School Psychology Review,2007, Volume 36, No. 2, pp. 217-236

Consultation-Based Academic Intervention for ChildrenWith Attention Deficit Hyperactivity Disorder:

School Functioning Outcomes

Asha K. Jitendra and George J. DuPaulLehigh University

Robert J. VolpeNortheastern University

Katy E. Tresco, Rosemary E. Vile Junod, and J. Gary LutzLehigh University

Kristi S. ClearySyracuse City School District

Lizette M. Elammer-Rivera and Mark C. MannellaLehigh University

Abstract. This study evaluated the effectiveness of two consultation-based modelsfor designing academic interventions to enhance the educational functioning ofchildren with attention deficit hyperactivity disorder. Children (N = 167) meetingDiagnostic and Statistical Manual (4th ed.—text revision; American PsychiatricAssociation, 2000) criteria for attention deficit hyperactivity disorder were ran-domly assigned to one of two consultation groups: intensive data-based academicintervention (interventions designed using a data-based decision-making modelthat involved ongoing feedback to teachers) and traditional data-based academicintervention (interventions designed based on consultant-teacher collaboration,representing "consultation as usual"). Teachers implemented academic interven-tions over 15 months. Academic outcomes (e.g., curriculum-based assesstnent,repwrt card grade, and individual goal attainment) were assessed on four occasions(baseline, 3 months, 12 months, and 15 months). Hierarchical linear modelinganalyses indicated significant positive growth for 9 of the 10 dependent variables;however, trajectories did not differ significantly across consultation groups.Implications for practice and future research are discussed.

The preparation of the nnanuscript was supported by National Institute of Mental Health Grant R01-MH62941. Theauthors gratefiilly acknowledge the efforts of all teachers and students who participated in this pHoject as well as LisaMarie Angello, Andrea Deatline-Buchman, Anuja Divatia, Lauren Dulltim, Rebecca Eng, Karen Hailstones, JildaHodges, Jayne Leh, Stacy Martin, Jennifer Mautone, Erin Post, Eve Puhalla, Hillary Rogers, Timothy Scholten, CodeStrong, Deanna Tipton, and Yan Ping Xin, who served as research assistants for this study.

Correspondence regarding this article should be addressed to Asha K. Jitendra, Department of Educationand Human Services, Lehigh University, Bethlehem, PA 18015; E-mail: akj2@lehigh.edu

Copyright 2007 by the National Association of School Psychologists, ISSN 0279-6015

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The 2001 reauthorization of the Eletnentaryand Secondary Education Act, No Child LeftBehind, reflects the federal government'scomtnitment to and reinforcement of state ac-countability systems with the intent to im-prove school functioning outcomes for all stu-dents. Further, the concept of "scientificallybased research" has received increased atten-tion in an effort to address the low levels ofachievement among students with disabilities,minority students (i.e., African American, Na-tive American, and Latino), limited Englishproficient students, and students of low socio-economic status. In particular, closing theachievement gap for children with leamingand behavior difficulties (e.g., attention deficithyperactivity disorder [ADHD]), who are atrisk for school failure, is especially critical.

ADHD is a chronic disorder characterizedby developmentally inappropriate levels of inat-tention and/or hyperactivity-impulsivity that af-fects about 3-10% of school-age children(American Psychiatric Association, 2(X)0). Infact, the strong association between the behav-ioral symptoms (i.e., inattention, impulsivity,and overactivity) of ADHD and conctirrent orlater academic underachievement has been dem-onstrated across many investigations (e.g., Bark-ley, DuPaul, & McMurray, 1990; DuPaul et al.,2(X)4). Converging evidence suggests that sam-ples of children with ADHD encounter signifi-cant academic difficulties (e.g., failing grades,failure to complete assignments; American Psy-chiatric Association, 2000; DuPaul & Stoner,2003) that persist throughout their school yearsand continue into college (Barkley, Eischer,Edelbrock, & Smallish, 1990; Mannuzza, Gittel-man-Klein, Bessler, Malloy, & LaPadula, 1993;Montague, Enders, & Castro, 2005). Evidently,children with ADHD typically function approx-imately 1 standard deviation below their class-mates on standardized achievement tests (forreview, see DuPaul & Stoner, 2003; Hinshaw,1992). Not surprisingly, children with ADHDare at higher than average risk for grade retentionand school dropout (Barkley, 2(X)6).

Although numerous investigations haveaddressed the behavioral symptoms of ADHD,relatively few studies have focused directly onameliorating the academic problems that chil-

dren with ADHD may have with respect toreading and math skills that are critical forsuccess as an adult (Wirt et al., 2004). To date,the vast majority of treatment outcome studieshave exclusively addressed symptom reduc-tion rather than enhancement of academicfunctioning. This research has shown that themost effective treatments for ADHD includepsychostimulant medication (e.g., methyl-phenidate) and contingency management strat-egies (Barkley, 2006; Multimodal Treatmentof Children with ADHD [MTA] CooperativeGroup, 1999). Interestingly, results from pre-vious group treatment outcome studies suggestthat effect sizes associated with behavioralinterventions for classroom disruptive behav-ior are in the moderate range, whereas onlysmall effects are found for interventions ad-dressing academic problems in this population(DuPaul & Eckert, 1997). In addition, al-though stimulants and other medicationsmay improve productivity on academic tasks,long-term outcome studies indicate that stim-ulants have negligible effects on educationalachievement. Eor example, findings from theMTA study indicated small, statistically non-significant effect sizes (ES ranges from 0.0to 0.20) for reading and mathematics achieve-ment when children received carefully titratedpsychotropic medication either alone or incombination with psychosocial treatment at 14months (MTA Cooperative Group, 1999)or 24 months (MTA Cooperative Group,2004) after initiation of treatment.

Over the past decade, studies using sin-gle-subject research designs have providedinitial support for the effectiveness of variousacademic interventions for children withADHD (DuPaul & Eckert, 1997; DuPaul &Stoner, 2003). For example, a study by Du-Paul, Ervin, Hook, and McGoey (1998) estab-lished the benefits of ClassWide Peer Tutoringfor 18 students with ADHD who were placedin first- through fifth-grade general educationclassrooms. ClassWide Peer Tutoring not onlyled to reductions in these students' off-taskbehavior, but also improved their academicperformance. Results showed clinically signif-icant improvements in academic skills, withmoderate to large effects for math and spelling

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as measured by brief classroom-based assess-ments. Also, studies of computer-assisted in-struction have demonstrated clinically signifi-cant gains in oral reading fluency (Clarfield &Stoner, 2005) and scores on curriculum-basedmeasurement (CBM) mathematics probes(Mautone, DuPaul, & Jitendra, 2005; Ota &DuPaul, 2002) for small samples of childrenwith ADHD. These studies have used eitherclassroom-based measurements (Tindal &Marston, 1990) or CBMs that assess studentacademic competence and progress and aretypically used to make instructional decisions(Fuchs, Fuchs, & Courey, 2005). Researchregarding the effectiveness of these interven-tions with large samples in the context of agroup research design has not been conductedto date. Further, previous studies have usedsingle (e.g., curriculum-based assessments)rather than multiple measures to assess aca-demic competence and progress. The impor-tance of multiple measures to understand stu-dent performance is well documented (e.g.,Gersten, Baker, & Lloyd, 2000). Althoughnorm-referenced, standardized achievementtests are important measures of academic suc-cess, these measures typically are not used indetermining student academic progress. Alter-natively, measures that are used on a regularbasis in school settings (e.g., report cardgrades) or informal teacher measures (e.g.,ratings of teacher perceptions of a student'sprogress-to-target behavior) that evaluate stu-dent improvement in specific areas addressedby intervention have not been included in priorstudies with this population.

In summary, previous research regard-ing the effects of academic interventions onthe achievement of children with ADHD islimited by the following: (a) small samples;(b) interventions implemented over relativelyshort time periods (e.g., several weeks ormonths); (c) use of single academic measuresof student progress over time; and (d) use of a"one size fits all" approach, wherein all par-ticipants receive the identical intervention re-gardless of differences in academic profiles.Further, prior studies have not involved class-room teachers in the design, planning, andimplementation of interventions even though

they are critical to the implementation andintegrity of classroom-based academic inter-ventions. Involving classroom teachers in allphases of the intervention is important forseveral reasons. They are likely to implementwith integrity an intervention that they believehas beneficial effects for their students. Also,the viability of treatment plans is increasedwhen teachers are invested in them.

In most public school settings, teacherswork in collaboration with others, such as aconsultant (e.g., school psychologist), to planclassroom interventions, with the teacher se-lecting a specific treatment strategy, presum-ably based on both perceived effectivenessand feasibility of the various intervention op-tions. A data-based consultation decision-making model (Daly, Witt, Martens, & Dool,1997; Sheridan, Kratochwill, & Bergan, 1996)is known to improve educational outcomes.This consultation model entails classroomteachers working with consultants to developindividualized interventions based on initialperformance data, which consider studentstrengths and weaknesses as well as importantcontextual variables (e.g., antecedent and con-sequent events prompting and maintaining be-haviors) related to the target behavior. In ad-dition, consultants monitor treatment imple-mentation and provide teachers with feedbackdesigned to enhance intervention fidelity.

Unfortunately, the literature is mixedwith regard to the effectiveness of a data-based, decision-making consultation model.For example, findings from some studies indi-cate large, positive effects for this model(Sheridan, Eagle, Cowan, & Mickelson, 2001;Sheridan, Welch, & Ormi, 1996); however,most investigations of data-based consultationoutcomes have not used control groups. In oneof the few studies employing a control group.Beavers, Kratochwill, and Braden (2004)showed no advantage of the data-based con-sultation model over a traditional, less individ-ualized approach in designing interventionsfor children with reading difficulties. Unfortu-nately, conclusions based on the Beavers et al.study are limited given the relatively smallsample size (A' = 32) and the lack of focus onstudents with ADHD. Interestingly, the use of

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a data-based consultation model is not com-mon among school psychologists (Bramlett,Murphy, Johnson, & Wallingsford, 2002;Constenbader, Swartz, & Petrix, 1992). Fur-ther, when school psychologists report using aconsultation model in schools, only 37% fol-low all stages of the model and less than 50%use empirical research to select interventionsand collect evaluation data to assess academicoutcomes (Bramlett et al., 2002). Clearly,"consultation as usual" as it is applied in ac-tual school settings typically does not followthe recommended practice of designing aca-demic interventions based on empirical data.Thus, it is important to compare consultationmodels that incorporate academic interven-tions derived from evidence-based practicesand differ with regard to intensive or custom-ary level of data utilization in selecting inter-ventions for children with ADHD.

The purpose of this study was to com-pare the effects of two different models ofschool-based consultation on the academicfunctioning of a large sample of students withADHD. One approach involved consultationusing customary or traditional levels of datautilization, wherein teachers selected aca-demic interventions proposed by a school psy-chologist or special educator based on per-ceived effectiveness and feasibility, with min-imal follow-up once interventions had beenimplemented. The other consultation ap-proach, which was deemed intensive in datautilization, involved the selection and devel-opment of academic interventions based ondata collected by the consultant regarding in-dividual student skills and present classroomconditions. Teachers also were provided withfeedback regarding treatment integrity and in-terventions were modified based on outcomedata. Potential interventions (e.g., peer tutor-ing, direct instruction, and computer-assistedinstruction) used in both groups were empiri-cally supported by prior single subject re-search studies. It was hypothesized that thechildren receiving individualized academic in-terventions would exhibit greater growth inacademic achievement as measured by reportcard grades, CBM mathematics and readingtests, and teacher ratings of changes in target

skills than would children whose interventionswere selected in the context of a typicalschool-based consultation model.

Method

Student Participants

Participants included 175 children (133boys, 42 girls; mean age = 104.3 months,SD = 14.7) attending first through fourthgrade in public elementary schools located inurban, rural, and suburban settings in eastemPennsylvania. Children who were experienc-ing significant difficulties with ADHD symp-toms and academic achievement were referredto the project by their classroom teachers. Thesample consisted of primarily White children(58%; 26.9% Hispanic; 11.4% Black) andfamilies were in the lower middle class andmiddle class range based on the Hollingsheadindex (Hollingshead, 1975), with a mean in-dex score of 48.0 (SD = 24.8).

The sample for this study included chil-dren with ADHD who were experiencingachievement problems in either math or read-ing, according to teacher report. Further, thesechildren met strict research diagnostic criteriafor ADHD. To be identified as a child withADHD, both parent and teacher ratings on theADHD Rating Scale—IV (DuPaul, Ervin etal., 1998) exceeded the 90th percentile oneither the Inattention or Hyperactive-Impul-sivity subscales using appropriate age andgender norms. In addition, children met Diag-nostic and Statistical Manual of Mental Dis-orders (4th ed.—text revision, DSM-IV-TR;American Psychiatric Association, 2000) cri-teria for one of the three ADHD subtypesbased on parent interviews using the NationalInstitute of Mental Health Diagnostic Inter-view Schedule for Children—IV (Shaffer,Fisher, & Lucas, 1998). Children with all threeADHD subtypes were included, with the ma-jority (65%) being the combined type. Thesample included children with comorbid op-positional defiant disorder (38%) and conductdisorder (15%). A total of 51 students (29.1%)were receiving part-time special education ser-vices, while 50 children (28.5%) were receiv-ing psychotropic medication. Some students

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were receiving more than one type of psych-otropic medication, which included psycho-stimulants (n = 38), antidepressants (n = 8),and other medications (n = 26).

The final sample, however, comprised 167students (127 boys and 40 girls), because threestudents moved to schools out of the area beforeintervention could begin, two teachers decidednot to participate given their preference for be-havioral rather than academic interventions tar-geted in the study, two parents withdrew theirinitial consent for consultation with their child'steacher, and one student moved to a school thatdeclined participation. The majority (n = 132;79.04%) of the remaining 167 students wererandomly assigned to one of two educationalconsultation groups: intensive data-based aca-demic intervention (IDAI; n = 81; 61 boys) andtraditional data-based academic intervention(TDAI; « = 86; 66 boys) and received interven-tion in reading (n = 126) and/or mathematics(« = 95).' Table I presents demographic dataseparately for consultation groups within bothmath and reading samples The two groups didnot differ with respect to gender, ethnicity, med-ication, educational placement, soeioeconomicstatus, ADHD subtype, and comorbid opposi-tional defiant disorder or conduct disorder diag-nosis. The mean age (p < .01) and grade (p <.05) for children in the IDAI group was signifi-cantly greater than that for children in the TDAIcondition, for the math sample only. In the read-ing sample, the father's occupation was signifi-cantly higher for the IDAI group (p < .01).

It must be noted that 50 of the 167students did not receive consultation servicesduring their second year of participation (Se-mesters 2 and 3) for various reasons (e.g.,teacher or parent declining consultation ser-vices). However, given the intent-to-treatmethodology, assessment data for these par-ticipants were collected and included in allanalyses. Data for 18 additional students weremissing, because assessment information wasnot collected and/or students did not receiveconsultation services for some of the above-mentioned reasons. At the same time, groupmembership was not cited as a reason to declineconsultation services in the study. The x^ anal-yses revealed no significant differences between

consultation models for either the type of ser-vices received (consultation, data collectiononly, nothing) or the reason for refusal.

Teacher Participants

A total of 204 teachers across 52 schoolsparticipated in this study. Teachers were primar-ily female (87.7%) and White (96.5%) and heldeither a bachelor's degree (48.01%) or master'sdegree (51.98%). Most were general educationteachers, with 14.4% identified as teachers inspecial education classrooms. Once a student ina teacher's classroom was randomly assigned toa consultation model, all other student partici-pants in that classroom were assigned to thatgroup as well. The rationale for this was to avoidconfusion resulting fVom a teacher being askedto participate simultaneously in two differentmodels of consultation as well as working withmore than one consultant. It must be notedthat 27 teachers (13.2% of total teachers) workedwith multiple students. In 13 of these teach-ers' classrooms, students were first random-ized to TDAI. Students were randomly assignedto IDAI in the other 14 teachers' classrooms.As a result, the placement of 35 students(i.e., 20.96%) in a treatment group was manda-tory; 13 students were in a TDAI classroomand 22 students were in an IDAI classroom.Given the number of schools and our status asconsultants and guests, we could not control forfuture classroom placement (i.e., second aca-demic year). Fifteen (7.35%) of the 204 teachers,however, worked with consultants from bothmodels at some point in the study. On only oneoccasion did a teacher woric with consultantsfrom both models simultaneously. After elimi-nating the 15 teachers who received both con-sultation model services, x^ analyses revealed nosignificant differences between the groups ongender, ethnicity, or highest degree eamed. TheIDAI consultants, however, did work with moremale teachers (« = 17) than did the TDAI [n =

Consultant Participants

Eleven school psychology and specialeducation doctoral students served as consult-ants in the study and were assigned to either

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Academic Interventions for ADHD

the TDAI or IDAI consultation groups. It mustbe noted that throughout the course of thestudy, consultants remained with the assignedgroup (TDAI or IDAI). Each consultant, whohad either completed or was completing rele-vant coursework in consultation and school-based intervention, was supervised by the firstand second authors. At the start of the study,the mean age of consultants in the TDAI groupwas 24.00 (SD = 1.83) years and that of theIDAI group was 26.20 (SD = 3.19) years.Both groups were primarily female(TDAI = 85.7%; IDAI = 80.0%) and nonewere of a minority ethnic group. Students andtheir teachers were yoked to a TDAI or IDAIconsultant after they had been randomly as-signed to a specific consultation group, andwhenever possible retained the same consul-tant throughout the study.

Screening Measures

The ADHD Rating Scale—IV (DuPaul,Power et al., 1998) is a behavior rating scale thatincludes items directly related to the 18 symp-toms of ADHD based on the DSM-IV-TR(American Psychiatric Association, 2000).Home and school versions are available for com-pletion by parents and teachers, respectively.Items are scored on a scale of 0 (never or rarely)to 3 (very often). Normative data based on ageand gender are available, and the psychometricproperties of this instrument are well established(DuPaul, Power et al., 1998).

The Computerized NIMH DiagnosticInterview Schedule for Children (Parent Ver-sion, CDISC 4.0; Shaffer et al., 1998) is astructured diagnostic interview administeredusing computer software. Parents report cur-rent (present state) symptoms and symptomsover the past year on this interview. A trainedinterviewer (i.e., doctoral student in schoolpsychology) administered the Disruptive Be-havior Disorders module either in person or byphone. The entire CDISC was not adminis-tered because of time constraints (i.e.. Disrup-tive Behavior Disorder module took approxi-mately 1 hr to complete) and because the focusof the treatment outcome study was on exter-nalizing difficulties (as well as academic

achievement). The research project coordina-tor, who was a master's level psychologist,trained interviewers. Diagnostic decisionsbased on this interview have been found to behighly reliable (Shaffer et al., 1998). AllCDISC 4.0 interviews were audiotaped, and asecond trained interviewer (i.e., doctoral stu-dent in school psychology) reviewed a randomsubsample (21%) of interviews to assess inter-diagnostician agreement. Agreement was100% across all interviews with respect tooverall diagnosis and subtype designation.

Dependent Measures

Dependent measures included CBMreading and mathematics tests, individualizedacademic goal attainment or progress of targetbehavior (POTB) scores, and report cardgrades. CBM measures and report card gradeswere collected on four occasions (baseline, 3months, 12 months, and 15 months) acrosstwo school years. In contrast, POTB data werecollected on nine occasions during the study toprovide pre-, mid-, and postintervention as-sessments between baseline and 3 months, 3and 12 months, and 12 and 15 months.

Words correct per minute and digits cor-rect from the CBM reading and mathematicsmeasures served as indicators of academicachievement in reading and mathematics. Stu-dent progress in reading was monitored usingpassages developed by the Children's Educa-tional Services, Inc. (Deno, Deno, & Marston,1987). Before the start of the academic inter-ventions, each student was screened usingCBM reading passages to determine his or herinstructional grade level (see Deno et al.,1987). Once a student's instructional level wasestablished, progress in reading was assessedat each assessment phase using a CBM in-structional grade level passage test and an-other CBM reading test based on the nexthigher grade level passage. Passage levelswere adjusted at each assessment period basedon student performance. That is, if a studentperformed at a first-grade instructional level atbaseline, that student would complete bothfirst- and second-grade probes at the 3-monthassessment. Performance at this assessment

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was examined to determine grade level pas-sages for the next assessment. Thus, if thestudent performed at an instructional level onthe second-grade passage at 3 months, he orshe would be given a second- and third-gradepassage at the next assessment, and so forth.The instructional grade level at each assess-ment phase was used as the dependent variablein these analyses. During the CBM passagereading assessment, children were asked toread an excerpt of connected text in 1 min.Reading performance was determined by scor-ing the total number of words read correctlyper minute (i.e., WCPM). Correlations for cri-terion-related validity of oral reading fluency(ORF) with published nonn-referenced read-ing achievement tests range from .73 to .91,with most coefficients above .80 (Marston,1989). Investigations of both alternate-formand test-retest reliability of ORF report coef-ficients of .92-.97, with most estimates above.90. Interrater agreement coefficients havebeen found to be .99 (Marston, 1989).

Student progress in mathematics com-putation was evaluated using basic math com-putation probes (Fuchs, Hamlett, & Fuchs,1998). Similar to the CBM reading measure,each student was screened before the start ofthe academic interventions to determine his orher instructional grade level using establishednorms. Progress in mathematics was assessedat each assessment phase using a CBM in-structional grade level math computation testand another CBM math computation testbased on the next higher grade level. Notunlike CBM reading tests, probe levels wereadjusted at each assessment period based onstudent progress. The instructional level ateach assessment was used as the dependentvariable for this investigation. Each CBMcomputation test included 25 problems thatrepresented the sample of items found in aspecific grade level curriculum, which mayinvolve problem types that require adding,subtracting, multiplying, and dividing wholenumbers, fractions, and decimals. Perfor-mance was scored as total number of digitscorrect in a fixed time (i.e.. 2 min for Grades1-3, and 4 min for Grades 4 and 5). This

assessment system is known to have adequatereliability and validity (see Fuchs et al., 1998).

Ratings of teacher perceptions of a stu-dent's POTB were used as a measure of goalattainment scaling (Kiresuk, Smith, & Car-dillo, 1994), which has been validated andused previously in consultation research toevaluate client improvement (e.g., Busse,Kratochwill, & Elliott, 1995; Grissom, Erchul,& Sheridan, 2003; Sheridan et al., 2001). Inthis study, consultants worked with teachers atthe beginning of each assessment phase toensure that targeted academic goals were ob-jective, measurable, and stated in a positivefashion. Examples of goals included the num-ber of words read correctly in a minute, num-ber of mathematics problems completed in agiven time period, and the score on a school-specific assessment that the teacher monitoredon a frequent basis. Given the critical role ofteachers in making instructional decisionsabout student performance, assessments ormonitoring systems used by individual teach-ers and schools served as the basis for ratingstudent progress on academic goals. Teachersthen made POTB ratings regarding the fre-quency of student demonstration of the spe-cific academic goal using a 4-point rating scalethat ranged from 0 (never) to + 3 (very often).Ratings of the targeted academic goals werecollected pre-, mid-, and postintervention, re-sulting in nine ratings across three assessmentphases. Considering student performance andteacher choice were the basis for goal deter-mination, each assessment period began withdistinct targeted academic goals. Each set ofpre-, mid-, and postintervention ratings weretherefore used as dependent variables in sep-arate analyses.

Student report card grades in readingand mathematics were converted to numericalscores ranging from 1 (F) to 5 (A). Grades inreading and mathematics represented studentperformance in the classroom. In the first yearof participation, grades from the second mark-ing period were used at pretreatment andfourth marking period grades were used forAssessment 1 (3 months). To remain consis-tent, the second and fourth marking periodgrades were also used the following year to

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indicate performance at Assessments 2 (12months) and 3 (15 months).

Procedures

Data collection. Following receipt ofwritten parental consent, baseline data collec-tion of all dependent measures took place overapproximately a 1-month period during themiddle of the school year (December to Feb-ruary). CBM reading and mathematics testdata were collected on three additional occa-sions (3 months, 12 months, and 15 months)across 2 school years. Trained graduate stu-dents in school psychology, special education,and counseling psychology served as researchassistants and administered the CBM mathe-matics and reading measures using standard-ized procedures. The research assistants wereblind to the purpose of the study and to thegroup membership of participating children.Classroom teachers provided consultants withthe pre-, mid-, and postintervention ratings ofstudent POTB (i.e., three POTB ratings perassessment phase). Student report card gradeswere collected from schools at the end of eachacademic year. Parents signed separate releaseof information forms, allowing project staff tocollect this information.

Both treatment groups. Consultationwas provided beginning in the second half ofthe year (approximately February) and contin-ued through the next year, if the participant'ssecond-year teacher was willing. Overall, theconsultation procedure lasted approximately 15months. Several elements of consultation werecommon across both TDAI and IDAI groups.First, following the identification of a studentand teacher as participants, the consultant as-signed to the case scheduled a meeting with theteacher to provide information regarding ADHDand its effects on school {performance. Teacherswere given two resource materials—an over-view chapter on ADHD, taken from Pfiffner(1996), and a handout from the National Asso-ciation of School Psychologists entitled ADHDStudents in the Classroom: Strategies for Teach-ers (Brock, 1998)—and their contents were re-viewed. Second, the initial and second inter-views with the teachers by either TDAI or IDAI

consultants were audiotaped and procedural in-tegrity was determined using a checklist of in-terview steps. Feedback was provided to theconsultant on any steps not completed ade-quately (i.e., if integrity <100%).

Third, consultants in both groups collab-orated with classroom teachers to design aca-demic interventions, with teachers eventuallyselecting the intervention(s) that they believedwas most appropriate for their classroom andeach student's needs. Next, consultants de-signed specific intervention plans that detailedthe instructional steps and provided teacherswith the necessary materials to implement theselected interventions.

Fourth, both groups used a range of inter-ventions including teacher-mediated, peer-medi-ated, computer-assisted, and self-mediated strat-egies (DuPaul & Stoner, 2003). An examina-tion of the percentage use of the differentintervention types revealed no significant dif-ferences with regard to teacher-meditated[X^(l) = 0.003], peer-mediated [x^(l) = 2.42],computer-assisted [x^(l) = 0.17], and self-me-diated [x^(l) = 0.63] interventions. Teacherswere the most common mediator for bothgroups, with 84.9% of TDAI and 86.2% of IDAIparticipants receiving at least one teacher-mediated intervention across the 15-monthperiod. Peer-mediated interventions were thesecond most common (TDAI = 52.3%;IDAI = 64.8%), followed by self-mediated(TDAI = 15.1%; IDAI = 19.9%) and com-puter assisted (TDAI = 5.8%; IDAI = 7.4%).

Fifth, interventions for both groups fo-cused on math and/or reading skills dependingon the difficulties exhibited by specific chil-dren. Sixth, the TDAI and IDAI consultationgroups had access to the same interventionmaterials and all interventions were supportedby empirical research, most typically in thearea of leaming disabilities. Common readinginterventions for both groups included re-peated readings (O'Shea, Sindelar, & O'Shea,1985; Samuels, 1979; Tingstrom, Edwards, &Olmi, 1995), listening passage preview (Rath-von, 1999), collaborative strategic reading(Vaughn & Klingner, 1999), and story map-ping (Idol, 1987). In the area of mathematics,interventions such as cover-copy-compare

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(Skinner, Turco, Beatty, & Rasavage, 1989),reciprocal peer tutoring (Fantuzzo, King, &Heller, 1992), classwide student tutoring teams(Harper & Maheady, 1999), and schema-basedproblem solving (Jitendra & Hoff, 1996; Jiten-dra, Hoff, & Beck, 1999) vv'ere found in bothgroups. Finally, intervention integrity was mon-itored at least three times per intervention phasein both groups and treatment acceptability wasassessed as well (see DuPaul et al., 2006). IDAIteachers implemented interventions with signif-icantly (p < .01) greater integrity (math,M = 92.3%; reading, M == 93.1%) than didTDAI teachers (math, M == 60.1%; reading,M = 57.1%). Altematively, the two groups didnot differ with respect to treaitment acceptability,which was uniformly high in both conditions.

TDAI. The design of academic inter-ventions in the TDAI group was based onteacher choice (i.e., "consultation as usual"control condition). Following the ADHD in-formational session, teachers were informedof the procedures involved in the consultationprocess and what they could expect regardingthe number and duration of future meetings.Such meetings consisted of two consultationinterviews, with additional meetings sched-uled as needed. The TDAI consultation groupwas designed to be a close approximation towhat typically takes place in the schoolsetting.

During the initial interview, academicareas of concem were identified, current per-formance was discussed, and goals for inter-vention were determined. The consultants setup a time for a second inten/iew in which theyretumed with a menu of empirically supportedintervention options addressing the goals tar-geted in the initial interview. After explainingeach intervention, teachers chose the interven-tion(s) and consultants provided the necessaryresources (intervention plan, materials) to im-plement the intervention(s). Weekly contactwith teachers was arranged by phone or e-mailfor the teachers to provide updates and addressquestions or concems. Data on studentprogress were not collected and any changesin intervention were based solely on teacherreport. Intervention integrity was monitored

(using checklists reflecting the steps of theintervention plan) by a TDAI consultant notassigned to the case, and neither the teachernor the consultant responsible for designingthe intervention were provided with feedbackregarding integrity, progress, or outcomes.

The above procedure was implementedfor each teacher involved in the consultationprocess. When a child changed teachers (i.e.,advancing to the next grade level), the proce-dure was repeated. Master's level psycholo-gists completed procedural integrity of the ini-tial and second interviews. Approximately20% of the audiotapes were randomly chosen,resulting in 95.7% integrity for the initial in-terview and 97.6% integrity for the second.Feedback regarding treatment integrity wasprovided to the consultant by the supervisor.

IDAI. The design of academic interven-tions in the IDAI group was based on assess-ment data using a consultative problem-solv-ing model involving three consultant-teacherinterviews (Bergan & Kratochwill, 1990).During the Problem Identification Interview(PII), consultants in the IDAI group identifiedacademic areas of concem, antecedent condi-tions, student's response to these conditions,as well as consequent conditions. Pattems toacademic behavior problems were also identi-fied; goals were set and prioritized. Teachersand consultants then agreed on additional ob-servational procedures.

Before conducting the Problem AnalysisInterview (PAI), consultants in the IDAI treat-ment groups conducted functional academicassessments of the classroom to obtain infor-mation regarding teacher routines, behaviors,and procedures as well as student and peerbehaviors (e.g., Daly et al., 1997). Consultantsalso reviewed student work products in com-parison to peers and completed a basic skillsassessment using curriculum-based assess-ment data in the content areas of reading andmath. Once these observation and assessmentprocedures were completed, consultants con-ducted the PAI with the teacher. In this meet-ing, specific interventions were discussedbased on teacher input as well as direct obser-vation and assessment data. After explaining

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Table 2Means and Standard Deviations for CBM and Report Card Grade

Measure

CBM instruct,level-math

CBM instruct,level-reading

Report card grade-math

Report card grade-reading

TDAIBL

1.00(0.71)

1.85(1.58)

3.07(1.01)

3.00(0.89)

TDAI3 Months

1.04(0.74)

2.20(1.72)

3.14(1.02)

3.39(0.93)

TDAI12 Months

1.44(0.85)

2.80(1.91)

3.29(0.94)

3.36(0.96)

TDAI IDAI15 Months BL

1.78(0.97)

3.30(1.90)

3.45(0.98)

3.66(0.88)

1.27(0.66)

1.72(1.57)

3.19(0.94)

3.16(1.08)

Note. TDAI = traditional data-based academic intervention; BL = baseline;intervention; CBM = curriculum-based measurement. Instruct = instructional

IDAI3 Months

1.26(0.66)

2.18(1.97)

3.32(0.91)

3.34(0.99)

IDAI12 Months 15

1.69(0.89)

3.31(L78)

3.50(1.14)

3.62(0.83)

IDAI = intensive data-based

IDAIMonths

2.03(1.05)

3.36(1.86)

3.46(1.26)

3.62(1.00)

academic

each intervention and teachers chose the inter-vention(s), consultants not only provided thenecessary resources (intervention plan, mate-rials) to implement the intervention(s) as in theTDAI group, but also trained teachers andstudents, if necessary, on the steps of the in-tervention. In contrast to the TDAI group,progress monitoring data were collected on aweekly basis by either the teacher or the con-sultant. The type of data collected was basedon the goals and academic subject targeted andincluded such procedures as curriculum-basedprobes in beginning reading skills, oral read-ing fluency, and/or math fluency, as well ascomprehension and problem-solving skillswhen appropriate.

In contrast to the TDAI group, the con-sultants responsible for the interventions con-ducted integrity checks (using checklists re-flecting the steps of the intervention plan) andprovided teachers with feedback that was tiedto the intervention plan on a biweekly basis. Atreatment evaluation interview was also con-ducted approximately 4 weeks into the inter-vention. Consultants used visual analysis ofthe graphed displays of the progress monitor-ing data to determine the level of progressmade (i.e., mastery, no progress, adequateprogress, inadequate progress, motivationproblems; Browder et al., 1989). It was then

determined whether it was appropriate to leavethe plan in place, intensify or simplify theintervention, provide for improved anteced-ents, change the intervention, redefine thegoals, or retrain the teacher and/or students inthe procedures.

This procedure was implemented foreach teacher involved in the consultationprocess, including a discussion of pastperformance and progress in the projectwith additional teachers. Further, the secondauthor determined procedural integrity ofthe interview. Approximately 20% of theaudiotapes were randomly chosen, resultingin 94.8% integrity for the PII and 96.6%integrity for the PAI. Procedural integritywas also completed on audiotapes of thetreatment evaluation interviews and wasfound to be 91.3%. Feedback regardingtreatment integrity was provided to the con-sultant by the second author.

Results

Means and standard deviations for alldependent measures are presented for CBMand report card grades (Table 2) and POTBratings (Table 3). Separate hierarchical linearmodeling analyses for each dependent variablewere conducted to assess possible differences

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Table 3Means and Standard Deviations for Progress of Target Behaviors

TDAI TDAI TDAI IDAl IDAI IDAIMeasure Pretreatment Midtreatment Posttreatment Pretreatment Midtreatment Posttreatment

POTB 0.51(0.48) 1.43(0.79) 1.88(0.94) 0.81(0.43) 1.62(0.77) 1.75(0.86)AssessmentPhase 1 (math)

POTB 0.37(0.54) 1.05(0.53) 1.55(0.84) 0.77(0.44) 1.47(0.63) 1.89(0.85)AssessmentPhase 2 (math) ' ^

POTB 0.63(0.54) 1.28(0.87) 1.76(1.16) 1.03(0.88) 1.56(0.93) 1.87(0.94)AssessmentPhase 3 (math) ' ' .

POTB 0.84(0.54) 1.59(0.76) 1.92(0.87) 0.71(0.50) 1.53(0.70) 1.74(0.74)AssessmentPhase 1 , .(reading)

POTB 0.51(0.45) 1.40(0.73) 1.74(0.80) 0.86(0.67) 1.67(0.75) 1.77(0.70)Assessment ;Phase 2(reading)

POTB 1.45(0.85) 1.80(0.86) 2.06(1.03) 1.55(0.57) 1.95(0.71) 2.10(0.75)AssessmentPhase 3 •(reading)

Note. TDAI = traditional data-based academic intervention; IDAI = intensive data-based academic intervention;POTB = progress of target behavior.

iti ititercept (baselitie value) and slope (aca-demic growth) between the two cotisultatiotigroups (usitig at! intetit to tteat tnethodology).For CBM and report card grades, interceptsand slopes represented trajectories over a 15-month period (i.e., one data point per assess-ment phase). Alternatively, because POTBdata were collected three times per assessmentphase, hierarchical linear tnodeling analyseswere conducted within each assessment phase.For all analyses, at Level 1, individual trajecto-ries (i.e., intercept [baseline value] and slope)were calculated for each participant. At Level 2,group level parameters of individual changewere examined, including mean initial perfor-mance for TDAI (7oo)' difference in mean initialperformance between TDAt and IDAI (^Q,),

mean growth rate (per assessment period) forTDAI (7io), and difference in mean growth ratebetween TDAI and IDAI (7,,).^

Because participants in the IDAI mathsample were significantly older than thechildren in the TDAI math sample, age inmonths was used as a Level 2 covariate formath analyses only. Although there also wasa significant difference in grade level be-tween math intervention groups, grade wasnot added as a covariate because it is anordinal variable and was highly correlated(v(^ = 0.85) with age. In similar fashion,because paternal occupation was signifi-cantly higher in the IDAI group for thereading sample, paternal occupation wasused as a Level 2 covariate for reading anal-yses of POTB data. Paternal occupation wasnot included as a covariate for analyses ofCBM reading level or reading report cardgrades because this potential covariate wasnot significantly related to scores on thesedependent measures.

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Academic Interventions for ADHD

Table 4Hierarchical Linear Modeling Analyses of Mathematics

Achievement Outcomes

Measure

CBM instructional levelReport card gradePOTB Phase 1POTB Phase 2POTB Phase 3

TDAIIntercept

0.98***3.02***0.60***0.42**0.84**

A Intercept(IDAI)

0.120.190.35**0.53**0.19

TDAISlope

0.31***

0.100.67***0.56***0.61***

A Slope(IDAI)

-0.070.01

-0.200.00

-0.19

Note. TDAI = traditional data-based academic intervention; IDAI = intensive data-based academic intervention;POTB = progress of target behavior.** p < .01.***p < .001.

For all dependent measures, 7oo wasstatistically significant {p < .05), indicatingthat the TDAI group started out at a nonzerolevel of performance (see Tables 4 and 5).For all but four measures, 7oi was not sta-tistically significant; thus, there was no sig-nificant difference in initial performance be-tween the two treatment groups. Statisticallysignificant (p < .05) group differences inintercept were found for POTB in both mathand reading (Phases 1 and 2). Specifically,IDAI group scores were greater than TDAIscores. Although statistically significant(p = <.O1 to <.OO1), positive growth wasobtained for 9 of the 10 dependent variables(not significant for math report card), the twoconsultation groups did not differ in rate ofgrowth for any measure.

To estimate the magnitude of change,within-group effect sizes were calculated usingthe formula (Mi^.^^, - A/BL)/(pooled SD),^which accounted for the correlation between pre-and postintervention scores. Thus, these effectsizes represent change over baseline functioningin standard deviation units (Cohen, 1988; seeTable 6). Effect sizes were in the small range(ES < 0.50) for report card grade in math (bothgroups) and reading (IDAI only). Alternatively,a moderate effect size (0.50 < ES < 0.80) wasobtained for report card grade in reading (TDAIonly). Finally, large effect sizes (ES > 0.80)

were found for CBM instructional level in bothmath and reading, as well as all POTB scores(both groups).

Discussion

This study compared the effects of twodifferent consultation models (TDAI andIDAI) on the academic achievement of chil-dren with ADHD. Specifically, we examinedschool functioning as measured by CBM as-sessments, report card grades, and individualgoal attainment (i.e., POTB). The results indi-cated that the two consultation groups did notdiffer with respect to growth on any of theacademic measures.

At the same time, the finding about apositive outcome for the TDAI group on thePOTB combined with the fact that interven-tions designed through the IDAI approachwere implemented with greater integrity thanthose designed through the TDAI approach isnot consistent with research that clearly indi-cates a link between treatment integrity andoutcome (Witt & Elliott, 1985). One explana-tion for the finding may be that TDAI teach-ers, on average, implemented academic strat-egies with a sufficient level of integrity toproduce positive outcomes. The level of integ-rity with the TDAI intervention was greaterthan 50% integrity, which is higher than pre-

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Table 5Hierarchical Linear Modeling Analyses of Reading Achievement Outcomes

Measure

CBM instructional levelReport card gradePOTB Phase 1POTB Phase 2POTB Phase 3

TDAIIntercept

1.53***3.05***0.59***0.61***1.56***

A Intercept(IDAI)

0.110.130.25*0.50**0.04

TDAISlope

0.62***0.20**0.74***0.59***0.29**

A Slope(IDAI)

0.03-0.03-0.17-0.15

0.00

Note. TDAI = traditional data-based academic intervention; IDAI = intensive data-based academic intervention;POTB = progress of target behavior.*p < .05.**p < .01.***p < .001.

vious findings for behavioral interventions un-der similar no-feedback conditions (Noell etal., 2005).

In general, both TDAI and IDAI modelsof consultation were comparable in terms oftheir effect on the academic achievement ofstudents with ADHD. That is, positive growthtrajectories were evident on the majority ofmeasures across a 15-month period. The ex-ception was report card grade in mathematics,which did not indicate a significant growthrate over time. Overall, these positive trajec-tories are encouraging, giiven that childrenwith ADHD typically experience significantacademic difficulties over time as a function ofthe interaction between their symptoms andincreased academic challenges across gradelevels. Although it is unclear whether theseslopes are educationally significant given thelack of prior research with this sample over anextended period, the findmg of growth oncommonly used measures of school function-ing appears to have social validity. These pos-itive trajectories for academic achievement areparticularly noteworthy given the more typicalmaintenance or worsening of achievement dif-ficulties of this population through the schoolyears (Barkley, Fischeret al., 1990; Lambert,1988; Latimer et al., 2003; Mannuzza, Gittel-man-Klein, Bessler, Malloy, & LaPadula,1993) as well as the relative intractability ofacademic problems in response to treatment

among children with both ADHD symptomsand academic difficulties (MTA CooperativeGroup, 1999; Rabiner, Malone, & the ConductProblems Prevention Research Group, 2004).For example, in the MTA study (MTA Coop-erative Group, 1999), slopes (over a 14-monthperiod) for reading achievement test scoresranged from 0 (community treatment controlgroup) to 0.20 (combined treatment group),whereas slopes for math achievement rangedfrom 0.13 (community treatment controlgroup) to 0.20 (medication management andbehavioral treatment groups). These slopesrepresent very small effects in the context ofachievement test standard scores having amean of 100 and a standard deviation of 15. Incontrast, slopes for CBM and POTB in thepresent study were larger and representedgreater effects given the much smaller rangeof possible scores on these measures relativeto an achievement test.

An examination of effect sizes repre-senting change over a 15-month period indi-cated large effects (0.80 to 1.49) across bothgroups for CBM mathematics and reading in-structional levels and POTB. These effects areconsiderably larger than the effects (0 to 0.58)for norm-referenced, standardized measures ofmathematics and reading achievement (seeDuPaul et al, 2006). The difference in effectsizes between these two types of measuresmay be explained in part by the fact that CBM

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Academic Interventions for ADHD

Table 6Effect Sizes for Change from

Baseline to 15 Months

Measure

CBM instructional level (math)Report card grade (math)POTB Phase 1 (math)POTB Phase 2 (math)POTB Phase 3 (math)CBM instructional level (reading)Report card grade (reading)POTB Phase 1 (reading)POTB Phase 2 (reading)POTB Phase 3 (reading)

TDAI

1.080.251.241.191.251.360.551.491.460.08

IDAI

0.940.280.941.130.861.480.251.161.340.94

Note. TDAI = traditional data-based academic interven-tion; IDAI = intensive data-based academic intervention;CBM = curriculum-based measurement; POTB =progress of target behavior.

measures are more sensitive to short-term ac-ademic skill change than standardized, norm-referenced assessments (Shapiro, 2004). Theeffects in this study also compare favorablywith effects (0.73-0.94) for achievementfound by Hechtman et al. (2004). However,their study examined the effects of stimulantmedication and/or multimodal psychosocialtreatment rather than interventions that specif-ically targeted academic skills. Further, theirsample demonstrated higher levels of baselinefunctioning (i.e., baseline academic achieve-ment scores averaged >100, with very fewparticipants scoring in the below averagerange) when compared to the participants ofthe present study.

In contrast, a small effect was found formath report card grades for both groups. Onthe report card grade for reading, the effectwas small for the IDAI group and medium forthe TDAI group. A plausible hypothesis is thatreport card grades are relatively stable acrosstime (Guskey, 2005). Further, it may be thecase that the mathematics skills targeted forintervention in both groups did not match thecontent addressed in the classroom. Becausestudents in this study were those with seriousacademic deficits and were functioning below

grade level as reported by their teachers, themajority of interventions in mathematics tar-geted remediation of basic skills (e.g., facts,operations) that were prerequisites for doingmore complex math. However, grades inmathematics were based on information cov-ered in grade level content (e.g., graphing) thatmay not have been mastered by these students.On the other hand, the medium effect size forthe TDAI group for reading report card gradeis particularly noteworthy. The focus primar-ily on reading for the majority of students inthe study combined with the implementationof effective and varied research-based inter-ventions may have accounted for the positivechange. Fluency in basic reading is robustenough to affect many areas (e.g., comprehen-sion), unlike basic mathematics skills, whichare necessary but not sufficient for higher or-der skills, such as reasoning and problem solv-ing. However, it is unclear why this changewas not as large for students in the IDAIgroup, who also received empirically basedreading interventions.

In summary, the equivalence in treatmenteffects for the two consultation models in thisstudy is discrepant from prior reviews of data-based consultation (e.g., Sheridan, Welch, et al.,1996). Most prior investigations have examinedbehavior change at an individual level with nobetween-group comparisons that included a con-trol condition. Alternatively, otir finding is sim-ilar to outcomes found for assessment-ba.sed be-havioral consultation in one of the few studies toinclude a control group (Beavers et al., 2004).Specifically, Beavers and colleagues found nodifference in treatment effects for reading diffi-culties between a consultation approach usingfunctional assessment and consultation withoutsfjecific functional assessment data. Further, itshould be noted that the TDAI group in thepresent study was similar to the IDAI conditionin most aspects and differed primarily withregard to intensity of data utilization. In fact,the TDAI condition included all the typicalstages of consultation such as structured con-sultation interviews, implementation of empir-ically supported interventions, and assessmentof outcomes.

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Limitations and Directions for FutureResearch

Results of the present study must beinterpreted in light of several limitations. First,because of a lack of a no-treatment controlgroup (because of ethical concerns about with-holding academic interventions from childrenover an extended period of time), it is impos-sible to draw conclusions about whether oneor both consultation models were effective.Future studies addressing the extent to whichthe two consultation models compare to a no-treatment control group should clarify the ex-tent to which the treatments are effective. Atthe same time, it is important to note thatchanges in academic skills obtained in thisstudy were greater in magnitude than thosefound in prior school-based intervention stud-ies (DuPaul & Eckert, 1997). In addition, fu-ture research involving a comparison to agroup of nonstruggling, non-ADHD studentsis needed to demonstrate normalization ofslope. Second, participants in this study had tomeet diagnostic criteria for ADHD as well asexhibit significant impairment in academicachievement on the basis of teacher reports.As such, these results may have limited exter-nal validity and may not generalize to theADHD population as a whole. However, thepresent sample most likely represents the pop-ulation of children with ADHD who requireacademic interventions (i.e., a significant ma-jority of the ADHD population), because di-agnosis of ADHD is based, in part, on symp-toms associated with significant academicand/or social impairment (American Psychiat-ric Association, 2000).

Third, because different teachers acrosssemesters implemented the tteatment, we do notknow whether findings would differ when thesame teachers, who are familiar with the proce-dures, implement the treatment. Clearly, addi-tional research that investigates such compari-sons is required. Fourth, the use of instructionallevel data rather than words correct per minute inreading or digits correct in mathematics is notoptimal given that the instnictional level scalehas a restricted range of values, limiting its sen-sitivity to change. Future research should con-

sistently monitor instructional reading text ormathematics skills at the following year's gradelevel to make the assessment strategy more sen-sitive to change in student performance. Fi-nally, an intent to treat design was usedwherein all available data (including fromthose who dropped out of active treatment)were used for all statistical analyses. It is pos-sible that clearer differences in outcomes be-tween groups would be evident if data wererestricted to treatment completers. From thisstudy, it is unclear whether the between-con-ditions comparisons were influenced by attri-tion effects. Because at least one-third of thesample did not receive consultation services inthe second year, it is critical that subsequentanalyses are conducted to investigate the ef-fects of attrition on outcomes.

Implications for Practice

Despite the limitations, results of thecurrent study suggest several important impli-cations for school psychologists and practitio-ners. The most obvious implication is thatpractitioners must emphasize the use of in-structional methods based on evidence-basedpractices. That empirically valid strategiesemployed in both consultation groups weregenerally equivalent with regard to their effec-tiveness in addressing the academic perfor-mance deficits of children with ADHD is con-sistent with the response to intervention modelin that a large number of children can bemaintained with effective teaching strategies(Fuchs & Fuchs, 2006). Recent research inresponse to intervention suggests that only asmall group of children may need more inten-sive services at the classroom (20%) and in-dividual levels (2%; Bums, Appleton, & Ste-houwer, 2005). Thus, the more intensive, on-going consultation support (i.e., IDAI) may beneeded only for a select group of children withADHD rather than all children with ADHD.

Another implication is the need to con-sider the challenges of working with meagerschool resources. Consequently, the choice ofeither type of consultation should be guided bycost-effectiveness and teacher preference. Itmay be that a comprehensive, data-based

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model as employed in the IDAI condition maynot be necessary if the TDAI approach that isless intensive can also bring about effectivegrowth in academic skills.

Conclusions

In an age of accountability, schools areunder increased pressure to improve student out-comes. Identifying effective instructional prac-tices that enhance school functioning of childrenwith high needs (e.g., ADHD) is essenfial. Theconsultafion approaches in this study hold prom-ise in meeting the goal of closing the achieve-ment gap for these students. Contrary to theoriginal hypothesis, results of the current studysuggested that both IDAI and TDAI are compa-rable in improving the academic performance ofstudents with ADHD. Even though an a prioripower analysis suggested that the sample sizewas adequate, it is recommended that additionalstudy of these two approaches be conducted witha larger sample size to increase confidence in theresults. In addition, future research should exam-ine possible differences in outcomes over longerperiods of time. Finally, it is important to explorethe conditions under which the TDAI consulta-tion approach is sufficient and to identify theconditions in which more intensive IDAI con-sultation strategies are necessary.

Supplementary Material

For additional materials about the inter-ventions described in this article, go tohttp://lehigh.edit/~inpass/inpass.html.

Footnotes

'These two samples have 54 cases in com-mon (i.e., students who received both reading andmath interventions).

^Following the notation of Raudenbush andBryk (2002). hierarchical linear modeling with re-peated measures first attempts to model each sub-ject's performance over time (t) on a given depen-dent variable (Y) as

It then attempts to model the growth parameters(the 3 values) in terms of treatment (W) as

o; = 7oo

Piy = 7.0 + yuWj +«„

In the present study, time (0 was coded starting at 0in the pretreatment phase and increasing by 1 insubsequent treatment phases. Consequently, forsubject i, P,o represents initial performance leveland (3,1 represents rate of growth per assessmentperiod. The treatment variable (W) was coded 0 forthe TDAI condition and 1 for the IDAI condition.Consequently, y,^^ represents mean initial status forthe TDAI condition, 7,,, represents incremental ini-tial status for the IDAI condition, 7,,, representsmean growth rate per assessment period for theTDAI condition, and -y,, represents incrementalgrowth rate per assessment period for the IDAIcondition. Tests of significance for the 7 parameterswere of primary interest in the present study.

""The formula used for the denominator was thesquare root of the following term: The variance atbaseline plus the variance at 15 months minus twicethe correlation between baseline and 15 tnonthstimes the product of the two standard deviations(Cohen, 1988).

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Date Received: July 5, 2006Date Accepted: November 28, 2006

Action Editor: Thomas Power

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School Psychology Review, 2007, Volume 36, No. 2

Asha K. Jitendra received her PhD in Special Education from the University of Oregon. Sheis currently Professor of Special Education at Lehigh University. Her research interests includedesigning effective interventions for students with learning disabilities in the areas of math-ematics and reading, textbook analysis, and assessment practices.

George J. DuPaul received his PhD in Psychology (School) from the University of RhodeIsland in 1985. Curretitly, he is Professor of School Psychology and Associate Chair ofEducation and Humatt Services at Lehigh University. His research interests includeschool-based intervention for students with ADHD, early intervention for young childrenat risk for behavior disorders, and assessment and treatment strategies for college studentswith ADHD.

Robert J. Volpe, PhD, is Assistant Professor of Counseling and Applied EducationalPsychology at Northeastern University. His primary research interests concern academicproblems experienced by children with ADHD, academic and behavioral assessment, andacademic interventions.

Katy E. Tresco is a doctoral student in the School Psychology program at Lehigh University.Her research and professional interests include intervention and long-term behavioral, aca-detnic, and social outcomes for students with ADHD and other related behavior problems.

Rosemary E. Vile Junod received her PhD in School Psychology from Lehigh Universityin 2007. She currently works as a consultant with the B2EST Program of ArcadiaUniversity. Her research and professional interests include behavioral consultation as ameans of service delivery for students in schools as well as behavioral and academicassessment and intervention for students with ADHD and related behavior problems. Inaddition, she has developed a strong research interest in issues related to the assessment,intervention, and outcomes associated with ADHD and other disruptive behavior disor-ders among students from ethnically diverse backgrounds.

Gary Lutz received a BS in Engineering Physics, an MA in Education, and an EdD inEducational Measurements and Research—all from Lehigh University, where he has beenon the faculty since 1971. His areas of expertise include research design, data analysis,and psychometric theory, with particular emphasis on multivariate methods. He hascoauthored numerous articles stemming from collaborative research efforts, and hasauthored a number of technical papers dealing with data analytic methods, as well asseveral computer programs for statistical applications. <

Kristi S. Cleary received her doctorate from Lehigh University in 2003, where shespecialized in school-based consultation for students with ADHD. She currently isworking as a school psychologist in the Syracuse City School District in Syracuse, NewYork. Her current responsibilities include providing training on response to interventionand best practices in assessment and intervention planning, as well as conductingfunctional behavioral assessments for students with emotional and behavioral disorders.

Lizette M. Rammer-Rivera, MEd, is a doctoral student at Lehigh University with aspecial interest in students with ADHD. She worked on Project PASS as a consultant forteachers of students with ADHD who were encountering academic difficulties. Currentlya special projects consultant at the B2EST Program of Arcadia University, she works inthe School District of Philadelphia with teachers who require support for studentsexperiencing behavior problems.

Mark C. Mannella, MA, is currently pursuing his doctoral degree in school psychology atLehigh University. His professional interests focus on academic and behavioral interven-tions, including functional behavioral assessment. He is a school psychologist in theCentral Bucks School District.

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