ACHIEVEMENT GOALS OF STUDENTS WITH ADHD

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

A b s t r a c t . Achievement goal theory is one of the dominant the-o retical frameworks being used today to understand and impro v estudent motivation (Bro p h y, 2004). So far, little work has beendone to evaluate the achievement goals of students with ADHD.The current study introduced achievement goal theory and ad-d ressed four primary re s e a rch questions: What are the achievementgoals of students with ADHD? How do achievement goals of stu-dents with ADHD differ from those of students without ADHD?How are achievement goals related to other academic outcomevariables for students with ADHD? Can current instructional prac-tices be altered to promote optimal goals and motivation of stu-dents with ADHD? Results revealed several interesting diff e re n c e sfor students with ADHD, especially concerning perf o rm a n c e -avoidance goal. 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.ZEWELANJI N. SERPELL, Department of Psychology, James Madison University.

ELIZABETH BUVINGER, Department of Psychology, University of Georgia.

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 & Schunk, 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). Stu-dents with ADHD have significantlyhigher dropout rates, increased frequency of failinggrades, and poor academic outcomes compared toyouth without ADHD (e.g., Fischer, Barkley, Fletcher, &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 & Church, 1997; Harackiewicz, Barron, Tauer, &Elliot, 2002). Understanding the potential contributionof achievement goals in the typical path towards pooroutcomes of children with ADHD could help usdevelop prevention and intervention programs toreduce the likelihood of poor outcomes for theseyouth. Research is needed to address the followingquestions: What are the achievement goals of studentswith ADHD? How do the achievement goals of stu-

Volume 29, Summer 2006 1

dents with ADHD differ from those of students withoutADHD? How are achievement goals related to otheracademic outcome variables for students with ADHD?Finally, can current instructional practices be altered topromote optimal goals and motivation of students withADHD?

Theoretical BackgroundAchievement goal theory provides a framework for

understanding the reasons why we engage in achieve-ment-related behavior and the standards used to evalu-ate success (Ames, 1992; Dweck & Leggett, 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’s motivation.

Different labels have been used by differentresearchers; however, two general types of achievementgoals 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 performance goals, 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 patterns following 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 mastery goal perspective (see Barron & Harackiewicz,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 mastery goalperspective, suggesting that performance goals can alsopromote important achievement outcomes becausethey help orient individuals toward achievement andcompetence (Harackiewicz, Barron, Pintrich, Elliot, &Thrash, 2002). For example, Wentzel (1991) noted thathigh school students who adopted both mastery andperformance goals had higher GPAs than students whoonly adopted mastery goals. In fact, several studies havefound positive performance goal effects in some situa-tions and for certain individuals (see Barron &Harackiewicz, 2000, for a review). Thus, a number oftheorists endorse a multiple goal perspective in whichadopting both types of achievement goals may be moreadaptive.

Furthermore, more recent work on achievement goaltheory suggests that a mastery-performance goal distinction of motivation may be a simplistic dichot-omization. For example, Elliot and colleagues (Elliot & Church, 1997; Elliot & Harackiewicz, 1996) parti-tioned the performance goal construct into p e r f o r m-ance-approach goals, 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-avoidance goals, 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).2

Thus, an alternative implication of the multiple goalperspective would be to question whether certain stu-dent populations, like students with ADHD, are endors-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

Learning Disability Quarterly 2

measure the goals perceived to be created by a particu-lar classroom and teacher (often referred to as perceivedclassroom goal structure or classroom goal 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, severalresearchers have 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 expertise in a given subject. Furthermore, students are typicallytracked and grouped by ability into higher versus lowersections of particular subjects, making normative comparisons 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 &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, &Owens, 2001; Milich & Greenwell, 1991; Milich &Okazaki, 1991) and exhibit greater frustration with

tasks (Milich & Greenwell, 1991; Milich & Okazaki,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 only limited research usingcontemporary measures of achievement goals (which formally compare and contrast levels of mastery, per-formance approach, and performance-avoidance goals)has studied students with ADHD, a series of studieshave been conducted on children with ADHD utilizingseveral of the core concepts and research paradigmsfrom which achievement goal theory derived. Speci-fically, some ADHD researchers have been influencedby the early work of Dweck (e.g., Diener & Dweck,1978; Dweck, 1975) and her laboratory paradigm ofusing solvable and unsolvable academic tasks. Thiswork has been used to study learned helplessness andattribution patterns in achievement situations to deter-mine the conditions under which children respondwith an adaptive, mastery response vs. a maladaptive,helpless response. For example, Milich (1994) revieweda series of studies in which he and his collaboratorsemployed a research paradigm similar to Dweck’s toevaluate the response patterns of students with ADHDwhen faced with 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 & Greenwell, 1991; Milich &

Volume 29, Summer 2006 3

Learning Disability Quarterly 4

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, &Hoza, 1993; Milich, Carlson, Pelham, & Licht, 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 & Shapiro, 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) to promote better academicsuccess 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 & Leggett,1988). However, investigations that have attempted toevaluate more contemporary measures of achievementgoals on student samples with ADHD are lacking. Oneof the few studies that does provide an initial descrip-tion was conducted by Carlson, Booth, Shin, and Canu(2002), who examined motivational variables in chil-dren with ADHD through self, parent, and teacher rat-ings. Although a range of different motivationalinstruments were utilized, one measure included anassessment of children’s self-reported mastery goals andperformance goals (Schunk, 1996).1 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 & Oehler-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 procedure to identify goal orienta-tions, Dunn and Shapiro (1999) used 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 for this popu-lation. Understanding the goals for students with ADHDwill provide important clues on how to improve aca-demic success for this population of students who are atsuch a high risk for school failure and dropout.

Current StudyTo investigate the role of achievement goals among

students with ADHD, middle school students with adiagnosis of ADHD completed the goal subscales fromthe 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 PALS isone of the most widely used tools for assessing achieve-ment goals in middle schools (see Midgley, 2002). Itprovides an assessment of students’ goal orientations aswell as their perceptions of classroom goal structures.In addition, it differentiates between mastery goals,performance-approach goals, and performance-avoid-ance goals. While other goal measures have been devel-oped, we specifically used the PALS to be able to

connect to the already large body of findings based onthis 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 with ADHD?

2. How do goal orientations and perceptions of classroom goal structure for students with ADHD differ from those of students without ADHD?

3. How do goal orientations and perceptions of classroom goal structure relate to other academic variables for students with ADHD?

Regarding the first research question, we wouldexpect that students who are struggling academically inschool 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-avoidance goal-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 characteristic of youth with ADHD as they expe-rience frustration and failure with academics and socialinteractions on a regular basis. 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 structures would 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 structures wouldbe 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 investigate relationships between achievement goal variables andother academic outcome variables, students’ GPA andresponses to additional subscales from the PALS werecollected, 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 goal 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 thePALS due to the difficulty in school experienced by thecurrent ADHD sample.

METHODParticipants

Seventy students in 6th grade, ranging in age from 10to 13 years old, from five middle schools in theShenandoah Valley of Virginia, participated in thestudy. Data were collected during comprehensive evalu-ations for a longitudinal study of the effects of a school-based treatment program for children with ADHD.Parents referred their children to the study in responseto recruitment mailings requesting children with prob-lems related to impulsivity, hyperactivity, or inatten-tion. Eligibility criteria for the longitudinal studyincluded (a) meeting diagnostic criteria for one subtypeof ADHD, (b) having an IQ equal to or greater than 80,and (c) not meeting diagnostic criteria for bipolar disor-der or schizophrenia. Participants for the current studyincluded only students with complete data who wereaccepted into the program between the months ofSeptember and December (first semester of school) andwho returned for a follow-up visit between the monthsof March 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 ( D I S C -

IV; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone,2000). Administered during the initial visit to establishparticipant eligibility, the DISC-IV is a structured diag-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 to the parents, as well as a sub-stance use section to the participants. The instrumenthas adequate reliability and validity evidence (McGrath,Handwerk, Armstrong, Lucas, & Friman, 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 &Kaufman, 1990). The K-BIT is an individually adminis-tered measure of verbal and nonverbal intelligence forchildren, adolescents, and adults. This test, also used for eligibility, was administered at the initial visit. The

Volume 29, Summer 2006 5

Learning Disability Quarterly 6

K-BIT has adequate reliability and validity evidence(Kaufman & Kaufman, 1990) and yields standard scorescomparable to those provided by comprehensive intelli-gence batteries but requires only 15 to 30 minutes toadminister.

Wechsler Individual Achievement Tests-II ( W I A T - I I ;The Psychological Corporation, 2001). The WIAT-II is 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 ( D B D ;Pelham, Evans, Gnagy, & Greenslade, 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., “Is often eas-ily distracted by extraneous stimuli”), and parents areasked to indicate whether a behavior is “not at all,” “justa little,” “pretty much,” or “very much” characteristic ofa 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,& Strauss, 2005). The IRS is a brief rating scale com-pleted by parents that assesses their child’s general func-

Table 1Descriptive Statistics of Sample Demographic Variables

N 50

Male 74.0%

Age M = 11.70 (SD = .47)

Full Scale IQ M =104.56 (SD = 12.23)

WIAT- Word Reading M = 99.40 (SD = 12.67)

WIAT- Numerical Operations M = 96.38 (SD = 14.09)

WIAT- Spelling M = 97.70 (SD = 13.94)

RaceCaucasian 96%Hispanic or Latino 2%Other 2%

Type of ADHDInattentive Type 36%Hyperactive/Impulsive Type 0%Combined Type 64%

Any Comorbid Disorder 74%ODD 66%CD 12%Mania/Hypomania 4%Major Depression 2%Dysthymic Disorder 2%

GPA at end of 1st Semester M = 2.41 (SD = .95)GPA at end of 2nd Semester M = 2.27 (SD = .96)

Note. WIAT=Wechsler Individual Achievement Test, ODD=Oppositional Defiant Disorder, and CD=Conduct Disorder.

tioning across a variety of life domains, including rela-tionships with peers, siblings, parents; academic func-tioning; self-esteem; family impact; and overall severity.Parents indicate the degree to which they believe func-tioning in these domains is or is not a problem andrequires or does not require additional treatment. A 6-point visual response scale is used, and scores of 4-6indicate impaired behavior for the given domain. In thecurrent study, parents completed the IRS at the initialvisit.

Measures of Achievement Goals and Achievement-Related Outcomes

Pattern of Adaptive Learning Scales (PALS; Midgleyet al., 2000). Administered at both the initial and follow-up visits, the PALS consists of a set of measuresbased on contemporary approaches to studying stu-dents’ achievement motivation using achievement goaltheory. The version of the PALS administered for thecurrent study included 11 dimensions: Mastery GoalOrientation, Performance-Approach Goal Orientation,Performance-Avoidance Goal Orientation, ClassroomM astery 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 & Harackiewicz, 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 PALS was rated on a 5-point Likert-type scale with 1 being “Not at all True” and 5 being“Very True.” 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 felt bestapplied to them. Once a response was registered, thecomputer automatically displayed and played the nextquestion, so there was no opportunity to alter responsesonce entered. Participants were expected to provide aglobal rating for their classes, rather than rating one spe-cific class, as has been the case in previous studies con-ducted with similar age groups (Midgley et al., 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’ grades reported 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.

ProceduresParents contacted the research center in response to

recruitment 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. Baird Attention and LearningDisabilities Center (ALDC). Parents were asked to dis-tribute the teacher versions of the DBD and the IRS toeach of their child’s four core course teachers (science,math, history, and reading). The rating scales wereaccompanied by a self-addressed stamped 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 research pro-cedures was provided, and participants and their par-ents signed informed consent and assent forms. Afterthe informed consent procedures, the evaluations wereconducted. The initial evaluation lasted 6-8 hours andencompassed questionnaires, computer assessments,and clinical interviews with both the child and parent,administered by trained research assistants. To helpguard against fatigue during the evaluation, participantswere not engaged in the same activity for extended peri-ods of time. In addition, several short breaks wereincluded throughout the session as well as an hour-longlunch 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-IV criteria 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 partic-ipants had to meet diagnostic criteria for a subtype ofADHD on this measure. In addition, participants alsohad to meet criteria based on parent and teacher ratingsusing the DBD and IRS. Impairment across setting wasconsidered present if ratings on the IRS by parents andat 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

Volume 29, Summer 2006 7

Learning Disability Quarterly 8

Table 2Brief Description of PALS Dimensions

Dimensions Brief Definition

PERSONAL ACHIEVEMENT GOAL ORIENTATIONS

Mastery Goal Orientation Students’ purpose in learning is to develop competence in their coursework. (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 STRUCTURES

Classroom Mastery Goal Students’ perceptions that developing competence is emphasized Structure in the classroom and by teachers. This climate focuses on self-

improvement, understanding the material, and learning from mistakes. (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 on

getting good grades, high scores, and right answers. (alpha= .70)

Classroom Performance- Students’ perceptions that avoiding demonstrating incompetence Avoidance Goal Structure in the subject is emphasized in the classroom and by teachers. This

climate focuses on not doing worse than others, not making mistakes, and not looking dumb. (alpha= .83)

ACADEMIC RELATED PERCEPTIONS, BELIEFS, AND STRATEGIES

Academic Efficacy Students’ feelings of academic competence and ability to do their coursework. (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 Success

Disruptive 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).

DBD as present “pretty much” or “very much” of thetime. When teacher ratings were not available, as was thecase for three participants, only the parent report wasconsidered. These procedures are consistent with bestpractices in diagnosing children and adolescents withADHD (Pelham, Fabiano, & Massetti, 2005). If the resultsof the evaluation indicated that the child met criteria forany subtype of ADHD, did not meet criteria for a diag-nosis of bipolar disorder or schizophrenia, and had an IQof 80 or greater, he was invited to participate in the largertreatment study as well as this study. The follow-up visitwas scheduled approximately six months after the initialvisit, and lasted approximately four hours. This sessionwas considerably shorter than the initial session becausenone of the diagnostic interviews and assessments or IQand achievement tests was administered.

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

Volume 29, Summer 2006 9

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

Paired-Samplest-Test

Variable Mean SD Alpha p-ValueMastery Goal Orientation

Time 1 4.32 .62 .75 .14Time 2 4.50 .76

Performance-Approach Orientation

Time 1 2.85 1.05 .87 .84Time 2 2.88 1.27

Performance-Avoidance Orientation

Time 1 3.47 1.06 .79 .05Time 2 3.10 1.21

Mastery-ApproachClassroom Structure

Time 1 4.32 .628 .64 .50Time 2 4.39 .752

Performance-ApproachClassroom Structure

Time 1 2.85 .727 .59 .93Time 2 2.88 1.27

Performance-AvoidanceClassroom Structure

Time 1 2.46 .975 .82 .82Time 2 2.41 1.21

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

Learning Disability Quarterly 10

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<.001 (η2= . 4 6 ) ,and Time 2, F(2, 98) = 49.00, p<.001 (η2=.50). Masterygoal orientations (M = 4.32 and M = 4.50 for Time 1 andTime 2, respectively) were the goal most likely to beadopted across both time frames, and performance-approach goal orientations (M = 2.85 and M = 2.88, for Time 1 and Time 2, respectively) were least likely to be adopted across both time frames. Performance-avoidance goal orientations fell between mastery and

performance-approach goal adoption across both timeframes (M = 3.47 and M = 3.10 for Time 1 and Time 2,respectively). Pairwise comparisons using a Bonferroniadjustment showed that all three goal orientations weresignificantly different from each other at the p< . 0 0 1level at both Time 1 and Time 2.

Regarding perceptions of classroom goal structures,students with ADHD perceived significantly differentlevels of classroom goal structures across Time 1, F( 2 ,98)=91.59, p<.001 (η2=.65), and Time 2, F(2, 98)=95.89,p<.001 (η2=.66). Students with ADHD were more likelyto perceive their classrooms as being mastery-oriented(M = 4.32 and M = 4.39, for Time 1 and Time 2, respec-tively) and least likely to perceive their classrooms asbeing performance-avoidance oriented (M = 2.46 and M = 2.42, for Time 1 and Time 2, respectively). Percep-tions of performance-approach goal structures fell

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

Mastery OrientationInattentive 4.47 .49 .23 .36 .39 .26Combined 4.24 .67

Performance-Approach Orientation

Inattentive 2.80 1.11 .78 .08 .92 .03Combined 2.89 1.03

Performance-Avoidance Orientation

Inattentive 3.27 1.04 .31 .30 .19 .39Combined 3.59 1.07

Mastery-ApproachClassroom Structure

Inattentive 4.22 .72 .42 .24 .75 .09Combined 4.38 .57

Performance-ApproachClassroom Structure

Inattentive 2.77 .86 .56 .17 .92 .03Combined 2.90 .65

Performance-AvoidanceClassroom Structure

Inattentive 2.43 1.00 .87 .05 .98 .01Combined 2.48 .98

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

between 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<.001 level at Time 1 and Time 2.

Next, we investigated whether there were any addi-tional differences in goal orientation or perception ofclassroom goal structures according to type of ADHD(inattentive vs. combined hyperactive and inattentive)or severity of ADHD symptoms (as measured by theDBD; Pelham et al., 1992). Type of ADHD was adichotomous variable, so we used a series of independ-ent t-tests. Severity of ADHD symptoms was a c o n t i n u-ous variable, so we used regression analyses. As shownin Table 4 and 5, no significant relationships emerged,suggesting that goal orientations and perceived goalstructures did not differ as a function of subtype ors e v e r i t y .

Finally, to investigate any goal changes that mighthave occurred from the beginning of the academic yearto the end, we conducted a series of paired sample t-tests for the goal variables across Time 1 and Time 2.Regarding goal orientation variables, only perform-ance-avoidance goals were significantly different acrosstime, t(49) = 2.0, p = .05, with performance-avoidance

goal orientations being less endorsed at Time 2 than atTime 1. Regarding classroom goal structure variables,no significant changes emerged across time. Thus,achievement goal adoption and perceptions of theachievement goal environment remained relatively sta-ble across students’ first year in middle school.

Comparing the Achievement Goals of Studentswith ADHD to Those of Non-ADHD Samples

In addition to profiling the goal orientations and per-ceptions of classroom goal structure of our sample ofstudents with ADHD, we also examined how the goalsof students diagnosed with ADHD compared to thegoals of students who had not been diagnosed withADHD. To address this research question, we comparedour ADHD sample of 6th-grade students to two norma-tive samples. The first sample came from the PALS man-ual (Midgley et al., 2000), which reported means from asample of 6th-grade students from Michigan. The sec-ond sample came from 6th-grade students living in thesame geographic region as the students diagnosed withADHD. The demographic characteristics for the PALSsample were more heterogeneous in terms of race andgender than the ADHD sample in the current study. Ourlocal sample was more similar to our ADHD sample in

Volume 29, Summer 2006 11

Table 5Regression Analyses for Severity of ADHD

Beta p-Value R2 Beta p-Value R2

Variable Time 1 Time 1 Time 1 Time 2 Time 2 Time 2

Mastery Orientation -.18 .20 .034 .06 .70 .056

Performance-Approach -.08 .60 .006 .10 .51 .009Orientation

Performance-Avoidance -.02 .92 .000 .15 .30 .023Orientation

Mastery-Approach -.25 .08 .063 .00 1.00 .000Classroom Structure

Performance-Approach .027 .85 .001 .18 .22 .031Classroom Structure

Performance-Avoidance -.157 .28 .025 .16 .26 .026Classroom Structure

Note. Beta values represent the standardized coefficients.

Learning Disability Quarterly 12

terms of demographic characteristics and involved 42students.

Descriptive statistics for the normative samples foreach of achievement goal variables are reported in Table 6. Differences in achievement goals were com-pared between the ADHD sample and the PALS manualsample 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 thereported means for each of the goal variables in thePALS manual as the theoretical values that we tested oursample against. For example, to determine if there weresignificant difference between performance-avoidancegoal orientation between our ADHD sample and thePALS sample, we compared the mean for our ADHDsample (M’s = 3.40 and 3.10 for Time 1 and Time 2)against the published mean in the PALS manual for performance-avoidance goal orientation (M = 2.40).Because we did not know what time of year the goal

variables were collected for the PALS manual, we com-pared the PALS manual data to both our Time 1 andTime 2 data. The results from these analyses arereported in Table 7.

Results from one-sample t-tests showed that theADHD sample had significantly higher performance-approach and performance-avoidance goal orientationsthan the PALS manual sample at both Time 1 and Time2. In addition, the ADHD sample had marginally highermastery goal orientations at Time 1 than the PALS man-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 actualdata, we used more traditional comparisons of inde-pendent-sample t-tests. Furthermore, because local nor-

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

Variable Mean SD

Mastery Goal OrientationPALS 4.15 .88Local 4.13 .84

Performance-Approach OrientationPALS 2.46 1.15Local 3.87 .90

Performance-Avoidance OrientationPALS 2.40 1.04Local 3.45 1.03

Mastery-Approach Classroom StructurePALS 4.11 .72Local 4.17 .80

Performance-Approach Classroom StructurePALS 3.34 .98Local 3.30 .75

Performance-Avoidance Classroom StructurePALS 2.03 .90Local 2.67 .99

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

mative 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 <.001. The ADHD sample also perceived the classroom tobe less performance-approach oriented, t (90) = 2.68, p= .009. In sum, both normative samples and the ADHDsample endorsed mastery achievement goals the most.However, both normative samples endorsed perform-ance-approach and performance-avoidance goals atsimilar levels, whereas the ADHD students endorsed

performance-avoidance goals more than performance-approach goals.

Relationship of Achievement Goals to AcademicOutcome for Students with ADHD

To address the third research question regarding therelationships between achievement goals and academicbehaviors for students with ADHD, we first calculatedzero-order correlations. The correlation matrix in Table8 summarizes the results for both Time 1 and Time 2data collections (variables collected at Time 1 arereported in the top right of the correlational matrix andvariables collected at Time 2 are reported in the bottom

Volume 29, Summer 2006 13

Table 7Results of t-Tests Comparing Normative Samples to ADHD Sample

p-Value p-Value p-ValuePALS vs. PALS vs. Local vs.

Variable ADHD at Time 1 ADHD at Time 2 ADHD at Time 2

Mastery OrientationPALS .053 .346 –Local – – .136

Performance-Approach Orientation

PALS .011 .023 –Local – – .008

Performance-Avoidance Orientation

PALS <.001 <.001 –Local – – .223

Mastery-Approach Classroom Structure

PALS .022 .011 .178Local

Performance-Approach Classroom Structure

PALS <.001 <.001 –Local – – .009

Performance-Avoidance Classroom Structure

PALS .003 .029 –Local – – .286

Note. Descriptive means and SD for the ADHD sample are reported in Table 3; descriptive means and SD for the PALS and LOCAL samples are reported in Table 6.

Learning Disability Quarterly 14

left of the matrix). At Time 1, mastery goal orientationswere positively related to adaptive variables, such as aca-demic efficacy (r = .36), and were negatively correlatedwith maladaptive variables, such as avoiding novelty (r = -.36), skepticism about the relevance of school (r = -.26), disruptive behavior (r = -.24), and work-avoidance(r = -.46). Performance-approach goal orientations werenot strongly correlated with academic outcome vari-ables, with its highest correlations being only -.20 withdisruptive behavior and .20 with academic efficacy. Asexpected, performance-avoidance goal orientationswere positively correlated with maladaptive academicvariables like avoiding novelty (r = .35); however, sur-prisingly, they were also positively correlated with GPA(r = .32) and negatively correlated with skepticism aboutthe relevance of school (r = -.23), which has not beenfound in past research using normative samples.

At Time 2, mastery goal orientations again wererelated positively to adaptive academic behaviors likeacademic efficacy (r = .54) and negatively to a wide variety of maladaptive academic behaviors, such ascheating behavior (r = -.50). Similar to Time 1, perform-ance-approach goal orientations were not stronglyrelated to the other academic variables at Time 2, exceptthey now shared a negative correlation with GPA.Finally, performance-avoidance goal orientations wereagain positively related to avoiding novelty (r = .31), butwere not as linked to other academic variables.

At Time 1, perceived mastery classroom goal struc-tures were associated with adaptive variables, such asacademic efficacy (r = .47) and GPA (r =.32), and werenegatively correlated with maladaptive variables, suchas avoiding novelty (r = -.35) and work-avoidance (r = -.28). Perceived performance-approach classroom goalstructures were not strongly associated with other aca-demic variables except for being positively associatedwith avoiding novelty (r = .30). Similarly, perceived per-formance-avoidance classroom goal structures were notstrongly associated with other academic variablesexcept for being positively associated with avoidingnovelty (r = .24). At Time 2, perceived mastery class-room goal structures again were associated with adap-tive variables, such as academic efficacy (r = .30), andnegatively correlated with maladaptive variables, suchas cheating behavior (r = -.34) and skepticism about therelevance of school (r = -.21). In contrast to Time 1, bothperceived performance-approach and performance-avoidance classroom goal structures were now moreclearly associated to academic outcomes. Specifically,performance-approach classroom goal structures werepositively associated with a range of maladaptive out-comes, such as self-handicapping (r = .24), avoidingnovelty (r = .33), skepticism about relevance of school (r = .34), and disruptive behaviors (r = .34). Similarly,

performance-avoidance classroom goal structures werepositively associated with maladaptive outcomes likeself-handicapping (r = .32), avoiding novelty (r = .40),skepticism about relevance of school (r = .34), disruptivebehaviors (r = .24), and GPA (r = -.19).

Regression AnalysesAlthough correlations provide a first step in under-

standing the pattern of associations between variables,they are limited in their ability to evaluate and test theimpact of pursuing multiple goals (see Barron &Harackiewicz, 2001, for a review on how to test for mul-tiple achievement goal effects). Therefore, to more thor-oughly investigate the potential additive and interactiveeffects of achievement goals, we conducted a series ofmultiple regression analyses.

First, we tested a regression model that simultane-ously included all three goal orientation variables tolook at the independent and interactive effects of goalorientations on each of our academic outcomes.Second, we tested a regression model that included allthree perceived classroom goal structure variables tolook at the independent and interactive effects of class-room goal structures on each outcome. We thoughttesting separate regression models would best honorpast research using normative samples that may haveadopted a goal orientation approach vs. a classroomgoal structure approach and would allow cleaner com-parisons with our present sample of ADHD students toother studies using normative samples. In addition,because we collected data at two separate points duringthe school year, we conducted a set of regressions withTime 1 variables and another set of regressions withTime 2 variables. We followed recommendations byAiken and West (1991) and Frazier, Tix, and Barron(2004) to test for the main and interactive effects of ourgoal variables, such as standardizing all continuous vari-ables and using standardized variables to calculate inter-action terms. If interactions were significant, wecalculated predicted values to determine the nature ofthe interaction. However, due to our limited pool andsample size of ADHD students, it is important to notethat our power to detect interactive effects was quitelow. Below, we report only the significant effects thatemerged from each model. We also report squaredsemi-partial coefficients (s r2) to provide an index ofeffect size for each effect.

Goal Orientation ModelsTo investigate the effects of goal orientations on aca-

demic efficacy, self-handicapping, avoiding novelty,skepticism about the relevance of school, disruptivebehavior, cheating behavior, work avoidance, and GPA,we tested a model that included the main effect termsfor mastery, performance-approach, and performance-

Volume 29, Summer 2006 15

Learning Disability Quarterly 16

avoidance goal orientations as well as the two-wayinteractions between these terms. In addition, weincluded IQ score as a covariate in all goal orientationanalyses to look at the role of participants’ motivationabove and beyond their cognitive ability.

Time 1. On avoiding novelty, the overall model wassignificant, F(7, 42)=2.26, p=.048 (R2=.27). There weremain effects for both mastery goal orientations, F( 1 ,42)=4.23, p=.046 (B=-.34, s r2=.07), and performance-avoidance goal orientations, F(1, 42)=5.54, p=.023 (B=.38, s r2=.10). Students who reported higher levels ofmastery goals were less likely to avoid novelty, whereasstudents who reported higher levels of performance-avoidance goals were more likely to avoid novelty.

On work-avoidance, the overall model was signifi-cant, F(7,42)=2.38, p=.039 (R2=.28). A main effect wasrevealed for mastery goal orientation, F( 1 , 4 2 ) = 9 . 4 7 ,p=.004 (B=-.51, sr2=.16). Students adopting higher levelsof mastery goals reported less work avoidance.

On GPA, the overall model was nearly significant,F(7,42)=2.15, p=.059 (R2=.26). A main effect was foundfor performance-avoidance goal orientations, F( 1 ,42)=6.68, p=.013 (B=.42, s r2=.12). Students who re-ported higher levels of performance-avoidance goalshad higher GPAs.

Time 2. On academic efficacy, the overall model wassignificant, F(7,42)=3.08, p=.010 (R2=.34). A main effectwas found for mastery goal orientations, F(1, 42)=7.31,p=.010 (B=-.53, sr2=.11). Students reporting higher levelsof mastery goal orientation indicated higher levels ofacademic efficacy.

On self-handicapping, the overall model was signifi-cant, F(7, 42)=2.57, p=.027 (R2=.30). A main effect wasfound for mastery goal orientations, F(1, 42)=4.64,p=.037 (B=-.43, sr2=.08). Students reporting higher levelsof mastery goal orientation indicated lower levels ofself-handicapping.

On avoiding novelty, the overall model was signifi-cant, F(7, 42)=2.97, p=.013 (R2=.33). A main effect wasrevealed for performance-avoidance goal orientations,F(1, 42)=10.37, p=.002 (B=.53, sr2=.17). Students report-ing higher levels of performance-avoidance goals weremore likely to avoid new activities and challenges inschool. In addition, the interaction of performance-approach and performance-avoidance goals was signifi-cant, F(1, 42)=4.16, p=.048 (B=-.33, sr2=.07). The patternof the interaction revealed that students who hadhigher performance-avoidance and lower performance-approach goals were the most likely to avoid novelty.

On cheating behavior, the overall model was signifi-cant, F(7, 42)=2.48, p=.032 (R2=.29). Mastery goals, F( 1 ,42)=5.18, p=.028 (B=-.46, sr2=.09), were a significant pre-dictor. Student who were higher on mastery goals wereless likely to cheat.

On work-avoidance, the overall model was signifi-cant, F(7, 42)=2.64, p=.024 (R2=.31). Mastery goals, F(1, 42)=5.59, p=.023 (B=-.47, s r2=.09), performance-approach goals, F(1, 42)=8.63, p=.005 (B=.544, sr2= . 1 4 ) ,and performance-avoidance goals, F(1, 42)=9.65, p= . 0 0 3(B=-.522, sr2=.16), were significant predictors. Studentshigher on mastery goals and performance-avoidancegoals were less likely to be work avoidant, whereas stu-dents higher on performance-approach goals were morelikely to be work avoidant.

Perceived Classroom Goal Structure ModelTo investigate the effects of perceived classroom goal

structures on academic efficacy, self-handicapping,avoiding novelty, skepticism about the relevance ofschool, disruptive behavior, cheating behavior, workavoidance, and GPA, we tested a model that includedthe main effect terms for perceptions of mastery, per-formance-approach, and performance-avoidance goalsin the classroom as well as the two-way interactionsbetween these terms.

Time 1. On avoiding novelty, the overall model wassignificant F(6, 43)=3.577, p=.006 (R2=.33). Main effectswere found for mastery classroom goal structure, F( 1 ,43)=8.58, p=.005 (B=-.38, s r2=.13), and performance-approach classroom goal structure, F(1, 43)=5.89,p=.019 (B=.349, s r2=.09). Students who perceived theclassroom environment as being mastery-oriented wereless likely to avoid novelty, whereas students who per-ceived the environment as being performance-approachoriented were more likely to avoid novelty.

Time 2. On academic efficacy, the overall model wassignificant, F(7, 42)=2.88, p=.015 (R2=.32). A main effectwas found for mastery classroom goal structure, F( 1 ,42)=11.43, p=.002 (B=-.59, sr2=.18). Students reportinghigher levels of mastery classroom goal structure indi-cated higher levels of academic efficacy.

On avoiding novelty, the overall model was signifi-cant, F(6, 43)=2.781, p=.022 (R2=.28). However, no indi-vidual predictors that reached significance.

On skepticism about the relevance of school, theoverall model was significant, F(6, 43)=3.55, p= . 0 0 6(R2=.33). Main effects were found for mastery classroomgoal structure, F(1, 43)=4.16, p=.048 (B=-.348, sr2= . 0 6 ) ,and performance-approach classroom goal structure,F(1, 43)=4.32, p=.044 (B=.355, sr2=.07). Students whoperceived the environment as being mastery-orientedwere less likely to have skepticism about the relevanceof school for the future. In contrast, students who perceived the environment as being performanceapproach-oriented were more likely to have skepticismabout the relevance of school for the future.

On disruptive behavior, the overall model was signif-icant, F(6, 43)=3.314, p=.009 (R2=.32). A main effect was

revealed for performance-approach classroom goalstructure, F(1, 43)=4.55, p=.039 (B=.368, s r2= . 0 7 ) .Students who perceived the classroom as being per-formance-approach oriented were more likely to be dis-ruptive during class.

DISCUSSIONAchievement goal theory is one of the dominant the-

oretical frameworks being used today to understand andimprove student motivation (Brophy, 2004). So far, lit-tle work has been done to evaluate the achievementgoals of students with ADHD. At the outset of thispaper, we recommended a series of questions thatresearchers should begin pursuing to evaluate the con-tribution that achievement goal theory could play inunderstanding and improving the motivation of stu-dents with ADHD. In the present investigation, weaddressed three of the four questions.

Research Question 1The first question posed was: “What are the achieve-

ment goals of students with ADHD?” Specifically, thisquestion called for a more thorough assessment of theachievement goals of students with ADHD using morecontemporary and multi-dimensional measures ofachievement goals. Researchers have conducted studieson achievement goals from person-centered as well assituation-centered perspectives. A person-centered (goalorientation) approach asks students to report the goalsthat they personally adopt when learning, whereas a sit-uation-centered (perceived classroom goal structure)approach asks students to report the goals that they per-ceive are being promoted by their teachers and theirclassrooms. We were particularly interested in deter-mining whether students with ADHD endorsed goal ori-entations or perceived their classroom environmentsdifferently than did youth without ADHD.

Regarding goal orientations, we found that studentswith ADHD were most likely to endorse a mastery goalorientation for their schoolwork and least likely toadopt a performance-approach goal orientation for theircoursework. Students’ level of performance-avoidancegoal orientation fell in between their mastery and per-formance-approach goal orientation. This pattern ofgoal orientation adoption was true at both Time 1 andTime 2 assessments during students’ 6th-grade year.

Three things are striking based on the pattern thatemerged. First, the higher level of mastery goal orienta-tion and lower levels of performance goal orientationsmay be considered a positive result by many goal theo-rists, especially those who endorse the mastery goal per-spective (where optimal motivation is believed to occur.Second, the fact that the students were still struggling todo well in school may be interpreted by other goal the-orists as the result of failing to endorse performance-

approach goals, especially by goal theorists who endorsea multiple-goal perspective (where optimal motivationis believed to occur by adoption of both mastery goalsand performance-approach goals). When looking at themean level of goal adoption on the 1-5 scale, a masterygoal orientation is endorsed well above the midpoint ofthe scale, whereas a performance-approach goal orien-tation is endorsed below the midpoint. Third, the levelof performance-avoidance goal adoption is also abovethe midpoint of the scale; thus, failure to excel inschool may be due to elevated levels of performance-avoidance goals in this student population. We wouldlike to note, however, some caution in comparing theexact position that students report between differentgoal orientation measures. Since the various goal scaleshave not been standardized, we cannot assume they aredirectly comparable.

Regarding perceived classroom goal structures, wefound that our sample of students with ADHD weremost likely to perceive mastery classroom goal struc-tures and least likely to perceive performance-avoidanceclassroom goal structures for their coursework, with per-ceptions of performance-approach classroom goal struc-tures falling in between. This pattern of perceivedclassroom goal structures occurred at both Time 1 andTime 2 assessments during students’ 6th-grade year.When looking at the mean level of classroom goal struc-ture on the 1-5 scale, mastery classroom goal structureswere perceived well above the midpoint of the scale,whereas performance-approach and performance-avoidance classroom goal structures were perceivedbelow the midpoint.

Once again, three things are striking based on the pat-tern that emerged. First, proponents of the mastery goalperspective might argue that this pattern of findings isthe preferred pattern to help promote and reinforcemaximizing students’ mastery goal pursuits while mini-mizing their performance goal pursuits. Second, propo-nents of the multiple-goal perspective may see the lackof perceived performance-approach classroom goalstructures as a problem to encourage students to orientto both mastery and performance-approach goals (seealso Linnenbrink, 2005). Third, even though both per-ceived performance goal structures were rated below themidpoint of the scale, perhaps the optimal goal patternfor perceived classroom goal structures requires a moreradical shift of having performance goal structurescloser to 1 on the 1-5 scale. Again, some caution shouldbe noted in comparing the exact position that studentsreport between different perceived goal structure meas-ures. Like the goal orientation scales, they have notbeen standardized.

We next evaluated whether there were any differencesin goal orientations or perceptions of perceived class-

Volume 29, Summer 2006 17

Learning Disability Quarterly 18

room climate according to type of ADHD or severity ofADHD symptoms. Unlike previous research, which hasdocumented motivational profile differences based ontype of ADHD for other motivational variables (Carlsonet al., 2002), no significant relationships emerged in thecurrent sample for any of the achievement goal con-structs. However, due to the limited sample size, moreresearch is needed before any definitive conclusions canbe made, and we refer the reader to Carlson et al. (2002)for potential implications of how to approach interven-tions for different subtypes of ADHD when differentmotivational patterns emerge.

Finally, we evaluated whether there were any changesin students’ goal orientations or perceptions of per-ceived classroom climate over their 6th-grade year. Weconsidered it essential to evaluate goals at more thanone time point as students were transitioning into anew school environment that has been particularlyassociated with the development of greater performancegoal focus (Midgley & Edelin, 1998). Surprisingly, onlyone difference emerged among the six goal variables,with students reporting less performance-avoidancegoal orientation at the end of 6th grade than at thebeginning. These findings generate some potentiallyuseful hypotheses about children with ADHD. It isinteresting that the endorsement of a performance-avoidance orientation was unique to students withADHD and that the orientation declined over the courseof the year. This decline corresponds with the typicalpattern of declining grades that occur for students withADHD over the course of the academic year (Evans,Langberg, Raggi, Allen, & Buvinger, 2005). Althoughthese findings are not conclusive, they suggest that aperformance-avoidance orientation may play a uniquerole with these students and potentially facilitate class-room achievement.

Research Question 2The second question was: “How do achievement goals

of students with ADHD differ from those of studentswithout ADHD?” After describing and profiling the goalorientations and perceptions of the classroom goalstructure of our sample of students with ADHD, we wereinterested in normative comparisons with non-ADHDsamples of a similar age. To accomplish this we firstcompared the levels of achievement goals reported byour sample of ADHD students to the normative datareported in the PALS manual, which contains the mostthorough and complete summary of past research usingthese scales.

Regarding goal orientation variables, we did not findany differences between the two samples on masterygoal orientation. However, as might be expected, wefound that our sample of students with ADHD were

more performance-avoidance goal-oriented than thePALS sample. We also found that our ADHD sample wasmore performance-approach goal-oriented. Regardingclassroom goal structure variables, our ADHD sampleperceived their classrooms to reinforce more masterygoal structures, more performance-avoidance goal struc-tures, and less performance-approach goal structuresthan did the PALS sample. Thus, based on this first nor-mative comparison, it does not appear that masterygoals are being adversely affected, because there were no differences in mastery goal orientation between ourADHD sample and the PALS sample; our ADHD sampleactually perceived their classroom environment as more mastery oriented than the PALS sample. However,regarding performance goals, it would appear that ourADHD sample was adopting less optimal goals and per-ceiving less optimal goal structures (by reporting moreperformance-avoidance goal orientations and more per-ceived performance-avoidance goal structures).

We also compared our ADHD sample to a local sam-ple of 6th-grade students. Two differences emergedhere. Our ADHD sample was less performance-approachgoal-oriented than our local sample of non-ADHD stu-dents, and our ADHD sample perceived their classroomsto reinforce less performance-approach goal structuresthan the PALS sample. Thus, once again, no major dif-ferences were found with mastery achievement goalsbetween our ADHD sample and local normative sample.Instead, compared to our local normative sample, itcould be argued that our ADHD sample was adoptingless optimal goals and perceiving less optimal goal struc-tures (by reporting less performance-approach goal ori-entations and less perceived performance-approach goalstructures).

Interesting, although the two normative samples differed on their overall level of goal adoption based onthe 1-5 scale, they shared a similar pattern on whatgoals were most and least endorsed. For both norma-tive samples, mastery goal orientations were mostendorsed whereas performance-approach and perform-ance-avoidance goal orientations were endorsed at alesser but somewhat similar level. This is in contrast tohow our ADHD sample ranked their goals, with per-formance-avoidance goal orientations always beingendorsed at a level that fell between mastery and per-formance-approach goals. Thus, while all three groupsshared similar levels of mastery goals, our ADHD sam-ple consistently had higher levels of performanc e -avoidance goals relative to their performance-approachgoals. This rank order may be a key pattern to track infuture research to identify optimal goal patterns for thispopulation. Achievement goal theorists would unani-mously agree on the deleterious effects of endorsing per-formance-avoidance goals (whether they subscribe to

the mastery goal perspective or the multiple-goal per-spective of achievement goals theory).

Research Question 3The third question was: “How are achievement goals

related to other academic outcome variables for stu-dents with ADHD?” Past research has shown that asso-ciations between motivational variables for studentswith ADHD do not always operate in the same adaptiveway as normative, non-ADHD populations (see Milich,1994). Thus, we were interested to determine the rela-tionship of achievement goals to a series of academic-related behaviors and outcomes. We selected a range ofacademic behaviors that would allow us to evaluate therelationships of our achievement goal constructs toboth adaptive (e.g., GPA and academic efficacy) andmaladaptive (e.g., avoiding novelty, self-handicapping)outcomes.

An investigation of correlations revealed a number ofexpected patterns of association based on achievementgoal theory and past research involving normative sam-ples. Regarding goal orientation variables, mastery goalorientation positively predicted adaptive behaviors (likeacademic efficacy) and negatively predicted a range ofmaladaptive behaviors (e.g., avoiding novelty, disrup-tive behavior, skepticism about school). Similarly, as inpast research, performance-avoidance goal orientationswere found to be linked to maladaptive behaviors (e.g.,avoiding novelty). But in contrast to past research find-ings, performance-avoidance goal orientations were alsoassociated with a number of adaptive outcomes (likeobtaining a higher GPA and being less skeptical aboutschool). So, performance-avoidance goals were not act-ing the way they typically function for a normativesample, at least in the beginning of 6th grade. This pos-itive relationship between GPA and performance-avoid-ance goals was not replicated at the end of the year, butit is important to note that the only goal orientationthat changed over the course of the year involved ourADHD sample becoming less performance-avoidanceoriented. Similarly, GPA for our ADHD sample alsodecreased from the beginning of the year to the end.Consequently, performance-avoidance goals may beproviding our sample of students with at least somemotivation to remain focused on academics, and actu-ally striving to not be the worst may be a helpful moti-vator for this population. Changing one type ofmotivational drive (like not wanting to be the worst)without changing others (like wanting to strive to bethe best) may actually be detrimental. Goal theoristshave often noted that having some motivation (even ifit is based in performance goals) may be better thanhaving no motivation at all (Pintrich & Schunk, 2002).

Regarding classroom goal variables, as theory would

predict, mastery classroom goal structure positively pre-dicted adaptive variables (like academic efficacy andGPA) and negatively predicted a range of maladaptivevariables (such as avoiding novelty and work avoidance)at both the beginning and the end of the 6th-gradeyear. However, performance-approach classroom goalstructure and performance-avoidance classroom goalstructure were not strongly associated with academico utcomes at Time 1, but by Time 2 they were both pos-itively associated with a wide range of maladaptivebehaviors as theory would predict. Thus, collectingdata at two time points was helpful in showing impor-tant patterns that only emerged at certain times of theyear. If we had limited the data collection to the initialtime point, we would not have uncovered the morecommon deleterious effects that are often found forperformance goals.

In addition to correlations, we conducted a series ofregressions to evaluate multivariate effects that goalsmight have on academic behaviors. Specifically, weevaluated the potential for goals to predict outcomes inan additive or interactive fashion. While little supportemerged for unique interactive goal effects, the trendsestablished in the discussion of correlations were sup-ported in the regressions. However, the regression mod-els revealed fewer significant relationships, especiallyfor the classroom goal structure measures.

Various factors may be at play with regard to the lackof significant relationships found between goal vari-ables and the various academic variables, especiallywhen evaluated with regression. The small sample sizeand lack of power were already noted. Another meas-urement issue should be noted for future research. Animportant issue being debated by achievement goal the-orists involves the level of specificity that is most appro-priate for assessing an achievement goal and the level ofspecificity in the outcomes collected (see Baranik,Barron, Finney, & Sundre, 2005; Elliot, 2005). For exam-ple, we can assess students’ achievement goals for a spe-cific situation (e.g., for a math class or for an Englishclass), or more globally for all of their classes in a par-ticular semester. The ability of goals to best predict out-comes may depend on the level of specificity selected.In the current study, we followed past recommenda-tions with this age group from the PALS manual toassess goals globally, but future research would benefitby evaluating students’ specific goals for differentclasses. In addition to the specificity of the goal, thelevel of specificity of the outcome has also been shownto be an important component in other areas ofresearch, with matching the level of specificity of thepredictor and outcome yielding the best results (seePajares & Miller, 1995). Students with ADHD frequentlyreport variations in their desire to achieve in some

Volume 29, Summer 2006 19

Learning Disability Quarterly 20

classes and not others, and these differences are fre-quently attributed to the student’s relationship with theteacher. The relationship between a child and each ofhis/her teachers is quite variable in middle school(Evans, Allen et al., 2005) and, as a result, examininggoal orientation within each classroom is likely toincrease our understanding of this process. Finally,future research would benefit from looking at additionalcriteria. In the current study, we focused primarily onself-report variables from the Pattern of AdaptiveLearning Survey, and as for all self-report data we mustrecognize the limitations and potential biases inherentin this method. Future work will benefit by movingbeyond self-report measures to include other behavioraloutcomes of adaptive and maladaptive learning (e.g.,actual number of behavior problems, actual number ofschoolwork assignments completed correctly and ontime), and by evaluating ratings from other sources(e.g., teachers and parents).

Research Question 4The fourth and final research question posed at the

outset of thisr article was: “Can current instructionalpractices be altered to further promote optimal goalsand motivation of students with ADHD?” This questionwas not addressed in the current study, but is a topic forour future research. Before we can make specific recom-mendations for teachers, we must better understand theunique role of performance-avoidance with this popula-tion. Although this may be considered a counterpro-ductive goal by some, for youth with ADHD it maycontribute to academic success. That is not to claim thatit is the optimal strategy for these students as it may alsocontribute to some of the poor learning strategies exhib-ited by these children. Further research is needed tohelp us understand how to transition students withADHD to an optimal orientation without sacrificing thebenefits of a less-than-optimal approach. Moreover, theeffect of behavioral contingencies, self-appraisal, andimpulsivity may further interact with goal orientationand contribute to the prediction of achievement. Infact, one of the concerns about such a strong reliance onbehavioral techniques with youth with ADHD is thatthese techniques may diminish their intrinsic motiva-tion to achieve. Similar concerns exist regarding theimpact of relying on stimulant medication instead ofattributing success and failure to one’s own ability andeffort; however, studies suggest that children withADHD attribute success to internal factors and failure toexternal factors regardless of whether they are takingstimulant medication or not (Pelham, Hoza & Pillow,2002). An examination of the interactions betweentreatment effects (medication and psychosocial), envi-ronmental influences (teacher and parent expectations

and contingencies), treatment outcomes, and motiva-tional characteristics is needed to advance our under-standing of youth with ADHD and develop effectivemethods to help them.

Future research will also benefit by considering addi-tional achievement goals. The current study focused onthe three types of achievement goals that have receivedthe most empirical attention in the literature (mastery,performance-approach, and performance-avoidancegoals), but researchers are currently looking at modelsthat partition mastery goals into mastery-approach vs.mastery-avoidance goals grounded in the same logicthat resulted in partitioning performance goals intotwo separate constructs. A focus on avoidance tenden-cies again may be particularly relevant for an ADHDpopulation, and this may be a particularly interesting population for evaluating the utility of measuring mas-tery-avoidance goals. In addition, these data suggestthat improving our understanding of the role of goalorientation in children with ADHD may provide valu-able clues for understanding techniques that may beused to improve their academic functioning.

REFERENCESAiken, L. S., & West, S. G. (1991). Multiple regression: Testing and

interpreting interactions. Thousand Oaks, CA: Sage Publications,Inc.

Ames, C. (1992). Classrooms: Goals, structures, and student moti-vation. Journal of Educational Psychology, 84, 2 6 1 - 2 7 1 .

Baranik, L. E., Barron, K. E., Finney, S. J., & Sundre, D. A. (2005). Acomparison of general vs. specific measures of achievement goal ori-entation. Poster presented at the annual meeting of theAmerican Educational Research Association, Montreal, Canada.

Barron, K. E., & Harackiewicz, J. M. (2001). Achievement goals andoptimal motivation: Testing multiple goal models. Journal ofPersonality and Social Psychology, 31, 6 7 4 - 6 8 5 .

Brophy, J. (2004). Motivating students to learn (2nd ed.). Mahwah,NJ: Lawrence Erlbaum Associates.

Carlson, C. L., Booth, J. E., Shin, M. & Canu, W.H. (2002). Parent-,teacher-, and self-rated motivational styles in ADHD subtypes.Journal of Learning Disabilities, 35(2), 104-113.

Carlson, C. L., Pelham, W. E., Milich, R., & Hoza, B. (1993). ADHDboys’ performance and attributions following success and fail-ure: Drug effects and individual differences. Cognitive Therapy &Research, 17(3), 269-287.

Diener, C. I., & Dweck, C. S. (1978). An analysis of learned help-lessness: Continuous changes in performance, strategy, andachievement cognitions following failure. Journal of Personality& Social Psychology, 35(5), 451-462.

Dunn, P. B., & Shapiro, S. K. (1999). Gender differences in theachievement goal orientations of ADHD children. C o g n i t i v eTherapy & Research, 23(3), 327-344.

Dweck, C. S. (1975). The role of expectations and attributions inthe alleviation of learned helplessness. Journal of Personality andSocial Psychology, 80, 7 0 6 - 7 2 2 .

Dweck, C. S. (1986). Motivational processes affecting learning.American Psychologist, 41, 1 0 4 0 - 1 0 4 8 .

Dweck, C. S., & Leggett, E. L. (1988). A social–cognitive approach tomotivation and personality. Psychological Review, 95, 2 5 6 -2 7 3 .

Elliot, A. J. (2005). A conceptual history of the achievement goalconstruct. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of com-petence and motivation (pp. 52-72). New York: The GuilfordP r e s s .

Elliot, A. J., & Church, M. A. (1997). A hierarchical model ofapproach and avoidance achievement motivation. Journal ofPersonality and Social Psychology, 72, 2 1 8 - 2 3 2 .

Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidanceachievement goals and intrinsic motivation: A mediationalanalysis. Journal of Personality and Social Psychology, 70, 4 6 1 - 4 7 5 .

Evans, S. W., Allen, J., Moore, S., & Strauss, V. (2005). Measuringsymptoms and functioning of youth with ADHD in middleschools. Journal of Abnormal Child Psychology, 33, 695-706.

Evans, S. W., Langberg, J., Raggi, V., Allen, J., & Buvinger, E.(2005). Development of a school-based treatment program formiddle school youth with ADHD. Journal of Attention Disorders,9, 3 4 3 - 3 5 3 .

Fischer, M., Barkley, R. A., Fletcher, K. E., & Smallish, L. (1993).The adolescent outcome of hyperactive children: Predictors ofpsychiatric, academic, social, and emotional adjustment. Journalof the American Academy of Child & Adolescent Psychiatry, 32( 2 ) ,3 2 4 - 3 3 2 .

Frazier, P., Tix, A., & Barron, K. E. (2004). Testing mediation andmoderation in counseling psychology research. Journal ofCounseling Psychology, 51, 1 1 5 - 1 3 4 .

Grant, H., & Dweck, C. S. (2003). Clarifying achievement goalsand their impact. Journal of Personality & Social Psychology, 85(3),5 4 1 - 5 5 3 .

Harackiewicz, J. M., Barron, K. E., Pintrich, P. R., Elliot, A. J., &Thrash,T. M. (2002). Revision of achievement goal theory:Necessary and illuminating. Journal of Educational Psychology,94, 638-645.

Harackiewicz, J. M., Barron, K. E., Tauer, J. M., & Elliot, A. J. (2002).Predicting success in college: A longitudinal study of achieve-ment goals and ability measures as predictors of interest andperformance from freshman year through graduation. Journal ofEducational Psychology, 94, 6 3 8 - 6 4 5 .

Hinshaw, S. P. (1992). Academic underachievement, attentiondeficits, and aggression: Comorbidity and implications for inter-vention. Journal of Consulting and Clinical Psychology, 60, 8 9 3 -9 0 3 .

Hoza, B., Waschbusch, D. A., Owens, J. S., Pelham, W. E., & Kipp,H. (2001). Academic task persistence of normally achievingADHD and control boys: Self-evaluations, and attributions.Journal of Consulting & Clinical Psychology, 69(2), 271-283.

Kaufman, A. S., & Kaufman, N. L. (1990). Kaufman Brief IntelligenceTest. Circle Pines, MN: American Guidance Service.

Linnenbrink, E. A. (2005). The dilemma of performance-approachgoals: The use of multiple goal contexts to promote students’motivation and learning. Journal of Educational Psychology, 97,1 9 7 - 2 1 3 .

McGrath, A. M., Handwerk, M. L., Armstrong, K. J., Lucas, C. P., &Friman, P. C. (2004). The validity of the ADHD section of theDiagnostic Interview Schedule for Children. Behavior Modifi-cation, 28, 349-374.

Midgley, C. (2002). Goals, goal structures, and patterns of adaptivelearning. Mahwah, NJ: Lawrence Erlbaum Associates.

Midgley, C., & Edelin, K. (1998). Middle school reform and earlyadolescent well-being: The good news and the bad. EducationalPsychologist, 33, 195-206.

Midgley, C., Kaplan, A., Middleton, M., Urdan, T., Maehr, M. L.,Hicks, L., Anderman, E., & Roeser, R. W. (1998). Developmentand validation of scales assessing students’ achievement goalorientation. Contemporary Educational Psychology, 23, 1 1 3 - 1 3 1 .

Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman,L., Freeman, K. E., Gheen, M., Kaplan, A., Kumar, R., Middleton,M. J., Nelson, J., Roeser, R., & Urdan, T. (2000). Manual for thePatterns of Adaptive Learning Scales (PALS). Ann Arbor: Universityof Michigan.

Milich, R. (1994). The response of children with ADHD to failure:If at first you don’t succeed, do you try, try, again? S c h o o lPsychology Review, 23, 11-18.

Milich, R., Carlson, C. L., Pelham, W. E., & Licht, B. G. (1991).Effects of methylphenidate on the persistence of ADHD boysfollowing failure experiences. Journal of Abnormal ChildPsychology, 19(5), 519-536.

Milich, R., & Greenwell, L. (1991, December). An examination oflearned helplessness among attention-deficit hyperactivity dis-ordered boys. In B. Hoza & W. E. Pelham (Chairs), Cognitivebiases as mediators of childhood disorders: What do we know?Symposium presented at the annual meeting of the Associationfor the Advancement of Behavior Therapy, New York.

Milich, R., & Okazaki, M. (1991). An examination of learned help-lessness among attention-deficit hyperactivity disordered boys.Journal of Abnormal Child Psychology, 19(5), 607-623.

MTA Cooperative Group. (1999). A 14-month randomized clinicaltrial of treatment strategies for attention-deficit/hyperactivitydisorder. Archives of General Psychiatry, 56, 1 0 7 3 - 1 0 8 6 .

Nicholls, J. G. (1984). Achievement motivation: Conceptions ofability, subjective experience, task choice, and performance.Psychological Review, 91, 3 2 8 - 3 4 6 .

Pajares, F., & Miller, D. M. (1995). Mathematics self-efficacy andmathematics performance: The need for specificity of assess-ment. Journal of Counseling Psychology, 42, 1 9 0 - 1 9 8 .

Pelham, W. E., Evans, S. W., Gnagy, E., & Greenslade, K. E. (1992).Teacher ratings of DSM-III-R symptoms for the disruptivebehavior disorders: Prevalence, factor analyses, and conditionalprobabilities in a special education sample. School PsychologyReview, 21, 2 8 5 - 2 9 9 .

Pelham, W. E., Fabiano, G. A., & Massetti, G. M. (2005). Evidence-based assessment of attention deficit hyperactivity disorder inchildren and adolescents. Journal of Clinical Child and AdolescentPsychology, 34(3), 449-476.

Pelham, W. E., Hoza, B., & Pillow, D. R. (2002). Effects of methy-phenidate and expectancy on children with ADHD: Behavior,academic performance, and attributions in a summer treatmentprogram and regular classroom settings. Journal of Consultingand Clinical Psychology, 70(2), 320-335.

Pintrich, P. R., & Schunk, D. (2002). Motivation in education:Theory, research and applications (2nd ed.). Englewood Cliffs, NJ:Merrill Prentice-Hall.

The Psychological Corporation. (2001). Wechsler IndividualAchievement Test (2nd ed.). San Antonio, TX: Harcourt Brace &C o m p a n y .

Shaffer, D., Fisher, P., Lucas, C. P., Dulcan, M., & Schwab-Stone,M.E. (2000). NIMH Diagnostic Interview Schedule for ChildrenVersion IV (NIMH DISC-IV): Description, differences from pre-vious versions, and reliability of some common diagnoses.Journal of the American Academy of Child and AdolescentPsychiatry, 39(1), 28-38.

Schunk, D. H. (1996). Goal and self-evaluative influences duringchildren’s cognitive skill learning. American Educational ResearchJournal, 33, 359-382.

Sideridis, G. D. (2005a). Goal orientation, classroom goal struc-tures, academic achievement, and regulation in learning dis-abilities. In G. D. Sideridis & T. A. Citro (Eds.), Research topractice: Effective interventions in learning disabilities (pp. 193-219). Boston: Learning Disabilities Worldwide.

Volume 29, Summer 2006 21

Learning Disability Quarterly 22

Sideridis, G. D. (2005b). Goal orientation, academic achievement,and depression: Evidence in favor of a revised goal theory frame-work. Journal of Educational Psychology, 97, 3 6 6 - 3 7 5 .

Stinnett, T. A., & Oehler-Stinnett, J. (1992). Validation of theteacher rating of academic achievement motivation. Journal ofPsychoeducational Assessment, 10(3), 276-290.

Wentzel, K. R. (1991). Social and academic goals at school:Motivation and achievement in context. In M. L. Maehr, & P. R.Pintrich (Eds.), Advances in motivation and achievement (Vol. 7,pp. 185-212). Greenwich, CT: JAI Press.

FOOTNOTES1 . Researchers have used a variety of labels to differentiate between

mastery and performance goals. For example, mastery goals alsohave been called task goals (Nicholls, 1984), learning goals(Dweck & Leggett, 1988), and intrinsic goals (Pintrich & Garcia,1991). Performance goals also have been called ego goals(Nicholls, 1984), ability goals (Ames & Ames, 1984), relativeability goals (Midgley, et al., 1998), and extrinsic goals (Pintrich

& Garcia, 1991). In the goal measure by Schunk (1996), thelabels used to differentiate goals are task and ego following thetradition of Nicholls (1984).

2 . Readers new to this area should also note work by Grant andDweck (2003) and Brophy (2005) for alternative conceptions ofwhat a performance goal can represent.

AUTHOR NOTEThis research was supported by a grant from the Virginia TobaccoSettlement Foundation to the second author and the Alvin V.Baird Attention & Learning Disabilities Center at James MadisonUniversity. This work was also supported by a grant from theCollege of Integrated Science and Technology at James MadisonUniversity to both the first and second authors.

Please address correspondence concerning this article to: KennethE. Barron, MSC 7401, James Madison University, Harrisonburg, VA22807. Email: barronke@jmu.edu.