FROM RESEARCH SETTINGS TO PARENTS: THE ROLE OF PARENT SOCIAL
NETWORKS IN THE CHOICES THEY MAKE ABOUT SERVICES FOR THEIR CHILD
WITH ASD
By
Katherine Pickard

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Psychology-Master of Arts
2014

ABSTRACT
FROM RESEARCH SETTINGS TO PARENTS: THE ROLE OF PARENT SOCIAL NETWORKS IN
THE CHOICES THEY MAKE ABOUT SERVICES FOR THEIR CHILD WITH ASD

By
Katherine Pickard

Literature in the ASD field has repeatedly highlighted the need for a more effective
framework of disseminating evidence-based practices (EBPs) into community settings. Despite
research that has documented the types of services that are being used by parents of children with
ASD, research has yet to determine how intervention-related knowledge spreads to parents. The
current study sought to clarify the process by which interventions are disseminated to parents
through their social networks, and examined the following: 1) What ASD services are primary
caregivers accessing, and from whom are they seeking advice when choosing ASD services?; 2)
Do social network size, social network density, and social network makeup predict the use of
evidence-based or non-evidence-based practices (non-EBPs)?; 3) Do social network size, social
network density, and social network formality predict primary caregiver satisfaction with the
services that they access for their child?; and 4) Who are the referral sources of EBPs and nonEBPs? Results indicated that social network variables predict primary caregivers’ use of EBPs
above and beyond their income, education, and their child’s ASD symptom severity.
Additionally, recommendations to EBPs were significantly more likely to come from professions
such as speech-language pathologists. Family members, friends and other parents of a child with
ASD were more likely to make recommendations to non-EBPs. The results have significant
implications for more effective models of disseminating EBPs within the ASD fie

ACKNOWLEDGMENTS

There are many people whom I would like to thank for the time that they spent shaping
my experience while I was writing this thesis. First, thank you to my supervisor, Brooke
Ingersoll, for being a fabulous mentor and source of advice throughout the past year and a half.
Thank you to Allison Wainer and Natalie Berger for guiding me through my first year and a half
in the Clinical Psychology Ph.D program. I am so grateful for the helpful advice that you both
gave me about classes, lab work, and the PhD program more generally. Finally, and most
importantly, thank you to my parents and younger brother for being a incessant source of love
and support, and for always encouraging my intellectual pursuits.

iii

TABLE OF CONTENTS

LIST OF TABLES..................................................................................................................

v

LIST OF FIGURES................................................................................................................

vi

INTRODUCTION..................................................................................................................
Current Study Aims.......................................................................................................

1
6

METHODS…………………………………………………………………………………..
7
Participants and Procedures.........................................................................................
7
Measures.......................................................................................................................
8
Socio-demographic Information....................................................................................
8
ASD Symptom Severity................................................................................................... 8
Social Networks.............................................................................................................
9
.Autism Services Accessed............................................................................................. 10
Parenting Stress Index-Short Form.............................................................................. 11
RESULTS………………………………………………….……………....…………………..
Sample Characteristics.................................................................................................
Service Use...................................................................................................................
Social Network Characteristics......................................................................................
Predictors of EBP Use...................................................................................................
Predictors of Primary Caregiver Satisfaction................................................................
Service Recommendations...............................................................................................

13
13
13
14
15
17
17

DISCUSSION…………………………………………………………..………….…….….

19

APPENDICES…………………………………………………………………..…….........

26

APPENDIX A: Tables………………………………………………….…..…..…....
APPENDIX B: Figures…………………………………………………..….…...…..

27
34

REFERENCES…………………………………………………...……………..…….…........

39

iv

LIST OF TABLES

Table 1. Survey instrument……………………………………………………...............

28

Table 2. Sample characteristics…………………………………………………….........

29

Table 3. Correlation matrix of survey measures.………………………………………..

30

Table 4. Multiple linear regression: predictors of service use. …………………............

31

Table 5. Predictors of the total number of EBPs heard of in the community…………….

32

Table 6. Predictors of the total number of EBPs available in the community……...........

33

v

LIST OF FIGURES

Figure 1. Primary caregiver’s use of evidence-based, non-evidence based, and supplemental
services …………………………………………………………………….……………...
35
Figure 2. The percent of interventions being used as a primary source of intervention plotted
against the hours per week that it is being used…………………………………………...
36
Figure 3. Makeup of primary caregiver social networks and breakdown of the individuals who
are the referral sources to actual intervention use.………...........................………………
37
Figure 4. The percentage of referrals to evidence-based and non-evidence-based practices
coming from each profession.………………………………………………………………

vi

38

INTRODUCTION
The past two decades have brought about substantial improvement in the evidence-based
practices that are available for children with autism spectrum disorders (ASD; Dawson, 2008;
Richmond, 2011). Evidence-based practices (EBPs) have been explicitly defined as clinical
practices that are informed by evidence about interventions, clinical expertise, as well as patient
needs, values, preferences, and decisions about individual care (Kazdin et al., 2008). Moreover,
studies have indicated that the use of EBPs can result in significant improvements in IQ,
language and educational placement for many children with ASD. This is especially true when
intervention is begun early and is implemented intensively (Corsello, 2005; Dawson, 2008;
Richmond, 2011; Rogers & Vismara, 2008).
Despite the identification of a number of efficacious EBPs for children with ASD,
research efforts have not yet been paralleled in the effective dissemination and implementation
of these EBPs into the community (Dingfelder & Mandell, 2010; Lord, 2005; Smith et al., 2007).
Reports of this research-to-practice gap have been noted across many health fields (Glasgow,
Lichtenstein, & Marcus, 2003; Wandersman et al., 2003; Weisz, 2004; Weisz, 2000). However,
research has noted that this gap is especially pronounced within the ASD field, with a
hypothesized 20 year gap from when interventions are designed to when they are effectively
integrated into the community (Dingfelder & Mandell, 2011; Stahmer et al., 2007). Researchers
across many health fields, including the ASD field, have hypothesized that this void is primarily
due to an ineffective model of intervention development, in which interventions are designed and
efficacy trials are run in highly controlled environments that are not reflective of typical
community practice. Because early stages of intervention development do not weigh community

1

dynamics, many EBPs do not reflect the time, cost and complexity that is necessary for effective
dissemination (Dingfelder & Mandell, 2011; Glasgow et al., 2003; Storch & Crisp, 2004).
The ability to access EBPs in the community is additionally aggravated by the
availability of a plethora of other non-evidence-based practices (non-EBPs) for ASD (Heflin &
Simpson, 1998; Hyman & Levy, 2000; Simpson, 2005). Previous research has indicated that
parents report using an average of 7 treatments at one time for their child with ASD (Green et al.,
2006). Although about half of parents report using at least one EBP for their child, a large
proportion of the services that parents access lack an evidence-base (Simpson, 2005; Thomas et
al., 2007). In fact, 74% of parents report using at least one non-EBP for their child with ASD,
indicating that a scientific foundation is not the only criteria by which parents choose services for
their child (Hanson et al., 2007). It is important to note that the above studies were all conducted
before the best practice guidelines that were recently proposed by the National Standards Project
(NSP; National Autism Center, 2009), and the National Professional Development Center for
Autism Spectrum Disorders (NPDC; Odom et al., 2010), both of which proposed specific
standards for what is considered efficacious and scientifically validated interventions for children
with ASD. In light of this, it is critical to highlight how these guidelines may have shaped EBP
use over the past few years.
Moreover, research has only just begun to determine how parents come to access EBPs.
Recent research has documented that individual recommendations play a crucial role in the
decisions that parents make about ASD interventions for their child (Carlon, Carter, &
Stephenson, 2013). However, research has yet to explicitly determine who recommends parents
to certain interventions and, more generally, how intervention-related knowledge spreads to
parents. This gap in research is striking, especially given that parents are particularly influential

2

in choosing services and advocating for their child’s needs (Etscheidt, 2003; Pierce & Tincani,
2007; Simpson, 2005). In order to facilitate parent access to EBPs, it is first critical to understand
their current method of accessing information about ASD interventions from within their social
networks. Highlighting the current system of intervention uptake through parent social networks,
and the role that certain individuals play in the dissemination of EBPs, will clarify how
intervention techniques reach parents, and will facilitate the creation of more effective models of
disseminating EBPs to parents.
Research has consistently highlighted the impact that social networks (i.e. a group of
individuals and all of the connections between them) have on the transfer of both information
and social capital (Burt, 1973; Burt, 1999). Although social network research has yet to make its
way into the ASD field, research within other health fields has indicated that social networks are
particularly influential in the spread of health-related innovations and behavior (Coleman, Katz,
& Menzel, 1957; Smith & Christakis, 2008; Wejnert, 2002). In other words, individuals impact
the behavior of those they are connected to, and have been shown to influence anything from
adolescent smoking, to obesity, to more positive health related behaviors such as the propensity
to get health screenings or comply with doctor recommendations (Greenhalgh et al., 2004; Smith
& Christakis, 2008).
Additionally, social network theory suggests that social networks influence health-related
behaviors through five main pathways. First, social networks may provide social support to
individuals. Although not all social network ties may be positive, social network ties have the
potential to provide an individual with love, care, assistance, and empathy, thereby promoting
positive health. Second, social networks may impact health behavior via social influence
(Berkman et al., 2000; Berkman & Glass, 2000). This occurs when individuals shape their own

3

attitudes about health care based on health norms that are learned from those they associate with
in their social network (Berkman et al., 2000; Berkman & Glass, 2000). Third, social networks
may influence health behavior through social engagement, in which individuals define their
social roles, and the health behaviors associated with those roles, based on social participation in
larger groups. Fourth, social networks may impact health by person-to-person contagion. This
pathway specifically refers to the spread of disease, which occurs when individuals come into
close proximity with those they are socially connected to.
Finally, and critically, social networks greatly impact an individual’s access to healthrelated resources and material goods. Individuals learn about available health care options from
recommendations and advice that comes from within their social network (Berkman et al., 2000;
Berkman & Glass, 2000). Moreover, it is this final pathway that the current study focuses on.
Social networks greatly impact an individual’s access to health-related resources, and it is this
particular pathway that could play a significant role in the spread of important health information
related to services for ASD.
Not only are social network ties crucial in explaining the transfer of health-related
knowledge and the adoption of EBPs outside of the ASD field (Palinkas et al., 2011), but
research has highlighted that such knowledge and behavior may be dependent on three types of
social network variables. The first of such is social network size, or the number of individuals
that one person is socially connected to. Research outside of the ASD field has repeatedly
demonstrated that individuals with larger networks are exposed to more individuals, and have
more opportunities to acquire novel health-related knowledge (Kohler, Behrman, & Watkins,
2001; Rindfuss et al., 2004).

4

The second social network variable is social network density, or the interconnectedness
of social network ties. Network density refers to whether an individual’s social ties are connected
amongst themselves. There has been some research to suggest that highly dense and
interconnected social networks constrain the acquisition of novel information by limiting the
bridges that individuals may have to outside sources of information (Kohler, Behrman, &
Watkins, 2001). On the other hand, high levels of collaboration and communication across
individuals within a social network increases the likelihood that EBPs will be adopted and
implemented by community mental health directors (Palinkas et al., 2008). Therefore, it may be
that network density will facilitate the adoption of EBPs within the ASD field.
The final social network variable is social network formality, which includes two distinct
types of social network ties. The first of such are informal ties, which refer to emotionally close
individuals such as friends and family. These informal network ties have been shown to
influence health-related behaviors such as smoking and weight gain, as well as knowledge such
as that surrounding contraceptive use (Smith & Christakis, 2008; Kohler, Behrman, & Watkins,
2001; Palinkas et al., 2011). Despite a substantial focus on the impact that informal network ties
have on the transfer of health-related knowledge and behavior, research has documented that the
use of formal social ties (i.e. support that is paid for from professionals), is often used in
conjunction with informal support (Litwin & Attias-Donfut, 2009; Sundstrom, 2006), and is just
as important in influencing the transfer of health-related knowledge and behavior (Berkman &
Glass, 2000; Boyd, 2002; Carlon et al., 2013).
As in other health fields, parents of a child with ASD have their own distinct social
networks that are made up of both formal ties, such as therapists, teachers and other intervention
providers, as well as informal ties such as friends and family (Bailey et al., 1999; Boyd, 2002;

5

Carlon et al., 2013). Although parents make critical decisions about services and treatments for
their child based on advice they receive from both informal and formal network ties, it is likely
that they receive distinct types of information about interventions and services based on which
type of tie the information is coming from (Carlon et al., 2013). Formal network ties are often
members of professional organizations, whose goals are to inform its members of the most
scientifically-based treatments available for particular disorders. Because EBPs for ASD often
have their roots in research settings, membership in formal, professional organizations may
facilitate access to knowledge about EBPs. Thus, communication with formal ties may provide
parents with more scientifically-based recommendations to EBPs that they might not receive
from informal and emotionally close ties.
Current Study Aims
The current study sought to examine four research questions with regards to the social
networks of primary caregivers of a child with ASD: 1) What services are parents are accessing
for their child with ASD and from whom are they seeking advice when choosing ASD services?;
2) Do social network size, social network density, and social network makeup predict the use of
EBPs or non-EBPs?; 3) Do social network size, social network density, and social network
formality predict primary caregiver satisfaction with the services that they access for their child?;
and 4). Who are the referral sources of EBPs and non-EBPs?

6

METHODS
Participants and Procedure
320 primary caregivers began the current study survey. However, 76 were excluded on the
basis of non-completion. Analysis comparing completers to non-completers revealed that
participants who completed the survey had a significantly higher income than those who did not
complete the survey (p<0.05). However, the two groups did not differ in age, education, or child
symptom severity (p=n.s. for all). After excluding non-completers, the study sample consisted of
244 primary caregivers of a child with an ASD diagnosis (i.e. autistic disorder, Asperger’s
Syndrome, pervasive developmental disorder-not otherwise-specified) between the ages of 2 and
17 (M=6.41 years, SD=2.57). Recruitment for the present study occurred in two ways. 73%
(N=177) of the sample was recruited through the Interactive Autism Network (IAN) Research
database. The IAN Research database is an online resource that connects families of children
with ASD to ongoing research studies throughout the United States. Currently, a diverse sample
of over 43,000 families are enrolled in the IAN database. To increase the diversity and
representativeness of the study sample, 27% (N=65) of participants were non-IAN primary
caregivers who were recruited through both ASD resource centers and ASD clinical centers
across the United States. This method of recruitment was consistent with prior intervention
research within the ASD field (Green et al., 2006). Preliminary analyses indicated the two
recruitment samples did not significantly differ in age, geographical location, education level,
household income, marital status, child age, or child symptom severity (p=n.s). As a result, the
two samples were combined in all analyses. Families who qualified for the current study were
sent an email explaining the purpose of the study and containing a link to the online survey.

7

Informed consent approved by the Michigan State University IRB was provided to all
participants.
Measures
The study survey was designed using Qualtrics software. The survey was piloted on
five primary caregivers of a child with ASD to ensure that the survey was appropriately
constructed for its target population before it was sent out through IAN and various ASD centers
across the U.S. An overview of the survey is displayed in Table 1.
Socio-demographic Information: Primary caregivers provided basic demographic
information about both themselves and their child with ASD. This included primary caregiver
age, gender, education level, marital status, annual household income, zip code (county of
residence), as well as the age, gender, diagnosis and age of diagnosis for their child with ASD.
The 2006 NCHS urban-rural classification scheme (Ingram & Franco, 2012) was used to indicate
whether primary caregivers were living in one of four types of metropolitan regions based on
their county of residence: 1) Large central metro, 2) Large fringe metro, 3) Medium metro, 4)
Small metro, or one of two nonmetropolitan regions: 1.) Micropolitan, 2) Noncore. The 2006
NCHS Urban-Rural classification scheme is based on the 2000 United States census and has
been used in many types of research as a method to measure health disparities across the urbanrural continuum.
ASD Symptom Severity: Primary caregivers completed the Autism Behavior Checklist
(ABC; Krug, Arick, & Almond, 1980) as a measure of their child’s ASD symptom severity. The
ABC consists of 57 questions that ask about behaviors that are specific to ASD, as well as other
general behavior problems. Primary caregivers marked “yes” or “no” to indicate whether the
particular behavior applied to their child. Items on the ABC are weighted so that a “yes” to a less

8

severe behavior is scored as a 1, and a “yes” to a more severe behavior is scored as 4. Total
scores were summed with higher total scores indicating more severe ASD symptoms.
Chronbach’s alphas indicated good internal consistency (α=0.94).
Social Networks: Questions in this part of the survey were modeled off of the principle
of “ego” networks where primary caregivers serve as the “egos,” or the center of the network,
and all their connection serve as “alters” (Hanneman & Riddle, 2005; Luke, 2005). Primary
caregivers were asked to list the initials of all the people from whom they had received advice,
information, or support related to autism interventions, services, or care for their child over the
past 6 months. Primary caregivers were asked to indicate the role or profession of each tie, and
had the option to choose from a list 22 possible personal and professional relationships that are
commonly noted in the ASD literature. Primary caregivers also had the option to indicate the
type of tie was something other than those listed. Primary caregivers were asked the length of
time that they had known each of their social network ties, the frequency of their contact with
each tie, and their level of trust in the advice of each tie. Primary caregivers next were provided
with a matrix that contained all the initials of their social network ties across the top and down
the side of a matrix. Primary caregivers used this matrix to indicate whether their social network
ties communicated between themselves.
In order to create descriptive, structural variables of primary caregivers’ support
networks, support network data were converted into matrices and uploaded into Ucinet. Using
the network analysis procedure recommended by Hanneman and Riddle (2005). Network Size
was calculated by totaling the number of social network connections that each primary caregiver
had listed. Network Density was calculated by dividing the total number of within network
connections within each primary caregiver’s network by the total possible number of ties that the

9

primary caregiver’s network could have. This only included connections amongst individuals
that primary caregivers were seeking advice from, and did not include ties from the primary
caregiver. Doing so indicated the percentage of total ties that were present between all the
members of a primary caregiver’s network. Network Makeup was calculated as percent formal
by dividing the total number of formal ties in the primary caregiver’s network by the total
number of ties, both formal and informal, in the primary caregiver’s network. Two independent
coders calculated each of the three social network variables using Ucinet. To calculate reliability
between the two coders, Pearson’s correlations were run to compare the two coder’s social
network variables. Correlations indicated good reliability at r=0.97, p<0.001.
Autism Services Accessed: Primary caregivers were provided with a list of 52 services
that have been identified as common services used by primary caregivers of a child with ASD
(Bitterman et al., 2008; Green et al., 2006; Hess et al., 2008; Hyman & Levy, 2010; Odom et al.,
2010; Simpson, 2005; Thomas et al., 2007). Of these 52 services, 24 were common EBPs, 24
were known non-EBPs, and 6 were supplemental services that are often used to treat the
associated features of ASD (e.g., counseling, physical therapy, and respite care). From this list,
primary caregivers first selected all the services that they had heard of as treatment options for
ASD (Services-Heard) and all of the services that they knew as being available treatment options
for their child within their community (Services-Available). From this same list of 52 services,
primary caregivers next selected all of the services that they had actually accessed for their child
over the past 6 months (Service-Used). Caregivers were also provided with blanks to indicate
any service that they used that was not a part of the provided list. These services were
independently coded by five graduate-level coders as EBP, non-EBP, or supplemental based on

10

knowledge of the ASD intervention literature. Coders then discussed each intervention to make
sure that there was consensus on its overall categorization.
Primary caregivers then indicated the initials of the individual who recommended them to
the service, and whether the recommendation came from one of the individuals within their
social network. Primary caregivers indicated the profession of the recommender, and were also
able to indicate whether the referral came from an alternative source, such as the internet or a
book. Primary caregivers noted the amount of time the service was used per week, as well as
their satisfaction level with the particular service. Primary caregivers then indicated their primary
source of intervention for their child and their overall satisfaction with all the services they were
accessing for their child on a 7-point likert scale.
Based on this information, three variables were calculated to reflect the amount of
services that primary caregivers were accessing for their child. These variables were calculated
separately for both EBPs and non-EBPs. First, amount of service use was calculated as the
number of different services within a category that primary caregivers had accessed in the past 6
months (Use-Number). Amount of service was also calculated as the number of hours per week
of services within a category that caregivers used over the past 6 months (Use-Hours). It is
critical to note that service use was capped so that the maximum amount of intervention that
primary caregivers could be accessing was 40 hours per week. Finally, service use was
calculated as the proportion of a category of service that were used (Use- Proportion) by
dividing the total number of the category of service that primary caregivers used by the total
number of all services they used, both evidence-based and non-evidence-based.
Parenting Stress Index-Short Form (PSI-SF; Abdin, 1995). The PSI-SF is a 36-item selfreport questionnaire that reflects the amount of stress that parents experience in their relationship

11

with their child. Parents indicate the degree to which they agree with each of the questionnaire
statements using a 5-point likert scale, with a 1 indicating that the parent strongly disagrees with
the statement and a 5 indicating that the person strongly agrees with the statement. A high “Total
Score” on the PSI-SF (i.e. a raw score greater than 110) is indicative of significant stress in the
parent-child relationship. Chronbach’s alpha indicated good internal consistency (α =0.91).

12

RESULTS
Sample Characteristics
All participant characteristics, including both primary caregiver and child demographics
are presented in Table 2. Overall, the study sample was mostly consistent with other literature
documenting service access and use within the ASD field. Primary caregivers came from 35
different U.S. states, were 95% female, and were 38.10 years of age on average (Range: 23-64
years, SD: 6.68). Ninety percent of participating caregivers were White, 3.70% were Black,
4.10% were Hispanic, 0.80% were Asian/Pacific Islander, and 1.20% were Biracial/Other,
making the sample slightly less diverse than what is typically seen throughout the ASD
intervention literature (Liptak et al., 2008; Thomas et al., 2007; Mandell et al., 2009; Patten,
2012). Primary caregiver income and education was also on the higher end of intervention
research within the ASD field (Mandell et al., 2009; Patten, 2012; Thomas et al., 2007). An
analysis of primary caregiver responses indicated that 66.5% of their children had an autism
diagnosis, eleven percent had an Asperger’s Syndrome diagnosis, and twenty-one percent had a
PDD-NOS diagnosis. Eighty percent of children were male, and the average age of ASD
diagnosis was 3.19 years (Range: 1-14 years; SD: 1.79). All child characteristics were consistent
with prior research within the ASD field (Fombonne, 2003; Mandell et al., 2009).
Service Use
On average, primary caregivers reported having accessed an average of 6 interventions
(Range: 1-35, SD=0.58) for their child with ASD over the past 6 months. Sixty-three percent of
the services that primary caregivers were using came from recommendations within their
indicated social network. 94.67% of primary caregivers (N=231) were accessing at least one EBP
for their child, 58.20% (N=142) were accessing at least one non-EBP, and 81.97% (N=200) were

13

accessing at least one supplemental service for their child. On average, primary caregivers
reported using an average of 8.07 hours of EBPs and 8.47 hours of non-EBPs per week, and
indicated that they were significantly more satisfied with the ability of EBPs than non-EBPs to
meet their child’s needs, p<0.001.
Primary caregiver service use is depicted in Figure 1. The most common intervention
used was speech-language therapy, with seventy-three percent (N=188) of primary caregivers
indicating that they had accessed the service for their child in the past 6 months. This was
followed by the use of Applied Behavior Analysis (38%, N=98), social skills groups (27%,
N=70), and social narratives/social stories (25%, N=64). Primary caregivers also indicated their
primary intervention strategy for their child with ASD. This is depicted in Figure 2. Applied
Behavioral Analysis was the most commonly used primary intervention strategy (21.3%, N=51),
followed by speech-language therapy (19.2%, N=47) and occupational therapy (8.6%, N=21).
68.2% (N=163) of primary caregivers reported using an EBP as their primary source of
intervention, 10.5% (N=25) reported using a non-EBP as their primary source of intervention,
and 20.8% (N=51) of primary caregivers reported using some other supplemental service as their
primary intervention source. As is depicted in Figure 2, even though primary caregivers use
EBPs as their primary source of intervention, EBP use is often less than 15 hours per week.
Social Network Characteristics
Primary caregivers indicated that they had sought advice and support from an average of
6 individuals over the past 6 months about interventions and services their child with ASD
(Range: 0-20; SD=4.10). A full listing of primary caregiver social network characteristics is
given in Figure 3. Figure 3 also demonstrates that not all of the individuals that primary
caregivers sought advice from were the individuals the referral sources that primary caregivers

14

acted upon. Rather, 37% of the service referrals that primary caregivers acted upon came from
recommendations outside of their indicated social network. Of all of the social network ties that
primary caregivers listed seeking advice from, 32.7% (N=482) came from informal ties while
67.3% (N=990) came from formal ties. Primary caregivers indicated that they sought advice and
support about interventions and services for their child most from family members (consisting of
14.1% of ties), followed by special education teachers (consisting of 11.7% of ties), behavioral
specialists/analysts (consisting of 9.5% of ties), other parents of a child with ASD (consisting of
7.5% of ties), and speech language pathologists and friends (both consisting of 7.0% of ties).
Although primary caregivers noted that they had known informal ties significantly longer than
formal ties (F=5.81, p<0.001), and that they talked to informal ties more frequently than formal
ties (F=1.09, p<0.001), primary caregivers indicated that they trusted both sources of
information about ASD interventions about the same (p=n.s).
Predictors of EBP Use
Separate hierarchical linear regressions were run to examine whether social network
variables were unique predictors of caregivers’ use of both EBPs and non-EBPs. Control
variables were chosen based on preliminary bivariate correlational analyses, and defined as any
parent and child demographic variable that was significantly associated with the outcome
variable. The results from the preliminary correlational analyses are displayed in Table 3. These
variables were entered in the first step of each linear regression model. For all models, these
control variables included primary caregiver income, primary caregiver education, and child
ASD severity (ABC total). Network size, network density and network formality were entered
into the second step. Separate regression analyses were run for both EBPs and non-EBPs, with

15

the number of services (Use-Number), the hours per week of services (Use-Hours), and the
proportion of EBPs (Use-Proportion) as the metric of service use.
Social network size predicted a unique amount of the total number of EBPs that primary
caregivers were accessing for their child (β=0.29, p<0.001), with the final model explaining 15%
of the variance in the total number of EBPs that primary caregivers were accessing. Both social
network size (β =0.17, p<0.05) and social network formality (β =0.14, p<0.05) predicted a
unique amount of the number of hours per week primary caregivers were accessing EBPs for
their child, with the final model explaining eleven percent of the variance in the total hours of
EBPs that primary caregivers were accessing. Social network size also predicted a unique
amount of the total number of non-EBPs that primary caregivers were accessing for their child (β
=0.23, p<0.001), with the final model explaining eleven percent of the variance in the total
number of EBPs that primary caregivers were accessing at one time. Social network variables
did not predict a unique amount of the total hours per week that primary caregivers were
accessing for their child with ASD, p=0.18. Finally, social network variables did not predict a
unique amount of the variance in the proportion of EBPs that primary caregivers were accessing.
All regression models are displayed in Table 4.
As mentioned earlier, EBP use can be distinguished from the amount of EBPs that
primary caregivers have heard about and the amount of EBPs that are available to primary
caregivers in their community. As a result, two identical linear regression models were also run
to predict the total number of EBPs that primary caregivers had heard of, and the total number of
EBPs that were available to primary caregivers in their community. For both models, the control
variables that were entered in the first step were primary caregiver income, primary caregiver
education, child ASD symptom severity and primary caregiver age. Social network size was a

16

unique predictor of the EBPs that parents had heard of (β =0.23, p<0.001) with the final model
explaining twenty percent of the variance in the number of EBPs primary caregivers had heard
of. Similarly, social network size was a unique predictor of the number of EBPs that were
available to primary caregivers in their community (β =0.20, p<0.05), with the final model
explaining fifteen percent of the variation in the number of EBPs available to primary caregivers.
These regression models are displayed in Tables 5 and 6.
Predictors of Primary Caregiver Satisfaction
Next, a hierarchical linear regression was run to determine whether social network
variables were unique predictors of primary caregivers’ satisfaction with the services that they
were accessing for their child. Again, control variables were chosen based on preliminary
bivariate correlational analyses, and defined as any parent and child demographic variable that
was significantly associated with primary caregiver overall satisfaction. Primary caregiver
income, primary caregiver education, primary caregiver total stress (total PSI score), and child
ASD severity (ABC total) were entered into the first step, and network size, network density and
network formality were entered into the second step. The model was not significant, with no
social network variable explaining a significant amount of the variance in primary caregiver
satisfaction over and above the four control variables (R2 =0.01, p=n.s).
Service Recommendations
Next, the referral sources of both evidence-based and non evidence-based practices
were examined. Figure 4 breaks down service recommendations by profession in order to
provide a more nuanced picture of the referral sources to services for ASD. The percent of
recommendations to EBPs and non-EBPs are represented for each profession. As can be seen,
although informal professions provide only 15.7% (N=226) of the recommendations to services

17

for primary caregivers, the majority of non-EBP recommendations came from these individuals.
Sixty-seven percent (N=34) of the recommendations from family members and friends that
primary caregivers acted upon, and sixty-two percent (N=52) of the recommendations from
parents of another child with ASD that primary caregivers acted upon were to non-EBPs. On the
other hand, recommendations that primary caregivers acted upon from formal social ties were
more often recommendations to EBPs. Ninety-two percent of the recommendations (N=106) that
primary caregivers acted upon from speech-language pathologists were to EBPs. Eighty-three
percent (N=145) of the recommendations that primary caregivers acted upon from behavioral
specialists, 78% (N=18) of the recommendations that primary caregivers acted upon from regular
education teachers, and 69% (N=235) of the recommendations that primary caregivers acted
upon from special education teachers were to EBPs.
Finally, a Pearson’s chi-square was run in order to determine whether formal ties were
significantly more likely to be the referral sources of an EBP. The chi-square was significant
[X2(6)=155.87, p<0.001], indicating that the EBPs that primary caregivers were using were more
likely to have been recommended by a professional than family and friends. Moreover, an odds
ratio (likelihood of using an EBP from a formal recommendation/likelihood of using an EBP
from an informal recommendation) indicated that formal ties were 10.07 times more likely to be
the referral sources of an EBP.

18

DISCUSSION
In summary, primary caregivers seek out advice from many sources of information when
searching for information and support about interventions and services for their child with ASD.
This finding is consistent with recent research within the ASD field that has documented the
importance of individual recommendations in primary caregiver intervention choices (Carlon et
al., 2013). Interestingly, the individuals that parents seek advice from are not necessarily the
individuals who provide the recommendations that primary caregivers eventually act upon and
use. The current study also paralleled prior research within the ASD field by documenting that
primary caregivers use a wide variety of services for their child with ASD at one time that often
include both evidence-based and non-evidence-based practices.
Despite mostly paralleled findings with prior research within the ASD field, the current
study was the first to examine the impact of social networks on health care use within the ASD
field specifically. Like other social network research, the results highlight the importance of
social networks above and beyond social support, income, education and child ASD symptom
severity in predicting primary caregiver’s EBP use. Specifically, social network size predicted a
significant amount of the variance in the total number of EBPs that primary caregivers had heard
of in their community, the total number of EBPs available to them in their community, and the
total number and hours per week of EBPs that they accessed for their child with ASD.
Interestingly, the same social network variables that predicted primary caregiver EBP use also
predicted the number of non-EBPs that primary caregivers were using. Therefore, while social
network size played a significant role in the number of interventions that primary caregivers
were accessing, it played less of a role in the quality of services that they were using.

19

This finding is not entirely inconsistent with prior social network research which has
documented the influence of social networks on the acquisition of novel health care knowledge
and behavior (Berkman & Glass, 2000; Coleman et al., 1957; Smith & Christakis). Although
non-EBPs lack a scientific basis, they are novel health care practices. Moreover, research has
demonstrated that recommendations are crucial decision-making factors when primary
caregivers are choosing ASD services (Carlon et al., 2013). It could be, then, that primary
caregivers who are seeking more advice and support from a larger group of individuals are
inherently more motivated to find new services for their child with ASD. It may be an increased
motivation that also increases primary caregiver willingness to go above and beyond typical
recommended best practices in order to try a mixture of EBPs and novel non-EBPs.
Although it was predicted that social network variables would influence primary
caregiver satisfaction with ASD service use, this was not found to be the case. Rather, child
symptom severity and primary caregiver stress levels were the only predictors of primary
caregiver satisfaction. One possible explanation for this is that this particular survey question
was worded so that primary caregivers indicated the extent to which they were satisfied with the
ability of the services to meet their child’s ASD needs. Although social network variables may
be indicators of increased and integrated service use, the amount of advice that primary
caregivers seek out may not necessarily make their services better suited to address their child’s
ASD needs. Inquiring whether primary caregivers were satisfied about their overall knowledge
about interventions and services for their child may have been a more appropriate means to
address the impact of social network variables on primary caregiver satisfaction.
Prior research within the ASD field has demonstrated that primary caregivers frequently
use non-EBPs as intervention strategies for their children with ASD (Heflin & Simpson, 1998;

20

Simpson, 2005). In some regards the current study corroborated past research by demonstrating
that primary caregivers do use a variety of both EBPs and non-EBPs for their child with ASD.
On the other hand, nearly all primary caregivers (94.67%) were accessing at least one EBP for
their child, and 68% were using an EBP as their primary intervention strategy. Additionally,
even though half of primary caregivers were accessing at least one non-EBP for their child with
ASD, it was rare that primary caregivers were using a non-EBP as their primary intervention
strategy. These findings are promising and give a more current portrayal of intervention use
within the ASD field. An increasing number of ASD health guidelines published by the National
Standards Project (NSP; National Autism Center, 2009) and the NPDC (Odom et al., 2010) have
explicitly advocated EBPs as best practice for the treatment of ASD. The results of the current
study may highlight the impact of such websites on ASD service use, and may indicate an
increasing awareness of the importance of EBP use as a result of these guidelines. Additionally,
over the past few years, thirty U.S. states have passed autism insurance reform legislation that
now requires insurance companies to provide coverage of EBPs for children with ASD (Autism
Speaks, 2013). National best practice guidelines in combination with new insurance legislation
may have increased the awareness of EBPs, as well as the accessibility of EBPs to primary
caregivers.
Finally, and most importantly, the current study expanded prior research within the ASD
field by documenting the specific recommenders to both EBPs and non-EBPs. Previous research
had indicated that parents seek out recommendations when making decisions about services for
their child with ASD (Carlon et al., 2013). However, given that primary caregivers use a varied
assortment of EBPs and non-EBPs for their child, clarifying how they came to use specific
services for their child was critical to highlight intervention dissemination patterns. As was

21

hypothesized, professionals were significantly more likely to be the referral sources of EBP use.
Given the increasing notice of EBPs as best practice for ASD within the research literature
(National Autism Center, 2009; Odom et al., 2010), this finding was not necessarily surprising.
Nor was it surprising that recommendations to non-EBPs often came from family, friends, the
internet and other parents of a child with ASD. Even though the controversy surrounding many
non-EBPs may be more widely publicized than in the past, scientific journals and professional
organizations are not necessarily viewed by the general public.
More importantly, as mentioned earlier, it is not necessarily the case that primary
caregivers who are using non-EBPs are unaware of the importance of EBP use. Rather, it appears
that non-EBPs are used to supplement EBP use. Research within the ASD field has continually
noted the ineffective modes of delivering EBPs into the community (Dingfelder & Mandell,
2011), and this was clearly indicated in the current study. Although almost all primary caregivers
were using at least one EBP for their child, primary caregiver EBP use was often less than the
recommended 25 hours per week (Lord et al., 2001). This suggests that primary caregivers may
be aware of the importance of EBPs, but only have access to them in limited amounts. Moreover,
this limited access may increase primary caregivers’ tendency to search for services to add to the
insufficient amount of EBPs that they can access. By searching within their social networks,
primary caregivers stumble across varied suggestions including suggestions to non-EBPs that
primarily come from their more informal social ties.
What this suggests is threefold. First, the current study clearly calls for the need to
disseminate evidence-based research so that it is readily accessible to the public. Even minimal
amounts of non-EBP use reflect large expenses that are often associated with little to no gains in
child ASD symptoms (Heflin & Simpson, 1998). However, the vast majority of the information

22

noting best practice treatment comes from research journals and more formal autism resources. It
is our responsibility as researchers to publicly acknowledge the dissemination issues that our
field faces, and to emphasize to parents, family and friends directly that seeking alternative
solutions is neither cost effective nor beneficial for children with ASD. A more public
understanding of evidence-based and non-evidence-based practices may decrease alternative
intervention seeking behaviors. Second, and more importantly, the current study corroborates the
importance of facilitating access to EBPs in the community. As mentioned earlier, EBPs may not
be sufficiently available to meet primary caregiver needs, prompting them to seek out additional
services for their child. As a result, the current study calls for more efficient and cost-effective
methods to deliver EBPs in larger quantities into the community. Until EBPs are more readily
available, it may be inevitable that primary caregivers seek out non-EBPs for their child.
Finally, the current study explicitly highlighted the importance of parent social networks
in their decision making process about interventions and services. The role of social networks in
primary caregiver service use should be explored in future intervention research. As parents seek
out services, an important component of service delivery should be educating parents about
where to seek best-practices for their child. As primary caregivers are faced with an ASD
diagnosis and a plethora of available service options for their child, being explicit about the best
choices they can make for their child may decrease the inevitable stress of seeking out help from
a complex system.
There are several limitations to consider with the current study. First, because the study
survey was online, the sample was limited to English-speaking caregivers with internet access.
Moreover, the methodology of study recruitment, although in line with prior research within the
ASD field, may have been biased towards more motivated and service-seeking families. As a

23

result, the sample demographics may not be representative of all primary caregivers of a child
with ASD. The types of services that primary caregivers access, and the individuals that they
seek advice from may greatly vary in lower SES families and non-English speaking families.
Research within the ASD field has noted that cultural background greatly impacts how a family
views medical diagnoses, as well as the value that they place in health opinions from family
versus professionals (Mandell & Novak, 2005). Future research should pay closer attention to the
impact that cultural values and beliefs may have on the individuals from whom families seek
ASD health care advice.
Another limitation to consider in the current study is the manner in which services were
classified as being an EBP, a non-EBP, or a supplemental service. Service classification involved
a systematic review of the existing ASD literature to determine how services had previously been
classified. This was done in combination with a review of services by five graduate-level coders,
who each independently classified all services as evidence-based, non evidence-based or as a
supplemental service. Although the systematic process facilitated the classification of services
that were clearly EBPs and non-EBPs, some treatments for ASD, such as occupational therapy,
describe a profession rather than a specific practice. As a result, individual occupational
therapists may be providing an EBP (e.g., parent-implemented intervention), a non-EBP (e.g.,
auditory integration training) or a supplemental service (e.g., dietary supplement) depending on
the individual therapist who is providing the therapy. Moreover, many non-EBPs are not
implemented by a professional at a specific time. Rather, they are implemented and used
throughout the day (e.g., gluten/casein free diet, specialized eye glasses). As a result, the number
of hours per week that non-EBPs were being used by primary caregivers was likely inflated
because of the difficulty in classifying the total time they were used.

24

A final limitation of the current study was that it asked primary caregivers to report on
the service recommendations that they eventually acted upon and used for their child with ASD.
However, this questions does not depict all of the recommendations that primary caregivers hear
about before they act upon them. Additionally, the current study does not portray the perspective
of the individuals who are providing the ASD service recommendations to primary caregivers.
Future research should address this by surveying ASD providers and families to determine
whether the recommendations that providers say that they give reflect the same
recommendations that primary caregivers say that they hear about and use for their child.
In summary, the current study corroborated past research within the ASD field by
demonstrating that parents access a variety of interventions, both evidence-based and nonevidence-based, for their child with ASD. More importantly, the current study emphasized the
critical role that primary caregiver social networks play in their decisions about services for their
child with ASD. As primary caregivers seek out interventions for their child with ASD, they turn
to many individuals for advice and support. Given the significant challenges that the ASD field
faces in disseminating EBPs into the community, the current study has critical implications. Due
to the limited availability of EBPs for ASD, primary caregivers may be more likely to seek out
non-EBPs in an attempt to increase the service options for their child. A more focused effort on
increased public knowledge of best-practices and the places for parents to locate them is crucial.
Rather than leaving primary caregivers to search through the plethora of available interventions
for ASD, future research should assess the added utility of providing primary caregivers with this
explicit knowledge.

25

APPENDICES

26

APPENDIX A

Tables

27

Table 1. Survey instrument
Measure
Primary Caregiver Demographic Information
Child Demographic Questions
Autism Behavior Checklist
Social Network Information
ASD Services Accessed
Parenting Stress Index - Short Form
Medical Outcome Study - Social Support Survey
Center for Epidemiological Studies - Depression
Broad Autism Phenotype Questionnaire

Number of Items
8 items
5 items
57 items
6 items per network tie
10 items
36 items
18 items
20 items
36 questions

28

Table 2. Sample characteristics
Characteristic
Primary Caregiver
Age
Gender

Mean

Range

38.1 years

23-64

Percent

95% Female

Marital Status
Married/Living with Partner
Single, Widdowed, Divorced

83.9
16.1

Ethnicity
White/Caucasian
Black/African American
Hispanic
Asian/pacific Islander
Biracial/Other

90.10%
3.70%
4.10%
0.80%
1.20%

Household Income
< $24,999
$25,000-$49,999
$50,000-$74,999
$75,000-$99,999
>$100,000

13.30%
18.30%
19.20%
18.30%
30.80%

Education
High school or less
Some college/specialized training
4-year college graduate
Graduate degree

7%
33.30%
29.80%
30.20%

Child
Age
Gender

6.41 years

2--17
80% Male

Diagnosis
Asperger’s Syndrome
Autism
PDD-NOS
Other ASD
Age at Diagnosis

11%
66.50%
20.40%
1.20%
3.17 years

29

1--14

Table 3. Correlation matrix of survey measures.
ASD Length of
Child
Income Education
Urbanicity Symptom ASD
Age
Severity Diagnosis

PSI
Total

Total
Overall
Network Network Network Number
Satisfactio
Size Density Formality of EBPs
n
Used

Income

0.50***
Child Age
0.04
Urbanicity
0.08
Education

0.14*
0.07

ASD
Symptom
-0.32
-0.24
Severity
Length of
ASD
0.01
0.12
Diagnosis
PSI Total
-0.02
-0.04
Overall
0.21**
0.11
Satisfaction
Network
-0.06
0.01
Size
Network
-0.07
-0.09
Density
Network
0.02
0.06
Formality
Total
Number of 0.23*** 0.19**
EBPs Used
Total
Number of
0.12
0.13*
Non-EBPs
Used
***p<0.001 **p<.01, *p<.05

0.03
-0.12

-0.05

0.73**
*

0.04

-0.12

-0.01

0.02

0.30***

-0.09

-0.01

-0.01

-0.25***

0.04

-0.34***

-0.01

0.04

0.04

-0.01

-0.04

0.02

-0.04

0.02

0.07

-0.06

0.01

0.09

-0.05

0.21**

-0.05

0.04

-0.11

-0.06

0.05

-0.20**

0.11

-0.08

-0.03

0.04

-0.01

-0.03

0.12

0.28***

0.01

0.04

0.03

0.01

-0.01

0.03

0.01

0.05

0.23*** -0.06

-0.06

30

0.46***

Table 4. Multiple linear regression: predictors of service use.
Total Number of EBPs Used
Predictor

β

t

Step 1

R2

F Change

0.07

6.28***

Total Hours Per week of EBPs Used
β

t

R2

F Change

0.07

5.75**

Total Number of Non-EBPs Used
β

t

Income

0.21

2.73**

0.24

3.08**

0.09

1.16

Education

0.13

1.73

0.05

0.65

0.09

1.14

ASD Symptom Severity

0.12

1.74

0.18

2.70**

0.03

0.50

Step 2

0.16

7.57*

0.11

3.36*

Income

0.23

3.15**

0.26

3.36**

0.07

1.41

Education

0.11

1.51

0.03

0.34

0.11

1.00

ASD Symptom Severity

0.11

1.63

0.17

2.56*

0.03

0.50

Social Network Size

0.29

4.70***

0.16

2.53*

0.23

3.58***

Social Network Density

0.03

0.45

0.05

0.75

-0.05

-0.74

Social Network Formality

0.10

1.60

0.14

2.17*

-0.04

-0.65

***p<0.001 **p<.01, *p<.05

31

R2

F Change

0.02

1.67

0.08

5.27**

Table 5. Multiple linear regression: predictors of the total number of EBPs heard of in
community.
Predictor
β
t
R2
F Change
Step 1
0.14
9.31***
Income
0.14
1.88
Education
0.12
1.67
ASD Symptom Severity
-0.09
-1.41
Child Age
0.23
3.65***
Step 2
0.20
5.22**
Income
0.16
2.19*
Education
0.11
1.55
ASD Symptom Severity
-0.10
-1.59
Child Age
0.24
3.74***
Social Network Size
0.23
3.82***
Social Network Formality
0.01
0.14
Social Network Density
0.06
1.03

***p<0.001 **p<.01, *p<.05

32

Table 6. Multiple linear regression: predictors of the total number of EBPs available in the
community.
Predictor
β
t
R2
F Change
Step 1
0.11
8.29***
Income
0.22
2.90**
Education
0.11
1.52
ASD Symptom Severity
-0.07
-0/09
Child Age
0.14
2.14*
Step 2
0.15
3.74*
Income
-0.07
3.16**
Education
0.10
1.40
ASD Symptom Severity
-0.10
-1.13
Child Age
0.14
2.15*
Social Network Size
0.20
3.25**
Social Network Formality
-0.01
-0.04
Social Network Density
0.04
0.58

***p<0.001 **p<.01, *p<.05

33

APPENDIX B

Figures

34

Figure 1. Primary caregiver’s use of evidence-based, non-evidence based, and supplemental
services.

35

Figure 2. The percent of interventions being used as a primary source of intervention plotted
against the hours per week that each intervention is used.

36

Figure 3. Makeup of primary caregiver social networks and breakdown of the individuals who
are the referral sources to actual intervention use.

37

Figure 4. The percentage of referrals to evidence-based and non-evidence-based practices
coming from each profession.

38

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