APPLICATION OF BEHAVIOR CHANGE AND PERSUASION THEORIES TO A MULTIMEDIA INTERVENTION DESIGNED TO IMPROVE THE HOME FOOD ENVIRONMENT
AND DIET QUALITY OF RESOURCE-LIMITED PARENTS WITH YOUNG CHILDREN
By
Melissa Michelle Reznar

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Human Nutrition
2012

ABSTRACT
APPLICATION OF BEHAVIOR CHANGE AND PERSUASION THEORIES TO A MULTIMEDIA INTERVENTION DESIGNED TO IMPROVE THE HOME FOOD ENVIRONMENT
AND DIET QUALITY OF RESOURCE-LIMITED PARENTS WITH YOUNG CHILDREN
By
Melissa Michelle Reznar
Background. Few interventions have focused on a parent-based, home food-centered approach
as a way to improve the relatively poor quality of US children’s diets. This dissertation evaluated
one such intervention by combining two theoretical models taken from the fields of health
psychology/behavior change (Social Cognitive Theory; SCT) and health
communication/persuasion (Heuristic-Systematic Model; HSM). The novel combination of
these two theoretical orientations was intended to combine the often distinct fields of nutrition
and communication to ultimately uncover new ways to improve child diet quality.
Aims. 1) Explore whether and how the level of parent motivation and/or parent ability
(education level) affects cognitive processing of the intervention materials by parents who
receive intervention materials; 2) Compare intervention and control groups according to
knowledge gain and change in key personal factors in the SCT (self-efficacy, outcome
expectancies, skills); and 3) Compare intervention and control groups according to changes in
parent attitudes toward healthy eating, parent diet quality, parent modeling behaviors, home food
availability and accessibility, and child diet quality.
Methods. A newly developed intervention package designed for low-income parents of 3-5 year
old children in the Head Start preschool program was tested in an eight-week randomized
controlled trial. Researchers recruited 42 participants who were randomized into control (n=19)
and intervention (n=23) groups and who received the intervention package or nothing beyond

Head Start materials, respectively. Researchers collected cognitive and dietary data and a home
food inventory at pre-study (week 0) and post-study (week 8) in participant homes.
Results. Analyses of HSM constructs in intervention participants (n=16 who remained in the
study at week 8) revealed a significant positive relationship between the perceived similarity
heuristic and change in parent attitude toward the child eating healthfully during the study
(β=0.13, p=0.02) and significant negative relationship between systematic processing (number of
correct responses on knowledge test) and parent attitude toward the child eating healthfully
during the study (β=-0.09, p=0.02). The latter finding was in the opposite direction than
expected. No other Aim 1analyses were significant. No significant changes in SCT, home food
environment, or adult or child dietary constructs/measures were detectable in the intervention
group compared to the control group, and thus, Aims 2 and 3 were not supported. Post-hoc
analysis of the combined sample of intervention and control participants (n=35 who completed
week 8) revealed significant correlations between many measured constructs, and a well-fitting
path model (motivation and education  percent of nutrient-dense foods available in the home
 adult diet quality  child diet quality) was identified that explained 15.4% of the variance in
child diet quality.
Conclusions and Implications. Post-hoc path analysis demonstrated the importance of the home
food environment, particularly availability of nutrient-dense foods in the entire small sample of
parents. Although there were few significant findings in processing intervention materials and no
significant differences between the intervention and control groups in changes in attitude,
theoretical, or behavior change, some trends in the data, particularly in the Social Cognitive
Theory, home food environment, and adult diet quality variables presented interesting leads for
future research.

To my husband, Jason Reznar, my best friend and soul mate. Luf you,
hundee! Thanks for your unconditional love, support, and never-ending
encouragement.

iv

ACKNOWLEDGEMENTS
I must acknowledge the support of my advisor, Dr. Sharon Hoerr. I may very well have
not returned to grad school without her urging. One of most important things I learned from Dr.
Hoerr is the critical importance of project partnerships. It has been great to see her formulate and
work with teams of professionals, and it was because of her leadership and experience that I was
able to be part of so many different research teams and meet so many fantastic experts. I have
grown so much as a professional because of these experiences.
I want to thank my other committee members as well. Lorraine Weatherspoon is a
wonderful, strong woman with a great and warm sense of humor. Sandi Smith offers calm and
reason in a sea of uncertainty. Beth Olson has the experience and quick wit that any department
would be lucky to have in an administrator.
Finally, I have to send my love to all my family and friends. I would not have made it
through the process without all of your encouragement. Love to you all!

v

TABLE OF CONTENTS

List of tables ................................................................................................................................. viii
List of figures ................................................................................................................................. xi
Chapter 1. Introduction ....................................................................................................................1
1.1. Background ...................................................................................................................1
1.2. Specific aims and hypotheses .......................................................................................2
1.3. Significance...................................................................................................................4
Chapter 2. Review of literature ........................................................................................................5
2.1. Child diet quality in the U.S. ........................................................................................5
2.1.1. Poor diet quality in young children ................................................................5
2.1.2. Importance of child diet quality to health ......................................................7
2.1.3. Energy-dense and nutrient-dense dietary patterns .........................................8
2.2. Dietary trends during childhood ...................................................................................8
2.2.1. Development of taste preferences ..................................................................8
2.2.2. Trends in childhood dietary quality and patterns ...........................................9
2.2.3. Eating behaviors track throughout childhood ..............................................10
2.3. Home food environment .............................................................................................11
2.3.1. Parent and child dietary concordance ..........................................................11
2.3.2. Food availability ..........................................................................................12
2.3.3. Assessment of food availability ...................................................................14
2.3.4. Home food accessibility...............................................................................14
2.3.5. Successful modification of the home food environment .............................14
2.3.6. Food modeling .............................................................................................16
2.3.7. Relationship between socioeconomic status, dietary patterns, and home
food environment ........................................................................................17
2.4 Theoretical orientation .................................................................................................18
2.4.1. Enhanced efficacy of theory-based interventions ........................................18
2.4.2. Social Cognitive Theory ..............................................................................18
2.4.3. Heuristic-Systematic Model of Persuasion ..................................................19
Chapter 3. Methods ........................................................................................................................21
3.1. Preliminary study procedures. ....................................................................................21
3.2. Pilot study design ........................................................................................................24
3.3. Pilot test sample and recruitment ................................................................................25
3.4. Pilot test procedures ....................................................................................................26
3.5. Pilot study measurement and variables .......................................................................28
3.5.1. Outcome-relevant motivation ......................................................................31
3.5.2. Systematic processing/topical knowledge ...................................................34
3.5.3. Heuristic processing .....................................................................................35
3.5.4. Attitudes .......................................................................................................38
3.5.5. Social Cognitive Theory ..............................................................................43

vi

3.5.6. Home food inventory ...................................................................................50
3.5.7. Parent dietary intake ....................................................................................53
3.5.8. Child dietary intake ......................................................................................53
3.5.9. Diet quality...................................................................................................54
3.5.10. Demographic characteristics ......................................................................56
3.6. Statistical methods ......................................................................................................56
3.6.1. Analysis for aim 1 ........................................................................................57
3.6.2. Analysis for aims 2 and 3.............................................................................58
3.6.3. Post-hoc analysis ..........................................................................................58
Chapter 4. Results ..........................................................................................................................60
4.1. Overall results .............................................................................................................60
4.2. Post-hoc analysis .........................................................................................................68
Chapter 5. Discussion ....................................................................................................................74
5.1. Main findings ..............................................................................................................74
5.1.1. Aim 1 - Heuristic Systematic Model in practice ..........................................74
5.1.2. Aim 2 and 3 - Parent attitudes, SCT, home food environment, and diet
quality ..........................................................................................................79
5.1.3. Post-hoc analysis – pooled correlations and path model .............................80
5.2. Study strengths and limitations ...................................................................................82
5.3. Recommendations for future studies ..........................................................................84
5.4. Conclusions .................................................................................................................88
Appendices .....................................................................................................................................89
Appendix 1. Abbreviated intervention workbook .............................................................90
Appendix 2. IMMS survey ................................................................................................97
Appendix 3. Cognitive assessment script ........................................................................106
Appendix 4. Study flier ....................................................................................................110
Appendix 5. Abbreviated study protocol .........................................................................112
Appendix 6. Study progress phone call/visit script .........................................................127
Appendix 7. Compliance and comprehension abstraction form ......................................129
Appendix 8. Control group contact letter ........................................................................142
Appendix 9. Intervention evaluations ..............................................................................144
Appendix 10. Intervention evaluation data ......................................................................148
Appendix 11. Study instruments ......................................................................................151
References ....................................................................................................................................183

vii

LIST OF TABLES
Table 3-1. Demographic characteristics of Head Start children in the pilot sample, the CACS
2010-11 class, and the US in Fiscal Year 2009. ............................................................................26
Table 3-2. Summary of measures collected for dissertation listed by construct, instrument, item
format, and time of measure. .........................................................................................................29
Table 3-3. Summary of additional measures collected for grant project listed by construct,
instrument, item format, and time of measure ...............................................................................30
Table 3-4. Scales administered for dissertation Likert scale and semantic differential response
options, number of items retained from original scale and Cronbach’s alpha reliability of final
scales ..............................................................................................................................................30
Table 3-5. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the outcome-relevant motivation scale at week 0 for both intervention and control
participants .....................................................................................................................................33
Table 3-6. Knowledge test individual items, number and percent of intervention and control
participants answering each item correctly at week 8 ...................................................................34
Table 3-7. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the perceived similarity scale among intervention participants at week 8 .......................36
Table 3-8. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the perceived message quality scale among intervention participants at week 8 ............36
Table 3-9. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the perceived source credibility scale among intervention participants at week 8 ..........37
Table 3-10. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the parent attitude toward healthfulness of own diet, intervention and control
participants at week 0, week 8, and change between weeks 0 and 8 .............................................39
Table 3-11. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the parent attitude toward eating solid fats and added sugars, intervention and control
participants at week 0, week 8, and change between weeks 0 and 8 .............................................40
Table 3-12. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the parent attitude toward healthfulness of foods available in the home, intervention and
control participants at week 0, week 8, and change between weeks 0 and 8.................................41
Table 3-13. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the parent attitude toward healthfulness of child’s diet, intervention and control
participants at week 0, week 8, and change between weeks 0 and 8 .............................................42

viii

Table 3-14. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the self-efficacy scale, intervention and control participants at week 0, week 8, and
change between weeks 0 and 8. .....................................................................................................45
Table 3-15. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the outcome expectancy inhibitor scale intervention and control participants at week 0,
week 8, and change between weeks 0 and 8. .................................................................................47
Table 3-16. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual
items in the outcome expectancy promoter scale, intervention and control participants at week 0,
week 8, and change between weeks 0 and 8. .................................................................................48
Table 3-17. Means, standard deviations (SD), and Pearson’s correlation coefficients of fruit and
vegetable skill items, intervention and control participants at week 0, week 8, and change
between weeks 0 and 8. .................................................................................................................49
Table 3-18. Means, standard deviations (SD), and Pearson’s correlation coefficients of sugar
sweetened beverage skill items, intervention and control participants at week 0, week 8, and
change between weeks 0 and 8 ......................................................................................................49
Table 3-19. Means, standard deviations (SD), and Pearson’s correlation coefficients of energydense snack skill items, intervention and control participants at week 0, week 8, and change
between weeks 0 and 8. .................................................................................................................50
Table 3-20. Home food inventory energy-dense items by food category and food examples. .....51
Table 3-21. Home food inventory nutrient-dense items by food category and food examples. ....52
Table 3-22. Healthy Eating Index 2005 components and standards .............................................55
Table 4-1. Demographic characteristics of control and intervention participants. .......................61
Table 4-2. Generalized linear models for Hypothesis 1.1 predicting message processing at week
8 from motivation at week 0 ..........................................................................................................62
Table 4-3. Generalized linear models for Hypothesis 1.2 predicting attitude changes between
week 0 and week 8 from systematic and heuristic processing at week 8. .....................................64
Table 4-4. Mediation Models for Hypothesis 1.3 predicting change in adult diet quality from
attitude changes and systematic processing ...................................................................................65
Table 4-5. Pre-study to post-study (week 8) change in attitude, Social Cognitive Theory (SCT),
home food environment, and diet quality measure in intervention ( n=16), versus control groups
........................................................................................................................................................67

ix

Table 4-6. Correlation matrix for main study variables, intervention and control group combined
........................................................................................................................................................70

x

LIST OF FIGURES
Figure 3-1. Study activities and timeline. ......................................................................................23
Figure 3-2. Study concept model for dissertation ..........................................................................32
Figure 3-3. Hypothesis 1.3 Mediation Model ................................................................................58
Figure 4-1. Graphic summary of significant correlations. .............................................................72
Figure 4-1. Path model ...................................................................................................................73

xi

CHAPTER 1
INTRODUCTION
1.1.

Background
The quality of children’s diets in the US is poor despite decades of effort, potentially due

in part to lack of effective parent-based interventions that reach beyond traditional approaches.
Children consume far more energy-dense foods, like candy, sweets, and salty snacks, than
recommended and too few nutrient-dense foods, like fruit, vegetables, whole grains, and low-fat
dairy/dairy alternatives. Home food environments influence the eating preferences that children
adopt and consequently affect child diet quality. Few interventions have targeted changes in the
home food environment as an avenue to improve child diet quality. Therefore, a crossdisciplinary health communication and nutrition education intervention based on established
theory is especially promising to improve home food environment and child diet quality. Given
the challenges that resource-limited parents face in providing a healthy home food environment,
it is imperative that interventions target resource-limited parents with young children to establish
healthy eating patterns as early in the life-course as possible.
The work described in this dissertation was conducted under the guidance of Dr. Sharon
Hoerr. In June 2010, Dr. Hoerr was awarded an NIH R21 to develop and pilot test an
intervention to educate parents about appropriate home food environment and child-centered
feeding practices. The original NIH grant involved developing the intervention materials and
determining if they are effective. My dissertation is a supplement to the NIH grant and intended
to examine how the parent-centered intervention may produce positive changes in child diet
quality.

1

In entirety, the objective of my dissertation was to evaluate a parent-based, multimedia
intervention for parents with 3-5 year old children in the Head Start Program through the lens of
a persuasive theoretical framework. The theoretical models employed were the HeuristicSystematic Model (HSM) of persuasion and the Social Cognitive Theory (SCT). The HSM posits
that persons form or change attitudes using both systematic processing of messages (complex
cognitive thought) and heuristic cues (short-cut processing). SCT describes human behavior as
reciprocal interaction among personal factors (knowledge, skills, self-efficacy, and outcome
expectations) and a person’s social and physical environment, emphasizing learning through
modeling.
To achieve the objective of this project, the specific aims were as follows (See Figure 32 for conceptual model).

1.2.

Specific aims and hypotheses

Aim 1: Explore whether and how the level of parent motivation and/or parent ability (education
level) affects cognitive processing of the intervention materials by parents who receive
intervention materials.
Hypothesis 1.1: Parents with high motivation and ability will be more likely to use systematic
processing (knowledge acquisition) when processing intervention materials and parents with low
motivation and ability will be more likely to use heuristic processing when processing
intervention materials.
Hypothesis 1.2: Parents who use higher amounts of a combination of heuristic and systematic
processing compared to those who use less will have more positive change in attitudes toward
healthy eating.

2

Hypothesis 1.3: Parents who use more systematic processing compared to those who use less
systematic processing will have more positive change in attitudes toward healthy eating, leading
to improved diet quality of parents.
Aim 2: Compare intervention and control groups according to knowledge gain and change in key
personal factors in the SCT (self-efficacy, outcome expectancies, skills).
Hypothesis 2.1: Parents in the intervention group will experience greater knowledge gain and
thereby demonstrate greater changes in self-efficacy, outcome expectancies, and behavioral skills
compared to parents in the control group.
Research Question 2.1: How does the relationship between knowledge gain and SCT constructs
affect parent attitudes toward healthy eating, home food environment, and parent diet quality in
the intervention group compared to the control group?
Aim 3: Compare intervention and control groups according to changes in parent attitudes toward
healthy eating, parent diet quality, parent modeling behaviors, home food availability and
accessibility, and child diet quality.
Hypothesis 3.1: Intervention parents will experience more positive changes in parent attitudes
toward healthy eating and thereby experience more improvements in diet quality than control
parents.
Hypothesis 3.2: Intervention parents will exhibit greater improvements in modeling of nutrientdense (ND) foods, child diet quality, and availability and accessibility of ND foods in the home
than parents in the control group.
Hypothesis 3.3: Control parents will experience less change in modeling of energy-dense (ED)
foods and availability and accessibility of ED foods in the home compared to intervention
parents.

3

1.3.

Significance
This study used the HSM framework to investigate how parents processed SCT-derived

intervention materials. In the future, findings from this study will be used to refine intervention
materials and develop additional supportive materials for this and other populations.

4

CHAPTER 2
REVIEW OF LITERATURE
2.1.

Child diet quality in the U.S.
2.1.1. Poor diet quality in young children
The Dietary Guidelines for Americans, which represents federal dietary

recommendations, have identified several key nutrients of concern for children of all ages –
calcium, potassium, fiber, magnesium, and vitamin E – suggesting that nutrient-dense foods like
fruit, vegetables, whole grains, and low-fat dairy/dairy alternatives need to be emphasized to
ensure nutritional adequacy (US Department of Health and Human Services et al., 2005). The
US Department of Agriculture MyPlate food guide recommends that children 2-3 years of age
consume daily 1 cup of fruit, 1 cup of vegetables, 1½ ounce equivalents of whole grains, and 2
cup equivalents of low-fat dairy/dairy alternatives and that children 4-8 years consume 1 to 1½
cups of fruit, 1½ cups of vegetables, 2½ ounce equivalents of whole grains, and 2½ cup
equivalents of low-fat dairy/dairy alternatives (US Department of Agriculture, 2012). However,
the majority of children fail to meet these recommendations. Data from the 2001-2004 National
Health and Nutrition Examination Survey (NHANES) indicates that 32% of 2-3 year old
children eat less than the recommended amount of fruit, 80% eat less than the recommended
amount of vegetables, 99% eat less than the recommended amount of whole grains, and 10% eat
less than the recommended amount of dairy (Krebs-Smith et al., 2010). Children 4-8 years of age
are even less likely to meet the recommendations, with 63%, 92%, nearly 100%, and 42% failing
to consume enough fruit, vegetables, whole grains, and dairy respectively.
Overall assessments of diet quality in children support these data. The Healthy Eating
Index (HEI) is a diet quality indicator used to measure adherence to the Dietary Guidelines for

5

Americans (US Department of Agriculture, 2010). Children aged 2-5 years and 6-11 years of age
have an overall HEI score of 59.6 and 54.7 out of 100, respectively (Fungwe et al., 2009).
Subcomponent scores for total vegetables, dark green and orange vegetables and legumes, and
whole grains all fall far short of recommendations and older children also have poor scores for
total and whole fruit. Children, particularly younger children, do score well in the dairy category,
with children 2-5 years scoring an average 10 out of 10 (score of 10 achieved with ≥1.3 cup
equivalents of dairy). Dairy intake is lower in 6-11 year old children (HEI score = 8.7 out of 10)
and is even lower later in childhood (HEI score = 7.7 out of 10 for 12-17 year olds).
A cluster analysis of national data revealed that 2-3 year olds in low-income households
were most likely to conform to a pattern the authors called “Big Eaters,” which was marked by
excess energy intake and relatively high amounts of fat, sodium, and cholesterol (Knol et al.,
2005). Although the predominant pattern in 4-8 year age group was the “Light Eaters” pattern,
characterized by low energy intake compared to the other clusters, 40% of the children’s energy
was in the form of discretionary fat and added sugars and they did not consume enough servings
from nutrient-dense food groups. The authors expected, but failed to find, an ideal or balanced
diet pattern, in these young children from low-income households (Knol et al., 2005).
Research that compares diet quality of lower-income children to higher-income children
fails to find significant differences between the two groups. Guenther and colleagues (2008)
compared HEI scores for 2-18 year old children in households <185% of the Federal poverty
1

line to the same aged children in households ≥185% of the Federal poverty line. The authors
found no differences in overall HEI or subcomponent HEI scores, with the exception of total

1

Households income <185% of the Federal poverty line was chosen because this income level is
required for participation in the Special Supplemental Nutrition Program for Women, Infants,
and Children program (WIC) and free or reduced school meal plans.
6

vegetable scores, in which lower income children actually had significantly better HEI scores
than higher income children (total vegetables HEI=2.5 and 2.2 out of 5, respectively). The
authors offer that these findings may be attributable to low-income children’s participation in the
National School Lunch program. In the same report, researchers also analyzed diet quality of all
2

persons 2 years and older in households <130% of the Federal poverty line versus households
≥130% of the Federal poverty line. Individuals in higher income households had significantly
better scores in total vegetable (low income HEI = 3.0, high income HEI = 3.3), dark green and
orange vegetable (low income HEI =1.0, high income HEI=1.2), whole grain (low income=0.8,
high income=0.9) scores, but a worse sodium score (low income HEI=4.4, high income HEI=3.8,
with a score indicating lower sodium intake). These findings suggest the need for interventions
directed toward parents in low-income households to promote home food environments in which
the parent is able to model intake of nutrient-dense foods (see section 2.3.6) while the child is
developing his or her taste preferences (see section 2.2).
2.1.2. Importance of child diet quality to health
Childhood is a time of considerable growth and development that requires optimal
nutrition. The brain develops to promote cognitive advances in memory and attention (Casey et
al., 2000) and bone mass increases as long bones grow (Davies et al., 2005; Prentice et al., 2006).
Poor diet quality has traditionally also been associated with deficiency diseases such as irondeficiency, most recently determined to be at a prevalence of 5% among 3-5 year olds (Centers
for Disease Control and Prevention (CDC), 2002). In recent decades, diet quality has been
targeted in relation to childhood obesity and its affiliated health consequences—hypertension,

2

Household income <130% of the Federal poverty line was chosen because this level of income
is required for participation in the Food Stamp program, now known as the Supplemental
Nutrition Assistance Program.
7

dyslipidemia, type 2 diabetes, diseases formerly seen almost exclusively in adults (Dietz, 1998).
National survey data indicate that 21% of 2-5 year olds are overweight and 10.4% are obese
(Ogden et al., 2010). Conversely, high quality diets rich in fruits, vegetables, and whole grains
protect against development of cardiovascular disease (Joshipura et al., 2001), diabetes (Ford &
Mokdad, 2001), and other chronic diseases (World Health Organization, 2003). Intake of
calcium-rich foods, particularly those fortified with vitamin D like milk, yogurt and orange juice,
help promote bone health (Institute of Medicine, 2011).
2.1.3. Energy-dense and nutrient-dense dietary patterns
Energy dense foods appear to displace nutrient dense foods in children’s diets. Kant
(2003) analyzed dietary intake of all children aged 8-18 years of age included in the National
Health and Nutrition Examination Survey and found that children who consume high amounts of
energy-dense foods consume fewer nutrient-dense foods. High intake of energy-dense foods
increases total energy intake, making it more likely that children who consume energy-dense
diets will become overweight or obese (Kant, 2003). For example, one intervention that aimed to
increase fruit and vegetable intake in families with a 6-11 year old child also resulted in a
decrease in high-fat, high-sugar foods, suggesting that energy-dense and nutrient-dense foods
displace each other and that an increase in one may lead to an decrease in the other (Epstein et
al., 2001).

2.2.

Dietary trends during childhood
2.2.1. Development of taste preferences
Children develop preferences for foods and flavors early in life that may influence diet

quality. Children are genetically predisposed to accept salty and fat flavors like those in energy

8

dense foods and to reject bitter flavors like those in vegetables (Birch & Davison, 2001).
Children’s willingness to try new foods increases and peaks between 1-2 years of age and then
steadily declines through age 4 (Cashdan, 1994). At least 5-10 exposures to a new food may be
needed before a child will accept it (Birch & Fisher, 1998). Parent intake of energy and
macronutrients accounted for 30-40% of variance in child energy and macronutrient in one study
(Vauthier et al., 1996). Moreover, variance estimates were approximately 10% higher when
parents and children shared more than 45 meals a week together compared to ≤45 meals
(Vauthier et al., 1996). These results highlight the importance of shared environment and repeat
exposure. Given these findings about taste preference development at early ages, young
childhood (3-5 years) appears to be a key point of establishing preferences for healthy foods and
setting the stage and the child’s mindset to taste new ones.
2.2.2. Trends in childhood dietary quality and patterns.
As suggested by FVI and HEI data already presented, diet patterns do not remain stable
over time. A longitudinal study by Lytle and colleagues (2000) collected 24-hour food recalls of
291 children from third grade through eighth grade and found significant decreases in reported
rd

th

consumption of any fruit (65% reported fruit intake in 3 grade, 56% in 5 grade, and 37% in
rd

th

th

8th grade) in any vegetable intake (56% in 3 grade, 50% in 5 grade, and 42% in 8 grade),
rd

th

th

and in milk intake (99% in 3 grade, 98% in 5 grade, and 90% in 8 grade), but significant
rd

th

th

increases in soft drink consumption (21% in 3 grade, 31% in 5 grade, 57% in 8 grade).
However, other evidence indicates that these changes are universal for growing children and that
dietary patterns remain broadly similar during childhood (Johnson et al., 2008). For instance,
while the percent of children meeting recommendations for vegetable, fruit, and milk servings

9

decline during childhood, the rank-order of their diet quality remains the same (Mannino et al.,
2004). In other words, those children with the highest fruit, vegetable, and milk intake at age 5
also had the highest intake at age 9. In addition, one study found that food preferences remain
relatively stable during childhood, with bread, pasta, and desserts being the most liked and raw
and cooked vegetables the most disliked foods at ages 2, 4, and 8 years (Skinner et al., 2002).
2.2.3. Eating behaviors track throughout childhood
In a longitudinal study of 300 children using the parent-completed Child Eating Behavior
Questionnaire (Wardle et al., 2001), responsiveness to satiety (e.g. my child gets full easily),
slow eating (e.g. my child eats slowly), and emotional under-eating (e.g. my child eats less when
upset) decreased between 4 and 11 years of age while food responsiveness (e.g. my child is
always asking for food), enjoyment of food (e.g. my child loves food), food fussiness (e.g. my
child refuses new foods at first), and emotional overeating increased (e.g. my child eats more
when anxious) (Ashcroft et al., 2008). However, the rank-order of these traits was persistent over
time, such that, for example, the fussiest eaters at age 4 tended to be the fussiest eaters at age 11
(Ashcroft et al., 2008). It seems then that over time, children experience typical changes in eating
behaviors that increase responsiveness to a high-fat/high-sugar food environment (Larson et al.,
2009; Sallis & Glanz, 2009) through increases in factors like food responsiveness, enjoyment of
food, and emotional overeating and decreases in responsiveness to satiety and slow eating, and
that individual eating behavior patterns tend to be planted early in life (Ashcroft et al., 2008).
This might be due to parent feeding behaviors or due to genetic predispositions or an interaction
of both, within the home food and social environment (Birch, 1999).
In addition, mothers seem to overestimate the quality of their children’s diets. Kourlaba
and colleagues (2009) analyzed data on 2287 children 2-5 years of age, including children’s HEI

10

scores and their mother’s perceptions of child diet quality. Overall, 18% of children had a poor
diet (HEI < 50), 82% had a diet needing improvement (50 ≤ HEI ≤ 80), and 0.2% had a good
diet. Of all mothers of children with “poor” or “needs improvement” diets, 83% classified their
children’s diets as good. Mothers who chose foods based on health were more likely to
overestimate their children’s diet quality than mothers who chose foods based on child’s
preferences or other factors (Kourlaba et al., 2009).

2.3.

Home Food Environment
2.3.1. Parent and child dietary concordance
Fisk and colleagues (2010) found that mothers’ prudent diet scores were highly correlated

with 3 year-old child prudent diet scores (r 0.55, p <0.001), with mothers’ diet quality accounting
for the majority of the variance in the model for child diet quality. Other studies have found
correlations between parents and children of various ages for various foods: beverage
consumption (2-3 year old) (Hoerr, Lee et al., 2006), diet quality (3-5 year old; 24 month old)
(Hoerr, Horodynski et al., 2006; Papas et al., 2009), calcium intake (3-5 year old) (Hoerr et al.,
2009), healthy and unhealthy snack intake (9-13 year old) (Brown & Ogden, 2004), and fruit and
vegetable consumption (5 year old; 12-36 month old; 4-12 year old; grades 4-6) (Fisher et al.,
2002; Horodynski et al., 2010; Reinaerts et al., 2007; Sylvestre et al., 2007). Parent intake was
the strongest predictor of child fruit and vegetable intake, with child gender, food neophobia, and
child enjoyment of food being other significant predictors of vegetable intake (2-6 year old)
(Cooke et al., 2004).
Some studies report weaker associations between parent and child diet, including a recent
meta-analysis, which reported that studies of parent-child diet pattern similarity typically report

11

correlations in the 0.2 to 0.33 range (Wang et al., 2010), with correlations of less than 0.3
generally considered weak, between 0.3 to <0.5 considered moderate, and ≥ 0.5 considered
strong. Wang and colleagues suggest that parent-child diets are not as similar as some may
believe particularly since diet patterns and habit may be influenced by a number of different
factors like peer influence and offerings at school. However, the authors only examined total
energy and fat intake, which may not be the best marker of diet pattern similarity. Of note, the
authors report that studies with younger children (<10 years of age) tend to have stronger
correlations than studies with older children. Unfortunately, the authors did not analyze dietary
similarity in smaller units of age than 0-9 years versus 10 years or older. Nonetheless, the finding
that parent-child similarities are stronger for younger children is notable since the proposed study
will target parents of children 3-5 years of age.
Similarity in dietary intakes between parents and younger children may be due to several
factors like feeding behaviors (e.g., modeling, and similarity in taste preferences due to feeding
behaviors), genetic transmission of taste preferences, and neighborhood food access. One major
factor and certainly a prerequisite for dietary similarity is the home food availability – the
presence of foods in the home – and home food accessibility – the ease with which children can
obtain foods in the home. Food availability and accessibility are factors largely under the control
of parents. The home food environment is a major focus of this dissertation. The home food
environment is important because adverse food environments can contribute to childhood obesity
(Newby, 2007), and it comprises the context within which feeding behavior occurs.
2.3.2. Food availability.
The relationship between food intakes of parent and child is intuitive, especially for
younger children, given that parents control the foods that are brought into the home and foods

12

that are served during meals. In fact, one survey of nutrition educators suggested that nutrition
gatekeepers – caregivers that do the majority of food shopping and cooking – control
approximately 72% of the foods that their children consume both in and out of the home
(Wansink, 2006). A telephone survey of parents similarly found that, on average, parents
perceived that they influence 66% of the food their children consume (Wansink, 2006). Some
investigators have recognized the important role that parents play in child diet quality and
suggest that parents should be the primary target of interventions to improve child diet quality
and weight status (Golan & Crow, 2004a, 2004b; Gross et al., 2010; Haire-Joshu & Nanney,
2002; Nicklas et al., 2001; Reinaerts et al., 2007). Indeed, parents or caregivers will be the focus
of the proposed intervention.
Given that younger preschool children (3 years old) have the ability to regulate their food
intake in response to hunger and fullness whereas older preschool children (5 years old) may
consume more food when presented larger amounts (Rolls et al., 2000), it is important that a
child’s home environment supports healthy choices. One aspect of the home food environment is
availability, the presence of foods in the home. Fruit and vegetable (grades 4-6; grade 3; review
of all ages mostly elementary to middle school; grade 4; 4-12 year old; preschool) (Cullen et al.,
2003; Hearn et al., 1998; Jago et al., 2007; Kratt et al., 2000; Reinaerts et al., 2007; Spurrier et
al., 2008), high-fat food (6-10 years old) (Gable & Lutz, 2000), and sweet and salty snack
(preschool) (Spurrier et al., 2008) home availability has been shown to influence child intake in a
number of studies. The literature as a whole, however, is unclear as to whether high availability
of nutrient-dense food or low availability of energy-dense food has an impact on overall child
diet quality. Johnson et al. (2010) found that the mother’s intake of both core (“healthy”; e.g.
vegetables, all types of cereals) and non-core (“unhealthy”; e.g. biscuits, fats) foods categorized

13

according to the Australian dietary guidelines was associated with 11 year-old children’s intake
and that availability was only associated with non-core foods.
2.3.3. Assessment of food availability
As noted by Bryant, (Bryant & Stevens, 2006) availability assessments should be
performed shortly after grocery shopping, adjusted for number of days since the last shopping
trip and ask about usual food availability. Pantry or cupboard food items can indicate less
preferred food or long-term food storage, because these items can last for longer periods of time.
Availability is a “gauge of exposure” (Bryant & Stevens, 2006). It is not known if food
availability is moderated by anything like feeding behaviors or something else, because highavailability items might only be eaten by a single household member. Therefore, the home food
environment can be influenced by the composition of the household, including the ages and
number of persons living and dining in the household.
2.3.4. Home food accessibility
Home food accessibility, the existence of foods in a form and place that encourages
consumption, is also an important predictor of child intake beyond mere availability. One study
of 4th to 6th grade students found that FV availability was significantly related to intake among
those with high preferences for FV. Both FV availability and accessibility, on the other hand,
were significantly associated with intake among those with low preference for FV (Cullen et al.,
2003). This suggests that for children with high preference for FV, availability alone is sufficient
for intake, and that for children with low preference, FV must be convenient (e.g. peeled and
sliced carrot sticks in the refrigerator) to facilitate intake. No studies have been identified that
have examined food accessibility among young children 3-5 years of age.

14

2.3.5. Successful modification of home food environment
Krukowski et al. (Krukowski et al., 2010) found significant reductions of high-fat foods
in the home after a weight loss 6 month intervention, but the environmental changes were not
associated with weight changes. Other studies have found positive changes in both the home
food environment and participant weight status (Gorin et al., 2007; Gorin et al., 2008). However,
these studies focused only on adult intervention participants and did not extend to children living
in the household. A few intervention studies have aimed to improve diet quality of children
(Fulkerson et al., 2010; Pearson et al., 2010; Sweitzer et al., 2010; Wardle et al., 2003), but many
of them have been school-based and/or child-centered with the intent to reach parents indirectly
(Baranowski et al., 2000; Evans et al., 2006; Fitzgibbon et al., 2005; Sirikulchayanonta et al.,
2010). Few intervention studies have focused on the home food environment of preschool
children, especially those in low-income households (Haire-Joshu et al., 2008; Stark et al., 2011;
Wyse et al., 2010), and the literature acknowledge that this line of study is still in its formative
stage (Hingle et al., 2010; Skouteris et al., 2010).
In spite of the work presented, there are gaps in the literature that have not been
addressed or fully investigated, particularly among young children. First, the relationship
between availability and accessibility is unclear. Few studies, for example, have investigated
whether food availability or food accessibility is more important or if there are particular
circumstances under which one is more important than the other or whether dependent on the age
of the child. Second, many studies have focused on fruit and vegetable availability and
accessibility relative to intake and few have examined the impact of energy-dense food in the
home food environment. Third, no identified home environment investigations or interventions

15

have examined the impact of the home food environment on overall diet quality. Instead studies
have tended to focus on intake of specific foods, such as fruit and vegetables.
2.3.6. Food modeling
Child food intake is influenced by the intake of those around them. For instance,
preschool children showed increased preference for snacks when receipt of the snack was not
contingent on performing a specific behavior and the snack was presented by an attentive teacher
(Birch et al., 1980). However, other experimental studies with preschool children and teachers as
models have shown only some support for this finding (Hendy, 1999; Hendy & Raudenbush,
2000).
These differences may perhaps be explained by limited exposure that children have to
these teacher models, compared to exposure to parents. Indeed, family food preparers have been
shown to strongly influence intake of other family members, and the more meals that children
shared with food preparers, the stronger the relationship was between child and food preparer
fruit and vegetable intake for 5-12 year old children (Hannon et al., 2003). Another study
demonstrated that modeling by mothers (e.g. how often she ate something in front of the child
that she also wanted the child to eat) was strongly associated with an increase in healthy food
intake by the child and a moderate decrease in unhealthy foods (Kroller & Warschburger, 2009).
Sutherland et al. (2008) conducted an observational experiment comparing 2-6 year old food
choices in a simulated grocery store to parent reports of grocery shopping behavior and found
healthfulness of parent and child choices to be significantly associated.
A noteworthy study to specifically examine and improve modeling of food behaviors
among African American mothers (Tibbs et al., 2001) reported several interesting findings. First,
modeling in general was related to fruit and vegetable intake, but the authors noted that there was

16

room for improvement in the reported frequency of modeling behaviors. In addition, the authors
reported that the relationship between modeling and eating behaviors was stronger than between
modeling and intake, suggesting that examining only fruit and vegetables, while parsimonious,
does not account for the complexity of dietary patterns and behaviors. Finally, parents were more
likely to model some behaviors (e.g. sitting with the child at dinner, eating foods they wanted
their child to eat) than others (e.g. eating low-fat snacks), implying for example that parents may
question cost or taste and thus not have those items available.
2.3.7. Relationship between socioeconomic status, dietary patterns, and home food
environment
One comprehensive review indicates that children in low socioeconomic families eat less
fruits and vegetables, have parents who eat less fruit and vegetables, and live in homes with
lower fruit and vegetable availability (Rosenkranz & Dzewaltowski, 2008). Energy dense diets in
young children were associated with low household income and participation in the Federal Food
Stamp Program (Mendoza et al., 2006). These results are not surprising, given that healthy foods
have been found to be more expensive than energy dense foods (Drewnowski & Specter, 2004).
Indeed, data from the 2009 Michigan Behavioral Risk Factor Survey for adults indicates that
educational level and household income have an inverse relationship with consuming fewer than
five servings of fruit and vegetables per day. That is, 80.9% of Michiganders with less than a
high school education and 80.3% with a household income <$20,000 have inadequate fruit and
vegetable intake compare to 70.7% of college graduates and 73.0% of those with a household
education of ≥$75,000 (Fussman, 2010). Other studies have found that cost is second only to
taste in influencing food choices (Glanz et al., 1998) and that food cost is a barrier to maintaining
a healthy home environment (Ard et al., 2007). In addition, low-income neighborhoods tend to

17

have a higher concentration of fast-food restaurants and low concentration of grocery stores and
other outlets that offer fresh fruits and vegetables (Larson et al., 2009; Sallis & Glanz, 2009).
2.4.

Theoretical orientation
2.4.1. Enhanced efficacy of theory-based interventions
Literature reviews suggest that theory-based interventions are more efficacious than those

not informed by explicit theoretical frameworks (Baranowski et al., 2003; Baranowski et al.,
1999). One theoretical orientation that is often employed in behavior change research is Social
Cognitive Theory (Bandura, 1986). Social Cognitive Theory is a learning theory that indicates
that humans learn their behavior by watching others (i.e. models) as a function of their social
environment and cognitive structures. Bandura called this concept of behavior, cognitive factors,
and the environment all interacting to influence each other reciprocal determinism. Baranowski
is a prominent nutrition education researcher who has recognized the complementary fit between
SCT and dietary patterns in family environments (Baranowski, 1997) and several researchers
have subsequently applied SCT in a number of nutrition research studies (Cullen et al., 2003;
Dave et al., 2010; Gross et al., 2010; Reynolds et al., 1999; Young et al., 2004).
2.4.2. Social Cognitive Theory
In addition to examining and attempting to influence the home food environment, food
modeling, and dietary behavior, the current research measured and improved upon personal
cognitive constructs from SCT: outcome expectancies, self-efficacy, and skills (Bandura, 1986).
Outcome expectancies are positive or negative outcomes that a person expects to occur as a
result of a behavior (e.g. reducing risk of chronic disease as a result of a healthy diet). Selfefficacy is the confidence one has in his or her ability to carry out a behavior, even in the face of
difficulties (e.g. confidence that one can purchase healthy foods even when low on cash).

18

Outcome expectancies and self-efficacy are sometimes confounded in research, but the two are
not interchangeable and work in concert. In other words, one may have a positive outcome
expectancy about a behavior, but unless he or she has the confidence to perform the behavior, it
is less likely to occur. Finally, SCT addresses skill building and knowledge gain relevant to the
behavior to enhance one’s capability for carrying out the behavior.
2.4.3. Heuristic-Systematic Model of Persuasion.
The HSM posits that people judge information and messages using a combination of two
forms of processing, heuristic and systematic (Chaiken, 1980; Chaiken et al., 1989; Eagly &
Chaiken, 1993). Systematic processing is effortful, analytic assessment of information. Heuristic
processing is economical and requires much less cognitive effort. The predominant type of
processing varies according to motivation (e.g. messages that are personally relevant to the
recipient will induce more systematic processing) and ability (e.g. more available time will allow
for more systematic processing). This model posits that people try to conserve resources as much
as possible. As a result, processors will rely on heuristic processing as a short cut to make a
decision about the information, but supplement the heuristic cues with systematic processing
when not confident about the decision reached through heuristic processing. Heuristic cues that
lessen cognitive effort to assist in decision-making, and those to be examined in this research
project, include perceived similarity (messages from a similar source are perceived to be more
valid), source credibility (a message from a source perceived to be more expert or more
knowledgeable is more valid), and perceived message quality (higher quality messages are
perceived to be more valid). With respect to the current project, I examined how similar parents
perceived themselves to be to parents featured in video clips, how credible parents perceived the

19

source of both the written materials and videos, and the perceived quality of print and video
materials.

20

CHAPTER 3
METHODS
3.1. Preliminary study procedures
This methods chapter describes research activities supported by National Institutes of
Health grant 1R21HD064876-01A1, funded September 7, 2010 through August 31, 2011. This
grant supported my major professor’s project (hereafter referred to as “grant project”) as well as
my dissertation research (hereafter referred to as “dissertation”). It is necessary to describe
activities of both closely related projects as well as to distinguish between the two projects to
highlight my original contribution. Note that I was project manager for the entire grant period
and was heavily involved with all grant activities, including those not essential to achieve my
dissertation aims.
The objective of the original grant project was to develop a parent-centered feeding
intervention and to conduct a pilot test of the intervention. My dissertation elaborated upon the
grant project pilot test by applying two theoretical models and evaluating participants’ home
food environments and adult and child diet quality. There are two important considerations of the
remaining discussion of methodology. First, although my dissertation aims did not address
intervention development, I will describe the intervention materials and their development in
order to provide context for my dissertation. Second, the study sample for the grant project pilot
study is the same as for my dissertation, but my dissertation’s elaboration on the original grant
project required substantial revision of two original instruments and the addition of 13 new
instruments.
During the first phase of the grant period, September 2010 through April 2011, a research
team consisting of my major professor, a behavioral psychologist, a school psychologist, a

21

biostatistician, a video production team, two exceptional dietetic undergraduates, and I
developed and cognitively tested written intervention materials and recorded, edited, and tested
video intervention materials. The second phase, May 2011 through August 2011, was the pilot
test. The study time table and summary of study activities are shown in (Figure 3-1).
Again, intervention development was part of the grant project and not incorporated in my
dissertation aims. The intervention content drew on previous work in Dr. Hoerr’s Behavioral
Nutrition Laboratory that examined the relationship between seven parent feeding constructs and
child diet and weight status. Hoerr’s previous work found that the home food environment and
parent modeling behaviors were significantly related to child diet quality (Murashima et al.,
2011; Murashima et al., 2012). Therefore, the intervention developed for the grant project was
intended to be a self-guided parent workbook and video package designed to improve child diet
and weight status partly by reinforcing positive food modeling behaviors and by offering
practical ways to evaluate and improve the parents’ home food environments. The workbook and
video format was patterned after an intervention package called The Incredible Years, which is
also a workbook- and video-based intervention for parents, but focused on child behavior
problems (Webster-Stratton, 1992; Webster-Stratton et al., 1988; Webster-Stratton & Reid,
2003).
The final intervention package was a 100-page manual divided into 5 major topical
chapters, (1) food environment, (2) food modeling, (3) praise & encouragement, (4) making
mealtime fun, and (5) how to handle difficult behaviors at the table. Chapters were further
subdivided into a total of 23 subsections (3-6 per chapter; hereafter referred to as “lessons”), and
a companion DVD of 23 short (2-3 minute) clips matched to each lesson (see Appendix 1 for
abbreviated intervention manual; entire manual and DVD clips can be accessed at

22

Aug

Sep

2010
Oct

Nov

Dec

Jan

Feb

Mar

2011
Apr
May

Jun

Jul

Aug

Workbook content development, editing, and revision
Video
Video
Video editing
shoots
shoots

Video editing
Cog
Assess
assess
(other)
(HS)
Pilot test

Figure 3-1. Study activities and timeline. Cog assess (HS) = Cognitive assessments with Head Start parents, Assess
(other) = Assessments with non-Head Start parents and content experts. For interpretation of the color in this and all other
figures, the reader is referred to the electronic version of this dissertation.

23

http://amolpavangadkar.com/eat_healthy/videos.php?c=1). Each of the lessons was structured
with a brief introduction, followed by a prompt to watch the corresponding DVD segment,
questions about the DVD clip for the participant’s response, and an activity. In addition, many
lessons featured tips, key points, things to think about, and ideas or suggestions. DVD clips were
interview segment compilations featuring 14 sets of Head Start parents. Parents described their
experiences with each of the topics highlighted in the intervention. Researchers recruited video
interviewees according to race/ethnicity (2 white families, 4 black families, 4 families with a
white parent and a black parent, and 4 families of other single or combination race/ethnicities) so
that a variety of perspectives representative of Head Start families could be captured.
Researchers performed face validity and cognitive assessments of the material with 11
Head Start parents that did not participate in the video sessions. Head Start evaluators helped
with content validity by completing 1-2 manual chapters and watching the accompanying DVD
clips. They then completed the Instructional Materials Motivation Survey (IMMS; Keller, 1987a,
1987b). Researchers also interviewed the Head Start participants using IMMS concepts (see
Appendix 2 and Appendix 3 for IMMS surveys and interview scripts), and modified the
materials to accommodate feedback. After modification, the researchers gave the materials to
parents not enrolled in the Head Start program and to content experts. These evaluators provided
open-ended feedback and suggestions for changes.

3.2. Pilot study design
The pilot study was a 14 week randomized controlled trial, with an 8-week study phase
and a follow-up visit at week 14. Researchers collected data for this dissertation and the grant
project during the pilot study, with the primary points of data collection occurring at baseline

24

(week 0; pre-study), week 8 (post-study), and week 14 (follow-up visit), as described below.
Grant project data collected during week 14 were not used for this dissertation.

3.3. Pilot test sample and recruitment
Researchers recruited 42 parents of 3-5 year old children enrolled in the Capital Area
Head Start (CACS) program. Head Start is a national preschool program for children from 3 to 5
years of age. In order to be eligible for Head Start, gross household income must be at or below
100% of the federal poverty level
(http://cacsheadstart.org/LinkClick.aspx?fileticket=CjidWzMKNh8%3d&tabid=1172), although
10% of each class may consist of special needs children that are exempt from income
restrictions. The CACS program for this study services four Michigan counties: Ingham, Eaton,
Clinton, and Shiawassee. The demographic characteristics of the CACS enrollees are shown in
Table 3-1, along with the characteristics of children in the pilot sample and in the entire US
Head Start program.

25

Table 3-1. Demographic characteristics of Head Start children in the pilot
sample, the CACS 2010-11 class, and the US in Fiscal Year 2009.*
Pilot Sample
CACS Enrollees
US
(n=41)
(2010-11 School
Enrollees
Year, n=1,620)
(FY 2009,
n=904,153)
Child race
White
43.9%
58.0%
39.9%
Black
29.3%
26.0%
30.0%
Bi-Racial
17.1%
13.4%
7.8%
Other
9.8%
2.6%
23.0%
Child ethnicity
Hispanic
14.6%
17.0%
35.9%
Child BMI percentile
31.7%
42.0%
N/A
≥85th percentile
17.1%
21.0%
N/A
≥95th percentile
*Data for CACS enrollees were obtained via personal correspondence with CACS HS
administrative staff. Data for US Enrollees were obtained from
http://eclkc.ohs.acf.hhs.gov/hslc/mr/factsheets/fHeadStartProgr.htm.
Parents were invited to participate via verbal announcements at Head Start family fun
nights, fliers posted in Head Start schools and sent home with children (see Appendix 4), and
personal invitation by study personnel stationed near Head Start classrooms during child pick-up
and drop-off times. To qualify for the study, parents had to be 18 years old or older, reachable by
phone, and able to speak and read English. The target child could not have special needs other
than speech, language, and/or orthopedic issues, because special needs children often have
different food and mealtime needs compared to children without special needs (Allen, 2011;
Andrew et al., 2012; Hammons & Fiese, 2010; Provost et al., 2010).

3.4. Pilot test procedures
Researchers obtained human subjects research approval of all study procedures,
instruments, and consent forms from the Michigan State University Institutional Review Board
before beginning the study. The PI and study coordinator trained five research assistants to

26

conduct all research procedures (see Appendix 5 for abbreviated study protocol). Two research
staff conducted the first study visit at each participant’s home. First, the research aide obtained
informed consent from each participant. Next, one research aide assisted the participants with the
paper surveys that took approximately 30-45 minutes to complete, while the other aide
conducted the home food inventory. Aides also measured height and weight of both the children
and caregivers to fulfill requirements by the grant funding agency, although those data were not
used for this dissertation. Participants randomly drew numbers out of an opaque bag to determine
study group placement (even numbered draws were assigned to the intervention group and odd
numbered draws were assigned to the control group). Those randomized into the intervention
group were provided the first chapter of the intervention manual and a copy of the intervention
DVD.
Researchers contacted intervention participants weekly for 8 weeks to promote adherence
to the study schedule and answer any questions or concerns participants may have had. For
weeks 1, 3, 5, and 7, participants were contacted by phone and asked six questions about what
stood out in the assigned workbook section, if anything impeded progress with the workbook,
and how long that week’s workbook/DVD session took to complete (see Appendix 6 for full
script). Research staff also conducted interim study home visits for intervention participants at
weeks 2, 4, and 6 to administer the same script in person and provide workbook materials for the
subsequent two weeks until the next home visit. At these interim home visits, researchers
examined participant workbooks and completed forms to capture participant compliance and
comprehension (see Appendix 7 for abstraction form). Researchers did not contact control
participants during the eight week study period with the exception of a letter at week 4-5 to
remind control participants of their involvement in the study and to let them know that

27

researchers would be contacting them soon to schedule an end-of-study visit (see Appendix 8 for
control group mid-study letter).
Research staff visited both intervention and control participants for end-of-study visits at
week 8 and week 14. These home visits were similar to baseline visits in that participants
completed the full set of study surveys and researchers collected anthropometric measures and a
home food inventory. At week 8, intervention participants completed two program evaluation
surveys that were modeled after those used for the Incredible Years (see Appendix 9 and
Appendix 10 for evaluations and evaluation data). Participants received $40 at week 8 and $60
at week 14.

3.5. Pilot study measurement and variables
All measures collected for my dissertation are summarized in
Table 3-2 (see Appendix 11 for full instruments), and are differentiated from measures
collected for the original grant project in Table 3-3. Subsequent discussion will focus on
dissertation measures only. All instruments except for the knowledge test (described below) were
adapted from literature. Each of the instruments corresponds to one of the constructs included in
the dissertation concept model (Figure 3-2).
Instruments using Likert or semantic differential response options were evaluated for
internal consistency using Cronbach’s coefficient alphas (Cronbach, 1951). Alphas range
between 0 and 1, with a higher alpha indicating a more reliable instrument. Items were
individually removed from the scale to determine if the reliability of the scale increased, and if
so, those items were omitted from the scale. The researcher used this procedure to obtain the
maximum scale reliability, indicating that remaining items were those most highly correlated

28

items in the scale (Yu, 2001). All single time-point alphas (week 8 or week 0) for scales that
were used for final analysis were at or above 0.80 (Table 3-4). Researchers have suggested that
alphas of 0.7 or above are acceptable and lower alphas unacceptable (Nunnally & Bernstein,
1994).

Table 3-2. Summary of measures collected for dissertation listed by construct, instrument, item
format, and time of measure.
Measure/
Instrument
Number Item Format Week Week Week
Construct
of Items
0
8
14
1 Motivation
Cho & Boster
8
Likert
X
Outcome Relevant
Scale
2 Ability
Education
1
Multiple
X
Choice
3 Systematic
Knowledge Test
12
Multiple
X
X
Processing
Choice
4 Heuristic
Perceived Similarity
6
Likert
X
X
Processing
Source Credibility
8
Semantic Diff
Perceived Message
5
Likert
Quality
5 Attitude
Attitude Scales
4 sets of Semantic Diff
X
X
X
6
6 Social Cognitive Hildebrand & Betts
5 (SE) Likert
X
X
X
Theory (SCT)
Outcome Expectancy
6 (OE) Likert
Constructs
and Self-Efficacy
scales

7

Home Food
Environment

8

Parent Food
Intake

9

Child Food
Intake

10 Demographic
Information

6

Parent Feeding
Questionnaire*
Home Food Inventory
(completed by
research aides)
Block Brief 2000

195

70

Block Kids Food
Screener 2007*

39

Demographic
Questionnaire*

10

29

Likert
Yes/No
Multiple
Choice
Scaled
Frequency and
Quantity
Scaled
Frequency and
Quantity
Multiple
Choice and
Open-Ended
Short Answer

X

X

X

X

X

X

X

X

X

X

X

X

*Data from these instruments were also collected for the grant project, to answer different
research questions, see Table 2. Note that a subset of Parent Feeding Questionnaire items were
used for the dissertation and all items of the Parent Feeding Questionnaire were used for the
grant project.

Table 3-3. Summary of additional measures collected for grant project listed by construct,
instrument, item format, and time of measure.
Measure/
Instrument
Number Item Format
Week Week Week
Construct
of Items
0
8
14
1 Parent
Parent Feeding
31
Likert
X
X
X
Feeding
Questionnaire*
Behaviors
2 Child Food
Block Kids Food
39
Scaled Frequency
X
X
X
Intake
Screener 2007*
and Quantity
3 Child Height N/A
N/A
N/A
X
X
X
& Weight
4 Adult Height N/A
N/A
N/A
X
X
X
& Weight
5 Demographic Demographic
10
Multiple Choice and
X
X
X
Information
Questionnaire*
Open-Ended Short
Answer
*Data from these instruments were also collected for the dissertation, to answer different
research questions, see Table 2; note that a subset of Parent Feeding Questionnaire items were
used for the dissertation and all items of the Parent Feeding Questionnaire were used for the
grant project

Table 3-4. Scales administered for dissertation Likert scale and semantic differential
response options, number of items retained from original scale and Cronbach’s alpha
reliability of final scales.
Final
Cronbach's
# Items
Final
alpha,
retained/ Cronbach's
week 8-0
#original
alpha,
items
week 8
change
scale
Motivation: Cho & Boster Outcome Relevant Scale*
5/8
0.81
N/A
Heuristic: Perceived Similarity
6/6
0.92
N/A
Heuristic: Source Credibility
4/8
0.98
N/A
Heuristic: Perceived Message Quality
5/5
0.80
N/A
Attitude: Parent eating healthfully
3/6
0.96
0.87
Attitude: Solid fats & added sugar
5/6
0.95
0.91
Attitude: Home food availability
4/6
0.88
0.94
Attitude: Child eating healthfully
3/6
0.90
0.71

30

Table 3-4. Scales administered for dissertation Likert scale and semantic differential
response options, number of items retained from original scale and Cronbach’s alpha
Table 3-4 of final scales.
reliability (cont’d)
Final
# Items
Final
Cronbach's
alpha,
retained/ Cronbach's
#original
alpha,
week 8-0
items
change
week 8
scale
SCT: Self efficacy
5/5
0.93
0.46
SCT: Outcome expectancy inhibitors
0/3
0.49
0.49
SCT: Outcome expectancy promoters
3/3
0.83
0.86
SCT: Fruit & vegetable encouragement skills
2/2
0.80
0.22
1
SCT : Sugar sweetened beverage discouragement
2/2
0.82
0.75
skills
SCT: Sweet & salty snack discouragement skills
0/2
0.62
0.06
1
SCT=Social Cognitive Theory construct.
*Scale only administered at week 0, so alpha shown is week 0 alpha. N/A = not applicable for
instruments that were only administered at a single time point and thus lack change scales.

3.5.1. Outcome-relevant motivation.
The researcher used a modified version of Cho and Boster’s (2005) outcome-relevant
involvement scale to measure parents’ motivation to eat healthfully. The original scale includes
eight items with a 7-point Likert response option (1=strongly disagree, 7=strongly agree). Table
3-5 shows the mean, standard deviation, and correlation matrix for individual items in the
involvement scale. Standardized alpha reliability of the scale with all items was 0.75. Items 1, 2,
and 3 were omitted from the final scale, which improved the alpha reliability to 0.81.

31

Figure 3-2. Study concept model for dissertation. SCT = Social Cognitive Theory.

32

Table 3-5. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the outcome-relevant
motivation scale at week 0 for both intervention and control participants (n=41).
Item
Mean SD
1
2
3
4
5
6
7
1. The amount of healthy food that I
R 5.12 2.40
-------eat has little impact on my life.
2. My life would be changed if I ate
more healthy food.
3. My quality of life would not change
depending on the amount of healthy
food I eat.
4. It is easy for me to think of ways
that my intake of healthy food
influences my well-being.†
5. Consuming the recommended
amount of healthy food affects my
daily life.†
6. It is difficult for me to think of ways
the amount of healthy food impacts
my life.†
7. My well-being has little to do with
the amount of healthy food I eat.†

6.12

1.54

-0.12

--

--

--

--

--

--

5.85

1.56

-0.03

0.44***

--

--

--

--

--

5.54

1.43

0.13

-0.02

0.02

--

--

--

--

5.41

1.60

0.02

0.08

0.21

0.42***

--

--

--

R

5.63

1.53 0.33**

0.00

0.08

0.57*** 0.66***

--

--

R

6.12

1.27

0.47***

--

R

0.29*

0.40*** 0.49***

0.20

0.38*

8. All in all, the effects of eating
healthy food on my life would be
R 6.15 1.35 0.23
0.06
0.35**
0.27* 0.53*** 0.46*** 0.58***
little.†
*p<0.1, **p<.05, ***p<.01; R=reverse-coded; †Item retained in final scale. Likert response options ranged from 1=strongly disagree
to 7=strongly agree. Reverse scored (R) and non-reverse items were coded such that higher scores reflect positive evaluations of
healthy food.

33

3.5.2. Systematic Processing/Topical Knowledge.
A 12-item multiple choice knowledge questionnaire was developed de-novo. Each of the
items were directly adapted from workbook content and lesson objectives in order to ascertain
how well participants understood concepts related to home food environment, portion size, and
parent modeling. The knowledge test evaluated topical knowledge for all participants, whereby
participants with a higher number of correct responses demonstrated more knowledge of the
concepts. In addition, a higher number of correct items on the knowledge tests also indicated a
higher amount of systematic processing among intervention participants. Table 3-6 shows the
response profile to individual items. The knowledge measure used for subsequent analysis
included the total number of correct responses.

Table 3-6. Knowledge test individual items, number and percent of intervention
and control participants answering each item correctly at week 8 (n=35)
Item
n Percent
1. What is the best description of what a healthy food is?
20
57%
2. What is the best choice for a sweet snack?
24
69%
3. What is an appropriate serving size of a food for a
23
66%
preschooler?
4. Which of the following is the best way to keep healthy food
34
97%
visible and accessible?
5. What are the recommendations for beverages for
33
94%
preschoolers?
6. Which is the best description of snacking by preschoolers?
32
91%
7. Which of the following is the best description of preschoolers 22
63%
tasting new foods?
8. When your preschooler is about to try a new food that he or
29
83%
she has never tried, which of the following is the best way to
handle the situation?
9. Should the TV be ON or OFF during mealtimes and why?
10. Should two adults in the household discuss food
disagreement IN FRONT of preschool children or IN
PRIVATE and why?
11. What is the best example of modeling healthy eating using
words?
34

29
35

83%
100%

34

97%

Table 3-6. Knowledge test individual items, number and percent of intervention
Table 3-6 (cont’d)
and control participants answering each item correctly at week 8 (n=35)
12. Does eating healthy in front of your preschool child
35 100%
(modeling) make a difference and why or why not?

3.5.3. Heuristic Processing.
Three forms of heuristic processing were measured among intervention participants:
perceived similarity, perceived message quality, and perceived source credibility. The perceived
similarity scale was originally developed by Lakey et al. (1996) and subsequently modified by
Paukert et al. (2008). Participants used 9-point Likert response options (1=very dissimilar,
9=very similar) to evaluate six characteristics in response to the statement “How similar or
dissimilar are the people in the DVD clips to you in the following ways?” All items were
retained, with a standardized scale reliability of 0.92.
Perceived message quality was assessed using a five-item scale. Respondents used 7point Likert items (1=strongly disagree, 7=strongly agree) to appraise the guidebook (Smith et
al., In press; Smith et al., 2004). All items were retained (α=0.80).
Participants evaluated source credibility by responding to the statement “I feel the authors
of the parent guidebook are…” using eight sets of bipolar adjectives anchored at opposite ends of
a 7-point scale, 8-item scale (McCroskey & Teven, 1999; Smith et al., In press). The final scale
included item sets 2, 5, 6, and 7 and had a reliability of 0.98.
Means, standard deviations, and the correlation matrix for each of the heuristic scale
items are shown in Table 3-7, Table 3-8, and Table 3-9.

35

Table 3-7. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the perceived similarity scale
among intervention participants at week 8 (n=16).
Item
Mean SD
1
2
3
4
5
1. Values
6.38 1.59
-----2. Personality
6.13 1.45 0.79***
----3. Hobbies & interests
5.75 1.39 0.74*** 0.77***
---4. Overall
6.38 1.75 0.81*** 0.87*** 0.81***
--5. Ethnic background
5.38 2.50
0.25
0.55** 0.51** 0.41*
-6. Cultural background
5.13 2.55
0.35
0.68*** 0.63*** 0.54** 0.96***
*p<0.1, **p<.05, ***p<.01. All items retained. Likert response options ranged from 1=very dissimilar to 9=very similar. Higher
scores indicate higher perceived similarity.

Table 3-8. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the perceived message
quality scale among intervention participants at week 8 (n=16).
Item
Mean SD
1
2
3
4
1. I felt the materials in the guidebook were appropriate.
6.56 0.63
----2. I felt the materials in the guidebook were effective.
6.50 0.63 0.42*
---3. I felt the materials in the guidebook were informative. 6.63 0.62 0.24 0.85***
--4. I felt the materials in the guidebook were credible.
6.63 0.62 0.41*
0.34
0.30
-5. I felt the materials in the guidebook were effective.
6.56 0.63 0.49** 0.42*
0.24 0.75***
*p<0.1, **p<.05, ***p<.01. All items retained. Likert response options ranged from 1=strongly disagree to 7=strongly agree. Higher
scores indicate higher perceived message quality.

36

Table 3-9. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the perceived source
credibility scale among intervention participants at week 8 (n=16).
Item anchors
Mean SD
1
2
3
4
5
6
7
1. Inexpert/expert
6.13 0.81
-------2. Unethical/ethical†
6.06 1.61 0.35
------3. Honest/dishonest
R 5.56 2.19 -0.23
-0.24
-----4. Trained/Untrained
R 6.31 1.49 -0.15
-0.09 0.51**
----5. Uninformed/informed†
6.19 1.47 0.26 0.92*** -0.16
-0.15
---6. Unprincipled/principled†
6.19 1.47 0.20 0.92*** -0.14
-0.15 0.97***
--7. Incompetent/competent†
6.38 1.50 0.07 0.90*** -0.21
-0.12 0.96*** 0.96***
-8. Trustworthy/Untrustworthy R 6.25 1.61 -0.18
-0.08 0.52** 0.91*** -0.11
-0.11
-0.07
*p<0.1, **p<.05, ***p<.01; R=reverse-coded; †Items retained in final scale. Items were semantic differential items anchored by the
adjectives shown on a scale of 1 to 7. Reverse scored (R) and non-reverse items were coded such that higher scores reflect more
positive ratings of source credibility.

37

3.5.4. Attitudes.
Researchers assessed parent attitudes toward healthfulness of their own dietary habits,
their children’s dietary habits, and their home food environment. Parents responded to four
question stems (1) “For me, eating healthy food is…,” (2) “For me, eating foods high in solid
fats and added sugars is…,” (3) “Having healthy food available in the home is…,” and (4) “My
child eating healthy food is…” using semantic-differential scales. The scales each contained six
identical sets of bipolar adjectives anchored on opposite ends of 7-point response options.
The final scales include (1) parent eating healthy: item sets 1, 2, and 3 (α=0.96 at week 8 and
α=0.87 for change scores) (2) solid fats and added sugars: item sets 1, 2, 3, 4, and 6 (α=0.95 at
week 8 and α=0.91 for change scores) (3) healthy food available: item sets 1, 2, 3, and 4 (α=0.88
at week 8 and α=0.94 for change scores) (4) child eating healthy: item sets 1, 2, and 3 (α=0.90 at
week 8 and α=0.71 for change scores). Table 3-10 through Table 3-13 display attitude means,
standard deviations, and correlations for the intervention and control participants at week 0 and
week 8 and change between weeks 0 and 8.

38

Table 3-10. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the parent attitude toward
healthfulness of own diet, intervention and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 0 (n=38)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad
R 6.63 0.82
-----2. Positive/negative
R 6.76 0.59 0.71***
----3. Beneficial/harmful
R 6.79 0.47 0.56*** 0.69***
---4. Valuable/worthless
R 6.47 0.80 0.40*** 0.30* 0.41***
--5. Calming/distressing
R 5.58 1.37 0.32**
0.24
0.19
0.14
-6. Acceptable/unacceptable
R 6.39 1.05 0.71*** 0.50*** 0.50*** 0.38** 0.46***
Week 8 (n=35)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
R 6.69 0.80
-----2. Positive/negative†
R 6.66 0.76 0.93***
----3. Beneficial/harmful†
R 6.77 0.49 0.87*** 0.88***
---4. Valuable/worthless
R 6.71 0.62 0.88*** 0.84*** 0.84***
--5. Calming/distressing
R 6.00 1.28 0.60*** 0.51*** 0.51*** 0.63***
-6. Acceptable/unacceptable
R 6.69 0.68 0.90*** 0.87*** 0.75*** 0.83*** 0.58***
Week 8-0 (n=32)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
R 0.06 0.72
-----2. Positive/negative†
R -0.09 0.59 0.70***
----3. Beneficial/harmful†
R 0.00 0.51 0.53*** 0.86***
---4. Valuable/worthless
R 0.28 0.92 0.56***
0.29
0.21
--5. Calming/distressing
R 0.53 1.57 0.31*
0.09
0.04
0.25
-6. Acceptable/unacceptable
R 0.34 0.94 0.50***
0.12
0.20
0.26
0.33*
*p<0.1, **p<.05, ***p<.01; R=reverse-scored; †Items retained in final scale. Items were semantic differential items anchored by the
adjectives shown on a scale of 1 to 7. Items were reverse coded so that higher scores reflect more positive attitudes about parents
eating healthfully.

39

Table 3-11. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the parent attitude toward
eating solid fats and added sugars, intervention and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 0 (n=40)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad
5.25 1.85
-----2. Positive/negative
5.15 1.86 0.86***
----3. Beneficial/harmful
5.68 1.72 0.75*** 0.83***
---4. Valuable/worthless
5.38 1.61 0.72*** 0.67*** 0.80***
--5. Calming/distressing
4.20 1.77 0.31**
0.25
0.38*** 0.36**
-6. Acceptable/unacceptable
4.45 1.71 0.54*** 0.57*** 0.53*** 0.51*** 0.29*
Week 8 (n=35)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
4.09 1.80
-----2. Positive/negative†
4.23 1.73 0.89***
----3. Beneficial/harmful†
4.60 1.79 0.84*** 0.85***
---4. Valuable/worthless†
4.26 1.92 0.78*** 0.86*** 0.86***
--5. Calming/distressing
3.26 1.75 0.36** 0.46*** 0.46*** 0.57***
-6. Acceptable/unacceptable†
3.46 1.70 0.69*** 0.71*** 0.68*** 0.82*** 0.63***
Week 8-0 (n=34)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
-1.12 2.10
-----2. Positive/negative†
-0.82 2.05 0.84***
----3. Beneficial/harmful†
-1.03 2.25 0.69*** 0.75***
---4. Valuable/worthless†
-1.06 1.95 0.57*** 0.61*** 0.73***
--5. Calming/distressing
-0.88 2.18
0.23
0.23
0.43*** 0.47***
-6. Acceptable/unacceptable†
-1.00 1.86 0.49*** 0.59*** 0.70*** 0.62*** 0.28*
*p<0.1, **p<.05, ***p<.01. †Items retained in final scale. Items were semantic differential items anchored by the adjectives shown on
a scale of 1 to 7. Higher scores reflect more negative attitudes toward eating solid fats and added sugars.

40

Table 3-12. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the parent attitude toward
healthfulness of foods available in the home, intervention and control participants at week 0, week 8, and change between weeks 0 and
8.
Week 0 (n=39)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad
R 6.72 0.79
-----2. Positive/negative
R 6.67 0.84 0.88***
----3. Beneficial/harmful
R 6.77 0.54 0.77*** 0.88***
---4. Valuable/worthless
R 6.67 0.70 0.82*** 0.88*** 0.84***
--5. Calming/distressing
R 6.26 1.04 0.31** 0.37** 0.34**
0.26*
-6. Acceptable/unacceptable
R 6.74 0.59 0.85*** 0.83*** 0.80*** 0.86*** 0.41***
Week 8 (n=32)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
R 6.91 0.30
-----2. Positive/negative†
R 6.94 0.25 0.80***
----3. Beneficial/harmful†
R 6.94 0.25 0.80*** 0.47***
---4. Valuable/worthless†
R 6.91 0.30 0.63*** 0.36** 0.80***
--5. Calming/distressing
R 6.34 1.12 0.49*** 0.31*
0.31* 0.49***
-6. Acceptable/unacceptable
R 6.81 0.59 0.82*** 0.36** 0.80*** 0.82*** 0.58***
Week 8-0 (n=30)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
R 0.10 0.61
-----2. Positive/negative†
R 0.13 0.68 0.97***
----3. Beneficial/harmful†
R 0.10 0.31 0.69*** 0.76***
---4. Valuable/worthless†
R 0.20 0.66 0.80*** 0.78*** 0.75***
--5. Calming/distressing
R 0.07 1.34
-0.18
-0.20
-0.27
-0.25
-6. Acceptable/unacceptable
R 0.03 0.56 0.60*** 0.44***
0.18
0.63***
0.09
*p<0.1, **p<.05, ***p<.01; R=reverse-scored; †Items retained in final scale. Items were semantic differential items anchored by the
adjectives shown on a scale of 1 to 7. Items were reverse coded so that higher scores reflect more positive attitudes about having
healthy foods in the home.
41

Table 3-13. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the parent attitude toward
healthfulness of child’s diet, intervention and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 0 (n=40)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad
R 6.83 0.55
-----2. Positive/negative
R 6.80 0.61 0.97***
----3. Beneficial/harmful
R 6.80 0.61 0.89*** 0.93***
---4. Valuable/worthless
R 6.80 0.56 0.79*** 0.70*** 0.70***
--5. Calming/distressing
R 6.40 1.15 0.44*** 0.48*** 0.48*** 0.44***
-6. Acceptable/unacceptable
R 6.70 0.85 0.65*** 0.77*** 0.77*** 0.51*** 0.62***
Week 8 (n=33)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
R 6.91 0.29
-----2. Positive/negative†
R 6.85 0.36 0.75***
----3. Beneficial/harmful†
R 6.85 0.36 0.75*** 0.76***
---4. Valuable/worthless
R 6.85 0.36 0.75*** 0.76*** 1.00***
--5. Calming/distressing
R 6.61 0.93 0.44*** 0.55*** 0.65*** 0.65***
-6. Acceptable/unacceptable
R 6.91 0.29
0.27
0.16
0.45*** 0.45*** 0.32*
Week 8-0 (n=32)
Item anchors
Mean SD
1
2
3
4
5
1. Good/bad†
R 0.06 0.25
-----2. Positive/negative†
R 0.03 0.40 0.63***
----3. Beneficial/harmful†
R 0.03 0.40
0.31*
0.40**
---4. Valuable/worthless
R 0.00 0.44
0.30*
0.18
0.37**
--5. Calming/distressing
R 0.22 1.26
0.06
0.30*
0.37**
0.17
-6. Acceptable/unacceptable
R 0.16 0.85
-0.05
0.18
0.27
-0.35** 0.42**
*p<0.1, **p<.05, ***p<.01; R=reverse-scored; †Items retained in final scale. Items were semantic differential items anchored by the
adjectives shown on a scale of 1 to 7. Items were reverse coded so that higher scores reflect more positive attitudes toward the child
eating healthfully.

42

3.5.5. Social Cognitive Theory.
Three predominant constructs from SCT were assessed for the dissertation research: selfefficacy, outcome expectancies, and skills. Five self-efficacy and six outcome expectancy items
were adapted from a study by Hildebrand & Betts (2009) to evaluate parents’ confidence in
serving healthy foods and the value they placed on healthy foods for the family, respectively.
The self-efficacy scale used a 7-point Likert format (1=very sure I can, 7=very sure I cannot).
The outcome expectancy scale was also a 7-point Likert format (1=strongly disagree, 7=strongly
agree). Self-efficacy descriptive statistics are shown in Table 3-14. All items were retained in the
final scale (α=0.93 at week 8 and α=0.46 for change scores). Outcome expectancy items are
presented separately as behavioral inhibitors (Table 3-15) and promoters (Table 3-16).
Inhibitors had low alpha reliability (α=0.49 at week 8 and α=0.49 for change scores), suggesting
that items were not measuring the same construct (Cronbach, 1951). Thus, the inhibitor scale
was not used for the remainder of the analyses. The promoter scale exhibited acceptable
reliability (α=0.83 at week 8 and α=0.86 for change scores) and all items were retained.
The Parent Feeding Questionnaire (Murashima et al., 2011) was used to collect selfreported food modeling skills. Six of the original scale items were used regarding having fruit
and vegetables, sugar-sweetened beverages, and sweet and salty snacks available in the house
and eating those foods in front of the child (Table 3-17-Table 3-19). Parents responded using a
5-point Likert response (1=never, 5=always). Fruit and vegetable skill items had α=0.80 at week
8 and α=0.22 for change scores. Sugar-sweetened beverage skill items had α=0.82 at week 8 and
α=0.75 for change scores. Sweet and salty snack skill items had α=0.62 at week 8 and α=0.06 for
change scores. Sweet and salty snack items were eliminated from further analysis due to

43

unacceptable reliability. Fruit and vegetables items were retained because week 8 reliability was
acceptable and the intervention had a heavy focus on fruit and vegetable intakes and behaviors.
The PFQ also contains one item related to milk intake, “I drink milk in front of my
child.” This item was used by Murashima and colleagues (2011) in combination with fruit and
vegetable items as part of a “Nutrient Dense Encouraging” scale. For this dissertation, the
inclusion of the milk item with the fruit and vegetable items reduced the fruit and vegetable
alpha at week 8 from 0.80 to 0.67, indicating that the milk item was not a good fit with the fruit
and vegetable items. Thus, the milk item was omitted from further analysis.

44

Table 3-14. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the self-efficacy scale,
intervention and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 0 (n=42)
Item
Mean SD
1
2
3
4
1. Serve healthy foods to my family when preparing meals at home?
R 6.52 0.94
----2. Serve healthy foods to my family when eating meals away from
R 5.43 1.48 0.60***
---home?
3. Prepare tasty, easy recipes that are healthy?
R 6.33 0.95 0.40*** 0.55***
--4. Serve healthy food to my family when I am low on money for
R 5.60 1.45 0.57*** 0.41*** 0.54***
-buying food?
5. Serve my family healthy foods when I do not have a lot of time?
R 5.33 1.60 0.54*** 0.55*** 0.60*** 0.58***
Week 8 (n=35)
Item
Mean SD
1
2
3
4
1. Serve healthy foods to my family when preparing meals at home?
R 6.43 1.20
----2. Serve healthy foods to my family when eating meals away from
R 5.46 1.42 0.64***
---home?
3. Prepare tasty, easy recipes that are healthy?
R 6.17 1.07 0.84*** 0.64***
--4. Serve healthy food to my family when I am low on money for
R 5.60 1.35 0.74*** 0.74*** 0.84***
-buying food?
5. Serve my family healthy foods when I do not have a lot of time?

45

R

5.43

1.58 0.60*** 0.75*** 0.58*** 0.83***

Table 3-14. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the self-efficacy scale,
intervention(cont’d)
Table 3-14 and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 8-0 (n=35)
Item
Mean SD
1
2
3
4
1. Serve healthy foods to my family when preparing meals at home?
R -0.03 0.82
----2. Serve healthy foods to my family when eating meals away from
R 0.14 1.29
0.06
---home?
3. Prepare tasty, easy recipes that are healthy?
R -0.11 0.80
0.13
0.02
--4. Serve healthy food to my family when I am low on money for
R 0.17 1.34
0.19
0.10
0.32*
-buying food?
5. Serve my family healthy foods when I do not have a lot of time?
R 0.26 1.70
0.17
0.22
-0.04
0.28
*p<0.1, **p<.05, ***p<.01; R=reverse-coded. All items retained. Likert response options ranged from 1=very sure I can to 7=very
sure I cannot. Items were reverse coded so that higher scores reflect higher self-efficacy.

46

Table 3-15. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the outcome expectancy
inhibitor scale intervention and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 0 (n=41)
Item
Mean SD
1
3
1. Healthy foods can be expensive.
R 3.24 1.98
--3. Sometimes it takes too much time to prepare healthy foods. R 4.98 1.92 0.31**
-5. Buying healthy foods may mean more trips to the store.
R 3.68 2.20 0.46*** 0.34**
Week 8 (n=34)
Item
Mean SD
1
3
1. Healthy foods can be expensive.
R 3.09 1.80
--3. Sometimes it takes too much time to prepare healthy foods. R 4.71 1.80
0.28
-5. Buying healthy foods may mean more trips to the store.
R 3.74 1.97
0.12
0.33*
Week 8-0 (n=34)
Item
Mean SD
1
3
1. Healthy foods can be expensive.
R -0.09 1.76
--3. Sometimes it takes too much time to prepare healthy foods. R -0.09 1.52
0.11
-5. Buying healthy foods may mean more trips to the store.
R 0.21 2.43
0.10
0.09
*p<0.1, **p<.05, ***p<.01; R=reverse scored. Likert response options ranged from 1=strongly disagree to 7=strongly agree. Items
were reverse coded so that higher scores reflect more positive outcome expectancies.

47

Table 3-16. Means, standard deviations (SD), and Pearson’s correlation coefficients of individual items in the outcome expectancy
promoter scale, intervention and control participants at week 0, week 8, and change between weeks 0 and 8.
Week 0 (n=41)
Item
Mean SD
2
4
2. Healthy foods help protect my family from diseases like cancer and heart disease.
6.44 1.30
--4. Healthy foods can help keep my family from getting sick with colds and infections.
5.93 1.59 0.56***
-6. Healthy foods can help my family members have a healthy weight.
6.24 1.46 0.81*** 0.71***
Week 8 (n=35)
Item
Mean SD
2
4
2. Healthy foods help protect my family from diseases like cancer and heart disease.
6.21 1.25
--4. Healthy foods can help keep my family from getting sick with colds and infections.
5.91 1.46 0.67***
-6. Healthy foods can help my family members have a healthy weight.
6.41 1.21 0.62*** 0.55***
Week 8-0 (n=35)
Item
Mean SD
2
4
2. Healthy foods help protect my family from diseases like cancer and heart disease.
-0.14 1.87
--4. Healthy foods can help keep my family from getting sick with colds and infections.
-0.03 1.77 0.53***
-6. Healthy foods can help my family members have a healthy weight.
0.11 1.94 0.83*** 0.68***
*p<0.1, **p<.05, ***p<.01; R=reverse scored. All items retained. Likert response options ranged from 1=strongly disagree to
7=strongly agree. Higher scores reflect more positive outcome expectancies.

48

Table 3-17. Means, standard deviations (SD), and Pearson’s correlation coefficients of fruit and
vegetable skill items, intervention and control participants at week 0, week 8, and change
between weeks 0 and 8.
Week 0 (n=41)
Item
Mean SD
8.
8. I keep fruits and vegetables available that my child can eat.
4.49 0.55
-26. I eat fruits and vegetables in front of my child.
4.29 0.87
0.16
Week 8 (n=35)
Item
Mean SD
8.
8. I keep fruits and vegetables available that my child can eat.
4.34 0.59
-26. I eat fruits and vegetables in front of my child.
4.26 0.74 0.67***
Week 8-0 (n=34)
Item
Mean SD
8.
8. I keep fruits and vegetables available that my child can eat.
-0.06 0.55
-26. I eat fruits and vegetables in front of my child.
0.09 1.00
0.12
*p<0.1, **p<.05, ***p<.01. Both items retained. Likert response options ranged from 1=never to
5=always. Higher scores indicate more self-reported nutrient-dense encouraging behavior.
Table 3-18. Means, standard deviations (SD), and Pearson’s correlation coefficients of sugar
sweetened beverage skill items, intervention and control participants at week 0, week 8, and
change between weeks 0 and 8.
Week 0 (n=40)
Item
Mean SD
10.
10. I keep sugar-sweetened beverages where my child can
R 3.53 1.38
-reach them.
29. I drink sweetened beverages in front of my child.
R 3.15 1.19 0.72***
Week 8 (n=35)
Item
Mean SD
10.
10. I keep sugar-sweetened beverages where my child can
R 3.77 1.19
-reach them.
29. I drink sweetened beverages in front of my child.
R 3.29 1.15 0.69***
Week 8-0 (n=33)
Item
Mean SD
10.
10. I keep sugar-sweetened beverages where my child can
R 0.18 1.53
-reach them.
29. I drink sweetened beverages in front of my child.
R 0.12 1.05 0.61***
*p<0.1, **p<.05, ***p<.01; R=reverse-coded. Both items retained. Likert response options
ranged from 1=never to 5=always. Items were reverse-coded so that higher scores indicate more
self-reported energy-dense discouraging behavior.

49

Table 3-19. Means, standard deviations (SD), and Pearson’s correlation coefficients of energydense snack skill items, intervention and control participants at week 0, week 8, and change
between weeks 0 and 8.
Week 0 (n=41)
Item
Mean SD
9.
9. I keep sweets, candy or salty snacks where my child can
R 4.05 1.20
-reach them.
28. I eat sweets, candy or salty snacks in front of my child.
R 3.44 1.12 0.47***
Week 8 (n=41)
Item
Mean SD
9.
9. I keep sweets, candy or salty snacks where my child can
R 4.00 0.85
-reach them.
28. I eat sweets, candy or salty snacks in front of my child.
R 3.56 1.02 0.45***
Week 8-0 (n=41)
Item
Mean SD
9.
9. I keep sweets, candy or salty snacks where my child can
R 0.00 1.06
-reach them.
28. I eat sweets, candy or salty snacks in front of my child.
R 0.18 0.98
0.03
*p<0.1, **p<.05, ***p<.01; R=reverse-coded. Likert response options ranged from 1=never to
5=always. Items were reverse scored so that higher scores indicate more self-reported energydense discouraging behavior.

3.5.6. Home food inventory.
Research aides evaluated the home food environment using the 195-item Home Food
Inventory (HFI) developed by Fulkerson and colleagues (2008). The instrument consists of a list
of foods grouped into 17 categories (e.g. cheese, butter, fruit, vegetables), and research
evaluators mark ‘yes’ or ‘no’ to indicate availability of each item in the home Two questions
ascertain accessibility on the counter tops and in the refrigerator by asking which of a list of
items are visible and readily accessibility, including fresh vegetables, soda pop, and candy.
Foods were categorized into nutrient-dense and energy-dense categories using definitions
and examples put forth in the 2010 Dietary Guidelines for Americans (DGA). The 2010 DGA
states that nutrient-dense foods are those that provide vitamins and minerals and contain
relatively few calories (US Department of Agriculture & US Department of Health and Human
50

Services, 2010). Examples include fruit, dark green and orange vegetables, and whole grains,
legumes, and low-fat milk and milk products. Conversely, the DGA describes foods to reduce,
here after referred to as energy-dense foods, as those that are consumed in high quantities and
tend to displace nutrient-dense items. Examples of those that the DGA suggests are energy-dense
are those high in saturated fat (e.g. full-fat cheeses, pizza, desserts, chicken mixed dishes, and
sausages/franks/bacon/ribs); solid fats (e.g. grain-based desserts, pizza, full-fat cheese,
sausage/franks/bacon/ribs, fried white potatoes); added sugar (e.g. soda/energy drinks/sports
drinks, desserts, fruit-drinks, candy); sodium (e.g. chicken mixed dishes, pizza, pasta dishes);
and refined grains (e.g. breads, pizza, grain-based desserts, tortillas/burritos/tacos). Table 3-20
and Table 3-21 show the items categorized as energy-dense and nutrient-dense. It should be
noted that meat products were categorized with the beef/pork/lamb item as energy-dense and the
chicken/turkey and fish items as nutrient-dense. Given that all meat products offer a ready supply
of protein and other nutrients like iron, one might argue that all meats could be categorized as
nutrient-dense. The decision to include beef/pork/lamb in the energy-dense category was made
because the percent of calories from saturated fat of beef fat is 50% and for pork fat is 40%
whereas for chicken it is 30% (US Department of Agriculture & US Department of Health and
Human Services, 2010). In addition, sausage/franks/bacon/ribs, beef/beef mixed dishes, and
burgers together contribute to 13.4% of the saturated fat in the US diet (US Department of
Agriculture & US Department of Health and Human Services, 2010).

Table 3-20. HFI energy-dense items by HFI food category* and food examples.
Food category*
Individual foods
Instrument Item
Numbers
Cheese
Full-fat block and shredded cheese,
1a, 1b, 1f, 1g, 1i, 1k
cottage cheese, cream cheese, processed
cheese
51

Table 3-20 (cont’d)
3-20. HFI energy-dense items by HFI food category* and food examples.
Food category*
Individual foods
Instrument Item
Numbers
Milk/Dairy
Whole milk, heavy cream, full-fat sour
2c, 2d, 2e
cream
Butter, margarine, oils

Regular butter, margarine,
lard/shortening

3a, 3c, 3h

Salad dressing

Regular dressing

4a

Condiments
Deli, luncheon, sandwich
meat and sausage

Regular mayonnaise
Sliced ham/roast beef, bologna, salami,
bacon/breakfast sausage, hot
dogs/bratwurst/polish sausage
Beef/pork/lamb

5a
9b, 9c, 9d, 9e, 9f

Frozen desserts

Regular ice cream, frozen ice cream
treats

11a, 11d

Microwavable or quickcook foods

Pizza, burritos, chicken nuggets, French
fries, egg rolls, ramen noodles

12a-12h

Bread

White bread, English muffins, bagels,
pita bread, croissants

13b, 13d, 13f, 13k,
13l

Desserts

Regular cookies, cake, muffins, pastries

14a, 14c, 14e, 14g,
14h

Chips, crackers, and other
snack foods

Potato, corn, tortilla chips, cheese puffs,
bagel chips, granola bars

15d, 15f, 15g, 15i,
15k, 15q

Non-alcoholic beverages

Soda, iced teas, sports drinks, fruit drinks

20a, 20c, 20e, 20g

Candy

Chocolate, hard, gummi, fruit-based,
chewy candy

21a-21e

Meats and other protein

10b

*The food categories are presented in accordance with HFI authors’ groupings and are not meant
to represent DGA groupings. For instance, the HFI and the USDA (Ahuja et al., 2012) both
group heavy cream and sour cream conceptually with milk products, but neither heavy cream nor
sour cream contribute to dairy equivalencies, only to discretionary solid fats (Bowman et al.,
2008).

Table 3-21. HFI nutrient-dense items by HFI food category and food examples.
Food category
Individual foods
Instrument Item
Numbers
Milk/Dairy

Skim and low-fat milk, reduce-fat yogurt

52

2a, 2b, 2h, 2j

Table 3-21 (cont’d)
3-21. HFI nutrient-dense items by HFI food category and food examples.
Food category
Individual foods
Instrument Item
Numbers
Vegetables
Broccoli, carrots, green beans, lettuce,
7a-7t
potatoes, greens, tomatoes, mixed
vegetables, etc.
Fruit

Apples, bananas, grapes, melons, mixed
fruit, oranges, strawberries, clementines

8a-8z

Deli, luncheon, sandwich
meat and sausage

Sliced turkey or chicken

9a

Meats and other protein

Chicken/turkey, tofu, veggie burgers,
fish, lentils, beans, peanut butter, eggs

10a, 10c-10j

Frozen desserts
Bread

Frozen yogurt, frozen yogurt treats
Wheat bread, English muffins, bagels,
pita bread

11c, 11e
13a, 13c, 13e, 13j

Chips, crackers, and other
snack foods

Whole grain snack crackers; peanuts,
cashews, or other nuts

15a, 15p

Non-alcoholic beverages

100% fruit juice, bottled water, soy/rice
milk

20f, 20h, 20i

3.5.7. Parent Dietary Intake.
Parent intake was assessed using the Block Brief Food Frequency Questionnaire FFQ
2000. The Block Adult FFQ has 70-items and was developed using data from the National
Health and Nutrition Examination Survey (NHANES) III (NutritionQuest, 2009). The first Block
Brief FFQ was 60-items and validated in three different populations (Block et al., 1990). No
studies have validated the current Block Brief 2000.
3.5.8. Child Dietary Intake.
The Block Kids Food Screener 2007 for Ages 2-17 was used to measure child food intake.
The Food Screener is a shortened, 39-item FFQ that asks how many days the child ate a
particular food item in the last week (none, 1 day, 2 days, 3-4 days, 5-6 days, or everyday) and
item-specific quantity that is consumed in one day (e.g. a little/some/a lot; 1 bowl/2 bowls/3
bowls). Various versions of the full Block Kids FFQ have been tested for validity and reliability

53

(Block, 2008; Cullen et al., 2008; Marshall et al., 2008; Smith & Fila, 2006). The Block Kids
Food Screener 2007 has not been validated, but a validation study is currently in its third
manuscript revision (Hoerr SH, Guo W, NutritionQuest, personal communication, April 2012).
3.5.9. Diet quality.
Overall diet quality of adults and children was assessed using the Healthy Eating Index (HEI)
2005. HEI-2005 scores dietary quality according to the presence and amount of 12 components
(Patricia M. Guenther, Jill Reedy, & Susan M. Krebs-Smith, 2008; US Department of
Agriculture, 2010). HEI-2005 scores range from 0 to 100, and HEI subscore ranges vary for
different components, shown in Table 3-22. In a 2008 evaluation, Guenther and colleagues
(Patricia M. Guenther, Jill Reedy, Susan M. Krebs-Smith et al., 2008) performed a rigorous set
of tests to assess content validity, construct validity, and reliability of the HEI-2005 (an updated
version of the instrument released in 2005), and concluded that the HEI is highly valid and
predictive of health outcomes.
The mean HEI-2005 score of the pilot study at week 8 was 66.6 (90% confidence interval
63.4, 70.0) for parents and 65.0 (90% confidence interval 62.9, 67.0) for children. In comparison,
data from the National Health and Nutrition Examination Survey, 2003-2004, indicate a mean
HEI-2005 score of 57.5 (95% confidence interval 56.0, 59.0) for all individuals in the US 2 years
of age and older (P. M. Guenther et al., 2008) and 59.6 (no confidence interval provided) for
children 2-5 years of age (Fungwe et al., 2009). Although the data from the pilot study are
similar to national figures, the fact that pilot HEI data are slightly higher than national data may
be attributed, in part, to use of a food frequency questionnaire (over the past week) for pilot data
rather than a one-day dietary recall for national data.

54

HEI-2005 was calculated by using a publicly accessible SAS program and SAS-formatted
MyPyramid equivalency databases published by the USDA (available at
http://www.cnpp.usda.gov/healthyeatingindex.htm). This SAS program joined each individual’s
average daily quantity of each food in the respective food frequency questionnaires with SASformatted equivalency data by unique food code, enabling calculation of HEI-2005 for each
individual and the entire study population.
1

Table 3-22. Healthy Eating Index 2005 components and standards .
Component
Maximum Standard for
Standard for
Points
Maximum Score
Minimum Score
Total Fruit (includes 100% juice)
5
≥0.8 cup equiv.
No Fruit
per 1,000 kcal
Whole Fruit (not juice)
5
≥0.4 cup equiv.
No Whole Fruit
per 1,000 kcal
Total Vegetables
5
≥1.1 cup equiv.
No Vegetables
per 1,000 kcal
Dark Green and Orange Vegetables
5
≥0.4 cup equiv.
No Dark Green or
2
per 1,000 kcal
Orange
and Legumes
Total Grains
5
≥3.0 oz equiv. per No Grains
1,000 kcal
Whole Grains
5
≥1.5 oz equiv. per No Whole Grains
1,000 kcal
3
10
≥1.3 cup equiv.
No Milk
Milk
per 1,000 kcal
Meat and Beans
10
≥2.5 oz equiv. per No Meat or Beans
1,000 kcal
4
10
≥12 grams per
No Oil
Oils
1,000 kcal
5
Saturated Fat (R)
10
≥15% of energy
≤7% of energy
Sodium (R)
10
≤0.7 grams per
≥2.0 grams per
1,000 kcal
1,000 kcal
Calories from Solid Fats, Alcoholic
20
≤20% of energy
≥50% of energy
beverages, and Added Sugars
(SoFAAS; R)
1
Intakes between the minimum and maximum levels are scored proportionately, except for
Saturated Fat and Sodium (see note 5).
2
Legumes counted as vegetables only after Meat and Beans standard is met.
3
4

Includes all milk products, such as fluid milk, yogurt, and cheese, and soy beverages.
Includes nonhydrogenated vegetable oils and oils in fish, nuts, and seeds.
55

5

Saturated Fat and Sodium get a score of 8 for the intake levels that reflect the 2005 Dietary
Guidelines, <10% of calories from saturated fat and 1.1 grams of sodium/1,000 kcal,
respectively.
(R) The last 3 components – saturated fat, sodium, and SoFAAS – are reverse-scored, such that
the higher the intake of these foods and nutrients, the lower the score for that component.

3.5.10. Demographic characteristics.
Demographic items collected include age, race/ethnicity, and sex of adults and children
living in the household in a format modeled after the US Census format (US Census Bureau,
2011). In addition, education (multiple choice with choices of: no high school, some high school,
high school diploma/GED, some college, college graduate, graduate training beyond college) and
employment status of adults in the household (two multiple choice questions; employed, yes/no;
if employed, what type, full time, part time, other) were asked. Time since last major grocery
shopping trip and time until next major grocery shopping trip were also included (9 multiple
choice options for each, today, 1 day, 2 days, 3-4 days, 5-6 days, 1-2 weeks, 3-4 weeks, more
than 1 month, don’t know).

3.6. Statistical methods
Analyses were conducted using SAS software (version 9.3, SAS Institute, Inc). The
significance level alpha for all statistical tests was set at 0.10 because of the small sample size
and exploratory nature of the study. Descriptive univariate analysis of each questionnaire item
was conducted to inspect variable distribution (means, medians, standard deviations). As
previously noted, Cronbach’s alpha was used to examine the internal consistency of all scales at
baseline.

56

3.6.1. Analysis for Aim 1.
Relationships between motivation (mean of retained items in the outcome relevant scale
at week 0), ability (education level reported at week 0), heuristic processing (mean responses for
each of the three heuristic scales: similarity, source credibility, and perceived message quality at
week 8), systematic processing (number of correct responses from the knowledge test at week 8),
change attitudes (mean of retained items for each of the four attitude scales at week 8 minus at
week 0), and adult dietary quality (mean healthy eating index score at week 8) were evaluated
using a series of generalized linear models. For Hypothesis 1.1, I fitted four main models – one
with systematic processing as the dependent variable and the other three with heuristic
processing as the dependent variables. The main independent variables in these models were
motivation and ability.
For Hypothesis 1.2, changes in each of the four attitudes toward healthy eating (mean
value at week 8 – mean value at week 0) were the dependent variables and systematic and
heuristic processing were the independent variables.
For Hypothesis 1.3, a mediation analysis was conducted as described by Baron and
Kenny (1986; MacKinnon et al., 2007). The mediation model tested is depicted in Figure 3-3.
For each of the four attitude scales, model parts a, b, and c depicted in the figure were run as
generalized linear models, so that the independent variable was systematic processing; the
mediator was each of the parent attitudes; and the outcome variable was parent dietary quality.
Path a represents the relationship between the independent variable (systematic processing) and
mediator (parent attitude); path b represents the relationship between the mediator (parent
attitude) and the outcome variable (parent dietary quality); and path c represents the relationship
between the independent variable (systematic processing) and outcome variable (parent dietary

57

quality). Mediation occurs when path a is significant and path c is significant, but path c is
considerably reduced or no longer significant when path a and path b are both controlled. (Baron
& Kenny, 1986; MacKinnon et al., 2007)

Figure 3-3. Hypothesis 1.3 Mediation Model

3.6.2. Analysis for Aims 2 and 3.
Whereas aim 1 hypotheses focused only on processing of intervention materials by
intervention participants only, aims 2 and 3 were concerned with differences between
intervention and control participants on attitude change, changes in SCT measures, the home
food environment, and adult and child diet quality during the intervention period. To examine
these differences, student t-tests were conducted comparing change scores of intervention
participants for each of the measures to change scores of control participants.
3.6.3. Post-hoc analysis.
The post-hoc analysis included intervention and control participants, combined, to
explore overall relationships between motivation, education, SCT measures, attitudes, home food
environment, and adult and child diet quality. Pearson’s correlations were obtained for all
variable combinations. Significant correlations were displayed graphically using the original

58

dissertation concept model as a guide. Several path models were then run from the graphic
correlation graphic using PROC CALIS in SAS 9.3, and the model showing acceptable fit was
presented for future testing in a larger study population. The model fit statistics considered
include: (1) the model chi-square (when not statistically significant, the researcher fails to reject
the hypothesis that there are no discrepancies between model predictions and population co
variances); (2) the root mean square error of approximation (RMSEA, values below 0.05 may
indicate a good fit); (3) the comparative fit index (CFI, values ≥0.95 may indicate good fit); and
(4) standardized root mean square residual (SRMSR, values ≤0.08 may indicate good fit) (Kline,
2011). In addition, the effect size of the model is presented as percent of variance in the outcome
attributable to the model.

59

CHAPTER 4
RESULTS
4.1. Overall Results
Researchers recruited 42 adults into the study, for both the grant study and the
dissertation. Participants were overwhelmingly female (Table 4-1). Approximately half were
non-Hispanic white, a quarter non-Hispanic black, and the remainder, Hispanic or other/mixed
race. The majority of participants were in their late twenties or early thirties, although four
control parents were over 40 years of age, two of whom were grandparents to the target Head
Start child. With the exception of one control participant who did not have a high school degree,
all participants were high school educated or beyond. The study population was well-educated,
with 59% having some college education and 29% having a college degree or graduate training
beyond college. More participants were unmarried than married. No significant differences
between intervention and control participants were detected in the categorical variables shown.
Nineteen individuals were randomized to the control group and 23 were randomized to
the intervention group. A total of 19 control participants (100%) and 16 intervention participants
(70%) completed week 8 and week 14 (there was no loss to follow-up between weeks 8 and 14),
for an overall study completion rate of 83%. Seven of the original 23 intervention participants
withdrew before the end of the study. One of these attrition participants provided unreliable
information on the demographic form, but researchers visually assessed that this participant was
a white female, Hispanic ethnicity unknown. Of the other six attrition participants, 5 (83%) were
female; 5 (83%) had some college, 1 (17%) had a graduate degree or higher; 1(17%) was less
than or equal to 25 years of age, 3 (50%) were 26-30 years of age, 2 (33%) were greater than 30
years of age; and 2 (33%) were white, non-Hispanic, 2 (33%) were black non-Hispanic, 2 (33%)

60

were other race. There were no statistically significant differences between attrition and nonattrition participants according to sex, education, age, and race using Fisher’s exact test (data not
shown).
Table 4-1. Demographic characteristics of control (n=23) and
intervention participants (n=19).*†
Control
Intervention
n (%)
n (%)
Adult sex
Male
1 (5%)
1 (5%)
Female
18 (95%)
21 (95%)
Adult race/ethnicity
White, non-Hispanic
11 (58%)
12 (55%)
Black, non-Hispanic
5 (26%)
6 (27%)
Hispanic
2 (11%)
1 (5%)
Other
1 (5%)
3 (14%)
Adult age
18 - 25 years
2 (11%)
4 (18%)
26 - 30 years
5 (26%)
9 (41%)
31 - 35 years
6 (32%)
7 (32%)
36 - 40 years
2 (11%)
2 (9%)
> 40 years
4 (21%)
0 (0%)
Adult education
High school degree or less
3 (16%)
2 (9%)
Some college
9 (47%)
15 (68%)
College degree or more
7 (37%)
5 (23%)
Adult employment
Full time
3 (16%)
13 (59%)
Part time
6 (32%)
2 (9%)
Not employed
9 (47%)
7 (32%)
Other
1 (5%)
0 (0%)
Adult Marital Status
Married
8 (42%)
6 (27%)
Not married
11 (58%)
17 (77%)
*One intervention participant was excluded due to unreliable demographic data
†Chi-squares on all data in table for differences between control and intervention participants
were all non-significant

61

Aim 1 hypothesized that motivation and ability would affect whether parents in the
intervention group exposed to study materials would use systematic processing or heuristic
processing of the materials, that the type of processing would influence attitude change during
the intervention, and that the attitude change would be associated with improved diet quality of
parents. The results for hypothesis 1.1 regarding the effect of motivation and ability on
systematic and three types of heuristic processing are shown in Table 4-2. No models were
significant and Hypothesis 1.1 was not supported.
Table 4-2. Generalized linear models for Hypothesis 1.1 predicting
message processing at week 8 from motivation at week 0.
Beta
SE
T value p value
Model 1. Systematic Processing (n=15)
Motivation
0.37
0.49
0.76
0.46
Education
High school versus college
1.59
2.11
0.76
0.47
Some college versus college
1.95
1.33
1.46
0.17
2
F = 1.88 (3,11), p=.19, R = 0.34
Model 2. Heuristic Processing - Perceived Similarity (n=15)
Motivation
-0.05
0.42
-0.13
0.90
Education
High school versus college
1.28
1.79
0.71
0.49
Some college versus college
1.73
1.13
1.53
0.15
2
F = 1.03 (3,11), p=.42, R = 0.22
Model 3. Heuristic Processing - Perceived Message Quality (n=15)
Motivation
-0.01
0.14
-0.07
0.94
Education
High school versus college
0.45
0.59
0.76
0.46
Some college versus college
0.03
0.37
0.07
0.95
2
F = 0.22 (3,11), p=.88, R = 0.06
Model 4. Heuristic Processing - Perceived Source Credibility (n=15)
Motivation
0.14
0.44
0.32
0.75
Education
High school versus college
0.72
1.90
0.38
0.71
Some college versus college
-0.43
1.20
-0.36
0.73
2
F = 0.13 (3,11), p=.94, R = 0.03

62

Table 4-3 displays the results for hypothesis 1.2, regarding the effect of systematic and
heuristic processing on change in four types of parent attitudes towards themselves eating
healthfully, eating solid fats and added sugars, having healthy food available in the home, and
their children eating healthfully. The first three models were not significant. The fourth model,
regarding parents’ attitudes toward their children eating healthfully was significant. In contrast to
the hypothesized relationship, however, parents with lower amounts of systematic processing
and higher amounts of the heuristic perceived similarity of themselves to those in the DVD clips
had positive attitude change about their children eating healthfully. Hypothesis 1.2 was partially
supported.
2

Of interest is model 4, which was significant with an adjusted R of 39%. In that model,
systematic processing and attitude change about the child eating healthfully were significantly
negatively related, meaning that the more systematically that parents processed intervention
materials the more negatively their attitude changed between weeks 0 and 8. In that same model,
there was a significant positive relationship between parents who perceived themselves to be
similar to parents featured in the DVD clips and attitude change toward child eating healthfully.
Perceived similarity was also positively related to attitude change toward having food available
in the home in model 3, although the overall model was not significant.
Model 2 was also not statistically significant overall, but within that model source
credibility and attitude change toward eating solid fats and added sugars were positively related.
Taking into account the way the variables were coded, this relationship can be interpreted to
mean that the more credible that parents perceived the authors of the workbook to be, the more

63

negatively parents were to evaluate intake of solid fats and added sugars over the course of the
study.

Table 4-3. Generalized linear models for Hypothesis 1.2 predicting attitude changes
between week 0 and week 8 from systematic and heuristic processing at week 8.
Beta
SE
T value p value
Model 1. Attitude change - Adult eating healthy (n=16)
Systematic processing
-0.01
0.04
-0.32
0.76
Heuristic processing - perceived similarity
-0.03
0.06
-0.54
0.60
Heuristic processing - perceived message quality
0.23
0.21
1.13
0.28
Heuristic processing - perceived source credibility
-0.03
0.06
-0.49
0.63
2
F = .35 (4,11), p=.84, R = 0.11
Model 2. Attitude change - Adult eating solid fats and added sugars (n=16)
Systematic processing
0.41
0.24
1.68
0.12
Heuristic processing - perceived similarity
-0.13
0.33
-0.39
0.71
Heuristic processing - perceived message quality
-1.14
1.14
-1.00
0.34
Heuristic processing - perceived source credibility
0.65
0.35
1.84
0.09
2
F =1.65 (4,11), p=.23, R = 0.38
Model 3. Attitude change - Healthy food availability (n=15)
Systematic processing
0.02
0.05
0.48
0.64
Heuristic processing - perceived similarity
-0.13
0.07
-1.81
0.10
Heuristic processing - perceived message quality
0.27
0.24
1.12
0.29
Heuristic processing - perceived source credibility
-0.03
0.08
-0.42
0.69
2
F =1.03 (4,10), p=.44, R = 0.29
Model 4. Attitude change - Child eating healthy (n=15)
Systematic processing
-0.09
0.03
-2.67
0.02
Heuristic processing - perceived similarity
0.13
0.05
2.67
0.02
Heuristic processing - perceived message quality
0.08
0.16
0.54
0.60
Heuristic processing - perceived source credibility
-0.01
0.05
-0.22
0.83
2
F =3.2 (4,10), p=.06, R = 0.56

The results for hypothesis 1.3, which posited that parents with more systematic
processing would experience more attitude change and would thereby improve diet quality
during the study as a result of receipt of intervention materials, are shown in Table 4-4.
Although the relationship between systematic processing and change in adult diet quality was
64

significant, none of the other relationships in the mediation models were significant. Therefore,
hypothesis 1.3 was not supported. However, it is noteworthy that the relationship between
systematic processing and adult diet quality change was significant, but in a direction opposite of
that expected. In other words, those that processed intervention materials more systematically
had a more negative change in adult diet quality during the study.

Table 4-4. Mediation Models for Hypothesis 1.3 predicting change in adult diet quality from
attitude changes and systematic processing.
Mediation Model 1. Systematic processing → Attitude change - parent eating healthfully →
Adult diet quality change (n=16)
Independent Variable

Dependent Variable

Systematic processing

Attitude change - adult eating
healthfully
Attitude change - adult eating Adult diet quality change
healthfully
Systematic processing
Adult diet quality change

Beta

SE
0.04

T
value
-0.25

p
value
0.80

-0.01
4.33

8.66

0.50

0.62

-2.18

1.10

-1.99

0.07

Mediation Model 2. Systematic processing → Attitude change - solid fats and added sugars →
Adult diet quality change (n=16)
Systematic processing
Attitude change - solid fats
and added sugars
Systematic processing

Attitude change - solid fats
and added sugars
Adult diet quality change

0.32

0.24

1.38

0.19

-1.08

1.29

-0.84

0.42

Adult diet quality change

-2.18

1.10

-1.99

0.07

Mediation Model 3. Systematic processing → Attitude change - healthy food availability →
Adult diet quality change (n=15)
Systematic processing
Attitude change - healthy
food availability
Systematic processing

Attitude change - healthy
food availability
Adult diet quality change

0.01

0.05

0.20

0.85

-0.77

7.17

-0.11

0.92

Adult diet quality change

-2.18

1.10

-1.99

0.07

65

Table 4-4. Mediation Models for Hypothesis 1.3 predicting change in adult diet quality from
attitude changes and systematic processing.
Mediation Model 4. Systematic processing → Attitude change - child eating healthfully →
Adult diet quality change (n=15)
Systematic processing

Attitude change - child eating
healthfully
Attitude change - child eating Adult diet quality change
healthfully
Systematic processing
Adult diet quality change

-0.06

0.04

-1.54

0.15

12.15

7.93

1.53

0.15

-2.18

1.10

-1.99

0.07

Aims 2 and 3 predicted changes in SCT constructs, home food environment, attitudes,
child diet quality, and parent diet quality in the intervention group compared to the control
group. Table 4-5 shows the results of t-tests comparing the amount of change in each of the
measures between baseline and the end of the study in the intervention versus the control group.
The intervention group did not experience significant positive changes in the attitude, theoretical,
or behavior measures compared to the control group. Neither Aim 2 nor Aim 3 was supported.
Although not significant, the intervention group did experience positive changes during the
intervention in promoter outcome expectancies, fruit and vegetable encouraging skills, sugar
sweetened beverage discouraging skills, and adult diet quality.

66

Table 4-5. Pre-study to post-study (week 8) change in attitude, Social Cognitive Theory (SCT), home food environment, and diet
quality measure in intervention (n=16), versus control groups ( n=19).
Construct
Individual measure
Intvn Control
Diff
St
SE
T
p
mean
mean
(Intvn - Dev
(n=16) (n=19) Control)
Parent eating healthfully attitude change
Solid fats and added sugars attitude change

SCT Change

Healthy food availability attitude change

-0.09
-1.05
0.12

0.17
0.11
-0.01

0.51
1.74
0.53

0.17
0.59
0.18

1.04
0.19
-0.06

0.31
0.85
0.95

1

Attitude Change

0.08
-0.94
0.11
0.04

0.04

0.01

0.28

0.10

0.07

0.94

-0.03
0.31
0.16
0.34

0.18
-0.30
-0.16
-0.18

-0.20
0.61
0.31
0.53

0.70
1.65
0.60
1.21

0.24
0.56
0.20
0.42

-0.86
1.01
1.54
1.26

0.40
0.32
0.13
0.22

0.02

0.04

-0.02

0.08

0.03

-0.68

0.50

0.00

-0.02

0.02

0.08

0.03

0.87

0.39

0.88
-0.69
1.13
-0.62

0.79
-0.42
-1.08
1.72

0.09
-0.27
2.21
-2.35

1.77
1.67
8.91
8.25

0.60
0.57
3.02
2.80

0.14
-0.47
0.73
-0.84

0.89
0.64
0.47
0.41

1

Child eating healthfully attitude change
Self-efficacy change
Outcome expectancy promoter change
FV encouraging modeling skills change
2

SSB discouraging skills change
Change in percent nutrient dense foods
available
Change in percent energy dense foods
Home Food
Environment Change available
Change in access to nutrient dense foods
Change in access to energy dense foods
Adult diet quality change
Diet Quality Change
Child diet quality change
Intvn = Intervention; SCT = Social Cognitive Theory
1
Intervention n=15, Control n=18
2

Sugar Sweetened Beverages

67

4.2. Post-hoc analysis.
Although not expressly articulated in the dissertation aims and hypotheses, it was of
interest to explore relationships between motivation, education, social cognitive theory, home
food environment, and adult and child diet quality in intervention and control parents combined.
Means, standard deviations, and correlation between these variables are shown in Table 4-6.
Other than motivation and education, all other variables were those collected post-study at week
8.
One interesting finding is that child diet quality is significantly positively correlated with
parent diet quality and negatively correlated with energy dense food accessibility and not with
any other parent-focused variables, with the exception of a weak correlation with parent selfreported sugar-sweetened beverage discouragement. This suggests that parent behavior and
physical environment are more important direct contributing factors to child diet quality than
parent cognitive constructs.
Parent diet quality, in turn, was also positively correlated with percent of nutrient-dense
foods available in the home and nutrient-dense accessibility and negatively related to energydense food availability and accessibility. In addition, education, self-efficacy, and sugarsweetened beverage discouragement were significantly correlated with parent diet quality. None
of the attitude scales were significantly correlated with adult diet quality.
In order to visualize relationships between all the variables included in Table 4-6 and to
propose tentative path models, significant correlations are summarized graphically in Figure 4-1,
using the dissertation concept model as the theoretical foundation. From that model, path models
were fit. The path model shown in Figure 4-2 is the path model that described the data the most

68

comprehensively and fit the data well. The model described 15.4% of the variance in child diet
quality.

69

Table 4-6. Correlation matrix for main study variables, intervention and control group combined.
Variable
N Mean
Std
1
2
3
4
5
Dev
1 Motivation
41 5.83
1.14 -----2 Education
41 2.17
0.63 0.10
----3 SCT: Self-efficacy
35 5.82
1.17 0.42*** -0.12
---4 SCT: Outcome exp promoters
35 6.20
1.12 0.73*** 0.32*
0.22
--5 SCT: PFQ FV encouragement
35 4.30
0.61 0.13
-0.02
0.36**
0.15
-6 SCT: PFQ SSB discouragement
35 3.53
1.08 0.16
0.38**
0.18
0.25
0.05
7 Attitude: Parent eating healthy
35 6.70
0.66 0.35**
0.03
0.45*** 0.40**
0.18
8 Attitude: solid fats & added sugar
35 4.13
1.64 0.12
-0.09
-0.18
0.07
-0.07
9 Attitude: Healthy food availability
33 6.83
0.46 0.28
0.05
0.15
0.26
0.06
10 Attitude: Child eating healthy
33 6.87
0.31 0.50*** 0.12
0.13
0.56*** 0.21
11 HFE: Percent nutrient dense
35 0.51
0.09 0.32*
0.38**
0.16
0.24
0.06
12 HFE: Percent energy dense
35 0.38
0.09 -0.35** -0.30*
-0.13
-0.27
0.03
13 HFE: Nutrient dense access
35 3.49
1.40 0.19
0.22
0.15
0.10
0.20
14 HFE: Energy dense access
35 2.63
1.33 0.03
-0.29*
0.13
-0.01
0.36**
15 Adult diet quality
35 66.63 11.39 0.22
0.33**
0.28*
0.27
0.07
16 Child diet quality
35 64.98 7.25 0.13
0.06
-0.01
0.01
0.04
***p<0.01, **p<0.05, *p<0.10; SCT=Social cognitive theory, PFQ=Parent feeding questionnaire, FV=Fruit and vegetable,
SSB=Sugar sweetened beverage, HFE=Home food environment

70

6
------0.18
-0.14
-0.05
0.03
0.52***
-0.53***
0.17
-0.41***
0.67***
0.29*

Table 4-6. (Cont’d)
Variable
7
8
9
10
11
12
13
14
15
-----1 Motivation
---------2 Education
---------3 SCT: Self-efficacy
---------4 SCT: Outcome exp promoters
---------5 SCT: PFQ FV encouragement
---------6 SCT: PFQ SSB discouragement
---------7 Attitude: Parent eating healthy
---------8 Attitude: solid fats & added sugar
-0.17
--------9 Attitude: Healthy food availability 0.58*** -0.33*
-------10 Attitude: Child eating healthy
0.49*** -0.30* 0.82***
------11 HFE: Percent nutrient dense
0.09
0.00
0.09
0.18
----12 HFE: Percent energy dense
-0.10
-0.12
-0.06
-0.15 -0.89***
---13 HFE: Nutrient dense access
-0.03
0.06
-0.03
0.01
0.45*** -0.34**
--14 HFE: Energy dense access
0.04
-0.12
-0.10
-0.07 -0.34** 0.43***
0.00
-15 Adult diet quality
0.19
-0.04
-0.04
0.01
0.52*** -0.48*** 0.32* -0.39**
16 Child diet quality
0.01
-0.25
-0.01
0.09
0.21
-0.27
0.25 -0.37** 0.39**
***p<0.01, **p<0.05, *p<0.10; SCT=Social cognitive theory, PFQ=Parent feeding questionnaire, FV=Fruit and vegetable,
SSB=Sugar sweetened beverage, HFE=Home food environment

71

Figure 4-1. Graphic summary of significant correlations. Numbers shown are Pearson’s r from Table 4-6. *p<0.1, **p<0.5, ***p<.01

72

Chi-square model = 2.2064 (p=0.8199)
RMSEA = 0.0
CFI = 1.0
SRMSR = 0.0496

Figure 4-2. Path model. Numbers shown are the standardized path coefficients. *p<0.1, **p<0.5, ***p<.01. RMSEA = root mean
square error of approximation; CFI = comparative fit index; SRMSR = standardized root mean square residual

73

CHAPTER 5
DISCUSSION
5.1. Main findings.
The objective of this research was to evaluate a newly developed intervention for lowincome parents to improve feeding practices and home food environment. The framework for
evaluation was based largely on two theoretical models: the Social Cognitive Theory and the
Heuristic-Systematic Model (HSM). The Social Cognitive Theory has been used in a number of
different ways related to dietary interventions aimed in part at parents of infants, toddlers,
preschool, and school-aged children; namely, to inform future interventions (Byrd-Bredbenner et
al., 2011; Kolopaking et al., 2011; Wiig & Smith, 2009) and in the development and evaluation
of such interventions (Baranowski, et al., 2003; Beech, et al., 2003; Burgess-Champoux, et al.,
2008; Haire-Joshu, et al., 2008; Harvey-Berino & Rourke, 2003; Horodynski, et al., 2011;
Klohe-Lehman, et al., 2007; Monteiro, et al., 2011; Nyberg, et al., 2011; Story, et al., 2003).
Conversely, no studies have been identified that have used the HSM for dietary interventions in
this study population. Therefore, this dissertation was an attempt to bridge the fields of
behavioral nutrition and health communication by combining these theories and to thereby
uncover potential new ways of improving child diet quality.

5.1.1. Aim 1 - Heuristic Systematic Model in practice
The first aim of this dissertation was to investigate how low-income parents of preschoolaged children processed materials of the intervention using HSM as framework. In particular, the
three hypotheses of aim 1 proposed that (1) parents with high motivation and ability would use
more systematic processing whereas those with low motivations and ability would use more

74

heuristic processing; (2) higher use of systematic and/or heuristic processing would be associated
with more positive change in pare attitudes from pre- to post-study; and (3) that high use of
systematic processing would be associated with positive change in attitudes and, in turn, would
result in positive change in adult diet quality. Neither hypotheses 1.1 nor 1.3 were supported by
the data, but 1.2 was partially supported because one of the attitudes, “parent attitude toward the
child eating healthfully,” was significantly explained by a model containing three types of
heuristic processing and systematic processing.
One specific component of this model that was significant was the positive relationship
between attitude change and perceived similarity of parents featured in the intervention DVD
clips. In developing our intervention, particular attention was paid to maximizing perceived
similarity. The parents in the video were recruited from the same Head Start program as the pilot
intervention participants were. In addition, we sought racial/ethnic diversity in video participants,
striving to recruit equal numbers of participants who were of white, black, Hispanic, and other
races/ethnicities to represent the diversity of Head Start enrollment.
The perceived similarity results agree with a long line of evidence in communication
research suggesting that perceived similarity, also known as homophily, can enhance
persuasiveness of a message (McQueen et al., 2011; Moyer-Gusé, 2008; Rogers & Bhowmik,
1970; Slater & Rouner, 2002). Slater and colleagues (2003) found that identification with a
message source modified the relationship between message type and three types of dietary
change message characteristics: (1) message believability, (2) message usefulness, and (3)
message clarity. The importance of perceived similarity is also supported by the Social Cognitive
Theory (Bandura, 1986). Bandura, in his landmark publication introducing the Social Cognitive
Theory, cited research suggesting that a person will be more likely to try a behavior and

75

experience increased self-efficacy, if a similar rather than a less similar model demonstrates the
behavior. This is supported by quantitative and qualitative data from the Witness Project, in
which culturally similar role models and health advisors were key to increasing mammography
rates among African American women (Bailey et al., 2000).
The second significant model component was a negative relationship between attitude
change and systematic processing that which was opposite of the predicted direction. It is well
documented that changing another’s attitude is difficult, particularly when the attitude is strongly
held (Eagly & Chaiken, 1995). Indeed, in the dissertation pilot study, the mean of parent
attitudes toward children eating healthfully at baseline was already very positive with 6.85 on a
7-point scale. Wood, Rhodes, and Biek (1995) suggest that individuals with strong attitudes and
extensive working knowledge, or “beliefs and prior experiences that come to mind when one is
confronted with an attitude object [in this case, feeding children healthfully; p. 293],” can engage
in selective information processing, or biased receipt and evaluation of information that in
agreement with currently held attitudes and discard of information in disagreement with current
attitudes. In other words, “people who believe themselves knowledgeable might reject an appeal
because they think they already possess substantial support for their own judgment, rendering
further consideration of the topic unnecessary (Wood et al., 1995, p. 298).” This is demonstrated
in a recent study of breast cancer advocates, in which the education and scientific literacy were
inversely related to attitude toward regulation of a potential carcinogen (Silk et al., 2012). The
authors surmise that the more highly educated and scientifically literate participants may
perceive the carcinogen presented in the study to be of lesser significance than other carcinogens
that they have more experience with.

76

One manifestation of resistance to attitude change is counter arguing, or generating
arguments that contradict intervention content (Petty & Cacioppo, 1986; Slater & Rouner, 2002).
Resistance and counter arguing were observed anecdotally during the study, particularly by one
participant who repeatedly emphasized that she was currently in college and implied that the
simplicity of the intervention materials was not designed for well-educated parents like her.
Attitudes that are ego-defensive, or those that are held to protect one’s self-concept
against threats, are associated with discounting information and tend to be very resistant to
change (Eagly & Chaiken, 1993). Given that parenting feeding style and the underlying practices
that characterize the style are internally governed (Hughes et al., 2005; Ventura et al., 2010), it is
reasonable to surmise that attitudes toward child feeding could be highly ego-involved. Lapinski
and Boster (2001) proposed and tested a model of ego-involvement regarding college students’
attitudes toward being serious students. In it, they suggest that an ego-defensiveness reaction
against information threatening their concept of being serious students leads to message
1

2

discounting, source derogation, and lack of adherence to message recommendations. Although
there is no evidence that participants in this dissertation research engaged in disparaging
intervention source, given the high source credibility (mean=6.2 on a 7-point scale), it would be
interesting to explore the extent to which parent feeding attitudes are ego-defensive and whether
that influences intervention processing and effectiveness.
As noted in Chapter 3 on Methods, systematic processing was evaluated using a
knowledge test developed de-novo for this dissertation, with the intent of measuring systematic
processing, or how thoroughly participants deliberated the intervention materials. Development

1

Lack of trust in message

2

Expression of lack of trust in the message source
77

of an instrument such as this is typically a rigorous process that involves development and
design, pilot testing, and psychometric evaluation (Friedenberg, 1995). Specifically,
psychometric tests are expected to be reliable, which can refer to consistency in test scores over
time (test-retest reliability) or correlation between items with a scale measuring a single
construct (internal reliability). Validity is the another important property of psychometric tests,
including construct validity, that the test is measuring what it was intended to measure, and
content validity, that the test measures all aspects of the tested domain. Test developers confer
with subject matter experts and conduct pilot testing to evaluate test reliability and validity as
well as to improve individual item quality and clarity (Friedenberg, 1995; Kline, 2000; Kline,
2005). Unfortunately, the restricted timeline and budget of the dissertation project precluded
such in-depth assessment of the knowledge test for this study. Thus, it is likely that the
knowledge test did not optimally measure systematic processing. If so, the relationship between
systematic processing and attitude change may have been an artifact of the knowledge test
design.
Overall, the HSM did not fit the dissertation concept model well, given that hypotheses
1.1. and 1.3 were not supported and one of the findings of hypothesis 1.2 was in a direction
opposite to that expected. It could be that the HSM truly does not describe the process by which
low-income parents engage in parent feeding interventions materials. In contrast, one similarly
conceived study that used HSM to investigate message processing found that ability, including
education level, significantly predicted knowledge gain after exposure to breast cancer messages
(Smith et al., Under review). It should be noted that the sample in the Smith et al. study was
highly educated, with nearly three-quarters of the sample (72.4%) having a college degree or
post-college graduate training compared to 29.3% of the dissertation pilot study sample.

78

However, in the breast cancer study, even lay messages written at low reading level enhanced
knowledge gain, suggesting that the messages used could be effective for a less educated
population. It is important to reiterate that HSM testing in my dissertation research was
conducted only among intervention participants, which was a sample size of 16 at the week 8
post-study visit. Adjusted model R2 for several of the models were upwards of 20%, indicating
that 20% of the variance of the outcome variable is explained by the model. This is encouraging,
despite the fact that the models were largely non-significant and bode well for re-testing the
HSM in a larger population.
A potential change that may make the HSM more useful for studies with low-income
parents like the target population is to consider alternate ways of conceptualizing ability other
than just education level. Some variables thought to influence ability as provided in the
Elaboration Likelihood Model (Petty & Cacioppo, 1986), the predecessor to the HSM, include
presence of distractions while processing, message repetition, and message comprehensibility.
Eagly and Chaiken (1993) provide time pressure as another example of a variable that may
influence ability. Low-income parents may have different demands and time constraints than
higher-income parents, which could justify considering different measures of ability in future
models.

5.1.2. Aims 2 and 3 – parent attitudes, SCT, home food environment, and diet
quality
No significant differences were detected in pre- to post-study change in Aim 2 and 3
variables in the intervention group compared to the control group. Thus, neither Aim 2 nor Aim
3 were supported. It is noteworthy, however, that several changes were in the expected direction,

79

including positive changes in the intervention group on parent attitude toward eating healthfully,
outcome expectancy promoters, fruit and vegetable modeling skills, sugar-sweetened beverage
discouraging skills, and adult diet quality compared to the control group which experience
negative changes in each of these variables between pre- and post-study. The lack of significant
findings is likely due in large part to the limited sample size.

5.1.3. Post-hoc analysis – pooled correlations and path model
In order to investigate the relationships of the dissertation concept model constructs more
comprehensively, I pooled the intervention and control groups and calculated correlation
between all variables measured in both groups (i.e. non-HSM variables) at post-test. This was a
valid procedure because there were no significant differences between groups from pre-test to
post-test. One interesting finding is that none of the attitude measures were significantly
correlated with the respective behavioral measures, i.e. adult diet quality, child diet quality, and
home food accessibility and availability. A number of reviews and meta-analyses have found
many variables that moderate the relationship between attitudes and behavior (Crano & Prislin,
2006; O'Keefe, 2002). By definition, then, presence of an inhibiting moderator or absence of a
promoting moderator may attenuate the relationship between attitude and behavior. Crano and
Prislin (2006) report that attitude-behavior moderators tend to fall into three categories: metaattitudinal (e.g. temporal stability, attitude accessibility, certainty, attitude strength); self-interest
(how closely an attitude is tied to self); and attitude assessment (explicit measurement is more
predictive of attitude-behavior consistency than implicit measures). A recent study on organ
donation found that self-efficacy mediated the relationship between attitude and organ donation
(Anker et al., 2010). This is especially intriguing because parent attitude toward eating healthy is

80

significantly correlated with self-efficacy in Table 4-6 and Figure 4-1, and self-efficacy, in turn,
is correlated with adult diet quality.
In contrast to the HSM, the Social Cognitive Theory explained the data well, which
squares with its frequent use in dietary change interventions (Hingle et al., 2010; Sahay et al.,
2006). Particularly, the sugar-sweetened beverage (SSB) discouraging skill was strong correlated
with several variables, including adult diet quality, nutrient dense availability, and energy dense
availability and accessibility, and SSB discouraging skill was also moderately correlated with
education and child diet quality. I attempted to fit a path model from education  SSB
discouraging  adult diet quality  child diet quality, but the model did not fit the data well.
However, the numerous correlations are encouraging and intriguing, especially in light of the
negative publicity that SSBs have received in last several years in relation to child weight and
diet quality (Hawkes, 2010; Ludwig et al., 2001; Malik et al., 2006; Reedy & Krebs-Smith,
2010; Wang et al., 2008). Several studies have examined the relationship between parent and
child consumption, and/or home availability of SSB, with promising results (Berge et al., 2011;
Bjelland et al., 2011; Ezendam et al., 2010; Hoerr, Lee et al., 2006; Hoerr et al., 2009; Pinard et
al., 2011; Spurrier et al., 2008).
Another striking finding was the correlation between adult and child diet quality. As
previously described, most evidence indicates that children’s diets and parent’s diets are
correlated, whether overall patterns (Beydoun & Wang, 2009; Fisk et al., 2010; Hoerr,
Horodynski et al., 2006; Papas et al., 2009), specific foods (Brown & Ogden, 2004; Fisher et al.,
2002; Horodynski et al., 2010; Reinaerts et al., 2007; Sylvestre et al., 2007), or specific nutrients
(Cullen et al., 2002) are considered. Often, concordance is low to modest, as suggested by the
meta-analysis by Wang and colleagues (Wang et al., 2010), with correlations in the 0.2 to 0.3

81

range, but overall relationship do vary from study to study according to which component is
considered, how the data were modeled, and the statistics that are reported. In this dissertation,
adult and child diet quality described by the Healthy Eating Index were correlated with an r=0.39
(p<0.05). Other than adult diet quality, the only other variables that were directly significantly
correlated with child diet quality were modeling (SSB discouragement modeling skills) and
energy dense food accessibility, suggesting that research focusing on the home food environment
and parental skills related to mealtime behaviors are potentially important ways to positively
influence children’s diet quality.

5.2. Strengths and limitations.
This dissertation research based on a pilot study had a number of strengths. It was a unique
intervention development and evaluation project that combined all of the following aspects: (1)
target population of low-income parents with young children, (2) home-based, (3) use of the
Social Cognitive Theory and Heuristic Systematic Model, (4) objective in-home assessment of
the home eating environment, (5) diet quality of children and parents. This combination of
features adds to the emerging literature on the contribution of home food environment and
modeling to diet quality of preschool children. Perhaps the most enlightening part of the data
analysis and results is that of the post-hoc analysis. In particular, it was quite encouraging that a
well-fitting path model was identified with a sample of 35 parents.
This is one of few interventions, if not the only intervention, with the goal of improving diet
quality or preventing obesity in young children, that has used a dual-media format, i.e. workbook
and DVD clips. This format has the potential to appeal to those who learn best using different
modalities, like tactile versus aural versus visual. Overall, parents responded well to the

82

intervention, both in terms of format and content. In the qualitative evaluation data collected,
94% of the intervention participants agreed or strongly agreed that they would recommend the
program to a friend; 100% liked the activities; and 94% agreed that the program was effective in
changing their families’ mealtimes. When asked in an open-ended question what they learned
that helped them and their families, parents most often mentioned concepts in Chapters 2
(“Different ways to talk to her and encourage her to eat healthy,” “To ignore some behaviors and
use positive encouragement to reinforce desired behaviors”) and Chapter 4 (“I've learned that
getting my child involved in prepering [sic] the food helps her want to eat it and helps her with
trying new foods.”).
One positive aspect of the limited sample size is that we were able to conduct multiple inhome visits with parents in the intervention group to evaluate intervention progress and enhance
compliance. This also allowed us to collect detailed process evaluation measures that will
enhance future iterations of the intervention and allow us to streamline intervention content and
deployment.
This dissertation research did have limitations that must be described. Most importantly, the
sample size was a limiting factor of the data analysis in that there was not enough statistical
power to detect many significant findings. This occurred because the grant project had distinct
aims and hypotheses and a different primary outcome. Thus, the grant project was not
specifically powered to detect differences in HSM variables, home food environment, or child
diet quality. For the grant project, a sample size of approximately 40 participants was determined
to be adequate to detect a 0.4 unit change on a 5-point average Likert scale of the Parent Feeding
Questionnaire. Because the sample size was fixed prior to development of dissertation aims and
hypotheses, the sample size of the dissertation research was accordingly budget-limited. Despite

83

that, the relatively high R2 of the HSM, the non-significant data trends of Aims 2 and 3, and the
significant correlations and path model identified in post-hoc analysis provide indicate that a
larger sample would yield significant results. It is important to bear in mind that this project was
conceived as a small pilot project from its inception and did allow us to obtain baseline and
expected change data to use to power future studies.
There was a fairly high respondent burden with this study. Questionnaires took 30-45
minutes to complete, which may have compromised the integrity of the data. However, there
were research aides present at each data collection visit, and the aides reported that parents made
mention of the large number of questionnaires but overall were quite compliant. In addition to
the length of time the surveys take to complete, the high number of study home visits will not be
realistic in a larger study.

5.3. Recommendations for future studies.
The data gathered for this pilot study will be used to generate a larger-scale R01 grant
application. To prepare for the larger study, intervention materials are currently being revised in
a number of ways: chapters are being packaged as separate manuals so that the material is less
daunting to parents, graphic appeal of materials are being enhanced by a professional graphic
design firm, and parent evaluation interviews are being conducted to further tailor content to
low-income parents. The internet is also being considered as an intervention distribution channel,
the feasibility of which is also being investigated in parent interviews. We also plan to conduct a
survey of the Head Start parent population to ascertain prevalence of internet connection in
homes, internet connection speed, and familiarity of parents with internet so that parents do not
perceive participation to be unnecessarily burdensome. Current data indicates that 21% of

84

Americans do not use the internet and only 66% have high-speed internet access (Smith & Pew
Research Center, 2010). Those least likely to have internet access include those living in
households earning less than $30,000 annually and those with less than a high school education
(Zickuhr et al., 2012). Thus, we will need to make arrangements for those without reliable
internet usage to participate, such as providing hard copies of intervention materials and
conducting surveys via mail or telephone, to ensure that the results of the larger study are as
generalizable to the target population as possible.
Future, larger versions of this study may benefit from including additional information in
analyses. One such variable is parent BMI. In this dissertation 49% of the adults were obese
(BMI ≥30) and 78% were overweight (BMI ≥ 25). In comparison, 36% of US adults are obese
and 69% are overweight (Flegal et al., 2012). Anthropometric data were collected for the grant
project and not used for the dissertation because the primary outcome of the dissertation was diet
quality rather than BMI. However, research suggests that trends in obesity prevalence correspond
to increased eating out of the home, fewer family meals, and increased portion sizes (Lachat et
al., 2012; Nicklas et al., 2001) and that diet quality and BMI (Boynton et al., 2008; Laraia et al.,
2007) and diet quality and abdominal obesity (Wolongevicz et al., 2010) are inversely related.
Thus, it may be important to consider the effect of BMI of parents in future studies, given that
diet quality of the parents may differ according to weight status, which could conceivably affect
the home food environment and cognitive factors affecting eating decisions.
It may also be advantageous to consider other demographic characteristics in future studies,
like the number and ages of children in the household and parent race/ethnicity and income level.
Although parents and their children share the home food environment and thus share moderate
dietary concordance, as discussed previously, each member of the family also experiences

85

individual environments outside of the home that also shape eating behaviors. General parenting
behaviors differ across children in response to child characteristics like sex, birth order, and
abilities (Holden & Miller, 1999; Keller & Zach, 2002). Therefore, it would be interesting to
examine the impact of family composition characteristics on diet quality of parents and children.
For all aspects of this dissertation research - home food environment, diet quality, and HSM – it
would be informative to obtain a more diverse population. For example, recent HSM studies
have primarily been done in largely white, highly educated female populations (Silk et al., 2012;
Smith et al., in press). A larger sample of both men and women and a larger spectrum of income
and education level would yield additional information about the utility of the HSM. In addition,
involvement of a larger group of both mothers and fathers, again of more diverse socioeconomic
strata, would provide more breadth analyses of diet quality and home food environment.
It is important to use the knowledge garnered from the pilot feasibility study to improve data
collection methods and precision of results. Logistically, the pilot study revealed that the
response burden of participants was high, the number of study visits unrealistic in a much larger
study, and there was no data collected to evaluate the effect of timing of low-income food benefit
distribution on data collection, particularly with respect to the home food environment. The
surveys took 30-45 minutes, which may discourage participants from participating regardless of
incentive, and those that do participate may not be able to dedicate undivided attention that
acquisition of high quality data entails. In particular, the survey noted to consume the greatest
amount of time by research aides was the 8-page Adult Block Brief 2000, for which participants
were requested to report usual frequency and serving size of 67 different food items. This may
have been overwhelming and tedious to some participants. Alternatively, researchers could
perform 3-day dietary recalls over the telephone. Dietary recalls are generally considered the

86

gold standard in dietary assessment instruments and may not be as cost-prohibitive as generally
believed (Kristal et al., 2005). Thus, conducting dietary recalls for adult dietary data could both
reduce perceived participant burden and improve accuracy and cost. In addition, the number of
study visits would have to be drastically reduced or eliminated in a larger study to ensure that
data collection is manageable for research staff. Finally, it would be useful to collect time when
food benefits are received each month. If, for instance, families stock up on food items shortly
after receiving food benefits, the food environment may be more (or less, depending on purchase
habits) nutrient-dense. Likewise, families who often use up benefits for some time before receipt
of the next month’s benefits may then experience dwindling of food in the home. This could also
affect quality of the home food environment. It would be important to characterize these
potential differences in the quality of the home food environment with respect to food assistance
benefits in future research.
The sample size of the R01 study can be calculated using data from this dissertation. To
determine the appropriate sample size for a study that detects differences in parent diet quality
from pre-study to post-study in the control group compared to the intervention group, the
following data from this dissertation were used: a change score of 1.1 for the intervention group
(i.e. an increase 1.1 units of the HEI from pre- to post-study in the intervention group), a change
score of -1.1 for the control group, a pooled standard deviation of 8.9, and a desired power of
80%. These specifications yielded a required sample size of 512 persons. The attrition rate in this
study was 17%. Adjusting for losses to follow-up, then, the requested sample size for an R01
would be approximately 615 persons. It would also be desired to retest the path model confirmed
in this dissertation (Figure 4-2). Structural equation modeling, including path analysis, typically
requires large sample sizes of 200 or more participants (Kline, 2011). Kline (2011) indicates that

87

to estimate a sample size to maximize confidence in results, one should strive for 20 participants
for each parameter in a model. The path model shown in Figure 4-2 includes eight parameters,
four direct effect parameters, three variance parameters, and one covariance parameters, which
would therefore best be retested with a sample size of at least 160 participants. Thus, the
previously identified sample size of 615 would be more than adequate for path analysis as well.

5.4. Conclusions
In summary, parent and child diet quality were highly correlated in this dissertation.
Moreover, parent modeling, particularly of SSB, and home food environment were correlated
with parent and child diet quality. Future studies with larger samples will be able to further
explore how modeling and food environment moderate or mediate the relationship between adult
and child diet quality. Further, it would interesting to tease out the relationships between parent
attitudes and behaviors, such as whether self-efficacy indeed mediates or moderates the
relationship between them. Finally, the possibility of future use of a modified version of the
HSM in a larger population is interesting. These applications will certainly be a valuable
contribution to the burgeoning field of parent-centered interventions to improve child diet quality
and prevent child overweight.

88

APPENDICES

89

APPENDIX 1
ABBREVIATED INTERVENTION WORKBOOK

90

91

92

93

94

95

96

APPENDIX 2
IMMS SURVEY

97

Instructional Materials Motivation Survey

John M. Keller
Florida State University
307 Stone Building
Tallahassee, Florida 32306-3030

COPYRIGHTED MATERIAL
Contact author for permission and scoring
information before using.
Copyright ♥ 1993, John M. Keller. All rights reserved.

98

INSTRUCTIONS

1. There are 36 statements in this questionnaire. Please think about each
statement in relation to the instructional materials you have just studied, and
indicate how true it is. Give the answer that truly applies to you, and not what
you would like to be true, or what you think others want to hear.

2. Think about each statement by itself and indicate how true it is. Do not be
influenced by your answers to other statements.

3. Record your responses on the answer sheet that is provided, and follow any
additional instructions that may be provided in regard to the answer sheet that is
being used with this survey. Thank you.

1 (or A) = Not true
2 (or B) = Slightly true
3 (or C) = Moderately true
4 (or D) = Mostly true
5 (or E) = Very true

1. When I first looked at this activity, I had the impression that it would be easy
for me.
99

2. There was something interesting at the beginning of this activity that got my
attention.
3. This material was more difficult to understand than I would like for it to be.
4. After reading the introductory information, I felt confident that I knew what I
was supposed to learn from this activity.
5. Completing the exercises in this activity gave me a satisfying feeling of
accomplishment.
6. It is clear to me how the content of this material is related to things I already
know.
7. Many of the pages had so much information that it was hard to pick out and
remember the important points.
8. These materials are eye-catching.
9. There were stories, pictures, or examples that showed me how this material
could be important to some people.
10. Completing this activity successfully was important to me.
11. The quality of the writing helped to hold my attention.
12. This activity is so abstract that it was hard to keep my attention on it.
13. As I worked on this activity, I was confident that I could learn the content.
14. I enjoyed this activity so much that I would like to know more about this topic.
15. The pages of this activity look dry and unappealing.
16. The content of this material is relevant to my interests.
17. The way the information is arranged on the pages helped keep my attention.

100

18. There are explanations or examples of how people use the knowledge in this
activity.
19. The exercises in this activity were too difficult.
20. This activity has things that stimulated my curiosity.
21. I really enjoyed studying this activity.
22. The amount of repetition in this activity caused me to get bored sometimes.
23. The content and style of writing in this activity convey the impression that its
content is worth knowing.
24. I learned some things that were surprising or unexpected.
25. After working on this activity for awhile, I was confident that I would be able to
pass a test on it.
26. This activity was not relevant to my needs because I already knew most of it.
27. The wording of feedback after the exercises, or of other comments in this
activity, helped me feel rewarded for my effort.
28. The variety of reading passages, exercises, illustrations, etc., helped keep
my attention on the activity.
29. The style of writing is boring.
30. I could relate the content of this activity to things I have seen, done, or
thought about in my own life.
31. There are so many words on each page that it is irritating.
32. It felt good to successfully complete this activity.
33. The content of this activity will be useful to me.

101

34. I could not really understand quite a bit of the material in this activity.
35. The good organization of the content helped me be confident that I would
learn this material.
36. It was a pleasure to work on such a well-designed activity.

Copyright ♥ 1993, John M. Keller. All rights reserved.

102

Manual for the Instructional Materials Motivation Survey (IMMS)
John Keller
Florida State University1
Purpose
The Instructional Materials Motivation Survey is intended to be a situational
measure of students' motivational reactions to instructional materials. It was
designed in accordance with the theoretical foundation represented by the ARCS
Model (Keller, 1987a, 1987b). This theory is derived from the current literature
on human motivation; hence, many of the items in the IMMS are similar in intent
(but not in wording) to items in established measures of psychological constructs
such as need for achievement, locus of control, and self-efficacy, to mention
three examples.
Method
After reviewing the concepts and strategies that comprise the ARCS model and a
variety of instruments used to measure motivational constructs, a pool of items
was prepared.
Results
Reliability estimates based on Cronbach's alpha measure were obtained for each
subscale and the total scale. They were:
Attention: .89 Confidence: .90 Total Scale: .96
Relevance: .81 Satisfaction: .92
In a validational study, differences in two sets of instructional materials with
respect to format, content, and other features affecting motivation were reflected
in the differences in scores on the IMMS.
Note:
Additional information concerning the development of this survey and the results
of the validation study will be included in the next draft of this document.
103

References
Keller, J.M. (1987). Strategies for stimulating the motivation to learn.
Performance & Instruction, 26(8), 1-7.
Keller, J.M. (1987). The systematic process of motivational design.
Performance & Instruction, 26(9), 1-8.

IMMS SCORING GUIDE

Instructions: The response scale ranges from 1 to 5. This means that the minimum
score on the 36 item survey is 36, and the maximum is 180 with a midpoint of 108. The
minimums, maximums, and midpoints for each subscale vary because they do not all
have the same number of items.
An alternate scoring method is to find the average score for each subscale and the total
scale instead of using sums. For each respondent, divide the total score on a given
scale by the number of items in that scale. This converts the totals into a score ranging
from 1 to 5 and makes it easier to compare performance on each of the subscales.
There are no norms for the survey. As it is a situation specific measure, there is no
expectation of a normal distribution of responses. As data become available from a
variety of applications of the scales, descriptive statistical information will be published.

Attention Items

Confidence Items
2 15 (reverse) 24

1 13 35

8 17 28

3 (reverse)

11 20 29 (reverse)

19 (reverse)

12 (reverse) 22
(reverse) 31 (reverse)

4 25
7 (reverse)

104

34 (reverse)
Relevance Items

Satisfaction Items

6 18 33

5 32

9 23

14 36

10 26 (reverse)

21

16 30

27

Scores are determined by summing the responses for each subscale and the total
scale. Please note that the items marked reverse are stated in a negative manner. The
responses have to be reversed before they can be added into the response total. That
is, for these items, 5 = 1, 4 = 2, 3 = 3, 2 = 4, and 1 = 5.

105

APPENDIX 3
IMMS INTERVIEW SCRIPT

106

What did you think about Lesson X? What do you think Lesson X was trying to communicate?

ATTENTION
1. Anything catch your attention in Chapter X? What was engaging?

2. Quality of writing?

3. Parts that were boring?

4. Learn anything that you didn’t know before?

5. Anything to improve materials in Chapter X?

6. Distractions that kept you from the materials?

7. Length of time: too long, too short, or just right?

8. What type of graphics or pictures might help with attention and understanding?

CONFIDENCE/COMPREHENSION
1. Difficulty too easy, too hard, just right?

2. Was there anything confusing or that you had trouble with? How can we clarify?

3. Amount of information: too much, too little, just right?

4. Exercises: too difficult, too easy, just right?

5. Confidence in test on the material?
107

6. How accurate did you perceive the materials/videos to be? Similarity? PMQ? Credibility?

7. Was there anything you disagreed with?

8. Do you see yourself incorporating any of the ideas or suggestions in the materials? If so
what? How easy will this be for you?

9. Are there suggestions that you would like to incorporate but seem too hard or unrealistic?

RELEVANCE/FEASIBILITY
1. How relevant is the content to your life? How useful is the material to your own life?

2. Did you learn anything or did you already know most of the information?

3. If you learned, did this do a good job of building on what you already know?

4. Did you feel that reviewing this material was worth your time?

5. Do you think parents like you will find this information helpful?

6. Do you see yourself incorporating the information into your lives? How?

SATISFACTION
1. How much did you enjoy on a scale of 1 to 5 (highest)?

2. Were there things that you would like to learn more about?

3. Did you feel that the format flowed well?

108

Any suggestions for changes or additions?

Anything you particularly liked/disliked?

How can we make it better?

109

APPENDIX 4
STUDY FLIER

110

111

APPENDIX 5
ABBREVIATED STUDY PROTOCOL

112

113

114

115

116

117

118

119

120

121

122

123

124

REFERENCES

125

126

127

APPENDIX 6
STUDY PROGRESS PHONE CALL/VISIT SCRIPT

128

SCRIPT & NOTES:
Last week, you had chapter 1.1 to 1.3 about: What’s in your Fridge, What are Healthy Foods,
and Having Healthy Snacks Visible & Accessible.

What stands out to you in these sections?

What new things did you learn?What went particularly well? Why?

Did you run into any problems with the material, writing, or video clips? What?

Did you run into any problems with any specific activity?

How long did it take you to complete?

Is there anything this week that interfered with your ability to get the activities done (e.g. lack of
time; environmental distractions; family issues)?

Thank you! (If not already scheduled) Can we schedule the next home appointment?

Other notes:

129

APPENDIX 7
COMPLIANCE AND COMPREHENSION ABSTRACTION FORM

130

ID#______________

Objectives Question Form

1.1 What’s in your fridge?
Questions/Activities
Completed?

WEEKS 1 & 2

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Activity

Y

N

Y

N

Notes about 1.1

1.2 What are healthy foods?
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Question 4

Y

N

Y

N

Activity

Y

N

Y

N

Notes about 1.2

1.3 Have healthy snacks visible and accessible for children
Questions/Activities
Completed?
Understood?
Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Question 4

Y

N

Y

N

Question 5

Y

N

Y

N

Notes about 1.3 Questions

131

1.3 continued
Do you have healthy foods where your child can see and reach them?
Yes
No
Why not?___________________________

Okay, what’s an example of how you’ve made a healthy food accessible?

__________________________________________________________________

1.4 How much does your child need?
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Question 4

Y

N

Y

N

Notes about 1.4

1.4 continued
Does your child need the same amount of food as you?
Yes
No
How much does he/she need? ________________

Okay, why do you think so?

______________________________________________________

132

1.5 Healthy eating helps children develop.
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Activity

Y

N

Y

N

Notes about 1.5

1.6 The Challenges of Getting Healthy Foods
Questions/Activities
Completed?

Understood?

Pre-video questions

Y

N

Y

N

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Activity part 1

Y

N

Y

N

Activity part 2

Y

N

Y

N

Notes about 1.6

133

ID#______________

Objectives Question Form

WEEKS 3 & 4

2.1 Modeling overview
Questions/Activities

Completed?

Question 1

Y

N

Y

N

Activity

Y

N

Y

N

Understood?

Notes about 2.1

2.2 Talking about eating healthy
Do you talk about eating healthy with your child?
Yes
No
Ok, why not? __________________

Ok, please give an example:

2.3 Tasting new foods
Questions/Activities

Completed?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Question 4

Y

N

Y

N

Question 5

Y

N

Y

N

Activity

Y

N

Y

N

Notes about 2.3

134

Understood?

2.3 Continued
Do you believe it is important for your child to taste new foods?
Yes
No
Why not? ______________________

2.3 Tasting new foods (continued)
Ok, please explain why you think it is important ___________________________

2.4 Creating a good mealtime environment
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Activity 1

Y

N

Y

N

Activity 2

Y

N

Y

N

Activity 3

Y

N

Y

N

Notes about 2.4

135

2.5 Challenges and contradictions in modeling
Does your child and family benefit when all caregivers are consistent?
Yes

No

Why not? ___________________________

_____________________________________________________________
Does your child and family suffer when caregivers are inconsistent?
Yes
No
Why not? _______________________

Ok, how?
___________________________________________________________
___________________________________________________________

136

ID#______________

Objectives Question Form

WEEKS 5 & 6

3.1 Types of praise
Questions/Activities

Completed?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Question 4

Y

N

Y

N

Activity 1

Y

N

Y

N

Activity 2

Y

N

Y

N

Understood?

Notes about 3.1

3.2 Encouraging tiny steps
Has your child taken any small steps towards eating healthy or trying something
new?
Yes
No
Okay, what could you do to encourage your
child to take a small step in this direction?
__________________________________
__________________________________

Okay, what have you done to encourage your child to take this step?
__________________________________________________________________

137

3.3 Praise yourself and your partner
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Activity part 1

Y

N

Y

N

Activity part 2

Y

N

Y

N

Notes about 3.3

3.3 continued
Is praising yourself or your partner part of being a good model for your child?
Yes

No

Ok, why not? ____________________________

Ok, how is it part of being a good model?

___________________________________________

138

3.4 Catch them in the act
If your child is behaving well at mealtime is it important to acknowledge the
specific behavior?
Yes
No
Ok, why don’t you think so? ________________

Ok, please give an example
______________________________________________________
Are there some bad behaviors (that aren’t safety issues) you can ignore at
mealtime?
Yes
No
Ok, why can’t you ignore them?______________

Ok, please give an example
_________________________________________________________________
Activity notes:
_________________________________________________________________
3.5 Body language of praise
Does body language make praise more powerful? Like making eye contact,
hugging or smiling at your child.
Yes

No

if not, what effect does body language have?_____

Ok, how does your child benefit when you use body language with praise?
__________________________________________________________________

139

4.1 parent feeding behaviors
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Question 4

Y

N

Y

N

Question 5

Y

N

Y

N

Activity part 1

Y

N

Y

N

Activity part 2

Y

N

Y

N

Activity part 3

Y

N

Y

N

Notes about 4.1

4.2 Can you use food rewards for good behavior?
Is it a good idea to use food as a reward?
Yes
No
Ok, why not? ______________________

Ok, what makes it a good idea?
___________________________________________________

Can you use things other than food as rewards?
Yes
No
Why not? _________________________

Okay, please give an example of what can be used
__________________________________________________________________

140

4.3 Some rules and limits can help
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Activity

Y

N

Y

N

Notes about 4.3

Are rules at mealtime good for preschoolers?
Yes
No
Ok, why not? _____________________

Ok, why do preschoolers like rules?
__________________________________________________________________
4.4 involving the child and making mealtime fun
Questions/Activities
Completed?

Understood?

Question 1

Y

N

Y

N

Question 2

Y

N

Y

N

Question 3

Y

N

Y

N

Activity part 1

Y

N

Y

N

Activity part 2

Y

N

Y

N

Notes about 4.4

141

5.1 Pressure to eat vs. encouragement
Is using pressure the best way to get your child to eat?
Yes
No
Ok, what is the best way and why? ____________

Ok, why do you think pressure is best?
__________________________________________________________________
Does using encouragement at mealtimes work for your child?
Yes
No
what happens when you use encouragement? ____

Ok, why do you think it works better than pressure?
__________________________________________________________________

5.2 How to handle “sometimes” foods
Are there foods that should be for adults only?
Yes
No
Ok, how do you handle sometimes foods? ______

Ok, please give an example of an adult’s only food in your home and why your
child is restricted from eating it.
__________________________________________________________________
5.3 Is punishment necessary?
Is punishing your child at mealtimes necessary?
Yes
No
Ok, why does punishment have a negative effect?
___________________________________________________
Ok, what makes the punishment necessary?
_________________________________________
142

APPENDIX 8
CONTROL GROUP CONTACT LETTER

143

144

APPENDIX 9
INTERVENTION EVALUATIONS

145

146

147

148

APPENDIX 10
INTERVENTION EVALUATION DATA

149

Mean

This was an acceptable program
for our family's mealtimes.
Most parents would find this
program appropriate for
mealtimes.
This program was effective in
changing my family's
mealtimes.
I will suggest this program to
other parents
My family and I needed this
program because our mealtimes
needed to be better.
Most parents would find this
program good for improving
mealtimes.
The program suggested good
ways to handle problems in my
family's mealtimes.
I liked the activities used in the
program.
Overall, the program was
beneficial for my family.

Std

Range

4.4

0.62

3-5

Strongly
disagree
n (%)
0 (0%)

4.7

0.48

4-5

0 (0%)

0 (0%)

0 (0%)

4.2

0.41

4-5

0 (0%)

0 (0%)

1 (6%)

12 (75%)

3 (19%)

4.5

0.63

3-5

0 (0%)

0 (0%)

1 (6%)

6 (38%)

9 (56%)

3.6

0.81

2-5

0 (0%)

1 (6%)

6 (38%)

7 (44%)

2 (13%)

4.5

0.63

3-5

0 (0%)

0 (0%)

1 (6%)

6 (38%)

9 (56%)

4.1

0.89

2-5

0 (0%)

1 (6%)

2 (13%)

7 (44%)

6 (38%)

4.3

0.48

4-5

0 (0%)

0 (0%)

0 (0%)

11 (69%)

5 (31%)

4.5

0.63

3-5

0 (0%)

0 (0%)

1 (6%)

6 (38%)

9 (56%)

150

Disagree

Neutral

Agree

Strongly
Agree
n (%)
7 (44%)

n (%)
0 (0%)

n (%)
1 (6%)

n (%)
8 (50%)

5 (31%) 11 (69%)

How much have the following things changed since you began the program?
n

Home food
environment
Food modeling
Praising
Making mealtimes
fun
Difficult behavior

Mean

Std

Range

13

1.8

0.44

1-2

A lot
positive
n (%)
3 (23%)

A little
positive
n (%)
10 (77%)

13
13
15

1.6
1.8
1.7

0.65
0.83
0.46

1-3
1-3
1-2

6 (46%)
6 (46%)
4 (27%)

6 (46%)
4 (31%)
11 (73%)

1 (8%)
3 (23%)
0 (0%)

0 (0%)
0 (0%)
0 (0%)

0 (0%)
0 (0%)
0 (0%)

15

1.9

0.46

1-3

2 (13%)

12 (80%)

1 (7%)

0 (0%)

0 (0%)

151

No change
n (%)
0 (0%)

A little
negative
n (%)
0 (0%)

A lot
negative
n (%)
0 (0%)

APPENDIX 11
STUDY INSTRUMENTS

152

Outcome-relevant scale
Strongly
Disagree
The amount of healthy food that I eat has little
1
2
impact on my life.
My life would be changed if I ate more healthy
1
2
food.
My quality of life would not change depending
1
2
on the amount of healthy food I eat.
It is easy for me to think of ways that my intake
1
2
of healthy food influences my well-being.
Consuming the recommended amount of healthy 1
2
food affects my daily life.
It is difficult for me to think of ways the amount 1
2
of healthy food impacts my life.
My well-being has little to do with the amount
1
2
of healthy food I eat.
All in all, the effects of eating healthy food on
1
2
my life would be little.

3

4

5

Strongly
Agree
6
7

3

4

5

6

7

3

4

5

6

7

3

4

5

6

7

3

4

5

6

7

3

4

5

6

7

3

4

5

6

7

3

4

5

6

7

Knowledge Test
1. What is the best description of what a healthy food is?
a. A food that is organic and pesticide-free
b. A food that has a lot of vitamins and minerals with little added sugar and
solid fat.**
c. A food that is low-fat and has more than 3 grams of fiber.

2. What is the best choice for a sweet snack?
a. 100% juice
b. An orange**
c. Applesauce

3. What is an appropriate serving size of a food for a preschooler?
a. 1 Tablespoon per year of age**
153

b. Enough to cover the bottom of the plate with a thin layer
c. About 1 cup

4. Which of the following is the best way to keep healthy food visible and
accessible?
a. By stocking the freezer with frozen fruit juice pops
b. By putting healthy food in clear, plastic containers at the front of the fridge
where kids can reach them**
c. By making sure chips are on top of the fridge out of reach

5. What are the recommendations for beverages for preschoolers?
a. Children should have as much 100% fruit juice as they want, because it is
packed with nutrients
b. Children should have whatever they want to drink, because beverages
don’t count as much as food
c. Children should only have ½ cup of fruit juice a day because there is
natural sugar in it that can cause cavities**

6. Which is the best description of snacking by preschoolers?
a. Preschool children need to eat every 2-3 hours and many children have 2
snacks a day**
b. Preschool children should snack whenever they want, even right before
meals, because they are growing
c. Preschool children should never snack so that they are hungry at
mealtime

7. Which of the following is the best description of preschoolers tasting new foods?
a. Preschoolers should have at least four bites of every new food, because
that’s the only way to know for sure if they like it

154

b. If the child just puts the food on his or her tongue it’s okay, because it
takes up to 15 times before they like it**
c. A child should be made a different food, if they don’t want to taste a new
food

8. When your preschooler is about to try a new food that he or she has never tried,
which of the following is the best way to handle the situation?
a. Telling your child that you don’t like a food so they know that you won’t eat
it
b. Letting the child taste a food to decide for him or herself, if they like it
before saying anything**
c. Making the child have at least 4 bites of the food, before giving up on it

9. Should the TV be ON or OFF during mealtimes and why?
a. TV ON, because the TV helps keep the kids quiet
b. TV OFF, because children will not eat too much when distracted by TV**
c. TV OFF, because the child will learn to enjoy the taste of their food more
when there are less distractions.

10. Should two adults in the household discuss food disagreement IN FRONT of
preschool children or IN PRIVATE and why?
a. IN FRONT so children know how to handle conflicts
b. IN PRIVATE so that both adults teach the same thing and do not confuse
the child**
c. IN FRONT so that the child can decide who to agree with

11. What is the best example of modeling healthy eating using words?
a. “These apples are a great sweet snack, because they have lots of
nutrients.”**

155

b. “Broccoli is not bad, when you put cheese on it.”
c. “I hate zucchini, but you might like it.”

12. Does eating healthy in front of your preschool child (modeling) make a difference
and why or why not?
a. NO, they are too young to know what is going on and will figure out their
own tastes when they are older
b. NO, what I eat has no affect on what they eat
c. YES, they learn by watching me and others in the house, even when we
do not know they are watching**

Heuristic Cues: Perceived Similarity
How similar or dissimilar are the people in Clip 1 to you in the following ways?
Very
Similar
Values
1
Personality
1
Hobbies & interests 1
Overall
1
Ethnic background 1
Cultural background 1

2
2
2
2
2
2

3
3
3
3
3
3

4
4
4
4
4
4

5
5
5
5
5
5

6
6
6
6
6
6

7
7
7
7
7
7

Very
Dissimilar
8
9
8
9
8
9
8
9
8
9
8
9

Heuristic Cues: Source Credibility
I felt the source of the materials in Lesson 1 of the workbook was…
Inexpert
1
Unethical
1
Dishonest
1
Untrained
1
Uninformed
1
Unprincipled

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

2

3
3

4
4

5
5

6

156

Expert
7
Ethical
7
Honest
7
Trained
7
Informed
7
Principled

1
2
Incompetent
1
2
Untrustworthy
1
2

6
3

4

5

6

3

4

5

7
Competent
7
Trustworthy
7

6

Heuristic Cues: Perceived Message Quality
Strongly
Disagree
I felt the materials in Lesson 1 were appropriate. 1
2
I felt the materials in Lesson 1 were effective.
1
2
I felt the materials in Lesson 1 were informative. 1
2
I felt the materials in Lesson 1 were credible.
1
2
I felt the materials in Lesson 1 were thorough.
1
2

3
3
3
3
3

4
4
4
4
4

5
5
5
5
5

Strongly
Agree
6
7
6
7
6
7
6
7
6
7

5

Very
Sure I
CANNOT
6
7

Attitudes
For me, eating healthy food is…
For me, eating snack food is…
Having healthy food available in the home is…
My child eating healthy foods is…
Good
1
Positive
1
Beneficial
1
Valuable
1
Calming
1
Acceptable
1

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

Bad
7
Negative
7
Harmful
7
Worthless
7
Distressing
7
Unacceptable
7

Social Cognitive Constructs: Self-Efficacy
How sure are you that you can do these things?

Serve healthy foods to my family when

Very
Sure I
CAN
1
2
157

Unsure
3

4

preparing meals at home?
Serve healthy foods to my family when eating
meals away from home?
Prepare tasty, easy recipes that are healthy?
Serve healthy food to my family when I am low
on money for buying food?
Serve my family healthy foods when I do not
have a lot of time?

1

2

3

4

5

6

7

1
1

2
2

3
3

4
4

5
5

6
6

7
7

1

2

3

4

5

6

7

Social Cognitive Constructs: Outcome Expectancies

Healthy foods can be expensive.
Healthy foods help protect my family from
diseases like cancer and heart disease.
Sometimes it takes too much time to prepare
healthy foods.
Healthy foods can help keep my family from
getting sick with colds and infections.
Buying healthy foods may mean more trips to
the store.
Healthy foods can help my family members
have a healthy weight.

Strongly
Disagree
1
2
1
2

3
3

4
4

5
5

Strongly
Agree
6
7
6
7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

Social Cognitive Constructs: Skills: Parent Feeding Questionnaire
Never Rarely Sometimes
I beg my child to eat dinner.
I spoon-feed my child to get him or her to eat
dinner.
I physically struggle with my child to get him or
her to eat (for example, putting my child in the
chair so he or she will eat).
I warn my child that you will take away
something other than food if he or she doesn’t
eat (for example, “If you don’t finish your meal,
there will be no TV tonight after dinner”).
I promise my child to something other than food
if he or she eats (for example, “If you eat your
beans, we can play ball after dinner”).

158

Most Always
of the
time
4
5
4
5

1
1

2
2

3
3

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

Never Rarely Sometimes
I encourage my child to eat something by using
food as a reward (for example, “If you finish
your vegetables, I’ll get you some ice cream”).
I warn my child that I will take a food away if
the child doesn’t eat (for example, “If you don’t
finish your vegetables, you won’t get dessert”).
I keep fruits and vegetables available that my
child can eat.
I keep sweets, candy or salty snacks where my
child can reach them.
I keep sugar-sweetened beverages* where my
child can reach them. *Drinks like Coke, 7-Up,
Sunny Delight, Hawaiian Punch, or aguas
frescas (DO NOT include 100% fruit juice and
diet soda)
I limit my child’s access to sweets, candy, salty
snacks or sweetened beverages by not having
them readily available.
I allow my child to play and watch TV during
meals.
We eat dinner together as a family.
I allow my child to eat whenever he/she is
hungry during a day.
I allow my child to decide when to eat meals and
snacks.
I allow my child to eat an hour before meals.
I set regular meal times for my child.
I have my child sit down at home while eating.
I allow my child to eat while standing or
walking.
I say something positive about the food my child
is eating during dinner.
I reason with my child to get him or her to eat
(for example, “Milk is good for your health
because it will make you strong”).
I help my child to eat dinner (for example,
cutting the food into smaller pieces).
I compliment the child for eating food (for
example, “What a good boy! You’re eating your
beans”).
I encourage my child to eat by arranging the
food to make it more interesting (for example,
making smiley faces on the pancakes).
159

Most Always
of the
time
4
5

1

2

3

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1
1

2
2

3
3

4
4

5
5

1

2

3

4

5

1
1
1
1

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

Never Rarely Sometimes
I ask my child questions about the food during
dinner.
I eat fruits and vegetables in front of my child.
I drink milk in front of my child.
I eat sweets, candy or salty snacks in front of my
child.
I drink sweetened beverages in front of my
child.
I worried that my child is overweight right now
I am worried that my child will become
overweight.

1

2

3

1
1
1

2
2
2

3
3
3

4
4
4

5
5
5

1

2

3

4

5

1
1

2
2

3
3

4
4

5
5

Home Food Inventory (Completed by research aide)

160

Most Always
of the
time
4
5

161

162

163

164

165

166

167

168

169

Block Brief 2000

170

171

172

173

174

175

176

Block Kids Screener 2007

177

178

Demographic Questionnaire
1. How many adults older than 18 years are living in your home, including you? _________
2. Please complete the table below for each adult older than 18 years living in the home (if there
are more than 3, please let the research staff know).
You (Adult 1)

Adult 2

Adult 3

Relationship
to You

N/A

 Husband or wife
 Other relative
 Roomate
 Unmarried partner
 Other (___________)

 Husband or wife
 Other relative
 Roomate
 Unmarried partner
 Other (___________)

Sex

 Male
 Female

 Male
 Female

 Male
 Female

Age

______

______

______

Date of Birth

____ ____ / ____ ____ /
m m
d
d

____ ____ / ____ ____ /
m m
d
d

____ ____ / ____ ____ /
m m
d
d

____ ____ ____ ____
y
y
y
y

____ ____ ____ ____
y
y
y
y

____ ____ ____ ____
y
y
y
y

Hispanic,
Latino, or
Spanish
origin?

 Yes
 No

 Yes
 No

 Yes
 No

Race (all that
apply)

 White
 Black/African
American
 American Indian
 Asian Indian
 Asian non-Indian
(Chinese, Japanese,
etc.)
 Other
(_____________)

 White
 Black/African
American
 American Indian
 Asian Indian
 Asian non-Indian
(Chinese, Japanese,
etc.)
 Other
(_____________)

 White
 Black/African
American
 American Indian
 Asian Indian
 Asian non-Indian
(Chinese, Japanese,
etc.)
 Other (___________)

Education

 No high school

 No high school

 No high school

179

You (Adult 1)

Adult 2

Adult 3

 Some high school
 High school diploma
or GED
 Some college
 College graduate
 Graduate training
beyond college
Employed?

 Some high school
 High school diploma
or GED
 Some college
 College graduate
 Graduate training
beyond college

 Some high school
 High school diploma
or GED
 Some college
 College graduate
 Graduate training
beyond college

 Yes
 No

 Yes
 No

 Yes
 No

 Full time
 Part time
 Other

 Full time
 Part time
 Other

If employed,  Full time
what type of  Part time
employment?  Other

3. How many children 18 years or younger are living in your home, including the Head Start
Child in this study? __________
4. Please complete the table below for each child 18 years or younger living in the home (if
there are more than 4, please let research staff know).

Head Start Child
(Child 1)
Relationshi  Biological son
p to You
or daughter
 Adopted son or
daughter
 Grandchild
 Other
(___________)

Child 2

Child 3

Child 4

 Biological son or
daughter
 Adopted son or
daughter
 Grandchild
 Other
(___________)

 Biological son or
daughter
 Adopted son or
daughter
 Grandchild
 Other
(___________)

 Biological son or
daughter
 Adopted son or
daughter
 Grandchild
 Other
(___________)

Sex

 Male
 Female

 Male
 Female

 Male
 Female

 Male
 Female

Age

______

______

______

______

Date of
Birth

__ __ / __ __ /
m m d d

__ __ / __ __ /
m m d d

__ __ / __ __ /
m m d d

__ __ / __ __ /
m m d d

__ __ __ __

__ __ __ __

__ __ __ __

__ __ __ __

180

Head Start Child Child 2
(Child 1)
y y y y
y y y
Hispanic,
Latino, or
Spanish
origin?

 Yes
 No

Race (all
 White
that apply)  Black/African
American
 American
Indian
 Asian Indian
 Asian nonIndian
(Chinese,
Japanese, etc.)
 Other
(___________)

Child 3
y

y y y

Child 4
y

y y y

y

 Yes
 No

 Yes
 No

 Yes
 No

 White
 Black/African
American
 American Indian
 Asian Indian
 Asian non-Indian
(Chinese,
Japanese, etc.)
 Other
(___________)

 White
 Black/African
American
 American Indian
 Asian Indian
 Asian non-Indian
(Chinese,
Japanese, etc.)
 Other
(___________)

 White
 Black/African
American
 American Indian
 Asian Indian
 Asian non-Indian
(Chinese,
Japanese, etc.)
 Other
(___________)

5. How often do you go grocery shopping?
 Less than once a month
 Once a month
 2-3 times a month
 Once a week
 2-3 times a week
 Other (
)
6. How long ago did someone in the home last go on a major grocery trip?
 Today
 1 day
 2 days
 3-4 days
 5-6 days
 1-2 weeks
 3-4 weeks
 More than 1 month
 Don’t know
7. How long from now is someone in the home planning on going on the next major grocery
trip?
 Today
 1 day
 2 days
181








3-4 days
5-6 days
1-2 weeks
3-4 weeks
More than 1 month
Don’t know

8. Is anyone in the home on Food Stamps or SNAP?
 Yes
 No
 Don’t know
9. Is anyone in the home receiving nutrition education from the Food Stamp program or SNAPEd?
 Yes
 No
 Don’t know
10. Is anyone in the home in the Supplemental Nutrition Program for Women, Infants, and
Children (WIC)?
 Yes
 No
 Don’t know

182

REFERENCES

183

REFERENCES
Ahuja, J. K. A., Montville, J. B., Omolewa-Tomobi, G., Heendeniya, K. Y., Martin, C. L.,
Steingeldt, L. C., et al. (2012). Food and Nutrient Database for Dietary Studies, 5.0.
Documentation and User Guide. from US Department of Agriculture, Agricultural
Research Service, Food Surveys Research Group: http://www.ars.usda.gov/ba/bhnrc/fsrg
Allen, K. (2011). Managing Prader-Willi syndrome in families: an embodied exploration. Social
Science and Medicine, 72(4), 460-468.
Andrew, M. J., Parr, J. R., & Sullivan, P. B. (2012). Feeding difficulties in children with cerebral
palsy. Arch Dis Child Educ Pract Ed.
Anker, A. E., Feeley, T. H., & Kim, H. (2010). Examining the attitude-behavior relationship in
prosocial donation domains. Journal of Applied Social Psychology, 40(6), 1293-1324.
Ard, J. D., Fitzpatrick, S., Desmond, R. A., Sutton, B. S., Pisu, M., Allison, D. B., et al. (2007).
The impact of cost on the availability of fruits and vegetables in the homes of
schoolchildren in Birmingham, Alabama. American Journal of Public Health, 97(2), 367372.
Ashcroft, J., Semmler, C., Carnell, S., van Jaarsveld, C. H., & Wardle, J. (2008). Continuity and
stability of eating behaviour traits in children. European Journal of Clinical Nutrition,
62(8), 985-990.
Bailey, E. J., Erwin, D. O., & Belin, P. (2000). Using cultural beliefs and patterns to improve
mammography utilization among African-American women: the Witness Project.
Journal of the National Medical Association, 92(3), 136.
Bandura, A. (1986). Social foundations of thought and action : a social cognitive theory.
Englewood Cliffs, N.J.: Prentice-Hall.
Baranowski, T. (1997). Families and health actions. In D. S. Gochman (Ed.), Handbook of health
behavior research (Vol. 1, pp. 173-206). New York: Plenum Press.
Baranowski, T., Cullen, K. W., & Baranowski, J. (1999). Psychosocial correlates of dietary
intake: advancing dietary intervention. Annual Review of Nutrition, 19, 17-40.
Baranowski, T., Cullen, K., Nicklas, T., Thompson, D., & Baranowski, J. (2003). Are current
health behavioral change models helpful in guiding prevention of weight gain efforts?
Obesity Research, 11(10), 23-43.
Baranowski, T., Davis, M., Resnicow, K., Baranowski, J., Doyle, C., Lin, L. S., et al. (2000).
Gimme 5 fruit, juice, and vegetables for fun and health: outcome evaluation. Health
Education and Behavior, 27(1), 96-111.

184

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social
psychological research: conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51(6), 1173-1182.
Berge, J. M., Larson, N., Bauer, K. W., & Neumark-Sztainer, D. (2011). Are Parents of Young
Children Practicing Healthy Nutrition and Physical Activity Behaviors? Pediatrics,
127(5), 881-887.
Beydoun, M. A., & Wang, Y. (2009). Parent-child dietary intake resemblance in the United
States: Evidence from a large representative survey. Social Science and Medicine, 68(12),
2137-2144.
Birch, L. L. (1999). Development of food preferences. Annual Review of Nutrition, 19, 41-62.
Birch, L. L., & Davison, K. K. (2001). Family environmental factors influencing the developing
behavioral controls of food intake and childhood overweight. Pediatric Clinics of North
America, 48(4), 893-907.
Birch, L. L., & Fisher, J. O. (1998). Development of eating behaviors among children and
adolescents. Pediatrics, 101(3 Pt 2), 539-549.
Birch, L., Zimmerman, S., & Hind, H. (1980). The influence of social-affective context on the
formation of children's food preferences. Child Development, 51(3), 856-861.
Bjelland, M., Lien, N., Grydeland, M., Bergh, I. H., Anderssen, S. A., Ommundsen, Y., et al.
(2011). Intakes and perceived home availability of sugar-sweetened beverages, fruit and
vegetables as reported by mothers, fathers and adolescents in the HEIA (HEalth In
Adolescents) study. Public Health Nutrition, 14(12), 2156-2165.
Block, G. (2008). Validating Block Kids Questionnaire. Journal of the American Dietetic
Association, 108(12), 1995-1996; author reply 1996.
Block, G., Hartman, A. M., & Naughton, D. (1990). A reduced dietary questionnaire:
development and validation. Epidemiology, 1(1), 58-64.
Bowman, S. A., Friday, J. E., & Moshfegh, A. (2008). MyPyramid Equivalents Database, 2.0 for
USDA Survey Food, 2003-2004. Retrieved July 23, 2012, from Food Surveys Research
Group. Beltsville Human Nutrition Research Center, Agricultural Research Service, US
Department of Agriculture: http://www.ars.usda.gov/ba/bhnrc/fsrg
Boynton, A., Neuhouser, M. L., Sorensen, B., McTiernan, A., & Ulrich, C. M. (2008). Predictors
of diet quality among overweight and obese postmenopausal women. Journal of the
American Dietetic Association, 108(1), 125-130.
Brown, R., & Ogden, J. (2004). Children's eating attitudes and behaviour: a study of the
modelling and control theories of parental influence. Health Education Research, 19(3),
261-271.

185

Bryant, M., & Stevens, J. (2006). Measurement of food availability in the home. Nutrition
Reviews, 64(2 Pt 1), 67-76.
Byrd-Bredbenner, C., Abbot, J. M., & Cussler, E. (2011). Relationship of social cognitive theory
concepts to mothers' dietary intake and BMI. Maternal & Child Nutrition, 7(3), 241-252.
Casey, B. J., Giedd, J. N., & Thomas, K. M. (2000). Structural and functional brain development
and its relation to cognitive development. Biol Psychol, 54(1-3), 241-257.
Cashdan, E. (1994). A sensitive period for learning about food. Human Nature, 5(3), 279-291.
Centers for Disease Control and Prevention (CDC) (2002). Iron deficiency--United States, 19992000. MMWR; Morbidity and Mortality Weekly Report, 51(40), 897-899.
Chaiken, S. (1980). Heuristic versus systematic information-processing and the use of source
versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5),
752-766.
Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic information
processing within and beyond the persuasions context. In J. S. Uleman & J. A. Bargh
(Eds.), Unintended thought. New York: Guilford Press.
Cho, H., & Boster, F. J. (2005). Development and Validation of Value-, Outcome-, and
Impression-Relevant Involvement Scales. Communication Research, 32(2), 235-264.
Cooke, L. J., Wardle, J., Gibson, E. L., Sapochnik, M., Sheiham, A., & Lawson, M. (2004).
Demographic, familial and trait predictors of fruit and vegetable consumption by preschool children. Public Health Nutrition, 7(2), 295-302.
Crano, W. D., & Prislin, R. (2006). Attitudes and persuasion. Annual Review of Psychology, 57,
345-374.
Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3),
297-334.
Cullen, K. W., Baranowski, T., Owens, E., Marsh, T., Rittenberry, L., & de Moor, C. (2003).
Availability, accessibility, and preferences for fruit, 100% fruit juice, and vegetables
influence children's dietary behavior. Health Education and Behavior, 30(5), 615-626.
Cullen, K. W., Lara, K. M., & de Moor, C. (2002). Familial concordance of dietary fat practices
and intake. Family & Community Health, 25(2), 65-75.
Cullen, K. W., Watson, K., & Zakeri, I. (2008). Relative reliability and validity of the Block
Kids Questionnaire among youth aged 10 to 17 years. Journal of the American Dietetic
Association, 108(5), 862-866.

186

Dave, J. M., Evans, A. E., Pfeiffer, K. A., Watkins, K. W., & Saunders, R. P. (2010). Correlates
of availability and accessibility of fruits and vegetables in homes of low-income Hispanic
families. Health Education Research, 25(1), 97-108.
Davies, J. H., Evans, B. A., & Gregory, J. W. (2005). Bone mass acquisition in healthy children.
Arch Dis Child, 90(4), 373-378.
Dietz, W. H. (1998). Health consequences of obesity in youth: childhood predictors of adult
disease. Pediatrics, 101(3 Pt 2), 518-525.
Drewnowski, A., & Specter, S. E. (2004). Poverty and obesity: the role of energy density and
energy costs. American Journal of Clinical Nutrition, 79(1), 6-16.
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt
Brace Jovanovich College Publishers.
Eagly, A. H., & Chaiken, S. (1995). Attitude strength, attitude structure, and resistance to
change. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: antecedents and
consequences. Mahwah, NJ: Lawrence Erlbaum Associates.
Epstein, L. H., Gordy, C. C., Raynor, H. A., Beddome, M., Kilanowski, C. K., & Paluch, R.
(2001). Increasing fruit and vegetable intake and decreasing fat and sugar intake in
families at risk for childhood obesity. Obesity Research, 9(3), 171-178.
Evans, A. E., Dave, J., Tanner, A., Duhe, S., Condrasky, M., Wilson, D., et al. (2006). Changing
the home nutrition environment: effects of a nutrition and media literacy pilot
intervention. Family and Community Health, 29(1), 43-54.
Ezendam, N. P., Evans, A. E., Stigler, M. H., Brug, J., & Oenema, A. (2010). Cognitive and
home environmental predictors of change in sugar-sweetened beverage consumption
among adolescents. British Journal of Nutrition, 103(5), 768-774.
Fisher, J. O., Mitchell, D. C., Smiciklas-Wright, H., & Birch, L. L. (2002). Parental influences on
young girls' fruit and vegetable, micronutrient, and fat intakes. Journal of the American
Dietetic Association, 102(1), 58-64.
Fisk, C. M., Crozier, S. R., Inskip, H. M., Godfrey, K. M., Cooper, C., & Robinson, S. M.
(2010). Influences on the quality of young children's diets: the importance of maternal
food choices. British Journal of Nutrition, 1-9.
Fitzgibbon, M. L., Stolley, M. R., Schiffer, L., Van Horn, L., KauferChristoffel, K., & Dyer, A.
(2005). Two-year follow-up results for Hip-Hop to Health Jr.: a randomized controlled
trial for overweight prevention in preschool minority children. Journal of Pediatrics,
146(5), 618-625.
Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of obesity and
trends in the distribution of body mass index among US adults, 1999-2010. JAMA,
307(5), 491-497.
187

Ford, E. S., & Mokdad, A. H. (2001). Fruit and vegetable consumption and diabetes mellitus
incidence among U.S. adults. Preventive Medicine, 32(1), 33-39.
Friedenberg, L. (1995). Psychological testing: Design, analysis, and use: Allyn and Bacon
Boston.
Fulkerson, J. A., Nelson, M. C., Lytle, L., Moe, S., Heitzler, C., & Pasch, K. E. (2008). The
validation of a home food inventory. International Journal of Behavioral Nutrition and
Physical Activity, 5, 55.
Fulkerson, J. A., Rydell, S., Kubik, M. Y., Lytle, L., Boutelle, K., Story, M., et al. (2010).
Healthy Home Offerings via the Mealtime Environment (HOME): feasibility,
acceptability, and outcomes of a pilot study. Obesity (Silver Spring), 18 Suppl 1, S69-74.
Fungwe, T., Guenther, P. M., Juan, W. Y., Hiza, H., & Lino, M. (2009). The Quality of
Children’s Diets in 2003-04 as Measured by the Healthy Eating Index-2005. Alexandria,
VA: US Department of Agriculture.
Fussman, C. (2010). Health Risk Behaviors in the State of Michigan: 2009 Behavior Risk Factor
Survey. Lansing, MI: Michigan Department of Community Health, Bureau of
Epidemiology, Division of Genomics, Perinatal Health, and Chronic Disease
Epidemiology, Surveillance and Program Evaluation Section, Chronic Disease
Epidemiology Unit.
Gable, S., & Lutz, S. (2000). Household, Parent, and Child Contributions to Childhood Obesity.
Family Relations, 49(3), 293-300.
Glanz, K., Basil, M., Maibach, E., Goldberg, J., & Snyder, D. (1998). Why Americans eat what
they do: taste, nutrition, cost, convenience, and weight control concerns as influences on
food consumption. Journal of the American Dietetic Association, 98(10), 1118-1126.
Golan, M., & Crow, S. (2004a). Parents are key players in the prevention and treatment of
weight-related problems. Nutrition Reviews, 62(1), 39-50.
Golan, M., & Crow, S. (2004b). Targeting parents exclusively in the treatment of childhood
obesity: long-term results. Obesity Research, 12(2), 357-361.
Gorin, A. A., Raynor, H. A., Niemeier, H. M., & Wing, R. R. (2007). Home grocery delivery
improves the household food environments of behavioral weight loss participants: results
of an 8-week pilot study. International Journal of Behavioral Nutrition and Physical
Activity, 4, 58.
Gorin, A. A., Wing, R. R., Fava, J. L., Jakicic, J. M., Jeffery, R., West, D. S., et al. (2008).
Weight loss treatment influences untreated spouses and the home environment: evidence
of a ripple effect. Int J Obes (Lond), 32(11), 1678-1684.

188

Gross, S. M., Pollock, E. D., & Braun, B. (2010). Family influence: key to fruit and vegetable
consumption among fourth- and fifth-grade students. Journal of Nutrition Education and
Behavior, 42(4), 235-241.
Guenther, P. M., Juan, W. Y., Lino, M., Hiza, H., Fungwe, T., & Lucas, R. (2008). Diet quality
of low-income and higher income Americans in 2003-04 as measured by the Healthy
Eating Index-2005. Alexandria, VA: US Department of Agriculture.
Guenther, P. M., Reedy, J., & Krebs-Smith, S. M. (2008). Development of the Healthy Eating
Index-2005. Journal of the American Dietetic Association, 108(11), 1896-1901.
Guenther, P. M., Reedy, J., Krebs-Smith, S. M., & Reeve, B. B. (2008). Evaluation of the
Healthy Eating Index-2005. Journal of the American Dietetic Association, 108(11), 18541864.
Haire-Joshu, D., & Nanney, M. S. (2002). Prevention of overweight and obesity in children:
influences on the food environment. Diabetes Educator, 28(3), 415-423.
Haire-Joshu, D., Elliott, M. B., Caito, N. M., Hessler, K., Nanney, M. S., Hale, N., et al. (2008).
High 5 for Kids: the impact of a home visiting program on fruit and vegetable intake of
parents and their preschool children. Preventive Medicine, 47(1), 77-82.
Hammons, A. J., & Fiese, B. (2010). Mealtime interactions in families of a child with cystic
fibrosis: a meta-analysis. Journal of Cystic Fibrosis, 9(6), 377-384.
Hannon, P. A., Bowen, D. J., Moinpour, C. M., & McLerran, D. F. (2003). Correlations in
perceived food use between the family food preparer and their spouses and children.
Appetite, 40(1), 77-83.
Hawkes, C. (2010). The Worldwide Battle Against Soft Drinks in Schools. American Journal of
Preventive Medicine, 38(4), 457-461.
Hearn, M., Baranowski, T., Baranowski, J., Doyle, C., Smith, M., Lin, L., et al. (1998).
Environmental influences on dietary behavior among children: Availability and
accessibility of fruits and vegetables enable consumption. Journal of Health Education,
29(1), 26-32.
Hendy, H. (1999). Comparison of five teacher actions to encourage children's new food
acceptance. Annals of Behavioral Medicine, 21(1), 20-26.
Hendy, H. M., & Raudenbush, B. (2000). Effectiveness of teacher modeling to encourage food
acceptance in preschool children. Appetite, 34(1), 61-76.
Hildebrand, D. A., & Betts, N. M. (2009). Assessment of stage of change, decisional balance,
self-efficacy, and use of processes of change of low-income parents for increasing
servings of fruits and vegetables to preschool-aged children. Journal of Nutrition
Education and Behavior, 41(2), 110-119.

189

Hingle, M. D., O'Connor, T. M., Dave, J. M., & Baranowski, T. (2010). Parental involvement in
interventions to improve child dietary intake: a systematic review. Preventive Medicine,
51(2), 103-111.
Hoerr, S. L., Horodynski, M. A., Lee, S. Y., & Henry, M. (2006). Predictors of nutritional
adequacy in mother-toddler dyads from rural families with limited incomes. Journal of
the American Dietetic Association, 106(11), 1766-1773.
Hoerr, S. L., Lee, S. Y., Schiffman, R. F., Horodynski, M. O., & McKelvey, L. (2006). Beverage
consumption of mother-toddler dyads in families with limited incomes. Journal of
Pediatric Nursing, 21(6), 403-411.
Hoerr, S. L., Nicklas, T. A., Franklin, F., & Liu, Y. (2009). Predictors of calcium intake at dinner
meals of ethnically diverse mother-child dyads from families with limited incomes.
Journal of the American Dietetic Association, 109(10), 1744-1750.
Holden, G. W., & Miller, P. C. (1999). Enduring and different: A meta-analysis of the similarity
in parents' child rearing. Psychological Bulletin, 125(2), 223-254.
Horodynski, M. A., Stommel, M., Brophy-Herb, H., Xie, Y., & Weatherspoon, L. (2010). Lowincome African American and non-Hispanic White mothers' self-efficacy, "picky eater"
perception, and toddler fruit and vegetable consumption. Public Health Nursing, 27(5),
408-417.
Hughes, S. O., Power, T. G., Orlet Fisher, J., Mueller, S., & Nicklas, T. A. (2005). Revisiting a
neglected construct: parenting styles in a child-feeding context. Appetite, 44(1), 83-92.
Institute of Medicine (2011). Dietary Reference Intakes for Calcium and Vitamin D.
Washington, D.C.: The National Academies Press.
Jago, R., Baranowski, T., & Baranowski, J. C. (2007). Fruit and vegetable availability: a micro
environmental mediating variable? Public Health Nutrition, 10(7), 681-689.
Johnson, L., Mander, A. P., Jones, L. R., Emmett, P. M., & Jebb, S. A. (2008). Energy-dense,
low-fiber, high-fat dietary pattern is associated with increased fatness in childhood.
American Journal of Clinical Nutrition, 87(4), 846-854.
Johnson, L., van Jaarsveld, C. H., & Wardle, J. (2010). Individual and family environment
correlates differ for consumption of core and non-core foods in children. British Journal
of Nutrition, 1-10.
Joshipura, K. J., Hu, F. B., Manson, J. E., Stampfer, M. J., Rimm, E. B., Speizer, F. E., et al.
(2001). The effect of fruit and vegetable intake on risk for coronary heart disease. Annals
of Internal Medicine, 134(12), 1106-1114.
Kant, A. K. (2003). Reported consumption of low-nutrient-density foods by American children
and adolescents: nutritional and health correlates, NHANES III, 1988 to 1994. Archives
of Pediatrics and Adolescent Medicine, 157(8), 789-796.
190

Keller, H., & Zach, U. (2002). Gender and birth order as determinants of parental behaviour.
International Journal of Behavioral Development, 26(2), 177-184.
Kline, P. (2000). The handbook of psychological testing (2nd ed.). London ; New York:
Routledge.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New
York: Guilford Press.
Kline, T. (2005). Psychological testing : a practical approach to design and evaluation.
Thousand Oaks, Calif.: Sage Publications.
Knol, L. L., Haughton, B., & Fitzhugh, E. C. (2005). Dietary patterns of young, low-income US
children. Journal of the American Dietetic Association, 105(11), 1765-1773.
Kolopaking, R., Bardosono, S., & Fahmida, U. (2011). Maternal Self-efficacy in the Home Food
Environment: AÂ Qualitative Study among Low-income Mothers of Nutritionally At-risk
Children in an Urban Area of Jakarta, Indonesia. Journal of Nutrition Education and
Behavior, 43(3), 180-188.
Kourlaba, G., Kondaki, K., Grammatikaki, E., Roma-Giannikou, E., & Manios, Y. (2009). Diet
quality of preschool children and maternal perceptions/misperceptions: the GENESIS
study. Public Health, 123(11), 738-742.
Kratt, P., Reynolds, K., & Shewchuk, R. (2000). The role of availability as a moderator of family
fruit and vegetable consumption. Health Education and Behavior, 27(4), 471-482.
Krebs-Smith, S. M., Guenther, P. M., Subar, A. F., Kirkpatrick, S. I., & Dodd, K. W. (2010).
Americans do not meet federal dietary recommendations. J Nutr, 140(10), 1832-1838.
Kristal, A. R., Peters, U., & Potter, J. D. (2005). Is It Time to Abandon the Food Frequency
Questionnaire? Cancer Epidemiology Biomarkers & Prevention, 14(12), 2826-2828.
Kroller, K., & Warschburger, P. (2009). Maternal feeding strategies and child's food intake:
considering weight and demographic influences using structural equation modeling.
International Journal of Behavioral Nutrition and Physical Activity, 6, 78.
Krukowski, R. A., Harvey-Berino, J., & West, D. S. (2010). Differences in home food
availability of high- and low-fat foods after a behavioral weight control program are
regional not racial. International Journal of Behavioral Nutrition and Physical Activity,
7, 69.
Lachat, C., Nago, E., Verstraeten, R., Roberfroid, D., Van Camp, J., & Kolsteren, P. (2012).
Eating out of home and its association with dietary intake: a systematic review of the
evidence. Obesity Reviews, 13(4), 329-346.

191

Lakey, B., Ross, L., Butler, C., & Bentley, K. (1996). Making social support judgments: The role
of similarity and conscientiousness. Journal of social and clinical psychology, 15(3),
283-304.
Lapinski, M. K., & Boster, F. J. (2001). Modeling the ego-defensive function of attitudes.
Communication Monographs, 68(3), 314-324.
Laraia, B. A., Bodnar, L. M., & Siega-Riz, A. M. (2007). Pregravid body mass index is
negatively associated with diet quality during pregnancy. Public Health Nutrition, 10(9),
920-926.
Larson, N. I., Story, M. T., & Nelson, M. C. (2009). Neighborhood environments: disparities in
access to healthy foods in the U.S. [Review]. American Journal of Preventive Medicine,
36(1), 74-81.
Ludwig, D. S., Peterson, K. E., & Gortmaker, S. L. (2001). Relation between consumption of
sugar-sweetened drinks and childhood obesity: a prospective, observational analysis. The
Lancet, 357(9255), 505-508.
Lytle, L. A., Seifert, S., Greenstein, J., & McGovern, P. (2000). How do children's eating
patterns and food choices change over time? Results from a cohort study. American
Journal of Health Promotion, 14(4), 222-228.
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of
Psychology, 58, 593-614.
Malik, V. S., Schulze, M. B., & Hu, F. B. (2006). Intake of sugar-sweetened beverages and
weight gain: a systematic review. The American Journal of Clinical Nutrition, 84(2),
274-288.
Mannino, M. L., Lee, Y., Mitchell, D. C., Smiciklas-Wright, H., & Birch, L. L. (2004). The
quality of girls' diets declines and tracks across middle childhood. International Journal
of Behavioral Nutrition and Physical Activity, 1(1), 5.
Marshall, T. A., Eichenberger Gilmore, J. M., Broffitt, B., Stumbo, P. J., & Levy, S. M. (2008).
Relative validity of the Iowa Fluoride Study targeted nutrient semi-quantitative
questionnaire and the block kids' food questionnaire for estimating beverage, calcium,
and vitamin D intakes by children. Journal of the American Dietetic Association, 108(3),
465-472.
McCroskey, J., & Teven, J. (1999). Goodwill: A reexamination of the construct and its
measurement. Communication Monographs, 66(1), 90-103.
McQueen, A., Kreuter, M. W., Kalesan, B., & Alcaraz, K. I. (2011). Understanding narrative
effects: The impact of breast cancer survivor stories on message processing, attitudes, and
beliefs among African American women. Health Psychology, 30(6), 674-682.

192

Mendoza, J. A., Drewnowski, A., Cheadle, A., & Christakis, D. A. (2006). Dietary energy
density is associated with selected predictors of obesity in U.S. Children. Journal of
Nutrition, 136(5), 1318-1322.
Menon, G., Block, L. G., & Ramanathan, S. (2002). We're at as much risk as we are led to
believe: Effects of message cues on judgments of health risk. Journal of Consumer
Research, 28(4), 533-549.
Moyer-Gusé, E. (2008). Toward a Theory of Entertainment Persuasion: Explaining the
Persuasive Effects of Entertainment-Education Messages. Communication Theory, 18(3),
407-425.
Murashima, M., Hoerr, S. L., Hughes, S. O., & Kaplowitz, S. (2011). Confirmatory factor
analysis of a questionnaire measuring control in parental feeding practices in mothers of
Head Start children. Appetite, 56(3), 594-601.
Murashima, M., Hoerr, S. L., Hughes, S. O., & Kaplowitz, S. A. (2012). Feeding behaviors of
low-income mothers: directive control relates to a lower BMI in children, and a
nondirective control relates to a healthier diet in preschoolers. American Journal of
Clinical Nutrition.
Newby, P. K. (2007). Are dietary intakes and eating behaviors related to childhood obesity? A
comprehensive review of the evidence. Journal of Law, Medicine and Ethics, 35(1), 3560.
Nicklas, T. A., Baranowski, T., Baranowski, J. C., Cullen, K., Rittenberry, L., & Olvera, N.
(2001). Family and child-care provider influences on preschool children's fruit, juice, and
vegetable consumption. Nutrition Reviews, 59(7), 224-235.
Nicklas, T. A., Baranowski, T., Cullen, K. W., & Berenson, G. (2001). Eating patterns, dietary
quality and obesity. Journal of the American College of Nutrition, 20(6), 599-608.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGrawHill.
NutritionQuest (2009). Our Research: Questionnaires - Company - NutritionQuest Retrieved
December 30, 2010, from http://www.nutritionquest.com/company/our-researchquestionnaires/
Ogden, C. L., Carroll, M. D., Curtin, L. R., Lamb, M. M., & Flegal, K. M. (2010). Prevalence of
high body mass index in US children and adolescents, 2007-2008. JAMA, 303(3), 242249.
O'Keefe, D. J. (2002). Persuasion : theory & research (2nd ed.). Thousand Oaks, CA: Sage
Publications.

193

Papas, M. A., Hurley, K. M., Quigg, A. M., Oberlander, S. E., & Black, M. M. (2009). Lowincome, African American adolescent mothers and their toddlers exhibit similar dietary
variety patterns. Journal of Nutrition Education and Behavior, 41(2), 87-94.
Paukert, A., Pettit, J., & Amacker, A. (2008). The Role of Interdependence and Perceived
Similarity in Depressed Affect Contagion. Behavior Therapy, 39(3), 277-285.
Pearson, N., Atkin, A. J., Biddle, S. J., & Gorely, T. (2010). A family-based intervention to
increase fruit and vegetable consumption in adolescents: a pilot study. Public Health
Nutrition, 13(6), 876-885.
Petty, R., & Cacioppo, J. (1986). The elaboration likelihood model of persuasion. In L.
Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123-205).
Pinard, C. A., Davy, B. M., & Estabrooks, P. A. (2011). Beverage intake in low-income parentchild dyads. Eating Behaviors, 12(4), 313-316.
Prentice, A., Schoenmakers, I., Laskey, M. A., de Bono, S., Ginty, F., & Goldberg, G. R. (2006).
Nutrition and bone growth and development. Proc Nutr Soc, 65(4), 348-360.
Provost, B., Crowe, T. K., Osbourn, P. L., McClain, C., & Skipper, B. J. (2010). Mealtime
behaviors of preschool children: comparison of children with autism spectrum disorder
and children with typical development. Phys Occup Ther Pediatr, 30(3), 220-233.
Reedy, J., & Krebs-Smith, S. M. (2010). Dietary Sources of Energy, Solid Fats, and Added
Sugars among Children and Adolescents in the United States. Journal of the American
Dietetic Association, 110(10), 1477-1484.
Reinaerts, E., de Nooijer, J., Candel, M., & de Vries, N. (2007). Explaining school children's
fruit and vegetable consumption: the contributions of availability, accessibility, exposure,
parental consumption and habit in addition to psychosocial factors. Appetite, 48(2), 248258.
Reynolds, K., Hinton, A., Shewchuk, R., & Hickey, C. (1999). Social cognitive model of fruit
and vegetable consumption in elementary school children. Journal of Nutrition
Education, 31(1), 23-30.
Rogers, E. M., & Bhowmik, D. K. (1970). Homophily-heterophily: Relational concepts for
communication research. Public Opinion Quarterly, 34(4), 523-538.
Rolls, B. J., Engell, D., & Birch, L. L. (2000). Serving portion size influences 5-year-old but not
3-year-old children's food intakes. Journal of the American Dietetic Association, 100(2),
232-234.
Rosenkranz, R. R., & Dzewaltowski, D. A. (2008). Model of the home food environment
pertaining to childhood obesity. Nutrition Reviews, 66(3), 123-140.

194

Sahay, T. B., Ashbury, F. D., Roberts, M., & Rootman, I. (2006). Effective components for
nutrition interventions: a review and application of the literature. Health Promotion
Practice, 7(4), 418-427.
Sallis, J. F., & Glanz, K. (2009). Physical activity and food environments: solutions to the
obesity epidemic. [Research Support, N.I.H., Extramural Research Support, Non-U.S.
Gov't Review]. The Milbank quarterly, 87(1), 123-154.
Silk, K., Nazione, S., Neuberger, L., Smith, S., & Atkin, C. (2012). Investigating the Impact of
Message Format, Involvement, Scientific Literacy, and Education on Attitude Toward
Reducing Cancer Risk Through Regulation. Journal of Cancer Education, 27(1), 172178.
Sirikulchayanonta, C., Iedsee, K., Shuaytong, P., & Srisorrachatr, S. (2010). Using food
experience, multimedia and role models for promoting fruit and vegetable consumption
in Bangkok kindergarten children. Nutrition & Dietetics, 67(2), 97-101.
Skinner, J. D., Carruth, B. R., Wendy, B., & Ziegler, P. J. (2002). Children's food preferences: a
longitudinal analysis. Journal of the American Dietetic Association, 102(11), 1638-1647.
Skouteris, H., McCabe, M., Swinburn, B., Newgreen, V., Sacher, P., & Chadwick, P. (2010).
Parental influence and obesity prevention in pre-schoolers: a systematic review of
interventions. Obesity Reviews.
Slater, M. D., & Rouner, D. (2002). Entertainment—Education and Elaboration Likelihood:
Understanding the Processing of Narrative Persuasion. Communication Theory, 12(2),
173-191.
Slater, M. D., Buller, D. B., Waters, E., Archibeque, M., & LeBlanc, M. (2003). A test of
conversational and testimonial messages versus didactic presentations of nutrition
information. Journal of Nutrition Education and Behavior, 35(5), 255-259.
Smith, A., & Pew Research Center (2010). Pew Internet & American Life Project. Home
Broadband 2010. Retrieved from http://pewinternet.org/Reports/2010/Home-

Broadband-2010.aspx
Smith, C., & Fila, S. (2006). Comparison of the Kid's Block Food Frequency Questionnaire to
the 24-hour recall in urban Native American youth. Am J Hum Biol, 18(5), 706-709.
Smith, S. W., Hitt, R., Nazione, S., Russell, J., Silk, K., & Atkin, C. K. (In press). The effects of
heuristic cues, motivation, and ability on systematic processing of information about
breast cancer environmental factors. Journal of Health Communication.
Smith, S. W., Kopfman, J. E., Lindsey, L. L., Yoo, J., & Morrison, K. (2004). Encouraging
family discussion on the decision to donate organs: the role of the willingness to
communicate scale. Health Commun, 16(3), 333-346.

195

Spurrier, N. J., Magarey, A. A., Golley, R., Curnow, F., & Sawyer, M. G. (2008). Relationships
between the home environment and physical activity and dietary patterns of preschool
children: a cross-sectional study. International Journal of Behavioral Nutrition and
Physical Activity, 5, 31.
Stark, L. J., Spear, S., Boles, R., Kuhl, E., Ratcliff, M., Scharf, C., et al. (2011). A pilot
randomized controlled trial of a clinic and home-based behavioral intervention to
decrease obesity in preschoolers. Obesity (Silver Spring), 19(1), 134-141.
Sutherland, L. A., Beavers, D. P., Kupper, L. L., Bernhardt, A. M., Heatherton, T., & Dalton, M.
A. (2008). Like parent, like child: child food and beverage choices during role playing.
Archives of Pediatrics and Adolescent Medicine, 162(11), 1063-1069.
Sweitzer, S. J., Briley, M. E., Roberts-Gray, C., Hoelscher, D. M., Harrist, R. B., Staskel, D. M.,
et al. (2010). Lunch is in the bag: increasing fruits, vegetables, and whole grains in sack
lunches of preschool-aged children. Journal of the American Dietetic Association,
110(7), 1058-1064.
Sylvestre, M. P., O'Loughlin, J., Gray-Donald, K., Hanley, J., & Paradis, G. (2007). Association
between fruit and vegetable consumption in mothers and children in low-income, urban
neighborhoods. Health Education and Behavior, 34(5), 723-734.
Tibbs, T., Haire-Joshu, D., Schechtman, K. B., Brownson, R. C., Nanney, M. S., Houston, C., et
al. (2001). The relationship between parental modeling, eating patterns, and dietary
intake among African-American parents. Journal of the American Dietetic Association,
101(5), 535-541.
US Census Bureau (2011). Explore the Form - 2010 Census Retrieved January 5, 2011, from
http://2010.census.gov/2010census/pdf/2010_Questionnaire_Info.pdf
US Department of Agriculture (2010). Healthy Eating Index-2005, from
www.cnpp.usda.gov/HealthyEatingIndex.htm

US Department of Agriculture (2012). ChooseMyPlate.gov, Accessed July 10, 2012, from
http://www.choosemyplate.gov/
US Department of Agriculture, & US Department of Health and Human Services (2010). Dietary
guidelines for Americans, 2010 (7th ed.). Washington, D.C.: US Government Printing
Office.
Vauthier, J. M., Lluch, A., Lecomte, E., Artur, Y., & Herbeth, B. (1996). Family resemblance in
energy and macronutrient intakes: the Stanislas Family Study. International Journal of
Epidemiology, 25(5), 1030-1037.
Ventura, A. K., Gromis, J. C., & Lohse, B. (2010). Feeding Practices and Styles Used by a
Diverse Sample of Low-income Parents of Preschool-age Children. Journal of Nutrition
Education and Behavior, 42(4), 242-249.

196

Wang, Y. C., Bleich, S. N., & Gortmaker, S. L. (2008). Increasing Caloric Contribution From
Sugar-Sweetened Beverages and 100% Fruit Juices Among US Children and
Adolescents, 1988-2004. Pediatrics, 121(6), e1604-e1614.
Wang, Y., Beydoun, M. A., Li, J., Liu, Y., & Moreno, L. A. (2010). Do children and their
parents eat a similar diet? Resemblance in child and parental dietary intake: systematic
review and meta-analysis. Journal of Epidemiology and Community Health.
Wansink, B. (2006). Nutritional gatekeepers and the 72% solution. Journal of the American
Dietetic Association, 106(9), 1324-1327.
Wardle, J., Cooke, L. J., Gibson, E. L., Sapochnik, M., Sheiham, A., & Lawson, M. (2003).
Increasing children's acceptance of vegetables; a randomized trial of parent-led exposure.
Appetite, 40(2), 155-162.
Wardle, J., Guthrie, C. A., Sanderson, S., & Rapoport, L. (2001). Development of the Children's
Eating Behaviour Questionnaire. [Research Support, Non-U.S. Gov't Validation Studies].
Journal of child psychology and psychiatry, and allied disciplines, 42(7), 963-970.
Webster-Stratton, C. (1992). Individually administered videotape parent training:“Who
benefits?”. Cognitive Therapy and Research, 16(1), 31-52.
Webster-Stratton, C., & Reid, M. J. (2003). The incredible years parents, teachers and children
training series: A multifaceted approach for young children with conduct problems. New
York: The Guilford Press.
Webster-Stratton, C., Kolpacoff, M., & Hollinsworth, T. (1988). Self-administered videotape
therapy for families with conduct-problem children: comparison with two cost-effective
treatments and a control group. Journal of Consulting and Clinical Psychology, 56(4),
558-566.
Wiig, K., & Smith, C. (2009). The art of grocery shopping on a food stamp budget: factors
influencing the food choices of low-income women as they try to make ends meet. Public
Health Nutrition, 12(10), 1726-1734.
Wolongevicz, D. M., Zhu, L., Pencina, M. J., Kimokoti, R. W., Newby, P. K., D'Agostino, R. B.,
et al. (2010). An obesity dietary quality index predicts abdominal obesity in women:
potential opportunity for new prevention and treatment paradigms. J Obes, 2010.
Wood, W., Rhodes, N., & Biek, M. (1995). Working knowledge and attitude strength: An
information-processing analysis. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength:
antecedents and consequences. Mahwah, NJ: Lawrence Erlbaum Associates.
World Health Organization (2003). Diet, Nutrition and the Prevention of Chronic Diseases.
Geneva, Switzerland.
Wyse, R. J., Wolfenden, L., Campbell, E., Brennan, L., Campbell, K. J., Fletcher, A., et al.
(2010). A cluster randomised trial of a telephone-based intervention for parents to
197

increase fruit and vegetable consumption in their 3- to 5-year-old children: study
protocol. BMC Public Health, 10, 216.
Young, E. M., Fors, S. W., & Hayes, D. M. (2004). Associations between perceived parent
behaviors and middle school student fruit and vegetable consumption. Journal of
Nutrition Education and Behavior, 36(1), 2-8.
Yu, C. H. (2001). An introduction to computing and interpreting Cronbach Coefficient Alpha in
SAS. Paper presented at the Twenty-Six Annual SAS Users Group International
Conference, Cary, NC.
Zickuhr, K., Smith, A., & Center, P. R. (2012). Pew Internet & American Life Project. Digital
Differences. Retrieved from http://pewinternet.org/Reports/2012/Digital-differences.aspx

198