UNDERSTANDING DEVELOPMENTAL DIFFERENCES IN ADHD:
EXPLORING PATTERNS OF SYMPTOMS, IMPAIRMENT, RISK, AND
COMPENSATORY SKILLS BASED ON AGE OF INITIAL DIAGNOSIS
by
LAURA E. HLAVATY
Submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Department of Psychological Sciences
CASE WESTERN RESERVE UNIVERSITY
January 2020
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of
LAURA E. HLAVATY candidate for the degree of DOCTOR OF PHILOSOPHY *.
Committee Chair
ELIZABETH J. SHORT, PH.D.
Committee Member
SANDRA RUSS, PH.D.
Committee Member
MICHAEL J. MANOS, PH.D.
Committee Member
CHRISTOPHER BURANT, PH.D.
Date of Defense
APRIL 30, 2019
*We also certify that written approval has been obtained
for any proprietary material contained therein.
I dedicate this thesis to my loving husband and family. Without your patience, assistance, and encouragement, this pursuit would have never been possible. Table of Contents List of Tables ...... vi List of Figures ...... vii Acknowledgements ...... viii Abstract ...... 1 Introduction ...... 3 ADHD Symptom Continuity Across Development ...... 9 Persistence Over Time ...... 10 Early versus Late Symptom Onset ...... 13 Factors that influence ADHD diagnosis...... 15 Temperament ...... 15 IQ ...... 18 Behavioral Problems and Assets ...... 21 Current Study ...... 24 Questions and Hypotheses ...... 26 Method ...... 33 Participants ...... 33 Measures...... 35 Procedure ...... 39 Results ...... 49 Descriptives ...... 49 Results of Exploratory and Confirmatory Factor Analyses ...... 52 CFQL ...... 52 Behavioral Assets ...... 62 Structural Models ...... 73 Model 1: ADHD symptoms predicting age of ADHD diagnosis ...... 76 Model 2: Impairment predicting age of ADHD diagnosis ...... 81 Model 3: IQ, Temperament, and Behavioral Assets predicting age of ADHD diagnosis ...... 90 Model 4: Testing Risk/Compensatory variables as confounders on the relationship between impairment and age of ADHD diagnosis variables ...... 97 Testing confounder effects ...... 104 Final Model Description ...... 107 Discussion ...... 116 Results of Research Questions ...... 116 Implications ...... 124 Limitations ...... 130 Appendix ...... 132 References ...... 143
List of Tables
Table 1. Participant Demographics and Diagnostic Subtype ...... 50 Table 2. Participant Demographics and Diagnostic Subtype based on Age Grouping .... 51 Table 3. CFQL EFA 5 Factor Solution ...... 54 Table 4. CFQL Factor Correlation Matrix ...... 55 Table 5. Model Development of CFQL 5 Factor Model ...... 57 Table 6. SEM Parameter Estimates for CFQL 5-Factor Model...... 60 Table 7. Items from Behavioral Assets that were eliminated during EFA ...... 62 Table 8. Behavioral Assets EFA 6 Factor Solution ...... 64 Table 9. Factor Correlation Matrix for the Behavioral Assets Questionnaire ...... 65 Table 10. Model Development of Behavioral Assets Questionnaire 6-Factor Model ...... 68 Table 11. SEM Parameter Estimates for Behavioral Assets 6-Factor Model ...... 70 Table 12. Description of Impairment, Risk/Compensatory, and Outcome Variables ...... 74 Table 13. Description of Impairment, Risk and Compensatory factors based on Age Groupings ...... 75 Table 14. SEM Parameter Estimates for ARS Symptoms on Age ...... 78 Table 15. SEM Parameter Estimates for Impairment predicting Age of ADHD diagnosis ...... 84 Table 16. SEM Parameter Estimates for Risk/Compensatory factors impact on Age of ADHD Diagnosis ...... 93 Table 17. Regression Paths from Impairment to Age of Diagnosis tested for confounding effect of Risk/Compensatory Factors ...... 106 Table 18. SEM Parameter Estimates for SEM Model of Relationship between Risk/Compensatory Factors, Impairment, & Age of ADHD Diagnosis ...... 109 Appendix Table 1-A. Descriptives on Parent Education and Occupation ...... 132 Table 2-A. CFQL Item Correlations ...... 134 Table 3-A. Behavioral Assets Item Correlations ...... 137 Table 4-A. Model Development of Impairment as a predictor of Age of ADHD Diagnosis...... 139 Table 5-A. Model Development for Risk/Compensatory variables as predictors of Age of ADHD Diagnosis ...... 140 Table 6-A. Model Development of Risk/Compensatory variables impact on Impairment to Age of ADHD Diagnosis ...... 141
List of Figures
Figure 1. Hypothesized Model of ADHD Symptoms Impacting Age of ADHD Diagnosis...... 26 Figure 2. Hypothesized Model of Impairment Impacting Age of ADHD Diagnosis ...... 27 Figure 3. Hypothesized Model of IQ impacting Age of ADHD Diagnosis, and IQ as a Confounder on the Relationship between Impairment and Age of ADHD Diagnosis ..... 29 Figure 4. Hypothesized Model of Temperament impacting Age of ADHD Diagnosis, and Temperament as a Confounder of the Relationship between Impairment and Age of ADHD Diagnosis ...... 31 Figure 5. Hypothesized Model of Behavioral Assets impacting Age of ADHD Diagnosis, and Behavioral Assets as a Confounder on the Relationship between Impairment and Age of ADHD Diagnosis...... 32 Figure 6. Flow chart of Participants' Inclusion and Exclusion ...... 34 Figure 7. Proposed Conceptual Model of IQ, Temperament, and Behavioral Assets as potential Confounding Factors on Impairment effects on Age of ADHD Diagnosis, and ADHD Symptoms impacting Age of diagnosis ...... 48 Figure 8. Initial Standardized CFA Model of CFQL ...... 56 Figure 9. Standardized Results of CFQL 5 Factor Solution ...... 59 Figure 10. Proposed Model of Behavioral Assets 6 Factor Solution ...... 67 Figure 11. Standardized Results of Behavioral Assets 6 Factor Solution ...... 69 Figure 12. Initial Hypothesized Model of ADHD Symptoms Impacting Age of ADHD Diagnosis ...... 76 Figure 13. Standardized Results of ARS Symptoms predicting Age of ADHD Diagnosis...... 77 Figure 14. Initial Hypothesized Model of Impairment Impacting Age of ADHD Diagnosis...... 82 Figure 15. Standardized Results of SEM Model of Impairment Variables Predicting Age of ADHD Diagnosis ...... 83 Figure 16. Initial Hypothesized Model of Risk and Compensatory Factors Impacting Age of ADHD Diagnosis ...... 91 Figure 17. Standardized Results of SEM Model of Behavioral Assets, IQ, and Temperament Predicting Age of ADHD Diagnosis ...... 92 Figure 18. Standardized Results of SEM Model of Relationship between Risk/Compensatory Factors, Impairment, and Age of ADHD Diagnosis prior to testing for Confounder effects ...... 98 Figure 19. Standardized Results of SEM Model of Relationship between Risk/Compensatory Factors, Impairment, and Age of ADHD Diagnosis ...... 108
Acknowledgements
I am indebted to my exceptional advisor, Elizabeth Short, for her unwavering patience, support, and encouragement throughout the course of this project. I greatly appreciate the time and assistance offered by my committee members, Michael Manos, Chris Burant, and Sandy Russ. I also owe great thanks to the staff at ACET, particularly Ralph
D’Alessio and Rana Ulman, who offered significant time and data entry support that helped make this project possible. In addition, I need to acknowledge the financial support I received from the Schubert Center for Child Studies and Department of
Psychological Sciences in the form of the Freedheim Graduate Student Fellowship, which allowed me to dedicate time and energy into my dissertation project that would have otherwise not been possible. I would like to also thank my UNC colleagues for their general support and commiseration, as they have made this process more bearable during a productive internship year. Finally, thank you to the children and their families who participated in this research and helped make this study possible. 1
Understanding Developmental Differences in ADHD: Exploring Patterns of
Symptoms, Impairment, Risk, and Compensatory Skills Based on Age of Initial
Diagnosis
Abstract by
LAURA E. HLAVATY
Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic disorder associated with functional impairments in childhood, adolescence, and adulthood. Although externalizing behavior problems are the most common reason children are referred for
ADHD diagnosis, disparities exist in when parents seek treatment. Differences between children who are referred for psychological evaluations in early childhood versus adolescence are not well understood. The purpose of this study is to explore the differences between children and adolescents that are referred for ADHD diagnosis and determine whether protective factors emerge for individuals referred at later ages.
Participants included 1,331 youth referred for psychological evaluation at the ADHD
Center for Evaluation and Treatment at Cleveland Clinic Children’s Hospital. Prior to evaluation, parents completed questionnaires about the child’s history (medical history, developmental history, family history), temperament, behavior problems and assets, and previous mental health services. In addition, parents and teachers completed rating scales on ADHD symptoms, stress, and impairment. Also in-person diagnostic assessment and intellectual assessment was completed. Structural equation modeling techniques were
2 used to explore predictors of Age of ADHD diagnosis, including number of ADHD symptoms, parent and teacher ratings of impairment, and compensatory factors related to
IQ, temperament, and behavioral assets. Severe behavioral problems at school, higher numbers of referrals concerns, and lower family and caregiver quality of life contributed to younger age of ADHD diagnosis. Older age of diagnosis was predicted by parents who rated low support in their relationships with their spouse. Examination of developmental assets revealed that easy temperament, better social functioning, and greater compliance at school predicted older age of ADHD diagnosis, whereas younger age of diagnosis predicted more positive attitudes toward school and greater emotional awareness. Results add to our understanding of developmental processes underlying age of diagnosis and factors that may delay diagnosis. Additionally, ADHD symptom presentation and severity of behavioral symptoms affected family quality of life in young children. In contrast, older children may benefit from focused treatments aimed at addressing motivation toward achievement and emotion regulation. Overall, these results lend to the development of psychoeducation and intervention strategies designed to promote better outcomes for individuals with ADHD.
3 Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurobehavioral disorders diagnosed worldwide and among the most highly researched
(Furman, 2005; Kessler et al., 2006; Polanczyk, de Lima, Horta, Biederman, & Rohde,
2007). Despite the wealth of research on ADHD using clinical and community-based samples, there is still much work to do in understanding changes over the course of development. Historically, the stereotypical ADHD patient has been depicted as a hyperactive school age boy that will grow out of their fidgetiness in later childhood or adolescence. While there are some cases of ADHD that align with this depiction, more recent research has expanded our understanding of ADHD as a lifespan disorder that affects both males and females and is heterogeneous in symptom presentation over the course of development (Turgay et al., 2012; Wilens & Dodson, 2004). It is now widely acknowledged that the behavioral expression of ADHD symptoms changes over the course of development. Symptoms of impulsivity in preschool may present as impatience, temper tantrums, and difficulty sitting still, whereas in adolescence, these symptoms may look more like unease/restlessness or engaging in risky behaviors (i.e., speeding, unsafe sexual practices) (Campbell, Halperin, & Sonuga-Barke, 2014; Short,
Fairchild, Findling, & Manos, 2007; Wilens & Dodson, 2004). Although these symptoms take different shape over the course of development, the diagnostic descriptors used are based on behaviors primarily seen in childhood and do not adequately capture the behavioral nuances of older patients. Thus, the nosology and diagnostic construct of
ADHD continues to be conceptualized within a medical model that emphasizes stable, core deficits and common etiologies, which do not take into account potential differences that occur as the result of developmental changes.
4 One way to improve our understanding of ADHD would be to reconceptualize it using a developmental psychopathology approach. In contrast to a medical model of
ADHD, which emphasizes a static taxonomy to be applied to all age ranges, the developmental psychopathology perspective emphasizes a life-span approach and suggests that psychopathology may present differently over the course of development
(Cicchetti, 2015). The developmental psychopathology approach is advantageous because it takes into account adaptive and maladaptive processes that may influence symptom presentation. This approach focuses on identifying not only risk factors, but also protective factors that may have helped compensate for symptom interference up until the point of diagnosis.
My study will highlight what we know about ADHD within a developmental context. Most previous studies have largely ignored potential developmental differences in ADHD symptomatology throughout the preschool, school age, and adolescent periods.
This generally occurred because small sample size negated the possibility of examining developmental differences and instead the ADHD sample spanned a wide age range (i.e.,
6 to 12, 13 to 17, or sometimes 6 to 17). Not only were age differences not examined in most studies of ADHD, but studies tended to highlight negative outcomes and/or predictive factors associated with ADHD while overlooking strengths or assets that could potentially allow youth to overcome their developmental problems. While it has been suggested that ADHD derives from multiple causal factors, there is a lack of understanding on how risk and protective factors interact to shape outcomes in ADHD.
Much of the literature on ADHD has emphasized the accumulation of risk in the face of adversity, and generally attributed absence of risk as being more protective. Therefore, a
5 broad goal of this study is to examine compensatory factors that may mitigate areas of impairment for children with ADHD, and whether these factors contribute to later age, and delayed identification, of ADHD.
Age Related Differences in ADHD Symptoms and Subtypes
It is well documented that there are three subtypes of ADHD: the predominantly inattentive presentation, the predominantly hyperactive-impulsive presentation, and a combined presentation (American Psychiatric Association, 2013). Each subtype presents with a unique cluster of behaviors that are hypothesized to occur at distinct developmental windows. In particular, findings from the DSM-IV field trials found that parents reported a later age of onset for children diagnosed with the predominantly inattentive presentation of ADHD (mean age of onset= 6.13), as compared to the combined presentation (mean age of onset=4.88) and the predominantly hyperactive- impulsive presentation (mean age of onset= 4.21) (Applegate et al., 1997). Results from the DSM-IV field trials also showed that children diagnosed with the predominantly inattentive presentation of ADHD were older at time of diagnosis in comparison to children (age of diagnosis= 9.80), in comparison to children with the combined presentation (age of diagnosis= 8.52), and children with the predominantly hyperactive- impulsive presentation (age of diagnosis= 5.68) (Lahey et al., 1994). Similarly, Faraone,
Biederman, Weber, and Russell (1998) revealed that children with the predominantly inattentive presentation of ADHD had later age of referral for treatment and age of assessment (referral age= 9.2; assessment age= 12.5), followed by children with combined presentation (referral age= 6.4; assessment age= 9.9), and children with the predominantly hyperactive-impulsive presentation (referral age= 5.2; assessment age=
6 8.1). This study also illustrated that children with the predominantly inattentive presentation had longer time periods between reported age of onset and referral treatment
(years from onset to referral= 4.4) in comparison to the combined presentation (years from onset to referral= 3.5) and predominantly hyperactive-impulsive presentation (years from onset to referral= 1.9) (Faraone, Biederman, Weber, & Russell, 1998). These findings not only attest to the significant age differences among ADHD subtypes, but also highlight how the subtype presentation plays a role in parents receiving and following up on referrals for treatment.
The developmental trajectory of ADHD symptoms has also been shown to change over time. The change in ADHD symptoms from childhood to adolescence is well documented in longitudinal studies of clinical samples. One noteworthy change during this period of time is that symptoms of hyperactivity decrease while symptoms of inattention become more predominant (Biederman, Mick, & Faraone, 2000; Faraone,
Biederman, & Mick, 2006; Hill & Schoener, 1996). Parent-reported symptoms of hyperactivity-impulsivity have also been shown to decrease over time, whereas the results for symptoms of inattention have been somewhat mixed. Some studies have identified trajectories of inattention symptoms that demonstrate stable, persistent patterns of clinical significance over time (Sasser, Kalvin, & Bierman, 2016) whereas others have found trajectories that suggest increasing symptoms of inattention across the lifespan
(Larsson, Dilshad, Lichtenstein, & Barker, 2011). One factor that may contribute to the change in ADHD symptom endorsement over time is the fact that behavioral impairments due to inattention also become more obvious during later childhood and adolescence, making attentional difficulties easier to assess at older ages. This enhanced ease of
7 symptom identification may also account for the change seen in the developmental trajectories of ADHD subtypes, with the general trend of increasing prevalence in inattentive presentation and decreasing hyperactive-impulsive presentation from early childhood (e.g., preschool period) to later childhood (Applegate et al., 1997; Lahey,
Pelham, Loney, Lee, & Willcutt, 2005; Polanczyk et al., 2007).
Much of what we know about ADHD symptom presentation has been established through cross-sectional and longitudinal studies that follow cohorts of children over time.
In fact, there are few studies that examine differences across development in samples of newly diagnosed individuals with ADHD. One study attempted to examine differences in symptom presentation and impairment in children (age range 6-12) and adolescents (age range 13-17) newly diagnosed with ADHD (N=811) (Faraone, Biederman, &
Monuteaux, 2002). Although children and adolescents with ADHD did not apparently differ in number of ADHD symptoms endorsed, there was a tendency for children to be rated as more globally impaired than adolescents (Faraone et al., 2002). While the findings suggest symptom prevalence was similar in child and adolescents with ADHD,
Faraone, Biederman, and Monuteaux (2002) focused on differences among broad age bands (e.g., 6-11 years old vs 12-17 years old), and they did not report on differences among subtype presentation or on specific continuity among types of symptoms, which could have provided better insight to important developmental differences. Mahajnah,
Sharkia, Shorbaji, and Zelnik (2017) also examined differences among Israeli children
(n=373; age range 7-12; mean age 9.26) and adolescents (n=450; age range 13-17; mean age= 14.22) newly diagnosed with ADHD using retrospective data from medical records.
Subtype differences emerged, such that the combined presentation was more prevalent in
8 children and the predominantly inattentive presentation was more prevalent in adolescents. While secondary concerns were common across age groups, adolescents showed higher rates of learning disabilities and children showed higher rates of behavioral difficulties and anxiety (Mahajnah, Sharkia, Shorbaji, & Zelnik, 2017). The authors reported that while ADHD is more commonly diagnosed in childhood, there are also children who may demonstrate compensatory skills that allow them to forgo diagnosis of ADHD until the transition to middle school or high school introduces new challenges, which exacerbate ADHD symptoms and make symptom impact more obvious to the observer. Unfortunately, Mahajnah and colleagues (2017) examined broad age groupings of children, ranging in age from 6 to 12, and adolescents, ranging in age from
13 to 17. The breadth of these age groups, particularly the child group, appears to ignore potentially important developmental differences that might occur in symptom patterns and changes in environmental demands with advancement from elementary to middle school and middle school to high school.
In general, these studies reinforce the importance of viewing ADHD within a developmental framework. It is possible that by using broad age groupings and small sample sizes, important differences in ADHD presentation across development may be overlooked. Painting a picture of ADHD at unique developmental periods (early childhood, childhood, middle school) instead of using broad age groupings (e.g., children vs adolescents) has the potential to provide a better understanding of the disorder across the developmental continuum. My study will use age as a continuous variable to allow for the exploration of the relationship between age of diagnosis and ADHD symptoms
9 and impairment. The framework in my study is more appropriate for taking into account differences in development, academic environment, and ADHD symptom expression.
ADHD Symptom Continuity Across Development
In an effort to better understand the changing face of ADHD across the developmental continuum, the factor structure of ADHD symptoms from childhood to adolescence was examined by Toplak and colleagues (2012). They examined ADHD symptoms within a large clinical sample of children with ADHD (age range 5 to 17,
N=1373; mean age 10.95). A hierarchical 2-factor model of ADHD symptoms emerged, reflected by two orthogonal (non-correlated) factors of inattention and hyperactive- impulsive symptom domains, and a general overarching ADHD factor that accounts for covariation among individual ADHD symptoms. This study also assessed for invariance by creating age groupings (ages 9 and under, ages 10-12, and 13 and older) to compare across models and found that models were stable across age groupings. Further support for this model and the stability of the structure from childhood to adulthood has been obtained by Martel, von Eye, and Nigg (2012). Using a community-based sample of children (age range 6 to 18, n=302; mean age=11.7) and adults (age range 18 to 37, n=145; mean age=24) diagnosed with ADHD, they replicated the 2-factor model of
ADHD, which separated inattentive and hyperactive-impulsive symptoms into specific factors that share common variance within an overarching latent variable of ADHD.
While Martel and colleagues (2012) provided further support for the fact that the ADHD structure did not differ among children and adults, they suggested symptom structure was different for children and adults due to change in factor loadings across age groupings.
Specifically, the adult-based model found that 6 symptoms of hyperactivity-impulsivity
10 symptoms (fidgets, runs/climbs, leaves seat, talks a lot, blurts, and interrupts/intrudes) and 1 symptom of inattention (listens) were not significant within their individual factors, and 2 symptoms of hyperactivity-impulsivity (runs/climbs, driven by a motor) and 1 symptom of inattention (forgetful) were not significant with the overall factor of ADHD as compared to the model found with children (Martel, Von Eye, & Nigg, 2012). These findings support the idea that while the general structure of ADHD (as defined by inattentive and hyperactive-impulsive factors and an overarching ADHD factor) may be stable over time, the individual symptoms of ADHD change with development.
Understanding ADHD within a developmental framework appears essential for both diagnosis and treatment and the current conceptualization of ADHD symptoms does not appear to capture the change in symptom presentation across the lifespan.
Persistence Over Time
Although ADHD was once thought to be limited to childhood, more research is documenting the progression of ADHD symptoms over time and the prevalence of this disorder in adulthood (Rohde, Verin, & Polanczyk, 2012). There has been a reconceptualization of ADHD from a remitting disorder to a more chronic, pervasive disorder (American Psychiatric Association, APA, 2013). Some studies estimate that between 50 to 70% of children who experience symptoms of ADHD in childhood will continue to experience these symptoms into adulthood regardless of treatment (Rohde et al., 2012). While this may suggest that 30-50% of children with symptoms of ADHD in childhood recover as shown by remission of ADHD, methodological issues with how we define symptom persistence make such a statement difficult at best. A meta-analysis examining ADHD persistence found that rates of persistence varied significantly based
11 on how persistence was defined (Faraone et al., 2006). Specifically, when persistence was defined as continuing to meet full criteria for ADHD at follow-up, results suggested approximately 15% of individuals with childhood ADHD showed persistent ADHD at age 25. However, if partial remission was taken into account, which suggests ADHD symptoms are present but full criteria are not met, then persistence rates were closer to
40-60% (Faraone et al., 2006). The authors suggested that while it is possible that ADHD symptoms wane over time, developmentally insensitive diagnostic criteria make adult diagnosis of ADHD less likely to occur. At present, the DSM 5 does little to capture developmental variations in ADHD and adult presentation profiles.
Developmentally insensitive diagnostic criteria likely hinders diagnosis of those with partial remission of ADHD symptoms over time or initial symptom presentation in adolescence and adulthood, which may make identifying newly diagnosed cases of late onset ADHD more difficult. Researchers have also shown that a history of ADHD during childhood is associated with greater impairment later in life regardless of whether ADHD symptom have declined or not (Hurtig et al., 2007; Lahey et al., 2016). Caye and colleagues (2016) examined differences in outcomes and ADHD symptoms in individuals who were diagnosed with ADHD in childhood as compared to controls without ADHD.
Results indicated that the majority of individuals with childhood ADHD did not continue to meet criteria for ADHD in young adulthood (73.3% did not meet ADHD criteria in young adulthood vs 15.3% with persistent ADHD). Although comparisons were not made between individuals with child-onset ADHD that had remitted and those that persisted, comparisons to controls revealed poorer outcomes in young adulthood.
Specifically, those with child-onset ADHD had higher rates of comorbidities, substance
12 use, suicide attempts, risky sexual behavior, criminality, and lower IQ in comparison to typically developing peers (Caye et al., 2016). In general, those who present with ADHD in childhood and go on to demonstrate sub-threshold symptom severity in adolescence and adulthood continue to have functional impairments because they fail to develop compensatory skills (Biederman et al., 2006, 2000; Weiss, Hechtman, Milroy, &
Perlman, 1985). It therefore seems likely that ADHD does not diminish or remit over time, but that the conceptualization of the disorder does not adequately capture the manifestation of ADHD beyond childhood.
Although ADHD is understood as a chronic disorder that affects individuals across the life span, there is still much work to do in improving our conceptualization of the disorder over time. Researchers have documented problems with tracking symptoms over time. First, as previously highlighted, symptom criteria for ADHD does not adequately capture changes in the behavioral expression of ADHD symptoms across development. In particular, the symptoms of hyperactivity and impulsivity seem more appropriate when applied to younger age groups compared to adolescents or adults
(Barkley, 2003; Wilens & Dodson, 2004). There are also issues with assessment of symptoms over time. As children transition to adolescence, there is greater emphasis on self-report ratings. Adolescents with ADHD have been shown to underreport symptoms and functional impairment (Barkley, Fischer, Smallish, & Fletcher, 2002; Kramer et al.,
2004; Smith, Pelham Jr, Gnagy, Molina, & Evans, 2000). During adolescence, the structure of academic environment also changes, such that adolescents may have up to 7 different teachers that they interact with for a short amount of time (i.e., less that 1 hour class periods) during their school day. This can contribute to difficulties getting accurate
13 teacher ratings and often requires obtaining reports from multiple teachers (Wolraich et al., 2005). Furthermore, researchers have shown that there is generally poor agreement among secondary school teacher ratings (Molina, Pelham, Blumenthal, & Galiszewski,
1998). Studies tracking ADHD symptom persistence into adulthood often request retrospective report of childhood symptoms, which has been shown to be unreliable
(Barkley et al., 2002). In addition, few if any researchers seem to take into account changes that occur over development or adaptations and improvements that might result from involvement in treatment. Lastly, while longitudinal studies have been helpful for understanding change in developmental symptoms within an individual, they cannot shed insight into differences in youth who present for diagnosis for the first time at later developmental ages. In the present study I cannot examine symptom persistence over time, rather I aim to better understand the experience of individuals presenting for initial diagnosis at different periods of development.
Early versus Late Symptom Onset
Age of onset of ADHD has been found to be of diagnostic significance. Early onset of ADHD symptoms appears to predict chronicity of the disorder over time. Early onset is associated with higher rates of psychiatric comorbidities, greater cognitive impairments, and poorer academic and social functioning (McGee, Williams, & Feehan,
1992; E. Taylor, 1999; Eric Taylor & Rogers, 2005). Some researchers have found that children with early-onset (prior to age 7) ADHD symptoms were more likely to be involved in mental health services as compared to those with late-onset (after age 7) symptoms (Willoughby, Curran, Costello, & Angold, 2000). Thus there is preliminary evidence that suggests children with early-onset ADHD symptoms are at risk for poorer
14 outcomes compared to those with late-onset symptoms, as shown by higher symptom counts, higher rates of comorbidity, and poorer academic and cognitive functioning when symptom onset occurs early.
Before we conclude that individuals with early-onset ADHD symptoms fare poorly compared to individuals with late-onset ADHD symptoms, a careful examination of the data is warranted. Most of the studies addressing outcome differences as a function of age-of-onset appear to rely on retrospective parent report to estimate when symptoms first emerged. Unfortunately, this retrospective parent report method has been shown to have poor reliability within clinician diagnosis (Barkley, 1997; Kieling et al., 2010). For example, the Willoughby, Curran, Costello, and Angold (2000) study showed that parents commonly describe their child as “always” having ADHD symptoms. Failure to identify a specific age or date associated with symptom emergence can lead to issues confirming true age of onset. Secondly, sociodemographic factors may also affect “age-of-onset”.
One study suggested that when parents are asked when their child first demonstrated
ADHD symptoms, non-Hispanic white, high-income families more often reported earlier onset, whereas, non-white, low income families more often reported later onset symptoms (Voort, He, Jameson, & Merikangas, 2014). Willoughby, Curran, Costello, and Angold (2000) also found that individuals with late-onset ADHD symptoms utilized fewer services and were generally less likely to be involved in services as compared to those with early-onset ADHD symptoms. It is possible that children with later-onset symptoms may represent a population with largely unmet service needs, since low income families generally have less access to services and more barriers to treatment
(Bussing, Zima, Gary, & Garvan, 2003; Wright et al., 2015). With this in mind, the
15 current study will explore age-related differences within a clinical sample of children newly diagnosed with ADHD who have access to specialized mental health services.
Factors that influence ADHD diagnosis
ADHD has been shown to negatively impact children’s view of themselves (i.e., sense of efficacy, regulatory abilities) and externally (i.e., academic achievement, social functioning) within their environment (Antshel & Barkley, 2009; Foley, 2011). There are a variety of factors that may exacerbate or alleviate effects of ADHD and, in turn, influence the timing of ADHD diagnosis (i.e., childhood versus adolescence, versus adulthood). Using a developmental psychopathology framework as my lens, the current study will examine the complex interactions among factors that may influence the timing of ADHD diagnosis, such as those that increase risk of diagnosis and those that contribute to higher functioning that may delay detection of ADHD symptoms. The following sections will summarize what is known about factors of interest for the current study, including temperament, IQ, and behavioral assets in relation to ADHD.
Temperament
Temperamental traits within the child may assist in understanding the developmental pathways of ADHD (De Pauw & Mervielde, 2011; Martel, Nigg, & von
Eye, 2009; Nigg, Goldsmith, & Sachek, 2004). First, temperamental traits may provide insight into the expression of ADHD in early childhood and act as early indicators of pathology. Temperament has been defined as biologically based traits that present during early childhood and remain stable over time (Rothbart, Ahadi, & Evans, 2000a; Thomas
& Chess, 1977). A strong association between difficult temperament patterns in children and increased risk of childhood behavioral problems and mental health diagnoses has
16 been found (Bussing, Gary, et al., 2003; Thomas & Chess, 1977). ADHD symptoms and externalizing behavior problems, such as aggression, disruptive behavior, and noncompliance, have been associated with temperament patterns of high activity, more distractibility, and lower inhibition (Kagan, Snidman, & Arcus, 1998; Kagan, Snidman,
Zentner, & Peterson, 1999; White, 1999). Although some symptoms of ADHD closely resemble extremes within the temperamental domains of effortful control, arousal, and activity level (Nigg et al., 2004), this overlap has been cited as supporting these as potential early indicators of ADHD rather than methodological error. Bussing and colleagues (2003) also showed that temperamental domains were predictive of ADHD symptom categories, such that low levels of inattention symptoms were predicted by high levels of flexibility and task orientation, while higher levels of temperamental activity predicted diagnostic levels of hyperactivity symptoms. Further evidence for the diagnostic significance of temperament can be garnered from twin studies. There appears to be a genetic link between temperament and behavior in children with ADHD, as evidenced by a general disposition toward higher levels of activity and problems with attention in early childhood (Foley, McClowry, & Castellanos, 2008; White, 1999).
Within individuals with ADHD, temperamental characteristics may play a role in the development of maladaptive behaviors that influence impairment and comorbidity. For example, DePauw and Mevielde (2011) found that children with ADHD presented with higher temperamental characteristics of emotional intensity and negative affect, which presented as higher irritability, lower self-confidence, lower frustration tolerance, and higher anxiety. The authors described how these temperamental traits can impact self- efficacy and compliance in the face of emotional distress (De Pauw & Mervielde, 2011).
17 In addition to difficulties with emotion regulation, children with ADHD have also been shown to have lower temperamental characteristics of effortful control and task persistence, which can contribute to difficulties with flexibility in new situations and attending to social cues (Martel & Nigg, 2006; Nigg et al., 2004; Steinberg & Drabick,
2015). Nigg, Goldsmith, and Sachek (2004) suggested that the interaction of temperament and socialization might influence individuals with ADHD to develop comorbid conditions, such as conduct disorder, oppositionality, and anxiety. Specifically, children with ADHD may receive negative responses from their social environment as a result of their temperament (e.g., high reactivity, low persistence, high activity, low inhibition), and begin to develop patterns of antisocial behavior, withdrawal, or irritability that increase risk for comorbid problems (Nigg et al., 2004).
Though temperamental factors have been shown to predict ADHD symptoms and play a role in contributing to the heterogeneous outcomes associated with ADHD, it remains unclear whether temperament factors play a role in age of ADHD diagnosis.
Given that symptoms of inattention and hyperactivity have been associated with particular temperamental domains, there has been little evidence as to whether these associations are stable over time. In addition, models of temperament suggest that individual temperament characteristics shape their experience as they interact within their environment and influence risk toward pathology. For example, temperament characteristics may heighten an individual’s response to stress or events within their environment, evoke reactions from others, and shape an individual’s self-concept and view of their environment; all of which may facilitate development of psychopathology depending on whether these interactions are adaptive or maladaptive ȋ ǡͳͻͻǢ
18 ǡǡƬǡʹͲͲͲȌ. Although parents of children with difficult temperaments have been shown to report higher levels of stress and poorer parenting practices, there is mixed information as to whether these results emerge within populations of children with ADHD (Bussing, Gary, et al., 2003; Rettew & McKee,
2005). The developmental psychopathology perspective would also argue that it is plausible that some positive temperamental characteristics may enable children with
ADHD to evidence greater resiliency than their peers without these positive characteristics. Since research has shown that parent-child interactions influence social skill development, and higher levels of parental warmth and responsiveness contributes to improved social functioning and greater peer acceptance (Deault, 2010), it will be important to examine whether the same is true within ADHD. The current study will examine whether temperamental factors are associated with age of ADHD diagnosis and also how these factors may impact the relationship between level of impairment and age of diagnosis. It is hypothesized that individual differences in temperament will contribute to differences in age of diagnosis in youth diagnosed with ADHD, such that children with more difficult temperaments may present for earlier diagnosis. Clarifying how temperamental characteristics impact the age at which children present for ADHD diagnosis has the potential to increase our understanding of the developmental pathways of ADHD and improve strategies for early detection through examination of temperament.
IQ
The range of cognitive and academic functioning in children diagnosed with
ADHD is quite diverse. In an effort to better understand the relationship between
19 intelligence and the diagnosis of ADHD, Frazier, Demaree, and Youngstrom (2004) conducted a meta-analysis of studies describing the relationship of ADHD and performance on tests of intelligence and neuropsychological functioning. Results of the meta-analysis suggested that children with ADHD had lower IQ scores than children in control groups, with no differences noted across ADHD subtypes (Frazier, Demaree, &
Youngstrom, 2004). While their data appeared quite convincing, Jepsen, Fagerlund, and
Mortensen (2009) conducted a meta-analysis that somewhat challenged this position. The authors agreed that children with ADHD often perform more poorly than children without ADHD, but argued that these low scores are largely a function of the attentional demands rather than intellectual deficits (Jepsen, Fagerlund, & Mortensen, 2009). Thus, the question of whether children with ADHD have lower cognitive capacity than their peers with out this diagnosis is unclear. While the cross sectional nature of the current study and the failure to include a typical group precludes an answer to this question, the importance of examining IQ as a predictor of age of ADHD onset is critical.
It is well documented that children with low IQ display poorer adaptive functioning and greater impairment compared to individuals with average IQ (American
Psychiatric Association, 2013). Researchers have also suggested that children with
ADHD and intellectual disability (FSIQ ӊ70) are more likely to demonstrate earlier symptom onset and greater persistent symptoms compared to children with ADHD and average IQ (FSIQ Ӌ85) (Neece, Baker, Blacher, & Crnic, 2011). Children with ADHD that have comorbid intellectual disability have been shown to have higher rates of ODD and CD symptoms in comparison to those without comorbid intellectual disability
(Ahuja, Martin, Langley, & Thapar, 2013). Although symptom onset and impairment is
20 more severe within individuals with intellectual disability and ADHD, it is unclear whether similar patterns are seen for individuals who have borderline IQ (70 ӊFSIQ ӊ
85) and ADHD. The current study will not include individuals with ADHD that have comorbid intellectual disability, however, individuals with FSIQ above 70 will be described. The current study will also explore whether individuals with lower IQs present for diagnosis at younger ages and display greater levels of impairment.
Studies have documented that children with ADHD do not always present with low IQs and in fact, some may present with IQs in the gifted range (FSIQ Ӌ 120; (Antshel et al., 2007). The question then becomes whether higher IQ may improve behavioral and academic functioning in children with ADHD by helping children compensate for symptom impairment. Researchers have shown that high IQ is associated with improved self-regulation skills and academic performance in typically developing populations
(Calero, García-Martín, Jiménez, Kazén, & Araque, 2007; Gutman, Sameroff, & Cole,
2003). Given that impairments in self-regulation abilities and academic performance are commonly seen in children with ADHD, it is unclear whether the positive effects of high
IQ are limited to those in lower risk samples. Antshel and colleagues (2008) followed children with ADHD and high IQ over 4 years and discovered that, regardless of IQ strengths, children with ADHD continued to display greater impairment and comorbidity in comparison to typically developing peers. It is important to note that Antshel and colleagues (2008) compared relatively small samples (ADHD group, n=38; Control group, n=79) composed of a broad age range (age range 10-24; ADHD group mean age=16.9; Control group mean age=16.4). In addition, the authors indicated that the majority of the ADHD sample met criteria for the combined presentation, however,
21 subtype differences were not explored (Antshel et al., 2008). Therefore, the current study will aim to explore whether differences in IQ contribute to differences in age of ADHD diagnosis.
Despite a plethora of research, the role of IQ in the diagnosis of ADHD is somewhat unclear. Within a population of children with ADHD, little is known about the differences in impairment among those with high, average, and low IQ. In addition, the question remains whether these differences, should they exist, are in fact diagnostically useful. For instance, does high IQ protect against academic or social impairments, whereas low IQ increases these risks? This study will explore whether varying levels of cognitive functioning (≥ 70) contribute to the timing of detection of ADHD during childhood. This information will help increase the understanding of how cognitive and environmental factors interact throughout development within the context of ADHD.
Behavioral Problems and Assets
More recently researchers have acknowledged the importance of the complex interaction between an individual and their environment in the context of developmental disorders. Adopting a lens of positive youth development, researchers have turned to examining whether children-specific internal and external behavioral assets provide a level of protection against negative outcomes or promotion toward positive outcomes
(Leffert et al., 1998; Sesma Jr, Mannes, & Scales, 2013). Internal assets reflect characteristics of the individual, such as commitment to learning, positive values, social competencies, and positive identity, while the external assets incorporate positive features of the environment, such as social support, empowerment, boundaries and expectations, and constructive use of time (Leffert et al., 1998; Sesma Jr et al., 2013). Short, Fairchild,
22 Findling, and Manos (2007) examined developmental and subtype differences in behavioral assets and behavioral problems within a sample of newly diagnosed children
(N=318; age range 4 to 15) with ADHD. Behavioral assets and problems were assessed using the Social Medical Questionnaire (SMQ; Manos, 2004), which is also included in the current study. In order to examine developmental differences, this clinical sample was divided into 3 age groupings: 4-6.9 years old (young; n=95), 7-9.9 years old (middle; n=136), and 10-15 years old (old; n=87). With regard to ADHD subtypes, this study collapsed those with predominantly hyperactive-impulsive and combined presentations of
ADHD into one category (hyperactive/combined). Their findings indicated age-related differences in ADHD subtypes, such that the hyperactive/combined presentation of
ADHD was more prevalent in the youngest age group and the predominantly inattentive presentation was more prevalent in the oldest age group. The middle age group demonstrated no significant differences by subtype, which may suggest that age related changes in ADHD symptoms might begin to shift after age 10. Age group and subtype differences emerged in parent reports of behavioral problems, behavioral assets, and referral concerns. Young children showed more problems with hyperactivity than the middle and old age groups, and the oldest age group had more externalizing and inattention problems than the middle and young age groups. Across subtypes, children in the hyperactive/combined presentation demonstrated more problems with social network, hyperactivity, internalizing behaviors, and externalizing behaviors compared to those with predominantly inattentive presentation. In examining behavioral assets, children in the young and middle age groups were described as having higher self-esteem and better attitudes toward school compared to children in the oldest age group. Additionally,
23 children with predominantly inattentive presentation demonstrated better emotional adaptability and attitudes toward school in comparison to those with hyperactive/combined presentation. Parent-reported referral concerns also differed among age groups, such that children in the youngest group were more likely to be referred for assessment to address externalizing behavior (i.e., aggression, hyperactivity, disruptive behavior, impulsivity, and anxiousness), children in the middle age group were more likely to be referred for concerns with handwriting, and children in the oldest group were more likely to be referred due to concerns with academics (i.e., reading, spelling, math, written expression) and poor self-esteem. Within subtypes, children with predominantly inattentive presentation were more likely to be referred for concerns with academics (i.e., reading and math) and those with combined/hyperactive presentation were more likely to be referred for concerns with externalizing behavior (i.e., oppositional defiant behavior, aggression, hyperactivity, disruptive behavior, low frustration tolerance, impulsivity, and anxiousness). Their findings also indicated that children with more behavioral assets had fewer behavioral problems and fewer referral concerns, whereas children with more behavioral problems had more referral concerns and fewer behavioral assets (Short et al.,
2007). Overall, these findings offered insight into age related differences among ADHD subtypes and behavioral problems and assets that may influence diagnosis. Given that
Short et al. (2007) utilized a smaller sample size, the current study will explore whether similar results emerge within a larger sample of children and adolescents newly diagnosed with ADHD. In addition, the current study will also expand upon these findings by using structural equation modeling techniques that may better capture the
24 complex relationship among age and factors impacted by expression of ADHD symptoms and impairment over development.
Current Study
The face of ADHD is quite variable over time and in part dependent on the developmental window under consideration. Researchers have heavily focused on symptom continuity over time to help support a medical framework of ADHD, indicating that the disorder is static and stable over time. A more dynamic framework of assessment and diagnosis is needed to better understand the complex processes that occur throughout development and contribute to ADHD diagnosis. In addition, many studies that claim to examine developmental differences between age groups have neglected to parse out the more subtle differences occurring within each group. For example, many studies separate children into two groups (children ages 6 to 12 and adolescents ages 13 to 17). However, there are likely significant differences between a 6 and 12 year old both in social structure and academic expectations. We also know that there is change in frequency and intensity of hyperactivity and inattention symptoms during the school age period. By clustering school-aged children into one sample, developmental changes are likely to be overlooked. In this study I plan to superimpose a developmental framework on the data in order to better understand the face of ADHD across the developmental continuum.
The current study will use the developmental psychopathology (DP) model
(Cicchetti & Toth, 2009; Rutter, 2013; Rutter & Sroufe, 2000) as a framework for understanding pathways that contribute to ADHD diagnosis in children at different points in development. The developmental psychopathology perspective allows me to consider the pathology of ADHD as emerging from multiple internal (i.e., temperament, cognitive
25 ability) and external (i.e., behavioral assets) causal pathways. In addition, this approach accounts for individual differences by highlighting how risk and protective factors can influence the likelihood of maladaptive outcomes and impaired functioning. Prevention of developmental psychopathology considers both the path of those who develop specific disorders and the path of those who experience these adverse events but were able to overcome dysfunction along the way (Felner & DeVries, 2013). Thus, in examining the relationship between developmental pathways and ADHD pathology, it will be important to consider the ways that risk factors increase the likelihood of ADHD diagnosis, as well as how and what compensatory factors might lower the impact of risk factors and contribute to better functioning. Within the context of this study, structural equation modeling techniques will be used to examine developmental differences within ADHD, as well as the role of risk and compensatory factors as they related to the age that the child presents for ADHD diagnosis. This information will be used to articulate developmental pathways of ADHD that have the potential to aid in our understanding differences in diagnostic age of onset. In general, I will attempt to describe patterns of impairment, risk, and potential protective or compensatory factors that influence the age at which children present for ADHD diagnosis.
Within the context of the current study, structural equation modeling techniques will be used to add to the empirical base of ADHD research by increasing our understanding of developmental pathways of ADHD within a large, clinic based sample.
Unique features of the current sample position us to make a significant contribution to the field. First, the sheer number of children available for inclusion in this study (N=3,537) allows us to perform complex analyses designed to examine relationships among
26 variables. The wide age range (3 to 18) of the current sample also enables a more thorough examination of developmental differences within ADHD, rather than simply comparing school-aged children to adolescents. Unlike large longitudinal studies that follow children with established diagnoses, the current study utilizes a clinical-referred managed care sample of children seeking initial diagnosis of ADHD. As a result, the current study aims to better understand patterns of impairment, as well as resilience that may impact the timing of initial diagnosis. The current study will investigate several questions and hypotheses outlined below.
Questions and Hypotheses:
The primary question of interest in this study is does the face of ADHD change based on age of diagnosis. Thus I will be addressing the following questions:
Differences in ADHD across Developmental Periods
Research Question 1. Do ADHD symptoms impact age of ADHD diagnosis?
Hypothesis 1. Children with fewer ADHD symptoms of hyperactivity and more
ADHD symptoms of inattention will be diagnosed with ADHD at later/older ages
as compared to children with more ADHD symptoms of hyperactivity and fewer
ADHD symptoms of inattention (see Figure 1, H1).
Predictors Outcome Variable
ADHD Symptoms Age of ADHD Diagnosis
Inattention Symptoms H1 Age
Hyperactivity/ Impulsivity Symptoms
Figure 1. Hypothesized Model of ADHD Symptoms Impacting Age of ADHD Diagnosis
27 Research Question 2. Does impairment impact age of ADHD diagnosis?
Hypothesis 2. Children with greater impairment (as indicated by parent reported
severity of symptom impairment, number of referral concerns reported by parents,
parent ratings on the Child and Family Quality of Life scale, and teacher ratings
of behavioral problems and academic achievement) will be diagnosed with
ADHD at younger ages in comparison to children diagnosed with ADHD at older
ages (see Figure 2, H2).
Predictors Outcome Variable
Impairment Age of ADHD Diagnosis
Symptom Severity-P H2 Age
CFQL
Referral Concerns
Classroom behavior-T
Achievement-T
Figure 2. Hypothesized Model of Impairment Impacting Age of ADHD Diagnosis
Risk and Compensatory factors that may affect developmental pathway of ADHD
IQ
Research Question 3. Does IQ impact age of ADHD diagnosis?
Hypothesis 3. Individuals with higher IQs will have a later age of ADHD
diagnosis, while lower IQ will be associated with younger age of ADHD
diagnosis (see Figure 3, H3).
28 Research Question 4. Does IQ (as a confounder) impact the relationship between impairment and age of ADHD diagnosis?
Hypothesis 4. In examining IQ as a confounder, the following relationships are
hypothesized: Individuals with higher IQs will have a later age of ADHD
diagnosis in comparison to individuals with lower IQ. In other words, it is
hypothesized that individuals with higher IQs will demonstrate better
compensatory skills that may help them overcome domains of impairment, and
contribute to delayed diagnosis of ADHD. In addition, individuals with higher
IQs will have lower levels of impairment in comparison to individuals with lower
IQs. As mentioned in Hypothesis 2, children with greater impairment will be
diagnosed with ADHD at younger ages; however, this relationship will diminish
or no longer exist when IQ is added to the regression (see Figure 3, H4).
i. Logic. It is assumed that children with higher IQ have better self-
regulatory skills and academic functioning, which may allow them
to better compensate for ADHD symptom interference/impairment
at younger ages. As a result, it is hypothesized that children with
higher IQ will use their intelligence to develop better
compensatory skills and thus have lower levels of impairment at
time of diagnosis as compared to individuals with lower IQ.
Therefore, it is hypothesized that the relationship between
impairment and Age of ADHD diagnosis will no longer be
significant once IQ is taken into account.
29
Compensatory Factor Predictors Outcome Variable H3 Age of ADHD Diagnosis IQ ImpairmentIi t Age H4 Symptom Severity-P
CFQL
H4H4 Referral Concerns
Classroom Behavior- T
Achievement-T
Figure 3. Hypothesized Model of IQ impacting Age of ADHD Diagnosis, and IQ as a Confounder on the Relationship between Impairment and Age of ADHD Diagnosis
Temperament
Research Question 5. Does temperament impact age of ADHD diagnosis?
Hypothesis 5. Individuals with easier temperaments (lower ratings on
temperament domains indicate more positive or easy temperament) will have later
age of ADHD diagnosis as compared to individuals with difficult temperaments
(higher ratings on temperament domains indicate more difficult temperament)
(see Figure 4, H5).
Research Question 6. Does temperament (as a confounder) impact the relationship between impairment and age of ADHD diagnosis?
Hypothesis 6. In examining temperament as a confounder, the following
relationships are hypothesized: Individuals with easier temperaments will have a
later age of ADHD diagnosis in comparison to individuals with more difficult
30 temperaments because it is hypothesized that individuals with easier
temperaments will demonstrate better compensatory skills that may help them
overcome domains of impairment (e.g., reductions in parent reported severity of
symptom impairment, number of referral concerns reported by parents, parent
ratings on the Child and Family Quality of Life scale, and teacher ratings of
behavioral problems and academic achievement), and contribute to delayed
diagnosis of ADHD. As a result, it is hypothesized that children with easier
temperaments will be diagnosed with ADHD at later ages because they may
appear to have better compensatory skills as a result of positive temperament
characteristics, thus leading individuals with easy temperaments to have lower
levels of impairment. In contrast, children with more difficult temperaments
(higher scores on temperament domains) will demonstrate effects in the opposite
direction, such that they have greater impairment and earlier age of ADHD
diagnosis. Taken together, although it is hypothesized that children who
demonstrate greater impairment will have earlier/younger age of ADHD diagnosis
(see Hypothesis 2), this relationship will diminish or no longer exist when
temperament is added to the regression and taken into account (see Figure 4, H6).
31 Compensatory Factor Predictors Outcome Variable H5 Age of ADHD Diagnosis Temperament Impairment Age H6 Symptom Severity-P
CFQL
H6H6
Referral Concerns
Classroom Behavior- T
Achievement-T
Figure 4. Hypothesized Model of Temperament impacting Age of ADHD Diagnosis, and Temperament as a Confounder of the Relationship between Impairment and Age of ADHD Diagnosis
Behavioral Assets
Research Question 7. Do behavioral assets impact the age of ADHD diagnosis?
Hypothesis 7. Individuals with more behavioral assets will have later age of
ADHD diagnosis; individuals with fewer behavioral assets will have earlier age of
ADHD diagnosis (see Figure 5, H7).
Research Question 8. Do behavioral assets (as a confounder) impact the relationship between impairment and age of ADHD diagnosis?
Hypothesis 8. In examining behavioral assets as a confounder, the following
relationships are hypothesized: Individuals with more behavioral assets will have
a later age of ADHD diagnosis, in comparison to individuals with fewer
behavioral assets. It is hypothesized that individuals with more behavioral assets
will demonstrate better compensatory skills that may help them overcome
32 domains of impairment at earlier ages (e.g., better social functioning, better
compliance with adults, better attitudes toward school, etc.), and contribute to
delayed diagnosis of ADHD. As a result, children with more behavioral assets
will have lower levels of impairment in comparison to individuals with fewer
behavioral assets. Therefore, while it is hypothesized that children who
demonstrate greater impairment will have earlier/younger age of ADHD diagnosis
(see Hypothesis 2), this relationship will diminish or no longer exist when
behavioral assets are added to the regression and taken into account (see Figure 5,
H8).
Compensatory Factor Predictors Outcome Variable H7 Behavioral Age of ADHD Diagnosis Assets Impairment Age H8 Symptom Severity-P
CFQL
H8H8 Referral Concerns
Classroom Behavior- T
Achievement-T
Figure 5. Hypothesized Model of Behavioral Assets impacting Age of ADHD Diagnosis, and Behavioral Assets as a Confounder on the Relationship between Impairment and Age of ADHD Diagnosis
33 Method
Participants
A total of 3537 children ages 3 to 18 and their parents participated in this study.
Participants for this study were derived from a two-phase study of ADHD detection and service use in children. Phase one included parents completing background information, and children were assessed for ADHD using standardized parent and teacher reports. The second phase of this study involved children and parents completing an in-person diagnostic assessment at Cleveland Clinic’s ADHD Center for Evaluation and Treatment
(ACET). The diagnostic assessment involved a semi-structured interview with parent and child, as well as completing a standardized assessment of cognitive functioning. Families enrolled in this study consented to review of deidentified medical records and the
Institutional Review Board of the Cleveland Clinic approved this study. Archival data gathered from this study between 2000 and 2016 will be utilized.
Diagnosis. Children and their parents completed a semi-structured diagnostic interview administered by a Ph.D. level clinician or Master’s level counselor, social worker, or psychology trainee that were supervised by a PhD level clinician. The semi- structured interview included DSM-IV or DSM-V descriptors of ADHD that were rated as present or not present by parent. Consistency among symptom endorsement was checked against parent and teacher rating scales and discrepancies were discussed in interview. Additional information regarding impairment in domains of school and home, parenting practices, social functioning, and secondary symptom concerns (i.e., sleep problems, oppositionality, conduct, anxiety) were also assessed during semi-structured interview with parent and child. Diagnosis of ADHD was granted based on DSM
34 guidelines of symptom quantity and impairment, with subtype presentation and symptom severity specified.
Inclusion/exclusion. A total of 3537 participants were screened for inclusion in this study. Participants were included for this study if they presented for initial diagnosis of ADHD and received a diagnosis of ADHD following completion of full evaluation at
ACET. Participants were excluded from the current study if they had a history of intellectual disability (FSIQ ӊ70 , a history of head trauma, neurological problems, bipolar disorder, or psychotic features (i.e., delusions, hallucinations). In addition, since the aims of this study were to understand the experience of individuals who are newly diagnosed with ADHD, individuals were also excluded if they had a history of ADHD diagnosis or involvement in pharmacological and/or psychological services to address
ADHD symptoms.
3537 Child patients
1423 patients did 2114 patients not complete completed evaluations evaluations
338 had 1776 had full incomplete or evaluations and partial evaluations parent paperwork
265 had a previous 151 did not have a 29 had an IQ less diagnosis of ADHD 1331 met all diagnosis of ADHD than or equal to 70 and were receiving inclusion criteria current medication treatment
ǤFlow chart of Participants' Inclusion and Exclusion
35 Measures
Descriptive variables.
Sociodemographic information and developmental history. The Social Medical
Questionnaire (SMQ; Manos, 2004) is a 12-section parent-report questionnaire that includes information on child demographics (i.e., age, grade, school, gender, race), parent demographics (i.e., age, marital status, education, occupation) family structure (i.e., number of siblings, presence of multiple caregivers), referral concerns, family history, birth and developmental history, medical history, family transition in the last 3 years (i.e., parent separation/divorce, moved to new location, death of family member), child behavior problems and assets, and history of mental health service use and diagnosis
(i.e., involvement in therapy, involvement in psychological testing, history of diagnosis).
Although the SMQ is unpublished, it has been used in previous work with samples of children with ADHD (see Short et al., 2007) for a complete description).
Predictor variables.
ADHD symptoms. Parents confirmed the presence or absence of 18 ADHD symptoms based on DSM criteria and whether symptoms interfered with their child’s functioning (i.e., are problematic) during semi-structured interview. In addition, parents and teachers completed the ADHD Rating Scale (ARS) Home and Teacher versions
(DuPaul, Power, Anastopoulos, & Reid, 1998) prior to in-person diagnostic assessment.
The ARS is an 18-item questionnaire that assesses the frequency of ADHD symptoms, as based on the DSM-IV criteria, in the last 6 months. Both the parent and teacher versions of this measure were included in this study. Parents and teachers independently rate the frequency of child behaviors on a 4-point Likert scale (0=Never/Rarely, 3=Very Often).
36 Items are divided across two subscales representing inattentive and hyperactive- impulsive symptoms. Normative data is available for children age 5 to 18. Both parent and teacher versions have shown good internal consistency, strong test-retest reliability, and good validity against other similar measures (Conners rating scales, DuPaul et al.,
1998).
Impairment. Parents and teachers individually completed separate measures that captured ratings of overall impairment of behavioral problems. On the SMQ (Manos,
2004), parents completed a checklist of 21 referral concerns across domains of academic concerns (e.g., math, writing, reading), behavior (e.g., impulsive, hyperactive, oppositional), and development (e.g., fine motor, speech/language); parents indicated whether referral concerns listed were present or absent for their child and that they interfered with the child’s functioning. An overall total composite score indicating the number of referral concerns was used as one indicator of impairment in this study.
Parents also completed the Side Effect/Behavior Monitoring Scale (SEBMS; Manos,
1996), which included a section on severity of target symptoms related to ADHD. Parents were asked to rate the severity of the child’s behavioral concerns on 10 items using a 7 point Likert Scale based on the Clinical Global Impressions-Severity (CGI-S) anchored scale (1= Normal/Not at all; 7=Most Extreme Problem). An overall count of symptom severity was computed based on the number of symptoms parents reported as problematic. To be conservative, symptoms were considered problematic if parents rated them as 3 or greater. Teachers completed a rating of the child’s behavioral concerns in the classroom (“…rate how much of a problem the child is in the classroom”) using a 7- point Likert Scale based on the Clinical Global Impressions-Severity (CGI-S) anchored
37 scale (1= Normal/Not at all; 7=Most Extreme Problem). Teachers also completed a rating of the child’s level of academic achievement using a 5-point Likert scale (1=Well Below
Grade Level; 5=Well Above Grade Level). In order to capture diagnostic impact on child and family functioning, the Child and Family Quality of Life Scale (CFQL; Markowitz et al., 2016) will be included as a measure of impairment. This 32-item parent report questionnaire that assesses quality of life in individuals who either have an established diagnosis or whom are at risk for diagnosis if a developmental disorder is not currently assigned. The CFQL items are dispersed into seven a priori developed subscales including child, family, caregiver, financial, external support, partner relationship, and coping. Each subscale is composed of 4 or 5 items that are rated on a 5 point Likert scale indicating agreement (with 1 indicating strongly disagree and a 5 indicating strongly agree), frequency of behavior (1 = never, 5=always), or quantity of resources (i.e., 1=no support, 5=very much support). Psychometric assessment of this scale supports a 6-factor structure (with family and caregiver subscales merging into one subscale). A confirmatory factor analysis will be conducted to confirm the factor structure of the
CFQL in the current sample, and results will be presented in the Results section.
Outcome variable.
Age of ADHD diagnosis. Age of ADHD diagnosis was quantified as the age of the child at the time of evaluation.
Confounder variables.
IQ. Children’s current cognitive functioning was assessed using age-appropriate intelligence tests. Children completed an assessment battery when they presented for diagnostic evaluation. Two different IQ assessment were used, with choice dictated by
38 time frame of Prior to 2016, the Kaufman Brief Intelligence Test (K-BIT; (Kaufman &
Kaufman, 2004) was used as a screening measure to estimate participants’ cognitive ability. The K-BIT includes two subtests assessing verbal ability and one subtest assessing nonverbal ability, which can then be combined to obtain an estimate of overall cognitive ability. Beginning in 2016, children completed the Wechsler Primary
Performance Scale of Intelligence- Fourth Edition (WPPSI-IV; Wechsler, 2012), the
Wechsler Intelligence Scale for Children- Fifth Edition (WISC-V; (Wechsler, 2014), or the Wechsler Adult Intelligence Scale- Fourth Edition (WAIS-IV; (Wechsler, 2008) based on age at the time of assessment. The overall composite measure from the intelligence test administered (e.g., FSIQ for Wechsler tests, Overall IQ Composite for
KBIT-2, etc.) was used to capture the participant’s global score of cognitive functioning.
Temperament. Assessment of child temperament during the infancy and toddler period was derived from the SMQ (Manos, 2004). Parent report of child’s temperament was assessed across 9 domains: 1) Activity Level, 2) Rhythmicity (of bowel habits, sleep, and eating), 3) Approach-Withdrawal toward new situations, 4) Adaptability, 5)
Threshold of response to stimulation, 6) Intensity of response, 7) predominant Mood, 8)
Distractibility, and 9) Attention Span-Persistence. Each item was rated on a 3 point Likert scale individually anchored for each domain (i.e., Activity Level: 1= High Activity,
3=Low Activity; Approach/Withdrawal, 1=Approach, 3=Withdrawal). In order to explore easy and difficult temperament patterns, a total score was derived using the 5 temperament categories of Rhythmicity, Approach-Withdrawal, Adaptability, Intensity of
Response, and Predominant Mood. These 5 domains were described by Thomas and
Chess (1977) as capturing children with easy and difficult temperament patterns. Three
39 of the temperament categories were reverse recoded so that all 5 of the categories reflected easier temperament characteristics in the same direction. A composite temperament score was created by taking the mean of the 5 temperament categories
(Rhythmicity, Approach-Withdrawal, Adaptability, Intensity of Response, and
Predominant Mood), with lower scores indicating easier temperament style and higher scores indicating more difficult temperament style.
Behavioral assets. Assessment of behavioral assets was also derived from the
SMQ (Manos, 2004). Behavioral assets were assessed across 38 questions that paralleled those argued by the Search Institute in their creation of Developmental Assets questionnaires (Scales & Roehlkepartain, 2003). Items on the SMQ have been proposed to form a 4-factor structure composed of social networking, school attitude, emotional adaptability, and self-efficacy (Short et al., 2007). Parents rated the frequency of child behavioral assets on a 5-point Likert scale (1=Seldom/Never Exhibited, 5=Often/Always
Exhibited). Exploratory and confirmatory factor analyses will be conducted to assess the factor structure of the Behavioral Assets measure within the context of the current sample, and results will be presented in the Results section.
Procedure
Parents completed measures online as part of their Cleveland Clinic Pre- appointment procedures. Additional information regarding risk and protective factors,
ADHD diagnosis, and secondary symptom domains were gathered during semi-structured interview with parent and child.
40 Data Preparation.
Child evaluation. Participants were required to complete parent, teacher, and self
(if child was over the age of 12) report measures before their diagnostic evaluation was scheduled at the Cleveland Clinic Children’s. The diagnostic evaluation process involved semi-structured interviews with parent and child to assess child behaviors and symptoms, as well as IQ testing with the child. Information obtained from the diagnostic evaluation had to be retrieved and manually entered by 3 coders (the author, and 2 trained research assistants). This information included child date of birth, age at time of evaluation, history of mental health service use (e.g., assessment, therapy, or medication management to address mental health concerns), parent and child risk and protective factors, parent report of ADHD symptoms, child IQ testing results, diagnosis, and comorbid or secondary concerns.
Parent Occupation. Parent occupation was coded based on the Hollingshead
Occupational Index (Hollingshead, 1975). The Hollingshead Occupational Index reflects a ranking of occupational prestige ranging from 1 (lowest ranking; includes occupations categorized as menial service workers and farm laborers) to 9 (highest ranking; includes occupations categorized as high level executives, owners of businesses valued at greater than $250,000, and high ranking professionals). This index was modified for the current study to reflect 4 categories of occupation ranging from 1 (lowest rank: generally included Hollingshead categories 1 and 2; unskilled workers, service workers etc.) to 4
(highest rank: generally included Hollingshead categories 7, 8, and 9; higher level executives, large business owners, administrators, managers, etc.). In addition, individuals who were unemployed or classified themselves as a homemaker, on
41 disability, or in prison were coded at 0. As indicated in Hollingshead (1975), individuals who described themselves as retired were assigned codes that reflected their most recent occupation if listed; otherwise, they were coded as 0.
A total of 3516 occupational codes were assigned within this sample (Biological mother n=1620; biological father n=1539; Stepmother n=58; Stepfather n=146; Adopted mother n=73; Adoptive father n=67; Other female caregiver n=9; Other male caregiver n=4). In order to address potential concerns with reliability, approximately 15% of the entire sample was randomly selected and coded by a second coder. Initially, 10% of the data was randomly selected and coded, and there was substantial agreement between the two raters (κ= .733, p < .001). However, it was decided to review cases of disagreement to come to a consensus on coding and then code an additional 5% of the data. Results suggested strong agreement (κ= .834, p < .001) between the raters.
Data Analytic Strategy. A variety of statistical techniques were used to clarify research questions. First, SPSS was used to screen data for patterns of missing data (i.e., missing items or parents that completed paperwork but did not follow through with in- person diagnostic assessment) and assumption fulfillment/requirements for analyses (i.e., multivariate normality and outliers, linearity, multicollinearity and singularity). In addition, analysis of variance and chi-square analyses were conducted for the purposes of illuminating sample descriptives based on age, and demographic variables (i.e., parent level of education/SES, child ethnicity, and family history of ADHD).
Next, AMOS version 24 was used to build and run structural equation modeling
(SEM) procedures to evaluate hypothesized models. The use of SEM procedures has been documented in psychology research (Morton & Frith, 1995; Nelson, Aylward, &
42 Steele, 2007). A structural equation model is a depiction of casual processes based on theoretical underpinnings or empirical support. SEM combines multiple analytic procedures of regression, confirmatory factor analysis, and path analysis to assess model fit with the data. SEM procedures generally consist of analyzing a measurement model and a structural model. The measurement model uses confirmatory factor analysis to assess latent constructs. SEM is well known for its ability to depict constructs using latent variables, which are not directly observable, and indicator variables, which are directly observed. As an example, the latent construct of inattention would be modeled using parent report ratings of behaviors that capture inattention (i.e., forgetfulness, difficulty sustaining attention, distractibility) as observed indicators. SEM offers advantage over other statistical procedures because it explicitly accounts for measurement error within model constructs. Next, the structural model assesses the relationship among latent variables using multiple regression paths. Overall, SEM has the ability to analyze complex relationships among constructs by implementing multiple statistical approaches simultaneously and controlling for error variance parameters, which is beyond the capabilities of other statistical techniques ȋ ƬǡͳͻͻͶǢǡʹͲͳͷȌ.
Within the current study, SEM procedures will be employed to test both measurement and structural models. Measurement models will be developed to provide a base to build upon when assessing regression paths in structural models. Given that there are measures being included in this study that are not validated, initial steps will be taken to confirm the factor structure of these measures. Strategies used in exploratory factor analysis (EFA) and confirmatory factor analyses are described in the next section.
43 Strategies for Exploratory and Confirmatory Factor Analyses. SEM models will be developed to answer research questions to understand the impact age of ADHD diagnosis has ADHD symptom presentation (structural portion of SEM model, research question 1) and on impairment in domains of quality of life (measurement portion of research question 1) and behavioral assets (measurement portion of SEM for research question 7). The two measures used to help answer these research questions, the Child and Family Quality of Life (CFQL) measure and the Behavioral Assets questionnaire have been used in limited research, and therefore, their factor structure and validity within the current sample needs to first be determined through a series of exploratory and confirmatory factor analyses. Factor analyses will clarify how these measurement models fit the current data, and then the final best-fitting models for the CFQL measure and the
Behavioral Assets questionnaire derived from the factor analyses can then be used as the measurement portion of the full SEM models answering research questions.
Exploratory factor analysis (EFA) within SPSS was used to first examine the grouping of items based on hypothesized groupings selected from previous research.
Specifically, the CFQL was hypothesized to have 6 factors (Markowitz et al., 2016) and the Behavioral Assets questionnaire was hypothesized to have 4 factors (Short et al.,
2007). EFA using principal axis factoring (PAF) will be utilized because this approach extracts the shared variance between observed variables and maximizes the intercorrelation of items when forming latent factors (Tabachnick & Fidell, 2012). The
PAF approach acknowledges that both random and systematic measurement error, which are unmeasurable, can contribute to common variance among latent factors and attempts to remove this error variance and reduce noise by concentrating only on the variance
44 shared from one observed variable to the next following an iterative process (Tabachnick
& Fidell, 2012). After factor extraction, rotational techniques are implemented to guide interpretation of solution results. Oblique rotation method, which allows factors to be correlated with each other, were the preferred approach for the current study because the measures of interest tap constructs that are likely highly interrelated (e.g., domains of quality of life that impact both parent and child on the CFQL). Direct oblimin rotation, which is a type of oblique rotation, was selected to minimize cross-loadings between variables and simplify produced factors. This helps to reduce selecting factors where items fall across multiple factors, making factors easier to interpret. The number of factor solutions specified represented a range of factor solutions higher and lower than the hypothesized model (e.g., the CFQL was hypothesized to have 6 factors, so between 4 to
8 factor solutions were examined). Factors were selected by examining scree plots for change in eigenvalues (elbow) and examining eigenvalues that were greater than or equal to 1.0. Factor solutions were assumed to have good fit with the data if the factor solutions were not highly correlated with each other (>.60), and fewer than 50%of all residual correlations were greater than .05, which represents the difference between the correlations and the implied correlations generated by SPSS to fit the data. Items were retained for EFAs if they had primary factor loadings greater than .40 and secondary factor loadings less than .30; otherwise, items were removed one at a time and new EFAs were run with the designated ranges of specified factor solutions (e.g., 4 to 8 factor solutions). Once factor solutions were chosen, the reliability of individual factors was assessed using Cronbach’s alpha, which measures internal consistency (in this case, within each factor), with values greater than or equal to .70 deemed acceptable.
45 Following EFA, confirmatory factor analysis (CFA) was conducted in AMOS to determine if the factor structure fit the data. CFA is used to assess the validity of the hypothesized factor structure within the data. Within SEM procedure, there is not 1 gold standard for evaluating model fit, therefore, it is generally recommended that multiple goodness of fit statistics are used to determine model fit, including the chi-square statistic, Bentler Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and the
Steiger-Lind Root Mean Square Error of Approximation (RMSEA). The chi-square statistic tests the (null) hypothesis that there is no difference between the predicted covariance matrix generated by the model and the observed covariance matrix generated by the population. For the model to demonstrate good fit with the data, the chi-square statistic should be significant at p<.001 level (given the large size of the current sample).
Although chi-square statistic is always the first step in SEM, it has been shown to be sensitive to sample size and less reliable with larger sample sizes (<200), therefore, additional goodness-of-fit indices will be used to qualify model fit. Since chi square is influenced by large sample sizes, two additional goodness-of-fit indices (i.e., Bentler
Comparative Fit Index (CFI), and the Tucker-Lewis Index (TLI)) were performed in an effort to insure the hypothesized model’s fit in comparison to an independent, fully restricted null model. For both the CFI and TFI, values range from 0 to 1 with values
>.90 indicating acceptable model fit. Lastly, the Steiger-Lind Root Mean Square Error of
Approximation (RMSEA) examines the lack of fit between the hypothesized model and a fully saturated model, representing the least restricted model where all estimated parameters are set equal to the number of observed variables. For the RMSEA, values ≤
.08 indicate the model has low level of misspecification and demonstrate good fit with
46 the data (Byrne, 2016; Kline, 2015; Tabachnick & Fidell, 2012). Within the CFAs, post hoc modifications were conducted based on modification indices indicating improvement in chi-square.
Strategies for Structural Equation Models. Structural equation models will be developed to examine the relationships among variables as described in research questions. Predictor variables will include ADHD symptoms of hyperactivity-impulsivity and inattention from the ARS-P and ratings of impairment, which will include an overall composite of parent-reported symptom severity, total number of parent reported referral concerns, teacher ratings of severity of behavior problems in the classroom, teacher rating of academic achievement, and parent report of impairment in familial functioning
(CFQL). The outcome variable will be age as a continuous variable. Lastly, IQ, temperament, and behavioral assets will be examined as predictor variables to age of
ADHD diagnosis, but also examined as potential confounding variables, which impact the relationship between the predictor and outcome variables. The statistical procedure for identifying confounding effects is similar to that of mediation, however, confounding factors occur in temporal order prior to the predictor variable. More specifically, a mediation model examines the impact of mediator (M) on the relationship between predictor (X) and outcome (Y), where the mediator is an intermediate variable between predictor and outcome (e.g., XÆ MÆ Y); in contrast, a confounder model examines the impact of confounder (C) on the relationship between predictor (X) and outcome (Y), but the confounder is not intermediate and must precede the predictor in time (e.g., CÆ
XÆY). Therefore, within a mediation model, the relationship between predictor and
47 outcome is thought to occur as a result of the predictors indirect path through the mediator leading the path from predictor to outcome to be nonsignificant (e.g., paths from
XÆM and MÆ Y are significant, and path from XÆY becomes nonsignificant or reduced), whereas, in a confounder model, the association between predictor (X) and outcome (Y) is occurring because both are outcomes of confounder (e.g., paths from CÆ
X and CÆY both significant, and path from XÆY becomes nonsignificant or reduced)
(MacKinnon, Krull, & Lockwood, 2000)Ǥ
In this study examination of the confounding effects in the models was addressed in three steps. The first model had to show that the Impairment variables (b) predict the
Outcome variable of Age of ADHD diagnosis (c) before Risk and Compensatory factors were introduced into the model. The second model had to show that the Risk and
Compensatory factors (IQ, temperament, and behavioral assets) (a) predicted the
Outcome variable of Age of ADHD diagnosis (c). Finally, assuming that the paths between Impairment variables to Outcome variable (b Æc) as well as the risk and protective factors to outcome variable (a Æc) were significant, the final model had to show that Risk and Compensatory factors (IQ, temperament, and behavioral assets) predicted Impairment variables (aÆb), and also that the path from Impairment variables to Age of ADHD diagnosis (bÆc) was nonsignificant or reduced in magnitude when the
Risk and Compensatory factors (a) were introduced to the model. This pattern of results would suggest that Risk and Compensatory factors account for the initial relationship between Impairment and Age of ADHD diagnosis. Figure 7 depicts the full hypothesized model with Risk and Compensatory factors, Impairment variables, and ADHD symptoms predicting Age of ADHD diagnosis, and the confounding effects of Risk and
48 Compensatory factors on the relationship between Impairment and Age of ADHD diagnosis. In general, model parameters will be estimated using Full Estimation
Maximum Likelihood (FIML) estimation procedures and model fit will be evaluated using the following criterion: non-significant chi-square values, a Comparative Fit Index
(CFI) value greater than or equal to .90, a Tucker Lewis Index (TLI) value greater than or equal to .90, the root mean square error of approximation (RMSEA) value less than .08.
Risk/Compensatory Factors Predictors Outcome Variable ADHD Symptoms
Inattention Symptoms
Hyperactivity/ Impulsivity Symptoms IQIQ Age of ADHD AD Diagnosis
Impairment Age Temperament
Symptom Severity-P
Behavioral Assets CFQL
Referral Concerns
Classroom behavior-T
Achievement-T
Figure 7. Proposed Conceptual Model of IQ, Temperament, and Behavioral Assets as potential Confounding Factors on Impairment effects on Age of ADHD Diagnosis, and ADHD Symptoms impacting Age of diagnosis
49 Results Descriptives
The final sample consisted of 1,331 children and adolescents between the ages of
3 and 18 years old (M= 9.13, SD=3.57). The sample was predominantly male (70.7%) and Caucasian (75.8%). The majority of the sample was diagnosed with the Combined
(50.1%) or Predominantly Inattentive (45%) subtype of ADHD. Table 1 contains the full descriptive characteristics of the sample. In addition, to provide some context to the range demographic and ADHD subtype information within the context of development, seven age groupings were created (age 3-5, 6-7, 8-9, 10-12, 13-14, 15-16, 17-18). This information is presented in Table 2.
In addition, information on parental education and occupational levels (included in Table 1-A in the Appendix) suggested a fairly consistent level of education and occupational level for caregivers. Specifically, most caregivers earned at least a high school diploma (biological mothers=37.7%, biological fathers=46.2%, non-biological caregivers= 54.2%). In addition, most caregivers in the sample held jobs classified as higher level professions (biological mothers=42.7%, biological fathers=38.3%, non- biological caregivers=41.2%). Overall, the sample was composed of caregivers that were well educated and in more skilled labor occupations.
50
Table 1. Participant Demographics and Diagnostic Subtype M SD N Age 9.13 3.57 1331 n % Gender 1254 Male 887 70.7% Female 367 29.3% Race 1196 Caucasian 906 75.8% African American 173 14.5% Biracial 72 6.0% Asian 22 1.8% Native American 2 0.1% Other 21 1.8% Ethnicity 1209 Hispanic 87 7.2% Non-Hispanic 1122 92.8% ADHD Subtype 1331 Predominantly Inattentive 599 45.0% Predominantly Hyperactive/Impulsive 53 4.0% Combined 667 50.1% Other or Unspecified 12 0.9% Birth Family History 1220 Family History of Attention Problems 492 37% Family History of Hyperactivity 294 22.1% Note: Family history of problems with attention or hyperactivity was endorsed if there was any member of the child’s biological family that had experienced these concerns during childhood or adolescence. 51 % 7.3% 0.0% 0.0% 5.5% 0.0% 0.0% 61.4% 38.6% 70.9% 16.4% 10.7% 89.3% 79.7% 20.3% (n=59) 9 4 0 0 3 6 0 0 n Ages 17-18 35 22 39 50 47 12 % 8.1% 7.0% 0.0% 0.0% 1.2% 3.5% 1.1% 1.1% 65.5% 34.5% 83.7% 96.5% 67.4% 30.4% (n=92) 7 6 0 0 1 3 1 1 n Ages 15-16 57 30 72 82 62 28 % 1.1% 1.1% 0.0% 1.1% 5.4% 1.0% 1.9% 66.3% 33.7% 77.2% 19.6% 94.6% 81.6% 14.6% (n=103) 1 1 0 1 5 1 2 Ages 13-14 n 67 34 71 18 88 85 15 % 5.9% 3.2% 0.0% 1.8% 8.1% 2.4% 0.8% 70.4% 29.6% 76.8% 12.3% 91.9% 62.5% 33.9% (n=248) 7 0 4 6 2 n Ages 10-12 69 27 13 18 84 164 169 205 156 Age Groupings % 3.5% 2.7% 0.4% 1.2% 6.1% 2.3% 1.0% 67.4% 32.6% 76.8% 15.1% 93.9% 50.0% 46.6% (n=298) Ages 8-9 9 8 1 3 7 3 n 88 39 16 182 199 246 149 139 % on Age Grouping on Age 6.9% 1.3% 0.0% 1.9% 6.5% 6.0% 0.9% 74.9% 25.1% 77.8% 11.9% 93.5% 24.8% 68.4% (n=351) Ages 6-7 5 0 6 3 n 85 38 22 21 87 21 253 249 302 240 % 0.6% 0.6% 1.8% 7.2% 9.4% 0.6% 76.8% 23.2% 65.2% 21.3% 10.4% 10.8% 89.2% 82.8% (n=180) Ages 3-5 1 1 3 1 n 39 35 17 18 13 17 129 107 149 149 andbased Diagnostic Subtype Male Female Caucasian African American Biracial Asian Native American Other Hispanic Non-Hispanic InattentivePredominantly Predominantly Hyperactive/Impulsive Combined or Unspecified Other Table 2. Participant Demographics (SD) Domains, M Gender Race Ethnicity ADHD Subtype 52
Results of Exploratory and Confirmatory Factor Analyses
CFQL. The approach to this EFA and CFA was based on the results of
Markowitz et al. (2016) that showed the CFQL had a 6 factor solution within a population of parents bringing their children in for evaluation of Autism Spectrum
Disorder. Prior to running the EFA, 3 items on the CFQL were reverse coded to match the directionality of responses within their hypothesized factor. For example, Items 22 and 24 were both included within a section of items where higher scores (scale 1-5) indicated better functioning and higher quality of life. Similarly, Item 15 was reverse coded to match item responses where higher scores indicate worse functioning and lower quality of life.
EFA using principal axis factoring (PAF) with direct oblimin rotation was conducted on the 32 items of the CFQL measure. A range of 4 to 8 factor solutions were tested, with items being removed if they demonstrated low primary loadings (<.40) or high secondary factor loadings (>.30). In total, 4 items were removed (#5: “My child appears happy and content”, #15: “I think I manage stress related to caring for my child very well”, #19: “I think we are living beyond our financial means”, and #32: “I accept my child for who he/she is regardless of their problems”) because they did not contribute to a simple factor structure and failed to meet a minimum criteria of having a primary factor loading of at least .40 and secondary factor loadings less than .30. After these steps, a 5-factor solution was retained. The 5-factor solution (see Table 3) revealed factors similar to those reported by Markowitz et al. (2016) and included the following domains of quality of life: Family/Caregiver (13 items; #6-14, 22, 29-31),
Partner/Relationship (4 items; #25-28), Financial (3 items, #16-18), External Support (4 53 items, #20, 21, 23, 24), and Child (4 items, #1-4). Although Markowitz et al. (2016) reported a 6-factor solution, the 5-factor solution showed the 3 items from their Cope factor, that includes items assessing the caregiver’s ability to cope with their child’s illness, merging together with the Family/Caregiver factor. The 5 factor solution contained 13% of nonredundant residuals, and factor correlations ranged from .11 to .37
(See Table 4). In comparison, the 4-, 6-, 7-, and 8-factor solutions all demonstrated poor fit with the data as evidenced by primary factor loadings falling below .40, secondary factor loadings falling above .30, and factor correlations rising above .60. The final solution suggested that 5 factor solution accounted for 64.4% of the total variance, with
Factor 1 (Family/Caregiver) accounting for 31%, Factor 2 (Partner/Relationship) accounting for 11%, Factor 3 (Financial) accounting for 8.8%, Factor 4 (External
Support) accounting for 7.1%, and Factor 5 (Child) accounting for 6.5%. The 5 individual factors each demonstrated good reliability, ranging from Cronbach’s α= .74 to
.93 (see Table 4 for details). The correlation table for the 28 items retained for the 5 factor solution is presented in Table 2-A in the appendix. 54 A .12 .14 5 .41 .82 .67 .55 -.22 -.14 0.75 10.50 B 4 .12 .77 .73 .73 .56 0.81 12.92 A 3 .91 .93 .87 -.19 0.93 2.75 -.13 Factor B 2 .85 .89 .78 .85 0.90 11.33 A 1 .57 .83 .84 .81 .82 .77 .73 .82 .72 .60 .49 .47 -.28 -.46 -.13 -.20 -.13 0.93 134.05 ning. SD 1.09 0.82 0.99 0.62 0.53 0.61 1.22 1.18 1.11 1.11 1.21 1.18 1.34 1.38 1.18 1.41 1.31 1.40 1.41 1.27 0.75 0.99 1.00 0.95 0.93 1.10 1.00 0.83 M 3.79 4.37 4.12 1.19 1.13 1.19 3.53 3.60 3.19 2.52 2.87 3.67 2.29 2.26 1.97 2.68 2.66 2.82 2.81 2.34 4.67 2.60 1.72 1.69 4.23 3.99 4.31 2.95 Variance e factors indicate better functioe factors indicate better Reliability coefficients (α) problems d’s behaviors d’s needs a result of my child’s problems child’sa result of my hat are appropriate for him/her for hat are appropriate s problem disagreements d my child’s problems my child’s ely on ential e coded) of my child’s of my child’s ld ning. B= Higher scoresning. B= Higher on thes and family’s and rs avoid me because of my chil of me because avoid rs y needs in caring for my child my for in caring y needs been reduced as a result of been reduced as a result s been difficults been as a result ell about our child friends to rely upon (revers upon to rely friends on how to best care for my chil my care for best to how on a result of caring for my chi for a result of caring r family from social activities from r family everyday tasks and expectations t and expectations tasks everyday hassles andhassles daily activities wel l wing whether my child will have a wing whether child will have my d’s behavior is unmanageable d’s tor 3= Financial, Factor 4= External Support, Factor 5=Child. 5=Child. Factor External Support, Fac tor4= 3= Financial, Factortor 2= Partner/Relationship, my child’s condition difficulties have added stress to our home life have to our added stress difficulties our home others at difficult to entertain make it problems think my friends and family membe family and friends think my child’s managingin difficultie my me friendshave that support My child’s My child’s My limit ou child’s difficulties places difficult to go My make child’s it problems My child’s relax family to our difficult for My make it child’s problems as energy without I feel tired or not kno to due I feel on-guard I am frustrated that my chil dominates my life my child for I feel that caring I my child’s difficulties with difficult to cope I find it family? my happen to did this Why me? or I think Why about I feel angry is positive My partner relationship with my I andMy see eye-to-eye partner and differences our I are and to resolving My partner committed w I communicate andMy partner My situation ha family’s financial as has beenbasic family reduced needs availablefor The money has for extras available The money I m understands family My extended and consistently others I can r systems support I have a strong isolated andI feel alone, without My child deals with daily life My at his/her child full is ablepotential to function his/herpot My child for appropriately is able to learn age and with through My child follows Table 3. CFQL EFA 5Solution Factor Item 6 7 8 9 10 11 12 13 14 22 29 30 31 25 26 27 28 16 17 18 20 21 23 24 1 2 3 4 Notes: A= Lower scores onse factors indicate better functio the Factor Fac 1= Family/Caregiver, 55
Table 4. CFQL Factor Correlation Matrix Factor 1A 2B 3A 4B 5A 1. Family/CaregiverA 1.00 2. Partner/RelationshipB -.26 1.00 3. FinancialA .27 -.12 1.00 4. External SupportB -.26 .28 -.17 1.00 5. ChildA -.37 .15 -.11 .11 1.00 Notes: A= Lower scores on these factors indicate better functioning. B= Higher scores on these factors indicate better functioning.
Following exploratory factor analysis, a confirmatory factor analysis was conducted in AMOS to determine if the factor structure fit the data. CFA analyses were performed using the 993 participants that had complete listwise data across all items on the CFQL. Models of the individual factors were first examined to determine fit with the data and apply any post hoc model modifications to improve the individual factors.
There were 3 covariances added between the error terms of Items 7 to Item 8 (residual of
#7 “My child’s difficulties limit our family from social activities” ↔ residual of #8 “My child’s problems make it difficult to go places”), Item 8 to Item 9 (residual of #8 “My child’s problems make it difficult to go places” ↔ residual of #9 “My child’s problems make it difficult to entertain others at our home”), Item 7 to Item 9
(residual of #7 “My child’s difficulties limit our family from social activities” ↔ residual of #9 “My child’s problems make it difficult to entertain others at our home”), and Item 30 to Item 31 (residual of #30 “I think Why me? or Why did this happen to my family?” ↔ residual of #31 “I feel angry about my child’s condition”) within the hypothesized Family/Caregiver factor, which helped improve the fit of the overall model. The full hypothesized model is presented in Figure 8 with ovals representing latent variables, rectangles representing indicator variables, and circles representing error variances. Despite a significant chi-square statistic (χ2 (346, N=993)=2134.84, 56 p<.001), the initial model demonstrated only moderate fit with the data when examining the other goodness-of-fit statistics (TFI=.89, CFI=.90, and RMSEA=.072
[90% Confidence Intervals=.069, .075]).Therefore, post hoc modifications were performed in an attempt to develop a model that better fit the data. Post hoc modifications retained are included in Table 5.
Figure 8. Initial Standardized CFA Model of CFQL 57
Table 5. Model Development of CFQL 5 Factor Model Chi Model Step df p TLI CFI RMSEA Square 1. Initial Model 2134.84 346 .00 .887 .897 .072 2. Add covariance between error terms of item CFQL_ExtSupp_20 (e20) and item 1924.57 345 .00 .900 .909 .068 CFQL_ExtSupp_21 (e21) 3. Add covariance between error terms of Item CFQL_Reltnshp_25 (e25) and Item 1880.77 334 .00 .902 .911 .067 CFQL_Reltnshp_27 (e27) 4. Added covariance between Latent Factor of Family/Caregiver (CFQL_FamCargivr) and 1711.09 343 .00 .913 .921 .063 Child (CFQL_Child) 5. Added covariance between Latent Factor of Family/Caregiver (CFQL_FamCargivr) and 1614.99 342 .00 .919 .926 .061 External Support (CFQL_ExtSupp) 6. Added covariance between Latent Factor of External Support (CFQL_ExtSupp) and 1536.97 341 .00 .923 .931 .059 Partner/Relationship (CFQL_Reltnshp) 7. Add covariance between error terms of Item CFQL_ExtSupp_22 (e22) and Item 1492.01 340 .00 .926 .933 .058 CFQL_ExtSupp_24 (e24) 8. Add covariance between error terms of Item CFQL_Child_2 (e2) and Item 1400.03 339 .00 .932 .939 .056 CFQL_Child_3 (e3)
The final model (depicted in Figure 9) was run using FIML procedures, and the
model demonstrated good fit with the data as indicated by a significant chi-square
statistic (χ2 (339, N=993)=1400.03, p<.001), as well as TFI=.932, CFI=.939, and
RMSEA=.056 [90% C.I.=.053, .059]. Parameter estimates for the CFQL 5-Factor
solution are presented in Table 5. Overall, the standardized regression weights, which
represent factor loadings, indicated primary factor loadings ranging from .42 to .93, and
all results (covariances, correlations) are statistically significant. Of note, there was a
covariance added between the error terms of Item 22
(CFQL_ExtSupp_22, e22) and Item 24 (CFQL_ExtSupp_24, e24), which occurred on separate
factors. This decision was made because the items were originally included on the same factor
by Markowitz et al 58
(2016) and the items reflected similar content (Item 22: “I think my friends and family members avoid me because of my child’s behaviors” and Item 24 (reverse coded): “I feel alone, isolated and without friends to rely upon”). In addition, there was a large correlation between the
Family/Caregiver factor and the Child factor (r= -.53, p<.001), a medium correlation between the External Support factor and the Partner/Relationship factor (r= -.32, p<.001), and a small correlation between the External Support factor and the Family/
Caregiver factor (r= -.28, p<.001). These results suggest that as the caregiver’s quality of life increases, the caregiver’s perception of their child’s quality of life increases. In addition, as caregiver’s sense of external support from family and friends increases, the caregiver’s quality of life and caregiver’s sense of support within their relationship also increases. These correlations indicate the impact that the caregiver’s quality of life and the caregiver’s perception of their child’s quality of life, as well as how the caregiver’s quality of life can be impacted by the level of support they have from a significant other and extended family and friends. 59
Figure 9. Standardized Results of CFQL 5 Factor Solution 60
Table 6. SEM Parameter Estimates for CFQL 5-Factor Model Measurement Weights Standardized Unstandardized Standard P- Paths Weights Weights Error (SE) value Family/Caregiver Latent (CFQL_FamCargvr) Item 6 (CFQL_Fam_6) .67 1.00 -- p<.001 Item 7 (CFQL_Fam_7) .72 1.22 .06 p<.001 Item 8 (CFQL_Fam_8) .70 1.22 .06 p<.001 Item 9 (CFQL_Fam_9) .67 1.01 .05 p<.001 Item 10 (CFQL_Fam_10) .81 1.46 .06 p<.001 Item 11 (CFQL_Caregivr_11) .82 1.37 .06 p<.001 Item 12 (CFQL_Caregivr_12) .74 1.32 .06 p<.001 Item 13 (CFQL_Caregivr_13) .82 1.47 .07 p<.001 Item 14 (CFQL_Caregivr_14) .80 1.29 .06 p<.001 Item 22 (CFQL_ExtSupp_22) .42 .40 .03 p<.001 Item 29 (CFQL_Cope_29) .67 .85 .04 p<.001 Item 30 (CFQL_Cope_30) .52 .66 .04 p<.001 Item 31 (CFQL_Cope_31) .53 .64 .04 p<.001 Financial Latent (CFQL_Fin) Item 16 (CFQL_FinQL_16) .93 1.00 -- p<.001 Item 17 (CFQL_FinQL_17) .92 .85 .02 p<.001 Item 18 (CFQL_FinQL_18) .89 .95 .02 p<.001 Child Latent (CFQL_Child) Item 1 (CFQL_Child_1) .68 1.00 -- p<.001 Item 2 (CFQL_Child_2) .68 1.00 .07 p<.001 Item 3 (CFQL_Child_3) .45 .72 .07 p<.001 Item 4 (CFQL_Child_4) .67 .74 .05 p<.001 External Support Latent (CFQL_ExtSupp) Item 20 (CFQL_ExtSupp_20) .54 1.00 -- p<.001 Item 21 (CFQL_ExtSupp_21) .55 .99 .05 p<.001 Item 23 (CFQL_ExtSupp_23) .91 1.50 .10 p<.001 Item 24 (CFQL_ExtSupp_24) .73 1.09 .07 p<.001 Partner/Relationship Latent (CFQL_Reltnshp) Item 25 (CFQL_Reltnshp_25) .81 1.00 -- p<.001 Item 26 (CFQL_Reltnshp_26) .81 1.18 .04 p<.001 Item 27 (CFQL_Reltnshp_27) .79 .86 .03 p<.001 Item 28 (CFQL_Reltnshp_28) .91 1.20 .04 p<.001 Structural Covariances Paths P- Correlations Covariances S.E. value Family/Caregiver Latent (CFQL_FamCargvr) ↔ Child Latent -.53 -.31 .03 p<.001 (CFQL_Child) 61
Family/Caregiver Latent (CFQL_FamCargvr) ↔ External -.26 -.13 .02 p<.001 Support Latent (CFQL_ExtSupp) Partner/Relationship Latent (CFQL_Reltnshp) ↔ External .32 .16 .02 p<.001 Support Latent (CFQL_ExtSupp) Measurement Covariances Paths P- Correlations Covariances S.E. value e2 ↔ e3 .41 .36 .04 p<.001 e7 ↔ e8 .71 .64 .04 p<.001 e8 ↔ e9 .64 .55 .04 p<.001 e7 ↔ e9 .56 .45 .03 p<.001 e20 ↔ e21 .47 .47 .04 p<.001 e22 ↔ e24 -.24 -.11 .02 p<.001 e25 ↔ e27 .28 .08 .01 p<.001 e30 ↔ e31 .55 .38 .03 p<.001 Squared Multiple Correlations Endogenous variable R2 Item 1 .47 Item 2 .47 Item 3 .20 Item 4 .45 Item 6 .44 Item 7 .51 Item 8 .49 Item 9 .45 Item 10 .66 Item 11 .68 Item 12 .55 Item 13 .67 Item 14 .64 Item 22 .17 Item 29 .45 Item 30 .27 Item 31 .28 Item 16 .86 Item 17 .84 Item 18 .80 Item 20 .29 Item 21 .31 Item 23 .83 Item 24 .53 Item 25 .65 Item 26 .65 Item 27 .63 Item 28 .82 62
Behavioral Assets. EFA using principal axis factoring (PAF) with direct oblimin rotation was conducted on the 38 items of the Behavioral Assets questionnaire. A range of 4 to 8 factor solutions were tested, with items being removed if they demonstrated low primary loadings (<.40) or high secondary factor loadings (>.30). In total, 18 items were removed (#5, 6, 7, 9, 10, 13, 16, 17, 19, 20, 22, 23, 27, 28, 31, 33,
34, and 35, see Table 6 for item wording) because they did not contribute to a simple factor structure and failed to meet a minimum criteria of having a primary factor loading of at least .40 and secondary factor loadings less than .30.
Table 7. Items from Behavioral Assets that were eliminated during EFA Item 5 Willingly participates in activities with family members 6 Wants to be accepted by others 7 Demonstrates leadership in groups 9 Participates cooperatively in a group 10 Willingly accepts help from family members 13 Works well in small groups 16 Usually completes homework 17 Works well in large groups 19 Engages in creative activities 20 Initiates activities by him/herself 22 Waits his/her tum 23 Has a good sense of humor 27 Is generally enthusiastic 28 Is friendly and outgoing 31 Likes to be helpful 33 Responds to acknowledgment 34 Shows self-confidence 35 Easily acknowledges others
The remaining 20 items were retained in a 6 factor solution. The 6 factors retained (see
Table 8) represented asset domains similar to those reported by Short et al. (2007), including Social Network (5 items, #1, 2, 3, 4, 8), Academic Compliance (2 items; #15,
18), Emotional Intelligence (2 items, #29, 30), Academic Attitude (3 items, #11, 12, 14),
Emotional Adaptability 63
(5 items, #21, 24, 25, 26, 32), and Compliance (3 items, #36, 37, 38). The 6-factor solution contained 5% of nonredundant residuals, and factor correlations ranged from .05 to .43 (see Table 9). In comparison, the 4-, 5-, 7-, and 8-factor solutions all demonstrated poor fit with the data as evidenced by primary factor loadings falling below .40, secondary factor loadings falling above .30, factor correlations rising above .60, or factors with fewer than 2 items. The retained 6-factor solution accounted for 67.8% of the total variance, with Factor 1 (Social Network) accounting for 29.9%,
Factor 2 (Academic Compliance) accounting for 10.1%, Factor 3
(Emotional Intelligence) accounting for 8.7%, Factor 4 (Academic Attitude) accounting for 7.5%, Factor 5 (Emotional Adaptability) accounting for 6.7%, and Factor 6
(Compliance) accounting for 4.9%. The 6 individual factors each demonstrated high reliability (see Table 8 for details), ranging from Cronbach’s α= .74 to .85. The correlation table for the 20 items retained for the 6 factor solution is presented in Table
3-A in the appendix. 64 6 .10 .14 .13 .22 .65 .68 .60 -.17 0.74 7.20 (Compliance) 5 -.64 -.69 0.76 -.57 -.49 -.48 15.91 Adapt) (Emot’l (Emot’l 4 .92 .76 .42 0.76 8.81 Attitude) (Academ (Academ Factor 3 .11 .11 .94 .69 .21 .15 IQ) 0.84 5.06 (Emot’l (Emot’l 2 .21 .13 .21 - 0.85 4.71 -.10 -.73 -.77 -.19 -.11 -.18 (Academic (Academic Compliance) 1 .82 .79 .78 .56 .56 .10 0.83 21.65 (Social (Social Network) .99 SD 1.13 1.20 1.24 1.19 1.29 1.11 1.17 1.16 1.14 1.08 1.02 1.13 1.17 1.19 0.99 1.33 1.13 1.35 1.22 M 3.56 3.89 3.53 3.61 3.51 4.10 3.07 2.87 3.15 3.08 2.79 3.13 3.15 2.98 3.80 3.94 3.85 4.02 3.69 3.55 Variance iability coefficients (α) coefficients iability Rel Follows rules at school. at rules Follows Makes friend easily. friend Makes children. by other liked Is friends. close two least at Has group. a of part to be Likes once least at friends with activities Plays/shares school. of outside per week in school. authority to appropriately Responds feelings. Easily expresses feelings. identify to able Is school. likes Generally willingly. school Attends activities. school about Talks routine. in changes Easily tolerates easily. up give Doesn't a good sport. Is to activity one from transitions manages Easily another. well. challenges Handles home. at rules Follows of property. care Takes proper wrongdoing. Admits Table 8. Behavioral Assets EFA 6 Factor Solution Assets Behavioral Table 8. Item 1 2 3 4 8 15 18 29 30 11 12 14 21 24 25 26 32 36 37 38 65
Table 9. Factor Correlation Matrix for the Behavioral Assets Questionnaire Factor 1 2 3 4 5 6 1. Social Network 1.00 2. Academic Compliance -.27 1.00 3. Emotional Intelligence .31 .05 1.00 4. Academic Attitude .34 -.18 .30 1.00 5. Emotional Adaptability -.43 .23 -.29 -.35 1.00 6. Compliance .27 -.34 .23 .22 -.40 1.00
Following exploratory factor analysis, a confirmatory factor analysis was conducted in AMOS to determine if the factor structure fit the data. Initial factor structure was tested using the participants that had complete listwise data across all items on the
Behavioral Assets questionnaire (n=1243). Model of the individual factors were first examined to determine fit with the data and apply any post hoc model modifications to improve the individual factors. On the Social Network factor, 3 covariances were added between the error terms of Item 3 and Item 8 (residual of #3 “Has at least two close friends” ↔ residual of #8 “Plays/shares activities with friends at least once per week outside of school”), Item 2 and Item 4 (residual of #2 “Is liked by other children” ↔ residual of #4 “Likes to be part of a group”), and Item 3 and Item 4 (residual of #3 “Has at least two close friends” ↔ residual of #4 “Likes to be part of a group”); on the
Emotional Adaptability factor, 1 covariance was added between the error variance of
Item 24 and Item 32 (residual of #24 “Doesn't give up easily” ↔ residual of #32
“Handles challenges well”). Individual factors were added into the CFA one by one in order to identify the model by identifying covariances between latent factors. As a result, covariances were added between the latent factor of Social Network and Emotional
Adaptability and Emotional Intelligence, between Academic Compliance and 66 Compliance, and between Emotional Adaptability and Emotional Intelligence. In addition, one covariance was added between the error term of Item 1 and Item 29
(residual of #1 “Makes friend easily” ↔ residual of #29 “Easily expresses feelings”). The initial hypothesized model is depicted in Figure 10 with ovals representing latent variables, rectangles representing indicator variables, and circles representing error variances. Despite a significant chi-square statistic (χ2 (161, N=1243)=1470.61, p<.001), the initial model demonstrated poor fit with the data when examining the other goodness- of-fit statistics (TFI=.84, CFI=.87, and RMSEA=.081 [90% C.I.=.077, .085]). Therefore, post hoc modifications were performed in attempt to develop a model that better fit the data. Post hoc modifications retained to develop the final model are included in Table 10.
67
Figure 10. Proposed Model of Behavioral Assets 6 Factor Solution
68 Table 10. Model Development of Behavioral Assets Questionnaire 6-Factor Model Chi Model Step df p TLI CFI RMSEA Square 1. Initial Model 1859.062 165 .00 .802 .828 .091 2. Added covariance between latent factors of Social Network (Social) 1699.438 164 .00 .819 .844 .087 and Emotional Adaptability (EmoAdapt) 3. Added covariance between latent factors of Compliance 1632.108 163 .00 .826 .851 .085 (Compliance) and Emotional Adaptability (EmoAdapt) 4. Added covariance between latent factors of Social Network (Social) 1488.938 162 .00 .842 .865 .081 and Emotional Intelligence (EmoIQ) 5. Added covariance between error terms of Item Behav_Asst_1 (e1) 1422.985 161 .00 .849 .872 .079 and Item Behav_Asst_29 (e29) 6. Added covariance between latent factors of Academic Compliance 1273.113 160 .00 .866 .887 .075 (Academ_Compl) and Emotional Adaptability (EmoAdapt) 7. Added covariance between latent factors of Compliance 1193.613 159 .00 .874 .895 .072 (Compliance) and Emotional Intelligence (EmoIQ) 8. Added covariance between latent factors of Academic Attitude 1137.576 158 .00 .880 .900 .071 (Academic) and Social Network (Social) 9. Added covariance between latent factors of Academic Attitude 1096.905 157 .00 .884 .904 .069 (Academic) and Emotional Adaptability (EmoAdapt) 10. Added covariance between latent factors of Academic Attitude 1028.101 156 .00 .892 .911 .067 (Academic) and Emotional Intelligence (EmoIQ) 11. Added covariance between error terms of Item Behav_Asst_4 (e4) 986.943 155 .00 .896 .915 .066 and Item Behav_Asst_29 (e29) 12. Added covariance between error terms of Item Behav_Asst_21 938.982 154 .00 .902 .920 .064 (e21) and Item Behav_Asst_26 (e26)
69
The final model demonstrated a good fit with the data as indicated by a significant chi-square statistic (χ2 (154, N=1243)=9384.982, p<.001), as well as TFI=.902,
CFI=.920, and RMSEA=.064 (90% C.I.: 059, .067). The final retained model is depicted in Figure 11, and the parameter estimates for the Behavioral Assets 6-Factor solution are presented in Table 11.
Figure 11. Standardized Results of Behavioral Assets 6 Factor Solution 70
Table 11. SEM Parameter Estimates for Behavioral Assets 6-Factor Model Measurement Weights Standardized Unstandardized Standard P- Paths Weights Weights Error value BA Social Network factor (Social) Item 1 (Behav_Asst_1) .83 1.00 p<.001 Item 2 (Behav_Asst_2) .84 .85 .03 p<.001 Item 3 (Behav_Asst_3) .70 .94 .04 p<.001 Item 4 (Behav_Asst_4) .68 .78 .03 p<.001 Item 8 (Behav_Asst_8) .53 .73 .04 p<.001 BA Academic Attitude factor (Academic) Item 11 (Behav_Asst_11) .84 1.00 p<.001 Item 12 (Behav_Asst_12) .79 .81 .04 p<.001 Item 14 (Behav_Asst_14) .54 .62 .04 p<.001 BA Academic Compliance factor (Academ_Compl) Item 15 (Behav_Asst_15) .86 1.00 p<.001 Item 18 (Behav_Asst_18) .86 1.06 .05 p<.001 BA Emotional Adaptability factor (EmoAdapt) Item 21 (Behav_Asst_21) .59 1.00 p<.001 Item 24 (Behav_Asst_24) .40 .68 .06 p<.001 Item 25 (Behav_Asst_25) .66 1.08 .07 p<.001 Item 26 (Behav_Asst_26) .63 .99 .05 p<.001 Item 32 (Behav_Asst_32) .56 .83 .06 p<.001 BA Emotional IQ factor (EmoIQ) Item 29 (Behav_Asst_29) .79 1.00 p<.001 Item 30 (Behav_Asst_30) .89 1.07 .06 p<.001 BA Compliance factor (Compliance) Item 36 (Behav_Asst_36) .80 1.00 p<.001 Item 37 (Behav_Asst_37) .68 .97 .05 p<.001 Item 38 (Behav_Asst_38) .63 .92 .05 p<.001 Structural Covariances Standard P- Correlations Covariances Paths Error value BA Emotional Adaptability factor p<.001 (EmoAdapt) ↔ BA Emotional IQ factor .37 .24 .03 (EmoIQ) BA Emotional Adaptability factor p<.001 (EmoAdapt) ↔ BA Compliance factor .52 .28 .03 (Compliance) BA Emotional Adaptability factor p<.001 (EmoAdapt) ↔ BA Academic .40 .26 .03 Compliance factor (Academ_Compl) BA Emotional Adaptability factor p<.001 (EmoAdapt) ↔ BA Social Network .39 .26 .03 factor (Social) 71
BA Emotional Adaptability factor p<.001 (EmoAdapt) ↔ BA Academic Attitude .30 .22 .03 factor (Academic) BA Compliance factor (Compliance) ↔ p<.001 BA Academic Compliance factor .57 .44 .03 (Academ_Compl) BA Compliance factor (Compliance) ↔ p<.001 .27 .21 .03 BA Emotional IQ factor (EmoIQ) BA Academic Attitude factor p<.001 (Academic) ↔ BA Social Network .35 .37 .04 factor (Social) BA Academic Attitude factor p<.001 (Academic) ↔ BA Emotional IQ factor .28 .29 .04 (EmoIQ) BA Social Network factor (Social) ↔ p<.001 .32 .30 .04 BA Emotional IQ factor (EmoIQ) Measurement Covariances Standard P- Correlations Covariances Paths Error value e1 ↔ e29 .38 .19 .02 p<.001 e2 ↔ e4 -.32 -.14 .02 p<.001 e3 ↔ e8 .26 .29 .04 p<.001 e4 ↔ e29 .24 .15 .02 p<.001 e12 ↔ e36 .21 .09 .02 p<.001 e21 ↔ e26 .27 .20 .03 p<.001 e24 ↔ e32 .31 .27 .03 p<.001 Squared Multiple Correlations Endogenous variable R2 Item 1 .68 Item 2 .71 Item 3 .48 Item 4 .46 Item 8 .28 Item 11 .71 Item 12 .62 Item 14 .30 Item 15 .75 Item 18 .74 Item 21 .35 Item 24 .16 Item 25 .43 Item 26 .40 Item 32 .32 Item 29 .63 Item 30 .79 Item 36 .65 Item 37 .47 Item 38 .39 72
Overall, results show strong primary factor loadings (represented as standardized regression weights) ranging from .40 to .89, and all results (covariances, correlations) are statistically significant. Of note, there are large correlations between the Compliance factor and the Academic Compliance factor (r= .57, p<.001) and the Emotional
Adaptability factor (r= .52, p<.001). Since the Compliance factor includes items representing following rules and taking responsibility for one’s actions, it seems logical that these behaviors would also relate to other latent factors representing following rules at school (Academic Compliance) and adjusting to changes in routine and challenge
(Emotional Adaptability). Medium correlations emerged among the Social Network factor and the Emotional Adaptability factor (r= .39, p<.001), Emotional Intelligence factor (r= .32, p<.001), and the Academic Attitudes factor (r= .35, p<.001). These results help to illustrate how children with positive social skills/ peer relationships (as reflected in the Social Network factor) are associated with also having better management of emotions in the face of challenge or transition (Emotional Adaptability), better understanding of emotions in self and others (Emotional Intelligence), and having a positive attitude toward school (Academic Attitude). There were also medium correlations between the Emotional Adaptability factor and the Emotional Intelligence factor (r= .37, p<.001) and Academic Compliance factor (r= .40, p<.001), which indicates that children who are better able to adapt to challenges or transitions (Emotional
Adaptability) were also associated with having better understanding emotions in self and others (Emotional Intelligence) and being compliant/respectful toward authority figures at school (Academic Compliance). On the item level, there were 3 medium correlations and 3 small correlations. Of note, there were 2 correlations that occurred between factors, 73 with Item 29 (e29, residual of Behav_Asst_29: “Easily expresses feelings”) from the
Emotional Intelligence factor being correlated with Item 1 (e1, residual of Behav_Asst_1:
“Makes friend easily”; r= .37, p<.001) and Item 4 (e4, residual of Behav_Asst_4: “Likes to be part of a group”; r= .24, p<.001) on the Social Network factor. These correlations suggest that children who are better able to express their emotions are also more likely to make friends and enjoy being included with groups of peers.
Structural Models
Structural models were conducted in 4 separate models that then combined into the full overall model in order to address research questions and hypotheses. The measurement model discussed in previous sections included the 5 factor solution of the
CFQL questionnaire and the 6 factor solution of the Behavioral Assets questionnaire.
Composite variables that represented the latent factors in the measurement model were created by taking the mean score of non-missing items on each factor, which was calculated for participants who had greater than 50% of non-missing items on each factor
(e.g., the Behavioral Assets Social Network factor is composed of 5 items, so means scores were computed for individuals who had at least 3 or more non-missing items on that factor). In addition, the reliability coefficients generated from the CFA results were included as the variance of the composite score error terms for each factor. This approach was used instead of creating a general sum of non-missing data in order to maximize the number of included participants and prevent potential errors for individuals who had missing items, which would distort composites scores for individuals with missing data points. 74
Table 12 provides the means, standard deviations, and range in scores for the impairment variables, risk and compensatory variables, and the outcome variable. In addition, to provide some context to the range of scores within certain age ranges, seven age groupings were created (age 3-5, 6-7, 8-9, 10-12, 13-14, 15-16, 17-18) and the breakdown of variable means and standard deviations are included in Table 13.
Table 12. Description of Impairment, Risk/Compensatory, and Outcome Variables M (SD) # of Items/ N Score Range ADHD Symptoms (Predictor) ARS-Parent # of Symptoms Range: 0-9 1257 Inattention 5.55 (2.83) Hyperactivity/Impulsive 3.95 (3.03) ARS Teacher # of Symptoms Range: 0-9 1125 Inattention 5.18 (2.95) Hyperactivity/Impulsive 3.55 (3.18) Impairment (Predictor) CFQL Composites Score Range: 1-5 1211 Child 11.55 (3.22) Total items: 4 Family/Caregiver 31.15 (11.76) Total items: 13 Financial 3.66 (1.96) Total items: 3 External Support 15.18 (3.73) Total items: 4 Partner/Relationship 16.51 (3.37) Total items: 4 Number of Referral Concerns 6.91 (3.31) Range: 0-18 1219 SEMBS Severity (# of items ≥ 3) 6.46 (2.63) Range: 0-10 1246 Teacher Ratings 1124 Problematic Behavior 3.61 (1.75) Range: 1-7 Academic Achievement 2.61 (0.92) Range: 1-5 Risk/Protective Factors Behavioral Assets Composites Score Range: 1-5 1247 Social Network 19.31 (4.65) Total items: 5 Academic Attitude 11.16 (2.97) Total items: 3 Academic Compliance 7.45 (2.17) Total items: 2 Emotional Adaptability 14.97 (3.99) Total items: 5 Emotional Intelligence 7.14 (2.25) Total items: 2 Compliance 9.26 (2.68) Total items: 3 Temperament 8.49 (1.73) Range: 5-15 1180 IQ Composite 102.87 (13.91) Range: 71-160 1176 Outcome Age of ADHD Diagnosis 9.13 (3.57) Range: 3-18 1331 75 SD 2.96 2.16 2.49 2.20 3.85 1.84 4.33 4.08 2.92 2.92 1.41 0.97 3.95 3.28 1.89 4.16 2.62 2.95 1.90 13.19 14.53 (n=59) M Ages 17-18 4.96 1.60 3.58 1.36 3.64 5.45 5.49 2.32 2.74 8.00 6.32 9.46 8.30 11.38 28.36 13.55 15.23 20.89 10.51 15.82 101.62 SD 2.88 2.12 3.19 2.65 3.34 0.97 3.59 3.58 2.59 2.73 1.54 0.88 4.51 2.89 1.69 4.24 2.43 3.00 1.56 10.34 13.43 (n=92) M Ages 15-16 5.67 1.87 3.70 1.96 3.34 5.62 5.64 2.52 2.54 8.22 6.26 9.45 7.90 11.51 28.47 15.36 16.19 20.49 10.26 16.18 102.13 SD 2.62 2.34 3.08 2.50 2.87 9.67 1.73 3.64 3.71 2.97 2.52 1.60 0.91 4.76 3.11 1.88 3.85 2.33 2.48 1.70 11.83 (n=103) M Ages 13-14 6.43 2.16 4.36 1.77 3.55 5.81 5.72 2.58 2.63 8.01 6.35 9.08 8.30 11.05 29.07 15.59 15.88 19.11 10.22 15.36 102.22 SD 2.75 2.62 3.08 2.79 3.30 9.77 1.82 3.19 3.01 3.63 2.58 1.70 0.82 4.61 2.91 1.98 3.97 2.29 2.84 1.64 14.26 (n=248) M Ages 10-12 5.89 3.02 4.88 2.44 3.54 6.88 6.00 3.10 2.60 8.25 6.92 9.48 8.25 11.46 28.09 15.15 16.99 19.63 11.00 15.23 103.08 Age Groupings SD 2.85 2.85 2.76 2.98 3.20 1.95 3.86 3.15 3.19 2.64 1.68 0.94 4.49 2.58 1.92 4.00 2.06 2.49 1.66 11.42 14.99 (n=298) Ages 8-9 M 5.58 3.61 5.73 3.31 3.58 6.97 6.26 3.73 2.57 7.89 7.33 9.83 8.30 11.98 28.79 15.52 16.77 19.87 11.64 15.46 104.19 Risk/Protective Factors Impairment (Predictor) ors based on Age Groupings on Age based ors SD 2.89 2.90 2.78 3.12 3.25 2.04 3.70 3.46 3.39 2.48 1.61 0.94 4.62 2.90 2.21 3.85 2.08 2.67 1.74 11.57 13.75 ADHD Symptoms (Predictor) (n=351) Ages 6-7 M 5.25 5.18 5.75 4.67 3.70 7.58 6.94 4.18 2.58 6.93 7.56 9.07 8.75 11.54 32.67 15.25 16.50 18.81 11.53 14.44 102.38 SD 2.73 2.53 2.75 2.81 2.99 2.41 4.03 3.29 3.04 2.25 1.46 1.01 4.79 3.34 2.04 3.77 2.21 2.37 1.72 12.13 12.97 (n=180) Ages 3-5 M 5.20 6.29 5.29 5.81 4.08 7.31 7.64 4.53 2.72 5.80 7.51 8.36 9.10 11.35 39.46 14.75 16.46 17.95 11.14 13.74 102.53 t, Risk and Compensatory fact t, Risk and Compensatory Inattention Hyperactivity/Impulsive Inattention Hyperactivity/Impulsive Child Family/Caregiver Financial External Support Partner/Relationship Problematic Behavior Academic Achievement Social Network Academic Attitude Academic Compliance Emotional Adaptability Emotional Intelligence Compliance Table Impairmen of 13. Description Domains ARS-Parent # of Symptoms Teacher # of Symptoms ARS CFQL Composites Concerns Referral of Number SEMBS Severity (# of items ≥ 3) Teacher Ratings Behavioral Assets Composites Temperament Composite IQ 76 Model 1: ADHD symptoms predicting age of ADHD diagnosis. The first model tested whether parent and teacher report on the ADHD Rating Scale (ARS) impacted age of ADHD diagnosis. It was hypothesized that children with fewer ADHD symptoms of hyperactivity and more ADHD symptoms of inattention would have later/older age of ADHD diagnosis as compared to younger children (see Figure 1, H1).
Predictor variables included 4 variables that captured the number of symptoms endorsed on the Inattentive subscale and the Hyperactivity/Impulsivity subscale on the ARS-Parent report and the ARS-Teacher report.
Figure 12. Initial Hypothesized Model of ADHD Symptoms Impacting Age of ADHD Diagnosis The initial hypothesized model (depicted in Figure 12) showed poor fit with the data as indicated by a significant chi-square statistic (χ2 (6, N=1331)=802.97, p<.001), as well as TFI=.562, CFI=.375, and RMSEA=.316 [90% C.I.= .298, .335]. The model was run using Full Information Maximum Likelihood (FIML) estimation procedures to handle missing data. FIML procedures allow one to run analyses using the original data file that includes missing data, and it uses the parametric estimates and goodness of fit indices for the full dataset while adjusting the standard errors and goodness of fit indices to account
77 for the missing data. Therefore, the model was developed by starting with all possible regression and covariance paths, and then removing nonsignificant paths (p>.01) individually and rerunning the model. In total, two nonsignificant correlation paths were removed from the model (ARS-T Inattention symptom total and the ARS-P Inattentive and ARS-P Hyperactivity/Impulsivity symptom totals). The final model (depicted in
Figure 13) was tested using FIML procedures and demonstrated a good fit with the data as indicated by a nonsignificant chi-square statistic (χ2 (2, N=1331)=6.95, p=.031), as well as TFI=.971, CFI=.996, and RMSEA=.043 [90% C.I.=.011, .080]. About one third
(32%) of the variance in age of ADHD diagnosis was accounted for by ARS parent and teacher reported symptoms of inattention and hyperactivity/impulsivity. Parameter estimates for the ARS Symptom Model are presented in Table 14.
Figure 13. Standardized Results of ARS Symptoms predicting Age of ADHD Diagnosis
78 Table 14. SEM Parameter Estimates for ARS Symptoms on Age Structural Regression Weights Paths Standardized Unstandardized Standard P-value Weights Weights Error ARS-P Inattention Symptoms .267 .336 .034 p<.001 (ARSP_INsx) Æ Age ARS-P Hyperactivity/Impulsivity -.514 -.603 .036 p<.001 Symptoms (ARSP_HIsx) Æ Age ARS-T Inattention Symptoms -.167 -.201 .033 p<.001 (ARST_INsx) Æ Age ARS-T Hyperactivity/Impulsivity -.086 -.096 .036 p<.01 Symptoms (ARST_HIsx) Æ Age Structural Covariances Paths Standard Correlations Covariances P-value Error ARS-P Inattention Symptoms (ARSP_INsx) ↔ ARS-P .374 3.211 .258 p<.001 Hyperactivity/Impulsivity Symptoms (ARSP_HIsx) ARS-P Inattention Symptoms (ARSP_INsx) ↔ ARS-T -.116 -1.041 .247 p<.001 Hyperactivity/Impulsivity Symptoms (ARST_HIsx) ARS-P Hyperactivity/Impulsivity Symptoms (ARSP_HIsx) ↔ ARS-T .426 4.071 .284 p<.001 Hyperactivity/Impulsivity Symptoms (ARST_HIsx) ARS-T Inattention Symptoms (ARST_INsx) ↔ ARS-T .377 3.510 .260 p<.001 Hyperactivity/Impulsivity Symptoms (ARST_HIsx) Squared Multiple Correlations Endogenous variable R2 Age .321
The final model had 4 significant regression paths. On the ARS Parent report, children with greater Inattentive symptoms (higher scores on the ARS-P Inattentive symptom total, ARSP_INsx) had older age of ADHD diagnosis (standardized beta= .27, unstandardized beta= .34, S.E. = .03, p<.001), and children with greater
Hyperactivity/Impulsivity symptoms (higher scores on the ARS-P
Hyperactivity/Impulsivity symptom total, ARSP_HIsx) had younger/earlier age of
ADHD diagnosis (standardized beta= -.51, unstandardized beta= -.60, S.E. = .04,
79 p<.001). On the ARS Teacher report, children with greater Inattentive symptoms (higher scores on the ARS-T Inattention symptom total, ARST_INsx) and with greater
Hyperactivity/Impulsivity symptoms (higher scores on the ARS-T
Hyperactivity/Impulsivity symptom total, ARST_HIsx) had younger/earlier age of
ADHD diagnosis (Inattention: standardized beta= -.17, unstandardized beta= -.20, S.E. =
.03, p<.001; Hyperactivity/Impulsivity: standardized beta= -.09, unstandardized beta= -
.10, S.E. = .04, p<.01). In general, these results show that the strongest predictor of Age of ADHD diagnosis is ARS-P total symptoms of Hyperactivity/Impulsivity, which evidence that for every 1 point increase in ARS-P Hyperactivity/Impulsivity scores
(which represents 1 symptom) age decreases by 0.60 points (which equates to about 7 months).
Overall, these results suggest that parents report higher numbers of Inattentive symptoms in older children diagnosed with ADHD, and both parents and teachers reported higher Hyperactivity/Impulsivity symptoms in younger children diagnosed with
ADHD. These results are consistent with the literature on changes in ADHD symptom endorsement from childhood to adolescence (Biederman et al., 2000; Faraone et al.,
2006; Hill & Schoener, 1996). In addition, the fact that teachers reported higher
Inattentive symptoms in younger children diagnosed with ADHD, while parents report showed patterns in the opposite direction, also helps to illuminate some of the inconsistencies in the literature regarding the patterns of Inattentive symptom endorsement increasing over childhood (Larsson et al., 2011; Sasser et al., 2016). In addition, the current results also illuminate some of the difficulties with teacher report in older children, given that older children may have multiple teachers during their school
80 day, which can lead to difficulties capturing symptom presentation in school settings for older children . In sum, these results suggest differences in parent reported symptoms based on age of ADHD diagnosis, while teachers generally report greater symptoms for children diagnosed with ADHD at younger ages.
Additional results for model 1. This model also contained 4 statistically significant correlations (p<.001) between ARS symptom totals. There were 3 correlations with a medium effect size: higher ARS-P Hyperactivity/Impulsivity symptom total to
ARS-P Inattentive symptom total (r= .37) and ARS-T Hyperactivity/Impulsivity symptom total (r= .43); and ARS-T Inattentive symptom total to ARS-T
Hyperactivity/Impulsivity symptom total (r= .38). The remaining correlation had a small effect size: ARS-P Inattentive symptom total to ARS-T Hyperactivity/Impulsivity symptom total (r= -.12). Taken together, these results reveal that higher numbers of hyperactivity/impulsivity symptoms on parent report were associated with higher numbers of inattentive symptoms on parent report and higher numbers of hyperactivity/impulsivity symptoms on teacher report. In addition, higher number of teacher-reported inattentive symptoms was associated with higher teacher-reported hyperactivity/impulsivity symptoms. Lastly, lower numbers of inattentive symptoms on parent report was associated with higher numbers of hyperactivity/impulsivity symptoms on teacher report.
81 Model 2: Impairment predicting age of ADHD diagnosis. The second model examined how impairment impacts age of ADHD diagnosis, with measures of impairment including parent global ratings of symptom severity, teacher ratings of problem behavior, teacher ratings of achievement, number of parent reported referral concerns, and parent ratings on the Child and Family Quality of Life (CFQL) scale. It was hypothesized that children with greater impairment would be diagnosed with ADHD at younger ages in comparison to children with lower levels of impairment. The initial hypothesized model (depicted in Figure 14) was tested using FIML procedures and showed poor fit with the data as indicated by a significant chi-square statistic (χ2 (36,
N=1331)=1902.71, p<.001), as well as TFI=-.322, CFI=.135, and RMSEA=.197 [90%
C.I.= .190, .205]. Therefore, the model was developed by starting with all possible regression and covariance paths, and then removing nonsignificant paths (p>.01) individually and rerunning the model. In total, 3 nonsignificant regression paths and 8 nonsignificant covariances were removed from the model (shown in Table 4-A, in the
Appendix).
82
Figure 14. Initial Hypothesized Model of Impairment Impacting Age of ADHD Diagnosis
The final model (depicted in Figure 15), which was tested using FIML procedures, demonstrated good fit with the data as indicated by a nonsignificant chi- square statistic (χ2 (11, N=1331)=20.45, p=.04), as well as TFI=.978, CFI=.996, and
RMSEA=.025 [90% C.I.=.005, .042]. Parameter estimates for the Impairment model are presented in Table 15; the components of the measurement model (e.g., standardized and
83 unstandardized regression weights between error terms and items and items and latent variables) were not included to simplify the table.
Figure 15. Standardized Results of SEM Model of Impairment Variables Predicting Age of ADHD Diagnosis
84 Table 15. SEM Parameter Estimates for Impairment predicting Age of ADHD diagnosis Structural Regression Weights Paths Standardized Unstandardized Standard P- Wts Wts Error value CFQL Child Latent (Child)Æ Age -.235 -.301 .055 p<.001 CFQL Family/Caregiver Latent -.205 -.064 .012 p<.001 (FamCargvr)Æ Age CFQL Partner/Relationship Latent -.104 -.116 .034 p<.001 (Reltnshp)Æ Age Referral Concerns Latent (RefCon)Æ -.135 -.146 .035 p<.001 Age Teacher Quality of Relationship Latent -.321 -.654 .055 p<.001 (TQltyRltnshp) Æ Age SEBMS Severity Latent -.129 -.175 .047 p<.001 (SEBMS_Sev)Æ Age Structural Covariances Paths Standard P- Correlations Covariances Error value CFQL Child Latent (Child)↔ CFQL -.512 -16.127 1.166 p<.001 Family/Caregiver Latent (FamCargvr) CFQL Child Latent (Child)↔ CFQL -.223 -1.182 .182 p<.001 Financial Latent (Financial) CFQL Child Latent (Child)↔ CFQL .183 1.718 .345 p<.001 External Support Latent (ExtSupp) CFQL Child Latent (Child)↔ CFQL .204 1.822 .342 p<.001 Partner/Relationship Latent (Reltnshp) CFQL Child Latent (Child)↔ Referral -.529 -4.853 .333 p<.001 Concerns Latent (RefCon) CFQL Child Latent (Child)↔ Teacher Academic Achievement Latent .156 .401 .087 p<.001 (TLrning) CFQL Child Latent (Child)↔ SEBMS -.520 -3.796 .262 p<.001 Severity Latent (SEBMS_Sev) CFQL Family/Caregiver Latent (FamCargvr)↔ CFQL Financial .314 6.770 .674 p<.001 Latent (Financial) CFQL Family/Caregiver Latent (FamCargvr)↔ CFQL External -.330 -12.651 1.284 p<.001 Support Latent (ExtSupp) CFQL Family/Caregiver Latent (FamCargvr)↔ CFQL -.278 -10.131 1.252 p<.001 Partner/Relationship Latent (Reltnshp) CFQL Family/Caregiver Latent (FamCargvr)↔ Referral Concerns .425 15.871 1.190 p<.001 Latent (RefCon) CFQL Family/Caregiver Latent (FamCargvr)↔ Teacher Quality of .178 3.530 .521 p<.001 Relationship Latent (TQltyRltnshp)
85 CFQL Family/Caregiver Latent (FamCargvr)↔ Teacher Academic .133 1.396 .296 p<.001 Achievement Latent (TLrning) CFQL Family/Caregiver Latent (FamCargvr)↔ SEBMS Severity .569 16.947 .990 p<.001 Latent (SEBMS_Sev) CFQL Financial Latent (Financial)↔ CFQL External Support Latent -.203 -1.307 .213 p<.001 (ExtSupp) CFQL Financial Latent (Financial)↔ CFQL Partner/Relationship Latent -.163 -.997 .209 p<.001 (Reltnshp) CFQL Financial Latent (Financial)↔ .250 1.573 .187 p<.001 Referral Concerns Latent (RefCon) CFQL Financial Latent (Financial)↔ SEBMS Severity Latent .199 .996 .147 p<.001 (SEBMS_Sev) CFQL External Support Latent (ExtSupp) ↔ CFQL .355 3.856 .410 p<.001 Partner/Relationship Latent (Reltnshp) CFQL External Support Latent (ExtSupp) ↔ Referral Concerns Latent -.136 -1.519 .349 p<.001 (RefCon) CFQL External Support Latent (ExtSupp) ↔ SEBMS Severity Latent -.153 -1.361 .278 p<.001 (SEBMS_Sev) CFQL Partner/Relationship Latent (Reltnshp)↔ Referral Concerns Latent -.126 -1.338 .345 p<.001 (RefCon) CFQL Partner/Relationship Latent (Reltnshp)↔ SEBMS Severity Latent -.158 -1.331 .275 p<.001 (SEBMS_Sev) Referral Concerns Latent (RefCon) ↔ Teacher Quality of Relationship Latent .097 .557 .150 p<.001 (TQltyRltnshp) Referral Concerns Latent (RefCon)↔ Teacher Academic Achievement -.184 -.562 .089 p<.001 Latent (TLrning) Referral Concerns Latent (RefCon)↔ SEBMS Severity Latent .499 4.323 .274 p<.001 (SEBMS_Sev) SEBMS Severity Latent (SEBMS_Sev)↔ Teacher Quality of .181 .831 .121 p<.001 Relationship Latent (TQltyRltnshp) SEBMS Severity Latent (SEBMS_Sev)↔ Teacher Academic .115 .279 .070 p<.001 Achievement Latent (TLrning) Squared Multiple Correlations Endogenous variable R2 Age .240
86 The final model had 6 significant regression paths. First, children with lower quality of life (lower scores on the CFQL Child factor) had older age of ADHD diagnosis
(standardized beta= -.24, unstandardized beta= -.30, S.E. = .06, p<.001). Second, children whose families had lower quality of life (higher scores on the CFQL Family/Caregiver factor) had earlier/younger age of ADHD diagnosis (standardized beta= -.21, unstandardized beta= -.06, S.E. = .01, p<.001). Third, children whose parents reported lower quality of life or low support in spouse/partner relationships (lower scores on the
CFQL Partner/Relationship factor) had children with older age of ADHD diagnosis
(standardized beta= -.10, unstandardized beta= -.12, S.E. = .03, p<.001). Fourth, children with higher numbers of referral concerns had earlier/younger age of ADHD diagnosis
(standardized beta= -.14, unstandardized beta= -.15, S.E. = .04, p<.001). Fifth, children rated by their teachers as having more problematic behavior in the classroom (higher scores on Teacher Quality of Relationship) had earlier/younger age of ADHD diagnosis
(standardized beta= -.32, unstandardized beta= -.65, S.E. = .06, p<.001). And sixth, children whose parents rated them as having more severe behavioral problems (higher scores on the SEBMS Severity composite) had earlier/younger age of ADHD diagnosis
(standardized beta= -.13, unstandardized beta= -.18, S.E. = .05, p<.001). Overall, these results suggest that greater impairment on family/caregiver quality of life, poorer relationships with teachers, higher numbers of referral concerns, and more severe behavioral concerns predict younger age of ADHD diagnosis. In addition, older age of
ADHD diagnosis was predicted by poorer quality of life and parents who reported lower support in their partner or spouse relationship. Taken together, children that have a greater number of impairments that impact number of parent reported concerns and
87 severity of behavior at home and school, which contribute to significant stress on the family and caregiver, are likely to be diagnosed with ADHD at earlier ages. In contrast, older age of ADHD diagnosis was predicted by fewer domains of impairment and lower severity of concerns, but was also predicted by poorer child quality of life and caregivers that reported having poorer relationships with their partners or spouses. These results suggest that while children that are rated by parents and teachers as having more problematic behaviors may be diagnosed with ADHD at earlier ages, these children are also rated as having higher quality of life and less of an impact on parental relationship satisfaction. This could suggest that older children who are newly diagnosed with ADHD have more awareness of their struggles that have either emerged as a result of increasing demands at home and school or have been accumulating over time due to delay in seeking diagnosis; either way, older children newly diagnosed with ADHD are observed by their parents as having poorer quality of life; in addition, diagnosing with ADHD at older ages may also take more of a toll on parental relationships and satisfaction.
Additional results for model 2. This model also contained 28 statistically significant correlations (p<.001) between latent impairment variables. There were 5 correlations that had a large effect size: CFQL Child to CFQL Family/Caregiver (r= -
.51), Referral Concerns (r= -.53), and SEBMS Severity (r= -.52), CFQL
Family/Caregiver to SEBMS Severity (r= .57), and Referral Concerns to SEBMS
Severity (r= .50). These correlations suggested that as a child’s quality of life improves, the family/caregiver’s quality of life also improves, the number of referral concerns decreases, and the severity of the child’s behavioral problems decreases. In addition, as the severity of a child’s behavioral problems decreases, the number of referral concerns
88 also decreases, and the family/caregiver quality of life is perceived to improve. There were 4 correlations with a medium effect size: CFQL Family/Caregiver to Referral
Concerns (r= .43), CFQL External Support (r= -.33), and CFQL Financial (r= .31), and
CFQL External Support to CFQL Partner/Relationship (r= .36). These correlations suggest that as the family/caregiver’s quality of life improves, the number of referral concerns decreases, the family’s sense of social support increases, and their financial stability increases. In addition, as family’s sense of social support increases, their sense of support from their spouse or partner also increases. The remaining 11 correlations had small effect sizes: CFQL Child to CFQL External Support (r= .18), CFQL Financial (r= -
.22), CFQL Relationship (r= .20), and Teacher Rating of Academic Achievement (r=
.16); CFQL External Support to Referral Concerns (r= -.14) and SEBMS Severity (r= -
.15); CFQL Family/Caregiver to CFQL Partner/Relationship (r= -.28), Teacher Quality of Relationship (r= .18), and Teacher Rating of Academic Achievement (r= .13); CFQL
Financial to CFQL Partner/Relationship (r= -.16), CFQL External Support (r= -.20),
Referral Concerns (r= .25), and SEBMS Severity (r= .20); CFQL Partner/Relationship to
Referral Concerns (r= -.13) and SEBMS Severity(r= -.16); Referral Concerns to Teacher
Quality of Relationship (r= .10) and Teacher Rating of Academic Achievement (r= -.18); and SEBMS Severity to Teacher Quality of Relationship (r= .18) and Teacher Rating of
Academic Achievement (r= .12). These correlations suggest the following associations: as a child’s quality of life increase, the family’s sense of social support increases, the family’s financial stability is perceived to increase, the caregiver’s sense of support from spouse or partner increases, and teacher’s rate the child as having higher levels of academic achievement. Further, as the family’s sense of social support increases, there is
89 a perceived decrease in referral concerns and the severity level of a child’s behavior problems. As the family/caregiver’s quality of life improves, there is also improvement in the caregiver’s sense of support from their partner or spouse, lower severity of behavior problems at school, and higher levels of academic achievement. As a family’s sense of financial stress decreases, the caregiver’s sense of support from their partner and general sense of social support is perceived to increase, as well as there being a reduction in number of referral concerns and severity of child’s behavior problems at home. Given that the current sample is composed of caregivers in higher level occupations, these results help to illustrate how financial stress can have a negative impact on access to social resources. Improvements in the caregiver’s sense of support from their partner/spouse is associated with fewer referral concerns and lower severity rating of child’s behavior problems. Next, as the number of referral concerns decreases, the severity of behavior problems at school decreases and teacher ratings of academic achievement improve. And lastly, as the severity of child’s behavior problems decreases, the severity of behavior problems at school also decreases, but the teacher’s rating of academic achievement is perceived to decrease. Overall, these correlations show the cumulative effect that domains of impairment can have on both the child’s behavioral presentation and the quality of life of caregivers/family members.
90 Model 3: IQ, Temperament, and Behavioral Assets predicting age of ADHD diagnosis. The third model explored how risk and compensatory variables, which included IQ, temperament, and parent report of child’s behavioral assets, impacted age of
ADHD diagnosis. It was hypothesized that children with higher IQs, easier temperaments, and more behavioral assets would have a later age of ADHD diagnosis in comparison to children with lower IQ, more difficult temperaments, and fewer behavioral assets. The initial hypothesized model (depicted in Figure 16) was tested using FIML procedures and showed poor fit with the data as indicated by a significant chi-square statistic (χ2 (28, N=1331)=1695.65, p<.001), as well as TFI=-.367, CFI=.149, and
RMSEA=.212 [90% C.I.= .203, .220].
In order to improve model fit with the data, the model was developed by starting with all possible regression and covariance paths, and then removing nonsignificant paths
(p>.01) individually and rerunning the model. In total, 11 nonsignificant paths were removed from the model (shown in Table 5-A, in the Appendix). The final model was also tested using FIML procedures.
91
Figure 16. Initial Hypothesized Model of Risk and Compensatory Factors Impacting Age of ADHD Diagnosis
Although the final model (depicted in Figure 17) had a significant chi-square statistic (χ2 (11, N=1331)=25.894, p=.007) that may indicate poor fit, other goodness-of- fit indicators were used as comparison given the large sample size. Other indicators demonstrated good fit with the data: TFI=.969, CFI=.992, and RMSEA=.032 [90%
C.I.=.016, .048]. Parameter estimates for the Risk/Compensatory model are presented in
Table 16; the components of the measurement model (e.g., standardized and
92 unstandardized regression weights between error terms and items and items and latent variables) were not included to simplify the table.
Figure 17. Standardized Results of SEM Model of Behavioral Assets, IQ, and Temperament Predicting Age of ADHD Diagnosis
93 Table 16. SEM Parameter Estimates for Risk/Compensatory factors impact on Age of ADHD Diagnosis Structural Regression Weights Paths Standardized Unstandardized Standard P- Weights Weights Error value BA Social Network Latent (SocNet)Æ .182 .155 .031 p<.001 Age BA Academic Attitude Latent -.296 -.412 .052 p<.001 (AcademAtt)Æ Age BA Academic Compliance Latent .343 .615 .057 p<.001 (AcademCompl)Æ Age BA Emotional IQ Latent (EmoIQ)Æ -.228 -.397 .057 p<.001 Age Temperament Latent (Temp) Æ Age -.168 -.348 .057 p<.001 Structural Covariances Paths Standard P- Correlations Covariances Error value BA Social Network Latent (SocNet)↔ BA Academic Attitude Latent .422 4.553 .402 p<.001 (AcademAtt) BA Social Network Latent (SocNet)↔ BA Academic Compliance Latent .357 2.988 .283 p<.001 (AcademCompl) BA Social Network Latent (SocNet)↔ BA Emotional Adaptability Latent .530 7.655 .552 p<.001 (EmoAdpt) BA Social Network Latent (SocNet)↔ .365 3.152 .294 p<.001 BA Emotional IQ Latent (EmoIQ) BA Social Network Latent (SocNet)↔ .346 3.317 .353 p<.001 BA Compliance Latent (Compl) BA Social Network Latent (SocNet)↔ -.293 -2.116 .237 p<.001 Temperament Latent (Temp) BA Academic Attitude Latent (AcademAtt) ↔ BA Academic .327 1.674 .179 p<.001 Compliance Latent (AcademCompl) BA Academic Attitude Latent (AcademAtt) ↔ BA Emotional .438 3.872 .343 p<.001 Adaptability Latent (EmoAdpt) BA Academic Attitude Latent (AcademAtt) ↔ BA Emotional IQ .375 1.981 .190 p<.001 Latent (EmoIQ) BA Academic Attitude Latent (AcademAtt) ↔ BA Compliance Latent .350 2.051 .225 p<.001 (Compl) BA Academic Attitude Latent (AcademAtt) ↔ Temperament Latent -.241 -1.068 .150 p<.001 (Temp) BA Academic Compliance Latent (AcademCompl) ↔ BA Emotional .429 2.950 .246 p<.001 Adaptability Latent (EmoAdpt)
94 BA Academic Compliance Latent (AcademCompl)↔ BA Compliance .523 2.381 .170 p<.001 Latent (Compl) BA Academic Compliance Latent (AcademCompl)↔ Temperament Latent -.154 -.529 .108 p<.001 (Temp) BA Emotional Adaptability Latent (EmoAdpt)↔ BA Emotional IQ Latent .377 2.668 .248 p<.001 (EmoIQ) BA Emotional Adaptability Latent (EmoAdpt)↔ BA Compliance Latent .561 4.410 .317 p<.001 (Compl) BA Emotional Adaptability Latent (EmoAdpt)↔ Temperament Latent -.474 -2.814 .212 p<.001 (Temp) BA Emotional IQ Latent (EmoIQ)↔ BA .357 1.678 .161 p<.001 Compliance Latent (Compl) BA Emotional IQ Latent (EmoIQ)↔ -.178 -.631 .113 p<.001 Temperament Latent (Temp) BA Compliance Latent (Compl)↔ -.273 -1.072 .136 p<.001 Temperament Latent (Temp) Squared Multiple Correlations Endogenous variable R2 Age .269 The final model had 5 significant regression paths. First, children with better functioning in social settings (higher composite scores on the Behavioral Assets- Social
Network factor) had older age of ADHD diagnosis (standardized beta= -.18, unstandardized beta= .16, S.E. = .03, p<.001). Second, children described as having more positive attitudes toward school (higher composite scores on the Behavioral Assets-
Academic Attitude factor) had younger age of ADHD diagnosis (standardized beta= -.30, unstandardized beta= -.41, S.E. = .05, p<.001). Third, children with better compliance at school (higher composite scores on the Behavioral Assets- Academic Compliance factor) had older age of ADHD diagnosis (standardized beta= .34, unstandardized beta= .62, S.E.
= .06, p<.001). Fourth, children described as having better understanding emotions of oneself and others (higher composite scores on the Behavioral Assets- Emotional
Intelligence factor) had younger age of ADHD diagnosis (standardized beta= -.23,
95 unstandardized beta= -.40, S.E. = .06, p<.001). Lastly, children with more difficult temperament styles (higher scores on Temperament composite) had younger age of
ADHD diagnosis (standardized beta= -.17, unstandardized beta= -.35, S.E. = .06, p<.001). Overall, these results suggest that younger children diagnosed with ADHD demonstrate more difficult temperaments, but have strengths in approach toward school and emotional awareness. In contrast, older children diagnosed with ADHD demonstrated strengths in social functioning and compliance at school. It is quite possible that older children that demonstrate fewer concerns at school and with peers are viewed as functioning better, which could contribute to delayed diagnosis of ADHD.
Additional results for model 3. This model also contained 20 statistically significant (p<.001) correlations between latent variables. There were 3 correlations that had a large effect size: BA Academic Compliance to BA Compliance (r= .52), BA
Emotional Adaptability to BA Compliance (r= .56), and BA Social Network to BA
Emotional Adaptability (r= .53). These correlations suggest that as a child’s compliance at home increases, the child’s compliance at school and their adaptability to new situations also increases. In addition, as a child’s emotional adaptability increases, their social functioning is perceived to increase as well. There were 12 correlations with a medium effect size: BA Academic Attitude to BA Academic Compliance (r= .33), BA
Compliance (r= .35), BA Emotional Adaptability (r= .44), and BA Emotional
Intelligence (r= .38); BA Emotional Adaptability to BA Academic Compliance (r= .43),
BA Emotional Intelligence (r= .38), and Temperament (r= -.47); BA Emotional
Intelligence to BA Compliance (r= .36); and BA Social Network to BA Academic
Attitude (r= .42), BA Academic Compliance (r= .36), BA Compliance (r= .35), and BA
96 Emotional Intelligence (r= .37). These correlations suggest that as a child’s positive attitude toward school increases, their compliance at school, compliance at home, adaptability to new situations, and their understanding of other’s emotions all also increase. In addition, as a child’s emotional adaptability increases, they also show improvement in understanding of other’s emotions, compliance at school, and appeared to have easier temperament patterns. As a child’s emotional understanding of others increases, their compliance at home is perceived to increase. And lastly, as a child’s social functioning improves, their positive attitude toward school, compliance at school, compliance at home, and emotional understanding of others is also perceived to improve.
The remaining 5 correlations had a small effect size: Temperament to BA Academic
Attitude (r= -.24), BA Academic Compliance (r= -.15), BA Compliance (r= -.27), BA
Emotional Intelligence (r= -.18), and BA Social Network (r= -.29). These patterns suggest that as children demonstrate easier temperament patterns, they are also perceived to have improved attitudes toward school, compliance at school, compliance at home, emotional understanding of others, and better social functioning.
97 Model 4: Testing Risk/Compensatory variables as confounders on the relationship between impairment and age of ADHD diagnosis variables. The final model examined whether Risk and Compensatory factors of IQ, temperament, and parent report of child’s behavioral assets, impacted the relationship between Impairment variables (CFQL factors, Referral Concerns, Teacher ratings of classroom behavior and achievement, and Severity ratings) predicting the age of ADHD diagnosis. All possible paths were added between risk Risk and Compensatory variables and Impairment variables were included, and the paths from model 2 (Impairment variable to Age of
ADHD diagnosis) and model 3 (Risk and Compensatory variables to Age of ADHD diagnosis) were maintained. The initial hypothesized model was tested using FIML procedures and demonstrated good fit with the data (χ2 (22, N=1331)=54.043, p<.001;
TFI=.953, CFI=.994, and RMSEA=.034 [90% C.I.=.023, .045]) but contained a number of nonsignificant paths. Therefore, post hoc modifications were performed in attempt to develop a model that better fit the data. Post hoc modifications retained are included in
Table 6-A (see Appendix). The final model (depicted in Figure 18) was also tested using
FIML procedures and demonstrated good fit with the data: (χ2 (77, N=1331)=170.444, p<.001; TFI=.962, CFI=.983, RMSEA=.030 [90% C.I.=.024, .036]). The significant paths from Risk/Compensatory factors will first be described in order to build toward examining potential confounding effects on the relationship of Impairment to Age of
ADHD diagnosis. A final table with all parameter estimates will be presented after confounding effects are examined.
98 Compensatory Factors, Impairment, and Age of Age and Impairment, Factors, Compensatory Standardized Results of SEM Model of Relationship between Risk/ Relationship of Model SEM of Results Standardized Figure 18. Figure ADHD Diagnosis prior to testing for Confounder effects 99
There were 24 significant paths from Risk/Compensatory variables to Impairment variables, and these will be subsequently described following individual Risk/Protective factors. First, children with better functioning in social settings (higher composite scores on the Behavioral Assets- Social Network factor) had families with greater sense of external support (higher scores on the CFQL External Support factor; standardized beta=
.21, unstandardized beta= .17, S.E. = .03, p<.001). There were also small, but significant regressions suggesting that children with better functioning in social settings (higher composite scores on the Behavioral Assets- Social Network factor) had families with lower financial stress (lower scores on the CFQL Financial factor; standardized beta= -
.10, unstandardized beta= -.05, S.E. = .02, p<.01) and parents with higher quality of life in spouse/partner relationships (higher scores on the CFQL Partner/Relationship factor; standardized beta= .12, unstandardized beta= .09, S.E. = .03, p<.01). These results suggest that as children’s social functioning improves, there is also improvement in families’ sense of social support, and some mild association with improvements in caregiver’s sense of support from their spouse/partner and lower financial stress on families.
Children with better attitude toward school (higher composite scores on the
Behavioral Assets- Academic Attitude factor) had higher quality of life (higher scores on
CFQL Child factor; standardized beta= .25, unstandardized beta= .27, S.E. = .04, p<.001). In addition, there were 2 small but significant regressions that suggested children with better attitudes toward school were also rated by teachers as having more problematic classroom behavior (higher scores on Teacher Quality of Relationship; standardized beta= .13, unstandardized beta= .09, S.E. = .02, p<.001) and that they had 100 higher levels of academic achievement (higher scores on Teacher Rating of Academic
Achievement; standardized beta= .13, unstandardized beta= .05, S.E. = .01, p<.001). This suggests that children with more positive attitudes toward school are perceived as having higher quality of life, and that there are mild associations with higher levels of academic achievement and poorer classroom behavior. Children with better compliance at school
(higher scores on the Behavioral Assets- Academic Compliance factor) had families with better quality of life (lower scores on CFQL Family/Caregiver factor; standardized beta=
-.29, unstandardized beta= -1.62, S.E. = .17, p<.001), less problematic classroom behavior per teacher report (lower scores on Teacher Quality of Relationship; standardized beta= -.69, unstandardized beta= -.61, S.E. = .03, p<.001), and less problematic behavioral difficulties per parent report (lower scores on the SEBMS
Severity composite; standardized beta= -.23, unstandardized beta= -.30, S.E. = .04, p<.001). There were also small, but significant regressions showing an association between better compliance at school (higher scores on the Behavioral Assets- Academic
Compliance factor) and fewer referral concerns (lower scores on the Referral Concerns composite; standardized beta= -.15, unstandardized beta= -.25, S.E. = .05, p<.001), lower financial stress (lower scores on the CFQL Financial factor; standardized beta= -.16, unstandardized beta= -.16, S.E. = .03, p<.001), and poorer academic performance (lower scores on Teacher Rating of Academic Achievement; standardized beta= -.11, unstandardized beta= -.05, S.E. = .02, p<.001). Given that these regression weights are small, results should be interpreted with caution. These results suggest that children with better compliance at school are more likely to have higher family quality of life and lower behavioral impairment at home and school; there was also some mild association 101 with better compliance at school and lower levels of referral concerns, families with lower financial stress, and poorer academic performance. When taken together with the findings on academic attitude, it appears that compliance has a strong impact on parent and teacher impressions of behavioral impairment and impacts family quality of life, whereas better attitude toward school has more impact on the child’s quality of life.
Children with higher emotional adaptability (higher scores on the Behavioral
Assets- Emotional Adaptability factor) had better child quality of life (higher scores on the CFQL Child factor; standardized beta= .36, unstandardized beta= .29, S.E. = .04, p<.001), fewer referral concerns (standardized beta= -.41, unstandardized beta= -.39, S.E.
= .03, p<.001), and less problematic behavioral difficulties (lower scores on the SEBMS
Severity composite; standardized beta= -.29, unstandardized beta= -.22, S.E. = .03, p<.001). This suggests that children who are more adaptability to new situations and changes in routine are more likely to have better quality of life, fewer referral concerns, and less severe behavioral problems at home. Next, there was a small, but significant regression suggesting that children with higher emotional awareness (higher BA
Emotional Intelligence composite scores) had lower levels of academic achievement
(lower scores on Teacher Rating of Academic Achievement; standardized beta= -.12, unstandardized beta= -.05, S.E. = .02, p<.001). However, given that this is a weak association, these results should be interpreted with caution and be considered as inconsequential given the large sample size.
Children with better compliance (higher scores on the Behavioral Assets-
Compliance factor) had better child quality of life (higher scores on the CFQL Child factor; standardized beta= .20, unstandardized beta= .24, S.E. = .05, p<.001), families 102 with better quality of life (lower scores on CFQL Family/Caregiver factor; standardized beta= -.40, unstandardized beta= -1.96, S.E. = .17, p<.001), parents with higher quality of life in spouse/partner relationships (higher scores on the CFQL Partner/Relationship factor; standardized beta= .20, unstandardized beta= .28, S.E. = .06, p<.001), and less problematic behavioral difficulties (lower scores on the SEBMS Severity composite; standardized beta= -.20, unstandardized beta= -.23, S.E. = .04, p<.001); however, they were also rated by their teachers as having more problematic classroom behavior (higher scores on Teacher Quality of Relationship; standardized beta= .23, unstandardized beta=
.18, S.E. = .03, p<.001). This suggests that children with better compliance at home are more likely to have better quality of life, families and caregiver’s with better quality of life, caregivers that report more support in their relationship with their partner/spouse, and fewer severe behavioral problems at home. Despite the positive effects of compliance at home, it was shown that the same effect did not transfer to the school environment.
Unfortunately, teachers rated the children as having more severe behavioral problems in the classroom. Taken together, while children were able to manage behavior in the home situation, they appeared to continue to struggle to comply in other settings like school.
Children with higher IQ scores had higher levels of academic achievement
(higher scores on Teacher Rating of Academic Achievement; standardized beta= .42, unstandardized beta= .03, S.E. = .002, p<.001). There were also small, but significant regressions suggesting that children with higher IQ scores also had families with lower financial stress (lower scores on the CFQL Financial factor; standardized beta= -.11, unstandardized beta= -.01, S.E. = .004, p<.001), fewer referral concerns (lower scores on the Referral Concerns composite; standardized beta= -.09, unstandardized beta= -.02,
103 S.E. = .01, p<.001), families with lower quality of life (higher scores on CFQL
Family/Caregiver factor; standardized beta= .10, unstandardized beta= .08, S.E. = .02, p<.001), and more severe behavioral problems (higher scores on the SEBMS Severity composite; standardized beta= .10, unstandardized beta= .02, S.E. = .004, p<.001).
Overall, higher IQ predicted higher academic achievement, and there was some mild association with children with higher IQs as having fewer referral concerns, lower financial stress, more severe behavioral problems and have families with lower quality of life. These mild effects should be interpreted with caution given the small effect and large sample size. But these results may suggest that while IQ may act as a compensatory skill to assist with academic performance and reduce the number of concerns parents report, having a higher IQ is not enough to relieve caregiver stress and reduce the severity of behavioral problems at home. It is important to note that IQ was also not found to predict age of ADHD diagnosis, suggesting that IQ factors also did not play a role in expediting or delaying diagnosis in this managed care sample. It seems that children with higher IQ are not necessarily demonstrating advantageous behavioral functioning and that their caregivers may find this finding frustrating and stressful.
Lastly, there were small, but significant regressions that suggested children with easier temperaments (lower scores on Temperament composite) had families with higher quality of life (lower scores on CFQL Family/Caregiver factor; standardized beta= .13, unstandardized beta= .83, S.E. = .16, p<.001) and lower financial stress (lower scores on the CFQL Financial factor; standardized beta= .08, unstandardized beta= .09, S.E. = .03, p<.01). These results should be interpreted with caution given the large sample size and
104 small effect size, but may suggest that easier temperament patterns contributes to reductions in caregiver stress and financial stress of families.
Testing confounder effects. In order to test the impact of IQ, Temperament, and the 6 Behavioral Assets factors have on the relationship between Impairment variables and Age of ADHD diagnosis, the paths from potential confounding factors to Impairment variables were added in the final step of the model. As mentioned previously, a model is defined as having confounding variables when the confounder is shown to significantly impact the Predictor and the Outcome variables, and the previously significant path between the Predictor variable and the Outcome variable is diminished or no longer significant when the path between Confounder and Predictor is added into the model.
Therefore, even though all possible paths from Confounder variables to Predictor variables were tested in this model, only the 5 Cofounding variables that significantly predicted the Outcome variable (BA- Social Network, BA-Academic Attitude, BA-
Academic Compliance, BA-Emotional Intelligence, and Temperament) and the 6
Predictor variables that significantly predicted the Outcome variable (CFQL Child,
CFQL Family/Caregiver, CFQL Partner/Relationship, Referral Concerns, Teacher
Quality of Relationship, and SEBMS Target Symptom Severity) were examined for confounding effects in the full model.
Additionally, to examine the strength of confounding effects, Risk/Compensatory variables with significant paths to Predictor and Outcome variables were examined individually by setting the regression path from the Risk/Compensatory variable to the
Predictor variable to 0 in order to examine the strength of the confounding effects on the
Impairment to Outcome path. In instances where multiple Risk/Compensatory variables
105 had potential impacts on Predictor to Outcome paths, all paths from Risk/Confounder variables to Predictor variables were set to 0 and then reintroduced to the model individually to examine the impact of each individual Risk/Compensatory Factor. The standardized beta weights of the eight paths tested for confounding effects are presented in Table 17. Three paths between Impairment and Outcome variables showed change in significance level or change in standardized beta when BA Academic Attitude and BA
Academic Compliance were present and then removed from the model. However, all of the effects observed were below the magnitude of even a small effect (less than .10), indicating these results were inconsequential. It is important to note that within large sample sizes, small differences can be picked up as significant, therefore, it is important to compare significant findings against effect sizes (small ≥.10, medium ≥ .30, and large
≥ .50) in order to lower the possibility of incurring Type I error. Therefore, the results suggested that the four Risk/Compensatory factors examined did not fully confound the relationship between impairment and outcome variables.
106 p=.002 p=.010 p<.001 p<.001 p<.001 p<.001 p=.006 p<.001 p-value -.12 -.11 -.09 Beta Standardized Standardized -.10-.15 -.09 -.55-.04 -.28 -.11 -.55-.12 -.13 -.27 -.04 -.14 Beta Unstandardized Unstandardized Testing Regression Path with Confounder with Path Testing Regression in (SocNet)Added Latent Network Social BA as Confounder (AcademAtt) Latent Attitude Academic BA as Confounder in Added Latent Compliance Academic BA Confounder as added (AcademCompl) (Temp) Latent Temperament in as added Confounder p=.004 p=.002 p<.001 p<.001 p<.001 p<.001 p=.004 p<.001 p-value -.13 -.11 -.10 Beta Standardized Standardized tested for confounding effect of Risk/Compensatory Factors of Risk/Compensatory effect confounding for tested -.10-.17 -.09 -.56-.04 -.28 -.11 -.56 -.13 -.13 -.28 -.04 -.14 Beta Unstandardized Testing Regression Path without Confounder without Path Testing Regression Æ Æ Æ Age Æ Age Æ Age Æ hip Latent (Reltnshp) Latent hip Age Age Æ Æ Regression Paths Regression Partner/Relations CFQL (Child) Latent Child CFQL Latent Relationship of Quality Teacher (TQltyRltnshp) (FamCargvr) Latent Family/Caregiver CFQL (RefCon) Latent Concerns Referral Latent Relationship of Quality Teacher (TQltyRltnshp) (SEBMS_Sev) Latent SEBMS Severity (FamCargvr) Latent Family/Caregiver CFQL Age Age Age Table 17. Regression Paths from Impairment to Age of Diagnosis Diagnosis of to Age Impairment from Paths Regression 17. Table 107
Final Model Description. Since the hypothesized model was not found to support the Risk/Compensatory factors as confounders to the relationship of Impairment to Age of ADHD diagnosis, the final model was adjusted to remove the 2 nonsignificant paths that resulted from Risk/Compensatory factors being added into the model. The final model (depicted in Figure 19) was tested using FIML procedures and it demonstrated good fit with the data: (χ2 (79, N=1331)=181.484, p<.001; TFI=.959, CFI=.981,
RMSEA=.031 [90% C.I.=.025, .037]). Parameter estimates for the Final Standardized
Results of Risk/Compensatory factors, Impairment, and Age of ADHD Diagnosis are presented in Table 18. 108 Compensatory Factors,Impairment, of and Age ADHD Standardized Results of SEM Model of Relationship between Risk/ Relationship of Model SEM of Results Standardized Figure 19. Figure Diagnosis 1 value .003 - p<.01 p<.01 p<.01 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 P S.E. .011 .033 .029 .064 .029 .049 .077 .055 .055 .016 .029 .027 .039 .023 .013 .173 .032 .050 .032 .015 Wts .127 .239 .269 -.040 -.105 -.102 -.530 -.379 -.342 -.309 -.047 Unstandardized Wts .149 .134 .123.214.250 .094 .133 .172 .133 .091 .047 -.127 -.091 -.094 -.262 -.273 -.197 -.149 -.104 -.289-.164-.153 -1.622 -.690 -.156 -.109 -.252 -.610 -.051 Standardized Standardized between Risk/Compensatory Factors, Impairment, & Age of of & Age Impairment, Factors, Risk/Compensatory between Age Æ Age CFQL CFQL Financial Latent Concerns Referral of Quality Teacher Academic Teacher Structural Regression Weights Regression Structural Æ Æ Æ Æ Æ Æ Age Age Age (Child) Latent Child CFQL of Quality Teacher Academic Teacher Æ Æ Æ Æ Æ Æ CFQL Financial Latent (Financial) Latent Financial CFQL Latent Partner/Relationship CFQL Latent Support External CFQL Age Age Age Æ Æ Æ Æ Æ Æ Age Æ hip Latent (Reltnshp) Latent hip (Reltnshp) (ExtSupp) ADHD Diagnosis (TQltyRltnshp) Latent Relationship (TLrning) Latent Achievement (FamCargvr) Latent Family/Caregiver (Financial) (RefCon) Latent (TQltyRltnshp) Latent Relationship (TLrning) Latent Achievement Table 18. SEM Parameter Estimates for SEM Model of Relationship Model SEM for Estimates 18. SEM Parameter Table Paths (FamCargvr) Latent Family/Caregiver CFQL Partner/Relations CFQL (RefCon) Latent Concerns Referral (TQltyRltnshp) Latent Relationship of Quality Teacher (SocNet) Latent Network Social BA (AcademAtt) Latent Attitude Academic BA (AcademCompl) Latent Compliance Academic BA (EmoIQ) Latent IQ Emotional BA (Temp) Latent Temperament (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (AcademAtt) Latent Attitude Academic BA (AcademAtt) Latent Attitude Academic BA (AcademAtt) Latent Attitude Academic BA (AcademCompl) Latent Compliance Academic BA (AcademCompl) Latent Compliance Academic BA (AcademCompl) Latent Compliance Academic BA (AcademCompl) Latent Compliance Academic BA (AcademCompl) Latent Compliance Academic BA 1 p<.01 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 P-value S.E. .040 .037 .030 .027 .015 .053 .166 .055 .030 .044 .019 .004 .006 .002 .004 .156 .033 .739 .144 .240 .067 .176 .516 .288 .235 .082 .028 .018 .834 .090 -.230 -.015 -.022 -.480 -5.401 -2.035 -1.374 Covariances .359 .194 .200.225.102 .280 .174 .416 .096 .128 .082 .225.237 .393 3.646 -.229-.407 -.301 -.288-.121 -.389 -.219 -.397 -.054 -1.953 -.200 -.107 -.094 -.302 -.125 -.339 -.310 Correlations Structural Covariances Structural SEBMS Severity Latent Latent SEBMS Severity Æ CFQL Child Latent (Child) Latent Child CFQL Latent Concerns Referral Latent Severity SEBMS CFQL Financial Latent Æ Æ Æ ↔ Teacher Academic Achievement Latent Latent Achievement Academic Teacher CFQL Child Latent (Child) Latent Child CFQL Latent Family/Caregiver CFQL Latent Partner/Relationship CFQL Latent Relationship of Quality Teacher (SEBMS_Sev) Latent Severity SEBMS Æ Æ Æ Æ Æ Æ CFQL Family/Caregiver Latent (FamCargvr) Latent Family/Caregiver CFQL (Financial) Latent Financial CFQL CFQL Family/Caregiver Latent (FamCargvr) Latent Family/Caregiver CFQL (Financial) Latent Financial CFQL (RefCon) Latent Concerns Referral Latent Achievement Academic Teacher (SEBMS_Sev) Latent SEBMS Severity Æ Æ ↔ ↔ ↔ ↔ ↔ CFQL Family/Caregiver Latent (FamCargvr) Latent Family/Caregiver CFQL (Financial) Latent Financial CFQL Referral Concerns Latent (RefCon) (TLrning) Latent Achievement Academic Teacher (SEBMS_Sev) Latent Severity SEBMS Æ Æ Æ Æ Æ (TLrning) (FamCargvr) (Reltnshp) (TQltyRltnshp) (SEBMS_Sev) (TLrning) (Financial) BA Academic Compliance Latent (AcademCompl) (AcademCompl) Latent Compliance Academic BA (EmoAdpt) Latent Adaptability Emotional BA (EmoAdpt) Latent Adaptability Emotional BA (EmoAdpt) Latent Adaptability Emotional BA (EmoIQ) Latent IQ Emotional BA (Compl) Latent Compliance BA (Compl) Latent Compliance BA (Compl) Latent Compliance BA (Compl) Latent Compliance BA (Compl) Latent Compliance BA (IQ) Latent IQ (IQ) Latent IQ (IQ) Latent IQ (IQ) Latent IQ (IQ) Latent IQ LatentTemperament (Temp) (Temp) Latent Temperament Paths (Child) Latent Child CFQL (Child) Latent Child CFQL (Child) Latent Child CFQL (Child) Latent Child CFQL (Child) Latent Child CFQL (FamCargvr) Latent Family/Caregiver CFQL (RefCon) (SEBMS_Sev) 11 .002 .002 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 .939 .912 .770 .604 .196 .191 .154 .116 .389 .069 .190 .400 .281 .548 .293 .349 .237 .178 .340 .190 .838 .363 1.987 3.178 3.430 -2.129 .250.308 6.025 5.472 .162 .095 .320.333 3.245 .419.367.536 4.520 .368 3.074 .359 7.730 .324.443.384 1.656 3.913 2.029 -.269-.157 -7.510 -4.088 -.150-.108 -.897 -.606 -.237 -.559 -.294 SEBMS Severity Latent Latent SEBMS Severity CFQL External Support Support External CFQL Partner/Relationship CFQL Latent Concerns Referral BA Academic Compliance Compliance Academic BA Adaptability Emotional BA Latent IQ Emotional BA ↔ ↔ ↔ ↔ CFQL Partner/Relationship Partner/Relationship CFQL ↔ ↔ ↔ ↔ BA Academic Attitude Latent Latent Attitude Academic BA Latent Compliance Academic BA Latent Adaptability Emotional BA (EmoIQ) Latent IQ Emotional BA (Compl) Latent Compliance BA (Temp) Latent Temperament CFQL Partner/Relationship Latent Latent Partner/Relationship CFQL Teacher Academic Achievement Latent Latent Achievement Academic Teacher (SEBMS_Sev) Latent SEBMS Severity CFQL External Support Latent Latent Support External CFQL (RefCon) Latent Concerns Referral (SEBMS_Sev) Latent Severity SEBMS ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ Latent (ExtSupp) Latent (Reltnshp) Latent (RefCon) (ExtSupp) (Reltnshp) (Reltnshp) Latent (AcademAtt) (AcademCompl) (EmoAdpt) (AcademCompl) Latent (EmoAdpt) Latent (EmoIQ) CFQL Family/Caregiver Latent (FamCargvr) Latent Family/Caregiver CFQL (FamCargvr) Latent Family/Caregiver CFQL CFQL Family/Caregiver Latent (FamCargvr) (FamCargvr) Latent Family/Caregiver CFQL (Financial) Latent Financial CFQL (Financial) Latent Financial CFQL (Financial) Latent Financial CFQL (Financial) Latent Financial CFQL (ExtSupp) Latent Support External CFQL (RefCon) Latent Concerns Referral (RefCon) Latent Concerns Referral (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (SocNet) Latent Network Social BA (AcademAtt) Latent Attitude Academic BA (AcademAtt) Latent Attitude Academic BA (AcademAtt) Latent Attitude Academic BA (SEBMS_Sev) (TLrning) 11 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 p<.001 .222 .149 .243 .169 .107 .246 .314 .211 .159 .113 .135 -.631 1.722 -1.060 -1.078 2 R .343.425 2.006 .527 2.907 .383 2.392 .590 2.717 .367 4.619 .338 .423 .429 .077 .046 .073 .251 .341 .196 .357 -.239 -.157 -.537 -.478-.178 -.275 -2.832 BA Emotional Emotional BA Latent Compliance BA Latent Temperament Squared Multiple Correlations Multiple Squared ↔ ↔ ↔ BA Emotional IQ Latent Latent IQ Emotional BA Latent Compliance BA Latent Temperament BA Compliance Latent Latent Compliance BA (Temp) Latent Temperament ↔ ↔ ↔ ↔ ↔ BA Compliance Latent (Compl) Latent Compliance BA (Temp) Latent Temperament Temperament Latent (Temp) ↔ ↔ ↔ hip Latent (Reltnshp) Latent hip (Compl) (EmoIQ) (Compl) (Temp) BA Academic Attitude Latent (AcademAtt) (AcademAtt) Latent Attitude Academic BA (AcademAtt) Latent Attitude Academic BA (AcademCompl) Latent Compliance Academic BA (AcademCompl) Latent Compliance Academic BA (AcademCompl) Latent Compliance Academic BA (EmoAdpt) Latent Adaptability Emotional BA (EmoAdpt) Latent Adaptability Emotional BA (EmoAdpt) Latent Adaptability Emotional BA (EmoIQ) Latent IQ Emotional BA (EmoIQ) Latent IQ Emotional BA (Compl) Latent Compliance BA variable Endogenous Age (Child) Latent Child CFQL (FamCargvr) Latent Family/Caregiver CFQL (Financial) Latent Financial CFQL (ExtSupp) Latent Support External CFQL Partner/Relations CFQL (RefCon) Latent Concerns Referral (TQltyRltnshp) Latent Relationship of Quality Teacher (TLrning) Latent Achievement Academic Teacher (SEBMS_Sev) Latent SEBMS Severity Adaptability Latent (EmoAdpt) Latent Adaptability (Compl) (Temp) 113
The final model had 37 significant regression paths. There were 28 significant paths between Risk/Compensatory factors and Impairment variables, described previously. And there were 9 significant paths to Age of ADHD Diagnosis, which were consistent with the paths from the previous 2 models. Specifically, children with higher numbers of referral concerns (standardized beta= -.11, unstandardized beta= -.11, S.E. =
.03, p<.001), classroom behavior rated as more severe by teachers (higher scores on
Teacher Quality of Relationship; standardized beta= -.26, unstandardized beta= -.52, S.E.
= .06, p<.001), and whose families had lower quality of life (higher scores on the CFQL
Family/Caregiver factor; standardized beta= -.13, unstandardized beta= -0.04, S.E. = .01, p<.001) had earlier/younger age of ADHD diagnosis. With regard to Compensatory factors, younger age of ADHD Diagnosis was predicted by having a more positive attitude toward school (higher composite scores on the Behavioral Assets- Academic
Attitude factor; standardized beta= -.23, unstandardized beta= -.32, S.E. = .05, p<.001), and having a better understanding of emotions in oneself and others (higher composite scores on the Behavioral Assets- Emotional Intelligence factor; standardized beta= -.19, unstandardized beta= -.34, S.E. = .06, p<.001). In contrast, older age of ADHD diagnosis was predicted by less positive attitudes towards school (lower scores on the Behavioral
Assets- Academic Attitude factor) and having poorer understanding of emotions in self and others (lower scores on Behavioral Assets- Emotional Intelligence factor).
Next, children whose parents endorsed lower quality of life in spouse/partner relationships (lower scores on the CFQL Partner/Relationship factor; standardized beta= -
.09, unstandardized beta= -.10, S.E. = .03, p<.01) had older age of ADHD diagnosis. In regard to Compensatory factors, older age of ADHD diagnosis was predicted by better 114 functioning in social settings (higher composite scores on the Behavioral Assets- Social
Network factor; standardized beta= .14, unstandardized beta= .12, S.E. = .03, p<.001), better compliance at school (higher composite scores on the Behavioral Assets-
Academic Compliance factor; standardized beta= .11, unstandardized beta= .20, S.E. =
.08, p<.01), and easier temperament styles (lower scores on Temperament composite; standardized beta= -.15, unstandardized beta= -.31, S.E. = .06, p<.001). In contrast, younger age of ADHD diagnosis was predicted by poorer functioning in social settings
(lower composite scores on the Behavioral Assets- Social Network factor), poorer compliance at school (lower composite scores on the Behavioral Assets- Academic
Compliance factor), and more difficult temperaments (higher scores on Temperament composite).
Consistent with previous models, these results suggest that factors such as teacher ratings of more severe behavioral problems, higher numbers of referrals concerns, and lower family and caregiver quality of life contribute to younger age of ADHD diagnosis.
However, younger children demonstrated strengths in having a positive approach toward school and greater emotional awareness. These results are consistent with findings from
Short, Findling, and Manos (2007), which showed children in the youngest and middle age groups (4-6.9 years old & 7-9.9 years old) had more positive attitudes toward school in comparison to the oldest age group (10-15 years old). In addition, their study also found that the youngest and middle age groupings had better self-esteem, which incorporated items from the BA-Emotional Intelligence factor, in comparison to the oldest group. While there is empirical support to suggest that this may be a function of the developmental process within the context of ADHD, it is also possible that the
115 strengths observed in younger children newly diagnosed with ADHD is a product of normative development, with older children having more life experience (not all of which are positive) that has impacted their interest in school and understanding of others. In addition, older age of ADHD diagnosis was predicted by parents who rated low support in their relationships with their spouse or partner. Older children diagnosed with ADHD also demonstrated strengths in areas related to easier temperaments, and having better social functioning and compliance at school.
Additional results for the final model. Our final SEM model contained 37 statistically significant correlations between latent variables that were also present in the previous models. There were 3 correlations that had a large effect size: BA Academic
Compliance to BA Compliance (r= .53, p<.001), BA Emotional Adaptability to BA
Compliance (r= .59, p<.001), and BA Social Network to BA Emotional Adaptability (r=
.54, p<.001). These correlations suggest that as a child’s compliance at home increases, the child’s compliance at school and their adaptability to new situations also increases
(i.e. compliance begets compliance and adaptability). In addition, as a child’s emotional adaptability increases, so too does their social functioning.
There were 18 correlations with a medium effect size: BA Academic Attitude to
BA Academic Compliance (r= .32, p<.001), BA Compliance (r= .34, p<.001), BA
Emotional Adaptability (r= .44, p<.001), and BA Emotional Intelligence (r= .38, p<.001); BA Emotional Adaptability to BA Academic Compliance (r= .43, p<.001), BA
Emotional Intelligence (r= .38, p<.001), and Temperament (r= -.48, p<.001); BA
Emotional Intelligence to BA Compliance (r= .37, p<.001); BA Social Network to BA
Academic Attitude (r= .42, p<.001), BA Academic Compliance (r= .37, p<.001), BA
116 Compliance (r= .36, p<.001), and BA Emotional Intelligence (r= .37, p<.001); CFQL
Child to CFQL Family/Caregiver (r= -.30, p<.001), Referral Concerns (r= -.34, p<.001), and SEBMS Severity (r= -.31, p<.001); CFQL External Support to CFQL
Partner/Relationship (r= .32, p<.001); and SEBMS Severity to CFQL Family/Caregiver
(r= .31, p<.001) and Referral Concerns (r= .33, p<.001). The remaining 16 correlations had a small effect size: Temperament to BA Academic Attitude (r= -.24, p<.001), BA
Academic Compliance (r= -.16, p<.001), BA Compliance (r= -.28, p<.001), BA
Emotional Intelligence (r= -.18, p<.001), and BA Social Network (r= -.29, p<.001);
CFQL Financial to CFQL Child (r= -.13, p<.001), CFQL Family/Caregiver (r= .24, p<.001), CFQL Partner/Relationship (r= -.11, p<.01), CFQL External Support (r= -.15, p<.001), Referral Concerns (r= .16, p<.001), and SEBMS Severity (r= .10, p<.01); CFQL
Family/Caregiver to CFQL External Support (r= -.27, p<.001), CFQL
Partner/Relationship (r= -.16, p<.001), and Referral Concerns (r= .25, p<.001); and
Teacher Rating of Academic Achievement to CFQL Child (r= .23, p<.001) and Referral
Concerns (r= -.24, p<.001).
Discussion
This section will first review the findings of the study as they address the research questions. Second, I will highlight the clinical implications of these findings. Finally, the limitations and possible directions for future research will be presented.
Results of Research Questions
Research Question 1. Do ADHD symptoms impact age of ADHD diagnosis?
It was hypothesized that children with fewer ADHD symptoms of hyperactivity and more
ADHD symptoms of inattention would have later/older age of ADHD diagnosis as
117 compared to younger children. The results of this study partially supported this hypothesis, and revealed that both parents and teachers reported higher
Hyperactivity/Impulsivity symptoms in younger children diagnosed with ADHD. Despite this hypothesized effect, I also found that parents report higher numbers of Inattentive symptoms in older children diagnosed with ADHD than younger children. These results are consistent with the literature on changes in ADHD symptom endorsement from childhood to adolescence (Biederman et al., 2000; Faraone et al., 2006; Hill & Schoener,
1996). In addition, the fact that teachers reported higher Inattentive symptoms in younger children diagnosed with ADHD, while parents report showed patterns in the opposite direction, also helps to illuminate some of the inconsistencies in the literature regarding the patterns of Inattentive symptom endorsement increasing over childhood
(Larsson et al., 2011; Sasser et al., 2016). In addition, the current results also shed light on some of the difficulties with teacher report in older children, given that older children may have multiple teachers during their school day, which can lead to difficulties capturing symptom presentation in school settings for older children (Molina et al., 1998;
Wolraich et al., 2005). In sum, these results suggest differences in parent reported symptoms based on age of ADHD diagnosis, while teachers generally report greater symptoms for children diagnosed with ADHD at younger ages.
Research Question 2. Does impairment impact age of ADHD diagnosis?
It was hypothesized that children with greater impairment (as indicated by parent reported severity of symptom impairment, number of referral concerns reported by parents, parent ratings on the Child and Family Quality of Life scale, and teacher ratings
118 of behavioral problems and academic achievement) would be diagnosed with ADHD at younger ages in comparison to children diagnosed with ADHD at older ages. This hypothesis was partially supported by the findings. Specifically, younger age of ADHD diagnosis was predicted by greater impairment on family/caregiver quality of life, more severe behavioral problems at school per teacher report, and higher numbers of referral concerns. In contrast, older age of ADHD diagnosis was predicted by poorer parental relationship support. In the final model, Age of ADHD diagnosis was not predicted by severity of behavioral problems per parent report, child’s quality of life, financial stress, parental sense of social support, or child’s level of academic achievement. These results generally suggest that younger children presenting for ADHD diagnosis may demonstrate greater domains of impairment that impact their functioning at home and school, and family/caregiver quality of life. Although older children presenting for ADHD diagnosis did not have as many domains of impairment, their parents reported lower sense of support within their relationship with their spouse/partner as a result of their child’s behavioral difficulties. Thus, indicating that delayed diagnosis of ADHD has negative impacts on caregiver’s sense of support and may also take more of a toll on parental satisfaction.
Confounding factors that may affect developmental pathway of ADHD
IQ
Research Question 3. Does IQ impact age of ADHD diagnosis?
It was hypothesized that IQ would impact the age of ADHD diagnosis, such that individuals with higher IQs will have a later age of ADHD diagnosis; however, results
119 suggest that there is no relationship between IQ and Age of ADHD diagnosis. This means that there is no difference in individuals age of diagnosis based on their IQ, thus this hypothesis was not supported.
Research Question 4. Does IQ (as a confounder) impact the relationship between age of
ADHD diagnosis and impairment?
It was hypothesized that IQ would act as a confounder and impact the relationship between impairment variables and Age of ADHD diagnosis. Since IQ was not a significant predictor of Age of ADHD diagnosis (see RQ3 above), IQ cannot be classified as a confounder. However, results found that IQ was a covariate for CFQL
Family/Caregiver factor, CFQL Financial Factor, number of Referral Concerns, Teacher rating of Academic Achievement, and SEBMS Severity Ratings. Specifically, children with higher IQ scores had families with lower financial stress, fewer referral concerns, and higher levels of academic achievement. In addition, children with higher IQ scores also had families with lower quality of life and had more severe behavioral problems.
These results suggest that higher intellectual functioning may not contribute to compensatory skills in areas of behavioral regulation in children with ADHD, given its relation to behavioral problems at home and caregiver stress. Instead, these findings suggest that while IQ may be acting as more of a compensatory factor in relation to academic functioning, which could be driving some of the reduction in referral concerns.
Temperament
Research Question 5. Does temperament impact age of ADHD diagnosis?
120 It was hypothesized that individuals with easier temperaments (higher ratings on temperament domains indicate more positive or easy temperament) will have later age of
ADHD diagnosis as compared to individuals with difficult temperaments (lower ratings on temperament domains indicate more difficult temperament). Results showed that
Temperament was significantly related to Age of ADHD diagnosis, and supported the hypothesis that children with more difficult temperaments received earlier/younger age of
ADHD diagnosis.
Research Question 6. Does temperament (as a confounder) impact the relationship between age of ADHD diagnosis and impairment?
Results revealed that Temperament was a covariate for CFQL Family/Caregiver quality of life and CFQL Financial quality of life. Specifically, children with more difficult temperaments had families with lower quality of life and more financial stress. Although
Temperament predicted Age of ADHD diagnosis, it did not have a significant impact on the relationship between CFQL Family/Caregiver quality of life and Age of ADHD diagnosis. Therefore, findings only partially supported the hypothesis that temperament would impact the relationship impairment and age of ADHD diagnosis, as temperament was found to predict age of ADHD diagnosis and act as a covariate for CFQL
Family/Caregiver quality of life. Results suggested that children with more difficult temperaments had earlier age of ADHD diagnosis and had families with lower quality of life.
121 Behavioral Assets
Research Question 7. Do behavioral assets impact the age of ADHD diagnosis?
It was hypothesized that children with a higher scores on multiple behavioral asset domains would have older age of ADHD diagnosis. Although children with better functioning in social settings (higher composite scores on the BA- Social Network factor) and better compliance at school (higher composite scores on the BA- Academic
Compliance factor) had older age of ADHD diagnosis, children with more positive attitude toward school (higher composite scores on the BA- Academic Attitude factor) and better understanding emotions (higher composite scores on the BA - Emotional
Intelligence factor) had younger age of diagnosis. These findings only partially support the initial hypothesis. Overall, findings suggest that younger children diagnosed with
ADHD may enjoy going to school and demonstrate good understanding of emotions, but they are struggling with following rules at school and making friends. In contrast, older children diagnosed with ADHD may present with better social and academic functioning, but they may have poorer attitude toward school and more difficulty understanding others emotions. It is possible that families seeking diagnosis for their older children may have delayed seeking treatment, such that concerns were accumulating, or that caregivers started to see a shift in their child’s ability to meet the challenges of school and social environment. In addition, the results regarding academic attitude are consistent with findings from Short, Findling, and Manos (2007), which showed children in the youngest and middle age groups (4-6.9 years old & 7-9.9 years old) had more positive attitudes toward school in comparison to the oldest age group (10-15 years old). While it seems possible that this experience is unique to older children with ADHD, given that they have
122 difficulties with attention and behavior regulation, which most often contributes to academic difficulties. In addition, the fact that older children with ADHD have poorer attitudes also attests to the greater life experience they have in comparison to younger children with ADHD, who may have fewer instances of academic struggle. However, it is also possible that waning positivity toward school is a fairly common experience among older children, regardless of whether they have symptomatology that hinders academic performance, as it is fairly common for older children to struggle with advancing grade levels that entail more challenging academic requirements paired with more independence and less support from teachers and caregivers.
In addition, Short, Findling, and Manos (2007) also demonstrated that children in the youngest and middle age groupings had better self-esteem in comparison to the oldest age grouping; coincidentally, the two items that compose the Behavioral Assets
Emotional Intelligence factor in the current study (“Easily expresses feelings” and “Is able to identify feelings”) were included on the self-esteem factor within their study.
These findings are consistent with empirical support suggesting that older children, particularly adolescents with ADHD may have impairments in understanding their own emotions and processing the emotions of others due to impairments with working memory, self-regulation, and reactivity/impulsivity (Barkley, 2010; Factor, Rosen, &
Reyes, 2016). However, it is also possible that these effects are reflective of developmental norms related to emotional awareness and expression, such that older children are trending toward phases of identity development that require more advanced emotional processing than younger children (Choudhury, Blakemore, & Charman, 2006).
123 Therefore, older children may be less adept at understanding emotions in themselves and others because the emotional arena is more challenging to process and interpret.
Research Question 8. Do behavioral assets (as a confounder) impact the relationship between age of ADHD diagnosis and impairment?
It was hypothesized that children with more behavioral assets would have lower levels of impairment and later age of ADHD diagnosis. Results revealed that BA-Academic
Attitude and BA-Academic Compliance were the only two factors that impacted the relationship between impairment and age of ADHD diagnosis, however, the magnitude of this effect was inconsequential and the results were not interpreted.
Although Risk and Compensatory factors were not found to confound the relationship between Impairment variables and Age of ADHD diagnosis, there were a number of Risk and Compensatory factors that significantly predicted impairment variables. Children with better functioning in social settings had families with lower financial stress, parents with higher quality of life in spouse/partner relationships, and families with greater sense of external support. Children with better attitude toward school had higher levels of academic achievement, but were also rated by teachers as having more severe behavioral problems. Children with better compliance at school had fewer referral concerns and families with lower financial stress, but they were rated by their teachers as having poorer academic performance. Children with higher emotional adaptability had better child quality of life, fewer referral concerns, and less problematic behavioral difficulties. Children with higher emotional intelligence were rated by their teachers as having lower levels of academic achievement. Children with better
124 compliance had better child quality of life, families with better quality of life, parents with higher quality of life in spouse/partner relationships, and were rated by their parents as having less problematic behavioral difficulties; however, they were also rated by their teachers as having more problematic behavior in the classroom. Overall, behavioral assets were shown to have a positive impact on quality of life ratings in multiple domains, as well as decreasing referral concerns and severity ratings for behavior.
However, there were some cases where behavioral assets did not impact academic achievement or teacher rating of problematic behavior in the classroom, suggesting that some domains of strength may not be enough to compensate for ADHD symptoms impairing academic functioning.
Implications
The variability of ADHD symptoms over the course of development poses significant challenges for researchers and clinicians involved in assessment and treatment of ADHD. In addition, the majority of research on symptom presentation on ADHD has been largely been based on prospective longitudinal studies of children diagnosed with
ADHD in childhood, which may not generalize to individuals who present for diagnosis for the first-time in adolescence and adulthood.
The results of this study focused on examining ADHD from a developmental perspective allows for a multitude of causal factors at each developmental window to explain the heterogeneity of symptoms, impairment, and developmental pathways.
Results suggested that factors related to severity of behavioral problems at home and school, number of referral concerns, and family and caregiver quality of life contributed
125 to earlier age of ADHD diagnosis. In contrast, factors that impacted delayed diagnosis were related to easier temperament patterns, better social functioning, and better compliance at school. However, later diagnosis of ADHD was also related to lower parental support in partner relationships. These findings are consistent with empirical evidence suggesting that children with ADHD often have families that experience significant stress and marital strain (Anastopoulos, Guevremont, Shelton, & DuPaul,
1992; Dadds, Sanders, Behrens, & James, 1987; Nurullah, 2013). Given that there is a natural push toward independence and autonomy in older children, it is possible that older children with ADHD may require more parental support, which reduces parental resources in their partner relationships. In adolescence, there are greater demands placed on social and academic performance, however, adolescents with ADHD are often less likely to seek external supports from parents and teachers, which can lead to poorer functioning. It is also important to note that factors related to IQ and academic achievement did not predict age of ADHD diagnosis, suggesting that factors related to behavioral compliance and functioning in social settings may play more of a role in treatment seeking behaviors.
It is important to recognize that ADHD is a chronic disorder and highlight the way ADHD interferes with the progression of normative processes in development (i.e., social, academic, and occupational functioning). In addition, The presence of ADHD can often lead to or be linked to other often comorbid conditions, such as secondary diagnoses or problem behaviors, that contribute to greater risk for poor outcomes. The findings from the current study suggest that children diagnosed with ADHD at younger ages will demonstrate greater impairments in home and school and more severe
126 behavioral problems as compared to children diagnosed with ADHD at older ages. One caveat to note when interpreting these findings is that in the present study did not include a control group of typically functioning peers in which to draw comparison. As such comparisons were made within a sample of individuals with ADHD that demonstrate significant impairment to qualify for diagnosis at different developmental windows.
Further, while older age of ADHD diagnosis was predicted by lower levels of impairment in comparison to younger age of ADHD diagnosis, it is important to highlight that all children within this study meant diagnostic criteria for ADHD and evidenced clinically significant impairment from symptoms in 2 or more settings. Thus, these results are not meant to suggest that children diagnosed with ADHD at older ages are without impairment, but rather highlight how impairment may present differently when developmental context is taken into account. In fact, older age of ADHD diagnosis was predicted by lower levels of caregiver partner/spousal support, lower understanding of emotions in themselves and others, and poorer attitude toward school. So even though older age of ADHD diagnosis was predicted by lower levels of behavioral severity per parent and teacher report, there were other domains of the child’s internal and family functioning that showed impairment that was not seen in children with younger age of
ADHD diagnosis. These results are likely to have significant implications for how clinicians understand and diagnose ADHD in childhood, and may impact the level of guidance given to parents seeking consultation for their child. For instance, researchers and clinicians alike should take special consideration of how ADHD may present differently in early childhood versus later childhood and how symptom descriptors may change over time. Although our understanding of ADHD has significantly grown even
127 within the last decade, there is much that could be done to improve our understanding of how this disorder presents across development in order to improve symptom descriptors used in the diagnostic system. In addition, inconsistencies in the literature on symptom emergence and persistence across childhood and into adulthood might be indicative of the fact that ADHD may be better understood as a disorder with waxing and waning symptom presentation rather than a stable presentation.
In addition, the current study highlights some of the risks that children with
ADHD experience related to academic functioning, particularly, with poorer attitudes toward school being associated with older age of ADHD diagnosis. Researchers have shown that older children and adolescents with ADHD are at greater risk for poor outcomes, one of which is dropping out of school (Barkley, Fischer, Smallish, &
Fletcher, 2006; Turgay et al., 2012). For youth experiencing significant educational impairments are likely to experience subsequent outcomes that are negative, such as lower likelihood of continued education after high school, fewer employment opportunities, greater occupational disruption (i.e., likelihood of unemployment, job termination), and lower overall earnings (Barkley et al., 2006; Kuriyan et al., 2013).
While many studies noted the negative impact of educational impairment on future educational attainment through completion of high school or higher education (Barkley et al., 2006; Kuriyan et al., 2013; Zendarski, Sciberras, Mensah, & Hiscock, 2016), few studies provided clear insight into how these needs should be addressed to promote academic motivation and achievement. Needless to say, special consideration should be given to developing interventions that may support children with ADHD within academic environments, such as training parents, teachers, and support staff at schools to help
128 provide guidance on how to effectively support youth with ADHD. Teachers and school staff are on the frontlines of recognizing initial signs of academic difficulties and children whose attitudes who shift toward disliking school. However, given the significant strain on our school systems, there are a number of barriers for ensuring that schools and teachers have enough resources (e.g., time, staff support, financial compensation, etc) to manage their classrooms let alone take on additional responsibility of recognizing early signs of behavioral and academic difficulties. Thus, it will take more than just adding support staff or continuing education to make a significant impact. It will take stronger advocacy for educational reform to ensure equal access to education in a safe and supportive environment in order to even begin to address systemic issues that impact teachers’ access to resources, classroom size, and the availability of educational support and mental health staff to help support comorbid issues like learning disorders and traumatic stress.
Given that ADHD presents with heterogeneous symptom profiles, causal pathways, and developmental outcomes, individualized treatments are likely needed to address the unique set of risk and impairments with which children with ADHD present.
This study highlights some of the difficulties that may be occurring during important developmental periods in childhood and adolescence. Many behavioral interventions aim to address common concerns that span from childhood to adolescence, such as organizational issues and reinforcement of compliance, however, it may be possible to identify concerns that are common within specific periods of development to further tailor treatment approaches. For example, given that older children presenting for ADHD diagnosis may be experiencing poorer attitudes toward school and poorer emotional
129 awareness, these individuals may benefit from learning strategies to build their confidence and self-efficacy. Focusing on additional skills related to self-identity development may also help to reduce the likelihood of later development of comorbid internalizing concerns (e.g., anxiety, depression) that are commonly seen in older children with ADHD (Barkley, Anastopoulos, Guevremont, & Fletcher, 1991; Steinberg
& Drabick, 2015). Preventative treatments should be based on the understanding of developmental processes of ADHD and factors that may impact the diagnosis of ADHD.
For instance, monitoring parent-child goodness of fit and temperamental domains related to ease vs difficulty in primary care settings (e.g., well-child checks) may help to address issues early on and better equip parents for managing more active, reactive children. This preventative approach may help boost parents’ sense of efficacy and reduce caregiver and familial stress. In addition, treatments that take into account individual differences (i.e. and that are developmentally appropriate are likely to be more efficacious. Interventions aimed at providing psychoeducation and guidance on symptom management during period of transition may be helpful for families of children with ADHD. Similar to well- child visits that provide education on developmental milestones within the first year of life, annual visits at integrated care settings over the course of childhood could incorporate psychoeducation on social-emotional and psychological “milestones” parents can be monitoring and fostering. For instance, teaching caregivers how to model appropriate emotional expression and communication may assist older children with
ADHD in developing better skills in emotional intelligence. In addition, these interventions could incorporate information on the shift from dependence to independence in familial relationships as to help parents cope with the power struggles
130 experienced during the transition periods (e.g., childhood to adolescence, adolescence to adulthood) time while also taking into account ways to help shift responsibilities more from the parent to the child. Overall, researchers and professionals may wish to consider how to address the evolving needs of youth with ADHD during different periods of development and design interventions aimed at assisting with navigating and managing the complexity of shifting roles and responsibilities over time.
Limitations
Despite the many strengths of this study including sample size, carefully documented history, low rates of missing data, there are important limitations as well.
First, this study utilized a clinic-based, largely middle-class managed care sample of youth referred for ADHD diagnosis and as such findings may not be representative of the general population. In addition, the cross-sectional nature of this study allowed for an examination of the face of ADHD across the broad developmental continuum, but does not allow us to ascertain how a child’s condition unfolds across this continuum. Thus, in contrast to longitudinal studies, the current study was not able to make inferences about developmental changes in ADHD. Further, there was no control group without mental health diagnosis available for comparison nor was a sample of typically developing youth available for comparison purposes. As such, we are not able to draw strong conclusions about this clinical group in comparison to other peers. Given the limitations within a clinical setting (e.g., low likelihood of control patient presenting for diagnosis, limitation on clinicians’ time, etc.), future studies may choose to recruit comparison groups of individuals diagnosed with disorders other than ADHD in similar clinic-based settings to
131 get a sense of whether factors related to impairment and compensatory factors are unique to an ADHD population. Lastly, although the Behavioral Assets parent report measure has been examined in another study with children diagnosed with ADHD (e.g., Short et al., 2007) and the measure itself was developed to capture similar constructs as the
Search Institute’s Developmental Assets questionnaires (Scales & Roehlkepartain, 2003), the measure has limited published research on its psychometric properties and application within other sample populations. Therefore, future research should use the Behavioral
Assets measure alongside other established measures of assets in children to validate findings and build the empirical base for the Behavioral Assets measure. Further research should also focus on helping to validate the individual factor domains on the Behavioral
Assets measure. For example, in order to validate the Emotional Intelligence factor from the Behavioral Assets measure, future research should also incorporate other validated measures of emotional intelligence as well as measures of emotion regulation and social skills to help provide information on content and discriminant validity. Since the current study did not include additional measures that would help validate the Behavioral Assets factors, the results pertaining to the Behavioral Assets factors should be interpreted with caution and considered to have limited generalizability.
132 Appendix
Table 1-A. Descriptives on Parent Education and Occupation n % N Biological Mother Level of Education Less Than High School 32 2.6 High School Diploma, Some 458 37.7 College College, Some Graduate work 418 34.4 Graduate Degree 277 22.8 Biological Mother Occupation 1214 Unemployed 235 19.4% Unskilled occupations 28 2.3% Semiskilled, Laborers 121 10.0% Semiskilled, Clerical/Admin 255 21.0% Skilled, Higher level 518 42.7% professionals Biological Father Level of Education Less Than High School 77 6.7 High School Diploma, Some 534 46.2 College College, Some Graduate work 299 25.9 Graduate Degree 216 18.7 Biological Father Occupation 1156 Unemployed 100 8.7% Unskilled occupations 76 6.6% Semiskilled, Laborers 255 22.1% Semiskilled, Clerical/Admin 162 14.0% Skilled, Higher level 443 38.3% professionals Non-Biological Parent Level of Education 260 Less Than High School 5 1.9% High School Diploma, Some College 141 54.2% College, Some Graduate work 45 17.3% Graduate Degree 56 21.5% Non-Biological Parent Occupation 260 Unemployed 26 10.0% Unskilled occupations 21 8.1% Semiskilled, Laborers 46 17.7% Semiskilled, Clerical/Admin 46 17.7% Skilled, Higher level professionals 107 41.2% Note: Non-biological parent categories included information combined from step-parents, adoptive parents, and individuals who were categorized as other primary caregivers for the child.
133
Table 2-A. Reference: Items included on CFQL 5-Factor Solution Item #
6 My child’s difficulties have added stress to our home life 7 My child’s difficulties limit our family from social activities 8 My child’s problems make it difficult to go places 9 My child’s problems make it difficult to entertain others at our home 10 My child’s problems make it difficult for our family to relax 11 I feel tired or without energy as a result of caring for my child 12 I feel on-guard due to not knowing whether my child will have a problem 13 I am frustrated that my child’s behavior is unmanageable 14 I feel that caring for my child dominates my life 22 I think my friends and family members avoid me because of my child’s behaviors 29 I find it difficult to cope with my child’s difficulties 30 I think Why me? or Why did this happen to my family? 31 I feel angry about my child’s condition 25 My relationship with my partner is positive 26 My partner and I see eye-to-eye on how to best care for my child 27 My partner and I are committed to resolving our differences and disagreements 28 My partner and I communicate well about our child and family’s needs 16 My family’s financial situation has been difficult as a result of my child’s problems The money available for basic family needs has been reduced as a result of my child’s 17 problems 18 The money available for extras has been reduced as a result of my child’s problems 20 I have friends that support me in managing my child’s difficulties 21 My extended family understands my needs in caring for my child 23 I have a strong support systems and others I can consistently rely on 24 I feel alone, isolated and without friends to rely upon (reverse coded) 1 My child deals with daily life hassles and daily activities well 2 My child is able to function at his/her full potential 3 My child is able to learn appropriately for his/her age and potential My child follows through with everyday tasks and expectations that are appropriate for 4 him/her 134 14. 1.00 0.28 0.26 0.21 0.28 0.27 0.27 -0.09 -0.12 -0.28 -0.38 -0.11 -0.14 -0.06 -0.06 13. 1.00 0.32 0.55 0.45 0.44 0.24 0.20 0.26 -0.13 -0.16 -0.31 -0.33 -0.20 -0.24 -0.13 -0.21 12. 1.00 0.66 0.34 0.58 0.43 0.47 0.14 0.11 0.16 -0.06 -0.09 -0.22 -0.25 -0.17 -0.25 -0.11 -0.20 11. 1.00 0.67 0.59 0.31 0.48 0.40 0.40 0.18 0.12 0.18 -0.03 -0.08 -0.21 -0.23 -0.12 -0.20 -0.08 -0.16 10. 1.00 0.62 0.65 0.72 0.33 0.54 0.43 0.42 0.22 0.17 0.24 -0.09 -0.14 -0.30 -0.32 -0.18 -0.26 -0.12 -0.21 9. 1.00 0.70 0.60 0.64 0.64 0.37 0.51 0.34 0.37 0.18 0.14 0.19 -0.09 -0.12 -0.25 -0.29 -0.16 -0.24 -0.13 -0.20 8. 1.00 0.67 0.53 0.50 0.54 0.49 0.54 0.39 0.31 0.29 0.22 0.19 0.22 -0.08 -0.07 -0.26 -0.32 -0.13 -0.18 -0.11 -0.11 7. 1.00 0.81 0.65 0.56 0.52 0.59 0.53 0.50 0.41 0.34 0.32 0.25 0.21 0.23 -0.05 -0.06 -0.25 -0.31 -0.15 -0.20 -0.11 -0.14 6. 1.00 0.85 0.77 0.65 0.58 0.53 0.58 0.56 0.48 0.43 0.36 0.32 0.25 0.20 0.24 -0.09 -0.11 -0.25 -0.30 -0.16 -0.21 -0.12 -0.15 5. 1.00 0.47 0.41 0.38 0.59 0.54 0.48 0.57 0.50 0.24 0.54 0.32 0.35 0.13 0.09 0.14 -0.06 -0.12 -0.17 -0.19 -0.15 -0.23 -0.07 -0.18 4. 1.00 -0.31 -0.26 -0.26 -0.25 -0.28 -0.26 -0.23 -0.30 -0.29 -0.18 -0.25 -0.15 -0.18 -0.14 -0.13 -0.13 0.04 0.06 0.12 0.11 0.11 0.15 0.07 0.14 3. 1.00 0.39 -0.20 -0.14 -0.11 -0.08 -0.14 -0.15 -0.14 -0.13 -0.13 -0.10 -0.16 -0.12 -0.12 -0.15 -0.15 -0.14 0.08 0.03 0.08 0.09 0.03 0.05 0.02 0.06 2. 1.00 0.58 0.48 -0.36 -0.26 -0.21 -0.19 -0.29 -0.27 -0.25 -0.26 -0.27 -0.16 -0.28 -0.16 -0.17 -0.13 -0.12 -0.12 0.10 0.07 0.12 0.15 0.11 0.14 0.06 0.13 1. 1.00 0.46 0.26 0.43 -0.32 -0.34 -0.35 -0.33 -0.34 -0.32 -0.36 -0.37 -0.32 -0.24 -0.29 -0.21 -0.28 -0.13 -0.10 -0.15 0.02 0.07 0.12 0.17 0.19 0.22 0.12 0.17 Table 2-A. CFQL Item Correlations CFQL 2-A. Table 1. 1 Item 2. 2 Item 3. 3 Item 4. 4 Item 5. 6 Item 6. 7 Item 7. 8 Item 8. 9 Item 9. 10 Item 10. 11 Item 11. 12 Item 12. 13 Item 13. 14 Item 14. 22 Item 15. 29 Item 16. 30 Item 17. 31 Item 18. 16 Item 19. 17 Item 20. 18 Item 21. 20 Item 22. 21 Item 23. 23 Item 24. 24 Item 25. 25 Item 26. 26 Item 27. 27 Item 28. 28 Item 135 27. 1.00 0.72 26. 1.00 0.63 0.74 25. 1.00 0.65 0.74 0.73 24. 1.00 0.28 0.26 0.25 0.27 23. 1.00 0.68 0.29 0.27 0.27 0.28 22. 1.00 0.52 0.38 0.17 0.17 0.17 0.20 21. 1.00 0.63 0.51 0.39 0.13 0.10 0.15 0.13 20. 1.00 -0.08 -0.05 -0.18 -0.26 -0.13 -0.13 -0.10 -0.11 19. 1.00 0.82 -0.06 -0.03 -0.14 -0.22 -0.10 -0.10 -0.09 -0.07 18. 1.00 0.85 0.83 -0.08 -0.05 -0.16 -0.24 -0.12 -0.13 -0.11 -0.10 17. 1.00 0.18 0.14 0.19 -0.13 -0.10 -0.23 -0.25 -0.14 -0.17 -0.09 -0.17 16. 1.00 0.67 0.21 0.16 0.19 -0.08 -0.10 -0.23 -0.24 -0.17 -0.20 -0.06 -0.15 15. 1.00 0.52 0.53 0.16 0.12 0.17 -0.11 -0.16 -0.24 -0.27 -0.14 -0.20 -0.07 -0.16 . Correlations, CFQL continued Item -A Table 2 15. 29 Item 16. 30 Item 17. 31 Item 18. 16 Item 19. 17 Item 20. 18 Item 21. 20 Item 22. 21 Item 23. 23 Item 24. 24 Item 25. 25 Item 26. 26 Item 27. 27 Item 28. 28 Item 136
Table 3-A Reference of items included in Behavioral Assets 6-Factor Solution Item # 1 Makes friend easily. 2 Is liked by other children. 3 Has at least two close friends. 4 Likes to be part of a group. 8 Plays/shares activities with friends at least once per week outside of school. 15 Follows rules at school. 18 Responds appropriately to authority in school. 29 Easily expresses feelings. 30 Is able to identify feelings. 11 Generally likes school. 12 Attends school willingly. 14 Talks about school activities. 21 Easily tolerates changes in routine. 24 Doesn't give up easily. 25 Is a good sport. 26 Easily manages transitions from one activity to another. 32 Handles challenges well. 36 Follows rules at home. 37 Takes proper care of property. 38 Admits wrongdoing. 137 10. 1.00 0.30 0.06 0.37 0.33 0.17 0.02 0.10 0.43 0.32 0.24 9. 1.00 0.74 0.27 0.07 0.33 0.32 0.16 0.03 0.08 0.44 0.32 0.24 8. 1.00 0.21 0.20 0.22 0.19 0.20 0.18 0.23 0.36 0.31 0.23 0.21 0.23 7. 1.00 0.40 0.26 0.26 0.24 0.14 0.24 0.24 0.20 0.15 0.15 0.26 0.18 0.13 6. 1.00 0.68 0.45 0.18 0.17 0.20 0.19 0.17 0.16 0.25 0.24 0.21 0.16 0.12 0.13 5. 1.00 0.15 0.21 0.23 0.27 0.28 0.24 0.14 0.27 0.26 0.19 0.15 0.15 0.20 0.20 0.16 4. 1.00 0.42 0.22 0.23 0.29 0.17 0.20 0.26 0.16 0.21 0.26 0.22 0.29 0.21 0.12 0.09 0.08 3. 1.00 0.42 0.53 0.13 0.19 0.21 0.27 0.28 0.23 0.18 0.28 0.27 0.18 0.18 0.23 0.23 0.25 0.18 2. 1.00 0.62 0.44 0.42 0.18 0.22 0.24 0.27 0.27 0.26 0.19 0.31 0.29 0.23 0.21 0.25 0.25 0.23 0.17 1. 1.00 0.69 0.57 0.56 0.42 0.24 0.26 0.31 0.13 0.16 0.29 0.24 0.23 0.26 0.26 0.37 0.29 0.15 0.17 0.17 Table 3-A. Behavioral Assets Item Correlations Table 3-A. Behavioral Assets Item 1. 1 Item 2. 2 Item 3. 3 Item 4. 4 Item 5. 8 Item 6. 11 Item 7. 12 Item 8. 14 Item 9. 15 Item 10. 18 Item 11. 21 Item 12. 24 Item 13. 25 Item 14. 26 Item 15. 32 Item 16. 29 Item 17. 30 Item 18. 36 Item 19. 37 Item 20. 38 Item 138 19. 1.00 0.45 18. 1.00 0.55 0.49 17. 1.00 0.22 0.24 0.31 16. 1.00 0.72 0.16 0.18 0.26 15. 1.00 0.25 0.29 0.25 0.23 0.19 14. 1.00 0.42 0.19 0.22 0.32 0.26 0.20 13. 1.00 0.45 0.38 0.14 0.20 0.41 0.34 0.28 12. 1.00 0.35 0.26 0.47 0.19 0.19 0.16 0.18 0.17 11. 1.00 0.21 0.38 0.57 0.42 0.23 0.25 0.32 0.21 0.24 Behavioral Assets Item Correlations (continued) Correlations Behavioral Assets Item -A. Table 3 11. 21 Item 12. 24 Item 13. 25 Item 14. 26 Item 15. 32 Item 16. 29 Item 17. 30 Item 18. 36 Item 19. 37 Item 20. 38 Item 139
Table 4-A. Model Development of Impairment as a predictor of Age of ADHD Diagnosis Chi Model Step df p TLI CFI RMSEA Square 1. Initial Model ------2. Remove nonsignificant covariance between Teacher Quality of 0.033 1 .856 1.025 1.00 .00 Relationship ↔ CFQL External Support 3. Remove nonsignificant covariance between Teacher Quality of 0.138 2 .933 1.024 1.00 .00 Relationship ↔ CFQL Relationship 4. Remove nonsignificant covariance between Teacher Rating of Academic 0.325 3 .955 1.023 1.00 .00 Achievement ↔ CFQL Relationship 5. Remove nonsignificant covariance between Teacher Rating of Academic 0.622 4 .961 1.022 1.00 .00 Achievement ↔ CFQL External Support 6. Remove nonsignificant covariance between Teacher Quality of 0.935 5 .968 1.021 1.00 .00 Relationship ↔ CFQL Child 7. Remove nonsignificant covariance between Teacher Quality of 2.018 6 .918 1.017 1.00 .00 Relationship ↔ Teacher Rating of Academic Achievement 8. Remove nonsignificant covariance between Teacher Quality of 12.095 7 .097 .981 .998 .023 Relationship ↔ CFQL Financial 9. Remove nonsignificant covariance between Teacher Rating of Academic 13.849 8 .086 .981 .997 .023 Achievement ↔ CFQL Financial 10. Remove nonsignificant path from 13.990 9 .123 .986 .998 .020 CFQL Financial Æ Age 11. Remove nonsignificant path from 16.874 10 .077 .982 .997 .023 CFQL External Support Æ Age 12. Remove nonsignificant path from Teacher Rating of Academic 20.447 11 .040 .978 .996 .025 Achievement Æ Age 140
Table 5-A. Model Development for Risk/Compensatory variables as predictors of Age of ADHD Diagnosis Chi Model Step df p TLI CFI RMSEA Square 1. Initial Model ------2. Remove nonsignificant path from .055 1 .815 1.022 1.00 .000 IQ Æ Age 3. Remove nonsignificant covariance 1.777 2 .411 1.003 1.00 .000 BA Social Network ↔ IQ 4. Remove nonsignificant covariance 3.220 3 .359 .998 1.00 .007 BA Emotional Adaptability ↔ IQ 5. Remove nonsignificant covariance 4.711 4 .318 .996 1.00 .012 BA Academic Attitude ↔ IQ 6. Remove nonsignificant covariance 6.030 5 .303 .995 .999 .012 Temperament ↔ IQ 7. Remove nonsignificant covariance 7.451 6 .281 .994 .999 .013 BA Academic Compliance ↔ IQ 8. Remove nonsignificant covariance 8.704 7 .275 .994 .999 .014 BA Compliance ↔ IQ 9. Remove nonsignificant path from 10.540 8 .229 .993 .999 .015 BA Compliance Æ Age 10. Remove nonsignificant covariance 13.823 9 .129 .988 .998 .020 BA Emotional IQ ↔ IQ 11. Remove nonsignificant covariance BA Emotional IQ ↔ BA Academic 19.055 10 .040 .979 .995 .026 Compliance 12. Remove nonsignificant path from 25.701 11 .007 .969 .993 .032 BA Emotional Adaptability Æ Age 141 .034 .032 .031 .030 .029 .028 .027 .026 .025 .024 .024 .023 .022 .022 .021 .021 .021 .020 .020 .020 .019 .019 .019 .019 .019 .019 .018 .017 .017 RMSEA CFI .994 .994 .994 .994 .995 .995 .995 .995 .995 .995 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 .996 TLI .953 .956 .959 .962 .965 .967 .969 .971 .973 .975 .976 .978 .979 .980 .981 .982 .982 .983 .984 .984 .984 .984 .985 .985 .985 .986 .987 .987 .988 p .000 .000 .000 .000 .001 .001 .002 .002 .003 .004 .005 .007 .009 .011 .012 .013 .015 .016 .018 .019 .019 .019 .019 .018 .017 .022 .027 .033 .033 df 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 2 χ 54.043 55.044 55.047 55.110 55.178 55.260 55.353 55.449 55.581 55.708 55.964 56.232 56.468 56.820 57.646 58.554 59.379 60.391 61.017 62.035 63.216 64.405 65.684 67.067 68.538 68.545 68.560 68.705 69.867 Relationship (e15) ↔ residual of Relationship (e15) er/Relationship (e15) ↔ residual of (e15) er/Relationship oncerns (e16) ↔ residual of Teacher ↔ residual (e16) oncerns of CFQL ↔ residual (e16) oncerns Teacher Rating of Academic of Teacher Rating CFQL Financial Teacher Relationship Quality of CFQL Partner/Relationship rtner/ CFQL Partner/Relationship External Support CFQL CFQL Child act on Impairment to Ageact on of ADHD Impairment Diagnosis Æ Æ Æ Æ Æ Æ Æ CFQL Partner/Relationship External Support CFQL Concerns Referral Æ Æ Æ CFQL Child Teacher Relationship Quality of Teacher Achievement Rating of Academic CFQL External Support CFQL CFQL Financial Æ Æ Æ Teacher Rating of Academic Achievement of Academic Rating Teacher Æ Æ SEBMS Severity External Support CFQL Quality of RelationshipTeacher Concerns Referral CFQL Child Achievement of Academic Rating Teacher Æ Æ Æ Æ Æ Æ Æ Teacher Quality of Relationship Teacherof Quality External Support CFQL Æ Æ between residual of Referral C of Referral between residual C of Referral between residual Partn of CFQL between residual isk/Compensatory variables imp isk/Compensatory variables rom BA Academic Compliance Academic Compliance BA rom Emotional Adaptability BA rom Academic Compliance BA rom Emotional Adaptability BA rom Emotional Adaptability BA rom Emotional Adaptability BA rom Academic Compliance BA rom path from Temperament Temperament from path Temperament from path Remove nonsignificant covariance nonsignificant Remove covariance nonsignificant Remove covariance nonsignificant Remove Quality of Relationship (e17) Partner/Relationship (e15) SEBMS Severity (e19) CFQL Child (e11) Achievement Table 6-A. Model Development of R Model Step 1. Initial Model 2. nonsignificant Remove 3. Academic Attitude BA from path nonsignificant Remove 4. Academic Attitude BA from path nonsignificant Remove 5. Emotional IQ BA from path nonsignificant Remove 6. Compliance BA from path nonsignificant Remove 7. Emotional IQ BA from path nonsignificant Remove 8.f path nonsignificant Remove 9. nonsignificant Remove 10. Temperament from path nonsignificant Remove 11. Academic Attitude BA from path nonsignificant Remove 12. Temperament from path nonsignificant Remove 13.f path nonsignificant Remove 14.f path nonsignificant Remove 15.f path nonsignificant Remove 16. Social Network BA from path nonsignificant Remove 17. IQ from path nonsignificant Remove 18. Social Network BA from path nonsignificant Remove 19.f path nonsignificant Remove 20. Temperament from path nonsignificant Remove 21.f path nonsignificant Remove 22. IQ from path nonsignificant Remove 23.f path nonsignificant Remove 24. Social Network BA from path nonsignificant Remove 25. Temperament from path nonsignificant Remove 26. 27. 28. of CFQL between residual Pa covariance nonsignificant Remove 29. 142 .017 .018 .018 .018 .018 .018 .019 .019 .019 .020 .021 .021 .022 .023 .023 .024 .024 .025 .025 .026 .026 .027 .028 .028 .029 .030 .030 RMSEA CFI .996 .996 .996 .996 .996 .995 .995 .995 .995 .994 .994 .993 .993 .992 .991 .991 .990 .990 .989 .989 .988 .987 .986 .985 .985 .984 .983 TLI .987 .987 .987 .987 .986 .986 .986 .985 .984 .983 .982 .981 .980 .979 .977 .977 .976 .975 .973 .972 .971 .969 .967 .966 .965 .963 .962 p .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .029 .026 .024 .021 .019 .016 .013 .010 .007 .005 .003 .002 .001 .001 .000 df 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 2 χ 71.733 73.653 75.158 77.052 78.965 80.901 83.249 85.715 88.809 92.383 95.706 99.368 103.286 107.817 112.713 115.199 119.041 122.871 127.907 131.958 137.240 142.947 148.889 153.925 159.121 164.564 170.444 (e12) ↔ residual of ↔ residual (e12) port (e14) ↔ residual of ↔ residual (e14) port Support (e14) ↔ residual of ↔ residual (e14) Support nal Support (e14) ↔ residual of ↔ residual (e14) nal Support ality of Relationship (e17) ↔ residual Relationship (e17) ality of CFQL Family/Caregiver External Support CFQL ternal act on Impairment to Ageact on of ADHD Impairment Diagnosis Rating of Academic Achievement Rating of Academic (e18) Rating of Academic Achievement (e18) Æ Æ CFQL Financial CFQL Family/Caregiver SEBMS Severity Æ Æ Æ SEBMS Severity CFQL Family/Caregiver Referral Concerns SEBMS Severity CFQL Family/Caregiver Concerns Referral Relationship Quality of Teacher CFQL Partner/Relationship CFQL Child CFQL Family/Caregiver Æ Æ Æ CFQL Financial Concerns Referral External Support CFQL Æ Æ Æ Æ Æ Æ CFQL Partner/Relationship Æ Æ Æ Æ residual of CFQL External Sup of CFQL residual CFQL Partner/Relationship CFQL Child Æ Æ between residual of Teacher Qu of Teacher between residual Exter of CFQL between residual isk/Compensatory variables imp isk/Compensatory variables rom BA Emotional Adaptability Emotional Adaptability BA rom Emotional Adaptability BA rom ily/Caregiver (e12) ily/Caregiver nonsignificant covariance between covariance nonsignificant of SEBMS Severity (e19) Teacher Relationship Quality (e17) of Remove nonsignificant covariance nonsignificant Remove covariance nonsignificant Remove SEBMS Severity (e19) (e16) Concerns Referral CFQL Child (e11) ↔ residual of SEBMS Severity (e19) ↔ residual of CFQL Fam Table 6-A. Model Development of R Model Step 30. Academic Attitude BA from path nonsignificant Remove 31. Emotional IQ BA from path nonsignificant Remove 32. Emotional IQ BA from path nonsignificant Remove 33. Emotional IQ BA from path nonsignificant Remove 34. Temperament from path nonsignificant Remove 35. IQ from path nonsignificant Remove 36.of between residual covariance nonsignificant Remove 37. Compliance BA from path nonsignificant Remove 38. Emotional IQ BA from path nonsignificant Remove 39. 40.f path nonsignificant Remove 41. Academic Attitude BA from path nonsignificant Remove 42. IQ from path nonsignificant Remove 43. Compliance BA from path nonsignificant Remove 44. Ex of CFQL between residual covariance nonsignificant Remove 45. 46. Remove 47.f path nonsignificant Remove 48.of Teacher between residual covariance nonsignificant Remove 49.of Teacher between residual covariance nonsignificant Remove 50. Emotional IQ BA from path nonsignificant Remove 51. Compliance BA from path nonsignificant Remove 52. Academic Attitude BA from path nonsignificant Remove 53. Social Network BA from path nonsignificant Remove 54. Social Network BA from path nonsignificant Remove 55. Emotional IQ BA from path nonsignificant Remove 56. Social Network BA from path nonsignificant Remove 143 References
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