 5
DISCUSSION
This study aimed to understand patterns in academic achievement, psychosocial
behaviors, and motivation in kindergarten and fifth grade for children with and without learning
disability (LD), as well as across subtypes of LD, by testing a theoretical, developmental model
of learning and behavior. Given the complexities of identifying LD and categorizing subtypes of
LD, many studies have not attempted to address possible group differences, particularly in
relation to psychosocial behavior and motivation. However, there is evidence to support that
differences in learning and behavioral outcomes do exist across subtypes of LD (Auerbach,
Gross-Tsur, Manor, & Shalev, 2008; Morgan, Farkas, & Wu, 2009; Scarborough & Parker,
2003; Willcutt et al., 2013). Moreover, research and clinical practice have focused on the
effectiveness of academic interventions in school to target learning deficits for children with LD,
however relatively less is known about the effects of parent variables, including parent
depression and parent school involvement, on short- and long-term outcomes for children with
LD. Latent variable structural equation modeling allowed for testing predictive pathways
between parent variables and children’s outcomes in kindergarten and fifth grade within a
nationally representative sample of children entering kindergarten in 1998.
This study examined the relations between symptoms of parent depression and parent
school involvement in kindergarten on academic achievement in reading and math and
psychosocial behavior in kindergarten, as well as student psychosocial behavior and student
motivation in fifth grade. This study also examined direct effects of kindergarten achievement
and psychosocial behavior on student’s academic achievement, psychosocial behavior, and

167

school motivation in fifth grade. Finally, this study compared these relations across groups of
students with and without LD’s.
The hypothesized model was only partially supported by the data. Results indicated that
symptoms of parent depression did not predict parents’ school involvement as defined by
behaviors within the school setting, however parent school involvement was directly related to
more positive kindergarten outcomes for children. LD status did not moderate this relationship,
suggesting that parent school involvement was a statistically significant factor in promoting more
positive kindergarten outcomes for children without LD only. Additionally, parent school
involvement in kindergarten had little long-term effect on student’s motivation or psychosocial
behaviors in fifth grade. This suggests that this type of parent engagement may confer more
short-term benefits for some students and other factors, such as kindergarten achievement and
psychosocial behaviors, have stronger effects on later outcomes. Thus, hypotheses involving
parent school involvement as a mediator of parent depression on kindergarten outcomes were not
supported, nor were effects of parent school involvement on later outcomes across children with
and without LD.
Additionally, complex relations between kindergarten and fifth grade outcomes arose.
Specifically, kindergarten math and reading achievement was predictive of fifth grade math and
reading achievement for children without LD, however for children with LD only kindergarten
math achievement was predictive of later reading and math performance. The hypothesis that
direct relations between kindergarten and fifth grade outcomes would be stronger for children
with LD and multiple LD’s was not fully supported. In fact, most relations between kindergarten
and fifth grade achievement were stronger for children without LD, with the exception of the
relation between kindergarten math achievement and fifth grade reading achievement. These

168

results suggest an important role for developing math proficiency early on, particularly for
children with learning disabilities, as higher math proficiency appears to result in long-term
academic benefits for all children. Additionally, these results suggest that other factors including,
biologically-driven neuropsychological factors and the potential compound effects of long-term
academic delays, may have a strong influence the developmental trajectory and later outcomes
for children with LD. Furthermore, kindergarten psychosocial behavior was predictive of fifth
grade psychosocial behavior for all students in the study sample, as well as fifth grade math
achievement and school motivation for children without LD. Thus, efforts to develop student’s
non-academic skills including interpersonal skills, executive functioning skills (attention, task
persistence, and organization), and social-emotional functioning early in childhood have lasting
effects on these behaviors in late-elementary school and resulting academic functioning.
Finally, hypotheses regarding the relation between fifth grade psychosocial behaviors,
student motivation, and academic achievement were only partially supported, and hypotheses
regarding the differential magnitude of these relations being greatest for children with multiple
LD’s or single LD were not supported. Psychosocial behavior in fifth grade was related to fifth
grade math and reading achievement for children without LD. Moreover, student motivation was
predictive of fifth grade math achievement for children with and without LD, however the
direction of this relation was opposite for children with LD and children without LD, such that
children with LD who rated themselves as more motivated in school were more likely to have
lower fifth grade math achievement. Thus, these findings provide counter evidence to previous
studies that indicate children with LD demonstrate clinically significantly lower school
motivation compared to their peers.

169

This study provides unique contributions to the literature regarding early parent variables
and academic and psychosocial outcomes for children with LD as compared to children without
LD, and controlled for various demographic variables that affect children’s outcomes using a
nationally representative data set and weights to allow results to generalize for the broader
population of children entering kindergarten in 1998. The following sections review the study
results in detail.
Parent Depression and School Involvement as Predictors of Kindergarten Outcomes
It was originally hypothesized that higher symptoms of parent depression would predict
lower academic achievement and less positive psychosocial behaviors in kindergarten via lower
levels of parent school involvement. However, the results of this study did not support this
mediational effect for children with or without LD. At the individual item level, children with
LD were more likely to have parents who reported worse appetite, feeling “blue,” trouble
focusing, feeling depressed, feeling fearful, and talking less compared to children without LD. At
the subgroup level, fewer significant differences arose; children with RD or RD-MD had parents
who endorsed more problems related to appetite, feeling fearful, and feeling “blue.” These
results indicated that parents of children with LD or multiple LD’s may endorse more depressive
symptoms. This novel finding contributes significantly to the literature as no other studies have
explicitly examined symptoms of depression in parents of children with LD. Additionally, this
finding is in line with other studies that have found higher rates of parent psychopathology,
including symptoms of anxiety and higher parental stress, in parents of children with LD
(Antshel & Joseph, 2006; Karande, Kumbhare, Kulkarni, & Shah, 2009) and other chronic
conditions such as, ADHD (National Research Council and Institute of Medicine, 2009).
Similarly, parents with lower SES also reported more problematic symptoms of depression. This

170

finding is not surprising given the overwhelming research evidence that poverty is associated
with higher rates of depression (Belle, 1990; Carlson & Corcoran, 2001; Johnson & Flake, 2007;
McLoyd, 1990; Pachter et al., 2006). It is likely that factors including family SES, and other
variables not accounted for in this study such as preschool enrollment, academic intervention,
and other home and school characteristics, contribute more significantly to the academic and
psychosocial outcomes in kindergarten for children with LD.
Despite significant group differences in symptoms of parent depression, hypothesized
relations between symptoms of parent depression and kindergarten psychosocial behaviors were
not significant. This finding is contrary to research indicating higher parental depression is
associated with more psychosocial problems in children in kindergarten and first grade (Ashman,
Dawson, & Panagiotides, 2008; Kurstjens & Wolke, 2001). Similarly, parent depression did not
significantly predict kindergarten math achievement for children without LD or math or reading
achievement for children with LD. These findings were contrary to original study hypotheses as
research suggests parental depression is linked to increased psychosocial problems and lower
academic achievement in children (Champion et al., 2009; Cortes et al., 2009; Goosby, 2007;
Gross, Shaw, Moilanen, Dishion, & Wilson, 2008; Martins & Gaffan, 2000; Mazza et al., 2009;
Rice, Lifford, Thomas, & Thapar, 2007). It may be that other demographic factors such as, SES,
play a more significant role in predicting early academic achievement and psychosocial
behavior. Likewise, factors not included in this study including, school and teacher variables and
parent engagement practices at home and in the community, may serve as protective factors
buffering children from possible negative influences associated with parent depression.
Interestingly, one exception was noted; higher rates of parent depression predicted lower
kindergarten reading achievement for children without LD. This finding aligns with research

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suggesting that increased parent depression is related to poorer academic outcomes for typically
developing children. Extant literature suggests that more depressed parents, mothers particularly,
may have fewer positive interactions with their child and read less frequently and for shorter
duration to their children, which may lead to reduced exposure to language and written text
(Bigatti, Cronan, & Anaya, 2001; Sohr-Preston & Scaramella, 2006). This significant finding did
not hold true for the LD sample. In this study children with LD represented students with
persistent academic difficulties as they either continued to demonstrate academic problems into
fifth grade despite potential intervention efforts. This group of students with persistent, severe
LD likely demonstrate significant neuropsychological deficits that impair their reading skills and
abilities (Pennington & Bishop, 2009), and it is likely that these neurobiological factors are
strong predictors of reading achievement in addition to or above and beyond some parenting
variables such as, parent depression. This is the first study known to the author to examine parent
depression on child outcomes specifically for children with LD and highlights the important role
of factors such as, SES and neuropsychological vulnerabilities, in predicting student achievement
and behavior.
Regarding parent school involvement, group differences emerged between children with
LD and children without LD. Parents with children without LD were more likely to attend open
houses, PTO meetings, volunteer at school, and participate in school fundraisers. Additionally,
parents of children with RD-MD engaged in these activities significantly less than parents of
children without LD, and participated in fundraising events and open house events significantly
less than parents of children with RD only. Despite group mean differences parent school
involvement did not significantly predict kindergarten outcomes for children with LD. Thus,
parent school involvement does not serve as a significant protective factor against academic

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underachievement as originally hypothesized. As with parent depression, the current study is one
of few to examine the influence of parent school involvement on outcomes for children with LD.
Prior studies (Hill et al., 2004; McLoughlin, Edge, & Strenecky, 1978) have suggested that
interactions between families and school staff allow for exchange of important information about
children’s development, encourage communication between settings, foster the relationship
between families and schools, and lead to more positive outcomes. However, the current findings
suggest that parent school involvement as defined by participation in various school events may
not be the only, or most significant, avenue by which parents of children with LD engage with
their child’s educational milieu. This finding does not suggest that parent school involvement is
not important in the larger context of child development nor that the potential additive effects of
parent school involvement along with other protective factors (e.g., parent engagement at home,
school resources, positive teacher factors, parent belief’s about their child’s education) have a
more profound influence on achievement and psychosocial behavior, but rather that parent
school involvement as narrowly defined in this study is only a small component of the larger
“puzzle” of child development.
In contrast to the null findings for children with LD, parent school involvement
significantly predicted higher math and reading achievement and more positive psychosocial
behavior in kindergarten for children without LD. This finding is consistent with research that
highlights the positive relations between parent involvement and early academic skills,
psychosocial behaviors and motivation (Christenson, Rounds, & Gorney, 1992; Dickinson &
DeTemple, 1998; Gauvain, Savage, & McCollum, 2000; Jeynes 2005, 2007). Parent involvement
in school-based activities may help children transition more smoothly from home to school
environments and may provide more opportunities for parents and teachers to develop rapport

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and communicate. Moreover, this finding supports research that suggests that parent
participation in school activities is related to children’s engagement in school, particularly in
kindergarten (Izzo, Weissberg, Kasprow, & Fendrich, 1999). Although the body of literature on
parent’s involvement in their child’s education as it relates to various learning outcomes is far
from conclusive, the current study provides some support for the positive effects of fostering
opportunities for parents to engage in school-based activities on children’s learning outcomes.
As noted in regards to the null effects of parent depression on outcomes for children with LD, the
differential findings between children with and without LD may be due in part to the role of
neuropsychological deficits in children with LD as a strong driver of outcomes in addition to
environmental factors highlighting the complex and powerful role of biological predispositions
(Geary et al., 2007; Pennington & Gilger, 1996).
In addition to understanding the influence of parent school involvement on kindergarten
outcomes, this study explored whether parent school involvement in kindergarten had lasting
effects on children’s psychosocial behavior and motivation in fifth grade. The data did not
support study hypotheses that higher levels of parent school involvement would predict more
positive outcomes in fifth grade as moderated by levels of children’s academic and psychosocial
behavior in kindergarten. Parent school involvement was not directly related to children’s
psychosocial behavior or motivation in fifth grade for any subset of the sample. Thus, early
parent school involvement may serve as a protective factor to promote children’s outcomes in the
short-term, however it is likely that complex interactions between other factors, such as
continued parental support at home and school, academic achievement, social-emotional
development, various home and school characteristics, and neuropsychological deficits,

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throughout a child’s elementary experience mitigate the academic and psychosocial trajectory for
students long-term.
Finally, studies have indicated that differences in parent school involvement, defined by
participation in school-based activities, and children’s academic and psychosocial outcomes
across socio-demographic characteristics as well (Hill & Craft, 2003). These claims were
supported by the current study’s results for the typically developing sample as family SES, race,
and gender did not significantly contribute to parent school involvement for the LD sample. In
this study, parents of typically developing children with higher SES were more involved in
school based activities. This finding aligns with previous research suggesting that family SES
was a stronger predictor of parent’s involvement in school and at home (Grolnick et al., 1997),
and suggests that families with lower SES do not access schools in the same way that families
with higher SES do. For example, families with lower SES may lack adequate transportation to
attend school functions, may have less flexibility in work hours, and may not have access to
childcare for other children, among other potential barriers. Schools that provide multiple modes
of communication (e.g., sending home memos regarding important school events, providing
information via phone, internet, and in writing), demonstrate more flexibility in accommodating
parent’s various needs (e.g., scheduling day and evening meetings, incorporating weekday and
weekend open houses, etc.), and support parent’s efforts to develop relationships with educators
and other parents in various ways (e.g., PTA/PTO, online parent groups, community-based
activities) will likely not only be more successful in promoting parent school involvement across
families from all SES’s, but may also promote the academic achievement and psychosocial
functioning of all students.

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Differences in parent school involvement and academic achievement across racial groups
also arose. Parents of African-American/Black children and Hispanic children were more likely
to be less involved in school-based activities across the full sample. This finding supports some
research that suggests African-American parents may be less involved in direct school activities
compared to other parents (Kohl et al., 2000; Reynolds, Weissberg, & Kasprow, 1992) despite
other research indicating no differences across racial groups (Harris, Kagey, & Ross, 1987).
Research, including this study, examining ethnic/cultural differences is often confounded by
socioeconomic variables (Hill & Craft, 2003), thus, it may be that some parents of AfricanAmerican or Hispanic children have lower SES and the effects of poverty lead to barriers to
parent school involvement (e.g., transportation, access to childcare, work hours, access to
information via internet or other communication modes). Additionally, different cultural values
and beliefs may drive parent’s school involvement behavior. For example, Hill and Craft (2003)
note that African American parents may place a higher value on monitoring their children’s
school performance and behavior and intervening within the home context, but not intervening
within the school context directly via school-based events. Research suggests that parent school
involvement in related to improved achievement for African-American/Black students (Hill et
al., 2004), however further research is needed to examine cultural variables that influence
parent’s school involvement behaviors across ethnic/cultural groups.
In regards to early academic achievement, children identified as African-American/Black
and Native American had lower kindergarten math scores, and children identified as Hispanic
had lower kindergarten reading scores. For Native American and Hispanic children with LD,
these relations were stronger as compared to their non-LD peers. Children from higher SES
households had higher achievement in kindergarten and fifth grade. These differences highlight

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deeply saddening and inequitable economic and racial disparities in student achievement from a
young age that have been highlighted in the research for more than thirty years (Reardon &
Galindo, 2009). Schools that implement policies and procedures to understand and address the
barriers that parents face in accessing school-based activities and practices that promote
achievement for disadvantaged youth (e.g., early childhood education opportunities), particularly
historically marginalized minorities, may be more successful at closing the racial disparity gap
and improving outcomes for all children.
Kindergarten Achievement and Psychosocial Behavior as Predictors of Fifth Grade
Outcomes
Kindergarten Academic Achievement. A third aim of the current study was to examine
the longitudinal effect of kindergarten achievement and psychosocial behavior on fifth grade
outcomes for children with and without LD. Basic group differences emerged. As expected,
children with one LD had lower kindergarten academic achievement than children without LD,
and children with multiple LD’s had the lowest academic achievement. The effect sizes for these
group differences was large and reflects the core nature of LD; difficulty in academic learning
and achievement.
Interestingly, kindergarten reading achievement predicted fifth grade academic
achievement only for children without LD. However, kindergarten math achievement predicted
fifth grade achievement for children with and without LD. The magnitude of the relationship
between kindergarten math achievement and fifth grade reading achievement was stronger for
children with LD. This finding provides evidence to support the importance of developing
reading and math skills early on and is in line with previous research demonstrating the
predictive value of kindergarten achievement on later academic achievement (Claessens,

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Duncan, & Engel, 2009; Duncan et al., 2007; Claessens & Engel, 2013; Jordan, Kaplan,
Ramineni, & Locuniak, 2009). However, this finding also suggests a unique role that early
mathematics learning and proficiency plays in predicting and promoting children’s long-term
achievement, particularly for children with or at-risk of developing LD. Several education
advocacy groups have put forth position statements espousing the importance of early
mathematics learning for long-term academic attainment (National Association for the Education
of Young Children; NAEYC, 2002; National Council of Teachers of Mathematics; NCTM;
2007; National Mathematics Advisory Panel; NMAP, 2008). Moreover, researchers have
theorized about mechanisms by which early mathematics learning contributes to children’s
improved learning disposition overall. For example, children with higher levels of math skills
may receive more reinforcement from the teacher. In turn, this may lead to more individual
instruction time and/or grouping with higher ability students (Claessens & Engel, 2013).
Furthermore, research suggests an uncoupling of intellectual functioning and reading
performance such that children with LD possess generally intact cognitive functioning, however
demonstrate marked difficulties in reading (Ferrer, Shaywitz, Holahan, Marchione, & Shaywitz,
2010). Thus, it is likely that reading performance in kindergarten does not predict future
academic performance, particularly in the LD sample, due to an uncoupling in general cognitive
skills that have greater predictive value on future academic performance. Given that a majority
of children with LD in this sample had an RD, this effect may be even more pronounced in this
study. Similarly, mathematics ability may serve as a proxy for general cognitive functioning (i.e.,
IQ) and thus may retain predictive value in estimating future academic performance. Higher
math skills may foster development of basic neuropsychological abilities that also serve

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development of reading skills such as sustained attention, working memory, and problem-solving
skills (Geary, 2004; Geary 2006).
Finally, early reading skills taught and emphasized in kindergarten tend to encompass
more associative learning processes as opposed to more assembled learning processes required
for many early math skills. For example, in kindergarten children are expected to memorize the
orthographic symbols associated with each letter in the alphabet, make associations between the
orthographic symbol and sound of a letter, and begin to blend and parse letter sounds. These
skills involve the ability to retrieve information from semantic memory and create associations,
or associative processing. In contrast, while basic learned math facts also involve associative
learning, early mathematics skills such as mentally representing a finite set of objects, counting
objects, and comparing quantities require abstract numerical representations requiring assembled
learning processes (Dennis, Landry, Barnes, & Fletcher, 2006). Thus, in kindergarten children at
risk for persistent learning difficulties may not be as readily differentiated from their peers
without learning difficulties in terms of reading ability due to the more basic nature of the
reading task. Moreover, the degree of associative learning required to be a proficient reader in
kindergarten may not have the same predictive weight on long-term learning outcomes as more
assembled learning processes. Assembled learning processes that are required for many early
math skills and support higher-order learning in reading and math (e.g., reading comprehension,
arithmetic, etc.) likely do differentiate children with and without learning disabilities earlier in
their schooling trajectory and thus are more predicative of long-term academic achievement.
Thus, it is especially important to bolster young student’s math skills, in addition to reading
skills, that may target neuropsychological processes that are involved in assembled learning

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processes to promote their long-term achievement, particularly for those students who have or
are at-risk of developing an LD in the future.
In regards to fifth grade psychosocial outcomes, higher kindergarten math achievement in
children without LD was predictive of more positive fifth grade psychosocial behavior, as
defined by children’s approaches to learning, interpersonal skills, self-control, and externalizing
and internalizing problem behaviors. This finding supports previous studies that suggest early
academic performance affects children’s interpersonal skills, mood, and behavior (Fleming,
Harachi, Cortes, Abbott, & Catalano, 2004; Lane, Beebe-Frankenberger, Lambros, & Pierson,
2001; Morgan, Farkas, & Wu, 2012; Wehby, Falk, Barton-Arwood, Lane, & Cooley, 2003).
Unexpectedly, the results of this current study did not indicate that academic performance in
kindergarten was predictive of later psychosocial behavior for children with or at-risk of
developing LD. Although children with LD demonstrated lower psychosocial behavior in fifth
grade compared to their non-LD peers in this study, the effect sizes were small for most
components of psychosocial behavior (interpersonal skills, self-control, externalizing and
internalizing behavior), except for approaches to learning (large effect size). There is some
research to suggest that children with LD are more vulnerable to psychosocial problems
(Gadeyne, Ghesquiere, & Onghena, 2004; La Greca & Stone, 1990; Maughan, Pickles, Hagell,
Rutter, & Yule, 1996; Ochoa & Palmer, 1995). For example, it is established that many children
with RD meet criteria for at least one behavioral or emotional disorder such as, ADHD
(Goldstone et al., 2007, Maughan et al., 2003). However, in this study, children with co-morbid
LD and ADHD were removed resulting in a “pure” LD sample. It may be that for children with
or at-risk of developing an LD other factors, such as co-morbid ADHD and increasing academic
demands throughout elementary school, mediate the relation between early academic

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performance and later psychosocial behaviors. Additionally, children with or at-risk of LD may
have lower approaches to learning (attention, task engagement, organization) and lower
academic achievement in kindergarten, however it may be the compound effects of repeated
academic underachievement from first grade through fifth grade that mediates the outcome of
psychosocial behaviors in fifth grade, rather than solely their baseline performance in
kindergarten.
Kindergarten Psychosocial Behavior. Children with or at-risk of developing LD
performed lower on all components of kindergarten psychosocial behavior compared to their
non-LD peers, however effect sizes were small except for approaches to learning (large effect
size) and interpersonal skills (medium effect size). Only small effect sizes were found in terms of
differences in psychosocial behavior between subtypes of LD, such that children with RD
performed higher on approaches to learning and lower on internalizing problem behaviors
compared to children with RD-MD. As noted above, this finding is aligned with extant literature
suggesting that children with or at-risk of LD demonstrate lower levels of task engagement,
attention, organization, and interpersonal skills compared to their non-LD peers (Goldston et al.,
2007; Martinez & Semrud-Clikeman, 2004; Willcutt et al., 2011), and indicate the importance of
and need for interventions targeted at improving executive functioning and social skills in
children with LD. Moreover, kindergarten psychosocial behavior predicted fifth grade
psychosocial behavior for children with and without LD. This finding suggests that there is a
long-term effect of early psychosocial behaviors, and fostering children’s ability to attend and
engage in academic content, as well as work alongside other children is equally important to
promote more positive long-term outcomes for all students.

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Despite the positive direct influence of kindergarten psychosocial behavior on fifth grade
psychosocial behavior, kindergarten psychosocial behavior only predicted fifth grade math
achievement and student-rated motivation for children without LD in fifth grade. For typically
developing children, this finding suggests that more positive kindergarten psychosocial
behaviors lead to more positive fifth math achievement, and ability beliefs and intrinsic
motivation in school. This finding is expected given the established support for long-term effects
of psychosocial behavior on later academic achievement and motivation (Moore, Lippman, &
Ryberg, 2015; Pintrich, 2000; Pintrich & De Groot, 1990; Pintrich & Zusho, 2002), and provides
support for initiatives that focus on developing positive psychosocial behaviors early on.
As with other null findings in the LD-only sample, it may be that other factors not accounted for
by this study, such as academic underachievement in first through fourth grade, teacher,
classroom, and school variables, level of academic remediation throughout elementary school,
presence of co-morbid conditions (e.g., ADHD), and measures of friendship and social support,
are more predictive of the relation between kindergarten psychosocial behavior and fifth grade
achievement and motivation for children with LD. Moreover, academic expectations and
psychosocial behavior expectations increase over time; in kindergarten children may be expected
to attend to short lessons, have less materials to remember and organize, and are often guided
through many social situations, whereas in mid-to-late elementary school the psychosocial
demands are much greater (e.g., managing multiple classrooms and teachers, higher cognitive
load, greater social demands). Thus, children at-risk of developing or with an LD may be rated
by their teachers as having kindergarten psychosocial skills that are highly related with their
academic achievement in kindergarten, however the developmental trajectory of psychosocial
behaviors and academic achievement may become uncoupled over time such that teacher’s

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perceptions of student’s psychosocial behaviors in kindergarten are not predictive of fifth grade
achievement due to the changing nature of academic expectations and psychosocial demands.
Finally, several demographic patterns related to levels of kindergarten psychosocial
behavior appeared in this study. First, children’s age at kindergarten entry significantly predicted
psychosocial behavior, such that older children had higher levels of psychosocial behavior. This
finding suggests that age-related maturation plays a strong role in children’s attention, school
engagement, organizational skills, interpersonal skills, self-control, and lower externalizing and
internalizing problems. Moreover, female students were more likely to have higher levels of
psychosocial behavior, and the magnitude of this relation was stronger for children with LD
compared to children without LD. There is evidence to suggest that gender differences in
psychosocial behavior exist (Duckworth & Seligman, 2006; Matthews, Ponitz, & Morrison,
2009; Nowell & Hedges, 1998; Weaver-Hightower, 2003). Given that psychosocial behavior was
based on teacher ratings, rater bias effects may also influence these findings. For example,
teachers are more likely to rate males as having higher externalizing behaviors. Additionally,
children identified as African-American/Black had lower levels of psychosocial behavior
compared to their peers (the magnitude of this relationship was even stronger for AfricanAmerican/Black children with or at-risk of developing LD). The racial disparity in teacher-rated
psychosocial behaviors may also be due in part to rater bias (Wright, 2015), but also parallels
previous research suggesting that a racial gap in academic achievement exists between minority
students, particularly African-American/Black students, and their peers. This finding highlights
several important considerations that should continue to be the subject of future research and
educational system policy including understanding the implicit bias in teacher-ratings of
minority student’s psychosocial behaviors (Gilliam, Maupin, Reyes, Accavitti, & Shic, 2016),

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the relation between early academic problems and early psychosocial behaviors in AfricanAmerican/Black children, and the dire need for teacher training programs and schools to better
understand the factors behind racial achievement gaps and to intervene early to promote
student’s psychosocial behaviors, particularly for minority students.
Fifth Grade Psychosocial Behavior and Motivation as Predictors of Academic Achievement
The fourth and final aim of this study was to examine the relation between fifth grade
psychosocial behavior and student-rated motivation on fifth grade achievement for children with
and without LD. Not surprisingly, children with LD had significantly lower academic
achievement compared to their non-LD peers in fifth grade. Children with LD also had lower
psychosocial behavior compared to non-LD peers, however the effect size was small for most
components except approaches to learning (large effect size) and there were no significant
differences between subtypes of LD as compared to children without LD. Interestingly, more
positive fifth grade psychosocial behavior predicted higher academic achievement in reading and
math for children without LD only. This finding is unique in that this study suggests that effects
of fifth grade psychosocial behavior on achievement operate differentially for children with and
without LD. Again, part of this variation may be explained by rater effects and little variation in
psychosocial behaviors across children with and without LD, as compared to great variation in
academic achievement between the two sample groups. In other words, teachers did not rate
students with LD as having significantly lower psychosocial behaviors in most areas compared
to their non-LD peers therefore psychosocial behaviors may explain relatively little to no
variance in achievement above and beyond the core nature of LD (i.e., academic difficulty due to
neuropsychological deficits). Additionally, other research has found that only approaches to
learning significantly predicts later academic achievement (Morgan, Farkas, Tufis, & Sperling,

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2008), thus it may be that only specific components of psychosocial behavior contribute to
academic achievement.
Regarding motivation, children with LD reported lower motivation in school compared to
their non-LD peers however effect sizes were small. Higher levels of self-reported motivation, as
defined by ability beliefs and intrinsic motivation, were predictive of higher fifth grade math for
children without LD. However, the opposite was true for children with LD, such that higher
levels of self-reported motivation were predictive of lower levels of math achievement in fifth
grade. These findings may suggest several phenomena. First, there may be issues related to
reliability of self-report measures in children that require caution when interpreting the relation
between student’s motivational beliefs and their academic outcomes (Fulmer & Frijters, 2009).
Second, children with LD may accurately perceive themselves as hard workers, and in many
cases potentially work harder in some academic subjects than their non-LD peers, and thus may
view themselves as highly intrinsically motivated in school and capable of completing the work
despite difficulties associated with their learning disability and lower academic
grades/achievement. Finally, it may be that children with LD with the greatest academic
underachievement endorse more positive ability beliefs and intrinsic motivation if they wish to
project to others that that they are highly motivated in school and have a high level of selfefficacy despite significant academic struggles. Overall, these findings support prior research
suggesting that children with LD do not demonstrate significantly lower intrinsic motivation or
more negative ability beliefs compared to their non-LD peers (Wilson & David, 1994;
Zisimopoulos & Galanaki, 2009). Efforts to continue to maintain student’s motivation in school
despite academic underachievement are warranted; these results are encouraging in the sense that

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children whom experience academic underachievement do not necessarily experience low school
motivation.
Finally, differences in fifth grade academic achievement and psychosocial behaviors
emerged across socio-demographic groups. The relation between higher SES and higher fifth
grade reading achievement was stronger for children with LD compared to children without LD,
and paralleled findings between higher SES and higher kindergarten achievement. This finding
indicates that the effects of poverty continue to affect children’s academic achievement in later
elementary school and contribute to a stable economic gap in achievement without our schools.
Similarly, differences in academic achievement and psychosocial across racial groups persisted
in fifth grade. Specifically, Hispanic students had lower reading achievement, AfricanAmerican/Black students had lower math achievement and lower psychosocial behavior, and
Asian students had higher psychosocial behavior. Interestingly, research suggests that similar
mechanisms involved in rater bias that lead African-American/Black, Hispanic, and Native
American students with lower teacher-rated psychosocial behaviors, also contribute to higher
teacher-rated psychosocial behaviors across Asian students (Chang & Demyan, 2007). Again,
these findings suggest that parity has not been achieved across all racial and SES groups in terms
of academic and psychosocial outcomes in early and late elementary school and highlights the
need for greater attention to issues of diversity and equity within educational systems. Finally, a
significant effect of gender was found for fifth grade math achievement, as males were more
likely to have higher math achievement compared to females. These findings suggest gender
differences in math are present for typically developing children in fifth grade and align with
research suggesting females are at-risk for lower math achievement compared to their male peers
(Fryer & Levitt, 2010; Jordan, Kaplan, Nabor Olah, & Locuniak, 2006). As with racial and

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economic disparities, this finding is disheartening and confirms the need for further research to
examine the factors that may contribute to this difference in late elementary school. For example,
so research indicates that female students may internalize societal messages that discourage them
from pursuing mathematics education and activities which has led to a surge of efforts to engage
females in science, math, and technology programs at earlier ages (Hyde, Lindberg, Linn, Ellis,
& Williams, 2008).
Conclusions and Clinical Implications
Conclusions about the predictive nature of parent variables on kindergarten outcomes for
children with or at-risk of developing LD, as well as effects of kindergarten and fifth grade
achievement and psychosocial behavior on fifth grade achievement for children with and without
LD were established. Although, parent depression did not affect parents school involvement,
parent depression was related to kindergarten reading achievement suggesting an important role
for intact parental mental health in supporting children’s achievement. Clinically, school and
mental health professionals involved in the community should be alert for symptoms of parent
depression and offer resources and support early on. Additionally, while parent school
involvement was not directly related to fifth grade psychosocial behavior and motivation, it was
related to children’s psychosocial behavior and achievement for children without LD. School
policies and procedures that address barriers to parents involvement in school-based activities,
and provide additional opportunities for parents to connect and communicate with their
children’s teachers and school staff outside traditional school activities would likely benefit more
students across socio-demographic groups, and potentially for parents of children with
disabilities, including LD. For children without LD, it appears that kindergarten math and
reading achievement and early psychosocial behaviors effect later outcomes, however for

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children with LD it appears that kindergarten math achievement has a particularly strong
predictive value on later achievement. Programs that focus on increasing early math skills, as
well as early psychosocial behavior, and interventions that target the core neuropsychological
deficits that characterize a child’s LD will likely support the greatest academic and psychosocial
outcomes for children with LD.
Limitations and Future Research
There are several limitations of this study due, in part, to the non-experimental nature of
the data. First, no causal inferences can be made about parent involvement in school or parental
depression on academic or psychosocial outcomes because an experimental design is necessary
to establish the extent to which altering early parent involvement, treating parent’s psychological
problems, or intervening in early academic achievement would influence child outcomes in fifth
grade. A second limitation is that although the ECLS-K began with a representative sample of
students, due to missing data, the representativeness of the final sample may have been affected.
A third limitation involves identification of students as LD. Since information about how the LD
was diagnosed (e.g., assessment procedures and data points) are not available, there may be
students who are inaccurately identified as LD or represent higher or lower functioning within
the heterogeneous spectrum of students with LD. However, the purpose of this study was to
examine whether students identified with LD in school, and presumably receiving services for
the LD and having awareness of their LD label, differed from students without LD in terms of
motivation, psychosocial behavior, and achievement, as well as early parent variables, and not
necessarily the validity of the method used to diagnose the student’s LD. Finally, there are
limitations involved in teacher and self-report of behaviors, cognitions, and moods, such as rater
bias and developmental limitations on metacognition and accuracy of self-reflection.

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Given that the long-term effects of kindergarten achievement and psychosocial behavior
on fifth grade outcomes was mixed for children with LD particularly, future research should
examine interim time points (e.g., first grade, third grade) to examine if there are pivotal time
points at which children who have or are at-risk of developing LD begin to show marked
decreases in skills. This type of research may inform the timing of interventions to mitigate the
negative effects of early academic underachievement and lower psychosocial behavior on future
outcomes. Additionally, future research should examine other aspects of home and school
environments that may play an important role in predicting achievement and psychosocial
outcomes for children with LD. These factors may include home literacy, enrollment in
preschool, type and effect of school academic intervention, and social support/relationship
variables. Finally, children with LD are a heterogeneous group, and although this study was
unable to examine the predictive pathways between kindergarten and fifth outcomes for subtypes
of children with LD, future studies should continue to examine differences in predicting
variables and outcomes for children across children with subtypes of LD.

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APPENDIX

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