ADHD, personal and interpersonal agency, and achievement: Exploring links from a social cognitive theory perspective

Accepted Manuscript ADHD, Personal and Interpersonal Agency, and Achievement: Exploring Links from a Social Cognitive Theory Perspective Andrew J. Martin, Emma C. Burns, Rebecca J. Collie PII: DOI: Reference:

S0361-476X(16)30061-3 http://dx.doi.org/10.1016/j.cedpsych.2016.12.001 YCEPS 1587

To appear in:

Contemporary Educational Psychology

Received Date: Revised Date: Accepted Date:

15 May 2016 17 November 2016 10 December 2016

Please cite this article as: Martin, A.J., Burns, E.C., Collie, R.J., ADHD, Personal and Interpersonal Agency, and Achievement: Exploring Links from a Social Cognitive Theory Perspective, Contemporary Educational Psychology (2016), doi: http://dx.doi.org/10.1016/j.cedpsych.2016.12.001

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SUBMISSION FOR SPECIAL ISSUE: AT-RISK STUDENTS, MOTIVATION, LEARNING

ADHD, Personal and Interpersonal Agency, and Achievement: Exploring Links from a Social Cognitive Theory Perspective

Andrew J. Martin, Emma C. Burns, Rebecca J. Collie School of Education, University of New South Wales, Australia

November 2016

Requests for further information about this investigation can be made to Professor Andrew J. Martin, School of Education, University of New South Wales, NSW 2052, AUSTRALIA. E-Mail: [email protected]. Phone: +61 2 9385 1952. Fax: +61 2 9385 1946.

Thanks are extended to Dr Marianne Mansour for data collection, the Australian Research Council (Grant # DP140104294) for funding, and participating schools and students.

Abstract Harnessing social cognitive theory (SCT), we investigated the roles of personal agency (selfefficacy and perceived control) and interpersonal agency (relational support) in the academic achievement (via literacy and numeracy testing) of students with attention-deficit/hyperactivity disorder (ADHD) and their non-ADHD peers. A sample of N=164 students diagnosed with ADHD were investigated alongside N=4658 non-ADHD peers in the same schools and year levels. Using structural equation modeling, findings showed that self-efficacy and relational support were

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consistently associated with better academic achievement for both groups, but with positive effects significantly stronger for students with ADHD than for students without ADHD. Although perceived control was significantly associated with achievement for students without ADHD and not significantly so for students with ADHD, there was not much difference in absolute size of perceived control effects for the two groups. Findings are relevant to theory, research, and practice identifying motivational factors and processes that may assist in closing well-known achievement gaps for students with ADHD whilst also maintaining positive outcomes for students without ADHD.

Keywords: attention-deficit/hyperactivity disorder; ADHD; social cognitive theory; self-efficacy; control; interpersonal relationships; achievement

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ADHD, Personal and Interpersonal Agency, and Achievement: Exploring Links from a Social Cognitive Theory Perspective

1. Introduction Students with attention-deficit/hyperactivity disorder (ADHD)1 face numerous academic challenges, often leading to problematic academic outcomes (Barkley, 2006). As an executive function disorder (i.e., affecting cognitive skills required for planning, organization, task initiation, working memory, etc.), students with ADHD have difficulties with many of the core skills essential to accomplishing academic tasks and meeting academic demands (Pennington & Ozonoff, 1996). Thus, over the course of their academic lives, students with ADHD are more likely to experience low academic achievement, reduced engagement, and decreased motivation as compared to their typical peers (Barkley, 2006). While numerous researchers (for review, see Burns & Martin, 2014) have examined clinical interventions that manage these executive function issues to enhance student outcomes, relatively few researchers have addressed psycho-educational factors and processes that promote and sustain positive academic outcomes for students with ADHD. Social cognitive theory (SCT; Bandura, 1986, 1991, 2001) identifies such factors and processes that are important for students’ academic functioning. SCT posits that personal agency and interpersonal agency have significant implications for individuals’ motivation, engagement, and achievement (Bandura, 1986, 1991, 2001). In this study, we investigate SCT and its factors as relevant to the academic success of students with ADHD. Specifically, we engaged SCT to examine the extent to which personal agency (operationalized as self-efficacy and perceived control) and interpersonal (or, contextual) agency (operationalized as relational support by teacher) are associated with the academic achievement (operationalized via a literacy and numeracy test) of students with ADHD who reside in the “mainstream”2 classroom. 2. Attention-Deficit/Hyperactivity Disorder (ADHD)

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In the DSM-5, ADHD is defined as “a persistent pattern of inattention and/or hyperactivityimpulsivity that interferes with functioning or development” (American Psychiatric Association, 2013, p. 59). Approximately 3–5% of children are diagnosed with ADHD, with a 3:1 male to female ratio (Purdie, Hattie, & Carroll, 2002). While this percentage is higher in primary school, approximately 50–70% of cases persist into adolescence (Barkley, 2006; Purdie et al., 2002). Major psychological models of ADHD emphasize impairments with self-regulation (e.g., selfmanagement) and executive functioning (Barkley, 2006). In relation to executive functioning, for example, research has suggested impairments with four executive neuropsychological abilities: working memory (holding information in mind, forethought, sense of time); internalization of speech (reflecting on behavior, self-questioning, self-instruction); self-regulation of affect, motivation and arousal (self-control, perspective taking, goal-directed action); and reconstitution (accurate and efficient communication of information) (Barkley, 2006). These impairments lead to difficulties with motor control, task-irrelevant responses, inability to execute goal-directed behavior, and problems re-engaging in tasks after being disrupted (Barkley, 2006; Pennington & Ozonoff, 1996; Purdie et al., 2002). The academic difficulties that students with ADHD experience are well documented (see Barkley, 2006). Many of the executive functions impaired by ADHD are essential for successfully navigating one’s academic life (Pennington & Ozonoff, 1996). As a result of these impairments, students with ADHD are often considered to be an academically at-risk population (Burns & Martin, 2014). In line with this, students with ADHD face increased risk of grade retention, lower academic achievement, and exhibit high levels of disruptive classroom behavior (Barkley, 2006; Biederman, Monuteaux, Doyle, Seidman, Wilens, Ferrero, Morgan, & Faraone, 2004; Martin, 2014a). Identifying factors and processes that may mitigate these risks and assist the achievement of students with ADHD is thus of critical importance. From an SCT perspective, we explored the roles of self-efficacy, perceived control, and relational support. 3. Social Cognitive Theory

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According to SCT, there are personal factors and interpersonal factors that contribute to individuals’ agentic thought, behavior, and emotion (Bandura, 1986, 1991, 2001). Two major constructs relevant to personal agency are self-efficacy and control (Bandura, 2001; Smith et al., 2000). In terms of academic outcomes, relational support from the teacher is a key element of interpersonal agency (Wentzel, 2010). 3.1 Key Elements of Personal and Interpersonal Agency Self-efficacy refers to an individual’s belief in their capacity to accomplish a given task (Bandura, 2001; Law, Elliot, & Murayama, 2012; Schunk & Miller, 2002). In academic endeavors, self-efficacy has a significant impact on the tasks students choose to undertake, including those that are central to their academic achievement, and the self-regulatory functions required to execute those tasks. In regard to perceived control, the construct is founded on the premise that humans are fundamentally motivated to control their own actions and their environment (Bandura, 2001). Control also encompasses individuals’ willingness and capacity to direct resources to courses of action that enhance and maintain their capacity for control (Schindler & Tomasik, 2010). Many control constructs have been posited (Skinner, 1996). As relevant to human agency, our approach is one reflecting a student’s perception that they know how to influence success and failure outcomes in their academic life (see also Bandura & Wood, 1989; Connell, 1985; Martin, 2007). In regard to interpersonal factors supporting human agency, a student’s sense of relatedness with others arises when they care for and accept others, and feel cared for and accepted by others (Deci & Ryan, 2000). Positive interpersonal relationships (including with teachers) are seen as a buffer against stress and risk, important for help on (academic) tasks, a source of emotional support in daily life, an energizing function, and a basis for social-emotional development and selffulfillment (Furrer & Skinner, 2003; Pianta, Hamre, & Allen, 2012) that each leads to positive behavioral and emotional responses. The present study centered on the role of relational support by the teacher (for the roles of parents/caregivers and peers, see Bempechat & Shernoff, 2012; Furrer & Skinner, 2003; Juvonen, Espinoza, & Knifsend, 2012; Pomerantz & Moorman, 2010).

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3.2 Effects of Personal and Interpersonal Agency on Academic Outcomes Personal agency and interpersonal agency significantly impact individuals’ capacity to attain desired outcomes (Bandura, 1991, 2001; Smith et al., 2000). With respect to self-efficacy, it has been found that students with higher self-efficacy experience greater engagement and achievement (Jacobs, Lanza, Osgood, Eccles, & Wigfield, 2002). Other work has also shown self-efficacy to be highly correlated with and uniquely predictive of achievement (for review, see Zimmerman, 2000). Similarly, students with a heightened sense of self-efficacy are more likely to embrace challenging tasks (Zimmerman, 2000). With respect to perceived control, Findley and Cooper (1983) found that students who experienced a greater sense of control were more likely to achieve better results than students who saw their academic outcomes in terms of external factors (i.e., not themselves) beyond their control. Similarly, in a review of motivation, Liem and Martin (2012) found that a sense of control over one’s academic development was associated with positive academic outcomes. Collie, Martin, Malmberg, Hall, and Ginns (2015) found control mediated the link between academic buoyancy and achievement, while Martin, Nejad, Colmar, Liem and Collie (2015) found perceived control reduced students’ failure dynamics. In regard to relational support, it has been shown that students who experience a strong sense of overall relatedness demonstrate increased behavioral and emotional engagement in school (Furrer & Skinner, 2003) and achieve more highly (Jang, Kim, & Reeve, 2012). When considering students’ relationships as relevant to the instructional process, research suggests the relational support from the teacher explains substantial variance in educational outcomes (Martin & Dowson, 2009). In a metaanalysis, Roorda, Koomen, Spilt, and Oort (2011) found that positive teacher-student relationships led to positive achievement outcomes (see also Klem & Connell, 2004; Martin & Dowson, 2009). 4. ADHD, Personal and Interpersonal Agency, and Academic Outcomes There has not been a great deal of research investigating the link between personal agency, interpersonal agency, and academic achievement for students with ADHD. Tabassam and Grainger (2002) found students with ADHD are lower in academic self-efficacy compared with non-ADHD peers achieving at typical levels. Dumas and Pelletier (1999) also found that children with ADHD

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evinced lower levels of perceived scholastic competence. In terms of control, major models of ADHD identify diminished self-control as a feature of ADHD (e.g., Barkley, 2006). Indeed, some have suggested that the positive effects of medication on outcomes for students with ADHD are in part due to the positive effects on control (Frankel, Cantwell, Myatt, & Feinberg, 1999). Students with ADHD also often experience interpersonal difficulties (e.g., Kendall, 2000), including with teachers (Krueger & Kendell, 2001) that can lead to a cycle of problematic academic interactions (Martin, 2012a). Notably, however, the bulk of these studies investigate mean-levels of personal and interpersonal agency for students with ADHD. Far less research has investigated their links with ADHD students’ achievement. Although not focused on SCT factors, prior work into personal best (PB) goals and academic buoyancy have shown markedly stronger associations between these constructs and achievement for students with ADHD than for students without ADHD (Martin, 2012b, 2014b). Extrapolating from these adaptive motivational factors (viz. self-efficacy, perceived control, relational support, buoyancy, PB goals) and the research showing different mean-levels of personal and interpersonal agency between students with and without ADHD (see summary above), we might expect that there may be different roles of personal and interpersonal agency in predicting achievement for students with ADHD when compared to students without ADHD. However, there appears to be no research investigating self-efficacy, perceived control, and relational support in an integrative model predicting achievement for students with and without ADHD. Thus, although we tentatively suggest a more salient role (by way of explained variance and predictive parameters) for these factors in the achievement of students with ADHD (consistent with research into other adaptive motivational factors; Martin, 2012b, 2014b), this is an empirical question and the focus of the present study. 5. Better Understanding Personal and Interpersonal Agency: The Role of Covariates To identify unique variance attributable to personal and interpersonal agency, it is important to control for factors known to be associated with ADHD and/or achievement. Some research has

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identified age as a factor relevant to motivational processes. For example, older students tend to report lower perceived competence (Jacobs et al., 2002) and lower quality of teacher-student relationships (Saft & Pianta, 2001). There are gender differences in regard to achievement (girls tend to achieve more highly than boys; for a summary, see Martin, 2007), relatedness (girls tend to report better relatedness; Furrer & Skinner, 2003), and efficacy (significant gender differences in domain-specific competency beliefs; see Jacobs et al., 2002), but not necessarily for control (Martin, 2007). Research has shown that socioeconomic status (SES) is positively associated with achievement (Sirin, 2005). It is also known that prior achievement significantly predicts future achievement (Hattie, 2009) and is also correlated with students’ sense of efficacy (Zimmerman, 2000). In regard to ADHD itself, males are significantly more likely to have ADHD (Purdie et al., 2002) and there may be age effects such that there is a slight decline in prevalence as children move into and through adolescence (Barkley, 2006; Purdie et al., 2002). It is also established that ADHD is comorbid with reading, writing, and numeracy difficulties/disabilities (Martin, 2014a). Based on this previous work, we therefore included age, gender, SES, disability status, and prior achievement as covariates in the model in order to control for their variance and thus better establish the unique effects of personal and interpersonal agency as relevant to achievement for students with and without ADHD. 6. Aims of the Present Study Social cognitive theory (SCT) has identified the importance of personal (self-efficacy and perceived control) and interpersonal (relational support) agency in optimal human functioning. We seek to identify the achievement-related yields of personal and interpersonal agency for a sample of students with ADHD. As noted earlier, we tentatively suggest a more salient role for these factors in the achievement of students with ADHD than for their non-ADHD peers (consistent with research into other adaptive motivational factors; Martin, 2012b, 2014b). We conducted our investigation in a multivariate set-up such that we estimated the joint operation of self-efficacy, perceived control, and teacher relational support on achievement (operationalized via a literacy and numeracy test),

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thereby controlling for shared variance among agency factors and enabling identification of their unique effects on the achievement of students with and without ADHD. We also controlled for factors known to be associated with personal and interpersonal agency, achievement, and/or ADHD status, and thus even better identify unique variance associated with self-efficacy, perceived control, and teacher relational support. 7. Method 7.1 Procedure Ethics approval was received from the University Human Research Ethics Committee. Signed consent was provided by the school Principal for the school’s participation. Parents/caregivers provided signed consent for their child to participate. A survey and achievement test were administered online to all participants in normally scheduled mathematics classes. A standard set of instructions was provided to each teacher responsible for supervising survey and test completion. To ensure anonymity and to allow them to respond as fully and frankly as possible, students did not identify themselves. Students completed the instrument on their own, but were instructed to ask teachers for assistance if they had difficulty reading or understanding. The response rate of total students taking the survey relative to the total eligible sampling frame was 75% (with the bulk of non-responders absent from class or school on the day of the survey or not receiving parental/carer consent to participate); however, we were unable to access data on non-responders and so could not carry out a formal analysis of how representative responders/non-responders were. 7.2 Participants Students with ADHD. The sample of students with ADHD (N=164) were in Year 7 (34%), Year 8 (33%), and Year 9 (33%) from 20 mainstream schools in major urban areas on the east and west coast of Australia. These students reported they had received a formal medical diagnosis of ADHD. In relation to these reports, we point out that the validity of ADHD self-report has received support (for meta-analysis of studies involving children, adolescents and adults, see Willcutt, 2012; for research among adults, see Kessler, Adler, Gruber, Sarawate, Spencer, & Van Brunt, 2007).

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Further, it is noted that N=164 in our study corresponds to a 3.4% incidence relative to the total sample, a prevalence broadly aligning with estimates of adolescents with ADHD (Barkley, 2006; Mannuzza & Klein, 2000; Shaw-Zirt, Popali-Lehane, Chaplin, & Bergman, 2005). Using DSM-5 criteria (American Psychiatric Association, 2013), this study’s proportions of presentation types were also in broad accord with meta-analytic results (Willcutt, 2012), with 52% predominantly inattentive presentation, 15% predominantly hyperactive-impulsive presentation, and 33% combined presentation (there were no significant differences in means on self-efficacy, control, relational support, and achievement factors as a function of the three ADHD presentations, F(8,266)=.37, p=.94). Just over half the sample (57%) was on medication to help manage their ADHD symptoms (there were no significant differences in means on self-efficacy, control, relational support, and achievement factors as a function of medication status, F(4,133)=.37, p=.83). Although this study focused on the ADHD group as a whole, for completeness in subsidiary analyses below we report findings from analyses run for students on medication and by ADHD presentation type. Of students with ADHD, the average age was 13.58 years (SD=.94). Consistent with population statistics is the prevalence of males (77%) to females (33%) with ADHD. Just under a third of students (29%) with ADHD reported having a diagnosed academic comorbidity in the form of difficulty in reading writing, and/or mathematics. A total of 11% of students with ADHD were from a non-English speaking background (NESB). ADHD students were from a range of SES levels, from 903 to 1213 on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage classification; with a mean of 1099 (SD=77) this is slightly higher than the national average of 1000. Schools were either systemic Catholic or private/independent. Seven schools were co-educational, seven were all female, and six were all male. Students in these schools generally had mixed ability levels, although the schools trended higher in achievement as indicated by literacy and numeracy data from the Australian Curriculum and Assessment Authority (ACARA).

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Students without ADHD. The non-ADHD group3 comprised N=4,658 students from the same schools and year levels as the ADHD group. Students without ADHD were in Year 7 (34%), Year 8 (33%), and Year 9 (33%). The average age was 13.57 years (SD=.94). Just over half (52%) were males, with 48% females. Very few (3%) students without ADHD reported having a diagnosed academic comorbidity in the form of difficulty in reading, writing, and/or mathematics. A total of 18% of students without ADHD were from a non-English speaking background (NESB). Non-ADHD students were from a range of SES levels, from 863 to 1214 on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage classification, with a mean of 1083 (SD=82). 7.3 Materials The independent variables. The independent variables were self-efficacy, perceived control, and relational support. Self-efficacy was assessed via the self-efficacy scale from the Motivation and Engagement Scale – High School (MES-HS; Martin, 2010). This scale assesses students’ belief, expectation, and confidence in their ability to understand or to do well in their schoolwork. It was measured via four items such as, “If I try hard, I believe I can do my schoolwork well”. For each item, respondents rated themselves from 1 (strongly disagree) to 7 (strongly agree). In prior measurement work, self-efficacy is normally distributed, reliable, and meaningfully differentiated from other factors in the MES-HS (e.g., Liem & Martin, 2012; Plenty & Heubeck, 2011, 2013; Nagabhushan, 2013). Table 1 presents factor loadings, reliability coefficients (Cronbach’s alpha and coefficient omega), and descriptive data for ADHD and non-ADHD groups. Perceived control was operationalized via the 4-item ‘uncertain control’ factor from the MES-HS (Martin, 2010). This scale assesses students’ sense of control and uncertainty/certainty about how to do well or how to avoid doing poorly at school and in schoolwork (e.g., “When I get a good mark I’m often not sure how I’m going to get that mark again”). In prior measurement work, it is shown to be normally distributed, reliable, and differentiated from other factors in the MES-HS (e.g., Liem & Martin, 2012; Plenty & Heubeck, 2011, 2013; Nagabhushan, 2013). For

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each item, respondents rated themselves from 1 (strongly disagree) to 7 (strongly agree). Consistent with Collie and colleagues (2015), to facilitate interpretation in the present study, the factor was reversed such that higher scores reflected greater personal control. Table 1 presents factor loadings, reliability coefficients (Cronbach’s alpha and coefficient omega), and descriptive data for ADHD and non-ADHD groups. Relational support was assessed via the teacher-student relationship scale by Martin and Marsh (2008) reflecting students’ perception of receiving support, interest, and help from the teacher. It consisted of four items, such as, “In general, my teachers give me the help and support I need” (Martin & Marsh, 2008). For each item, respondents rated themselves from 1 (strongly disagree) to 7 (strongly agree). In prior work, this scale is normally distributed and reliable (e.g., Martin & Marsh, 2008). Table 1 presents factor loadings, reliability (Cronbach’s alpha and coefficient omega), and descriptive data for ADHD and non-ADHD groups. The dependent variable. Achievement (the dependent variable) was assessed via an online literacy and numeracy test after respondents had completed the survey of SCT factors. The literacy component comprised 10 multiple choice items that escalated from relatively easy (e.g., The lid is very ______________ on this jar: [a] loss, [b] loos, [c] tight, or [d] tiht) to relatively more difficult (e.g., It was an _____________ way to behave: [a] entertament, [b] entertainment, [c] ignominus, or [d] ignominious) that required knowledge of spelling and comprehension to correctly answer. The numeracy component also comprised 10 multiple choice items that escalated from relatively easy (e.g., 22 + 30 + 44 = [a] 127, [b] 84, [c] 96, or [d] 106) to relatively more difficult (e.g., Integrate: 3 - 2x + 6x2 = [a] 3x2, [b] 3x– x2 + 2x3, [c] 3x2 – x, or [d] -2 x – 3 + x3) that required a broad range of mathematical knowledge and skill to correctly answer. The test is a subset of a longer quiz validated by Martin, Anderson, Bobis, Way, and Vellar (2012). The number of correctly answered questions were summed to form a student’s raw score for numeracy and literacy. Given the fact that older students tend to do better on this test, raw scores were converted to a z-score for each year level. The z-scores created for each of literacy and numeracy were the indicators of a latent achievement

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score subsequently estimated in confirmatory factor analysis and structural equation modeling. Table 1 presents factor loadings, reliability (Cronbach’s alpha and coefficient omega), and descriptive data for ADHD and non-ADHD groups. Covariates. Covariates were: gender, age, SES, academic disability status, and prior achievement. Age was retained as a continuous variable for analyses. Gender was coded 0 for females and 1 for males. Students’ SES was scored on the basis of their home postcode using the Australian Bureau of Statistics (ABS) relative advantage/disadvantage index, with higher scores reflecting higher SES. For academic disability status, participants reported whether they had received a formal diagnosis or categorization for one or more of reading, writing, and mathematics difficulties (0 for no disability; 1 for disability). Prior achievement was derived from students’ ratings of their year-level status for tests and assignments in each of English and mathematics (1=In the lower third of my year group; 2=In the middle third of my year group; 3=In the upper third of my year group). A latent prior achievement factor was estimated via these two indicators. To assist interpretation of the central substantive model, here we report on covariate effects significant at p<.001 (all other covariate effects are in Table 3). For non-ADHD students, age is positively associated with achievement (β=.09, p<.001), as is SES (β=.12, p<.001) and prior achievement (β=.51, p<.001); gender (males, β=-.13, p<.001) and disability status (disability, β=-.11, p<.001) are negatively associated with achievement. For ADHD students, only disability status (disability, β=.34, p<.001) is associated with achievement at p<.001 such that students with ADHD and an academic disability are significantly lower in achievement. 7.4 Data Analysis CFA and SEM. Data were analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM) with Mplus 7.3.1 (Muthén & Muthén, 2015). Maximum likelihood was the method of estimation used (Muthén & Muthén, 2015). Missing data were dealt with using the Mplus full information maximum likelihood defaults (FIML; Enders, 2013; Muthen & Muthen, 2015). Categorical variables were estimated as ordinal. The comparative fit index (CFI) and root

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mean square error of approximation (RMSEA) were the fit indices used. For CFI, values greater than .90 and .95 are considered acceptable and excellent fits respectively (Bentler, 1990). For RMSEA, values less than .08 and .05 are taken to reflect reasonable and excellent fits respectively (Byrne, 2012). Substantive factors (self-efficacy, control, relational support) were entered alongside covariates (age, gender, SES, disability, prior achievement) as predictors of achievement. Marsh and colleagues (1992) have emphasized that in order to obtain accurate estimates of relations for parallel constructs, correlations among their residuals must be included in modeling. Measurement error associated with matching items across parallel indicators is likely to be correlated, potentially leading to biased structural parameter estimates. Thus, we correlated the residual of prior English achievement with the residual of literacy test achievement; we also correlated the residual of prior mathematics achievement with the residual of numeracy test achievement. Multi-group analysis was conducted such that ADHD and non-ADHD parameters were estimated in the one model. Additional statistical considerations. The non-ADHD sample was much larger than the ADHD sample. This has implications for statistical significance such that non-ADHD effects will be biased to statistical significance relative to ADHD effects. Thus, when considering effects between ADHD and non-ADHD samples, there was also consideration of the absolute size of estimates using Keith’s (2006) guidelines proposing that standardized beta coefficients (β) less than .05 are considered too small to be meaningful, above .05 as small but meaningful, above .10 as moderate, and above .25 to be large effects. When testing for differences between ADHD and nonADHD standardized beta coefficients (β), we converted β-values to r-values using Peterson and Brown’s (2005) guidelines and then tested for statistically significant differences with these converted parameters using Preacher’s (2002) method. For completeness, we also conducted multigroup SEM where predictive substantive parameters were constrained across ADHD and nonADHD groups to further determine if there were differences in predictive paths between the two groups.

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8.1 Preliminary Descriptive and Psychometric Analyses Descriptive statistics (means, SDs) for ADHD and non-ADHD groups are shown in Table 1. Reliabilities (Cronbach’s alpha and coefficient omega) for multi-item factors for each of the two groups are also presented in Table 1. These indicate the scales are reliable for each of the two groups. Factor loadings for ADHD and non-ADHD samples are in Table 1 and are at acceptable levels (Tabachnick & Fidell, 2013). These factor loadings are derived from a multi-group (ADHD and non-ADHD) CFA of all factors in the study. This multi-group CFA yielded an excellent fit to the data, χ2=931.01, df=283, CFI=.98, RMSEA=.031. We also assessed for minimum assumptions of measurement invariance (i.e., metric invariance by way of invariant factor loadings for personal agency, interpersonal agency, and test achievement factors) between ADHD and non-ADHD groups. Compared to the multi-group CFA with all parameters freely estimated (i.e., configural invariance), RMSEA and CFI did not change beyond acceptable parameters (i.e., ΔRMSEA ≤ .015, Chen, 2007; ΔCFI ≤ .01, Cheung & Rensvold, 2002) when factor loadings were constrained across the two groups (metric invariance fit: χ2=1072.62, df=299, CFI=.97, RMSEA=.033). Thus, differences between the two groups in subsequent modeling are not a function of differences in fundamental measurement properties. 8.2 Latent Correlations Latent correlations from the CFA for each group are shown in Table 2. Here we report correlations among personal and interpersonal agency factors (self-efficacy, control, relational support) and achievement. All other correlations (i.e., with covariates) are shown in Table 2. For the ADHD group: self-efficacy (r=.53, p<.001) and relational support (r=.46, p<.001) are significantly correlated with achievement; however, perceived control has no significant correlation with achievement (r=.04, p=.71). For the non-ADHD group: self-efficacy (r=.40, p<.001), relational support (r=.30, p<.001), and perceived control (r=.38, p<.001) are significantly correlated with achievement.

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8.3 Structural Equation Modeling Analyses proceeded to SEM, with each of ADHD and non-ADHD groups estimated in the one analytic model. All personal and interpersonal agency factors were entered alongside covariates as predictors of achievement. This integrative SEM yielded an excellent fit to the data, χ2=931.01, df=283, CFI=.98, RMSEA=.0314. Table 3 shows standardized betas for all parameters. Figures 1a and 1b present standardized beta coefficients for personal and interpersonal agency factors for nonADHD and ADHD samples respectively. Results in Table 3 and Figures 1a and 1b show that after controlling for covariates, selfefficacy and relational support yield ‘medium’ to ‘large’ effects for the ADHD group, but ‘small’ effects for the non-ADHD group (based on Keith’s 2006 guidelines). For self-efficacy → achievement: non-ADHD β=.08, p<.05, ADHD β=.30, p<.05. For relational support → achievement: non-ADHD β=.06, p<.05, ADHD β=.23, p<.05. Findings for control were more equivocal, particularly for students with ADHD. First, relative to the medium-large self-efficacy and relational support effects for ADHD students, the effect of control was small; second, unlike the significant self-efficacy and relational support effects for ADHD students, control effects were not statistically significant (β=.09, p=.29). However, given the difference in sample sizes between the two groups, we suggest focusing on the absolute size of the beta parameters, which in the case of self-efficacy and relational support are quite different, and in the case of perceived control are not markedly different. Indeed, when testing for differences between betas using Preacher’s (2002) method (following Peterson & Brown’s (2005) conversion of β-values to r-values), two significant differences emerged between the ADHD and non-ADHD groups. First, the path between self-efficacy and achievement (selfefficacy → achievement) was significantly stronger for ADHD students, z=2.86, p<.01. Second, the path between relational support and achievement (relational support → achievement) was significantly stronger for ADHD students, z=2.17, p<.05. In both cases, there are markedly greater achievement yields from self-efficacy and relational support for students with ADHD compared

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with students without ADHD. As another approach to difference-testing, we estimated the hypothesized multi-group structural model, but constrained the three beta parameters (self-efficacy, control, and relational support to achievement) to be equal across ADHD and non-ADHD groups. This generated a significant decline in fit (∆χ2 = 9, ∆df = 3, p < .05), further supporting the conclusion that the two groups differ in predictive parameters. As noted in Method, we also had data on students’ medication status and also their ADHD presentation type. These are aspects of treatment and diagnosis that heavily feature in ADHD research and practice (Barkley, 2006) and into which the current study may provide additional insight. Thus, in subsidiary analyses we estimated the hypothesized structural model for students on medication (N=93, 57% of ADHD sample) finding, self-efficacy → achievement, β=.42, p<.01; perceived control → achievement, β=.06, p=.64; and relational support → achievement, β=.36, p<.01. This is a pattern of findings distinct from the non-ADHD sample and in line with the whole ADHD group. Again, employing the hypothesized structural model, we also estimated effects for students with only one of either hyperactive or inattentive presentation status (N=110, 67% of ADHD sample; we analyzed these single presentations together because there were insufficient numbers in the hyperactive presentation group to estimate them separately) finding, self-efficacy → achievement, β=.24, p=16; perceived control → achievement, β=.07, p=.52; and relational support → achievement, β=.32, p<.05. We also estimated effects for students with combined hyperactive/inattentive presentations (N=54, 33% of ADHD sample) finding, self-efficacy → achievement, β=.79, p<.05; perceived control → achievement, β=.20, p=.52; and relational support → achievement, β=-.01, p=.96. Given the relatively small sub-samples here, we see these findings as preliminary; however, it is interesting to note that our main findings are supported across presentation types, but with self-efficacy salient for the combined type and relational support salient for students with only one presentation type. 9. Discussion

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From a social cognitive theory (SCT) perspective, we sought to identify the achievement yields of personal agency (self-efficacy, perceived control) and interpersonal agency (relational support) for a sample of students with ADHD, alongside a large sample of their non-ADHD peers in the same schools and year levels. We found that self-efficacy and relational support were consistently associated with better academic achievement, with positive effects significantly stronger for students with ADHD than for students without ADHD. Although perceived control was significantly associated with achievement for students without ADHD and not significantly so for students with ADHD, there was not much difference in absolute size of perceived control effects for the two groups. We suggest these results are relevant to theory, research, and practice identifying motivational factors and processes that may assist in closing well-known achievement gaps for students with ADHD whilst also maintaining positive outcomes for students without ADHD. 9.1 Findings of Note The positive effects of self-efficacy generalized to students with ADHD. Notably, however, the yields of this personal agency factor were significantly greater for the ADHD group of students than for the non-ADHD group. It is possible that self-efficacy is particularly salient for students with ADHD given they are more likely to experience academic adversity and a sense of efficacy is important to surmount this adversity (e.g., Bandura, 2001; Barkley, 2006). As such, self-efficacy may mean the difference between these students giving up or persevering when faced with academic difficulties typical of ADHD. Indeed, this idea aligns with related work by Martin (2014a, 2014b; see also Martin & Burns, 2014) on the importance of students with ADHD being able to effectively navigate academic setbacks or challenges. Importantly, however, the greater strength of the association between self-efficacy and achievement for students with ADHD also signaled that ADHD students low in self-efficacy stand to achieve at a markedly lower level. This is especially concerning given that students with ADHD tend to report lower academic self-concept than students without ADHD (Tabassam & Grainger, 2002). To the extent that this is the case, and given our findings in regard to the strong self-efficacy

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 achievement link, there is a need for educational efforts directed at promoting self-efficacy among students with ADHD (discussed below). Turning to relational support, findings showed that the positive effects of relatedness and relational support generalized to students with ADHD. Notably however, as with self-efficacy, the greater strength of the association between relational support and achievement for students with ADHD also signaled that ADHD students experiencing poor relationships with teachers and/or not receiving adequate teacher support stand to achieve at a markedly lower level. It is known that students with ADHD report higher levels of teacher difficulty, leading to decreased relatedness (Whalen & Henker, 1992; Travell & Visser, 2006). To support and then sustain the achievement of students with ADHD, it is therefore critically important for teachers to look for ways to foster positive and supportive relationships with these students (discussed below). Another key finding from the present study was that perceived control played a relatively minor role in predicting achievement in the non-ADHD model and a non-significant role in the ADHD model (although the non-significance of this latter finding was in part a function of the smaller ADHD sample size). Thus, whereas variance explained by self-efficacy and relational support was quite different between ADHD and non-ADHD groups, this was not the case for perceived control, which explained relatively little variance in both groups. We operationalized control as the individual’s perception that they know how to succeed (or avoid failure) in future academic endeavors. Perhaps perceived control was not salient in the ADHD model because students with ADHD have a reduced capacity to consider medium- and long-term outcomes (Barkley, 2006; Young & Bramham, 2012) and thus a sense of knowing how to attain future success (or avoid future failure) was too distal for them, leading to no notable connection to achievement. At the same time, it may be the case that their non-ADHD peers had a sense of how to attain future success (or avoid future failure), resulting in relatively less variance on this factor and thus no meaningful connection to achievement. Future research that explores these issues in greater depth will be helpful for extending current knowledge.

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One other finding is worth explaining: the minor role played by prior achievement in predicting subsequent achievement (β=.03) for students with ADHD, when the correlation was notably higher for them (r=.33). We propose two reasons for this. First, the correlation between prior and subsequent achievement for students with ADHD (r=.33; 11% variance) is substantially lower than the correlation for their non-ADHD peers (r=.63; 40% variance), thus when controlling for covariates, the beta parameter for ADHD students is declining from a lower correlational base. Second, because ADHD is highly predominant among boys and co-morbid with disability (Barkley, 2006), and because both factors are associated with low achievement, when we included gender and disability as predictors of the achievement outcome, they accounted for more variance for students with ADHD than for their non-ADHD peers, leading to less variance to be accounted for by prior achievement in the ADHD group. Indeed, this is why the SEM is the important model to interpret (rather than the CFA) as it accounts for this shared variance in the one integrative multivariate setup. Notwithstanding these contentions, we also point out that prior achievement is to be interpreted in the context of its self-report methodology and its assessment using an ordinal measurement scale. 9.2 Implications for Practice We propose our findings have relevance to educational practice, but do so with due recognition that (1) our research design is suggestive rather than prescriptive, with the understanding that (2) the relationship between agency and achievement is likely to be reciprocal, and (3) there are likely to be other factors involved in the process that were not investigated here. In regard to the first point, our research design was not fully longitudinal and so the causal ordering of these factors for students with ADHD remains to be determined. Nevertheless, we point out that our findings control for prior variance in achievement and the achievement test was administered after students completed their survey of agency items. In regard to the second point, self-system research has demonstrated that motivation and achievement can be reciprocal (e.g., self-concept and achievement; see Marsh, 2007) and so intervention targeting both agency (self-efficacy and relational support) and achievement is likely to be optimal. In regard to the third point, there are

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likely to be other viable factors on which to focus that will raise both agency and achievement. For example, difficulty to focus on learning material or to organize learning processes, are issues for students with ADHD (Barkley, 2006; Purdie et al., 2002) and so teaching these skills will positively assist both achievement and self-efficacy. These qualifications notwithstanding, the results suggested that intervention approaches focusing on building self-efficacy and relational support may have particular relevance for students with ADHD. Efforts to build self-efficacy may include adapting lessons and activities to maximize opportunities for success (Martin & Burns, 2014); promoting mastery learning approaches; and, breaking lessons and activities into smaller, more manageable sections to optimize opportunities for completion and a sense of competence (Martin & Burns, 2014). Individualizing learning activities (Schunk & Miller, 2002), developing students’ goal-setting skills (Locke & Latham, 2002), and building students’ ability to problem-solve effectively (Young & Bramham, 2012) may also be helpful for building self-efficacy. With respect to relational support, teacher-student rapport is important for the effective implementation of strategies to aid students with ADHD (Geng, 2011). Building students’ skills for interacting positively with others (including with teachers) and their awareness of social cues through social skills training can be helpful for students with ADHD to build high quality relationships (e.g., Hoza, Waschbusch, Pelham, Molina, & Milich, 2000). Sherman, Rasmussen, and Baydala (2008) suggest the importance of teachers’ patience, tolerance, and prior experience working with students who have ADHD. In a similar vein, Geng (2011) establishes that teachers’ strategies for engaging students with ADHD are more likely to be effective when there is high quality teacher relational support underpinning their implementation. Research has also pointed to the importance of teacher professional development in assisting at-risk students. It is noteworthy that one of the key areas targeted for such professional development is improving teacher-student relationships (Becker & Luthar, 2002). 9.3 Limitations and Future Directions

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There were a number of potential limitations in the study and which suggest directions for future research. The first is that the personal and interpersonal agency data were self-reported. Future research might explore data from additional sources such as, for example, teacher reports of their relationships with students. Second, longitudinal data are needed to support present claims about the role of personal and interpersonal agency and achievement. Third, our substantive SCT factors were domain-general, leading us to represent the achievement measures in domain-general ways as well (thus, a latent factor of literacy and numeracy achievement). Future research would do well to capture SCT data in specific school subjects to determine their associations with specific dimensions of achievement. In doing so, however, it is important to represent domain-specific achievement using reliable measures. For example, the present domain-general achievement measure was reliable, but when disaggregated by literacy and numeracy sub-tests, reliability was lower due to fewer indicators (e.g., Cronbach’s  = .59 for numeracy and .69 for literacy). Thus, there would be a need to augment our existing measure with more numeracy and literacy achievement items in order to employ reliable and valid domain-specific models. Fourth, there was the possibility of ceiling effects and/or skewed distributions that could have reduced potential variance explained in the non-ADHD group (e.g., with respect to self-efficacy). Although we believe this was not a threat to the validity of this study (e.g., self-efficacy skew and kurtosis values indicated approximately normal distributions for both groups, non-ADHD: skew = -0.94, kurtosis = 0.89; ADHD: skew = -1.04, kurtosis = 1.24), this risk is important to guard against in future research. It is also the case that skew and kurtosis are dependent on sample size, with smaller samples likely to generate higher skew and kurtosis. It is also the case that skew and kurtosis can disproportionately affect regression parameters - though the precise effects can depend on other variables and parameters in the model (Yuan, Bentler, & Zhang, 2005). Given research involving students with ADHD typically comprises small samples, findings based on regression models (such as in this study) must be interpreted accordingly.

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Fifth, although we collected data on ADHD presentation type, we did not collect data on specific ADHD symptomatology and could not rule out variance associated with this among the non-ADHD sample (there will be some with ADHD or clinical symptoms who do not have a formal diagnosis). Related to this, although we did analyze data as a function of presentation type, given the relatively small sub-samples, we see these findings as preliminary; however, it is interesting to note that our main findings were supported across presentation types, but with self-efficacy salient for the combined type and relational support salient for students with only one presentation type. Future research with larger samples for each presentation type is needed to explore this pattern of effects further. Future research might also collect formal records of medication and diagnosis to compare effects in that research with the effects in our study that were based on survey data. Sixth, alternative approaches to comparing the effects of agency factors on achievement are possible. Propensity score matching is one such approach and future research would do well to purposefully recruit from ADHD and non-ADHD populations so as to enable valid matching in such analyses. Seventh, even among ADHD students, research has identified some symptoms (e.g., aspects of impulsivity) that are positively associated with achievement (Tymms & Merrell, 2011); these should be investigated alongside personal and interpersonal agency factors in future research. Finally, our suggested applied focus on self-efficacy and relational support for students with ADHD is not to preclude proven evidence-based treatments (e.g., medication, clinical therapy; Pliszka, 2009). 10. Conclusion Findings from this study showed that the positive roles of self-efficacy and relational support generalized across ADHD and non-ADHD samples, but appeared markedly stronger for students with ADHD. The role of perceived control was not salient in either group. The findings are informative for researchers studying issues relevant to ADHD, SCT, and achievement. Findings are also relevant to psycho-educational practitioners seeking to optimize the academic outcomes of a

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diversity of students that typically reside in the mainstream classroom, including students with ADHD.

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25 References

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34 Footnotes

1. The terms ‘students with ADHD’ and ‘ADHD students’ are sometimes used interchangeably. Importantly, the use of the latter term is not meant to indicate students are defined by their ADHD.

2. For the purposes of this study, the term “mainstream” is used to denote inclusive (or “regular”, “general”, “typical”) classrooms that comprise students with ADHD and students without ADHD (as well as other student types and presentations not the focus of this study).

3. It is recognized that the non-ADHD sample is substantially larger than the ADHD sample and that this has implications for statistical significance, biasing the non-ADHD parameters towards significance relative to the ADHD sample. However, because the study aimed to comprise a participant pool that reflected the real ratio of ADHD-to-non-ADHD students and because it was of interest to derive less biased standard error estimates through a larger sample size, comprehensive representation of non-ADHD students was of central interest.

4. Given the SEM was “fully-forward”, it yielded the same fit as the CFA. For completeness, we removed non-significant substantive paths from the SEM (viz. perceived control to achievement for the ADHD group), again generating excellent fit, χ2= 932.11, df=284, CFI=.98, RMSEA=.031.

ADHD, SCT, and Achievement

35

Table 1. Descriptive and Psychometric Statistics for ADHD and Non-ADHD Samples: Personal Agency, Interpersonal Agency, and Achievement Variables

Raw Means / SD

Factor Loadings

Cronbach’s α / CFA

(Range / Mean)

Coefficient Omega

Non-ADHD

ADHD

Non-ADHD

ADHD

Non-ADHD

ADHD

Self-efficacy

5.84 / 0.98

5.25 / 1.44

.68-80 / .74

.80-.85 / .83

.83 / .86

.89 / .91

Perceived Control

4.47 / 1.45

4.01 / 1.52

.68-.81 / .74

.69-.84 / .76

.83 / .85

.85 / .86

Relational Support

5.45 / 1.12

4.92 / 1.49

.75-.86 / .82

.77-.87 / .82

.89 / .91

.89 / .91

Achievement

0.03 /0.81

-.67 / 1.26

.59-.65 / .62

.73-.77 / .75

.71 / .73

.84 / .77

ADHD, SCT, and Achievement

36

Table 2. Latent Correlations from Multi-group CFA: ADHD and Non-ADHD Samples Age

Age

-

Gender

SES

Disability

Prior Achieve

Relational

Control

Support

.05

.03

-.07

-.07

-.07

-.09

-

.18*

.07

-.04

.04

-.08

-.05

.01

.06

-.13

-.01

.01

-.10

.07***

SES

.02

.20***

-

-.03

Disability (Y)

.02

.05**

-.01

-

-.10

Prior Achieve

-.03

-.02

.08***

-.22***

-

Self-efficacy

-.10***

-.02

.03

-.13***

.49***

Perceived Control

-.05**

.03

.09***

-.08***

.51***

Relational Support

-.09***

.01

.04*

-.09***

.15***

-.24***

.05*

Perceived

-.12

Gender (M)

Achievement

Self-efficacy

-.12***

Notes: * p<.05, ** p<.01, *** p<.001 ADHD group in upper right diagonal; non-ADHD group in lower left diagonal

-.24** .52***

Achieve

.02 -.24** .16 -.45***

.15

.38***

.33**

-.01

.59***

.53***

.37***

-

.31***

.04

.32***

.64***

-.05

.63***

.40***

-

.38***

-

.46*** .30***

-

ADHD, SCT, and Achievement

37

Table 3. Standardized Beta Parameters in Multi-group SEM: ADHD and Non-ADHD Samples Achievement Non-ADHD

ADHD

β

β a

Self-efficacy

.08* (Small)

Perceived Control

.07* (Small)

Relational Support

.06* (Small)

c

.30* (Large)

b

.09 (Small) .23* (Medium)

d

Covariates - Age - Gender (M) - SES - Disability (Y) - Prior Achievement

.09***

.04

-.13***

-.25**

.12***

.19*

-.11*** .51***

-.34*** .03

Notes: Superscripts a and b differ at p<.01; Superscripts c and d differ at p<.05 * p<.05, ** p<.01, *** p<.001 All parameters estimated in one multi-group SEM Standardized beta coefficients (β) above .05 are considered as Small (but meaningful), those above .10 as Medium, and those above .25 as Large (Keith, 2006).

ADHD, SCT, and Achievement

38

Figure 1a. Standardized Parameters for non-ADHD Sample

Self-efficacy a .08*

Perceived Control

.07*

Achievement

c .06*

Relational Support

Controlling for Covariates: - Age - Gender - SES - Disability - Prior Achievement

Figure 1b. Standardized Parameters for ADHD Sample

Self-efficacy b .30*

Perceived Control

.09

Achievement

d .23*

Relational Support

Controlling for Covariates: - Age - Gender - SES - Disability - Prior Achievement

Notes: Across Figures 1a and 1b, superscripts a and b differ at p < .01; Superscripts c and d differ at p < .05 All parameters estimated in one multi-group SEM * p<.05, ** p<.01, *** p<.001

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39

SUBMISSION TO SPECIAL ISSUE: AT-RISK STUDENTS, MOTIVATION, LEARNING

ADHD, Personal and Interpersonal Agency, and Achievement: Exploring Links from a Social Cognitive Theory Perspective

Highlights  ADHD, personal (self-efficacy, control) and interpersonal (teacher support) agency, and achievement were investigated.  Self-efficacy and teacher support were associated with higher academic achievement for both groups.  Effects for self-efficacy and teacher support were significantly stronger for students with ADHD.  Results hold implications for closing well-known achievement gaps for students with ADHD.