morgan, p. & sideridis, g. (2006)

Learning Disabilities Research

Learning Disabilities Research & Practice, 21(4), 191–210C© 2006 The Division for Learning Disabilities of the Council for Exceptional Children

Contrasting the Effectiveness of Fluency Interventions for Studentswith or At Risk for Learning Disabilities: A Multilevel Random Coefficient

Modeling Meta-Analysis

Paul L. MorganThe Pennsylvania State University

Georgios D. SideridisUniversity of Crete

This study had two purposes. First, we sought to compare the overall effectiveness of dif-ferent types of fluency interventions for students with learning disabilities (LD). Second, weattempted to identify how individual- and class-level characteristics moderated each interven-tion’s effectiveness. We used multilevel random coefficient modeling to analyze results from30 single-subject studies involving 107 students with or at risk for LD. Doing so allowed us toassess the intervention’s impact in terms of both intercept- (i.e., mean difference between studyphases) and slope-level (i.e., rate of growth) changes. Results indicated that two motivation-focused interventions were significantly more effective than other types of interventions acrossgender, age, and placement. The least effective intervention was word recognition training; thisfinding was consistent across all levels of analyzed variables.

Ensuring that children become proficient readers may be ateacher’s most important task (Mayer, 2004). To become aproficient reader, children must acquire fluency (e.g., Adams,1990; Fuchs, Fuchs, Hosp, & Jenkins, 2001; Snow, Burns, &Griffin, 1998), or the ability to read “quickly, accurately, andwith proper expression” (National Institutes of Child and Hu-man Development [NICHD], 2000, 3–5). Theoretically, flu-ency is important because it means that a child is able toexpend enough attention beyond word recognition to com-prehend a text’s meaning (e.g., Allington, 1983; LaBarge &Samuels, 1974; Perfetti, 1988; Stanovich, 1984). There isa strong empirical relation between children’s fluency andcomprehension skills (e.g., Fuchs et al. 2001; Hosp & Fuchs,2005).

Unfortunately, many children struggle with becoming flu-ent readers (Adams, 1990; Snow et al., 1998). Some of thesechildren’s fluency problems may be the result of a specifictype of reading disability (e.g., Archer, Gleason, & Vachon,2003; Fuchs et al., 2001; Lovett, 1987; Wolf, Bowers, &Biddle, 2000) that is resistant to decoding interventions(e.g., Lyon & Moats, 1997; Torgesen, Rashotte, Alexander,Alexander, & MacPhee, 2003). Thus, researchers are in-creasingly evaluating interventions that teachers might use

Requests for reprints should be sent to Paul L. Morgan, Department ofEducational and School Psychology and Special Education, The Pennsyl-vania State University, 211 CEDAR Building University Park, PA 16802.Electronic inquiries may be sent to [email protected].

to specifically target fluency (Wolf & Katzir-Cohen, 2001).Examples include repeated readings (e.g., Meyer & Felton,1999), assisted reading (e.g., Mefferd & Pettegrew, 1997), andmulticomponent interventions (e.g., Wolf & Bowers, 1999).Characteristics of these interventions vary substantially, asdo their effects across dependent variables (Swanson, 1999;Swanson, in press).

Limitations of the ExistingMeta-Analytic Literature

The growing number of evaluations of different types of flu-ency interventions has led to numerous literature syntheses.Some of these reviews have been traditional narratives (e.g.,Fuchs et al., 2001; Kuhn & Stahl, 2003; Myers & Felton,1999; Wolf & Katzir-Cohen, 2001). Although they makemany important contributions, these reviews do not attemptto quantify the effectiveness of the interventions using ef-fect size estimates. Other reviews have been meta-analytic(i.e., Chard, Vaughn, & Tyler, 2002; NICHD, 2000; Therrien,2004). For example, the NICHD estimated effect sizes for 77studies evaluating the impact of repeated or guided oral read-ing. The NICHD’s analysis yielded a weighted average effectsize of .41, indicating a small-to-moderate effect for thesetwo types of fluency interventions.

Yet the extant meta-analytic work is limited in at least twoways. First, recommendations for the “best” type of fluencyintervention have been constrained by each meta-analysis’

192 MORGAN AND SIDERIDIS: CONTRASTING THE EFFECTIVENESS OF FLUENCY INTERVENTIONS

scope. Chard and colleagues’ (2002) meta-analysis focusedonly on elementary-aged children with learning disabilities(LD). The NICHD’s (2000) meta-analysis did not includesystematic comparisons between different intervention types.Therrien (2004) only examined the effects of one type of flu-ency intervention. Second, none of these meta-analyses quan-tified results from single-subject studies. This is an importantlimitation because a substantial portion of the evaluations offluency interventions has used such designs. For example, thesearch and inclusion procedures used to conduct our meta-analysis yielded 43 single-subject studies. Most of these usedsamples of children with or at risk for LD.

Why have the extant meta-analyses not quantified resultsfrom single-subject studies? One offered justification (i.e.,Therrien, 2004) is that these studies’ designs are not appro-priate for computing effect sizes. However, there is increasingprecedent for doing so (e.g., Scruggs & Mastropieri, 2001;Olive & Smith, 2005). Indeed, single-subject meta-analyseshave been conducted on a number of other topics (e.g.,DuPaul & Eckert, 1997; Mathur, Kavale, Quinn, Forness,& Rutherford, 1998; Scotti, Evans, Meyer, & Walker, 1991;Scruggs & Mastropieri, 1994; Swanson & Hoskyn, 1998;Swanson & Sachse-Lee, 2000). What is notable, however, isthe intense controversy over the most appropriate effect sizemetric to employ (e.g., Campbell, 2004; Olive & Smith, 2005;Scruggs & Mastropieri, 1998). For example, researchers haveargued for—and against—regression-based effect sizes (e.g.,Faith, Allison, & Gorman, 1996; Francis et al., 2005; Olive &Smith, 2005), standardized mean differences (Busk & Serlin,1992; Onwuegbuzie, Levin, & Leach, 2003), percentage ofnonoverlapping data (Scruggs & Mastropieri, 2001; Scruggs,Mastropieri, & Casto, 1987), and percentage reduction(Campbell, 2004; O’Brien & Repp, 1990) as effect sizemetrics.

Problems Associated with Single-SubjectEffect Size Metrics

The controversy results from the nature of most single-subject studies’ data (Campbell, 2004). Single-subject studiestypically report time-series data. Such data may be auto-correlated, especially in treatment phases in which trends areapparent (e.g., Sideridis & Greenwood, 1997). Yet correlatederrors violate a key assumption in classical test theory and somay render traditional parametric procedures invalid. More-over, single-subject studies typically report only a small num-ber of data points (e.g., Huitema, 1985). A small set of datapoints makes statistical inference difficult. Data reported insingle-subject studies are sometimes reported across unequaltime intervals. Such data can lead to inaccurate effect size es-timates (Scruggs & Mastropieri, 2001). In addition, somesingle-subject effect size metrics result from tests of meandifferences. Potential variation in an intervention’s effective-ness over time may not be ascertained. Other limitations oftraditional effect size indices involve difficulties in calcu-lating confidence intervals as they are seriously affected by(a) homoscedasticity and nonnormality (Grissom & Kim,2005), (b) unequal population sizes, or even (c) cultural

factors (Matsumoto, Grissom, & Dinnel, 2001). Perhaps mostimportant, many of the more popular effect size approaches(e.g., percentage of nonoverlapping data) do not allow forstatistical tests of whether an intervention’s effectiveness isaffected by child- (e.g., age) or class-level (e.g., placement)factors. Yet this type of information is an important consid-eration when trying to determine which type of fluency inter-vention will work best for a particular group of students (e.g.,Does the intervention “work” equally well for both youngerand older students? Can it be used effectively in both generaleducation and special education settings?).

We used a nontraditional analytical approach that webelieve better accounts for a single-subject study’s time-series data. Specifically, we meta-analyzed the extant litera-ture using multilevel random coefficient modeling (MRCM).Use of MRCM has many advantages (see Roberts, 2004).First, MRCM takes into account correlated data structures(Pollack, 1998). Second, it estimates individual parametersand so is not unduly influenced by a limited number of ob-servations. Third, MRCM allows a researcher to estimategrowth trajectories, even when based on data from unequaltime intervals. Fourth, it provides robust tests of statisticalsignificance of both intercepts and slopes. Fifth, MRCM al-lows a researcher to include linear or nonlinear predictors(or outcomes) in the same model, and at different levels ofthe analysis (e.g., child or class levels). Thus, MRCM al-lows one to more accurately estimate an intervention’s actualtreatment effect because the time-series observations (level1) are examined after taking into account various child- orclass-level characteristics (level 1 and level 2), as well as ad-ditional covariates at either level. Sixth, multilevel modelstake into account the heterogeneity of studies within a cate-gory (in our case, participants) by applying a random effectsmodel. Thus, this information is not lost but modeled by ap-plying different weights to studies (or participants) that con-tribute heterogeneous effects (De Leeuw & Hox, 2003). Sev-enth, MRCM produces more accurate estimates comparedto classical ordinary least-squares solutions because the for-mer employs empirical Bayesian estimates whereas the lattersuffer from unconditional shrinkage (Raudenbush & Bryk,2002) and autocorrelation. In short, use of MRCM shouldyield a more accurate effect size estimate because the influ-ence of many potential confounds and statistical artifacts iscontrolled.

The purpose of this single-subject meta-analysis was tocompare the effectiveness of different types of fluency inter-ventions for children with or at risk for LD. We examined thefollowing five research questions:

1. Which type of intervention leads to the greatest gainsin oral reading fluency by students with LD?

2. To what extent is an intervention’s effectiveness mod-erated by a student’s gender?

3. To what extent is an intervention’s effectiveness mod-erated by a student’s age or grade level?

4. To what extent is an intervention’s effectiveness mod-erated by a student placement?

5. Do the different types of fluency interventions producedifferent types of growth patterns? That is, do some

LEARNING DISABILITIES RESEARCH 193

interventions lead to steady gains in a student’s fluency,while the effects of others wash out over time?

METHOD

Search Procedures

We searched the PsycINFO and ERIC databases for studiesthat met the inclusionary criteria described below. We usedthe same search terms used in the NICHD’s (2000) meta-analysis. These were chunking, echo reading, speech pitch,punctuation, reading rate, repeated readings, reading fluency,paired reading, reading speed, automaticity, prosody, parsing,intonation, expression, phrasing, reading accuracy, neurolog-ical impress, assisted reading, inflection, verbal fluency, in-stance theory, oral reading, and fluency. Our search yielded2,659 abstracts.

We reviewed each abstract to determine whether a studymet our inclusionary criteria. To be included in our review,the study must have (a) used a single-subject design consist-ing of at least two phases (i.e., AB), (b) included studentswith or at risk for LD enrolled in K–12th grade, (c) beenpublished in a refereed journal, (d) assessed the student’soral reading fluency in English, and (e) included measure-ment over at least three time points, in order for us to beable to assess growth patterns. Forty studies met our inclu-sionary criteria. We then conducted a hand search of ninemajor education and special education journals for articlesappearing between 1990 and 2006. These were BehavioralDisorders, Journal of Applied Behavior Analysis, Journalof Behavioral Education, Education and Treatment of Chil-dren, Journal of Learning Disabilities, Learning Disabil-ity Quarterly, Psychology in the Schools, Reading ResearchQuarterly, and School Psychology Review. We searched theseparticular journals because each yielded more than one studyvia the electronic search. Our hand search produced three ad-ditional studies. Thus, our initial pool involved 43 studies. Wesubsequently excluded 13 of theses studies for the technicalreasons described below.

We excluded (a) three studies (i.e., Lovitt & Hansen, 1976;Peterson, Scott, & Sroka, 1990; Scott, Stoutmore, Wolking,& Harris, 1990) for employing nonlinear scaling meth-ods (thereby rendering the reading of the figures useless);(b) two (i.e., Chafouleas, Martens, Dobson, Weinstein, &Gardner, 2004; Daly, Murdoch, Lillenstein, Webber, & Lenz,2002) because they included very brief phases (one data pointonly); (c) three (i.e., Morgan & Lyon, 1979; Rosenberg, 1986;Rousseau, Kai, & Tam, 1991) because they standardized flu-ency (used percentages) thus making their data incompati-ble with our analyses; (d) two because they reported errorsrather than correct words per minute (CWPM) (i.e., Salend& Nowak, 1988; Singh, 1990); (e) one because the depen-dent variable had to reach a criterion level following numer-ous trials; thus, there was no independent criterion on theeffectiveness of the intervention (i.e., Weinstein & Cooke,1992); and (f) two because the interventions could not beidentified within our classification system, and we could notrepresent them in independent categories due to their low

frequency (i.e., Jitendra et al., 2004; Rinaldi, Sells, &McLaughlin, 1997).

Participants

The 30 studies reported data on 144 experimental phases in-volving 107 students with or at risk for LD. Of these 107 stu-dents, 21 were girls and 86 were boys. The students wereenrolled in a variety of different grade levels. Seventy-fourstudents attended K–4th grade; 33 students attended 5th–12thgrade. Ninety-two students were educated in general or in-tegrated educational settings and 15 were placed in specialeducation classrooms. Researchers classified the students ashaving or being at risk for LD due to (a) a formal diagnosisby the local educational agency, (b) significant discrepancybetween achievement and ability (i.e., over 1.3 SD), (c) a sig-nificant discrepancy between ability and achievement usinggrade equivalent scores (e.g., more than 1.5 grades belowactual grade), (d) referral by the student’s teacher and/or par-ents as having a significant learning problem, or (e) provisionof Title I services. The sample also included a small num-ber of students exhibiting attention deficits or hyperactivityalong with their learning problems. If a student had a dualdisability diagnosis, we classified him or her as LD if LD wasthe primary diagnosis. With regard to race, 94 students wereWhite, 8 were Black, 4 were of Chinese origin, and 1 wasHispanic.

Intervention Types

Characteristics of the included studies’ fluency interven-tions varied substantially. To more parsimoniously analyzefor treatment effects, we collapsed each fluency interventioninto one of seven categories. An intervention could only beplaced in one category. We describe each category below.Appendix A displays both the characteristics and classifica-tion of the included studies.

Keywords and Previewing

During keywords and previewing, a teacher (or researcher)and a student first discussed the passage (e.g., O’Donnell,Weber, & McLaughlin, 2003; Skinner, Cooper, & Cole,1997). The teacher made sure that the student had someprior background knowledge about the passage topic. Next,the teacher identified potentially unfamiliar but importantkeywords in the passage. The teacher provided definitionsof each word and also modeled the word’s pronunciationbefore asking the student to repeat the word. The studentcould ask questions about specific words. The teacher thenread the entire passage while the student followed silentlyalong. The student was encouraged to follow along with hisor her finger under each word. Then the teacher asked thestudent to read the passage as quickly as he or she could.Twenty participants (13.9 percent) contributed data for thisintervention.


Listening and Repeated Readings

Here, the student followed along as the teacher read thepassage aloud (e.g., Jones & Wickstrom, 2002; Valleley &Shriver, 2003; Van Auken, Chafouleas, Bradley, & Martens,2002) or listened to the passage from a tape recorder. Thenthe student practiced reading the passage three to five times.During each practice attempt, the teacher marked the student’serrors and, in some studies, provided corrections. Sixty-sevenparticipants contributed data for this intervention (46.5 per-cent).1

Goal Setting Plus Performance Feedback

The student set goals for the amount of time it would takehim or her to read the passage aloud (e.g., Eckert, Ardoin,Daisey, & Scarola, 2000). The procedure included settingestimates of the amount of errors that the student mightmake. After reading the passage, the teacher told the student(a) how long it took to read the passage, (b) his or her accu-racy, and (c) the number of errors that were made. The teacherthen asked the student to record this information using bargraphs. Ten participants contributed data for this intervention(6.9 percent).

Contingent Reinforcement

Here, the teacher asked the student to choose two education-ally relevant reinforcers (e.g., eraser, pencil) from a pool ofreinforcers (e.g., Billingsley, 1977). The student’s first rein-forcer choice was awarded if the student read the text in lessthan a specified time (e.g., 3 minutes) while making fewerthan a specified number of errors (e.g., three). The student’ssecond reinforcer choice was awarded if the student tooklonger to read the passage, but was still able to do so withina specified time (e.g., 4 minutes) while making fewer than aspecified number of errors (e.g., eight). The specific criteriareported in these studies varied (e.g., Daly, Martens, Hamler,Dool, & Eckert, 1999). Sixteen participants contributed datafor this intervention (11.1 percent).

Goal Setting Plus Feedback and Reinforcement

During this intervention, the teacher asked the student to setgoals for both time to completion and accuracy (e.g., Eckert,Ardoin, Daly, & Martens, 2002). Before reading, the studentrecorded these goals on bar charts. The teacher allowed thestudent to choose two educationally relevant reinforcers. Af-ter reading, the student was awarded the reinforcer if he orshe met or exceeded the established goals. As in contingentreinforcement, the student received his or her first choiceof a reinforcer upon meeting a more difficult fluency crite-rion. Twelve participants contributed data for this interven-tion (12.2 percent).

Word Recognition

The student first read the passage as the teacher followedalong (e.g., Daly, Martens, Kilmer, & Massie, 1996). If the

student misread a word, the teacher supplied the correct wordand asked that the student repeat it. The teacher printed eachmisread word on index cards. These cards were then shownindividually to the student while the teacher pronounced themphonetically. The teacher and the student read the cards in uni-son by sounding out the word together. The student soundedout the word both aloud and in a whisper. Finally, the stu-dent read the card, sounded out the word quietly, and thenread the word aloud at a normal speed (Rosenberg, 1986).Seven participants contributed data for this intervention (4.9percent).

Tutoring

During tutoring, a higher skilled reader (e.g., classmate,parent) worked with a lower skilled reader (e.g., Duvall,Delquadri, Elliott, & May, 1992; Hook & DuPaul, 1999).The pairs followed structured protocols while practicing var-ious reading activities. These activities typically emphasizedboth word- (e.g., phonetic awareness, decoding, sight wordrecognition) and text-level (e.g., identifying the main idea ofa passage) strategies. Example procedures were developed byHook and DuPaul (1999) and were implemented by Fiala andSheridan (2003) and Wehby et al. (2003). Twelve participantscontributed data for this intervention (8.3 percent).

Characteristics of the Studies

Included Studies’ Methodological Quality

Although we did not use methodological quality as a criterionfor inclusion, we did code for it. All but two of the includedstudies reported interobserver agreement above .80. Inter-observer data were reported in 93.3 percent of the studies.Data on treatment fidelity were reported in 63.3 percent ofthe selected studies (see Appendix A).

Analyzed Observations

Our analyses are based on the data points reported within thefirst A and B phases reported for each study’s participant (or,in the case of ABCD or alternating treatment designs, theorthogonal phases). To be included in our analyses, each ofthese phases had to include a minimum of three data points(as noted in Appendix A, studies often contributed more thanthree data points). We limited our analysis to the first A andB phases of a study for two methodological reasons. First, weconsidered a study’s subsequent phases largely to be demon-strations of experimental control. In contrast, our purposewas to estimate the intervention’s treatment effect, which wefelt might better be estimated using data not confounded byan attempt to reverse fluency gains. In addition, collapsingmultiple A and B phase data together would inaccuratelyrepresent the data as uninterrupted time series.

As indicated above, students sometimes contributed datafor more than one intervention. This was because someof the studies involved within-individual comparisons that


contrasted different interventions (i.e., through use of an al-ternating treatments design). We used the weighting proce-dure described below to statistically adjust for the resultingpotential bias. Thus, the 107 participants provided data for144 phases.

Data Analysis

We estimated the multilevel models using the HierarchicalLinear Modeling (HLM) 6.1 program (Bryk & Raudenbush,1992; Raudenbush & Bryk, 2002). Multilevel modeling haspreviously been used to summarize effects in education (e.g.,De Leeuw & Hox, 2003; Dowrick, 1999; Watt & Eccles,1999), medicine (e.g., Thompson, Turner, & Warn, 2001),and developmental psychology (e.g., Maas, Hox, & Lensvelt-Mulders, 2004). We used MRCM over other single-subjecteffect size estimates because it allowed us to simultaneouslymodel both intercepts and slopes (Choi, 2001; Hox & deLeeuw, 2003; Kreft & de Leeuw, 1998). Modeling both in-tercepts and slopes allowed for a more in-depth evaluation ofthe effectiveness of a particular intervention. Use of MRCMalso allowed us to estimate how child- (e.g., gender) and class-level (e.g., special education placement) characteristics re-ported in the studies (e.g., Shin, Espin, Deno, & McConnell,2004) affected the impact of an intervention. We ran the mod-els by adjusting coefficients for a student’s baseline levelsof fluency, gender, age, placement, as well as the unequalnumber of students contributed by each study. Thus, bothintercepts and slopes were adjusted for these variables (asin a covariance analysis). We weighted for the number ofstudents contributed by each study to the meta-analysis us-ing the following formula (Asparouhov, 2004; Goldstein &Yang, 2000):

Wi j = 1

pi j,

where pij represents the probability that student i from studyj was included in the meta-analytic sample. Weighting thenis represented by the inverse of this probability. HLM furtherstandardizes this weight to have a mean of 1 because, forlow probabilities pij, weighting values tend to be quite large(Raudenbush & Bryk, 2002). We set the significance level toa more conservative level of .025 in order to adjust for thenumber of tests conducted. This practice is commonly usedin research employing multilevel modeling (e.g., Fleming,Cook, & Stone, 2002).

RESULTS

Establishing the Likelihood of Moderatorsto an Intervention’s Effectiveness

Our initial results indicated that gender, age, and placementdifferences likely impacted an intervention’s effectiveness.We found that the intercepts of each intervention were differ-ent for girls and boys (see Baron & Kenny, 1986). We foundthat girls, on average, read 19.1 more words per minute (wpm)than boys. Older students (i.e., those in grades 5th–12th), onaverage, read 15.4 wpm more than younger students (i.e.,

those in K-4th). Students in general education settings, onaverage, read 12.7 wpm more than student in special edu-cation, even after accounting for baseline levels of fluency.For all interventions, these means were significantly differ-ent from zero (see Table 1). The effect for placement did notreach statistical significance, suggesting that growth in flu-ency was not different between general/integrated and specialeducation settings.

Which Intervention Type Leadsto the Greatest Gains in Fluencyby Students with or at Risk for LD?

To answer this question, we ran the following two-level model(i.e., Equations 1–4) with inclusion of random effects, afteraccounting for students’ baseline level of achievement, gen-der, age, and placement (i.e., general education versus specialeducation setting). We fit the following model to the data us-ing CWPM as the dependent variable:2

Level 1 (1)

Y′(CWPM) = π0 + π1(Time) + π2(Baseline CWPM) + e

Level 2 (2)

π0 = β01(Gender) + β02(Age) + β03(Placement)

+ β04(Key Words/Previewing)

+ β05(Listening/Repeated readings)

+ β06(Goal setting and feedback) + β07(Reinforcement)

+ β08(Goal setting plus feedback and reinforcement)

+ β09(Word recognition) + β010(Tutoring) + r0

π1(Time) = β11(Gender) + β12(Age) + β13(Placement)

+ β14(Key Words/Previewing)

+ β15(Listening/Repeated readings)

+ β16(Goal setting and feedback)

+ β17(Reinforcement)

+ β18(Goal setting plus feedback and reinforcement)

+ β19(Word recognition) + β110(Tutoring) + r1

(3)

π2(Baseline CWPM) = β20 + r2 (4)

Here π0 and π1 represent the mean and linear growth rateof the interventions, respectively; π2 represents the meanof baseline levels of fluency. The term β01 represents thedifferent fluency estimates for boys compared to girls, β02represents the difference fluency estimate of younger par-ticipants compared to older participants, β03 represents thefluency rates for general education students compared to spe-cial education students; the terms β04 through β010 representmean fluency of each intervention controlling for gender, age,


TABLE 1Multilevel Random Coefficient Model Predicting Fluency as a Function of Intervention Type

Variables Coefficient S.E. t-Ratio p df

For intercept π0

Gender§ −19.143 5.509 −3.475 0.001∗ 134Age§ −15.406 4.492 −3.430 0.001∗ 134Placement§ 12.677 6.242 2.031 0.044 134Keywords and previewing 70.593 10.307 6.849 0.000∗ 134Listening and repeated readings 78.649 9.301 8.456 0.000∗ 134Goal Setting and Feedback 93.979 13.254 7.090 0.000∗ 134Reinforcement 84.718 13.943 6.076 0.000∗ 134Goal setting, feedback and reinf. 88.720 12.368 7.173 0.000∗ 134Word recognition 48.877 12.298 3.974 0.000∗ 134Tutoring 78.725 10.989 7.164 0.000∗ 134

For Slope π1

Gender −0.065 0.420 −1.552 0.123 134Age 0.499 0.342 1.459 0.147 134Placement 0.795 0.397 2.000 0.047 134Keywords and previewing 0.494 0.620 0.796 0.428 134Listening and repeated readings 0.871 0.521 1.672 0.097 134Goal setting and feedback 3.795 3.446 1.101 0.273 134Reinforcement 0.364 0.907 0.402 0.688 134Goal setting, feedback and reinf. 2.653 0.661 4.014 0.000∗ 134Word recognition 0.133 0.800 0.166 0.868 134Tutoring −0.381 0.643 −0.593 0.554 134

For Slope π2

Baseline levels of fluency 0.199 0.045 4.438 0.000∗ 143

Note: Coefficients represent the mean of the category coded as 1 compared to the reference category,which equals 0.

∗p < .05. §Gender: 0 = girls, 1 = boys; §Age: 0 = > grade 5–12; 1 = grades K–4; §Placement: 0 =special, 1 = general education setting or integrated.

placement, and baseline levels of fluency. Similarly, the termsβ11 through β13 represent difference estimates in slopes (asabove) whereas the coefficients β14 through β110 representpartial regression coefficients for each intervention, control-ling for gender, age, placement, and baseline levels of flu-ency. The term β20 represents the intercept of baseline levelsof fluency. The means for each intervention were estimatedby creating dummy variables for all interventions. Thus, fromall Level 2 models, the grand intercepts were deleted (Singer& Willet, 2003). This manipulation allowed us to make di-rect comparisons between the interventions using chi-squaredifference tests with one degree of freedom.

We then directly compared the different interventions byconducting a series of Chi-square difference tests (see alsoFigure 1 and Table 1). We found significant intercept-leveldifferences among the interventions. The most-to-least inter-ventions were (a) goal setting and feedback (M = 94 wpm),(b) goal setting with feedback and reinforcement (M = 89wpm), (c) reinforcement (M = 85 wpm), (d) listening andrepeated readings and tutoring (M = 79 wpm), (e) keywordsand previewing (M = 71 wpm), and (f) word recognition(M = 49 wpm), after controlling for baseline levels of flu-ency, gender, and placement (see Table 2).

We then tested whether these differences in gain betweenthe interventions were statistically significant (see Table 3).We found that goal setting plus feedback was more effec-tive compared to keywords and previewing [χ2(1) = 5.598,

p < .025]. Each intervention except keywords and preview-ing was significantly superior to word recognition [listen-ing and rereadings: χ2(1) = 11.378, p < .025; goal settingplus feedback: χ2(1) = 14.196, p < .025; reinforcement:χ2(1) = 9.214, p < .025; goal setting, feedback and rein-forcement: χ2(1) = 11.261, p < .025; and tutoring: χ2(1)= 7.970, p < .025]. Our inspection of the slopes indi-cated that only goal setting with feedback and reinforce-ment was associated with significant growth [t(134) = 4.014,p < .025]. However, this finding was rather conservativebecause the estimation of the slope parameter was basedon only one unit of change in time (i.e., one session).When comparing the growth parameters across interven-tions, the following significant differences emerged (seeFigure 1): Goal setting plus feedback and reinforcementwas significantly more effective compared to keywords andpreviewing [χ2(1) = 10.576, p < .025], listening and re-peated readings [χ2(1) = 7.220, p < .025], reinforcement[χ2(1) = 5.450, p < .025], tutoring [χ2(1) = 7.967, p <.025], and word recognition [χ2(1) = 13.043, p < .025].The above results regarding intercepts deviate markedly fromthose estimated using a traditional procedure3 (see AppendixB), although this is not surprising given that MRCM esti-mates the effect sizes in a much different (and, in our view,more accurate) way. We did not conduct a between-methodcomparison of slopes because, to our knowledge, traditionaleffect size methods cannot model time-series data.


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0 1 2 3 4 5

Number of Sessions

Co

rrec

t W

ord

s P

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KeyWords

Rereading

Goal Setting+F

Reinforcement

GoalSetting+F+R

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FIGURE 1 Comparisons between interventions at the intercept and slope level. The X -axis displays the sessions following the baseline condition.

Is Intervention Effectiveness Moderatedby a Student’s Gender?

We then ran within group analyses of intervention effective-ness separately for boys and girls. For statistical purposes,we compared interventions for the different levels of an inde-pendent variable (e.g., gender) using standard formulae.4 Fordescriptive purposes, we categorized interventions as pro-ducing “high,” “middle,” and “low” intercept values. Thesehigh, middle, and low categories serve only as a heuristic forcomparing the differences reported below.

Intervention Effectiveness in Boys

The two goal setting interventions produced the highest inter-cepts (see Figure 2), which were on average about 10 pointshigher compared to the middle-level interventions (i.e., lis-tening and repeated readings, keywords and previewing, re-inforcement and tutoring). Word recognition produced lowintercept levels. Direct contrasts of the interventions’ inter-cept values yielded statistically significant differences amongseveral comparisons. Specifically, word recognition producedsignificantly lower means compared to all other interventions,except reinforcement and tutoring (which were significant atthe marginal .05 level): [keywords and previewing: χ2(1) =6.916, p < .025; listening and rereadings: χ2(1) = 15.530,p < .025; goal setting plus feedback: χ2(1) = 8.249, p <.025; and goal setting, feedback and reinforcement: χ2(1) =

5.136, p < .025]. Slope level analyses indicated that onlygoal setting plus feedback was associated with a significantgrowth trajectory (t(103) = 4.711, p < .025).

Intervention Effectiveness in Girls

The relative effectiveness of the interventions changed sub-stantially for girls (see Figure 3). Reinforcement producedthe highest intercept value compared to both goal setting in-terventions (a finding that substantially deviated from that forboys). High intercepts were also evident by use of tutoring. Ofmiddle-level effectiveness was listening and repeated read-ings, followed by keywords and previewing at a much lowerlevel. Word recognition could not be evaluated due to low fre-quencies. Post hoc test results indicated that using keywordsand previewing was significantly less effective compared togoal setting plus feedback χ2(1) = 7.489, p < .025, reinforce-ment χ2(1) = 8.304, p < .025, and goal setting, feedback andreinforcement χ2(1) = 5.804, p < .025. Marginal intercepteffects (between .05 and .025) emerged for the comparisonof keywords and previewing versus tutoring (favoring thelatter) and repeated readings versus reinforcement (also fa-voring the latter). When inspecting the slopes, no significantgrowth emerged by any intervention, a finding that may bea function of ceiling levels because some intercepts were atvery high levels for girls. Listening and repeated readingswas associated with significant linear growth at the marginal.05 level.


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FIGURE 2 Comparison of interventions on fluency for boys. The X -axis displays the sessions following the baseline condition.

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FIGURE 3 Intervention effectiveness on fluency for girls. The X -axis displays the sessions following the baseline condition.


Comparing Interventions Across Gender

When comparing boys to girls across interventions usingslope difference tests, results indicated that there were sta-tistically significant differences between groups with regardto reinforcement only. Girls had higher scores comparedto boys based on intercept difference tests t(127) = 2.093,p < .05 at the marginal level.

Is Intervention Effectiveness Moderatedby Students’ Age?

Grades K through 4

Again, the two goal interventions were the most effective in-terventions, followed by reinforcement. Middle-level inter-cept values were produced by listening and rereading, key-words and previewing, and tutoring. Word recognition wassignificantly less effective compared to keywords and pre-viewing χ2(1) = 5.705, p < .025, listening and repeatedreadings χ2(1) = 9.966, p < .025, reinforcement χ2(1) =9.177, p < .025, goal setting plus feedback χ2(1) = 13.387,p < .025, goal setting plus feedback and reinforcementχ2(1) = 12.308, p < .025, and tutoring χ2(1) = 6.539,p < .025. No other comparison exceeded levels of signifi-cance. Visual inspection of the slopes (Figure 4) indicatedthat growth was substantial for the two goal setting inter-ventions, although neither slope was statistically significantfrom those of the other interventions. However, listening andrepeated readings as well as goal setting, feedback and rein-

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FIGURE 4 Intervention effectiveness on fluency for grades K through 4. The X -axis displays the sessions following the baseline condition.

forcement were associated with marginally significant growth(i.e., produced p-values between .05 and .025).

Grades 5 through 12

Two interventions (i.e., keywords and previewing, and listen-ing and repeated readings) provided sufficient data to modelintervention effectiveness for this age group. Results suggestthat listening and repeated readings might be more effective(its intercept was 12 wpm higher compared to keywords andpreviewing), although this difference did not exceed conven-tional levels of significance (i.e., p = .04). At the slope level,listening and repeated readings was indeed associated withsignificant positive growth [b = .87, t(31) = 3.287, p < .025],suggesting its superiority over keywords and previewing (seeFigure 5).

Comparing Interventions Across Grades

When comparing the interventions across the two gradegroups, no significant differences emerged for either of thetwo comparable interventions.

Is Intervention Effectiveness Moderatedby Student’s Placement?

General Education

All interventions produced statistically significant interceptvalues. The most-to-least effective interventions in general


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FIGURE 5 Intervention effectiveness on fluency for grades 5 through 12. The X -axis displays the sessions following the baseline condition.

education at the mean level were (a) goal setting plus feed-back, (b) goal setting plus feedback and reinforcement,(c) reinforcement, (d) tutoring, (e) listening and repeatedreadings, (f) keywords and previewing, and (g) word recog-nition (see Figure 6). Word recognition was once again sig-nificantly less effective compared to listening and repeatedreadings χ2(1) = 10.104, p < .025, reinforcement χ2(1) =8.781, p < .025, goal setting plus feedback χ2(1) = 13.357,p < .025, goal setting plus feedback and reinforcementχ2(1) = 10.784, p < .025, and tutoring χ2(1) = 9.222,p < .025. Significant growth emerged for keywords and pre-viewing [b = 1.24, t(120) = 2.850, p < .025], listening andrepeated readings [b = 1.70, t(120) = 3.686, p < .025], andgoal setting, feedback and reinforcement [b = 3.46, t(120) =5.271, p < .025].

Special Education

Due to limited data (n = 15) only listening and rereading con-tained enough frequencies to estimate effects in the specialeducation setting. This intervention produced a significantintercept (a = 40.39) but not significant growth over time(b = 0.07, p > .025).

Comparing Interventions Across Placement

Results based on slope difference tests indicated that listen-ing and repeated readings had differential effects in general

education settings compared to special education. Specif-ically, the intervention was significantly more effective ingeneral education settings [t(133) = 3.834, p < .025].

DISCUSSION

We conducted a single-subject design meta-analysis to test theeffectiveness of different types of interventions on fluencyfor students with or at risk for LD. By modeling both theintercepts and slopes of each intervention using MRCM, wewere able to identify a number of factors (i.e., gender, age,and placement) that affected an intervention’s effectiveness.

We might summarize this meta-analysis’ results in twoways. First, we found that the reviewed fluency interven-tions produced differing intercept-level effects. Here, themost effective interventions were the two goal-setting inter-ventions (i.e., goal setting plus feedback, goal setting plusfeedback and reinforcement) and reinforcement. Listeningand repeated readings, keywords and previewing, and tutor-ing produced average-to-above-average intercept effects. Useof word recognition training to boost a student’s fluency wasassociated with relatively low-level effects.

Second, at the slope level, most of the interventions wereassociated with positive or null trajectories over time, al-though these effects varied substantially across levels of themoderator variables. Once again, use of goal-setting inter-ventions led to significant growth over time. In contrast, the


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FIGURE 6 Intervention effectiveness on fluency levels in general education. The X -axis displays the sessions following the baseline condition.

most commonly studied fluency intervention (i.e., listeningand repeated readings) yielded treatment effects substantiallybelow the goal-setting interventions. Thus, our meta-analysisoffers some support for a practitioner’s decision to use a goal-setting intervention as his or her go-to strategy for boostingan LD student’s fluency.

There are a number of potential explanations of why goalsetting might function as a relatively more effective fluencyintervention. First, the goal-setting interventions involvedwell-defined plans with feedback and reinforcement and thus,target a skill (i.e., self-regulation, executive function, or self-management) that students with disabilities typically lack(Botsas & Padeliadu, 2003; Hughes, Ruhl, & Misra, 1989;Hughes & Suritsky, 1993, 1994). Second, goal setting pro-vides a means (i.e., a goal) to an end (i.e., reinforcement),which encourages a student’s active participation and re-sponding. At the same time, goal setting provides studentswith feedback and error correction. Third, goal setting, ineither form, tackles a potentially critical feature of LD, thecatalytic influence of emotions on achievement (Elksnin &Elksnin, 2004). Task avoidance (e.g., Morgan, Farkas, Tufis,& Sperling, 2006; Torgesen et al., 1999) and poor motiva-tion (Bouffard & Couture, 2003; Garcia & de Caso, 2004;Pintrich, Anderman, & Klobucar, 1994; Poskiparta, Niemi,Lepola, Ahtola, & Laine, 2003; Sideridis, Morgan, Botsas,Padeliadu, & Fuchs, 2006) are increasingly considered im-portant contributors to the skills deficits of students with orat risk for LD.

Modeling the Effects of Moderating Variables

We found that gender accounted for significant variabilityin an intervention’s effectiveness. Specifically, we found thateach intervention type except keywords and previewing wassubstantially more effective when used with girls. This meansthat the magnitude of an intervention’s effectiveness changeddepending on whether it was used with girls or boys. In ad-dition, the rank order of more-to-less effective treatmentschanged depending on gender. For example, for girls, rein-forcement was the most effective intervention at the interceptlevel (leading to a gain in 3 wpm compared to the second mosteffective intervention and 41 wpm gain compared to the leasteffective intervention). For boys, reinforcement was far lesseffective (deviating by almost 45 wpm compared to the effectson the girls and by about 15 wpm compared to the goal-settinginterventions). At the slope level, growth was always steeperfor girls compared to boys, except in the case when teach-ers used either reinforcement or goal setting plus feedback.The negative slopes of these two interventions when usedby girls may point to a ceiling effect, as the two interceptsneared 100 wpm. The two goal-setting interventions led tovery positive slopes when used with boys, which perhaps in-dicates a difference in motivation between the two groups ofstudents.

Age difference did not moderate intervention effective-ness. Intercept-level effects were similar across the two agegroups for listening and repeated readings and for keywords


TABLE 2Analysis of Intervention Effectiveness Based on Intercepts as a Function of Gender, Age, and Placement

Independent Variables Int1a Int2b Int3c Int4d Int5e Int6f Int7g

1. Full data 70.59a,c 78.65b,f 93.98c,a,f 84.72d,f 88.72e,f 48.88f,b,c,d,e,g 78.72g,f

2. Boys 58.14a,f 60.17b,f 69.25c,f 54.79d 65.16e,f 36.47f,a,b,c,e 55.51g

3. Girls 58.39a,c,d,e 73.04b 96.71c,a 99.60d,a 87.15e,a – 85.65g

4. Age: K–4 63.56a,f 66.37b,f 82.07c,f 73.97d,f 79.95e,f 37.04f,a,b,c,d,e,g 66.65g,f

5. Age: 5–12 45.39a 57.67b – – – – –6. General Ed. 83.91a 91.74b,f 106.95c,f 97.74d,f 101.91e,f 60.99f,b,c,d,f,g 95.73g,f

7. Special Ed. – 40.39 – – – – –

Note: Int1 = Keywords and previewing; Int2 = Listening and repeated readings; Int3 = Goal setting plus feedback; Int4 =Reinforcement; Int5 = Goal setting plus feedback and reinforcement; Int6 = Word recognition; Int7 = Tutoring. Differences are basedon chi-square difference tests with 1 degree of freedom. The significance level was set at p < .025. The presence of two subscriptsindicates statistically significant differences between a column intervention and any other intervention. Coefficients have been adjusted forthe contribution of gender, grade and placement when appropriate. Also, adjustments have been made to control for baseline levels of fluency.

TABLE 3Analysis of Intervention Growth or Decline Rates Based on Gender, Age, and Placement

Independent Variables Int1 Int2 Int3 Int4 Int5 Int6 Int7

1. Full data .49 .87 3.79 .36 2.65∗ .13 −.382. Boys −.07 .08 7.16∗ −.27 2.19 .09 −1.163. Girls 1.00 1.63 −1.37 −1.82 1.73 – 1.204. Age: K–4 2.48 2.69 5.46 2.13 6.13 1.94 1.305. Age: 5-12 .51 .87∗ – – – – –6. General Ed. 1.24∗ 1.70∗ 4.56 1.14 3.46∗ .93 .457. Special Ed. – .07 – – – – –

Note: Int1 = Keywords and previewing; Int2 = Listening and repeated readings; Int3 = Goal setting plus feedback; Int4 =Reinforcement; Int5 = Goal setting plus feedback and reinforcement; Int6 = Word recognition; Int7 = Tutoring. Differenceswere based on Chi-square difference tests using 1 degree of freedom. ∗Indicates significance of slope coefficient at p < .025.Coefficients have been adjusted for the contribution of gender, grade and placement when appropriate. Also, adjustmentshave been made to control for baseline levels of fluency, which were significant at p < .025.

and previewing. This suggests that each intervention wouldwork about equally well whether it was used with eitheryounger or older students. However, the small number ofstudents in the older age group may have influenced thisfinding. It may also be a reflection of the severity of cases.That is, these interventions were likely applied to studentswho had more pronounced fluency difficulties. Thus, thisgroup of older students may represent a special case (i.e., stu-dents whose learning problems were severe enough to persistthrough adolescence).

With regard to placement, we expected that the generaleducation setting might be associated with higher valuescompared to the effects of the same interventions in spe-cial education. We thought this might occur because we hy-pothesized that students with LD placed in special educationwould have more significant reading disabilities, and thustheir fluency deficits would be more resistant to remediation.Unfortunately, we could not evaluate those claims as therewere very few students with LD who were placed in specialeducation settings. Nevertheless, for the one intervention thatwe were able to estimate intercepts and slopes (i.e., listeningand repeated readings) students placed in special educationsettings had significantly lower fluency rates.

Contributions of This Meta-Analysis

Previous meta-analyses on fluency interventions have typi-cally relied on tests of mean-level differences of interventionsevaluated with group designs (e.g., NICHD, 2000; Therrien,2004). Yet this effect size approach does not account for thepatterns of growth or decline produced by a particular inter-vention (we recognize that this is itself a product of the orig-inal study’s design). Issues surrounding the continued use ofintervention, such as habituation, boredom, fatigue, and theresulting wash-out of effects have not been investigated.

In contrast, our use of MRCM allowed us to test the impactof seven different types on student’s fluency growth trajec-tories. As noted above, these analyses revealed several in-teresting growth patterns, as well as factors that may affectthese patterns. Additionally, our analytical strategy allowedus to examine the relative effectiveness of each interventionafter controlling for various potentially moderating variables.Thus, we believe we were better able to isolate the treatmenteffect associated with each of seven types of fluency interven-tions. Overall, our results consistently pointed to goal setting,whether used with feedback and with or without reinforce-ment as the most effective fluency intervention across all


analyzed variables. Reinforcement was also highly effective,especially for girls.

Limitations of This Meta-Analysis

The present study is limited by a number of factors. Stu-dent heterogeneity may account for some of the differencesbetween interventions. Students were included in the studywith or without a formal identification of LD, although theyall had severe reading difficulties. We assumed that the mostsevere cases would not be overly represented in specific inter-vention categories, but instead would be equally distributedacross interventions (as classical test theory would suggest).However, this is clearly a tentative assumption (Kavale &Forness, 1987). We should also note that word recognitiontraining, compared to the other types of interventions, did notinclude repeated reading. The lack of a repeated reading com-ponent may account for the limited effectiveness of this typeof approach. Furthermore, the type of single-subject designused may have worked to mask or inflate the effectivenessof certain interventions. This is because some designs arelikely more sensitive to carry-over effects (e.g., alternatingtreatments) than others (e.g., the ABAB). As in any meta-analysis, our conclusions are based on a constrained set ofstudies. Also, some of our analyses (e.g., those with regard toage) were based on low frequencies and thus may not be ro-bust due to small cell sizes. Nevertheless, the use of MRCMis not unduly influenced by a small number of participants(except for generalization purposes) as the method allowscalculation of individual growth curves based on multipletime points. The unequal number of data contributed by eachparticipant (some interventions lasted 3 sessions, others 20)may have masked the growth patterns of the interventions.If equal time were devoted to each intervention, we wouldbe able to ascertain the growth patterns of each interventionmore confidently.

Implications for Researchers and Practitioners

Our results have a number of implications. Our findings sug-gest the potential of fluency interventions that can explicitlytarget a student’s motivation to engage in (or at least attendto) the reading activity. This was true at both the interceptand slope level. Goal setting with feedback and goal settingwith feedback and reinforcement raised a student’s CWPM toabout 94 and 89 wpm, respectively, after controlling for stu-dent’s baseline CWPM. These two interventions also yieldedthe highest slope values, at 4 and 3 wpm, respectively. Thus,goal setting interventions work, and they seem to work betterthan other types of fluency interventions. Results from ourmeta-analysis of the single-subject literature seem to sup-port the use of goal setting as a primary tool for boosting astudent’s fluency. Future research that evaluates goal settingand other types of fluency interventions in side-by-side com-parisons using true experiments is necessary to confirm thisconclusion.

Our findings also support the idea that fluency deficitsmay be resistant to interventions focusing on improving word

recognition skills (e.g., Lyon & Moats, 1997; Torgesen et al.,2003). While it is very well established that certain system-atic types of word recognition instruction can result in gainsin these skills (e.g., NICHD, 2000), it is less clear whethersuch instruction is also sufficient to bolster the student’s flu-ency (Torgesen et al.). Here, word recognition training wasnotably less effective than the other fluency interventions.At the intercept level, it produced just over half the effect(i.e., 49 wmp) that goal setting plus feedback produced (i.e.,94 wpm). This suggests that practitioners should expect tosee less gain in a student’s fluency if they only target thestudent’s word recognition skills, at least using the methodwe examined. Instead, it may be necessary to combine wordrecognition instruction (which, in the case of phonics, shouldeffectively remediate a student’s decoding deficits and, indi-rectly, his or her dysfluency) with a goal-setting intervention,or even listening and repeated readings intervention (whichshould directly target the student’s dysfluency). By so doing,teachers may maximize the likelihood that the student willgrow to become a fluent reader.

NOTES

1. One reviewer thoughtfully suggested that repeatedreadings and a subsequent test on the practiced pas-sages would confound the picture regarding the ef-fectiveness of the intervention. Thus, in studies thatused practiced and independent passages, only the lat-ter were coded in the meta-analysis.

2. Similar results emerged with inclusion or not of a ran-dom component, ui. Thus, all results are presented withinclusion of random effects and using robust standarderrors.

3. We are grateful to the comments of an anonymousreviewer who suggested that the novelty of the MRCMmethod may be highlighted by paralleling its find-ings to those from traditional effect size models. Thus,Appendix B presents a table in which interventionswere summarized using the Standardized Mean Dif-ference statistic in order to evaluate intervention effec-tiveness using point estimates rather than simultane-ously evaluate random intercepts and slopes. The mosteffective intervention was goal setting plus feedback(SMD = 1.452) followed by keywords and preview-ing (SMD = .970) and listening and repeated readings(SMD = .931) for the whole sample. No other interven-tion produced significant gains using the standardizedmean difference statistic. However, this analysis doesnot exactly parallel the MRCM analysis because sin-gle participants that came from one study had to beexcluded (due to the lack of variance in estimating theSD). Nevertheless, with the exception of the most ef-fective intervention that both models agreed upon (i.e.,for goal setting plus feedback), the remaining findingswere in disagreement, pointing to the different meansof analyses.

4. When comparing two slopes representing two levelsof one independent variable (simple effects) we used


the following formula: t = B1 − B2√SE2

1 + SE22

in which the dif-

ference between two coefficients was expected to dis-tribute like a t statistic. The obtained t statistics werecompared based on critical values from published ta-bles, depending on their respective degrees of freedom(using two-tailed tests).

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APPENDIX ASingle Subject Intervention Studies on Fluency Included in the Meta-Analysis

Number of Data Points Reliability/TreatmentStudy Participants and Characteristics Intervention Type Research Design in Intervention Phase Fidelity

Billingsley (1977) Pupil 4: 10 years 5 months old withreading difficulties

Reinforcement ABAB 7, 9, and 9 for pupils 4through 6,respectively

Reliability

Pupil 5: 10 years old with readingdifficulties

Pupil 6: 9 years 9 months old withreading difficulties

Daly, Hintze, andHamler (2000)

Manny: 1st grader identified by teacheras being at “frustrating levels” inreading

Listening and repeatedreadings

Alternating treatments 21 None

Daly and Martens(1994)

Two students with identified LD. Theywere both 11 years 11 months old.

Listening and repeatedreadings only

Alternating treatments 7 Reliability

Daly et al. (1996) Jim and Jon were classified as having LDand were instructed in self-containedclassrooms. Their age rangedbetween 8 years 6 months to 12 years.

Word recognition AB 4 Reliability andTreatment fidelity

Daly, Persampieri,McCurdy, andGortmaker (2005)

Two students selected by their teacher ashaving reading difficulties.

Goal setting, feedback, andreinforcement

AB with experimentalprobes

13 and 14 for Joshua andElly, respectively

Reliability andTreatment fidelity

Joshua: 10 years 10 monthsElly: 9 years 3 months

Duvall et al. (1992) Steven: 8 years 11 months old with RD Parent tutoring Multiple baseline 10 for each student Reliability andTreatment fidelityAnna: 7 years 10 months old with RD

Dean: 7 years 5 months old with RDEckert et al. (2000) Four male students selected by their

teacher as having reading difficulties.Abel: 7 years 8 monthsGrafton: 7 years 3 monthsBrett: 8 years 5 monthsRubin: Age n/aAll students were subjected to all

conditions.

1. Listening and repeatedreadings

2. Goal setting and feedback3. Reinforcement4. Goal setting, feedback, and

reinforcementOther combinations were

excluded because of lowfrequency with regard tooccurrence across studies

Alternating treatments 3 Reliability andTreatment fidelity

Eckert et al. (2002) Hunter: 2nd grade student with teacheridentified reading difficulties


2. Goal setting and feedback3. Reinforcement4. Goal setting, feedback, and

reinforcement


Stephen: 3rd grade student with teacheridentified reading difficulties

Bethany: 7 years old with teacheridentified reading difficulties

Mason: 8 years old with teacheridentified reading difficulties

Alison: 9 years old with teacheridentified reading difficulties

Vilna: 9 years old with teacher identifiedreading difficulties

Fiala and Sheridan(2003)

Bob: 9 years old was receiving Title-Ireading services

Parent tutoring Multiple baseline 13 Reliability andTreatment fidelity

Freeman andMcLaughlin(1984)†

Six students with LD Listening and repeatedreadings

Multiple baseline 5, 6, 3, 4, 6, and 6 forstudents 1 through 6,respectively

Reliability

Gilbert, Williams,and McLaughun(1996)

Three students with LD ranging in agebetween 7 years 1 month and 7 years4 months.


Multiple baseline 28, 25, and 22 forstudents 1 through 3,respectively


Hitchcock, Prater,and Dowrick(2004)

Cinnamon: 7 years 3 months old withSLD

Indigo: 6 years 4 months old consideredfor special ed. services

Navy: 6 years 11 months old with SLD

Tutoring only was assessed.Tutoring plus video oraudio modelingrepresented a lowfrequency category andwas excluded.

Multiple baseline 6/7, 3/4, and 7/7 forCinnamon, Indigo,and Navy,respectively, fortutoring/ortutoring+modeling.


Hook and DuPaul(1999)

Joey: 2nd grader with reading difficultiesSusan: 3rd grader with reading

difficultiesThomas: 2nd grader with reading

difficulties and recipient of Chapter-Iservices

Scott: 2nd grader with readingdifficulties

Parent tutoring Multiple baseline 18, 12, 18, and 12 forJoey, Susan, Thomas,and Scott,respectively


(continued)


APPENDIX AContinued


Jones and Wickstrom(2002)

Jeff: 1st grade, referred for LD andrecipient of Title-I services

Jay: 2nd grade, referred for LD andrecipient of Title-I services

Dan: 2nd grade, referred for LD andrecipient of Title-I services

Mark: 3rd grade, referred for LD andrecipient of Title-I services

Joey: 3rd grade, referred for LD andrecipient of Title-I services


2. Listening and previewingplus error correction

Alternating treatments 4/8, 4/8, 5/6, 5/6, and 9for Jeff, Jay, Dan,Mark, and Joey forconditions (a)previewing and (b)repeated readings,respectively


Lionetti and Cole(2004)

Stephanie: 4th graderJohn: 4th graderSusan: 5th graderScott: 4th graderThey all had reading difficulties and

were reading 1 to 2 grades belowtheir grade level. Their ages rangedfrom 9 to 12 years old.

Previewing through listeningto text in a fast rate

Alternating treatments 8, 8, 9, and 9 forStephanie, John,Susan, and Scott,respectively


Nelson, Alber, andGordy (2004)

Student 1: 9 yeas 6 months old with LD Listening and rereading witherror correction

Multiple baseline 11, 8, 5, and 5 forstudents 1 through 4,respectively


Student 2: 8 years 10 months old withADHD

Student 3: 8 years 3 months old with LDStudent 4: 8 years 2 months old with LD

Noell et al. (1998) Three 4th graders with readingdifficulties and ADD.

1. Reinforcement2. Listening and repeated

rereadings

Multiple baseline For reinforcement: 4/3/2for the 3 students. Forlistening andrereading: 4/3/6 forthe 3 students


O’Donnell et al.(2003)

Ralph, 10 year old 5th grader, at-riskbecause of inability to think andprocess information that was inEnglish.

Keywords and previewing ABAB 10 Reliability

Rose (1984) Six students ranging in age between9 years 6 months and 13 years2 months. They were all receivingresource room instruction and severalof them had been retained in previousgrades. They had significant deficitsin reading.

1. Previewing2. Listening and repeated

readings

Alternating treatments For Previewing: 10, 9,10, 10, 10, and 10 foreach of Learners 1through 6,respectively. ForListening andrereading there were10 data points foreach Learner

Reliability

Rose and Beattie(1986)

Subject 1: 10 years old with LDSubject 2: 11 years 6 months old with LDSubject 3: 8 years 7 months old with LDSubject 4: 9 years 1 month old with LD.All students were receiving additional

instruction in a resource room

Listening and rereading onlywas selected because the“Taped” procedure wasidentical with onlydifference being the use ofthe tape player.

Alternating treatments 11, 12, 9, and 11 forSubjects 1 through 4,respectively

Reliability

Rose, McEntire, andDowdy (1982)

Five students ranging in age from 8 years7 months to 11 years 6 months whowere receiving LD resource roominstruction. They were 2–4 yearsbehind in academic achievement.

Word recognition only wasincluded. There was anerror correction procedurethat represented a lowfrequency category

Alternating treatments 10, 9, 10, 10, and 12 forLearners 1 through 5,respectively

Reliability

Rose and Sherry(1984)

Five students with a diagnosis of LD.Their age ranged between 14 years10 months and 16 years 2 months.They were also receiving resourceroom instruction

Listening and rereading wasevaluated only

Alternating treatments 11, 12, 12, 12, and 13 forLearners 1 through 5,respectively

Reliability

Shapiro andMccurdy (1989)

Five students with significant readingdifficulties (were reading below grade6) aged between 14 and 16 years.Three had LD and one EMR. Allpresented themselves with emotionalproblems as well.


Multiple baseline 18, 17, 13, 15, and 20 forstudents 1 through 5,respectively


Skinner et al. (1997) John: 12 years old with LDJack: 12 years old LD and EBD

Listening and repeatedreadings (slowpresentation)

Multiple baseline 6 for John and 8 for Jack Reliability andTreatment fidelity

(continued)


APPENDIX AContinued


Smith (1979) John: 12 years old with significantdeficits in reading

Listening and repeated readingswithout error correction wasassessed only

Multiple treatmentsABCD

5 None

Strong, Wehby, Falk,and Lane (2004)

Steve: 14 years old with LD plus ahistory of behavioral problems

Listening and repeated readingswith error correction

Multiple baseline 4 Reliability andTreatment fidelity

Swain and Allinder(1996)

C.C.: 8 years 1 month old with LDA.B.: 7 years 8 months old with LDR.B.: 7 years 8 months old with LD.All students were receiving special

education services for more than1 year. They were receiving readinginstruction in a resource room.

Listening and repeated readings Multiple baseline 18, 12, and 6 for C.C.,A.B., and R.B.,respectively

Reliability

Tingstrom, Edwards,and Olmi (1995)

Student 1: 9 years old at risk foracademic and behavioral problems

Listening and repeated readings Alternating treatments 4, 9, and 4 for students 1through 3,respectively

Reliability



All students were recommended by theteacher as needing remedial servicesin reading.

Van Auken et al.(2002)

Three students identified by their teacheras having reading difficulties.

William: 7 years 2 monthsJack: 8 years 3 monthsRachel: 8 years 10 months

Listening and repeated readingsintervention was selectedbecause the other conditionincluded material that wereeasier for students’ grade


Valleley and Shriver(2003)

John: 16 years old with LDSteve: 16 year old with LDDavid: 15 year old with LD

Listening and repeated readings Multiple baseline 22, 21, and 24 for John,Steve, and David,respectively


Note: The interventions described above may not be all the interventions described in a given study. Interventions may have been excluded for several reasons, one of the mostcommon being that they could not be classified into a category with adequate frequencies. All interventions included estimation of fluency in passages, except one that included wordlists†.

APPENDIX BMeta-Analysis of Single-subject Studies Using

Hedges g Statistic

95 PercentStudy N1 N2 Total SMD Confidence Intervals

Keywords and Previewing1 2 2 4 0.392 −6.104 6.8872 2 2 4 0.730 −9.308 10.7693 6 6 12 0.887 −0.491 2.2644 4 4 8 2.206 −0.360 4.7715 5 5 10 0.701 −0.830 2.231

Listening and Repeated Readings1 4 4 8 0.101 −1.631 1.8332 3 3 6 2.728 −1.638 7.0933 3 3 6 0.556 −1.835 2.9474 3 3 6 0.441 −1.905 2.7875 6 6 12 1.139 −0.294 2.5736 3 3 6 1.420 −1.565 4.4057 2 2 4 1.839 −21.405 25.0838 2 2 4 1.300 −15.411 18.0119 2 2 4 1.114 −13.372 15.600

10 6 6 12 1.290 −0.182 2.76211 4 4 8 1.605 −0.607 3.81612 5 5 10 1.277 −0.409 2.96313 5 5 10 0.215 −1.250 1.68114 4 4 8 1.438 −0.688 3.56415 3 3 6 0.881 −1.686 3.44916 3 3 6 −0.158 −2.435 2.11917 6 6 12 1.779 0.157 3.401Goal Setting plus Feedback1 4 4 8 1.425 −0.694 3.5452 6 6 12 1.469 −0.054 2.991

(continued)

APPENDIX BContinued

95 PercentStudy N1 N2 Total SMD Confidence Intervals

Reinforcement1 4 4 8 0.007 −1.724 1.7372 6 6 12 1.429 −0.082 2.9403 3 3 6 0.888 −1.684 3.4604 3 3 6 0.699 −1.761 3.160

Goal Setting plus Feedback and Reinforcement1 4 4 8 −0.041 −1.772 1.6892 6 6 12 1.610 0.043 3.1763 2 2 4 0.687 −8.868 10.242

Word recognition1 2 2 4 0.256 −5.097 5.6102 5 5 10 0.582 −0.927 2.090

Tutoring1 3 3 6 0.283 −2.017 2.5832 4 4 8 0.768 −1.083 2.6203 4 4 8 2.198 −0.362 4.759

Note: N1 = number of participants in experimental condition; N2 = number ofparticipants in baseline condition; SMD = Standardized Mean Difference. Studiesthat contributed only one participant were excluded from this analysis, thus, thenumber of participants that contributed data in the present analysis is relativelysmaller than the respective one used in the MRCM analyses. Some studies providedata for different interventions and were thus included in more than one condition.Analyses of the homogeneity of studies within an intervention suggested that acrossall intervention conditions studies were homogeneous [Q(4)keywords/previewing = 1,73,p = .785; Q(16)listening/repeated readings = 9.36, p = .00; Q(1)goal setting plus feedback = .01,p = .969; Q(3)reinforcement = 2.13, p = .546; Q(2)goal setting,feedback and reinforcement = 2.74,p = .254; Q(1)word recognition = .05, p = .817; Q(2)tutoring = 2.12, p = .346].


About the Authors

Paul L. Morgan is currently an Assistant Professor of Special Education at the Department of Educational Psychology,School Psychology, and Special Education at the Pennsylvania State University. He earned his Ph.D. in Education and HumanDevelopment from the Department of Special Education, George Peabody College of Vanderbilt University, where he wasawarded both a Dean’s Research Fellowship and an Office of Special Education Program’s Student-Initiated Research Projectgrant. Dr. Morgan’s research focuses on identifying the factors contributing to children’s identification as learning disabled oremotionally or behaviorally disordered, as well as evaluations of interventions designed to prevent or remediate these disabilities.

Georgios D. Sideridis, Ph.D. is currently an Associate Professor at the Department of Psychology at the University of Crete.He has been a research scientist at the Center for Social Development and Education and a research associate professor atthe Department of Psychology, both at the University of Massachusetts, Boston. He received his Ph.D. from the University ofKansas and did postdoctoral work at the Aristotle University of Thessaloniki. Dr. Sideridis conducts research on motivationand underachievement, particularly in populations of children with learning disabilities. He is interested in understanding theinterplay between emotional factors, personality characteristics, and cognition and their contribution to children’s achievementand well-being.

morgan, p. & sideridis, g. (2006)

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