UNLV Theses, Dissertations, Professional Papers, and Capstones
12-1-2020
Validity and Diagnostic Accuracy of an ADHD Symptom Rating Scale for Identifying Adults with ADHD
Stacy Jean Graves
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VALIDITY AND DIAGNOSTIC ACCURACY OF AN ADHD SYMPTOM RATING SCALE
FOR IDENTIFYING ADULTS WITH ADHD
By
Stacy J. Graves
Bachelor of Science – Psychology Virginia Polytechnic Institute & State University 2011
Master of Arts- Psychology University of Nevada, Las Vegas 2016
A dissertation submitted in partial fulfillment of the requirements for the
Doctorate of Philosophy – Psychology
Department of Psychology College of Liberal Arts The Graduate College
University of Nevada, Las Vegas December 2020
Dissertation Approval
The Graduate College The University of Nevada, Las Vegas
January 5, 2021
This dissertation prepared by
Stacy J. Graves entitled
Validity and Diagnostic Accuracy of an ADHD Symptom Rating Scale for Identifying Adults with ADHD is approved in partial fulfillment of the requirements for the degree of
Doctorate of Philosophy – Psychology Department of Psychology
Daniel Allen, Ph.D. Kathryn Hausbeck Korgan, Ph.D. Examination Committee Chair Graduate College Dean
Michelle Paul, Ph.D. Examination Committee Member
Noelle Lefforge, Ph.D. Examination Committee Member
LeAnn Putney, Ph.D. Graduate College Faculty Representative
ii Abstract
Specific rating scales of ADHD symptomatology are often included in neuropsychological assessments evaluating adults diagnosed with Attention Deficit
Hyperactivity Disorder (ADHD). The Barkley Adult ADHD Rating Scale-IV (BAARS-IV) is a widely used symptom rating scale that includes four questionnaires: self-report current, self- report childhood, other-report current, and other-report childhood. In addition to ADHD symptoms, each of the forms include items that assess functional impact and developmental history of symptomatology. While similar symptom rating scales have been found to have high validity and diagnostic utility in children, it is unclear the extent to which the BAARS-IV shares substantial common variance with other adult ADHD measures and if improves diagnostic accuracy when assessing for ADHD. The current study will examine these concerns in order to provide clinicians with evidence that may improve the diagnostic accuracy of ADHD evaluations.
Participants included approximately 379 adults who completed psychodiagnostic evaluations for clinical purposes at a community mental health clinic. Of these, approximately
156 were diagnosed with ADHD-Combined or ADHD-Inattentive, 201 were diagnosed with other disorders, and 22 had no diagnosis. The ADHD group had either a primary or secondary diagnosis of ADHD. There were 201 individuals diagnosed with other disorders including
Specific Learning Disability (n=107), Major Depressive Disorder (n=26), Anxiety Disorder
(n=23), and other clinical diagnoses (n=45). Finally, the group with no diagnosis (n=22) was included as a control sample. Comprehensive evaluations that included the administration of the
BAARS-IV questionnaire, clinical interviews, cognitive functioning, and thorough review of medical and mental health history were utilized to establish diagnoses.
iii The BAARS-IV self-report for current symptoms were the focus of this investigation.
Pearson correlations was used to examine convergent and discriminant validity between the participants’ ratings of Attention Problems/Inattention and Hyperactivity on the BAARS-IV and the ASEBA, a broader behavioral measure containing ADHD symptom rating subscales assessing symptoms of ADHD (convergent) as well as other domains that were not expected to be highly correlated with ADHD symptoms (discriminant). In order to evaluate differences in sensitivity and specificity, Receiver Operating Characteristic (ROC) analysis was used for the
BAARS-IV scores to differentiate between adult ADHD subtypes, as well as other psychiatric and healthy control groups.
Support for the discriminant validity was determined by examining the pattern of correlations between the BAARS-IV subscales. However, evidence of the pattern of correlations did not support convergent validity for the BAARS-IV subscales. Regarding discriminant validity, the ASEBA Anxiety and Depression subscales had lower correlations with the BAARS-
IV subscales than with the ASEBA Inattention and Hyperactivity-Impulsivity subscales. The
BAARS-IV Hyperactivity subscale had the lowest correlation with the ASEBA Anxiety subscale. However, in terms of convergent validity, the correlations between the BAARS-IV subscales and ASEBA Inattention and Hyperactivity-Impulsivity subscales provided weak support for the scales assessing three distinct symptom domains of ADHD. Similarly, the results of the Receiver Operating Characteristic (ROC) analyses indicated that BAARS-IV had poor discrimination between ADHD and a healthy, no psychiatric diagnosis group. Similarly, results revealed weak evidence for the BAARS-IV ability to discriminate between ADHD subtypes or between ADHD and other psychiatric groups, such as depression or anxiety. Notably, when
iv differentiating between ADHD and Specific Learning Disorder, the BAARS-IV demonstrated significantly better classification accuracy compared to the other comparison groups.
Findings from the current study support using the BAARS-IV cautiously as a screening measure when there is a question of ADHD, rather than as a stand-alone diagnostic measure. The results indicate that the BAARS-IV is not sensitive to differences in ADHD symptomatology based on the three core ADHD symptom domains of inattention, hyperactivity and impulsivity.
Therefore, the BAARS-IV is not an effective measure for determining ADHD subtypes.
Furthermore, when there is a potential for the presence of psychiatric disorders other than
ADHD, a broader behavioral measure should be used. The BAARS-IV may be utilized as a screening measure for assessing the presence of general ADHD symptomatology or as a part of a broader assessment. The BAARS-IV is also an appropriate measure to use when the referral question is focused on the differential diagnosis of ADHD and Specific Learning Disorder symptoms.
v Acknowledgments
I would like to extend my sincerest appreciation to my committee chair and advisor, Dr. Daniel
Allen, for his mentorship and kindness throughout this process and my graduate school career. I would also like to thank my committee members, Drs. Lefforge, Paul, and Putney for their time and meaningful contributions to this project as well as my academic and clinical journey from student to psychologist. Also, thank you to my fellow lab members for their hard work and dedication to complete this project. I would like to thank my late father, Donald, for the inspiration to pursue my dreams and to always be kind. Lastly, I would like to thank my husband, John, and my family, Iris and Ryan, for their encouragement and emotional support throughout my academic pursuits. This accomplishment would not have been possible without them.
vi Table of Contents
Abstract ...... iii
Acknowledgments ...... vi
Table of Contents ...... vii
List of Tables ...... ix
List of Figures ...... xi
Chapter 1: Introduction ...... 1
Chapter 2: Literature Review ...... 6
Importance of Studying ADHD in Adulthood ...... 6
Functional Impairment in Adult ADHD ...... 9
Neurocognition in ADHD ...... 11
ADHD Differential Diagnosis ...... 13
Summary ...... 24
Chapter 3: Psychometric Properties of the BAARS-IV ...... 5
Convergent and Discriminant Validity ...... 28
Diagnostic Utility ...... 28
Chapter 4: Methodology ...... 31
Participants ...... 31
Measures ...... 32
Procedure ...... 35
Hypotheses and Proposed Data Analysis ...... 36
Chapter 5: Results ...... 40
Convergent and Discriminant Validity ...... 40
Sensitivity, Specificity, and Positive and Negative Predictive Value ...... 41
vii
Chapter 6: Discussion ...... 50
Appendix A: Tables and Figures ...... 57
Appendix B: Extended Tables ...... 71
References ...... 85
Curriculum Vitae ...... 104
viii List of Tables
Table 1: Demographic and clinical information for clinic sample comparison groups ...... 57
Table 2: Correlations between Barkley-IV ADHD Symptom Rating Scale, Current Self-Report
(BAARS-IV) raw subscale scores and Achenbach System of Empirically Based Assessment,
Adult Self-Report (ASEBA) raw subscale scores ...... 58
Table 3: Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD vs. No Psychiatric Diagnosis (NPD),
ADHD-Combined vs. NPD, and ADHD-Inattentive vs. NPD scores ...... 59
Table 4: Classification Accuracy Statistics and Optimal Threshold Values for the Behavior
Assessment System for BAARS-IV subscale scores for ADHD vs. No Psychiatric Diagnosis
(NPD) ...... 60
Table 5: Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD-Inattentive vs. ADHD-Combined ..
...... 61
Table 6: Classification Accuracy Statistics and Optimal Threshold Values for the Behavior
Assessment System for BAARS-IV subscale scores for ADHD-Inattentive vs. ADHD-Combined
...... 62
ix Table 7 Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD vs. Mood and Anxiety Disorder (MAD),
ADHD-Combined vs. MAD, and ADHD-Inattentive vs. MAD ...... 63
Table 8: Classification Accuracy Statistics and Optimal Threshold Values for the Behavior
Assessment System for BAARS-IV subscale scores for ADHD vs. Mood and Anxiety Disorder
(MAD) ...... 64
Table 9: Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD vs. Specific Learning Disorder
(SLD), ADHD-Combined vs. SLD, and ADHD-Inattentive vs. SLD ...... 65
Table 10: Classification Accuracy Statistics and Optimal Threshold Values for the Behavior
Assessment System for BAARS-IV subscale scores for ADHD vs. Specific Learning Disorder
(SLD) ...... 66
x List of Figures
Figure 1a: ROC Curves comparing ADHD Groups to No Psychiatric Diagnoses (NPD) ...... 67
Figure 1b: ROC Curves comparing ADHD-Combined to No Psychiatric Diagnoses (NPD) .... 67
Figure 1c: ROC Curves comparing ADHD-Inattentive to No Psychiatric Diagnoses (NPD) ... 67
Figure 2: ROC Curves comparing ADHD-Combined to ADHD-Inattentive ...... 68
Figure 3a: ROC Curves comparing ADHD Groups to Mood and Anxiety (MAD) Group ...... 69
Figure 3b: ROC Curves comparing ADHD-Combined to Mood and Anxiety (MAD) Group .. 69
Figure 3c: ROC Curves comparing ADHD-Inattentive to Mood and Anxiety (MAD) Group .. 69
Figure 4a: ROC Curves comparing ADHD Groups to Specific Learning Disorder (SLD) ...... 70
Figure 4b: ROC Curves comparing ADHD-Combined to Specific Learning Disorder (SLD) .. 70
Figure 4c: ROC Curves comparing ADHD-Inattentive to Specific Learning Disorder (SLD) .. 70
xi Chapter 1: Introduction
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder present in both children and adults and represented by a pattern of persistent behaviors including symptoms of inattention, hyperactivity, and impulsivity that impair functioning (American
Psychiatric Association [APA], 2013). Adults with ADHD are referred for psychodiagnostic evaluations to confirm diagnosis of ADHD. These evaluations commonly include psychoeducational and neuropsychological testing. In addition, these evaluations often include symptom rating scales designed to assess the severity of current and past symptoms, with items ofen based on the18 DSM-5 ADHD Criteria A symptoms for ADHD. The DSM-5 ADHD diagnostic criteria also require these symptoms to impair functioning, such as interfering with work or academic performance, as well as in other environments. Therefore, these symptom rating scales also assess the nature and severity of impairment caused by behavioral or cognitive disturbances associated with ADHD.
Much of the existing literature on ADHD focuses on the development and functional impact of ADHD in children and adolescents (Abecassis et al., 2017). This is primarily due to onset of the disorder in childhood but also in part due to the early research arguing that ADHD often remits by the time an individual reaches adulthood. However, recently there has been an increased focus on the development and functional impact of ADHD in adults, supported by epidemiological findings that many cases of childhood ADHD persist into adulthood (Barkley,
Fischer, Smallish, & Fletcher, 2002; Kessler et al., 2011) and that these adults have diminished functioning across a number of important domains. While there are many overlapping characteristics, less is known about the nature of the symptoms and functional impact specific to
ADHD in adults.
1 Given the relatively recent recognition of ADHD as a significant condition in adulthood, much more is known about the characteristics, clinical course, diagnosis and other important features of ADHD in children. Based on research with children, it is clear that a clinical diagnosis of ADHD is best established by gathering historical evidence and identifying signs and symptoms of the disorder through various assessment tools. Among the available assessment tools, symptom rating scales are often utilized to assess behavior as well. For example, the Adult
ADHD Self-Report Scale Symptom Checklist (ASRS; Kessler, Adler, Ames, et al., 2005) and the Barkley Adult ADHD Rating Scale-IV ( BAARS-IV; Barkley, 2011 ) are commonly administered self-report measures for ADHD evaluations. In addition to self-report forms that can be completed by the individual with ADHD, these rating scales often include additional forms to be completed by a collateral informant who is familiar with the individual’s behaviors, such as a significant other, co-worker, or family member. In children, symptom rating scales that focus on the DSM ADHD symptom criteria have been determined to assist in identifying core symptoms of ADHD and improve DSM-IV and DSM-5 diagnostic accuracy (Allen et al., 2019, under review; Alloway et al., 2009; Martel et al., 2015). These scales are particularly useful because they focus on ADHD symptomatology rather than a general characterization of overall functioning. Notably, the core symptoms of ADHD, such as inattention hyperactivity and impulsivity, are common among other neurodevelopmental and psychiatric disorders. Therefore, these focused scales often include self-reported ratings for childhood and current ADHD symptoms to determine the course and history of the disordered symptoms.
Symptom rating scales have the advantage of providing a standardized approach to gathering information about ADHD symptoms and may also improve efficiency of the diagnostic process by providing a direct and structured means to gather specific information that is critical
2 for making an accurate diagnosis. These symptom rating scales are designed to supplement other broader assessment procedures, rather than to be used independently to determine diagnosis.
Because ADHD is primarily diagnosed by information provided to the clinician about behaviors that occur outside the office in the real world, ratings of these behaviors on standardized symptom rating scales must be concerned with psychometric issues such as reliability, convergent and discriminant validity, and predictive validity among others. Adequate reliability ensures consistency and accuracy in the measurement of ADHD symptoms.
Convergent and discriminate validity evidence support the extent to which the scale items assess the core symptom domains of ADHD (inattention, hyperactivity, impulsivity). Predictive validity evidence provides information regarding the degree to which the test is able to increase diagnostic accuracy. Previous research examining symptom ratings scales has primarily focused on comparing subscale and overall scores of individuals with ADHD to healthy control groups
(Caterino et al., 2009; Christansen et al., 2012; De Quiros & Kinsbourne, 2001). While this type of comparison is important for establishing the overall predictive validity and diagnostic utility of the scales when used to make a diagnosis of ADHD, it is also important to understand how symptom ratings might distinguish between various clinical populations, including between different gender- and age-related presentations of ADHD, and between ADHD and other disorders where inattention, hyperactivity and impulsivity symptoms are present.
In order to address the need for adult ADHD symptoms ratings scales with high reliability and validity, Barkley developed the Barkley Adult ADHD Ratings Scale, which is currently in its fourth edition (BAARS-IV; Barkley, 2011) and is used to assesses symptom severity and functioning based on DSM-IV ADHD criteria. The BAARS-IV has multiple forms including a long-form and brief-form for measuring current and childhood symptoms and
3 functional impairment. In addition, the BAARS-IV includes current and childhood measures to be completed by collateral informant familiar with the adult’s behaviors, typically a parent or partner, who has been around the individual and has had the opportunity to observe the subject’s symptoms and impairments in functioning over a period of time. Symptoms rating scales, such as the BAARS-IV, are expected to have diagnostic utility by increasing diagnostic accuracy and efficiency, as well as provide information that may be useful for treatment planning.
Psychometric analyses of self-report ADHD measures serves multiple purposes. Because
ADHD expresses differently in adults than in children, psychometric analyses are necessary to establish equivalency (or lack thereof) of ADHD rating scales when used with adults. Analyzing the psychometric properties of commonly used adult ADHD symptom rating scales will provide further evidence for the diagnostic utility and use of ADHD symptom rating scales in adults. In addition, investigating the relationships between ADHD symptoms and symptoms of other comorbid disorders will contribute to the development of ADHD measures with higher sensitivity and specificity to differentiate ADHD symptomatology from other comorbid disorders in adults.
Despite the common use of symptom ratings in the evaluation of children with ADHD, associations between the scores obtained with these different types of symptom ratings scales have been less examined in an adult population. Thus, the lack of information regarding relations between available scales commonly used to assess symptoms in adults with ADHD limits understanding of selection and application of these scales to address specific referral questions.
To address this issue, the current study will investigate diagnostic and behavioral ratings from a widely used ADHD symptom rating scale (BAARS-IV) in a large sample of adults with ADHD who were referred for psychoeducational evaluation and in comparison populations that are not
4 diagnosed with ADHD. The study is designed to address if the psychometric properties of the
BAARS-IV symptom ratings scale is consistent with findings for ADHD rating scales in adults, specifically:
a. Will BAARS-IV scores demonstrate convergent and discriminant validity based on
correlations with Achenbach System of Empirically Based Assessment (ASEBA) ADHD
subscale scores?
b. Will the BAARS-IV demonstrate improved classification accuracy for ADHD subtypes
in adults?
c. Will the BAARS scores discriminate between individuals diagnosed with ADHD and
individuals with other diagnoses?
The results of the current study will provide information useful for improving diagnostic accuracy of ADHD in adults and increase understanding of the latent structure of ADHD symptoms in adults. To provide background information for the current study, the following sections include information on ADHD including the diagnostic criteria, development and course of ADHD, latent structure of adult ADHD symptoms, age and gender differences of ADHD symptom presentation, review of psychometric properties of symptom rating scales used for diagnostic information, and hypotheses for the study.
5 Chapter 2: Literature Review
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by a persistent pattern of behavioral disturbances that includes high levels of inattention, hyperactivity, and/or impulsivity (APA, 2013). To meet diagnostic criteria, symptoms must exceed what is considered normative behavior of a person of the same age and cognitive level and symptoms must impair functioning or development (Abecassis et al., 2017;
Kessler et al., 2011). Diagnostic assessments with a referral question of ADHD often include a combination of structured interviews, cognitive assessments including attentional tasks, symptom ratings scales, and self-report questionnaires. ADHD was first identified in the early
1900s as a disorder occurring in children. At that time, it was conceptualized as a disorder that individuals would grow out of, so it was not recognized as a disorder that persisted into adulthood until 1976 (Adler & Chua, 2002). As a result, research has primarily focused on children and only in the past couple decades has there been an increased focus on the specific characteristics and functional impact of adult ADHD (Abecassis et al., 2017; Barkley et al.,
2002).
Importance of Studying ADHD in Adulthood
Much like children, diagnosis of ADHD in adults requires establishing that the core symptoms of ADHD are present at the time of the evaluation. However, there are a number of unique challenges associated with diagnosing ADHD in adults. First, adult ADHD diagnoses require collecting a reliable history of behavioral symptoms and level of functioning in childhood since the diagnostic criteria specify onset in childhood. The retrospective nature of reports about childhood symptoms of ADHD has the potential to diminish accuracy of these reports and complicate the diagnostic process. Unreliable reporting may contribute to current
6 prevalence estimates of ADHD that vary widely from one study to the next. For example, estimated rates of ADHD in children and adolescents range from approximately 6-9% worldwide
(Kessler, Adler, Ames, et al., 2005). Prevalence rates in the United states for adults with ADHD is 4.4% (Kessler et al, 2006), with worldwide rates of 5.3% (Hale et al., 2013). However, the
DSM-5 cites a lower adult ADHD prevalence rate of 2.5% (APA, 2013).
Relatedly, estimated prevalence of childhood ADHD persisting into adulthood widely varies. Earlier research characterized approximately half of childhood ADHD cases persisting into adulthood (Cantwell, 1996; Kessler, Adler, Ames, et al., 2005; Mendelson, Johnson, &
Stewart, 1971). However, a literature review by Mannuzza and colleagues (2003) found only four follow-up studies examining the rate of ADHD retention into adulthood, with extensive variability across studies ranging from 5% to 66% (Barkley et al., 2002; Mannuzza et al., 1998;
Rasmussen & Gillberg, 2000). Previous longitudinal studies report lower estimates, indicating
17.2% to 21.9% of children with ADHD will also meet diagnostic criteria between the ages of
18-19, based on current DSM-5 criteria (Abecassis et al., 2017; Agnew-Blais et al., 2016; Caye et al., 2016).
Several methodological issues that lead to wide ranges of prevalence estimates. There are varying age ranges across studies investigating ADHD across the lifespan. Notably, studies often focus on emerging adulthood to assess if ADHD symptoms carry into adulthood. However, there is evidence that adolescence and early adulthood is a unique period of development, where
ADHD symptoms abate in comparison to child and adult ADHD symptomatology (Arnett,
2000). As previously mentioned, concerns remain about the reliability of retrospective self- reported ADHD symptoms, which is significant given the fact that the presence of ADHD symptoms in childhood is required for a diagnosis of ADHD in adulthood (Gomez et al., 2020;
7 Mannuzza et al., 2002). Findings from longitudinal studies are significantly impacted by different durations of follow-up, ranging from three months to several years, causing variability among prevalence estimates. Despite wide ranges of prevalence estimates, when considering both epidemiological and longitudinal evidence, it is clear a significant subset of children diagnosed with ADHD continue to meet criteria for the disorder in adulthood. It is also relevant to note that some individuals are diagnosed with ADHD as adults, having not been diagnosed with ADHD in childhood, which may further contribute to apparent discrepancies in prevalence estimates for adult ADHD between one study and the next.
Researchers investigating prevalence rates of ADHD have also faced significant challenges for decades due to the changing operational definition and diagnostic criteria of
ADHD. Beyond prevalence studies utilizing various diagnostic methods and assessment tools, earlier characterizations of ADHD placed a heavy emphasis on hyperactivity as a key component to ADHD. However, there is growing evidence that inattention has an equally or greater impact on functioning and is especially important in the presentation of ADHD symptoms in adolescents and adults (Biederman et al., 2000; Millstein et al., 1997; Wilens et al., 2009). In contrast, hyperactivity-related symptoms are most present in young children and are the least endorsed symptoms in adults, suggesting symptoms of hyperactivity decline with age (Millstein et al.,
1997; Wender, Wolf, & Wasserstein, 2001; Wilens et al., 2009). Changes in recent DSM editions reflect this newer understanding, with inattentive ADHD recognized as a distinct subtype in the
DSM-IV (APA, 1994) and DSM-IV-TR (APA, 2000), and a distinct presentation in the DSM-5
(APA, 2013). The continuing evolution of the conceptualization of ADHD has been shown to effect rates of persistence of ADHD in longitudinal studies (Biederman, 2011). Symptom thresholds and symptom criteria used to define persistence of symptoms also widely varies from
8 one study to the next. For example, some researchers utilized DSM diagnostic criteria which have changed across editions, while other studies have frequently defined persistent symptoms by norm-based thresholds for meeting diagnostic criteria (e.g., four ADHD symptoms) that are adjusted for developmental age (Biederman, 2011; Kessler et al., 2011; Sibley et al., 2017).
Despite evidence suggesting a decreased incidence of ADHD in adults, ample evidence exists to support the continued investigation of this condition in adulthood for a number of reasons. For example, because ADHD negatively impacts functioning, and functional requirements are much different for children and adults, increased understanding of ADHD’s negative impact on adult functioning is required. There is also increased incidence of psychiatric comorbidity in adults with ADHD, which further complicates course and treatment of the disorder. Age and gender related differences have also been reported for adults with ADHD. The following sections provide more detailed information on functional impairment, psychiatric comorbidity, gender and age differences, and neurocognitive functioning in adults diagnosed with ADHD.
Functional Impairment in Adult ADHD
As with children, poorer academic outcomes are one of the domains most strongly associated with ADHD for adults who are attending vocational school, college, or university.
Adult students with ADHD demonstrate increased academic challenges and behavioral disturbances that result in underperformance during educational or vocational training. Namely, students with ADHD demonstrate significantly lower college admission rates, lower grade point averages, and lower graduation rates (Barkley et al., 2002; Barkley, Fischer, Smallish, &
Fletcher, 2004; Barkley & Murphy, 1998; Elliott, 2002). While these academic challenges can begin in early childhood, it is often the case that many adults with ADHD first present to
9 clinicians due to difficulties related to post-secondary education or vocational training (Adler &
Shaw, 2011). In fact, a major reason for referral to college and university disability resources centers are to establish a diagnosis of ADHD so that accommodations may be provided in the classroom. Poorer academic outcome can be associated with lower occupational achievement and therefore underperformance should be considered by clinicians when screening and diagnosing ADHD in adults.
Occupational challenges as a result of ADHD specific executive functioning deficits is also well established. Difficulties with manipulating and organizing information, planning, and time management is associated with poorer occupational outcomes (Adler & Shaw, 2011;
Barkley, Fischer, Smallish, & Fletcher, 2004; Faraone et al., 2006; Kessler et al., 2011). A longitudinal outcome study of children with ADHD and comorbid conduct disorder found that those diagnosed with ADHD in adulthood were more likely to be fired, display oppositional behavior or attitudes, and have lower work performance ratings (Adler & Shaw, 2011; Barkley et al., 2004). Other occupational problems include higher job turnover rates, difficulty finding and keeping jobs, underperforming and underachieving, and inability to perform at a level expected based on school or training (Elliott, 2002; Wender et al., 2001). As a result of these difficulties, adults with ADHD are more likely to be self-employed and have a lower socioeconomic status compared to healthy adults (Barkley & Murphy, 1998).
Increased impairments in familial and interpersonal functioning are also related to ADHD in adulthood. For instance, research suggests adults with ADHD may have difficulty meeting parental responsibilities such as preparing meals, assisting their children with getting ready, and helping their children with homework (Adler & Chua, 2002; Weiss et al., 2000). Similarly, other research has shown that families with a parent and a child with ADHD have even further
10 difficulties with organization, setting and keeping routines, stress tolerance, mood lability, and adhering to ADHD treatment plans (Faraone et al., 2000; Harpin, 2005; Miranda et al., 2014;
Wender et al., 2001). Research suggests increased familial discord may occur as a direct result of hyperactive/impulsive symptoms in adults with ADHD (Weiss et al., 2000). These adults may also have difficulty managing and/or sustaining intimate relationships and spend less time with their romantic partners (Barkley et al., 2004). Relationship difficulties stemming from ADHD also include complaints about the affected partner’s frequent interruptions, inattentiveness when the other partner is speaking, and disorganized or inattentive approach to household responsibilities (Miranda et al., 2014; Wender et al., 2001). The effects of these difficulties are reflected in higher separation or divorce rates, higher likelihood of receiving marital counseling, and increased reporting of relationship difficulties (Adler & Chua, 2002; Wender et al., 2001).
Identification and effective treatment of ADHD in adults may lessen or eliminate problems couples experience when one has ADHD and provide direction for how to improve the relationship.
Neurocognition in ADHD
Inattention is a hallmark feature of ADHD. However, cognitive deficits in ADHD include impairment in other cognitive domains. A recent study examining pattern and severity of cognitive deficits in ADHD children found that most children with ADHD exhibit marked working memory (WM) deficits but intact short-term memory abilities (Kofler et al., 2020).
Furthermore, they attribute the severity of inattention and hyperactivity/impulsivity symptoms to these WM deficits. These WM deficits are also believed to contribute to the hallmark behavioral symptoms in ADHD.
11 A meta-analysis by Pievsky and McGrath (2018) examined neurocognitive deficits associated with ADHD across the life-span and found an overwhelming evidence that ADHD groups across studies performed significantly worse than healthy controls across neurocognitive domains. Interestingly, the found age did mediate the level of impairment, with ADHD adolescents and emerging adults having fewer neurocognitive deficits than children and adults with ADHD. One possibility for this pattern is that this period of development is a time of increased neuroplasticity which leads to decreased observed between-groups differences
(Fuhrmann et al., 2015).
With inattention being the predominant presentation in adults with ADHD, further understanding of the neurocognitive deficits that impact inattention is warranted. Diamond
(2005) suggests that inattention is due to problems with cognitive processing, and the ADHD inattentive subtype is due to WM deficits rather than difficulties with response inhibition. In contrast, the hyperactive/impulsivity subtype of ADHD is argued to be more related to difficulty managing emotional stimuli and deficits in tasks involving rewards and motivation (Castellanos et al., 2005; Rubia, 2018). Other areas of cognitive deficits in adults with ADHD include response inhibition, motivation, temporal information processing, and regulation of activation, in addition to working memory and sustained attention deficits (Harvey et al., 2004; Pennington,
2005). Tasks that involve set-shifting, visuo-spatial planning, continuous performance and gambling, visuomotor speed, and stop-signal tasks are all believed to be impaired by these neurocognitive deficits (Lampe et al;, 2007; Malloy-Diniz et al., 2007; Mueller et al., 2007).
In addition to these cognitive deficits, Sluggish Cognitive Tempo (SCT) in relation to
ADHD has been gaining interest among researchers in recent years. SCT is characterized by symptoms of daydreaming, mental confusion, sluggish-lethargic behavior, hypoactivity, apathy,
12 and sleepiness, among others (Barkley, 2012; Becker et al., 2013; Flannery et al., 2014).
Childhood ADHD researchers have found strong support for a subset of children having SCT as a unique domain of symptoms, distinct from other subtypes of ADHD (Garner et al., 2010;
Hartman et al., 2004; Penny et al., 2009). SCT was also found to have larger correlation with the degree of internalizing symptoms and social withdrawal, but a weaker association with executive functioning measures. Penny and colleagues (2009) argue that the daydreaming and sleepy symptoms of SCT are distinctly different from slow, lethargic movement commonly seen in the inattentive subtype of ADHD. Some go as far as arguing inattention in these cases is distinctly different than inattention found in ADHD-C, to the point where they believe SCT is a separate disorder from ADHD (Carlson & Mann, 2002; Shepard, 2010). Although the majority of research on SCT and ADHD have been conducted on children, one might assume SCT is either similar or more pronounced in adults with ADHD, especially given the strong evidence for increased inattentive symptoms and decreased hyperactivity symptoms in adults when compared to children.
ADHD Differential Diagnosis
A number of factors complicate the accurate diagnosis of ADHD and the usefulness of rating scales in making this determination, including differences in symptoms that are associated with age and gender, as well as high rates of psychiatric comorbidities. In the following sections we discuss these factors.
Age Differences
Much of the recent literature investigating unique characteristics of adult ADHD have utilized large samples of young adults and college students, likely due to the accessibility of college-aged research participants. Some research suggests that emerging adulthood, defined as
13 the period between ages 18 and 29, is a unique period of development, characterized by neurocognitive development and unique life transitions and stressors (Arnett, 2000; Sussman &
Arnett, 2014). For example, young adults may be living independently and financially independent for the first time. In addition, it is likely that college-aged individuals may present with ADHD concerns due to increased challenges with managing significant life changes, such as increased independence from caregivers, increased academic workload in higher level education and/or gaining employment (Abecassis, Isquith & Roth, 2017). Namely, for many college students, the difficulty of school work increases significantly in college while simultaneously having less structure than secondary education. Additionally, this period of development usually involve significant changes to interpersonal relationships, challenges to pre- existing belief symptoms, and developing career paths (Abecassis, Isquith, & Roth, 2017). These transitions put an increased load on executive functioning tasks, such as organizing, planning and task-completion (Dorr & Armstrong, 2019). Executive functioning deficits have been found to be a reliable diagnostic indicator of adult ADHD, specifically difficulties with self-regulation
(Barkley, 2014; Barkley et al., 2010; Biederman et al., 2011; Kessler et al., 2010). This combination of increased responsibilities, challenging academics, and reduced structure can be particularly challenging for individuals with ADHD.
The unique characteristics associated with college ADHD samples (higher functioning, less severe symptoms, developmentally transitioning) limit the generalizability of research findings to the more general adult ADHD population. Based on the result of these studies, one would expect that college student samples of adults with ADHD are higher functioning and experience less severe symptoms than the more general adult ADHD population, because those individuals who have more severe symptoms would not be able to successfully attend college.
14 Gender Differences
Past and current diagnostic criteria used to evaluate ADHD, as well as norms and cut-offs for diagnostic measures, have most often been developed using samples of predominantly male children (Park et al., 2018). After an extensive review of the existing literature, Williamston and
Johnston (2015) offer further support for this finding, stating that although male children are more likely to present with hyperactive-impulsive symptoms (Rametakker et al, 2010), the normative age-related reduction in hyperactive-impulsive symptoms proportionately affects the prevalence of ADHD in adult males when compared to females. Therefore, the female ADHD latent structure is posited to remain relatively stable over the lifespan when compared to male
ADHD, that declines over time due to a normative, developmental reduction in hyperactive- impulsive symptoms.
While many studies have investigated the neurocognitive profiles of adults with ADHD, most did not investigate gender differences in cognitive deficits related to ADHD. Given the known biological and hormonal influences on cognitive development, parity across genders is unlikely. Researchers have posited gender-related developmental differences play a significant role in the clinical course and ADHD symptom presentation, with some even calling for separate, gender-specific criteria norms to correct for gender differences (Nadeau & Quinn, 2002;
Nussbaum, 2012; Quinn, 2011; William & Johnston, 2015).
Some researchers suggest observed neurocognitive gender differences are not due to true cognitive deficits, but instead are an artifact of flawed and gender-biased methodology and diagnostic criteria. Relevant to this study, previous psychometric research supports there are significant differences in the way adult males and females self-report ADHD symptoms. Barkley
(2011) analyzed gender differences in self-reported responses on the BAARS-IV using a sample
15 of 1242 men and women (621 males, 621 females). Females were more likely to endorse a greater number of symptoms then men on the Current ADHD Impulsivity subscale. In contrast, males reported a higher number of inattention symptoms on the retrospective Childhood
Inattention subscale. Barkley posited males reported higher inattention symptoms in childhood because they present with an overall greater number of symptoms than girls in childhood
(DuPaul et al., 1998).
Another meta-analysis (Fedele, et al., 2012) found similar gender-related differences in self-reported functional impairment related to ADHD. Their findings suggest adult ADHD females often report higher subjective ratings of impairment and distress as a result of their
ADHD symptomatology. Evidence also demonstrated females report higher impairment in other areas such as educational and occupational achievement, social functioning, interpersonal relationships, and activities of daily living. The authors offered a few possible explanations for their findings based on gender differences found in child ADHD literature: female subjective ratings may be influenced by better insight into their impairment than males; females may be more susceptible to perceived social consequences leading to lower self-esteem and sense of competence; ratings may reflect the result of under-diagnosis of ADHD in female children causing them to attribute potential difficulties to personal shortcomings rather than a mental disorder. In addition to the majority of early ADHD research focusing on male children, the aforementioned gender-related methodological issues further complicate the understanding of the symptom structure in adults with ADHD when both males and females are included in the sample.
16 Comorbid Psychiatric Disorders in Adult ADHD
Another factor that complicates ADHD diagnosis is the high rate of comorbid psychiatric disorders that occur among adults diagnosed with ADHD. These comorbid conditions are important to consider because they often complicate the course of ADHD in comparison to individuals with ADHD only (Barkley, 2006; Kooj et al., 2012; Yoshimasu et al., 2018). In general, individuals with ADHD experience higher rates of mood disorders, anxiety disorders, substance use, and impulse control disorders. ADHD is often conceptualized as a disorder of poor impulse control and subsequently several of its core symptoms overlap with other psychiatric disorders that also feature symptoms of impulsivity (Barkley, 1997; Pliszka, 2015). A large National Comorbidity Survey Replication study with a sample of 3000 adults compared prevalence rates of psychiatric disorders in normal controls vs adults diagnosed with ADHD and found differences in rates of major depression (8% vs 19%), bipolar disorder (3% vs 19%), anxiety disorders (20% vs 47%), substance use disorders (6% vs 15%), and intermittent explosive disorder (6% vs 20%) (Kessler et al., 2006), among other disorders.
Not unexpectedly, the literature is mixed when investigating specific comorbid disorder prevalence rates but ADHD and Major Depressive Disorder (MDD) have been found to frequently co-occur in both children and adult populations. Previous research has clearly shown that there are higher rates of MDD in adults presenting with ADHD, and also vice-versa (Kessler et al., 2006; Knouse et al., 2013; Prince, 2015). Similar to other ADHD comorbidity studies, differentiating depressive symptoms from ADHD symptoms proves difficult due to overlapping symptoms. Specific to MDD, overlapping symptoms include psychomotor agitation, restlessness, difficulty concentrating, distractibility, and inattention. Previous literature supports that those
17 with co-occurring ADHD and MDD experience earlier onset of depression, higher severity of depressive symptoms, and higher suicidality than those without ADHD (Prince, 2015).
The relationship between bipolar disorder (BD) and ADHD is less clear, with comorbidity rates varying from 5-20% across studies (McIntyre et al., 2010; Skirrow et al., 2012;
Wingo & Ghaemi, 2007). ADHD symptoms in adults can often resemble or co-occur with BD symptoms, most notably distractibility, psychomotor agitation, talkativeness, impulsivity and emotion dysregulation (Skirrow et al., 2012). The presentation of those with comorbid ADHD and BD often demonstrate a higher severity of disease course, mood disorder symptoms, and lower functional outcomes (Klassen, Katzman & Chokka, 2010). Some of this uncertainty may be due to previous skepticism of the continuity of ADHD into adulthood and skepticism of BD in pediatric samples (Joshi & Wozniak, 2015). In follow-up studies of ADHD children, rates of BD were not found to be elevated in adulthood (Barkley et al., 2002; Barkley et al., 2008; Kessler et al., 2006). However, when looking at adult BD samples subsequently diagnosed with ADHD, the pattern is much more clear, with a higher comorbidity rate ranging between 9% and 35%
(Klassen et al., 2010). Understanding the relationship between BD and ADHD is important for accurate diagnosis and appropriate psychological and psychiatric treatment.
Despite ADHD and anxiety disorders being distinctly classified disorders, there is a very high rate of comorbidity between them (Kessler et al;, 2006; Mick et al., 2004; Polanczyk et al.,
2007). Furthermore, despite having different development trajectories across the lifespan, with anxiety disorders more likely to present earlier in life, ADHD and anxiety disorder comorbidity has been found to frequently occur in both children and adults (Krone & Newcorn, 2015). In one study, 25% of children diagnosed with ADHD were found to also have comorbid anxiety disorder (Tannock 2000). A follow-up study determined about the same rate of comorbidity in
18 children whose ADHD persisted into adulthood. Furthermore, evidence suggests that adults with persistent ADHD have twice the risk of being diagnosed with an anxiety disorder(46%) than children whose ADHD did not persist into adulthood (23%) and a healthy control group (9%)
(Barkley et al., 2008). Other large epidemiological studies investigating ADHD comorbidities also found associated risk of anxiety disorders with ADHD (Kessler et al., 2006; Secnik et al.,
2005). However, these results are not consistent with other follow-up studies of children with persistent ADHD and comorbid anxiety disorders, where evidence suggests there is no elevated risk for comorbid ADHD and anxiety (Mannuzza, et al., 1998; Rasmussen & Gilberg, 2000).
These contrasting results may be due to methodological differences and difficulty with differential diagnosis.
Differential diagnosis between ADHD and anxiety disorders is complicated due to inattention and hyperactivity symptom overlap. This symptom overlap leads to the potential for misattribution or under-identification of symptoms. The differentiation of symptoms is critical to treatment outcomes, due to higher symptom severity and functional impairment in those with a comorbid presentation (Escamilla, 2013). Of interest to this study, Grogan and colleagues (2018) found provisional evidence that ADHD symptom rating scales have poor diagnostic accuracy and utility when differentiating between symptoms of ADHD and anxiety. However, there were several methodological limitations of their study, including a small sample size and determining diagnosis with self-report questionnaires rather than diagnostic interview or attentional tests to confirm diagnostic group.
Previous research suggests that 20-55% of adults with ADHD report a substance use disorder across their lifespan, which is two to four times higher than that of non-ADHD controls
(Murphy et al., 2002; Wilens, 2004, Wilens et al., 2009). Other studies have found individuals
19 with ADHD begin using substances at an earlier age than individuals without ADHD, and have increased lifetime rates of alcohol dependence or abuse (45%), cannabis (51%), amphetamines
(49%), opiates (16%), and tobacco use (Sullivan & Rudnik-Levin, 2001; Torgersen et al., 2006).
Furthermore, it was found that children with ADHD persisting into adulthood are at higher risk to have a current substance use disorder when compared to children with ADHD that did not persist into adulthood, and even greater risk compared to a healthy control group without an
ADHD diagnosis (Barkley et al., 2008). This suggests that the development of substance use disorders may be influenced by an ADHD diagnosis. It is important to consider the comorbidity of ADHD and substance use disorders, especially given that psychiatric treatment for ADHD symptoms often includes the use of stimulant-medications.
There is disagreement among researchers about the similarity of comorbidity rates between children and adults. However, age does seem to impact prevalence rates of specific comorbid disorders. Children that are diagnosed with ADHD that persists into adulthood are at a significantly higher risk to also carry a diagnosis of Oppositional Defiant Disorder (ODD;
Barkley et al., 2008). While there appears to be fewer adults with comorbid ADHD and ODD when compared to children, the likelihood of adults carrying both diagnoses is two to three times higher in adults with ADHD compared to a healthy control group (Barkley et al., 2008; Cumyn, et al., 2009; Wilens et al., 2009).
Gender has also been found to have a significant impact on comorbidity rates with
ADHD in children and adults. While ADHD females have higher rates of internalizing problems across the lifespan, there is some disagreement about if gender truly influences ADHD comorbidity in adults. Some researchers suggest comorbid conditions of depression and anxiety may be more problematic for adult ADHD females than males (Faraone et al., 2000; Gaub &
20 Carlson, 1997; Gershon, 2002; Nussbaum, 2012). In contrast, a large study conducted over the course of seven years found no evidence that gender moderated the correlation between the phenotypic expression of adult ADHD and the prevalence of comorbidity (Biederman et al.,
2004).
There appears to be a significant interaction effect between ADHD subtype and the rate of psychiatric comorbidity. Adults with ADHD-Combined subtype are more likely to have higher rates of psychiatric comorbidity compared to ADHD-Inattentive subtype (Wilens et al.,
2009). Wolf and Wasserstein (2001) found evidence that adults with ADHD often present with higher mood lability and lower self-esteem, above and beyond what can be accounted for by mood disorders such as depression or bipolar disorder, raising the possibility that increased mood lability is a unique feature of adult ADHD. However, other research suggests that high rates of mood lability in adults with ADHD is not a core feature of ADHD but is more related to comorbid disorders (Wilens, 2004).
Studies investigating ADHD comorbidity face challenges in distinguishing between symptoms of ADHD and overlapping symptoms of other comorbid disorders. It is difficult to distinguish whether comorbid disorders lead to a greater number and higher severity of symptoms compared to the severity and number of symptoms leading to increased comorbidity
(Wilens et al., 2009). Different diagnostic methodology in comorbidity studies further contributes to the lack of clarity, with some studies relying on cross-sectional observations while other studies are longitudinal and focus on symptom recall across the lifespan (Joshi & Wozniak,
2015). Similarly, ADHD comorbidity studies appear to use different cut-off diagnostic criteria and a variety of assessment and diagnostic techniques, such as clinical structured interviews, symptom rating scales, and cognitive measures. Furthermore, the distinction between core
21 symptoms of ADHD and symptoms of comorbid disorders can be difficult to identify. Clinical judgement and an understanding of the history of symptoms is required to reliably establish a differential diagnosis.
Thus, adults with ADHD experience a number of negative consequences associated with the disorder, which highlights the importance of increased detection when ADHD does occur in adults. The sensitivity and specificity of adult ADHD assessment measures is therefore critically important when distinguishing ADHD symptoms that may overlap with diagnostic criteria of other psychiatric disorders. With increased detection, targeted services may be provided to help ameliorate these negative outcomes and improve quality of life for adults affected with this disorder.
ADHD Symptom Rating Scales
ADHD rating scales are designed to assist in ADHD diagnosis by helping to ensure uniformity and increasing efficiency. Consistent with the DSM-5 guidelines for ADHD diagnosis specify that a clinician should obtain information from multiple informants whenever possible
(APA, 2013), most ADHD rating scales include forms for self-report and informant report (e.g., parent, teacher). Furthermore, the DSM-5 states that multiple informants are typically required in order to accurately confirm the presence of symptoms across settings. In a study examining differences in parent reporting styles of children with ADHD, mothers were found to report a higher mean ratings of inattention symptoms then fathers (Mayfield et al., 2018). This may be related to differences in the parent-child relationship, such as the interaction style and overall time spent with the child, in addition to other potential differences (Craig, 2006; Craig & Mullan,
2011; Lamb, 2010). The types of activities may also influence how ADHD symptomatology is observed and reported. For example, a father who regularly engages in physical activities with
22 his child may not recognize symptoms of hyperactivity as problematic, and therefore underreport hyperactivity symptoms. Therefore, it is important to use multiple informants to establish diagnostic criteria for ADHD in order to understand the influence of how different informants may respond on symptom rating scales and potentially influence diagnosis of ADHD and ADHD presentations.
The diagnostic process for adults commonly includes collecting information that is self- reported or provided by other informants, such as parents or spouses. When compared to children, adults with ADHD appear to be better informants with regard to their symptoms, though the concordance between self-reports and informant reports is lower in patients in their early 20s than in their late 20s or early 30s (Barkley et al., 2008). In addition, one study found that adults with ADHD are more likely to underreport the severity of their symptoms when compared to clinician ratings (Kooij et al., 2008).
One unique aspect of ADHD assessment and diagnostic criteria in adult ADHD is that in order to receive an ADHD diagnosis in adulthood, you must have had ADHD symptoms present in childhood. As a result, the BAARS-IV includes retrospective self-report and collateral informant forms regarding ADHD symptom severity in childhood. Several studies have investigated the differences between current and retrospective childhood ratings of ADHD, with the large majority of evidence suggesting adult self-reporting of childhood ADHD symptoms has limited accuracy (Breda et al., 2020; Dias et al., 2008; Mannuzza et al., 2002; Moffit et al.,
2015). Furthermore, Grogan and Bramham (2016) evaluated if mood symptoms influence the accuracy of retrospective self-ratings of childhood ADHD symptoms using the BAARS
(Barkley, 1998) and the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith,
1983). The self-ratings on both measures were found to be highly correlated, suggesting that
23 current mood symptoms do not have a significant effect on the accuracy of self-rated retrospective ADHD symptoms. Therefore, retrospective reporting of childhood ADHD symptoms is found to be unreliable, even when taking into account present mood symptoms.
Longitudinal studies are also impacted by the sources of information (self-report vs. informant) changing over time. For instance, parents and teachers are the primary informants in studies involving childhood ADHD while in adolescent and adult ADHD studies, the most common source of information is self-report (Barkley et al., 2002; Mannuzza et al., 2003). A study by Mannuzza and colleagues (2003) found that parents and teachers are more likely to report externalizing behaviors, such as hyperactivity and impulsivity, than children and adolescents report. Parent reports were also more strongly associated with educational, occupational and social functioning outcomes when compared to self-reported cases, suggesting parent reports were more valid indicators of ADHD symptoms. Furthermore, ADHD persistence rates were significantly higher in studies that combined parent-report with self-report (Halperin et al., 2008; Hinshaw et al., 2012; Klein et al., 2012). The changing source of information may contribute to declining rates of ADHD from adolescence to adulthood and variability in prevalence rates reported from one study to the next.
Summary
Based on this review of functional impairment, psychiatric comorbidity, gender and age differences, and neurocognitive functioning in adults diagnosed with ADHD, a number of research related questions are apparent:
First, given the potential differences in the expression of ADHD symptoms based on age, gender, and psychiatric comorbidity, it is unclear to which latent constructs identified for adult
ADHD rating scales demonstrate similar relationships with other broader behavioral rating
24 scales, cognitive measures, etc.. The validity of symptom rating scales is especially important in the case for adult ADHD, where symptoms may have several overlapping symptoms with other common psychiatric diagnoses and where an ADHD diagnosis requires retrospective self-reports of childhood ADHD symptoms. Demonstrating similar relationships is important to establish the convergent validity of the ADHD ratings scales. Relatedly, establishing the discriminant validity of the ADHD rating scales in adults is also important.
Second, the extent to which adult ADHD rating scales are useful for improving diagnostic accuracy requires additional investigation. For children, ADHD rating scales are a key component of the diagnostic evaluation and there is ample evidence that when used, they improve diagnostic accuracy. While there is significant evidence to support the validity of diagnosis of ADHD in adulthood, it remains unclear how to best understand the expression of
ADHD symptomatology in adults. Providing further evidence for the diagnostic utility of adult symptom ratings scales would provide empirical support for their use in the diagnostic process. It will also be informative to compare differences in sensitivity and specificity between adult
ADHD subtypes.
Finally, diagnostic accuracy in differentiating adults with ADHD from other clinical disorders should be examined, since in many cases differentiating between two disorders is the focus of evaluation. Comparing the sensitivity and specificity between groups of adults with
ADHD, mixed psychiatric diagnoses, and a healthy control group will provide more insight into the diagnostic utility of the BAARS-IV.
In the following section, each of these research related questions are given further consideration.
25 Chapter 3: Psychometric Properties of the BAARS-IV
Convergent and Discriminant Validity
Diagnostic evaluations for ADHD typically include clinical interviews with the individual and another informant, psychodiagnostic tests focusing on executive functioning and attention, and gathering information related to the developmental history of the individual. For adutls, neuropsychological and psychoeducational assessments of ADHD typically include administration of well-standardized behavioral rating scales that include ratings of ADHD symptoms and other disturbances of behavior (e.g., anxiety, depression). While there are several rating scales that have been developed specifically to assess ADHD symptomatology in adults, these measures have been less researched compared to children ADHD assessment measures
(Taylor et al., 2011).
Although it is common for diagnostic and behavioral rating scales to be included in the evaluation of adults with ADHD, it is not well understood how these different methods might impact the diagnostic accuracy of the assessment. Due to the high degree of overlapping symptoms among common psychiatric disorders, one might expect the same overlap to influence, namely, ratings of inattention to confirm an ADHD diagnosis and ratings of attentional disturbance that are endorsed on a comprehensive behavioral rating scale. It is possible that both share significant common variance, and that one scale might be substituted for the other scale in an effort to reduce administration time and increase efficiency. This is an important consideration when assessing individuals with ADHD who have difficulty tolerating long evaluations due to the nature of the disorder. However, it may also be that the symptom rating scale and comprehensive behavior rating scale may, when combined, provide additional, unique information about functioning and provide a clearer picture of the individual. It is also
26 common for the selection of the assessment tool to be determined by the referral question. For example, when the primary referral question is about a possible or probable ADHD diagnosis, clinicians may select a brief and specific symptom rating scale that focuses on the ADHD diagnostic criteria. A specific symptom rating scale could be expected to have higher sensitivity and specificity when compared to a general behavioral rating scale. However, it is also common for clinicians to select a comprehensive behavioral rating scale as part of the assessment battery then there is limited diagnostic information available or when there are concerns of impaired occupational or interpersonal functioning.
The setting of assessment and diagnosis is another important factor in the diagnosis of adult ADHD. Given that ADHD is already under-diagnosed and under-treated in the adult population, accurate, efficient diagnosis is critical. ADHD is frequently diagnosed in primary care settings by a physician, rather than by psychologists or psychiatrists. The current structure of the health care system supports the use of non-standardized assessment by physicians.
Namely, access to mental health care is limited and there is often inadequate compensation for the completion of extensive mental health evaluations by providers. One study reported that 68% of primary care providers (PCP) are uncomfortable diagnosing adult ADHD, in part due to unclear diagnostic criteria (Adler et al., 2009). In that same study, 85% of PCP’s reported they would be more confident in diagnosing adult ADHD if there were psychometrically valid, easy to use symptom-specific measures for screening purposes. However, primary care physicians do not frequently use the DSM diagnostic criteria to confirm diagnosis and only 50% reported that
ADHD symptom measures should be based on DSM criteria (Adler & Cohen, 2004; Adler et al.,
2009).
27 Symptom rating scales are often highly valued by providers when making diagnostic decisions, due to their efficiency in gathering diagnostic information and their low-cost (Volpe,
Briesch, & Gadow, 2011). However, broad behavioral rating scales can also be an important factor in the diagnosis of ADHD because they assess symptoms, behaviors, and mood across settings (Barkley, 2014). These broader behavioral assessments are often less cost- and time- efficient than more symptom-specific symptom rating scales. The widespread use of symptom rating scales, such as the BAARS-IV, can be especially valuable if found to be psychometrically sound with high predictive validity in adult populations, and therefore improve the rates of accurately diagnosed and treated adults that have previously gone undetected.
Due to the lack of research investigating the relationship between commonly used ADHD symptom rating scales for adults, there is a lack of understanding for which scales, or combination of these scales, should be selected to address differing referral questions. In order to establish convergent and discriminant validity of the BAARS-IV, the current study will examine the degree to which the BAARS-IV shares substantial common variance with other broad behavioral assessment measures that also include items assessing ADHD symptom domains. To address this issue, the current study will examine diagnostic and behavioral ratings in a sample of adults with ADHD.
Diagnostic Utility
Symptom rating scales of ADHD are a frequently used tool to help screen and subsequently diagnosis individuals with ADHD in both children and adults. While there are several widely-used valid clinical ADHD scales, less is known about the diagnostic utility of the individual scales, such as the BAARS-IV. Some possible difficulties that could impact the diagnostic utility of ADHD adult symptom rating scales include capturing the unique symptom
28 profile and presentation of adult ADHD. Adults demonstrate higher inattentive symptoms and less hyperactive symptoms when compared to child ADHD presentations. However, the majority of ADHD literature includes large samples of adults that are diagnosed using the same symptom criteria as children. Notably, the DSM-5 reflected some increased understanding of the phenotypic differences of adult ADHD from research conducted since the DSM-IV by reducing the required criteria from 6 to 5 for adults with ADHD. However, a diagnosis of ADHD in adults requires retrospective reporting of childhood symptoms, which can further impact the validity and reliability of these scales.
Previous psychometric studies investigating the properties of ADHD adult symptom rating scales have been conducted in small samples (n<100) with varying methodologies. In a systematic review of adult ADHD rating scales by Taylor, Deb and Unwin (2010), only 35 previous studies were found to have evaluated the diagnostic utility of adult ADHD rating scales.
Of those 35 studies, no analyses were conducted on the BAARS-IV or previous versions of the same scale. However, similar measures, such as the Conners’ Adult ADHD Rating Scale
(CAARS; Conners et al., 1999) and the Wender Utah Rating Scale (WURS; Ward et al., 1993) demonstrated strong psychometric properties and high content validity. In their review, they found only seven of the reviewed scales had calculated Positive Predictive Value (PPV) and
Negative Predictive Value (NPV). The CAARS demonstrated the highest PPV of 87%, and the
Adult Self Report Scale (ASRS-18 and ASRS-6) demonstrated NPV greater than 90%. Similarly,
Area Under the Curve (AUC) was only calculated in four of the reviewed scales by previous studies. Fields (2005) identified an AUC cutoff of 0.89 or greater, however all of the reviewed studies that evaluated AUC fell below this. The AUC for ASRS-18 and ASRS-6, as well as the two other reviewed scales, the ADHD Rating Scale (Mehringer et al, 2001) and the Assessment
29 of Hyperactivity and Attention (Du Paul et al., 1998) all fell between 0.72 and 0.79. Taylor and colleagues (2010) discussed a range of limitations of the existent literature on adult ADHD rating scales, acknowledging that the studies evaluated had small sample sizes and the findings had yet to be replicated in larger, representative samples. Furthermore, only 18 of the 35 studies utilized a gold standard clinical interview as a comparison measure, further weakening the study design.
Relevant to the current study, a study evaluated the psychometric properties and diagnostic utility of the BAARS-IV in an adult, male, prison population. Young and colleagues
(2016) conducted a cross-sectional study of 390 male prison inmates (124 with ADHD) that were administered the BAARS-IV as a diagnostic screening tool for ADHD. They analyzed the diagnostic utility and reported sensitivity (0.38), specificity (0.96), PPV (0.77), NPV (0.83), and percentage of correctly classified individuals (82.0). The overall AUC was 0.67. These results suggest the BAARS-IV was able to identify non-cases but was poor at identifying individuals with ADHD due to a high number of false positives. Although clinically, endorsement of 9 out of the 18 BAARS-IV items warrants a diagnosis, the researchers reported an optimized cut-off value of only >4 in their study. Limitations to this study include that it was a demographically homogenized sample (primarily young, white males) of prison inmates, which has a higher prevalence of ADHD than a general clinical sample (Young et al., 2015). While this study offers some initial information about the BAARS-IV diagnostic utility, it is still unknown if these findings generalize to a broader sample of adults. Furthermore, it is unknown how well the
BAARS-IV will differentiate clinical ADHD groups from other psychiatric groups, especially given the high comorbidity of ADHD and other disorders. Given that BAARS-IV is a symptom rating scale, and ADHD symptom criteria often overlaps with other common psychiatric disorders, it will be important to better understand the diagnostic utility of the scale.
30 Chapter 4: Methodology
Participants
Of the 379 adults included in the study, 156 adults carry a diagnosis of ADHD-
Inattentive or ADHD-Combined, 201were diagnosed with other psychiatric disorders (e.g., specific learning disorder, mood disorder), and 22 had no diagnosis and serve as a healthy control comparison group. Participants for this study were selected from archival data acquired at a community mental health clinic located on a university campus. These participants were selected from a consecutive series of 1200 cases that were evaluated at The PRACTICE over a
13-year period and were referred for a psychodiagnostic evaluation for ADHD or another mental disorder. Presenting symptoms were varied and included attention difficulties, mood and anxiety symptoms, and/or behavioral disturbances. Adults were either self-referred for psychodiagnostic evaluations or presented after academic, occupational, or interpersonal difficulties. To be included in this study, all participants must have completed the Barkley Adult Rating Scale-IV
(BAARS-IV) Current-Self questionnaire, be between the ages of 18 years, 0 months and 81 years, 11 months, and have a Weschler GIA or FSIQ of 70 or above. Participants were included in the adult ADHD group if they have a primary or secondary diagnosis of ADHD combined presentation (ADHD/C), predominately inattentive presentation (ADHD/PI), or predominantly hyperactive/impulsive presentation (ADHD/PHI). Participants were included in the control group if they had no clinical diagnosis of ADHD or another disorder based on the psychodiagnostic evaluation. Participants were included in the mixed diagnosis group if they were diagnosed with a DSM mental disorder based on the psychodiagnostic evaluation but did not have a primary or secondary diagnosis of ADHD.
31 In addition to these study specific criteria, The PRACTICE clinic has its own set of exclusionary criteria to screen for clients who are appropriate for outpatient services. Thus, our study’s exclusionary criteria also includes The PRACTICE’s exclusionary criteria for accepting clients for treatment. Clients are not accepted for psychological evaluation or treatment at The
PRACTICE if they: 1) have a current eating disorder severe enough that requires immediate medical monitoring; 2) are currently experiencing psychotic symptoms; or 3) are of imminent harm to themselves or others. Screening for these exclusionary criteria occurs by The
PRACTICE staff during the intake process.
The intake process at the PRACTICE testing clinic also assesses for current substance and alcohol abuse. Clients are encouraged to stay abstinent from substances and alcohol during the assessment process in order to ensure validity of results. Additionally, clients are asked to provide information about current prescription medications; ADHD testing clients are often encouraged to abstain from stimulant medication during testing process when possible, but some clients are assessed while actively taking prescribed medications.
In addition to ADHD measures, clinical interviews, behavioral assessments, clinical observation, neuropsychological testing, review of educational, occupational and medical history, and other relevant information were included. Research and archival data entry will be conducted in accordance with local Institutional Review Board (IRB) policies.
Measures
There were a number of interview procedures and measures that were examined in the current study, including demographic and diagnostic interview information, the BAARS-IV,
Achenbach System of Empirically Based Assessment, Adult Self-Report (BAARS-IV),
32 Wechsler Adult Intelligence Scale (WAIS-IV or WAIS-V), and the Continuous Performance
Test. A brief description of each of these measures is provided in the following sections.
Demographic and Diagnostic Information
Advanced level school and clinical psychology students collected diagnostic data during initial intake interviews. Requested information included age, sex, ethnicity, family history, developmental and medical history of complaints, developmental milestones, and symptomology associated with the client’s referral. A DSM-IV or DSM-V ADHD diagnosis was made based on a comprehensive review of psychodiagnostic documentation and approval of supervising licensed psychologist.
Barkley Adult Rating Scale-IV (BAARS-IV)
The BAARS-IV assesses current ADHD symptoms and domains of impairment in addition to recollections of childhood symptoms. The measure includes the 18 DSM-IV symptoms of ADHD (APA, 1994) and includes a current and childhood self-report and other- report forms. Administration on average is 5-7 minutes. Participants respond to each item using a
4-point scale, ranging from 1 (sometimes) to 4 (very often). Validity, reliability, and factor structure of the BAARS-IV has been established in a nationally representative sample of adults and the scales demonstrate satisfactory internal consistency and test-retest reliability over a 2- to
3-week period (Barkley, 2011). Interobserver agreement between adult respondents and a well- known informant for the ADHD symptom rating scores has been found to range from .59–.76
(Adler et al., 2008; Barkley, Murphy, & Fischer, 2008; Magnússon et al., 2006). Internal consistency was found to be moderate to strong for the three domains of ADHD that include
ADHD Inattention (α =.85), ADHD Hyperactivity (α =.67), and ADHD Impulsivity (α =.79)
(Becker et al., 2014).
33 Achenbach System of Empirically Based Assessment, Adult Self-Report (ASEBA)
The ASEBA is designed to assess adaptive functioning, problems and substance use as reported by individuals ages 18 to 59. Self-administration under many conditions is about 15-20 minutes. Profiles for scoring include normed scales for adaptive functioning, Personal Strengths, empirically-based syndromes, substance use, Internalizing, Externalizing and Total Problems.
The ASEBA profiles include DSM-oriented scales consistent with DSM-5 categories. The profiles also include a Critical items scale consisting of items of particular concern to clinicians and scale scores for each gender at ages 18-35 and 36-59, based on national probability samples.
The ASEBA has demonstrated good internal consistency with mean alpha coefficients .83 for the empirically based problem scales and .78 for the DSM-oriented scales. Good criterion-related validity and construct validity were also found.
Wechsler Adult Intelligence Scale (WAIS-IV or WAIS-V)
The WAIS-IV (Wechsler, 2008) is a comprehensive clinical instrument designed to assess intelligence. It is comprised of 15 subtests, 12 of which were standardized for adults ages
16 to 90. The WAIS-IV provides composite index scores representing intelligence in the domains of verbal comprehension, perceptual reasoning, working memory, and processing speed. Composite scores are based on sums of age-corrected scale scores. A composite score representing general intellectual ability (Full Scale IQ or FSIQ) is derived from each index scale.
The framework of these cognitive domains is based on current intelligence theory and current clinical research. Normative information is based on a sample of 2,200 participants in the United
States. The WAIS-IV has been found to be highly consistent with test-retest reliabilities ranging from .70 to .90, internal consistency ranging from .78 to .94 for subtests, from .90 to .96 for factor index scores, .98 for the FSIQ, and .98-.99 for the factor index scores (VCI, PRI, WMI,
34 PSI) (Canivez, 2010). Inter-scorer coefficients are high, ranging from .91 to .99 by age group, and intraclass correlation coefficients ranged from .91 to .97. The WAIS-IV also demonstrates strong content validity, convergent and discriminant validity as presented in the Technical and
Interpretive Manual (Wechsler, 2008).
Procedure
Adults were referred for clinical evaluations to a community mental health assessment clinic for concerns related to attention problems, mood disturbances, and other concerns.
Standard components of the clinical evaluations typically included assessment of IQ, achievement, behavior, ADHD symptoms, and psychodiagnostic evaluation of executive functioning and attention. Other procedures may also have been completed based on clinical considerations relevant to the specific case being evaluated. All assessments were administered according to standardized procedures by a clinical psychology or educational psychology doctoral candidate under supervision of a doctorate-level psychologist. Diagnoses of ADHD,
LD, and other clinical diagnoses (e.g., depression, anxiety) were made by the psychologist according to DSM-IV or DSM-V diagnostic criteria based on clinical interviews, psychodiagnostic testing, performance on intellectual functioning measures, behavioral assessment, and other relevant information from medical and educational records. Adults were individually assessed in typically two or three sessions in a quiet room at the community mental health clinic. When available, significant others and/or family members of adult subjects were routinely asked to complete the BAARS-IV when a diagnosis of ADHD was under consideration.
35 Hypotheses and Proposed Data Analysis
Convergent and Discriminant Validity
Hypothesis 1. It is hypothesized that the BAARS-IV will demonstrate good convergent and discriminant validity. Pearson product moment correlations will be used to test Hypothesis 1.
Support for convergent validity of the BAARS-IV hyperactivity, inattention, and impulsivity scores will be demonstrated by significant associations with corresponding ASEBA
Hyperactivity-Impulsivity (ASEBA H-I) and Inattention (ASEBA I) subscale scores, with the
Hyperactivity-Impulsivity scores demonstrating stronger correlations with the BAARS-IV
Hyperactivity and Impulsivity scores, and the Inattention score demonstrating a higher correlation with the BAARS-IV Inattention score. Support for discriminant validity will be provided by non-significant associations between the ASEBA Anxiety (ASEBA AX) and
Depression (ASEBA DP) subscales, and the BAARS-IV hyperactivity, inattention, and impulsivity scores. Depression and anxiety were selected for these analyses because these two disorders commonly co-occur with ADHD in adulthood. After correlations are calculated, the method described by Steiger (1980), which allows for comparison of dependent correlation coefficients, will be used to test whether there are significant differences between the patterns of correlations that support the convergent and discriminant validity of the BAARS-IV.
Sensitivity, Specificity, and Positive and Negative Predictive Value
Hypothesis 2. It is predicted that the BAARS-IV scores will demonstrate good classification accuracy when discriminating between individuals with ADHD and those with no psychiatric diagnosis (NPD). Receiver operating characteristic (ROC) analyses will be used to examine differences in sensitivity, specificity, positive predictive value, and negative predictive value between the BAARS-IV scores for ADHD and NPD groups. In this analysis, values will be
36 calculated using the ADHD group as the state variable group. The three BAARS-IV scores (IA,
HP, and IM) will be simultaneously entered into the ROC analyses. The area under the curve
(AUC) will be used to determine each test score’s ability to distinguish between the ADHD and
NPD groups. An AUC of 1.0 indicates perfect classification, and an AUC of 0.5 indicates classification that is no better than chance (Hosmer & Lemeshow, 2000). Thus, related to this study, a test score with a larger AUC would indicate increased predictive discrimination between participants with ADHD and those with no diagnosis. The method described by Hanley and
McNeil (1983) will be used to determine whether significant differences exist between the AUCs for the BAARS-IV subscale scores.
Hypothesis 3. Hypothesis 3 will also be evaluated using receiver operating characteristic
(ROC) analyses as described in Hypothesis 2. However, these analyses will focus on how effective the BAARS-IV is at differentiating between ADHD subtypes. ROC analysis will be used to examine differences in sensitivity, specificity, positive predictive value, and negative predictive value between the BAARS-IV subscale scores for ADHD subtype diagnoses. In these analyses, values will be predicted using the ADHD Inattentive subtype as the control, because they are expected to exhibit primarily symptoms of inattention, while the ADHD Combined subtype will have inattentive, hyperactive and impulsive symptoms. It is expected that scores reflecting hyperactive and impulsive symptoms from the BAARS-IV will have greater predictive discrimination than scores reflecting inattentive symptoms, since both subtypes are expected to have symptoms in inattention, with the combined subtype uniquely displaying symptoms of hyperactivity and impulsivity.
Hypothesis 4. Hypothesis 4 will also be evaluated using receiver operating characteristic
(ROC) analyses as described in Hypothesis 2. However, these analyses will focus on how
37 effective the BAARS-IV is at differentiating between participants with ADHD and from a mood and anxiety disorder (MAD) group. ROC analysis will be used to examine differences in sensitivity, specificity, positive predictive value, and negative predictive value between the
BAARS-IV subscale scores for ADHD and MAD groups. In these analyses, values will be predicted using the MAD group as the control, because they are expected to exhibit the least amount of ADHD symptoms overall. It is expected that scores reflecting ADHD symptom severity on the BAARS-IV will have greater predictive discrimination than scores reflecting symptom severity of other psychiatric disorders, since both diagnostic groups are expected to have symptoms but the ADHD group uniquely displaying higher scores of inattention, hyperactivity and impulsivity. It is also expected that all ADHD scores will perform less well when discriminating between ADHD and MAD than when discriminating between ADHD and
NPD, since the ADHD scores for the MAD group are expected to be higher than for the NPD group.
Hypothesis 5. Hypothesis 5 will also be evaluated using receiver operating characteristic
(ROC) analyses as described in Hypothesis 4. However, these analyses will focus on how effective the BAARS-IV is at differentiating between participants with ADHD and from a specific learning disorder (SLD) group. ROC analysis will be used to examine differences in sensitivity, specificity, positive predictive value, and negative predictive value between the
BAARS-IV subscale scores for ADHD and SLD groups. In these analyses, values will be predicted using the MAD group as the control, because they are expected to exhibit the least amount of ADHD symptoms overall. It is expected that scores reflecting ADHD symptom severity on the BAARS-IV will have greater predictive discrimination than scores reflecting symptom severity of other psychiatric disorders, since both diagnostic groups are expected to
38 have symptoms but the ADHD group uniquely displaying higher scores of inattention, hyperactivity and impulsivity. It is also expected that all ADHD scores will perform less well when discriminating between ADHD and SLD than when discriminating between ADHD and
NPD, since the ADHD scores for the SLD group are expected to be higher than for the NPD group.
39 Chapter 5: Results
Convergent and Discriminant Validity
Convergent and discriminant validity was examined for the BAARS-IV subscale scores, by calculating correlates between the BAARS-IV Inattention, Hyperactivity, and Impulsivity average scores and ASEBA Inattention, Hyperactivity-Impulsivity, Anxiety and Depression raw scores. First, Fisher’s r to z procedure was used to transform each correlation to a Z-score
(Fisher, 1915). Then the Z-scores were used to determine if there were significant differences between the dependent correlations using the equations 3 and 10 from Steiger (1980). Software designed by Lee and Preacher (2013) was utilized to conduct these calculations. Table 2 includes abbreviated correlation results between the BAARS-IV and ASEBA scores. A complete correlation matrix is provided in Appendix B, Table 2.
Hypothesis 1. Hypothesis 1 predicted that the BAARS-IV subscales would demonstrate convergent validity with the ASEBA Inattention and Hyperactivity-Impulsivity subscales and discriminant validity with the ASEBA Depression and Anxiety scales. As indicated in Table 2, for convergent validity, the correlation between the BAARS-IV IA and ASEBA I was greater than the correlations between BAARS-IV IA and ASEBA H-I, however these differences did not rise to the level of statistical significance (z=1.534, n=203, p>.05). Similarly, the correlation between the BAARS-IV HP and ASEBA H-I was larger than the correlation between the
BAARS-IV HP and ASEBA-I, but the difference between the correlations was not statistically significant (z=0.605, n=203, p>.05). Finally, in contrast to what was expected, the correlation between BAARS-IV IM and ASEBA H-I was slightly lower than the correlation between the
BAARS-IV IM and ASEBA I, although this difference did not rise to the level of statistical significant (z=0-0.633, n=203, p>.05).
40 For discriminant validity, the correlation between the BAARS-IV IA and ASEBA IA scores (r=.70) was found to be significantly larger than the correlations between the BAARS-IV
IA score and the ASEBA AX (z = 4.60, n = 203, p < .0001) or ASEBA DP (z = 3.17, n = 203, p
< .001) scores. Similarly, the correlation between the BAARS-IV HP and ASEBA H-I scores was significantly larger than the correlations between the BAARS-IV HP and the ASEBA AX (z
= 4.58, n = 203, p < .0001) or ASEBA DP (z = 2.83, n = 203, p < .01) scores. Finally, the correlation between the BAARS-IV IM and ASEBA H-I scores was significantly larger than correlations between the BAARS-IV IM and the ASEBA AX (z = 3.37, n = 203, p < .001) or
ASEBA DP (z = 2.03, n = 203, p < .05) scores.
Sensitivity, Specificity, and Positive and Negative Predictive Value
Hypothesis 2. To test hypothesis 2, receiver operating characteristic (ROC) analyses were used to compare the classification accuracy of the BAARS-IV when differentiating between individuals with ADHD and those with no psychiatric diagnosis (NPD). There were 156 individuals with ADHD and 22 NPD participants who were administered the BAARS-IV. Figure
1 panel A presents the ROC curves for the BAARS-IV Inattention, Hyperactivity, and
Impulsivity subscale scores when differentiating between the ADHD and NPD groups. The
AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 3. Significance levels indicate each score’s improvement over chance prediction. General guidelines for interpreting AUC’s specify that AUC’s falling below 0.5 indicate worse than chance discrimination, 0.5 is no better than chance, 0.5-0.7 is considered poor discrimination, 0.7-0.8 is considered acceptable discrimination, 0.8-0.9 is classified as excellent discrimination, and AUC’s above 0.9 is generally considered outstanding discrimination (Hosmer & Lemeshow, 2013). These guidelines were used to classify AUC
41 discrimination for all of the following ROC analyses. Comparisons between the AUCs for the
BAARS-IV and ASEBA scores were made using the method described by Hanley and McNeil
(1983). Statistical analyses were performed using MedCalc for Windows, version 19.6.1 (2020).
The results demonstrate that each of the BAARS-IV subscales had poor discrimination between the ADHD and control groups. In order of descending classification rate, the BAARS-
IV Inattention subscale and the BAARS-IV Impulsivity subscale had the highest AUC’s at 0.66, and the BAARS-IV Hyperactivity subscale had an AUC at 0.627 (see Figure 1a for the ROC curves). Asymptotic significance levels indicated the BAARS-IV Inattention and Impulsivity subscales provided significantly better classification than chance, but the BAARS-IV
Hyperactivity subscale did not significantly differ from chance. There were no significant differences present when comparing the AUC’s from the BAARS-IV Inattention, Hyperactivity,
Impulsivity scores. Results suggest no significant difference among subscales when differentiating ADHD and NPD groups.
Separate ROC analyses were also calculated to determine whether there were differences in the ability of the BAARS-IV scores to discriminate between ADHD subtypes and NPD.
Figure 1b presents the ROC curves for the BAARS-IV Inattention, Hyperactivity, and
Impulsivity subscale scores when discriminating between ADHD-Combined and NPD groups.
The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 3. The results demonstrate that each of the BAARS-
IV subscales also had poor discrimination between individuals with ADHD-Combined and individuals with no diagnosis. In order of descending classification rate, the BAARS-IV
Impulsivity subscale had the highest an AUC at 0.69, the BAARS-IV Inattention subscale had an
AUC at 0.68, and finally the BAARS-IV Hyperactivity subscale had an AUC at 0.67.
42 Asymptotic significance levels indicated the BAARS-IV Inattention subscale provided significantly better classification than chance, but the BAARS-IV Impulsivity and Hyperactivity subscale did not significantly differ from chance. There were no significant differences present when comparing the AUC’s from the BAARS-IV Inattention, Hyperactivity, Impulsivity scores.
Results suggest no significant difference among subscales when differentiating ADHD-
Combined and NPD groups.
Figure 1c presents the ROC curves for the BAARS-IV Inattention, Hyperactivity, and
Impulsivity subscale scores when discriminating between ADHD-Inattentive subtype and NPD groups. The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 3. The results demonstrate that each of the BAARS-IV subscales had poor discrimination between individuals with ADHD-Inattentive and controls. In order of descending classification rate, the BAARS-IV Inattention subscale had the highest AUC at 0.62, the BAARS-IV Impulsivity subscale had an AUC at 0.60, and finally the BAARS-IV Hyperactivity subscale had an AUC at 0.54. Asymptotic significance levels indicated none of the BAARS-IV subscales (Inattention, Impulsivity, Hyperactivity) discriminated between ADHD-Inattentive and NPD better than chance. Similarly, there were no significant differences present when comparing the AUC’s from the BAARS-IV Inattention,
Hyperactivity, Impulsivity scores. Results suggest no significant difference between subscales when differentiating ADHD-Inattentive and NPD groups.
Hypothesis 3. Hypothesis 3 was also evaluated using receiver operating characteristic
(ROC) analyses similar to those described in Hypothesis 2. However, these analyses focused on how effective the BAARS-IV was at differentiating between ADHD subtypes. ROC analysis was used to examine differences in sensitivity, specificity, positive predictive value, and negative
43 predictive value between the BAARS-IV scores for ADHD subtype diagnoses. In these analyses, values were predicted using the ADHD Inattentive subtype as the positive state groups, because they were expected to exhibit primarily symptoms of inattention, while the ADHD combined subtype had inattentive, hyperactive and impulsive symptoms. There were 95 individuals with
ADHD-Combined (ADHD-C) and 53 individuals with ADHD-Inattention (ADHD-I) who were administered the BAARS-IV. Figure 2 panel A presents the ROC curves for the BAARS-IV
Inattention, Hyperactivity, and Impulsivity subscale scores when differentiating between the
ADHD-C and ADHD-I groups. The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 5. Significance levels indicate each score’s improvement over chance prediction. The results demonstrate that each of the BAARS-IV subscales had poor discrimination between individuals with ADHD-C and ADHD-I. The BAARS-IV Hyperactivity subscale and the BAARS-IV Impulsivity subscale had the highest AUC’s at 0.63, and the BAARS-IV Inattention subscale had an AUC at 0.59 (see
Figure 2 for the ROC curves). Asymptotic significance levels indicated the BAARS-IV
Inattention and Impulsivity subscales provided significantly better classification than chance, but the BAARS-IV Hyperactivity subscale did not significantly differ from chance. There were no significant differences present when comparing the AUC’s from the BAARS-IV Inattention,
Hyperactivity, Impulsivity scores. Results suggest no significant difference among subscales when differentiating ADHD-Combined and ADHD-Inattentive groups.
Hypothesis 4. Hypothesis 4 was also evaluated using receiver operating characteristic
(ROC) analyses as described in Hypothesis 2. However, these analyses will focus on how effective the BAARS-IV was at differentiating between participants with ADHD and those with psychiatric disorders. ROC analyses were used to examine differences in sensitivity, specificity,
44 positive predictive value, and negative predictive value between the BAARS-IV scores for
ADHD and mood and anxiety (MAD) groups. In these analyses, values were predicted using the
MAD group as the control, because they are expected to exhibit the least amount of ADHD symptoms overall. There were 156 participants included in the ADHD group and 49 participants in the MAD group. Figure 3 panel A presents the ROC curves for the BAARS-IV Inattention,
Hyperactivity, and Impulsivity subscale scores when differentiating between the ADHD and
MAD groups. The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 7. Significance levels indicate each score’s improvement over chance prediction. The results demonstrate that each of the BAARS-
IV subscales had poor discrimination between the ADHD and MAD groups. In order of descending classification rate, the BAARS-IV Impulsivity subscale had the highest AUC at 0.69, the BAARS-IV Inattention subscale had the highest AUC’s at 0.68, and the BAARS-IV
Hyperactivity subscale had an AUC at 0.65 (see Figure 3a for the ROC curves). Asymptotic significance levels indicated all three of the BAARS-IV subscales provided significantly better classification than chance. There were no significant differences present when comparing the
AUC’s from the BAARS-IV Inattention, Hyperactivity, Impulsivity scores. Results suggest no significant difference among subscales when differentiating between ADHD participants and
MAD participants.
Separate ROC analyses were also calculated to determine whether there were differences in the ability of the BAARS-IV scores to discriminate between ADHD-Combined and MAD compared to ADHD-Inattentive and MAD. Figure 3b presents the ROC curves for the BAARS-
IV Inattention, Hyperactivity, and Impulsivity subscale scores when discriminating between
ADHD-Combined and MAD groups. The AUCs, standard error of the AUCs, 95% confidence
45 intervals, and asymptotic significance levels for each AUC are included in Table 7. The results demonstrate that each of the BAARS-IV Inattention and Impulsivity subscales had acceptable discrimination between individuals with ADHD-Combined and individuals with mood or anxiety disorder (MAD). However, the BAARS Hyperactivity subscale fell into the poor discrimination range. In order of descending classification rate, the BAARS-IV Impulsivity subscale had the highest an AUC at 0.72, the BAARS-IV Inattention subscale had an AUC at 0.70, and finally the
BAARS-IV Hyperactivity subscale had an AUC at 0.69. Asymptotic significance levels indicated all three of the BAARS-IV subscales provided significantly better classification than chance. There were no significant differences present when comparing the AUC’s from the
BAARS-IV Inattention, Hyperactivity, Impulsivity subscales, suggesting there is no significant difference among subscales when differentiating ADHD-Combined and MAD groups.
Figure 3c presents the ROC curves for the BAARS-IV Inattention, Hyperactivity, and
Impulsivity subscale scores when discriminating between ADHD-Inattentive and MAD groups.
The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 7. The results demonstrate that each of the BAARS-
IV subscales had poor discrimination between individuals with ADHD-Inattentive and MAD. In order of descending classification rate, the BAARS-IV Inattention and the BAARS-IV
Impulsivity subscale had AUC’s at 0.62, and finally the BAARS-IV Hyperactivity subscale had an AUC at 0.56. Asymptotic significance levels indicated the BAARS-IV Impulsivity and
Inattention subscales were better than chance when discriminating between ADHD-Inattentive and MAD. However, the BAARS Hyperactivity subscale did not perform significantly better than chance when differentiating between ADHD-Inattentive and MAD groups. Similar to the
ADHD-Combined comparisons, there were no significant differences present when comparing
46 the AUC’s from the BAARS-IV Inattention, Hyperactivity, Impulsivity subscales, suggesting there is no significant difference among subscales when differentiating ADHD-Inattentive and
MAD groups.
Hypothesis 5. Hypothesis 5 was evaluated using receiver operating characteristic (ROC) analyses as described in Hypothesis 4. However, these analyses focused on how effective the
BAARS-IV was at differentiating between participants with ADHD and those with Specific
Learning Disorder (SLD). ROC analyses were used to examine differences in sensitivity, specificity, positive predictive value, and negative predictive value between the BAARS-IV scores for ADHD and Specific Learning Disorder (SLD) groups. In these analyses, values were predicted using the SLD group as the control, because they are expected to exhibit the least amount of ADHD symptoms overall. There were 156 participants included in the ADHD group and 107 participants in the SLD group. Figure 4 panel A presents the ROC curves for the
BAARS-IV Inattention, Hyperactivity, and Impulsivity subscale scores when differentiating between the ADHD and SLD groups. The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 9. Significance levels indicate each score’s improvement over chance prediction. The results demonstrate that the BAARS-IV Inattention subscale had excellent discrimination between the ADHD and SLD groups, and the BAARS-IV Impulsivity and Hyperactivity subscales had adequate discrimination between the ADHD and SLD groups. In order of descending classification rate, the BAARS-IV
Inattention subscale had the highest AUC at 0.81, the BAARS-IV Impulsivity subscale had an
AUC at 0.79, and the BAARS-IV Hyperactivity subscale had an AUC at 0.78 (see Figure 4a for the ROC curves). Asymptotic significance levels indicated all three of the BAARS-IV subscales provided significantly better classification than chance. There were no significant differences
47 present when comparing the AUC’s from the BAARS-IV Inattention, Hyperactivity, Impulsivity scores. Results suggest no significant difference between each subscales ability to differentiate between ADHD participants and SLD participants.
Separate ROC analyses were also calculated to determine whether there were differences in the ability of the BAARS-IV scores to discriminate between ADHD-Combined and SLD compared to ADHD-Inattentive and SLD. Figure 4b presents the ROC curves for the BAARS-IV
Inattention, Hyperactivity, and Impulsivity subscale scores when discriminating between
ADHD-Combined and SLD groups. The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 9. The results demonstrate that all three of the BAARS-IV subscales (Inattention, Impulsivity, Hyperactivity) had excellent discrimination between participants with ADHD-Combined and Specific Learning
Disorder (SLD). In order of descending classification rate, the BAARS-IV Inattention and
Hyperactivity subscales had the highest AUC’s at 0.82, the BAARS-IV Impulsivity subscale had an AUC at 0.81. Asymptotic significance levels indicated all three of the BAARS-IV subscales provided significantly better classification than chance. There were no significant differences present when comparing the AUC’s from the BAARS-IV Inattention, Hyperactivity, Impulsivity subscales, suggesting there is no significant difference among subscales when differentiating
ADHD-Combined and SLD groups.
Figure 4c presents the ROC curves for the BAARS-IV Inattention, Hyperactivity, and
Impulsivity subscale scores when discriminating between ADHD-Inattentive and SLD groups.
The AUCs, standard error of the AUCs, 95% confidence intervals, and asymptotic significance levels for each AUC are included in Table 9. The results demonstrate that each of the BAARS-
IV subscales had acceptable discrimination between individuals with ADHD-Inattentive and
48 SLD. In order of descending classification rate, the BAARS-IV Inattention had an AUC at 0.77, the BAARS-IV Impulsivity subscale had an AUC at 0.73, and finally the BAARS-IV
Hyperactivity subscale had an AUC at 0.70. Asymptotic significance levels indicated that all three BAARS-IV subscales were better than chance when discriminating between ADHD-
Inattentive and SLD groups. Notably, the Inattention subscale had a significantly larger AUC compared to the Hyperactivity subscale (p= .0174, z = 2.378). This suggests significantly better classification accuracy for the BAARS-IV Inattention subscale compared to the BAARS-IV
Hyperactivity subscale, when differentiating between individuals with ADHD-Inattentive and
Specific Learning Disorder (Hanley & McNeil, 1983). No significant differences were present between the AUC’s of the Inattention and Impulsivity subscales or the Hyperactivity and
Impulsivity subscales, suggesting there is no significant difference in discrimination of ADHD-
Inattentive and SLD groups.
49 Chapter 6 : Discussion
The purpose of the current study was to evaluate the diagnostic utility of the BAARS-IV in adults with ADHD. To accomplish this, psychometric characteristics of the BAARS-IV were examined in a clinic-based sample of adults diagnosed with ADHD and other disorders, including its convergent and discriminant validity, as well as its capability to discriminate
ADHD from other clinical disorders and from healthy controls. The results from these analyses provided mixed support for the use of the BAARS-IV.
The results of the correlation analyses between the ASEBA and BAARS-IV scores provided stronger evidence for the discriminant validity of the BAARS-IV than for convergent validity. Regarding discriminant validity, support for the BAARS-IV was found by the pattern of the correlations between BAARS-IV and ASEBA subscale scores. Consistent with our hypotheses, the BAARS-IV subscales had lower correlations with the ASEBA Anxiety and
Depression subscales than they did with the ASEBA Inattention and Hyperactivity-Impulsivity scales. Notably, the BAARS-IV Hyperactivity subscale performed best when compared to the
ASEBA Anxiety subscale. This is relevant due to the high comorbidity and several overlapping symptoms of ADHD and anxiety disorders, such as restlessness and fidgeting. It is also notable that the BAARS-IV Inattention subscale and ASEBA Depression subscale had the highest correlation among the discriminant validity comparisons. While not surprising due to the overlap between DSM symptom criteria for ADHD-Inattentive and Major Depressive Disorder, it is clinically relevant for the BAARS-IV to accurately discriminate between individuals with
ADHD-Inattention and depressive disorders, due to very different pharmacological and behavioral treatment recommendations. Accurate discrimination is especially important when evaluating adults for ADHD, due to a higher likelihood of inattention symptoms being present in adults with ADHD compared to children with ADHD. Based on these results, one would expect
50 that the BAARS-IV would be better at discriminating between ADHD and anxiety-based disorders, compared to ADHD and depressive disorders.
However, inconsistent with our hypotheses, there was not strong support for convergent validity. For example, we expected the BAARS-IV Inattention subscale to be more strongly correlated with ASEBA Inattention subscale, compared to the ASEBA Hyperactivity-Impulsivity subscale. However, correlations between the BAARS-IV subscales and ASEBA Inattention,
Hyperactivity-Impulsivity subscales did not provide strong support that the BAARS-IV subscales were differentially assessing these three core ADHD constructs. While there were some differences between the magnitude of the correlations, consistent with the hypotheses, none of these differences were significant. Therefore, the pattern of correlations suggest that the
BAARS-IV ability to distinguish between ADHD subtypes is limited. Instead, these results suggest that the BAARS-IV scores are not measuring different underlying constructs of inattention, hyperactivity and impulsivity, but rather seem to be measuring a more general construct associated with the diagnosis of ADHD, which may also account for their poor ability to distinguish ADHD subtypes from each other in the ROC analyses.
In the current study, the BAARS-IV did a poor job of discriminating between ADHD as a group or ADHD subtypes from the normal, non-psychiatric group or the mixed anxiety and depression group. However, it performed well when discriminating between the ADHD group and ADHD subtype groups from Specific Learning Disorder. It also did not do well discriminating between ADHD subtypes. Regarding ADHD subtypes, based on the convergent and discriminant validity analysis, the BAARS-IV inability to discriminate between ADHD subtypes is expected given that it is not differentially sensitive to the core ADHD symptom domains. If it was, we would expect the Hyperactivity subscale would have differentiated
51 between the Inattentive and Combined subtypes because while both subtypes have inattention symptoms, the Combined subtype also includes hyperactivity symptoms. The descriptive data does suggest some differences in the mean scores between ADHD groups, but these differences do not appear to be significant enough to useful in making distinctions between ADHD subtypes in adults. For the normal, non-psychiatric diagnosis comparisons, the results again reflect poor discrimination. The results may have limited generalizability because the clinical sample was relatively small. It is also likely the individuals in that group, even though they did not have a formal diagnosis, were reporting to the clinic for treatment for emotional and/or behavioral disturbance which may have further diminished the ability of the BAARS-IV scales to distinguish the healthy control group from the ADHD group or subtypes. It may be that if a healthy community sample were to be examined, the BAARS-IV scores may have provided better discrimination. However, even so, you would still expect the BAARS-IV ability to discriminate between diagnostic groups and ADHD subtypes would be relatively diminished based on these findings.
Regarding the poor discrimination between the ADHD and ADHD subtype groups from the depression and anxiety group, referred to as the Mood and Anxiety Disorder group (MAD), the discriminant validity findings suggest the BAARS-IV may be better at discriminating between individuals with anxiety disorders from those with ADHD, than it would be with depressed individuals. Notably, the current study’s combined sample of individuals diagnosed with depression and anxiety into a single mood disorder group may have obfuscated meaningful discriminations based on psychiatric disorders. However, due to the lower-than-expected number of available participants with mood disorders, the groups were combined. If the depression and anxiety groups were evaluated separately, the BAARS-IV may discriminate significantly better
52 when differentiating between ADHD and anxiety disorders than ADHD and depressive disorders. Several symptoms of anxiety, such as fidgeting and restlessness, overlap and can be confused with ADHD symptoms. Therefore, if the BAARS-IV does well when discriminating between ADHD and anxiety disorders, then it would provide very useful clinical information for assisting in differential diagnosis and arriving at an accurate diagnosis of ADHD or anxiety.
A somewhat surprising finding is the BAARS-IV did an excellent job when discriminating between individuals with ADHD and Specific Learning Disorder (SLD). While these disorders often co-occur, the findings support the validity of using the BAARS-IV in the assessment of individuals with ADHD or learning disorders. This finding is important because of the high prevalence of comorbid ADHD and SLD (Mayes et al., 2000; McGillivray & Baker,
2009) and differential diagnosis is a common referral question that clinicians must address when individuals are presenting for psychodiagnostic testing. Unlike comorbid ADHD and mood disorder symptoms, there is not a high degree of overlapping symptoms between ADHD and pure learning disability symptoms. Individuals with learning disorders such as reading, mathematics, or written expression are not typically characterized by symptoms of inattention, impulsivity or hyperactivity, so it is important that the BAARS-IV is able to differentiate between these disorders to support accurate diagnosis.
Regarding diagnostic utility, the BAARS-IV may be a clinically useful measure for psychodiagnostic evaluation, depending on the referral question. For example, if the referral question is related to the potential presence of ADHD or a SLD, the BAARS-IV may be a useful measure to better understand the underlying symptomatology. However, other factors beyond the referral question should be considered when selecting a testing battery, such as the goal of the evaluation and its use in the diagnostic and treatment planning processes. For example, when the
53 goal of the assessment is to distinguish between ADHD and a potentially wide range of other possible clinical disorders, a broader behavioral measure other than the BAARS-IV may be preferred due to the lack of evidence for the BAARS-IV to accurately discriminate between symptoms of other psychiatric disorders. Furthermore, a broader behavioral assessment will more likely assess the internalized and externalized symptoms of several clinical disorders accurately, beyond just the presence of ADHD symptoms. However, if the primary referral question is directly related to an ADHD diagnosis, or there is a question about the rule-out of
ADHD and a learning disorder diagnosis, the BAARS-IV may be a more efficient measure, given that the items directly assess the symptoms related to the diagnostic criteria that is required for ADHD diagnosis.
There are some limitations to the current study that should be considered. The sample size of the mixed psychiatric group, consisting of individuals with anxiety, depression and bipolar disorder, was relatively small. Furthermore, separate analyses testing the ability of the
BAARS-IV to discriminate between anxiety, depression, or other psychiatric disorders were not conducted due to the small sample sizes. Results from the current study would be strengthened if they were found to be consistent in comparisons between ADHD and larger sample groups with separate psychiatric diagnoses.
Similarly, the control group, consisting of healthy individuals without psychiatric diagnoses, was relatively small. Due to this, it is questionable whether the current study’s results would generalize to other clinics that had larger sample groups or that used a more representative community control sample rather than a clinical sample. It would be beneficial for future research to examine the BAARS-IV performance in a larger, healthy community sample to see if
54 the BAARS-IV subscales provide better discrimination between diagnostic groups and ADHD subtypes, compared to the results of the current study.
Although the sample group of individuals were representative of outpatient clinics to some degree, there were also a significant number of university and college students included in the sample due to the clinic being located on a university campus. This may suggest that the sample demographics are more similar to a specific subset of the population, similar to a university counseling center, rather than a sample representative of the general population. This is an important factor to consider due to previous research finding evidence that emerging adulthood is a unique period of development where ADHD symptomatology may be different than in adults that are older (Adler & Shaw, 2011; Arnett, 2000). To strengthen evidence for the current study’s findings, future studies should consider the generalizability of the results in a larger community representative sample.
Finally, we were unable to examine the other BAARS-IV scales to determine if their convergent and discriminant validity, or ability to discriminate between diagnostic groups, would have been greater than the BAARS-IV self-report forms. Furthermore, it would be important to evaluate the diagnostic utility of the BAARS-IV when utilizing all of the BAARS-IV scales, including scales that assess the functional impairment of ADHD symptoms in addition to symptom severity. Furthermore, future research could elaborate on the current study by analyzing information from the BAARS-IV childhood and current ADHD symptom rating scales provided by collateral informants, in addition to self-reported information. Unique contributions of informant ratings to the BAARS-IV convergent and discriminant validity, as well as it’s ability to discriminate between ADHD and other psychiatric disorders, is an area that would benefit from additional research.
55 Despite these limitations, this is one of the few studies examining the usefulness of the
BAARS-IV from distinguishing between individuals with ADHD from other clinical samples.
Based on these results, some caution should be used if the BAARS-IV scales are used to describe the core symptoms of ADHD. This is because the correlations between the BAARS-IV and the
ADHD-related ASEBA subscales suggest the BAARS-IV does not appear to be sensitive to anticipated symptom differences based on ADHD subtypes. For example, based on the BAARS-
IV results, you may not want to conclude that an individual has more severe hyperactivity symptoms than inattention symptoms due to the lack of significant evidence for the ability to differentiate between inattention and hyperactivity symptoms. However, if used as a screening measure, it could identify symptoms in a general way that is consistent with a diagnosis of
ADHD. The BAARS-IV may also assist in differential diagnosis when there is a question of the presence of ADHD or a Specific Learning Disorder. The BAARS-IV may provide information related to ADHD symptomatology, in conjunction with information from other measures, to assist in the diagnostic and treatment planning process.
56 Appendix A: Tables and Figures
Table 1
Demographic and clinical information for clinical sample comparison groups ADHD-I ADHD-C ADHD Total MAD SLD NPD Total Sample
(n=53) (n=95) (n=156) (n=49) (n=107) (n=22) (N=379) M (SD) M (SD) M (SD) M (SD) M (SD) M (SD) M (SD) Age (years) 27.1 (8.4) 27.0 (6.4) 27.0 (7.1) 29.6 (9.6) 29.8 (10.6) 29.8 (9.6) 28.9 (9.7) FSIQ 106.0 (14.6) 108.7 (15.2) 108.0 (14.4) 104.0 (11.0) 100.3 (16.8) 115.1 (15.2) 105.6 (15.0) VCI 111.4 (15.4) 106.7 (24.4) 108.4 (21.5) 109.4 (16.7) 102.3 (15.1) 122.5 (14.5) 108.1 (19.1) PRI 107.9 (16.1) 103.4 (28.0) 104.0 (24.4) 97.3 (17.2) 101.0 (15.8) 106.0 (17.0) 100.9 (19.8) WMI 96.1 (9.1) 93.4 (29.0) 94.5 (24.8) 99.0 (14.1) 99.0 (15.2) 103.8 (14.4) 97.9 (19.8) PSI 100.1 (10.9) 97.2 (28.0) 98.7 (24.3) 95.0 (15.1) 94.9 (13.9) 104.5 (16.2) 95.8 (20.5) BAARS Inattention (average) 1.4 (0.6) 1.6 (0.6) 1.5 (0.6) 1.1 (0.6) 0.8 (0.6) 1.1 (0.8) 1.2 (0.7) BAARS Hyperactivity (average) 1.1 (0.7) 1.5 (0.7) 1.4 (0.7) 1.0 (0.7) 0.6 (0.6) 1.0 (0.9) 1.1 (0.8) BAARS Impulsivity (average) 1.4 (0.7) 1.7 (0.8) 1.6 (0.7) 1.1 (0.7) 0.8 (0.7) 1.1 (1.0) 1.2 (0.8) ASEBA Inattention 8.0 (3.4) 8.5 (3.5) 8.2 (3.4) 6.4 (3.3) 4.8 (3.2) 2.7 (2.0) 6.3 (3.6) ASEBA Hyperactivity-Impulsivity 4.6 (2.7) 6.4 (2.5) 5.8 (2.8) 5.1 (2.6) 3.5 (3.0) 1.7 (2.5) 4.6 (3.0) ASEBA Anxiety 6.6 (2.8) 7.4 (3.2) 7.1 (3.0) 8.8 (2.6) 5.8 (3.4) 5.0 (2.6) 6.8 (3.3) ASEBA Depression 9.4 (5.5) 10.6 (5.7) 10.3 (5.5) 12.8 (5.6) 6.9 (5.8) 4.3 (2.8) 9.1 (5.9) Gender % Male (n=169) 50.9% 53.7% 52.6% 38.8% 36.4% 36.4% 44.6% Female (n=210) 47.2% 46.3% 46.8% 61.2% 62.6% 63.6% 55.4% Comorbid Diagnoses % ADHD ------0% 0% 0% 41.2% Anxiety Disorder (n=23) 1.9% 1.1% 1.3% 32.7% 14.0% 0% 6.1% Depression Disorder (n=26) 3.8% 0% 1.9% 46.9% 15.0% 0% 6.8% Specific Learning Disability(n=124) 13.2% 9.5% 10.9% 0% -- 0% 28.2% Note. BAARS = Barkley Adult ADHD Rating Scale-IV; ASEBA= Achenbach System of Empirically Based Assessment, Adult Self-Report, Hyperactivity-Impulsivity Subscale; ASEBA AX = Achenbach System of Empirically Based Assessment, Adult Self-Report; FSIQ = Full Scale Intelligence Quotient; VCI= Verbal Comprehension Index; PRI = Perceptual Reasoning Index; WMI = Working Memory Index; PSI = Processing Speed Index; ADHD-I = ADHD-Inattentive; ADHD-C = ADHD-Combined; MAD = Mood and Anxiety Disorder; SLD = Specific Learning Disorder; NPD = No Psychiatric Diagnosis.
57 Table 2
Correlations between Barkley-IV ADHD Symptom Rating Scale, Current Self-Report (BAARS- IV) raw subscale scores and Achenbach System of Empirically Based Assessment, Adult Self- Report (ASEBA) raw subscale scores
Subscales Convergent Validity Discriminant Validity
ASEBA IA ASEBA H-I ASEBA AX ASEBA DP
a a a BAARS IA .70 .63 .44 .55
b b b BAARS HP .60 .63 .35 .47
c c c BAARS IM .66 .63 .42 .51
Note. All correlations significant at p < .01. N =203; ASEBA IA = Achenbach System of Empirically Based Assessment, Adult Self-Report, Inattention Subscale; ASEBA H-I = Achenbach System of Empirically Based Assessment, Adult Self-Report, Hyperactivity- Impulsivity Subscale; ASEBA AX = Achenbach System of Empirically Based Assessment, Adult Self-Report, Anxiety Subscale; ASEBA DP = Achenbach System of Empirically Based Assessment, Adult Self-Report, Depression Subscale; BAARS IA = Barkley Adult Rating Scale- IV, Inattention Subscale; BAARS HP = Barkley Adult Rating Scale-IV, Hyperactivity Subscale; BAARS IM = Barkley Adult Rating Scale-IV, Impulsivity Subscale. a. BAARS IA/ASEBA IA > BAARS IA/ASEBA AX; BAARS IA/ASEBA DP b. BAARS HP/ASEBA HI > BAARS HP/ASEBA AX; BAARS HP/ASEBA DP c. BAARS IM/ASEBA HA > BAARS IM/ASEBA AX; BAARS IM/ASEBA DP
58 Table 3
Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD vs. No Psychiatric Diagnosis (NPD), ADHD- Combined vs. NPD, and ADHD-Inattentive vs. NPD
Subscale Score AUC 95% CI of AUC SE of AUC p*
Total sample (n=156)
BAARS IA 0.66 0.515 to 0.800 0.073 .017*
BAARS IMP 0.66 0.018 to 0.515 0.071 .018*
BAARS HYP 0.63 0.490 to 0.769 0.071 .05
Combined (n=95)
BAARS IA 0.68 0.539 to 0.818 0.071 .009*
BAARS IMP 0.69 0.551 to 0.823 0.069 .069
BAARS HYP 0.67 0.490 to 0.769 0.071 .071
Inattentive (n =53)
BAARS IA 0.62 0.460 to 0.772 0.080 0.115
BAARS IMP 0.60 0.438 to 0.753 0.080 0.196
BAARS HYP 0.54 0.391 to 0.697 0.078 0.549 Note. *p value indicates asymptotic significance with null hypothesis = .05. BAARS-IV IMP = BAARS-IV Impulsivity; BAARS-IV HYP = BAARS-IV Hyperactivity; BAARS-IV IA = BAARS-IV Inattention.
59 Table 4
Classification Accuracy Statistics and Optimal Threshold Values for the Behavior Assessment System for BAARS-IV subscale scores for ADHD vs. No Psychiatric Diagnosis (NPD)
Youden’s BAARS-IV Subscales Score Sn+Sp Sn Sp PLR NLR Index BAARS-IV Inattention 0.06 1.05 1.00 0.05 1.05 0.00 0.05 0.39 1.19 0.96 0.23 1.24 0.19 0.19 0.61 1.31 0.95 0.36 1.49 0.15 0.31 Sn+SP cutoff score 1.06 1.38 0.79 0.59 1.93 0.36 0.38 1.65 1.20 0.47 0.73 1.74 0.72 0.20 2.28 1.07 0.16 0.91 1.74 0.93 0.07 2.83 1.01 0.01 1.00 -- 0.99 0.01 BAARS-IV Impulsivity 0.13 1.25 0.98 0.27 1.35 0.08 0.25 0.63 1.27 0.91 0.36 1.42 0.26 0.27 Sn+SP cutoff score 1.13 1.32 0.78 0.55 1.71 0.41 0.32 1.63 1.20 0.52 0.68 1.62 0.71 0.20 1.88 1.24 0.42 0.82 2.31 0.71 0.24 2.38 1.13 0.26 0.86 1.93 0.85 0.13 2.88 1.02 0.06 0.96 1.40 0.98 0.02 BAARS-IV Hyperactivity 0.10 1.13 0.99 0.14 1.15 0.08 0.13 0.30 1.25 0.98 0.27 1.35 0.08 0.25 Sn+SP cutoff score 0.45 1.31 0.95 0.36 1.49 0.15 0.31 1.10 1.25 0.66 0.59 1.62 0.57 0.25 1.90 1.12 0.31 0.82 1.68 0.85 0.12 2.30 1.12 0.21 0.91 2.32 0.87 0.12 2.70 1.00 0.04 0.96 0.93 1.00 0.00 Note. BAARS IA = BAARS-IV Inattention; BAARS IMP = BAARS-IV Impulsivity; BAARS HYP = BAARS-IV Hyperactivity. Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR= Negative Likelihood Ratio.
60
Table 5
Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD-Inattentive vs. ADHD-Combined
Subscale Score AUC 95% CI of AUC SE of AUC p*
BAARS IMP 0.63 0.539 to 0.720 0.046 0.009*
BAARS HYP 0.63 0.534 to 0.719 0.047 0.011*
BAARS IA 0.59 0.491 to 0.679 0.048 0.086 Note. *p value indicates asymptotic significance with null hypothesis = .05. BAARS-IV IMP = BAARS-IV Impulsivity; BAARS-IV HYP = BAARS-IV Hyperactivity; BAARS-IV IA = BAARS-IV Inattention.
61 Table 6
Classification Accuracy Statistics and Optimal Threshold Values for the Behavior Assessment System for BAARS-IV subscale scores for ADHD-Inattentive vs. ADHD-Combined
Youden’s BAARS-IV Subscales Score Sn+Sp Sn Sp PLR NLR Index BAARS-IV Impulsivity 0.13 1.04 0.98 0.06 0.94 1.04 0.37 0.63 1.08 0.91 0.17 0.83 1.09 0.56 1.13 1.12 0.78 0.34 0.66 1.18 0.65 1.63 1.20 0.52 0.68 0.32 1.61 0.71 Sn+SP cutoff score 2.13 1.24 0.37 0.87 0.13 2.79 0.73 2.38 1.21 0.26 0.94 0.06 4.61 0.78 2.88 1.04 0.06 0.98 0.02 3.32 0.96 BAARS-IV Hyperactivity 0.10 1.12 0.99 0.13 0.87 1.14 0.08 0.30 1.17 0.98 0.19 0.81 1.21 0.11 0.90 1.13 0.74 0.40 0.60 1.22 0.66 Sn+SP cutoff score 1.68 1.18 0.39 0.79 0.21 1.87 0.77 1.90 1.17 0.31 0.87 0.13 2.31 0.80 2.30 1.17 0.21 0.96 0.04 5.55 0.82 2.70 1.02 0.04 0.98 0.02 2.21 0.98 BAARS-IV Inattention 0.06 1.02 1.00 0.02 0.98 1.02 0.00 0.83 1.03 0.84 0.19 0.81 1.04 0.84 1.28 1.11 0.70 0.42 0.59 1.19 0.74 Sn+SP cutoff score 1.83 1.14 0.40 0.74 0.26 1.52 0.82 2.06 1.13 0.24 0.89 0.11 2.14 0.86 2.39 1.07 0.13 0.94 0.06 2.21 0.93 2.61 1.02 0.04 0.98 0.02 2.21 0.98 Note. BAARS IA = BAARS-IV Inattention; BAARS IMP = BAARS-IV Impulsivity; BAARS HYP = BAARS-IV Hyperactivity. Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR= Negative Likelihood Ratio.
62 Table 7
Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD vs. Mood and Anxiety Disorder (MAD), ADHD- Combined vs. MAD, and ADHD-Inattentive vs. MAD
Subscale Score AUC 95% CI of AUC SE of AUC p*
Total sample (n=156)
BAARS IA 0.68 0.589 to 0.761 0.044 <.001*
BAARS IMP 0.69 0.604 to 0.773 0.043 <.001*
BAARS HYP 0.65 0.562 to 0.736 0.044 .002*
Combined (n=95)
BAARS IA 0.70 0.608 to 0.787 0.046 <.001*
BAARS IMP 0.72 0.639 to 0.810 0.044 <.001*
BAARS HYP 0.69 0.597 to 0.780 0.047 <.001*
Inattentive (n =53)
BAARS IA 0.62 0.515 to 0.733 0.056 0.031*
BAARS IMP 0.62 0.510 to 0.729 0.056 0.038*
BAARS HYP 0.56 0.451 to 0.677 0.058 0.266 Note. *p value indicates asymptotic significance with null hypothesis = .05. BAARS IA = BAARS-IV Inattention; BAARS IMP = BAARS-IV Impulsivity; BAARS HYP = BAARS-IV Hyperactivity.
63 Table 8
Classification Accuracy Statistics and Optimal Threshold Values for the Behavior Assessment System for BAARS-IV subscale scores for ADHD vs. Mood and Anxiety Disorder (MAD)
Youden’s BAARS-IV Subscales Score Sn+Sp Sn Sp PLR NLR Index BAARS-IV Inattention 0.06 1.02 0.98 0.04 1.02 0.46 0.02 0.61 1.13 0.93 0.20 1.16 0.37 0.13 Sn+SP cutoff score 1.17 1.23 0.68 0.55 1.51 0.58 0.23 1.28 1.18 0.59 0.59 1.43 0.70 0.18 Sn+SP cutoff score 1.50 1.23 0.47 0.76 1.93 0.70 0.23 1.94 1.05 0.21 0.84 1.28 0.95 0.04 2.33 1.04 0.06 0.98 2.85 0.96 0.04 BAARS-IV Impulsivity 0.13 1.04 0.98 0.06 0.94 1.04 0.34 0.38 1.17 0.97 0.20 0.80 1.22 0.16 0.88 1.27 0.84 0.43 0.57 1.47 0.37 Sn+SP cutoff score 1.38 1.33 0.67 0.65 0.35 1.94 0.50 1.88 1.30 0.42 0.88 0.12 3.45 0.66 2.38 1.20 0.26 0.94 0.06 4.31 0.78 2.63 1.11 0.13 0.98 0.02 6.30 0.89 BAARS-IV Hyperactivity 0.10 0.91 0.87 0.04 0.91 3.22 -0.09 0.50 1.06 0.79 0.27 1.08 0.78 0.06 1.10 1.14 0.57 0.57 1.32 0.76 0.14 Sn+SP cutoff score 1.45 1.19 0.40 0.80 1.94 0.76 0.19 1.90 1.03 0.13 0.90 1.29 0.97 0.03 2.30 0.98 0.04 0.94 0.62 1.02 -0.02 2.70 1.00 0.02 0.98 0.95 1.00 0.00 Note. BAARS IA = BAARS-IV Inattention; BAARS IMP = BAARS-IV Impulsivity; BAARS HYP = BAARS-IV Hyperactivity. Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR= Negative Likelihood Ratio.
64
Table 9
Receiver Operating Characteristic (ROC) Area under the ROC Curve (AUC) differences between BAARS-IV subscale scores for ADHD vs. Specific Learning Disorder (SLD), ADHD- Combined vs. SLD, and ADHD-Inattentive vs. SLD
Subscale Score AUC 95% CI of AUC SE of AUC p*
Total sample (n=156)
BAARS IA 0.81 0.753 to 0.863 0.028 <.001*
BAARS IMP 0.79 0.732 to 0.843 0.028 <.001*
BAARS HYP 0.78 0.720 to 0.835 0.029 <.001*
Combined (n=95)
BAARS IA 0.82 0.766 to 0.880 0.029 <.001*
BAARS IMP 0.81 0.756 to 0.872 0.03 <.001*
BAARS HYP 0.82 0.759 to 0.873 0.029 <.001*
Inattentive (n =53)
BAARS IA 0.77 0.695 to 0.850 0.039 <.001*
BAARS IMP 0.73 0.653 to 0.815 0.041 <.001*
BAARS HYP 0.70 0.607 to 0.786 0.046 <.001* Note. *p value indicates asymptotic significance with null hypothesis = .05. BAARS IA = BAARS-IV Inattention; BAARS IMP = BAARS-IV Impulsivity; BAARS HYP = BAARS-IV Hyperactivity.
65 Table 10
Classification Accuracy Statistics and Optimal Threshold Values for the Behavior Assessment System for BAARS-IV subscale scores for ADHD vs. Specific Learning Disorder (SLD)
Youden’s BAARS-IV Subscales Score Sn+Sp Sn Sp PLR NLR Index BAARS-IV Inattention 0.06 1.06 0.99 0.06 1.06 0.09 0.06 0.50 1.32 0.94 0.37 1.50 0.16 0.32 0.88 1.50 0.83 0.66 2.48 0.25 0.50 Sn+SP cutoff score 1.19 1.55 0.74 0.80 3.80 0.32 0.55 1.59 1.31 0.43 0.88 3.55 0.65 0.31 1.94 1.21 0.28 0.93 3.76 0.78 0.21 2.28 1.11 0.12 0.99 12.78 0.89 0.11 BAARS-IV Impulsivity 0.13 1.20 0.97 0.23 1.26 0.14 0.20 0.71 1.38 0.89 0.50 1.75 0.23 0.38 Sn+SP cutoff score 1.13 1.42 0.74 0.67 2.28 0.38 0.42 Sn+SP cutoff score 1.38 1.42 0.63 0.79 3.05 0.47 0.42 1.88 1.28 0.35 0.94 5.32 0.70 0.28 2.38 1.17 0.19 0.98 9.79 0.83 0.17 2.88 1.04 0.05 0.99 5.00 0.96 0.04 BAARS-IV Hyperactivity 0.10 1.19 0.95 0.24 1.25 0.21 0.19 0.45 1.41 0.90 0.51 1.85 0.20 0.41 Sn+SP cutoff score 0.70 1.43 0.81 0.63 2.16 0.31 0.43 1.10 1.40 0.64 0.76 2.64 0.47 0.40 1.45 1.33 0.46 0.88 3.76 0.62 0.33 1.90 1.19 0.24 0.94 4.36 0.80 0.19 2.30 1.14 0.15 0.99 16.33 0.86 0.14 Note. BAARS IA = BAARS-IV Inattention; BAARS IMP = BAARS-IV Impulsivity; BAARS HYP = BAARS-IV Hyperactivity. Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR= Negative Likelihood Ratio
66 Figure 1a. ROC Curves comparing ADHD Groups to No Psychiatric Diagnoses (NPD)
Figure 1b. ROC Curves comparing ADHD-Combined to No Psychiatric Diagnoses (NPD)
Figure 1c. ROC Curves comparing ADHD-Inattentive to No Psychiatric Diagnoses (NPD)
67 Figure 2. ROC Curves comparing ADHD-Combined to ADHD-Inattentive
68 Figure 3a. ROC Curves comparing ADHD Groups to Mood and Anxiety (MAD) Group
Figure 3b. ROC Curves comparing ADHD-Combined to Mood and Anxiety (MAD) Group
Figure 3c. ROC Curves comparing ADHD-Inattentive to Mood and Anxiety (MAD) Group
69 Figure 4a. ROC Curves comparing ADHD Groups to Specific Learning Disorder (SLD)
Figure 4b. ROC Curves comparing ADHD-Combined to Specific Learning Disorder (SLD)
Figure 4c. ROC Curves comparing ADHD-Inattentive to Specific Learning Disorder (SLD)
70 Appendix B: Extended Tables
Table 1
Barkley Adult ADHD Rating Scale-IV, Current Symptoms Scale, Self-Report Form (DSM Symptom Criteria Items Only)
Never or Very Items Sometimes Often Rarely Often 1. Fail to give close attention to details or 0 1 2 3 make careless mistakes in my work 2. Fidget with hands or feet or squirm in my 0 1 2 3 seat 3. Have difficulty sustaining my attention in 0 1 2 3 tasks or fun activities 4. Leave my seat in situations in which sitting 0 1 2 3 is expected
5. Don’t listen when spoken to directly 0 1 2 3
6. Feel restless 0 1 2 3
7. Don’t follow through on instructions and 0 1 2 3 fail to finish work 8. Have difficulty engaging in leisure 0 1 2 3 activities or doing fun things quietly 9. Have difficulty organizing tasks or 0 1 2 3 activities
10. Feel “on the go” or “driven by a motor” 0 1 2 3
11. Avoid, dislike, or am reluctant to engage in 0 1 2 3 work that requires sustained mental effort
12. Talk excessively 0 1 2 3
13. Lose things necessary for tasks or activities 0 1 2 3
14. Blurt out answers before questions have 0 1 2 3 been completed
15. Am easily distracted 0 1 2 3
16. Have difficulty awaiting turn 0 1 2 3
17. Am forgetful in daily activities 0 1 2 3
18. Interrupt or intrude on others 0 1 2 3
71 Table 2
Correlations between all Barkley-IV ADHD Symptom Rating Scale, Current Self-Report (BAARS-IV) raw subscale scores and Achenbach System of Empirically Based Assessment, Adult Self-Report (ASEBA) raw subscale scores BAARS BAARS BAARS ASEBA ASEBA ASEBA ASEBA Subscales IA HYP IMP IA H-I ANX DP BAARS IA 1.00 0.77 0.82 0.70 0.63 0.44 0.55
BAARS HYP 0.77 1.00 0.81 0.60 0.63 0.35 0.47
BAARS IMP 0.82 0.81 1.00 0.66 0.63 0.42 0.51
ASEBA IA 0.70 0.60 0.66 1.00 0.54 0.42 0.60
ASEBA H-I 0.63 0.63 0.63 0.54 1.00 0.42 0.49
ASEBA ANX 0.44 0.35 0.42 0.42 0.42 1.00 0.63
ASEBA DP 0.55 0.47 0.51 0.60 0.49 0.63 1.00 Note. N =203; ASEBA IA = Achenbach System of Empirically Based Assessment, Adult Self- Report, Inattention Subscale; ASEBA H-I = Achenbach System of Empirically Based Assessment, Adult Self-Report, Hyperactivity-Impulsivity Subscale; ASEBA AX = Achenbach System of Empirically Based Assessment, Adult Self-Report, Anxiety Subscale; ASEBA DP = Achenbach System of Empirically Based Assessment, Adult Self-Report, Depression Subscale; BAARS IA = Barkley Adult Rating Scale-IV, Inattention Subscale; BAARS HP = Barkley Adult Rating Scale-IV, Hyperactivity Subscale; BAARS IM = Barkley Adult Rating Scale-IV, Impulsivity Subscale
72 Table 3
Classification Accuracy Statistics for BAARS-IV Inattention Subscale for ADHD vs. No Psychiatric Diagnosis (NPD)
Youden's Score Sn+SP Sensitivity Specificity PLR NPR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.06 1.05 1.00 0.05 1.05 0.00 0.05 0.17 1.14 1.00 0.14 1.16 0.00 0.14 0.28 1.17 0.99 0.18 1.21 0.06 0.17 0.39 1.19 0.96 0.23 1.24 0.19 0.19 0.50 1.27 0.95 0.32 1.39 0.17 0.27 0.61 1.31 0.95 0.36 1.49 0.15 0.31 0.72 1.29 0.88 0.41 1.50 0.28 0.29 0.83 1.25 0.84 0.41 1.43 0.39 0.25 0.94 1.31 0.81 0.50 1.62 0.38 0.31 1.06* 1.38 0.79 0.59 1.93 0.36 0.38 1.17 1.36 0.77 0.59 1.88 0.39 0.36 1.28 1.29 0.70 0.59 1.70 0.52 0.29 1.39 1.20 0.61 0.59 1.49 0.66 0.20 1.50 1.13 0.54 0.59 1.31 0.78 0.13 1.59 1.16 0.47 0.68 1.49 0.77 0.16 1.65 1.20 0.47 0.73 1.74 0.72 0.20 1.72 1.19 0.42 0.77 1.86 0.75 0.19 1.83 1.22 0.40 0.82 2.20 0.73 0.22 1.94 1.14 0.33 0.82 1.79 0.82 0.14 2.06 1.06 0.24 0.82 1.33 0.93 0.06 2.13 1.06 0.20 0.86 1.47 0.93 0.06 2.18 1.11 0.20 0.91 2.20 0.88 0.11 2.28 1.07 0.16 0.91 1.74 0.93 0.07 2.39 1.08 0.13 0.96 2.80 0.92 0.08 2.50 1.04 0.08 0.96 1.87 0.96 0.04 2.61 1.00 0.04 0.96 0.93 1.00 0.00 2.72 1.03 0.03 1.00 -- 0.97 0.03 2.83 1.01 0.01 1.00 -- 0.99 0.01 3.89 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
73 Table 4
Classification Accuracy Statistics for BAARS-IV Impulsivity Subscale for ADHD vs. No Psychiatric Diagnosis (NPD) Youden's Score Sn+SP Sensitivity Specificity PLR NLR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.13 1.25 0.98 0.27 1.35 0.08 0.25 0.38 1.29 0.97 0.32 1.42 0.10 0.29 0.63 1.27 0.91 0.36 1.42 0.26 0.27 0.88 1.30 0.84 0.46 1.55 0.35 0.30 1.13* 1.32 0.78 0.55 1.71 0.41 0.32 1.38 1.22 0.67 0.55 1.48 0.60 0.22 1.63 1.20 0.52 0.68 1.62 0.71 0.20 1.88 1.24 0.42 0.82 2.31 0.71 0.24 2.13 1.23 0.37 0.86 2.71 0.73 0.23 2.38 1.13 0.26 0.86 1.93 0.85 0.13 2.63 1.04 0.13 0.91 1.39 0.96 0.04 2.88 1.02 0.06 0.96 1.40 0.98 0.02 4.00 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
74 Table 5
Classification Accuracy Statistics for BAARS-IV Hyperactivity Subscale for ADHD vs. No Psychiatric Diagnosis (NPD)
Youden's Score Sn+SP Sensitivity Specificity PLR NPR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.10 1.13 0.99 0.14 1.15 0.08 0.13 0.30 1.25 0.98 0.27 1.35 0.08 0.25 0.45* 1.31 0.95 0.36 1.49 0.15 0.31 0.55 1.30 0.94 0.36 1.47 0.17 0.30 0.70 1.30 0.84 0.46 1.55 0.35 0.30 0.90 1.24 0.74 0.50 1.47 0.53 0.24 1.10 1.25 0.66 0.59 1.62 0.57 0.25 1.30 1.20 0.57 0.64 1.56 0.68 0.20 1.50 1.18 0.50 0.68 1.56 0.74 0.18 1.68 1.07 0.39 0.68 1.22 0.90 0.07 1.78 1.06 0.38 0.68 1.19 0.91 0.06 1.90 1.12 0.31 0.82 1.68 0.85 0.12 2.10 1.17 0.26 0.91 2.89 0.81 0.17 2.30 1.12 0.21 0.91 2.32 0.87 0.12 2.50 1.07 0.12 0.96 2.58 0.93 0.07 2.70 1.00 0.04 0.96 0.93 1.00 0.00 2.90 1.02 0.02 1.00 -- 0.98 0.02 4.00 1.00 0.00 1.00 -- 1.00 0.00 Note. Sn = Sensitivity; Sp = Specificity; *Sn+SP cutoff score; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
75 Table 6
Classification Accuracy Statistics for BAARS-IV Inattention Subscale for ADHD-Combined vs. ADHD-Inattentive
Youden's Score Sn+SP Sensitivity Specificity PLR NPR Index -1.00 1.00 1.00 0.00 1.00 1.00 -- 0.06 1.02 1.00 0.02 0.98 1.02 0.00 0.17 1.04 1.00 0.04 0.96 1.04 0.00 0.28 1.06 0.99 0.08 0.93 1.07 0.15 0.39 1.03 0.96 0.08 0.93 1.04 0.56 0.56 1.02 0.95 0.08 0.93 1.02 0.71 0.72 1.04 0.88 0.15 0.85 1.04 0.77 0.83 1.03 0.84 0.19 0.81 1.04 0.84 0.88 1.05 0.84 0.21 0.79 1.06 0.76 0.94 1.06 0.81 0.25 0.76 1.07 0.77 1.06 1.05 0.79 0.26 0.74 1.07 0.80 1.17 1.09 0.77 0.32 0.68 1.13 0.72 1.28 1.11 0.70 0.42 0.59 1.19 0.74 1.39 1.12 0.61 0.51 0.49 1.24 0.76 1.50 1.07 0.54 0.53 0.47 1.14 0.88 1.61 1.12 0.47 0.64 0.36 1.32 0.82 1.72 1.10 0.42 0.68 0.32 1.31 0.85 1.83* 1.14 0.40 0.74 0.26 1.52 0.82 1.94 1.12 0.33 0.79 0.21 1.57 0.85 2.06 1.13 0.24 0.89 0.11 2.14 0.86 2.17 1.13 0.20 0.93 0.08 2.67 0.87 2.28 1.10 0.16 0.94 0.06 2.77 0.89 2.39 1.07 0.13 0.94 0.06 2.21 0.93 2.47 1.05 0.08 0.96 0.04 2.21 0.95 2.53 1.07 0.08 0.98 0.02 4.42 0.93 2.61 1.02 0.04 0.98 0.02 2.21 0.98 2.72 1.03 0.03 1.00 0.00 -- 0.97 2.83 1.01 0.01 1.00 0.00 -- 0.99 3.89 1.00 0.00 1.00 0.00 -- 1.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
76 Table 7
Classification Accuracy Statistics for BAARS-IV Impulsivity Subscale for ADHD-Combined vs. ADHD-Inattentive
Youden's Score Sn+SP Sensitivity Specificity PLR NPR Index -1.00 1.00 1.00 0.00 1.00 1.00 -- 0.13 1.04 0.98 0.06 0.94 1.04 0.37 0.38 1.04 0.97 0.08 0.93 1.05 0.43 0.63 1.08 0.91 0.17 0.83 1.09 0.56 0.88 1.07 0.84 0.23 0.77 1.09 0.70 1.13 1.12 0.78 0.34 0.66 1.18 0.65 1.38 1.11 0.67 0.43 0.57 1.19 0.75 1.63 1.20 0.52 0.68 0.32 1.61 0.71 1.88 1.20 0.42 0.77 0.23 1.86 0.75 2.13* 1.24 0.37 0.87 0.13 2.79 0.73 2.38 1.21 0.26 0.94 0.06 4.61 0.78 2.63 1.09 0.13 0.96 0.04 3.32 0.91 2.88 1.04 0.06 0.98 0.02 3.32 0.96 4.00 1.00 0.00 1.00 0.00 -- 1.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
77 Table 8
Classification Accuracy Statistics for BAARS-IV Hyperactivity Subscale for ADHD-Combined vs. ADHD-Inattentive
Youden's Score Sn+SP Sensitivity Specificity PLR NPR Index -1.00 1.00 1.00 0.00 1.00 1.00 -- 0.10 1.12 0.99 0.13 0.87 1.14 0.08 0.30 1.17 0.98 0.19 0.81 1.21 0.11 0.45 1.16 0.95 0.21 0.79 1.20 0.26 0.55 1.15 0.94 0.21 0.79 1.18 0.30 0.70 1.13 0.84 0.28 0.72 1.17 0.56 0.90 1.13 0.74 0.40 0.60 1.22 0.664 1.10 1.10 0.66 0.43 0.57 1.17 0.78 1.30 1.12 0.57 0.55 0.45 1.25 0.79 1.45 1.10 0.50 0.60 0.40 1.25 0.84 1.55 1.12 0.50 0.62 0.38 1.31 0.81 1.68* 1.18 0.39 0.79 0.21 1.87 0.77 1.78 1.17 0.38 0.79 0.21 1.82 0.78 1.90 1.17 0.31 0.87 0.13 2.31 0.80 2.10 1.17 0.26 0.91 0.09 2.80 0.81 2.30 1.17 0.21 0.96 0.04 5.55 0.82 2.50 1.10 0.12 0.98 0.02 6.11 0.90 2.70 1.02 0.04 0.98 0.02 2.21 0.98 2.90 1.02 0.02 1.00 0.00 -- 0.98 4.00 1.00 0.00 1.00 0.00 -- 1.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
78 Table 9
Classification Accuracy Statistics for BAARS-IV Inattention Subscale for ADHD vs. Mood and Anxiety Disorder (MAD)
Youden’s Score Sn+Sp Sn Sp PLR NLR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.06 1.02 0.98 0.04 1.02 0.46 0.02 0.17 1.00 0.96 0.04 1.00 0.93 0.00 0.28 1.01 0.93 0.08 1.01 0.91 0.01 0.39 1.05 0.93 0.12 1.05 0.61 0.05 0.50 1.09 0.93 0.16 1.11 0.46 0.09 0.61 1.13 0.93 0.20 1.16 0.37 0.13 0.72 1.18 0.85 0.33 1.26 0.46 0.18 0.83 1.16 0.81 0.35 1.24 0.54 0.16 0.88 1.14 0.79 0.35 1.21 0.60 0.14 0.94 1.12 0.76 0.37 1.19 0.67 0.12 1.06 1.14 0.74 0.41 1.24 0.65 0.14 1.17* 1.23 0.68 0.55 1.51 0.58 0.23 1.28 1.18 0.59 0.59 1.43 0.70 0.18 1.39 1.19 0.49 0.69 1.60 0.73 0.19 1.50* 1.23 0.47 0.76 1.93 0.70 0.23 1.61 1.15 0.36 0.80 1.75 0.81 0.15 1.72 1.14 0.32 0.82 1.74 0.83 0.14 1.83 1.08 0.26 0.82 1.43 0.90 0.08 1.94 1.05 0.21 0.84 1.28 0.95 0.04 2.06 1.05 0.11 0.94 1.85 0.94 0.05 2.17 1.03 0.08 0.96 1.83 0.96 0.03 2.33 1.04 0.06 0.98 2.85 0.96 0.04 2.47 1.04 0.04 1.00 -- 0.96 0.04 2.58 1.02 0.02 1.00 -- 0.98 0.02 3.67 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
79 Table 10
Classification Accuracy Statistics for BAARS-IV Impulsivity Subscale for ADHD vs. Mood and Anxiety Disorder (MAD)
Youden’s Score Sn+Sp Sn Sp PLR NLR Index -1.00 1.00 1.00 0.00 1.00 1.00 -- 0.13 1.04 0.98 0.06 0.94 1.04 0.34 0.38 1.17 0.97 0.20 0.80 1.22 0.16 0.63 1.21 0.91 0.31 0.69 1.30 0.31 0.88 1.27 0.84 0.43 0.57 1.47 0.37 1.13 1.31 0.78 0.53 0.47 1.66 0.42 1.38* 1.33 0.67 0.65 0.35 1.94 0.50 1.63 1.27 0.52 0.76 0.25 2.11 0.64 1.88 1.30 0.42 0.88 0.12 3.45 0.66 2.13 1.29 0.37 0.92 0.08 4.49 0.69 2.38 1.20 0.26 0.94 0.06 4.31 0.78 2.63 1.11 0.13 0.98 0.02 6.30 0.89 2.88 1.06 0.06 1.00 0.00 -- 0.94 4.00 1.00 0.00 1.00 0.00 -- 1.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
80 Table 11
Classification Accuracy Statistics for BAARS-IV Hyperactivity Subscale for ADHD vs. Mood and Anxiety Disorder (MAD)
Youden’s Score Sn+Sp Sn Sp PLR NLR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.10 0.91 0.87 0.04 0.91 3.22 -0.09 0.30 0.97 0.81 0.16 0.97 1.16 -0.03 0.50 1.06 0.79 0.27 1.08 0.78 0.06 0.70 1.11 0.72 0.39 1.17 0.73 0.11 0.90 1.05 0.60 0.45 1.10 0.88 0.05 1.10 1.14 0.57 0.57 1.32 0.76 0.14 1.30 1.15 0.45 0.69 1.48 0.79 0.15 1.45* 1.19 0.40 0.80 1.94 0.76 0.19 1.55 1.17 0.38 0.80 1.85 0.78 0.17 1.70 1.09 0.21 0.88 1.70 0.90 0.09 1.90 1.03 0.13 0.90 1.29 0.97 0.03 2.10 0.99 0.09 0.90 0.92 1.01 -0.01 2.30 0.98 0.04 0.94 0.62 1.02 -0.02 2.50 0.96 0.02 0.94 0.31 1.04 -0.04 2.70 1.00 0.02 0.98 0.95 1.00 0.00 3.80 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
81 Table 12
Classification Accuracy Statistics for BAARS-IV Inattention Subscale for ADHD vs. Specific Learning Disorder (SLD)
Youden’s Score Sn+Sp Sn Sp PLR NLR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.06 1.06 0.99 0.06 1.06 0.09 0.06 0.17 1.19 0.99 0.21 1.24 0.06 0.19 0.28 1.21 0.97 0.24 1.28 0.13 0.21 0.39 1.27 0.95 0.32 1.39 0.16 0.27 0.50 1.32 0.94 0.37 1.50 0.16 0.32 0.59 1.41 0.94 0.47 1.77 0.12 0.41 0.65 1.42 0.94 0.48 1.80 0.12 0.42 0.71 1.42 0.88 0.54 1.92 0.23 0.42 0.76 1.43 0.88 0.55 1.96 0.22 0.43 0.83 1.50 0.84 0.66 2.50 0.24 0.50 0.88 1.50 0.83 0.66 2.48 0.25 0.50 0.94 1.49 0.80 0.69 2.60 0.29 0.49 1.06 1.50 0.78 0.72 2.79 0.30 0.50 1.14 1.54 0.74 0.79 3.61 0.32 0.54 1.19* 1.55 0.74 0.80 3.80 0.32 0.55 1.28 1.48 0.66 0.82 3.71 0.41 0.48 1.39 1.41 0.58 0.83 3.43 0.51 0.41 1.50 1.38 0.52 0.86 3.71 0.56 0.38 1.59 1.31 0.43 0.88 3.55 0.65 0.31 1.65 1.30 0.42 0.88 3.50 0.66 0.30 1.72 1.27 0.38 0.89 3.38 0.70 0.27 1.83 1.27 0.35 0.93 4.61 0.71 0.27 1.94 1.21 0.28 0.93 3.76 0.78 0.21 2.06 1.14 0.20 0.94 3.55 0.85 0.14 2.17 1.11 0.15 0.96 3.97 0.89 0.11 2.28 1.11 0.12 0.99 12.78 0.89 0.11 2.39 1.10 0.10 1.00 -- 0.90 0.10 2.47 1.06 0.06 1.00 -- 0.94 0.06 2.53 1.06 0.06 1.00 -- 0.94 0.06 2.61 1.03 0.03 1.00 -- 0.97 0.03 2.72 1.02 0.02 1.00 -- 0.98 0.02 2.83 1.01 0.01 1.00 -- 0.99 0.01 3.89 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
82 Table 13
Classification Accuracy Statistics for BAARS-IV Impulsivity Subscale for ADHD vs. Specific Learning Disorder (SLD)
Youden’s Score Sn+Sp Sn Sp PLR NLR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.13 1.20 0.97 0.23 1.26 0.14 0.20 0.38 1.34 0.96 0.38 1.55 0.12 0.34 0.58 1.37 0.89 0.49 1.72 0.24 0.37 0.71 1.38 0.89 0.50 1.75 0.23 0.38 0.88 1.41 0.83 0.58 1.96 0.30 0.41 1.13* 1.42 0.74 0.67 2.28 0.38 0.42 1.38* 1.42 0.63 0.79 3.05 0.47 0.42 1.63 1.33 0.45 0.88 3.71 0.63 0.33 1.88 1.28 0.35 0.94 5.32 0.70 0.28 2.13 1.25 0.28 0.97 10.07 0.74 0.25 2.38 1.17 0.19 0.98 9.79 0.83 0.17 2.63 1.07 0.09 0.98 4.74 0.93 0.07 2.88 1.04 0.05 0.99 5.00 0.96 0.04 4.00 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
83 Table 14
Classification Accuracy Statistics for BAARS-IV Hyperactivity Subscale for ADHD vs. Specific Learning Disorder (SLD)
Youden’s Score Sn+Sp Sn Sp PLR NLR Index -1.00 1.00 1.00 0.00 1.00 -- 0.00 0.10 1.19 0.95 0.24 1.25 0.21 0.19 0.30 1.35 0.92 0.43 1.62 0.18 0.35 0.45 1.41 0.90 0.51 1.85 0.20 0.41 0.55 1.41 0.89 0.51 1.83 0.21 0.41 0.70* 1.43 0.81 0.63 2.16 0.31 0.43 0.90 1.40 0.70 0.70 2.34 0.43 0.40 1.10 1.40 0.64 0.76 2.64 0.47 0.40 1.30 1.39 0.53 0.86 3.80 0.54 0.39 1.45 1.33 0.46 0.88 3.76 0.62 0.33 1.55 1.33 0.45 0.88 3.71 0.63 0.33 1.68 1.22 0.33 0.90 3.17 0.75 0.22 1.78 1.22 0.32 0.90 3.12 0.76 0.22 1.90 1.19 0.24 0.94 4.36 0.80 0.19 2.10 1.16 0.20 0.96 5.38 0.83 0.16 2.30 1.14 0.15 0.99 16.33 0.86 0.14 2.50 1.08 0.08 1.00 -- 0.92 0.08 2.70 1.03 0.03 1.00 -- 0.97 0.03 2.90 1.01 0.01 1.00 -- 0.99 0.01 4.00 1.00 0.00 1.00 -- 1.00 0.00 Note. *Sn+SP cutoff score; Sn = Sensitivity; Sp = Specificity; PLR= Positive Likelihood Ratio; NLR=Negative Likelihood Ratio.
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103 Curriculum Vitae
Office Address
Psychology Department University of Nevada, Las Vegas 4505 S. Maryland Parkway, MS 5030 Las Vegas, NV 89154 Email: [email protected]
Education Ph.D. in Clinical Psychology, January 2021 University of Nevada, Las Vegas, Las Vegas, NV GPA: 3.87/4.00
M.A., Clinical Psychology, December 2017 University of Nevada, Las Vegas, Las Vegas, NV GPA: 3.87/4.00 B.S., Psychology, Magna Cum Laude, December 2011
Virginia Polytechnic Institute and State University, Blacksburg, VA Honors Scholar Diploma GPA: 3.65/4.00
Clinical Postdoctoral Fellow in Group Psychotherapy, PRACTICE Clinic at UNLV, Experience August 2020 - Current • Participated as the lead facilitator in 6 co-facilitated psychotherapy groups weekly via telehealth platform to adult clients with a range of mental illnesses including mood disorders and personality disorders • Assisted in the development and implementation of a group therapy program at a community-based mental health clinic • Provided daily supervision to clinical trainees regarding group psychotherapy processes and CBT, DBT and ACT interventions and concepts • Provided supervision and consultation regarding risk management of adult clients, ethical and legal considerations, and general case management of adult group clients • Provided weekly integrative individual psychotherapy sessions to adult clients with a range of mental illnesses • Assisted in the development and co-facilitation of a skill-based psychotherapy group for adults with severe mental illness • Participated as a co-facilitator for an adult interpersonal process group • Provided crisis intervention to high-risk clients, including initial and
104 ongoing assessment of the nature and severity of the client’s suicidality and safety-planning in session • Conducted intakes for new group psychotherapy clients as well as justifying and discussing diagnosis and treatment planning from integrative therapeutic approach with clinical supervisors • Attended weekly case round meetings with an interdisciplinary treatment team to consult and provide diagnostic considerations and treatment recommendations • Responsible for managing billing, scheduling and administrative tasks at the clinic
Sharp Mesa Vista Psychiatric Hospital, August 2018- November 2019 • Developed efficient but purposeful case conceptualizations of the patients through chart review and staff consultation on all newly admitted patients. Identified bio-psycho-social factors that impact the patient’s functioning to individualize treatment planning and clinical goals • Assessed level of risk for patients on an on-going basis, with more formal assessments and treatment implemented in high-risk patients to protect the individual, other patients and staff • Facilitated daily clinical skills and process groups on locked adult and geriatric inpatient units to support crisis management and promote adaptive coping for various mental and physical pathologies • Conducted brief individual psychotherapy and informal psychology consults, based upon clinical need • Engaged in behavioral huddles with staff focusing on recommendations for working with different presenting concerns • Consistently implemented and improved awareness around safety practices (working with individuals who are danger to self, others, active AWOL risks) on psychiatric inpatient units • Conducted psychological testing based upon clinical need and psychiatric order with testing feedback provided to treatment team and patient within 1 week • Attended weekly multidisciplinary treatment team meetings and actively participated in treatment planning and providing clinical insights regarding patients • Consulted collaboratively with multidisciplinary treatment team in an ongoing manner with regular consultation with the attending psychiatrist about individual therapy progress and treatment recommendations • Completed all group, individual, and assessment clinical documentation by the end of day, including relevant information about consultation with treatment team and progress towards treatment goals • Utilized literature related to specific clinical presentations to enhance and guide interventions with supervisor support • Utilized review of current literature to support best practices and support
105 of the interdisciplinary staff and milieu environment • Presented quality improvement projects with the aim to improve the experience of patients and staff on adult inpatient units, with specific focus on clinical treatment for patients with traumatic brain injury and use of person-first language in a clinical setting • Presented 3 formal clinical case presentations to psychology department demonstrating the ability to refer to professional literature and evidence- based practices to inform case conceptualization and treatment planning • Exhibited sensitivity to all diversity issues including (but not limited to) chronological age and age cohort, race, ethnicity, gender, gender identity, sexuality, socioeconomic status, etc. during treatment, treatment planning, and development of clinical conceptualizations • Regularly engaged in review of clinical literature to inform case conceptualization, assessment, and intervention • Provided weekly supervision and ongoing consultation with psychology practicum students to assist in the development of clinical and case conceptualization skills • Actively participated in weekly individual supervision meeting with rotation supervisor and internship training director for a minimum of 2 hours • Participated actively in monthly Diversity Seminar and applied learnings to this rotation. • Participated actively in weekly group supervision meeting with training director and applied learnings to current clinical rotations • Assisted in hospital-wide and unit-specific program development through engagement on steering committees with faculty and medical director
SNAMHS Rawson-Neal Psychiatric Hospital and Muri Stein Forensic Hospital, August 2017- July 2018 • Developed and implemented a Dialectical-Behavior Therapy skill-based group psychotherapy curriculum for a population of adults with severe mental illness in a civil and forensic inpatient setting • Co-facilitated a Liebermann-module community-reentry psychotherapy group for acutely psychotic adults in a forensic inpatient setting • Provided brief, skill-based individual therapy with adults in a community and forensic inpatient setting • Conducted neuropsychological, psychodiagnostic and forensic competency assessments with adults with cognitive and neurodevelopmental disorders, under the supervision of a licensed neuropsychologist • Assessed the nature and severity of suicidality in high-risk patients and provided crisis intervention through individual therapy and psychodiagnostic assessment to assist in diagnosis and treatment • Conducted intakes with recently admitted patients, including justifying
106 and discussing diagnosis and treatment planning from integrative therapeutic approach with clinical supervisor • Attended weekly interdisciplinary treatment team meetings to consult and provide diagnostic considerations and treatment recommendations
Graduate Assistant and Student Clinician, PRACTICE Clinic at UNLV, August 2016-May 2018 • Provided weekly integrative psychotherapy sessions to adult clients with a range of mental illnesses • Assisted in the development and co-facilitation of a skill-based psychotherapy group for adults with chronic illness • Participated as a process observer and co-facilitator for an adult interpersonal process group • Co-facilitated a Cognitive Behavior Therapy skill-based group for adults coping with depression and anxiety • Participated in supervision of group therapy program and assisted in the development and implementation of a group therapy program at a community-based mental health clinic • Provided crisis intervention to high-risk clients, including initial and ongoing assessment of the nature and severity of the client’s suicidality and safety-planning in session • Conducted intakes for new clients as well as justifying and discussing diagnosis and treatment planning from integrative therapeutic approach with clinical supervisors • Attended weekly case round meetings with an interdisciplinary treatment team to consult and provide diagnostic considerations and treatment recommendations • Presented detailed case-presentations to other clinicians to assist in treatment planning • Responsible for managing billing, scheduling and administrative tasks at the clinic
Neuropsychological Assessment Practicum Clinician, Center for Applied Neuroscience, Las Vegas, NV, August 2016- May 2017 • Conducted comprehensive neuropsychological evaluations twice weekly with adults and children with cognitive and developmental disorders, under the supervision of a licensed neuropsychologist. • Wrote neuropsychological reports weekly with diagnostic information and treatment recommendations • Participated in supervision with didactic training on various psychological and cognitive disorders
Student Clinician, Sandstone Private Practice, Henderson, NV, August
107 2015- August 2016 • Provided weekly integrative psychotherapy sessions to adolescent and adult clients with severe depression, anxiety and related mental health conditions • Co-facilitated weekly interpersonal process group therapy for adolescent girls with social anxiety • Specialized in psychodynamic and interpersonal therapeutic interventions while also integrating other cognitive and behavioral interventions as needed for each client • Discussed diagnosis, treatment planning and case conceptualization from psychodynamic therapeutic approach with clinical supervisor during weekly supervision sessions • Specialized in providing psychotherapy for grief from a psychodynamic and interpersonal approach • Provided crisis intervention to high-risk clients, including assessing the nature and severity of the client’s suicidality and providing safety- planning in session • Conducted psychodiagnostic testing to adolescent and adult clients with learning and personality disorders, including scoring assessments, writing psychodiagnostic reports and providing feedback to clients • Conducted intake interviews for new testing and therapy clients to assess appropriate level of treatment and provide provisional diagnosis • Participated in weekly group supervision and case consultation meetings with other licensed and student clinicians
Student Clinician, PRACTICE Clinic at UNLV, August 2014- August 2015 • Provided weekly integrative psychotherapy sessions to adult clients with a range of mental illnesses (5-7 client caseload) • Provided crisis intervention to high-risk clients, including assessing the nature and severity of the client’s suicidality and safety-planning in session • Conducted intakes for new clients as well as justifying and discussing diagnosis and treatment planning from integrative therapeutic approach with clinical supervisor • Conducted psychodiagnostic testing to clients with various mental health concerns, including writing integrative psychodiagnostic reports and providing feedback to clients, under the supervision of a licensed psychologist • Conducted psychotherapy sessions through tele-communication services to provide mental health care for underserviced populations
Administrative Assistant, Family First Psychological Services, September
108 2012- April 2013 • Assisted in various administrative tasks for the practice including organizing and managing client invoices, payments and accounts • Scored psycho-educational assessment measures • Attended weekly peer consultation meetings for therapy cases
Adult Day Services Recreational Therapy Assessment, September 2011- November 2011 • Wrote progress notes for interviewed participants under supervision of therapist • Actively observed and assisted with group activities in Adult Day Services • Independently administered geriatric recreational therapy assessments to • Administered and developed an understanding of the MMSE, GDS, LSS, MARCC • Interacted with dementia patients on a weekly basis in group and individual settings
Caregiver Support Group Meeting, October 2011 – November 2011 • Student organized support group meeting for caregivers of elderly with dementia • Assisted in planning of activities and helped distribute marketing materials • Assisted in activities and care of elderly dementia patients during meeting
Research UNLV Neuropsychology Graduate Research Assistant, January 2015- Experience January 2021 • Administer neuropsychological assessments and assist in participant recruitment, screening and report writing with children and adolescents diagnosed with ADHD • Attend weekly lab meetings and contribute to discussion about current and future research projects • Combine large archival data sets in SPSS and Excel for data analysis • Assist in data entry of APA self-study material for accreditation visit
UNLV Doctoral Graduate Research Assistant, August 2014- December 2014 • Project coordinator for a qualitative interview project with Latino caregivers that received services from a local hospice center • Responsible for creating and distributing interview materials as well as delegating various research tasks to undergraduate research assistants • Coordinated financial compensation for participants • Tracked progress of interviews and collection of interview materials UNLV Stressful Transitions and Aging Lab Research Assistant, August
109 2013-June 2014 • Attend weekly lab meetings and share responsibility of leading meeting discussion about current and future research projects • Primary investigator of a pilot study assessing the utility of an electronic problem-solving therapy computer program developed for older adults • Actively involved in writing sections for research manuscripts with other lab members • Assist in managing lab resources and organization of lab materials
Research Assistant for Dr. Shannon Jarrott, December 2011- June 2012 • Assisted in various administrative tasks for research projects evaluating effectiveness of intergenerational education • Performed data analysis on research data and present in an organized report • Drafted activity guides for intergenerational activity instructors
Independent Research Contractor, Generations United, December 2011- March 2012 • Responsible for online survey design using Qualtrics Survey software • Conducted background research on childcare accreditation standards and intergenerational programming • Analyzed data using statistical methodology and SPSS software • Responsible for the design, production and distribution of survey materials • Prepared statistical reports for the team to present in evaluation
Undergraduate Honors Research with Human Development Dept., August 2011- December 2011 • Research attitudes on aging in college students and semantic age priming effects • Primary researcher responsible for developing research question and project design • Weekly research discussion meetings including discussion of literature, project design • Gained experience with the IRB process and helped edit IRB amendments • Responsible for data collection and analysis using SPSS • Research and findings presented at Quint State Research Conference, February 2012
Virginia Tech Cognition, Emotional and Self-Regulation Lab Research
110 Assistant, September 2010- August 2011 • Assisted in grant-funded research project on psychopathology in college students • Administered several common cognitive lab tasks and clinical diagnostic interviews • Administered galvanic skin response and electrocardiogram tests • Participated in weekly lab meetings to review current literature on psychopathy and Antisocial Personality Disorder as well as discuss research design and progress of research • Gained experience with SONA experiment system, E-Prime, and M.I.N.I • Attended lectures and presented research poster at APS 2011 psychology conference
Banner Sun Health Research Institute: Summer Intern, June 2010- August 2010 • Longevity research program in Arizona aimed at understanding the psychosocial, cognitive, medical and physical factors contributing to healthy aging • Completed 33 direct interviews and brief cognitive status assessments with 50-102 year olds • Developed skills for administration and scoring of psychometric tests • Responsible for scheduling, phone screening and completing in-person interviews with participants • Transcribed script of focus group on geriatric life satisfaction and intervention methods • Attended geriatric education lecture series 2-3 hours a week • Assisted in teaching 5-week memory improvement course for geriatric population • Shadowed geriatric medical physicians during medical visits with dementia patients • Observed psychometric testing and group counseling sessions • Observed a brain autopsy of healthy elderly person • Presented research in student symposium to professionals from the gerontology field
Virginia Tech Behavioral Neuroscience Lab Research Assistant, October 2009- May 2010 • Assisted in grant-funded research project entitled “Risk of Fall in the Elderly: A Neuropsychological Approach to Vestibular Function” • Administered questionnaires and neuropsychology tests of executive functions • Assisted in data collection and analysis of quantitative EEG
Department of Defense Education Activity, Research and Evaluation
111 Intern, June 2009- August 2009 • Coded and analyzed data from school evaluation surveys from schools internationally • Researched background information for various grant-funded research proposals on the prevalence of bullying in DoDEA international schools • Attended and participated in DoDEA executive director’s meetings
Teaching Introductory Psychology Part-time Instructor, Fall 2015- Spring 2016 Experience • Design introductory psychology course for two class sections • Present lectures twice weekly based on textbook and required course material • Design, administer and grade examinations and written assignments to assess understanding of students’ understanding of course material • Attend bi-weekly teaching course with other introductory psychology teachers to provide supplemental material and discuss course concepts
Graduate Teaching Assistant, August 2013- December 2013 • Assist professor with grading and lecture preparation • Attend Psychology of Aging class lectures weekly • Host office hours to assist students with course material
Student Athlete Academic Support Services, August 2010- December 2011 • Paid tutor position working with student athletes • Responsible for supplementary instruction in psychology and related courses • Help student athletes with time management, study skills and note- taking skills • Responsible for scheduling and evaluating tutor sessions with student- athletes
Articles in Graves, S. J., Freeman, A. J., Paul, M. G., Etcoff, L. M., Allen, D.N. (2017). Preparation Improving accuracy of ADHD-inattentive diagnoses with symptom rating scales. Manuscript in preparation.
Published Parke, E. M., Becker, M. L., Graves, S. J., Mayfield, A. R., Paul, M. G., Works Freeman, A. J., Allen, D. N. (2017). Social cognition in children with attention-deficit/hyperactivity disorder. Manuscript in preparation.
Holland, J. M., Graves, S. J., Klingspon, K. L., & Rozalski, V. (2016). Prolonged grief symptoms related to loss of physical functioning: Examining unique associations with medical service utilization. Disability and Rehabilitation: An International,
112 Multidisciplinary Journal, 38(3), 205-210. DOI:10.3109/09638288.2015.1031830
Graves, S. J.; Embler, S. & Watkins, S. (2010). Status Report: Implementation of DoDEA Graduation Requirements [White paper].
Conference Graves, S. J., Parke, E. M., Mayfield, A. R., Call, E. T., & Allen, D. N. (2017, Presentations October). Social Cognitive Deficits in Children with Attention Deficit Hyperactivity Disorder. Poster session presented at the Annual Meeting of the National Academy of Neuropsychology, Boston, MA.
Graves, S. J., Parke, E. M., Etcoff, L. M., & Allen, D. N. (2017, April). The relationship between ADHD symptomatology and BASC-2 ratings. Poster session presented at the American Academy of Pediatric Neuropsychology, Las Vegas, NV.
Graves, S. J., Parke, E. M., Etcoff, L. M., San Miguel, L., & Allen, D. N. (2016, October). The Relationship between the Woodcock-Johnson-III and the Batteria-III in Children with ADHD and Learning Disorders. Poster session presented at the Annual Meeting of the National Academy of Neuropsychology, Seattle, WA.
Graves, S. J., Parke, E. M., Etcoff, L. M., & Allen, D. N. (2015, November). The relationship between ADHD symptomatology and BASC-2 parent ratings. Poster session presented at the Annual Meeting of the National Academy of Neuropsychology, Austin, TX.
Holland, J.M., Graves, S.J., Thompson Kara L., Rozalski, V. (2014, February). Prolonged Grief Symptoms Related to Loss of Physical Functioning: Examining Unique Associations with Medical Service Utilization. Lecture presentation at American Association of Behavioral and Social Sciences Conference, Las Vegas, NV.
Graves, S.J., Burns, M., & Jarrott, S.E. (2012, February). The effects of age priming on images of future older selves. Poster session presented at the Southeastern Symposium on Child and Family Development, Blacksburg, VA.
Comer, C. S., Carmona, J. E., Oladosu, F., Golden, L. L., Grim, T. W., Graves, S. J., Rayher, D. A., & Harrison, D. W. (2010). Performance on an Extended Written Verbal Fluency Test as a Predictor of Trait Anxiety. Poster session presented at the Spring Conference of the Virginia Psychological Association, Norfolk, VA.
113 Clinical • Ethics and Risk Management in the Digital World Workshop with Dr. Training and Daniel Taube, May 2018 Workshops • Prolonged Exposure for PTSD Workshop with Dr. Thomas Mullin, February 2017 • Transitioning to ICD-10-CM Workshop with Dr. Greg Neimeyer, February 2016 • Dialectical Behavior Therapy Comprehensive 10-Day Training with Dr. Alan Fruzzetti, February 2015 - September 2016
Honors and • UNLV Summer Research Scholarship, $2000, May 2018 Awards • UNLV Access Grant, $2000, August 2017 • GPSA Travel Award, February 2016, October 2016 & October 2017 • GPSA Outstanding Poster Award, Honorable Mention, March 2016 • Mary Nolen Blackwood Endowed Scholarship in Psychology, $1000, August 2010 – May 2011 • Virginia Tech Honors Program, August 2009 – May 2011 • Virginia Tech Dean’s List, August 2008- December 2011
Advocacy • Nevada Psychological Association, UNLV Campus Representative, Positions May 2016-May 2018 • Outreach Undergraduate Mentoring Program, Student Mentor, August 2016- May 2018 • Nevada Student Delegate at State Leadership Conference, Washington D.C., February 2016
Professional • American Group Psychotherapy Association, Member Affiliations • American Psychological Association, Member • Society of Clinical Psychology, Division 12 of APA, Member • Nevada Psychological Association, Former Student Member
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