Achievement Goals of Students Wiith ADHD

7/30/2019 Achievement Goals of Students Wiith ADHD

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Achievement Goals of Students with ADHD

Author(s): Kenneth E. Barron, Steven W. Evans, Lisa E. Baranik, Zewelanji N. Serpell, ElizabethBuvingerReviewed work(s):Source: Learning Disability Quarterly, Vol. 29, No. 3, Motivation and Learning Disabilities(Summer, 2006), pp. 137-158Published by: Council for Learning DisabilitiesStable URL: http://www.jstor.org/stable/30035504 .

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ACHIEVEMENT GOALS OF STUDENTSWITH ADHD

Kenneth E. Barron, Steven W Evans, Lisa E. Baranik, Zewelanji N. Serpell,and Elizabeth Buvinger

Abstract. Although achievement goal theory is currently oneof the dominant theoretical frameworks used to understand andimprove student motivation (Brophy, 2004), little work has beendone to evaluate the achievement goals of students with ADHD.After an initial review of achievement goal theory, the currentstudy begins to address four research questions: What are theachievement goals of students with ADHD? How do achievementgoals of students with ADHD differ from those of students withoutADHD? How are achievement goals related to other academic out-come variables for students with ADHD? Can current instructionalpractices be altered to promote optimal goals and motivation ofstudents with ADHD? Results revealed a number of interestingdifferences for students with ADHD, especially concerning per-formance-avoidance goals. Implications are discussed.

KENNETH E. BARRON, Department of Psychology, James Madison University.STEVEN W. EVANS, Department of Psychology, James Madison University.

LISA E. BARANIK, Department of Psychology, James Madison University.ZEWELANJIN. SERPELL,Department of Psychology, James Madison University.ELIZABETHBUVINGER, Department of Psychology, James Madison University.

Over the past two decades, achievement goal theoryhas emerged as one of the predominant motivationalframeworks for understanding students' achievementmotivation (Brophy, 2004; Midgley et al., 1998;Pintrich CDATA[&chunk, 2002). Although achievement goaltheory has been widely used to understand the motiva-tion of students in a variety of educational settings, weknow very little about the achievement goals of stu-dents with attention-deficit/hyperactivity disorder(ADHD). Students with ADHD have significantlyhigher dropout rates, increased frequency of failinggrades, and poor academic outcomes compared toyouth without ADHD (e.g., Fischer,Barkley,Fletcher, CDATA[&Smallish, 1993). Furthermore, the school performance

of individuals with ADHD is often significantly lowerthan would be predicted by their cognitive abilities(Hinshaw, 1992).

Achievement goal theory has been found to predictacademic performance independent of cognitive ability(Elliot CDATA[&hurch, 1997; Harackiewicz,Barron,Tauer,CDATAElliot, 2002). Understanding the potential contributionof achievement goals in the typical path towards pooroutcomes of children with ADHD could help us de-velop prevention and intervention programs to reducethe likelihood of poor outcomes for these youth.

Researchis needed that can begin to address the fol-lowing questions: What are the achievement goals ofstudents with ADHD? How do the achievement goals

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of students with ADHD differ from those of studentswithout ADHD? How are achievement goals related toother academic outcome variables for students withADHD? Finally, can current instructional practices bealtered to promote optimal goals and motivation of stu-dents with ADHD?Theoretical Background

Achievement goal theory provides a framework forunderstanding the reasons why we engage in achieve-ment-related behavior and the standardsused to evalu-ate success (Ames, 1992; Dweck CDATA[&eggett, 1988;Nicholls, 1984). Thus, rather than simply determiningan overall amount or quantity of motivation, achieve-ment goals help us determine the type or quality ofsomeone'smotivation.Different labels have been used by differentresearchers;however, two general types of achievement

goals have been proposed: mastery and performance(see Elliot, 2005, for a review).1 When pursuing masterygoals, the purpose is to develop competence by acquir-ing new knowledge and skills. Success and failure arejudged through self-referential standards or absolutestandards of being able to complete a particular task.When pursuing performancegoals, on the other hand,the purpose is to demonstrate competence relative toothers (or to avoid demonstrating incompetence), andsuccess and failure are judged through normative com-parisons to others. According to Dweck (1986), the typeof achievement goal adopted shapes how studentsapproach, experience, and react to their school work,and has an influential impact on the affect, behaviors,and cognitions they experience.

For example, in one of the first comprehensivereviews of the achievement goal literature,Ames (1992)noted that students pursuing mastery goals useddeeper, more elaborate study strategies, selected morechallenging tasks, persisted in the face of difficulty, andheld more positive attitudes toward learning. In con-trast, students pursuing performance goals were morelikely to adopt superficial learning strategies, select eas-ier tasks, and engage in maladaptive behavior patternsfollowing difficulty or failure. Therefore, several re-searchers quickly concluded that mastery goals werethe optimal achievement goal for students to pursue.The perspective that mastery goals are adaptive andperformance goals are maladaptive has been labeledthe masterygoal perspective see BarronCDATA[&arackiewicz,2001). One obvious implication of this perspectivewould be to question whether certain student popula-tions who are struggling in school, like students withADHD, are maximizing their endorsement of masterygoals while minimizing their endorsement of perform-ance goals.

Other researchers disagree with a strict masterygoalperspective, uggesting that performance goals can alsopromote important achievement outcomes becausethey help orient individuals toward achievement andcompetence (Harackiewicz, Barron, Pintrich, Elliot, CDATThrash, 2002). For example, Wentzel (1991) noted thathigh school students who adopted both mastery andperformance goals had higher GPAsthan students whoonly adopted mastery goals. In fact, several studies havefound positive performance goal effects in some situa-tions and for certain individuals (see Barron CDATAHarackiewicz, 2000, for a review). Thus, a number oftheorists endorse a multiple-goal perspectivein whichadopting both types of achievement goals may be moreadaptive.Furthermore,more recent work on achievement goaltheory suggests that a mastery-performance goaldistinction of motivation may be a simplistic dichot-omization. For example, Elliot and colleagues (ElliotCDATA[&hurch, 1997; Elliot CDATA[&arackiewicz, 1996) parti-tioned the performance goal construct into perform-ance-approachgoals, where an individual's goal is toapproach a learning opportunity in order to demon-strate competence (e.g., "My goal is to do better thanother students") and performance-avoidanceoals, wherean individual's goal is to avoid demonstrating incom-petence (e.g., "I just want to avoid doing poorly com-pared to others"). When these refined measurementscales have been used, maladaptive learning patternshave been found to be more closely associated withperformance-avoidance goals and adaptive learningbehaviors to be associated with performance-approachgoals (Elliot, 2005).2Thus, an alternative implication of the multiple goalperspective would be to question whether certain stu-dent populations, like students with ADHD, areendors-ing the optimal combination of goals. Perhaps studentswith ADHD are adopting mastery goals but are notendorsing performance-approach goals. Ultimately,however, it is important to recognize that the optimalcombination of goals for achieving academic successfor students with ADHD may be different from thoseendorsed by successful children without such impair-ment.Goal Orientations vs. Classroom Goal StructureAnother distinction that has emerged in the achieve-ment goal literature centers on whether it is the goalsof the student or the goals being promoted in a class-room environment that are being assessed. In otherwords, researchers have adopted person-centeredapproaches that measure the achievement goals thatstudents personally endorse (typically referred to asgoal orientation)and situation-centered approaches that

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measure the goals perceived to be created by a particu-lar classroom and teacher (often referred to as perceivedclassroomgoal structure r classroomgoal climate).One important implication of this distinction is thatthe perceived classroom goal structure is argued toshape and influence students' goal orientation. Ames(1992) described classroom structures in terms of howthey make certain achievement goals prominent to stu-dents through the type of assignments, evaluationpractices, and distribution of authority used in theclassroom. For example, evaluation practices that nor-matively compare or track students by level of abilityreinforce performance goals. In particular, severalresearchershave studied how perceived classroom goalstructures change as students transition from one edu-cational environment to another, such as the transitionfrom elementary school to middle school (Midgley,2002). These authors have noted structural changes inmiddle school that are linked to a decline in personallypursued mastery goal orientations with subsequentnegative effects on academic and psychological well-being. For example, rather than remaining with oneprimary teacher who teaches all subjects to the samegroup of students, students in middle school are taughtby different teachers who have particular expertisein a given subject. Furthermore, students are typicallytrackedand grouped by ability into higher versus lowersections of particular subjects, making normativecomparisons among students more salient for both stu-dents and teachers. To counteract this shift in orienta-tions, researchers have attempted interventions thatcontinue to reinforce and promote mastery goal struc-tures in middle school environments (Midgley CDATA[&Edelin, 1998), as well as studying the impact of pro-moting mastery and performance-approach goals in aclassroom (Linnenbrink, 2005).Applying Achievement Goal Theory to Studentswith ADHD

Although researchers have begun to apply achieve-ment goal theory to investigations involving othertypes of educational disabilities (e.g., see work bySideridis, 2005a), little research exists on the achieve-ment goal orientations of students with ADHD or theirperceptions of classroom goal structures. This is unfor-tunate because children with ADHD are described ashaving performance deficits, not skill deficits(Hinshaw, 1992). In other words, they have the neces-sary skills to function at a higher level, but fail to usethem. Specifically, compared to peers without ADHD,these children tend to quit working on academic tasksmore often (Hoza, Pelham, Waschbusch, Kipp, CDATA[&Owens, 2001; Milich CDATA[&reenwell, 1991; Milich CDATA[&Okazaki, 1991) and exhibit greater frustration with

tasks (Milich CDATA[&reenwell, 1991; Milich CDATA[&kazaki,1991).Many of the academic behaviors that students withADHD display seem to be associated with the maladap-tive behaviors found in early research on having a per-formance goal orientation. Thus, students with ADHDmay be pursuing performance goals at a higher ratewhile pursing mastery goals at a lesser rate. However, itis not yet known whether promoting a masteryapproach is most conducive to the academic success ofyouth with ADHD. In order to understand the influ-ence of classroom environments on the achievementgoals of youth with ADHD, it is important to determinehow students with ADHD perceive their classroomenvironments. Once the optimal goal orientations forthis population are identified, their perception of class-room environments will help guide the developmentof classrooms conducive to their learning needs.Interesting, although there is little research usingcontemporary measures of achievement goals to studystudents with ADHD (formally comparing and con-trasting levels of mastery, performance approach, andperformance-avoidance goals), a series of studies havebeen conducted on children with ADHD utilizing sev-eral of the core concepts and research paradigms fromwhich achievement goal theory derived. Specifically,some ADHD researchers have been influenced by theearly work of Dweck (e.g., Diener CDATA[&weck, 1978;Dweck, 1975) and her laboratory paradigm of usingsolvable and unsolvable academic tasks. This work hasbeen used to study learned helplessness and attributionpatterns in achievement situations to determine theconditions under which children respond with anadaptive, mastery response vs. a maladaptive, helplessresponse. For example, Milich (1994) reviewed a seriesof studies in which he and his collaborators employeda research paradigm similar to Dweck's to evaluate theresponse patterns of students with ADHD when facedwith success and failure experiences.In her early work, Dweck found that some childrenresponded adaptively to unsolvable tasks by attributingfailure to lack of effort, increasing persistence, andmaintaining a positive outlook that they had been pre-sented a challenge to overcome. In contrast, other chil-dren responded maladaptively to unsolvable tasks byattributing failure to lack of ability, withdrawing, devel-oping a negative outlook, and avoiding subsequenttasks. Using similar techniques, Milich and his col-leagues demonstrated that when faced with unsolvableproblems, boys with ADHD displayed several of thecharacteristics that are associated with a maladaptive,helpless pattern (e.g., students with ADHD were lesslikely to persist and were more frustrated than studentswithout ADHD) (Milich CDATA[&reenwell, 1991; Milich CDAT



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Okazaki, 1991). However contrary to predictions, boyswith ADHD who made effort attributions consistentwith the adaptive, mastery response demonstrated lesseffort, greater helplessness, and more frequent quittingthan boys with ADHD who attributed failure to externalcauses. Thus, the attributional response that yieldsadaptive behaviors among students without ADHD(that failure reflects lack of effort and thus can be con-trolled by giving more effort) did not provide the samebenefit for boys with ADHD.

An additional component of this work evaluated theextent to which psychostimulant medication alteredthe achievement behavior of students with ADHD whenfaced with solvable and unsolvable problems. Resultsrevealed that students were more likely to persist andexperienced less frustration when on medication versusa placebo, especially when confronted with challengingand unsolvable problems (Carlson, Pelham, Milich, CDATA[&Hoza, 1993; Milich, Carlson, Pelham, CDATA[&icht, 1991). Inaddition, students with ADHD on medication weremore likely to make adaptive, mastery patterns of attri-butions than when taking a placebo. Thus, it was arguedthat medication helped promote (or normalize) a moreadaptive motivational response. These studies involvingmotivational constructs that were precursors to con-temporary achievement goal theory (as well as othersreplicating these findings for girls with ADHD, seeDunn CDATA[&hapiro, 1999) showcase the utility of incorpo-rating motivational variables to further our understand-ing of achievement behavior among students withADHD as well as assessing the efficacy of different inter-ventions (like medication) for promoting better aca-demic success for these students.

In addition to her early work on learned helplessness,which provided the groundwork for achievement goaltheory, Dweck played a pivotal role in developing theachievement goal construct, which she argued was abetter explanatory construct for capturing when stu-dents are likely to display adaptive or maladaptivelearning patterns (Dweck, 1986; Dweck CDATA[&eggett,1988). However, investigations that have attempted toevaluate more contemporary measures of achievementgoals on student samples with ADHD are lacking.

One of the few studies that does provide an initialdescription was conducted by Carlson, Booth, Shin, andCanu (2002), who examined motivational variables inchildren with ADHD through self, parent, and teacherratings. Although a range of different motivationalinstruments were utilized, one measure included anassessment of children's self-reported mastery goals andperformance goals (Schunk, 1996). No differences werefound for mastery goal adoption between children withADHD and a non-ADHD control group; however, dif-ferences in performance goal adoption were found

between children with various subtypes of ADHD, withchildren meeting criteria for ADHD-combined typeendorsing more performance goals than children withADHD-inattentive type. In addition, ratings provided byteachers and parents provided two other measures thatcould be considered a proxy for students' level of mas-tery and performance goal pursuit (Stinnett CDATA[&ehler-Stinnett, 1992). Both teachers and parents perceivedstudents with ADHD as having lower levels of masterygoals and lower levels of performance goals than stu-dents without ADHD.

Using a different approach to identify goal orienta-tions, Dunn and Shapiro (1999) chose a forced-choiceprocedure developed by Dweck to evaluate the goalpursuit of students with ADHD. Specifically, partici-pants were given two descriptions that either high-lighted working on a task that was more masterygoal-oriented or a task that was more performance goal-oriented and were asked to choose which of the twotasks they would prefer. The results revealed that stu-dents with ADHD preferred working on the perform-ance goal-oriented task more than a control group ofstudents without ADHD.

A major limitation of each of these studies is thatmeasures typically used to differentiate more contempo-rary distinctions of achievement goals (like goal orienta-tion vs. classroom goal structure or mastery vs.performance-approach vs. performance-avoidance goals)have not been used with this population. A next step inthe research process would be to identify the goal orien-tations and perceptions of goals that are promoted inclassroom environments that may be unique to this pop-ulation and then examine how achievement goals arerelated to academic achievement patterns. Understand-ing the goals for students with ADHD will provideimportant clues on how to improve academic success forthis population of students who are at such a high riskfor school failure and dropout.Current Study

To investigate the role of achievement goals amongstudents with ADHD, middle school students witha diagnosis of ADHD completed the goal subscalesfrom the Patterns of Adaptive Learning Survey (PALS;Midgley et al., 2000) at two time points (in the begin-ning of the academic year and at the end). The PALSis one of the most widely used tools for assessingachievement goals in middle schools (see Midgley,2002). It provides an assessment of students' goalorientations as well as their perceptions of classroomgoal structures. In addition, it differentiates betweenmastery goals, performance-approach goals, andperformance-avoidance goals. While other goal meas-ures have been developed, we specifically used the PALS

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to be able to connect to the already large body of find-ings based on this assessment tool.The purpose of the current study was to address thefollowing three research questions:1. What are the goal orientations and perceptionsof classroom goal structure for students withADHD?2. How do goal orientations and perceptionsof classroom goal structure for students withADHD differ from those of students withoutADHD?3. How do goal orientations and perceptions

of classroom goal structurerelate to otheracademic variables for students with ADHD?Regarding the first research question, we wouldexpect that students who are struggling academically in

school are adopting less optimal goal orientations andperceive less optimal classroom goal structures. Withregard to the second research question, we would fur-ther predict that students with ADHD are less masterygoal-oriented and/or more performance-avoidancegoal-oriented than a non-ADHD comparison group.Children with performance-avoidance orientationshave been described as having experienced a highernumber of negative life events and a greater likelihoodof producing negative life events than children withother goal orientations (Sideridis,2005b). This propen-sity to experience and contribute to negative life eventsis very characteristicof youth with ADHD as they expe-rience frustrationand failurewith academics and socialinteractions on a regularbasis. Furthermore,the patternof academic behavior noted in past studies of studentswith ADHD (e.g., demonstrating less persistence, expe-riencing greater frustration) are characteristic of chil-dren who would be less mastery-oriented and moreperformance-avoidant (Elliot, 2005).

Finally, regarding our third research question, wewould expect that goal orientation and perceptions ofthe classroom goal structureswould be related similarlyfor students with ADHD as for non-ADHD populations.Specifically,we expected that less optimal goal orienta-tions and less optimal classroom goal structureswouldbe linked to maladaptive outcomes. However, based onpast motivational research with an ADHD population(e.g., Milich, 1994), the relationship between motiva-tional variables like achievement goals and academicfunctioning may be different for children with ADHDthan for peers without the disorder. To investigaterelationships between achievement goal variables andother academic outcome variables, students' GPA andresponses to additional subscales from the PALSwerecollected, including academic self-efficacy and otheracademically related outcomes associated with mal-adaptive learning (such as self-handicapping, avoiding

novelty, and skepticism about school). The results ofthese additional subscales provide researchers withexternal criteria relevant for investigating adaptive andmaladaptive outcomes associated with particular goaladoption (see Midgley, 2002). We were particularlyinterested in evaluating the maladaptive scales of thePALSdue to the difficulty in school experienced by thecurrentADHD sample.

METHODParticipantsSeventy students in sixth grade, ranging in age from10 to 13 years old, from five middle schools in theShenandoah Valley of Virginia, participated in thestudy. Data were collected during comprehensive eval-uations for a longitudinal study of the effects of aschool-based treatment program for children withADHD. Parents referred their children to the study inresponse to recruitment mailings requesting childrenwith problems related to impulsivity, hyperactivity, orinattention.Eligibility criteriafor the longitudinal study included(a) meeting diagnostic criteriafor one subtype of ADHD,(b) having an IQ equal to or greaterthan 80, and (c) notmeeting diagnostic criteriafor bipolar disorderor schiz-ophrenia. Participants for the current study includedonly students with complete data who were acceptedinto the program between the months of Septemberand December (first semester of school) and whoreturned for a follow-up visit between the months ofMarch and May (second semester of school), resultingin a final sample size of 50. Descriptive data regardingparticipants are presented in Table 1.Measures Used to Determine EligibilityDiagnostic Interview Schedule for Children (DISC-IV; Shaffer, Fisher, Lucas, Dulcan, CDATA[&chwab-Stone,2000). Administered during the initial visit to establishparticipant eligibility, the DISC-IV s a structureddiag-nostic interview used to evaluate children for ADHDand other psychiatric disorders (e.g., major depressionand oppositional defiant disorder) based on DSM-IVdiagnostic criteria. Research assistants were trained toadminister the DISC-IV o the parents, as well as a sub-stance use section to the participants. The instrumenthas adequate reliabilityand validity evidence (McGrath,Handwerk, Armstrong, Lucas, CDATA[&riman, 2004; Shafferet al., 2000), and has been widely used for diagnosticpurposes in studies of children with ADHD (The MTACooperative Group, 1999).Kaufman Brief Intelligence Test (K-BIT;Kaufman CDATKaufman, 1990). The K-BITs an individually adminis-tered measure of verbal and nonverbal intelligence forchildren, adolescents, and adults. This test, also used

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Table 1Descriptive Statistics of Sample Demographic Variables

NMaleAgeFull Scale IQWIAT - Word ReadingWIAT - Numerical OperationsWIAT - SpellingRace Caucasian

Hispanic or LatinoOtherType of ADHDInattentive TypeHyperactive/Impulsive TypeCombined TypeAny Comorbid DisorderODDCD

Mania/HypomaniaMajor DepressionDysthymic DisorderGPA at end of 1st SemesterGPA at end of 2nd Semester

5074.0%

M= 11.70 (SD = .47)M =104.56 (SD = 12.23)M= 99.40 (SD = 12.67)M= 96.38 (SD = 14.09)M = 97.70 (SD = 13.94)

96%2%2%

36%0%64%74%66%12%

4%2%2%M = 2.41 (SD = .95)M = 2.27 (SD = .96)

Note. WIAT=Wechsler ndividual Achievement Test, ODD=OppositionalDefiant Disorder,and CD=ConductDisorder.

for eligibility, was administered at the initial visit. TheK-BIThas adequate reliability and validity evidence(KaufmanCDATA[&aufman, 1990) and yields standard scorescomparable to those provided by comprehensive intel-ligence batteries but requires only 15 to 30 minutes toadminister.Wechsler Individual Achievement Tests-II (WIAT-II;The Psychological Corporation, 2001). The WIAT-IIs ameasure of academic achievement that has been stan-dardized with a sample of school-age children carefullyselected to reflect the overall population of the UnitedStates.The revised version of this measure was adminis-tered during the initial visit. Studies demonstrate ade-quate test-retest reliability for students falling withinthe same age range as those targeted in the currentstudy (The Psychological Corporation, 2001).

Disruptive Behavior Disorders Rating Scale (DBD;Pelham, Evans, Gnagy, CDATA[&reenslade, 1992). Adminis-tered at both the initial and the follow-up visits to trackthe severity of participants'ADHD symptoms, the DBDis a symptom rating scale completed by parents. Thescale includes 18 symptoms of ADHD (e.g., "Isoften eas-ily distracted by extraneous stimuli"), and parents areaskedto indicate whether a behavior is "not at all,""justa little," "prettymuch," or "verymuch" characteristicofa child. An endorsement of "pretty much" or "verymuch" is considered to indicate the presence of a symp-tom.

Impairment Rating Scale (IRS;Evans, Allen, Moore,CDATA[&trauss, 2005). The IRS is a brief rating scale com-pleted by parents that assesses their child's general func-tioning across a variety of life domains, including

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relationships with peers, siblings, parents; academicfunctioning; self-esteem; family impact; and overallseverity. Parents indicate the degree to which theybelieve functioning in these domains is or is not a prob-lem for their child and requires or does not requireadditional treatment. A 6-point visual response scaleis used, and scores of 4-6 indicate impaired behaviorfor the given domain. In the current study, parentscompleted the IRSat the initial visit.Measures of Achievement Goals and Achievement-Related OutcomesPattern of Adaptive Learning Scales (PALS;Midgleyet al., 2000). Administered at both the initial andfollow-up visits, the PALSconsists of a set of measuresbased on contemporary approaches to studying stu-dents' achievement motivation using achievement goaltheory. The version of the PALSadministered for thecurrent study included 11 dimensions: Mastery GoalOrientation, Performance-Approach Goal Orientation,Performance-Avoidance Goal Orientation, ClassroomMastery Goal Structure, Classroom Performance-Approach Goal Structure, Classroom Performance-Avoidance Goal Structure,Academic Efficacy,AcademicSelf-Handicapping Strategies, Avoiding Novelty, Cheat-ing Behavior, Disruptive Behavior, and SkepticismAbout the Relevance of School for Future Success. AWork Avoidance subscale was added to evaluate analternative goal orientation that is currently debated asanother important achievement goal to evaluate(Barron CDATA[&arackiewicz, 2003). Specifically, workavoidance refers to an orientation in an achievementsetting to minimize the amount of effort or work thatone must exert. See Table 2 for brief descriptions ofeach dimension.

Each item on the PALSwas rated on a 5-point Likert-type scale with 1 being "Not at all True" and 5 being"VeryTrue."Strong reliability and validity evidence forthe PALS was reported by Midgley et al. (1998).Although typically administered as a paper-and-pencilmeasure, in the current study the scale was adminis-tered via computer. Participants were instructed to readthe question on the screen while listening to the entirequestion being read to them though headphones, andthen to enter the number on the keypad that corre-sponded to the response on the screen that they feltbest applied to them. Once a response was registered,the computer automatically displayed and played thenext question, so there was no opportunity to alterresponses once entered. Participants were expected toprovide a global rating for their classes, rather than rat-ing one specific class, as has been the case in previousstudies conducted with similar age groups (Midgley etal., 2000).

Grade point average (GPA). GPA was computed atthe end of the first and at the end of the second semes-ter by taking the average of students' gradesreported foreach of four core subjects: science, math, history, andreading. All schools used a 0 to 4.0 grading scale and thesame percentage cutoffs to assign letter grades.Procedures

Parents contacted the research center in response torecruitment mailings, and potential participants werepre-screened for eligibility using parent ratings on theDBD. If parent ratings indicated that the child met thepre-screening criteria (likely to meet diagnostic criteriafor ADHD), an evaluation was scheduled for the parentand child at the Alvin V. BairdAttention and LearningDisabilities Center (ALDC). Parents were asked to dis-tribute the teacher versions of the DBD and the IRStoeach of their child's four core course teachers (science,math, history, and reading). The rating scales wereaccompanied by a self-addressedstamped envelope anda cover letter asking teachers to send the completed rat-ing scales directly to the ALDC.

At the initial visit, an explanation of the researchprocedures was provided, and participants and theirparents signed informed consent and assent forms.After the informed consent procedures, the evaluationswere conducted. The initial evaluation lasted 6-8 hoursand encompassed questionnaires, computer assess-ments, and clinical interviews with both the child andparent, administered by trained research assistants. Tohelp guard against fatigue during the evaluation, par-ticipants were not engaged in the same activity forextended periods of time. In addition, several shortbreaks were included throughout the session as well asan hour-long lunch break.

As highlighted above, a number of criteria were usedduring the initial assessment to determine if the childwas eligible to participate. The initial visit encompasseda full eligibility assessment, including diagnostic inter-views conducted with both the child and parent todetermine if participants met DSM-IVcriteria for onesubtype of ADHD. DSM diagnoses require the presenceof impairment in functioning related to diagnoses andevidence of the presence of symptoms to an extent thatis inconsistent with developmental levels. The DISC-IVassesses both impairment and symptoms, and all par-ticipants had to meet diagnostic criteria for a subtypeof ADHD on this measure. In addition, participants alsohad to meet criteria based on parent and teacher rat-ings using the DBD and IRS.Impairment across settingwas considered present if ratings on the IRSby parentsand at least one teacher fell in the impaired range (4 orabove). Symptoms were considered present if a parentand at least one teacher reported the symptoms on the



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Table 2Brief Description of PALS Dimensions

Dimensions Brief Definition

PERSONAL ACHIEVEMENT GOAL ORIENTATIONSMastery Goal Orientation Students' purpose in learning is to develop competence in theircoursework. (alpha=.85)Performance-Approach Students' purpose in learning is to demonstrate their competenceGoal Orientation in their coursework. (alpha=.89)Performance-Avoidance Students' purpose in learning is to avoid demonstrating incompetenceGoal Orientation in their coursework. (alpha=.74)

PERCEIVED CLASSROOM GOAL STRUCTURESClassroom Mastery Goal Students' perceptions that developing competence is emphasizedStructure in the classroom and by teachers. This climate focuses on self-improvement, understanding the material, and learning frommistakes. (alpha=.76)Classroom Performance- Students' perceptions that demonstrating competence in the subject isApproach Goal Structure emphasized in the classroom and by teachers. This climate focuses ongetting good grades, high scores, and right answers. (alpha=.70)Classroom Performance- Students' perceptions that avoiding demonstrating incompetenceAvoidance Goal Structure in the subject is emphasized in the classroom and by teachers. Thisclimate focuses on not doing worse than others, not makingmistakes, and not looking dumb. (alpha=.83)

ACADEMIC RELATED PERCEPTIONS, BELIEFS, AND STRATEGIESAcademic Efficacy Students' feelings of academic competence and ability to do theircoursework. (alpha=.78)Skepticism About the Students' belief that doing well in school will not help them achieveRelevance of School for success in the future. (alpha=.83)Future SuccessDisruptive Behavior Students' use of behaviors that disrupt or disturb the class.(alpha=.89)Cheating Behavior Students' use of cheating in class. (alpha=.87)Avoiding Novelty Students' preference for avoiding new or unfamiliar work in class.

(alpha=.78)Academic Self- Students' use of strategies that prove that circumstances are at fault forHandicapping Strategies low performance rather than lack of ability. (alpha=.84)

Note. Reported alphas are from the PALS manual (Midgley et al., 2000).

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DBD as present "pretty much" or "very much" of thetime. When teacher ratings were not available, as wasthe case for three participants, only the parent reportwas considered. These procedures are consistent withbest practices in diagnosing children and adolescentswith ADHD (Pelham, Fabiano, CDATA[&assetti, 2005). If theresults of the evaluation indicated that the child metcriteria for any subtype of ADHD, did not meet criteriafor a diagnosis of bipolar disorder or schizophrenia,and had an IQ of 80 or greater, he was invited to par-ticipate in the larger treatment study as well as thisstudy.

The follow-up visit was scheduled approximately sixmonths after the initial visit, and lasted approximatelyfour hours. This session was considerably shorter thanthe initial session because none of the diagnostic inter-

views and assessments or IQ and achievement tests wasadministered.

RESULTSProfiling the Achievement Goals of Students withADHD

To address our initial research question of profilingthe goal orientations and perceptions of classroom goalstructure for our sample of students with ADHD, we firstconducted a series of descriptive statistics on each of thegoal variables assessed. Furthermore, because we con-ducted goal assessments both at the beginning and theend of the 6th-grade academic year (referred to as Time1 and Time 2, respectively), we were able to profile stu-dents with ADHD twice and to look at potentialchanges in goals occurring during their first year in mid-

Table 3Descriptive Statistics, Reliabilities, and Results from Paired-Samples t-Tests Comparing Time1 and Time 2 Variables

VariableMastery Goal OrientationTime 1

Time 2Performance-ApproachOrientation

Time 1Time 2Performance-AvoidanceOrientation

Time 1Time 2

Mastery-ApproachClassroom Structure

Time 1Time 2Performance-ApproachClassroom StructureTime 1

Time 2Performance-AvoidanceClassroom StructureTime 1

Time 2

Mean4.324.50

2.852.88

3.473.10

4.324.39

2.852.88

2.462.41

SD.62.76

1.051.27

1.061.21

.628

.752

.731.27

.981.21

Alpha.75

.87

.79

.64

.59

.82

Paired-Samplest-Test

p-Value.14

.84

.05

.50

.93

.82

Note. The possible range of scores for each variable was 1 to 5.

Volume 29, Summer2006 145


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Table 4Descriptive Statistics for ADHD Inattentive and Combined Subtypes and Results from t-Tests

p-Value Cohen's d p-Value Cohen's dVariable Mean SD Time 1 Time 1 Time 2 Time 2.23

.78

.31

.42

.56

.87

.36

.08

.30

.24

.17

.05

.39

.92

.19

.75

.92

.98

.26

.03

.39

.09

.03

.01

Mastery OrientationInattentiveCombinedPerformance-ApproachOrientation

InattentiveCombinedPerformance-AvoidanceOrientation

InattentiveCombined

Mastery-ApproachClassroom StructureInattentiveCombined

Performance-ApproachClassroom StructureInattentiveCombined

Performance-AvoidanceClassroom Structure

InattentiveCombined

4.47 .494.24 .67

2.80 1.112.89 1.03

3.27 1.043.59 1.07

4.22 .724.38 .57

2.77 .862.90 .65

2.43 1.002.48 .98

Note. Descriptive means and SD scores are presented for Time 1.

die school. The means and standard deviations for bothTime 1 and Time 2 goal variables are summarized inTable 3.

We then ran a series of one-way, repeated-measuresANOVAs to determine if students with ADHD adoptedone type of goal orientation more than another and todetermine if they perceived a particular type of class-room goal structure as being promoted more thananother.Regarding goal orientation, students with ADHDadopted significantly different levels of goal orientationacross both Time 1, F(2, 98) = 42.25, p


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being performance-avoidance oriented (M = 2.46 andM = 2.42, for Time 1 and Time 2, respectively). Percep-tions of performance-approach goal structures fellbetween mastery and performance-avoidance goal per-ceptions. Pairwise comparisons using a Bonferroniadjustment showed that all three perceptions of class-room goal structures were significantly different fromeach other at the p


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Table 6Descriptive Statistics for Two Separate Normative Non-ADHD Comparison Samples

Variable Mean SDMasteryGoal OrientationPALS 4.15 .88Local 4.13 .84Performance-ApproachOrientationPALSLocalPerformance-AvoidanceOrientationPALSLocalMastery-ApproachClassroom StructurePALS

LocalPerformance-ApproachClassroom StructurePALSLocalPerformance-AvoidanceClassroom StructurePALSLocal

Note. The possible range of scores for each variable was 1 to 5.

2.463.87

2.403.45

4.114.17

3.343.30

2.032.67

1.15.90

1.041.03

.72

.80

.98

.75

.90.99

heterogeneous in terms of race and gender than theADHD sample in the current study. Our local samplewas more similar to our ADHD sample in terms ofdemographic characteristics and involved 42 students.

Descriptive statistics for the normative samples foreach of achievement goal variables are reported inTable 6. Differences in achievement goals were com-pared between the ADHD sample and the PALSmanualsample through a series of one-sample t-tests. A one-sample t-test allows a researcher to determine if themean of a single sample is significantly different fromsome theoretical value.

In this case, we used the reported means for each ofthe goal variables in the PALSmanual as the theoreticalvalues that we tested our sample against. For example,to determine if there were significant differencesbetween performance-avoidance goal orientationbetween our ADHD sample and the PALS sample, wecompared the mean for our ADHD sample (M's = 3.40and 3.10 for Time 1 and Time 2) against the published

mean in the PALS manual for performance-avoidancegoal orientation (M = 2.40). Because we did not knowwhat time of year the goal variables were collected forthe PALS manual, we compared the PALS manual datato both our Time 1 and Time 2 data. The results fromthese analyses are reported in Table 7.

Results from one-sample t-tests showed that theADHD sample had significantly higher performance-approach and performance-avoidance goal orientationsthan the PALSmanual sample at both Time 1 and Time2. In addition, the ADHD sample had marginally highermastery goal orientations at Time 1 than the PALSman-ual sample. Furthermore, the ADHD sample perceivedthe classroom to be more mastery-oriented, less per-formance-approach oriented, but more performance-avoidance oriented at Time 1 and Time 2.

We adopted a different approach when we compareddifferences in achievement goals between the ADHDsample and our local normative sample. Because we col-lected our local sample and had access to the actual

LearningDisability Quarterly 148


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data, we used more traditional comparisons of inde-pendent-sample t-tests. Furthermore, because the localnormative sample was surveyed at the end of the schoolyear, we limited our comparisons to the Time 2 data ofour ADHD sample. The results from these analyses arealso reported in Table 7.

Compared to the local sample, the ADHD sample hadlower performance-approach goals, t (90) = 3.36, p CDATA[

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