How vocabulary interventions affect young children at risk: A meta-analytic review

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How Vocabulary Interventions AffectYoung Children at Risk: A Meta-AnalyticReviewLoren Marie Marulis a & Susan B. Neuman a ba University of Michigan , Ann Arbor , Michigan , USAb New York University , New York , New York , USA

To cite this article: Loren Marie Marulis & Susan B. Neuman (2013): How Vocabulary InterventionsAffect Young Children at Risk: A Meta-Analytic Review, Journal of Research on EducationalEffectiveness, 6:3, 223-262

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Journal of Research on Educational Effectiveness, 6: 223–262, 2013Copyright © Taylor & Francis Group, LLCISSN: 1934-5747 print / 1934-5739 onlineDOI: 10.1080/19345747.2012.755591

How Vocabulary Interventions Affect Young Childrenat Risk: A Meta-Analytic Review

Loren Marie MarulisUniversity of Michigan, Ann Arbor, Michigan, USA

Susan B. NeumanUniversity of Michigan, Ann Arbor, Michigan, USANew York University, New York, New York, USA

Abstract: This meta-analytic review examines how word-learning interventions affect young chil-dren, at risk for reading difficulties, on vocabulary outcomes. We quantitatively reviewed 51 studieswith 138 effect sizes (N = 7,403) to assess the association between vocabulary training and wordlearning. Using a random-effects model, we found a mean effect size of nearly 1 standard devi-ation indicating a strong training effect overall. Moderator analyses indicated that children fromlow-socioeconomic-status (SES) families experienced significantly lower word-learning gains thanthose from middle- and upper-SES families who had one or more risk factor (e.g., English Lan-guage Learner, language delays). This was true regardless of the total number of risk factors present.However, risk factors in addition to poverty did compound this SES disadvantage. Further, multi-variate meta-regression analyses indicated that the sole risk factor associated with lower effect sizeswas poverty controlling for all other risk factors. Subgroup moderator analyses indicated a numberof instructional and pedagogical factors associated with greater effect sizes. Taken together, theseresults highlight the importance of creating interventions powerful enough to accelerate children’svocabulary development if we are to narrow the reading achievement gap.

Keywords: Meta-analysis, multivariate meta-regression, oral word learning, vocabulary, cumulativerisk, poverty, early childhood

Students’ word knowledge plays a fundamental role in learning to read (Beck & McKeown,2007). As learners begin to read, they map the printed vocabulary encountered in texts ontothe oral language they bring to the task. Understanding text, therefore, depends on being ableto translate letter-sound correspondences into known words and comprehensible concepts(Kamil, 2004). Consequently, word knowledge seems to occupy an important middle groundin learning to read. It makes a critical contribution to beginning readers’ transition from oralto written forms and is crucial to the comprehension processes of a skilled reader (Beck &McKeown, 2007). Numerous studies (e.g., Anderson, Wilson, & Fielding, 1988; Nagy &Herman, 1987) have shown that the size of a student’s word knowledge is strongly relatedto how well that student understands what he or she reads, not only in the primary grades(Scarborough, 2001) but also to reading comprehension in high school (Cunningham &Stanovich, 1997).

Address correspondence to Loren Marie Marulis, University of Michigan, Combined Program inEducation and Psychology, 610 E. University Avenue, SEB 1400, Ann Arbor, MI 48109-1259, USA.E-mail: [email protected]

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224 L. M. Marulis and S. B. Neuman

The practical problem, however, is that there are profound differences in word knowl-edge among learners from different ability and socioeconomic groups, from toddlersthrough high school (Neuman & Celano, 2012). Studies have shown, for example, thatfirst graders from higher socioeconomic status (SES) backgrounds know as many as twicethe number of words as lower SES-students (Graves, 2006). Further, estimations by Hartand Risley (2003) have suggested what they describe as a “30-million word catastrophe,”the proportionate differential between the accumulated experience with words that the aver-age child from a professional family is likely be exposed to compared to that of the averagechild from a poor family. In a recent analysis, Rodriquez and her colleagues (Rodriquez &Tamis-LeMonda, 2011) estimated that environmental factors associated with vocabulary de-velopment and emergent literacy skills are already present among children 15 months of age.

Although poverty is one of the most potent risk factors (Neuman, 2009), there areothers as well that are known to influence students’ word knowledge and place them atrisk for low achievement. Denton, West, and Waltston (2003), in an analysis of the EarlyChildhood Longitudinal Study–Kindergarten class, found that children whose mothers hadhigher levels of education scored higher in word knowledge than those whose mothershad less education. When some of these factors were considered along with others (e.g.,single-parent family; primary home language other than English), children from familieswith multiple risk factors scored lower in reading upon kindergarten entry than childrenwith no risk factors, or even one factor. Other related factors were the literacy richness of thehome literacy environment, and the amount and quality of cognitively stimulating activities,all shown to have an important influence on word knowledge, even after controlling forchildren’s poverty status, and race/ethnicity. In short, studies (e.g., Neuman & Celano, 2006)have identified a number of key environmental factors associated with the development ofword knowledge that may place young children at risk for failure to learn how to read.

With the recognition of the substantial differences in word knowledge early on in chil-dren’s lives and their consequences for subsequent literacy development, there is an emerg-ing consensus that early intervention is critical if we are to prevent reading difficulties. Forexample, Farkas and Beron (2004) in a recent analysis of the children of the National Longi-tudinal Survey of Youth 1979 cohort, found that more than half of the social class effect onearly oral language was attributable to the years before five and, further, that rates of vocabu-lary growth declined for each subsequent age period. However, to date, there is strikingly lit-tle evidence on the key components of effective early vocabulary intervention for young chil-dren. Although a relatively large database now supports the linkages between oral languagedelays and later problems in learning to read (Kaiser, Roberts, & McLeod, 2011), researchon preventing reading deficits through early and effective vocabulary intervention for chil-dren at risk is sorely needed.

This meta-analysis builds on our previous research to examine how vocabulary in-terventions specifically affect young children at risk for word learning difficulties. Recentmeta-analyses have indicated only modest effect sizes for interventions among low-incomechildren compared to their more middle-class peers (e.g., Mol, Bus, deJong, & Smeets,2008). At the same time, however, there is emerging evidence that carefully targeted in-terventions can make substantial improvements. Bishop and Adams (1990) found, forexample, that children who received specialized language intervention at 5 years of agedid not have reading difficulties 3 years later. Catts and colleagues (Catts, Fey, Tomblin, &Xhang, 2002) replicated these findings for children who were slightly older; those childrenwith significant vocabulary delays in second and fourth grades performed significantlyworse on reading outcome measures than did their counterparts no longer identified ashaving vocabulary difficulties. Consequently, it appears that with more intentional andtargeted instructional programs, early intervention can potentially increase the odds that

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Word Learning Meta-Analysis for At-Risk Children 225

children will be successful in reading (Halle et al., 2009). This meta-analysis, therefore, wasconducted to examine the effects of these more targeted interventions for children at risk andthe potential factors that might moderate language outcomes particular to this specializedpopulation.

CHARACTERISTICS OF VOCABULARY INTERVENTIONS

Unlike the skills of alphabet knowledge or concepts of print, vocabulary is considered anunconstrained skill (e.g., unconstrained by the knowledge to be acquired or the duration oflearning; Paris, 2005; Scarborough, 2001). As such, there is no finite number of words tobe learned to achieve successful growth in vocabulary development. Therefore, vocabularyinterventions have varied considerably in what words to teach, when and how to teachthem, as well as how to best assess student learning. Briefly, we highlight these issues tounderstand how these factors may moderate student outcomes.

Age and Ability Effects on Word Learning

Despite its recognized importance for reading performance, there has been limited attentionto vocabulary intervention in the early years (Beck & McKeown, 2007). For example, of the73 grade-level samples examined in the National Reading Panel’s synthesis of experimentaland quasi-experimental research prior to 2000 (National Reading Panel Report, 2000), 53were in Grades 3 through 8, with relatively little research on vocabulary instruction in theearly grades. The authors speculated that one possible explanation for the dearth of studieswas that the teaching of vocabulary is often not separate from other instruction in the earlygrades; further, a second possible explanation was that early reading, at least theoretically,involves the use of texts that typically do not exceed the vocabulary levels of most earlyreaders. In this event, presumably there would be little need for vocabulary instruction.

Nevertheless, the authors of the National Reading Panel reported a trend in the databaseindicating that gains made from vocabulary intervention may be influenced by various abil-ity levels and age differences. Senechal and Cornell (1993), for example, reported moresustained benefits of a vocabulary intervention for 5-year-olds compared to 4-year-olds.In addition, certain instructional methods seemed to exacerbate the Matthew effect (e.g.,the rich get richer phenomenon in reading; Stanovich, 1986; Walberg & Tsai, 1983) ratherthan close the gap. Robbins and Ehri (1994) found that storybook readings helped to teachchildren the meanings of unfamiliar words; yet those with larger entering vocabularieslearned more words than those with more limited vocabulary. Similarly, Nicholson andWhyte (1992), examining the incidental exposure to stories, found that the largest effectswere for high-ability students compared to low-ability or average-ability students. In con-trast, Tomesen and Aarnoutse (1998), studying reciprocal teaching and direct instructionin deriving word meanings, found that the instruction was more helpful for poor readersrather than for average readers. Consequently, although we can conclude that vocabularyinstruction is generally effective, interventions clearly benefit some children more thanothers, suggesting the differential impacts that various vocabulary instruction techniquescan have for different ages and abilities.

Recent meta-analyses (Mol et al., 2008; National Early Literacy Panel, 2008) have sub-sequently examined age and ability effects on vocabulary learning. Recognizing that thereader’s ability to translate letter-sound correspondences to printed materials meaningfullycan come about only if the resultant oral representation is a known word in the learner’s oral

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vocabulary, these meta-analyses have focused on the effects of vocabulary interventionsand language-enhancement programs for children prior to conventional reading. The Na-tional Early Literacy Panel (2008) identified 19 studies in their meta-analysis of languageenhancement interventions and reported slight though nonsignificant differences betweenpreschoolers and kindergarten students. Yet when they contrasted four interventions forchildren younger than 3 with the other 15 interventions that targeted children older than 3(i.e., 3.5–5 years), they found that interventions for children birth through 3 years of agewere associated with significantly greater gains. Similar findings were reported for Moland her colleagues in their meta-analysis of dialogic storybook reading; 3- to 4-year-oldsmoderately benefited from dialogic reading (d = .50) with diminishing returns for the 5-and 6-year-olds (d = .14). For vocabulary in particular, therefore, intervening earlier mayimpact the magnitude of gains.

Nevertheless, each of these meta-analytic studies reported minimal effects of vocabu-lary interventions among children at greatest risk for school failure. For example, Mol andher colleagues (2008) found that dialogic reading explained only 1% of the variance forchildren at risk for language and literacy impairments. Notably, interventions that mightwork better for children who struggle with language or have other risk factors have not beendetected thus far, though such differences might emerge from a more thorough analysis ofthe interventions for those at risk.

This was our intention in the present meta-analysis of vocabulary interventions amongpreconventional readers: to examine studies specifically targeted to at-risk learners forevidence of instructional design features that may especially benefit these children. Incontrast to previous research syntheses which have identified at-risk status dichotomouslyas either present or absent (e.g., at risk or not at risk, our own 2010 meta-analysis included),in this more targeted meta-analysis, our goal was to code all factors associated with risk suchas levels and types of risk factors to more precisely examine how vocabulary interventionsmay differentially impact word learning for these young children.

Context of Vocabulary Instruction

Among factors associated with effective vocabulary instructional approaches is the capacityto deliver instruction carefully tailored to children’s needs in a timely manner. The groupsetting of instruction—whether it is delivered to all children in the setting (i.e., whole group)to small groups of children, or individually—may be viewed as an opportunity structure thatsupports different types of teacher behaviors and child engagement (Powell, Burchinal, File,& Kontos, 2008). In large-group settings, for example, children may have opportunitiesto develop a shared understanding of vocabulary and content through activities such asshared book reading. In small-group settings, on the other hand, children may be able toengage in discourse patterns that are highly interactive and responsive to their questionsand comments.

Although much instruction is delivered in whole-class groupings in typical classrooms,study results (Lowenthal, 1981; Morrow & Smith, 1990) have suggested that children at riskfor academic underachievement learn more rapidly in small-group or one-to-one instruction.Correlational evidence suggests that instruction provided to preschoolers in small groupsmay be up to four times as effective as instruction delivered to the entire class (Connor,Morrison, & Slominski, 2006). This is possibly because teachers may be more able todifferentiate instruction and can change instructional strategies and activities more flexiblyto optimize instruction.

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In fact, meta-analyses (Elbaum, Vaughn, Hughes, & Moody, 2000; Vaughn & Linan-Thompson, 2003) have consistently shown positive effects of grouping practices for studentsat risk in the elementary and middle grades indicating that small group configurations workbetter than whole-class instruction. However, one-to-one interventions in reading havenot been shown to be more effective than small-group interventions (Morrow & Smith,1990). Although Torgesen and his colleagues (Torgesen et al., 2001) demonstrated powerfulinstructional effects of one-on-one instruction, other studies (e.g., Rashotte, MacPhee, &Torgesen, 2001) have demonstrated similar rates of growth for reading disabled childrenusing small groups compared to one-on-one.

These findings stand in stark contrast to recent meta-analyses on the effects of inter-ventions on word learning for preconventional readers. For example, Mol, Bus, and deJong(2009) reported no differences in word learning gains resulting from different groupingpatterns. Similarly, the National Early Literacy Panel (2008) did not report any signifi-cant differences in effect sizes by group configuration. However, unlike Elbaum et al.’s(2000) and Vaughn et al.’s (2003) analyses, these meta-analyses did not focus exclusivelyon children at risk (in multiple, specific ways) for word learning difficulties. One possibleexplanation for these different findings, therefore, could be that more advantaged childrenmight benefit from a variety of classrooms structures—including whole class, small groupand individual time with a teacher—whereas children at risk might profit from the in-creased “time on task” in smaller groupings. Another possibility could be that interventionsin the early years do not differentiate instruction in a similar manner as interventions withreading-disabled children in the upper grades; rather, grouping patterns in early childhoodmay serve organizational (i.e., behavior management) more than instructional needs.

In our current meta-analysis, therefore, we examined the relationship between groupingpatterns and word learning gains. Given that our sample of studies represents vocabularyinterventions targeted to children at risk for failure to learn to read, we expect to find largereffect sizes for smaller group and one-on-one configurations than for whole-group settings.

Instructional Design and Pedagogical Features of Vocabulary Instruction

Converging evidence of research in early reading development indicates that the skillsrequired to become a successful reader are also critical for effective instruction of childrenwho may be at risk for reading difficulties (i.e., phonemic awareness and phonics; vocab-ulary, fluency and comprehension; National Reading Panel Report, 2000; Snow, Burns, &Griffin, 1998). It is important to note that children at risk will need to acquire the same setof word-learning skills and academic vocabulary to become good readers as other typicallyfunctioning students. However, a key difference according to many sources (e.g., Foorman& Torgesen, 2001; Torgesen et al., 2001) lies in the delivery of services, that is, the mannerin which instruction is provided. For example, the National Reading Panel report (2000)recommended that vocabulary intervention provide direct instruction, repetition, and re-view; actively engage students; restructure tasks for greater clarity; and employ a variety ofinstructional methods for optimal results. Recommendations from a number of consensuspanels (Bowman, Donovan, & Burns, 2000; Snow et al., 1998) have further strengthenedthese recommendations for children at risk: Specifically, instruction for children who maybe at risk for reading difficulties should be more explicit, more comprehensive, and moresupportive than the instruction required by the majority of children.

For example, in a study with kindergarten students from a high-poverty community,Coyne and his colleagues (Coyne, McCoach, Loftus, Zipoli, & Kapp, 2009) compared the

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effects of two explicit instructional techniques for teaching vocabulary words: embeddedinstruction and extended instruction with incidental exposure in which children heard wordsbut did not directly discuss them. Moderate to large effects indicated that direct, explicitinstruction of vocabulary resulted in reliably greater word learning than did incidentalexposure. This same pattern of results was reported in studies by Hamilton and Schwanen-flugel (2011; Schwanenflugel et al., 2010) with preschoolers and kindergarten students inthe heart of a high-poverty community in the Mississippi Delta. Using a three-part strategythat engaged their young children in explicit vocabulary enhancements (e.g., didactic-interactional book reading; definitions of novel words; additional target words throughoutthe instructional day) along with phonemic awareness instruction, two randomized con-trol trials reported significant positive impacts for student’s vocabulary development andacademic knowledge. Kindergartners, in particular, who received the intervention were 1month further ahead in vocabulary at the end of kindergarten compared with their peers.These studies indicate that explicit and systematic instruction with clearly defined targetwords identified prior to instruction may be particularly beneficial for at-risk students.

The second broad characteristic that these studies and others (e.g., Stone, Silliman,Ehren, & Apel, 2004) have emphasized for children at risk for reading difficulties is that,unlike instruction in most classrooms, instruction will need to be more comprehensive,leveraging instructional time by increasing the frequency of encounters with target words,reviewing, and practicing words in multiple contexts. This may be especially importantfor the more sophisticated or academically challenging words that children are less likelyto encounter in day-to-day activities (Neuman & Dwyer, 2011). In addition, special popu-lations, such as English Language Learners (ELLs) who may lack foundational words inearly vocabulary (e.g., same; different), are likely to need comprehensive supports to retainword knowledge. For example, Silverman (2007a) developed a multidimensional vocabu-lary program that used supplemental verbal explanations, gestures and facial expressionsto clarify word meanings, and opportunities for children to repeatedly say words aloud sothey that establish phonological representation of the words. Using this approach in fivekindergarten classrooms with mainstream and ELLs, she found that ELLs learned targetwords at the same rate and grew at an accelerated rate in general vocabulary compared toEnglish-only students. Therefore, successful interventions for at-risk learners have includedmore explicit and comprehensive instruction than one would typically find in classroominstruction.

The final general characteristic of instruction that studies have accentuated reflectsthe need for more scaffolded instruction for children at risk: the finely tuned interactionsbetween teacher and child that supports learning (Clay, 1991). Stone et al. (2004) havedescribed two forms of scaffolding: one type involves careful sequencing of activities sothat skills build gradually, which would likely be more evident in systematic programsthan in storybook reading activities where words unfamiliar to children are often selected.Neuman and Dwyer (2009) described these systematic vocabulary programs as includingfive key components: identifying target words, defining and clarifying their meaning;practice, review, and progress-monitoring to measure outcomes. Another type of scaffoldinginvolves teacher–student dialogue designed to maximize children’s opportunities to usewords meaningfully in new contexts (Dickinson & Porche, 2011). For example, Hamiltonand Schwanenflugel (2011) in their PAVEd intervention, emphasized three types of teacherinteraction: competence questions (i.e., asking children to label objects and basic recallinformation), abstract questions (i.e., prediction and problem solving), and relate questions(i.e., relation to personal lives). The point of this type of instructional interaction is to engagechildren in cognitively challenging conversations that use words in context. Dickinson and

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Smith (1994) found that the amount of child-initiated analytic talk was important forvocabulary gains, whereas Juel and her colleagues (Juel, Griffith, & Gough, 1986) notedthat the ability to offer scaffolded support while children are acquiring these skills mayhave increasing importance as the severity of the child’s disability increases.

Consequently, we posit that our present meta-analysis may reveal strong associationsbetween these instructional and pedagogical characteristics and word learning gain forchildren at risk. Previous meta-analytic studies (National Reading Panel Report, 2000),including the National Early Literacy Panel (2008) report, have been unable to addressthese sets of questions due to the small numbers of studies that met their inclusion criterion(e.g., published studies only; short-term trials excluded). In our analysis, we examine avariety of vocabulary interventions, published and unpublished, providing us with a largersample of studies and more opportunities to examine a number of these factors throughmoderator analysis.

Measurement of Vocabulary

We continue to learn vocabulary throughout our lives because there is more vocabulary tolearn. As a result, it is never clear how large a vocabulary a student must have in order tosuccessfully read (Anderson & Freebody, 1981). Instead, assessments often measure wordsthat have been taught—through teacher or author-generated assessments. Standardizedassessments, on the other hand, select words that differ widely in their familiarity, assumingthat people who can correctly identify unfamiliar words will have larger vocabularies(Kamil, 2004).

However, standardized assessments may not be sufficiently sensitive to measure effectsof vocabulary interventions. For example, Elleman and her colleagues in their meta-analysisof vocabulary interventions on comprehension outcomes (Elleman, Lindo, Morphy, &Compton, 2009) report a moderate effect size for author-created measures (d = .50) butwere far less effective for standardized measures (d = .10). Our previous meta-analysis(Marulis & Neuman, 2010), as well, reported stark differences between author-created andstandardized assessments. Given the small corpus of words taught in many vocabularyinterventions (e.g., nine target words in three readings; Coyne, McCoach, & Kapp, 2007),Senechal and Cornell (1993), as well as other vocabulary researchers (Biemiller, 2006)have argued that the use of author-created assessments represent a more valid indicator ofvocabulary improvements. Interventions for at-risk children are often taught in small unitsof instruction, using teacher generated assessments to check for understanding.

Further, author-created measures are more likely to examine proximal outcomes closelyaligned to the intervention than standardized measures (Elleman et al., 2009). These mea-sures most often assess what has been taught in the curriculum (and not taught in the controlgroup). At the same time, treatment-inherent measures only provide a demonstration thatstudents exposed to the curriculum have learned from it. In contrast, treatment-independentmeasures (Slavin & Madden, 2011) examine gains in skills that are presumably learned inboth treatment and control classrooms, such as receptive language. Although both typesof measures are likely to reveal growth, there is an assumption that effect sizes in treat-ment inherent-measures will be larger yet less consequential than treatment independentmeasures.

These issues suggest that there are trade-offs between author-created and standardizedassessments. Author-created assessments may be useful for giving corrective feedbackand differentiated responses; at the same time, they may lack reliability. Standardized

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assessments, on the other hand, may be too generic, tapping children’s background skillsand abilities rather than achievement gains. Given these considerations, therefore, weexpect that interventions, especially for children at risk for reading difficulties who arepreconventional readers, will rely on author-created assessments, which may be better ableto measure more subtle changes in vocabulary, with subsequently greater effect sizes thanassociated with standardized measures.

The Present Study

This meta-analysis examined vocabulary interventions targeted to at-risk young children,prekindergarten through kindergarten. Given that the studies involve preconventional read-ers, we defined at-risk status as some measurable characteristic of a child or the child’shome, school, or community that has been associated with poor progress in learning to read(Snow et al., 1998). It was designed to build upon our previous meta-analytic study (whichfocused on a general population of prekindergarten and kindergarten children; Marulis& Neuman, 2010) by targeting children at risk for word learning difficulties in multiple,specific ways. This was done through the inclusion of additional vocabulary interventionstargeted to at-risk young children and reclassification and recoding of previously includedstudies related to specifically how children were at risk. For example, in the present study,we reclassified a study with one sample of special education (language impairment) learnersin an inclusion school and an independent sample of ELLs as providing two separate effectsizes. Previously, we had collapsed the data from the ELLs and children with languageimpairments in this study. By reclassifying studies in this way, we were able to includeseparate effect sizes for each group of children in our moderator analyses allowing forgreater detection of differences between risk populations.

Further, this meta-analysis was designed to extend our previous study by employingmultivariate meta-regression analyses to disentangle the independent contribution of po-tential moderator factors, which may have been confounded in extant research. Such ananalysis, therefore, could provide important new information regarding the unique effectof each risk factor as it allowed us to control for the effects of other factors.

The purpose of the present study was to critically analyze and integrate the researchliterature on the effects of vocabulary interventions for children at risk for word learningdifficulties. It was designed to examine the overall effect sizes of interventions and thepotential factors that moderate child outcomes. Specifically, we addressed the followingquestions:

• To what extent are vocabulary interventions effective for at-risk children, prekindergartenthrough kindergarten prior to conventional reading instruction?

• Is the magnitude of gains from vocabulary interventions differentiated by type of riskfactor? By the number of risk factors?

• Are there certain methodological, pedagogical, or instructional characteristics that mod-erate effect sizes for children at risk?

METHOD

Search Strategy and Eligibility Criteria

Studies were included when they met the following inclusion criteria:

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1. Study participants were aged 0 to 6.0 (approximately birth through kindergarten) andwere free of developmental or neurological impairments such as Down’s syndrome,William’s Syndrome, or cerebral palsy but qualified as “at risk” such that 90% or more ofthe sample were from low-SES families, marginalized racial groups (African Americanand Hispanic) or urban/rural communities; were ELLs; and had language impairments,language delays (low previous vocabulary scores), or low academic achievement.

2. The study included a training, intervention, or specific teaching technique to increaseword learning (defined as receptive or expressive language).

3. A quasi- or experimental design was applied incorporating one or more of the following:a randomized controlled trial, a pretest–intervention–posttest with a control group thatdidn’t receive any instruction (including within-subject designs), or a pre–post interven-tion comparison between preexisting groups (e.g., two kindergarten classrooms) thatreceived different instruction.

4. The study was conducted with real English words and not pseudowords.5. Outcome variables included a measurement of English language word learning, iden-

tified as either expressive or receptive vocabulary development or both. The mode ofassessment could be standardized (e.g., Peabody Picture Vocabulary Test, ExpressiveOne-Word Picture Vocabulary Test, or Test of Language Development) or researcher-designed (e.g., the Researcher Vocabulary Assessment; Silverman, 2007a, 2007b).

Using both electronic and manual searches, we searched the following databases usingsearch terms based on our inclusion criteria: PsycINFO, ISI Web of Science, EducationAbstracts ProQuest Dissertations and Theses and Educational Resources Information Cen-ter (ERIC; CSA; OCLC FirstSearch). In addition, our manual search included contactingexperts in the field, reading the references of the relevant studies, and examining confer-ence papers and presentations. Both searches included published and unpublished studiesto address potential publication bias.

Inclusion Screening

To maintain accuracy in coding and ensure that all relevant studies were appropriately iden-tified, four language and literacy doctoral students participated in two 6-hr training sessionsthat included tutorials on research design, methodological characteristics, and related in-formation; practical coding techniques; and procedures specific to our project. Prior totraining, each coder was given required reading assignments specific to meta-analysis (e.g.,Cooper & Hedges, 1994; Lipsey & Wilson, 2001) and participated in related lectures anddiscussions on how inclusion coding relates to meta-analytic techniques. Subsequently,coders read and discussed several published educational meta-analysis, focusing on thecoding sections (e.g., S. J. Wilson, Lipsey, & Derzon, 2003). In addition, coders were givencoding manuals used in previous meta-analyses to review and study. At the conclusion ofthe training, coders discussed whether 10 full studies were eligible for our study basedon our full inclusion criteria. Discrepancies were resolved through discussion and consul-tation with the second author, an expert in early literacy research. Subsequently, codersindependently screened a common set of 50 studies and reached a level of “almost perfectagreement” (Fleiss’s κ = .96; Landis & Koch, 1977) on whether each study should beincluded in our analysis and why. The remainder of the studies was divided among the fourcoders; inter- and intrarater reliability checks were repeated on every 25th study to check

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for drift both within and between raters. Cohen and Fleiss’s kappas ranged from .87 to 1.0.Through this rigorous process, we identified 51 eligible studies.1

Study Variable Coding

Codebook

We then developed a detailed codebook2 to code and extract data from the 51 relevant studiesto address our research questions related to factors that may moderate at-risk children’soutcomes. The codebook was based on our research questions and characteristics of eachstudy and intervention that we believed, based on past research and theory (e.g., Ellemanet al., 2009; Halle et al., 2009; Marulis & Neuman, 2010; Mol et al., 2009; Mol et al., 2008;National Reading Panel Report, 2000) would influence child outcomes for at-risk childrenas well as all information related to participant risk factors. The study variables fell broadlyinto two categories. The first were source descriptors. These variables include bibliographicinformation about the study, such the author, date, and age of child in the research. Thesecond category was based on study characteristics, which include participant information,independent variable (i.e., intervention) and dependent variable (i.e., outcome measure),experimental research design information, and effect size data.

We developed not only codes related to whether children were at risk for word learningdifficulties but also in-depth coding as to what types of risks they faced and how these riskswere determined. Prospective longitudinal studies of sample surveys (e.g., Early ChildhoodLongitudinal Study; Lee & Burkam, 2002) have indicated key risk factors that appearto most potently affect development and receptive and expressive language outcomes.Such predictors include those that are (a) intrinsic to the individual child (e.g., languageother than English; nonstandard dialect; developmental delays); (b) identified in the familyenvironment (e.g., SES status); and (c) associated with the larger environment of thechildren including the neighborhood, school, and community in which the child lives (e.g.,neighborhoods of concentrated poverty). Therefore, we used researchers’ specificationsregarding the student population to identify individual or multiple risk factors in studies.

In addition, following recommendations by Lipsey and Wilson (2001), we developedcodes to determine the degree to which information on various aspects of the study weresufficiently provided. For example, if detailed information on the demographic characteris-tic of the at-risk sample was not provided, we created a code to indicate that our confidencerating in determining this factor was low (e.g., 0 = not enough information given to makedetermination). We also included “coder-determined” variables for key characteristics inwhich coders listed their interpretation and could compare this to the interpretation of theauthors, thereby producing further confidence ratings. For example, within the category ofassignment to study conditions, we created a code that indicated what the author stated

1Our previous meta-analysis examined 67 studies, 40 of which addressed at-risk children (125effect sizes) whose participants we coded dichotomously as being at risk or not (please see Marulis& Neuman, 2010, for details). For the current meta-analysis, we created a comprehensive, in-depthcoding scheme to distinguish particular risk factors in a more nuanced way, for example, specificallyhow the participants were at risk for reading difficulties (available upon request from the first author)which we applied to all studies. Subsequently, seven studies and 15 effect sizes (e.g., conditions orparticipants) were eliminated, and 18 studies with 28 effect sizes added, resulting in 51 studies and138 effect sizes.

2This study variable codebook, developed specifically to address vocabulary interventions forchildren at risk for reading failure, is available upon request from the first author.

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(e.g., the use of random assignment), as well as how the coders interpreted the assignmentto conditions (e.g., whether we coded it as random, convenience random or another typeof assignment to conditions altogether). Next, we created a code for the level of agreementbetween these codes. In this example, random assignment and convenience random assign-ment are not fundamentally different, and thus would receive a moderate to high level ofagreement. In addition, we created a code to calculate the percentage of study descriptorsthat were not reported by the authors, and within this, the percentage of descriptors relatedto study quality that were missing (Cooper & Hedges, 1994; Lipsey & Wilson, 2001).This was in adherence to validity procedures recommended for meta-analysis. Therefore,throughout our codebook, in cases where subjective judgment was required we created a“match” category to indicate our confidence rating.

Training. Due to the large number of variables and importance of accuracy, training wasconducted for 6 hr per week over an 8-week period by the first author and involved tutorialson research design, variable coding, and practical coding techniques. In addition, the firstauthor created a coding sheet with accompanying coding manual. Subsequently, the coderscollectively coded five studies in full with extensive discussion and revision of the codingmanual and sheet in accordance to the sample studies. Next, the coders individually codedfive studies and, though a moderate level of agreement was reached (Fleiss’s κ = .67),the standards for meta-analysis were not met, particularly for moderator analyses (e.g.,Borenstein, Hedges, Higgins, & Rothstein, 2009; Lipsey & Wilson, 2001), which werecentral to our research questions. Therefore, to reach the recommended agreement level of.81 or higher, we revised the coding sheets and the first author conducted an additional 6 hrof training with all coders. Following this training, coders independently coded five studiesand obtained an “almost-perfect agreement” of .89. We followed the same procedures aswith the inclusion screening; the remaining studies were coded individually with reliabilitychecks after every fifth study. Both within and between coders, we stayed within theacceptable range for study-variable coding (Fleiss’s kappa ranged .83–.91).

Analysis Plan

To compute effect size estimates, we entered all data into the statistical program calledComprehensive Meta-Analysis (CMA; Borenstein, Hedges, Higgins, & Rothstein, 2005).To avoid dependency in our effect size data (e.g., when a study included more than oneoutcome measure resulting in multiple effect sizes), we calculated one mean effect size(Borenstein et al., 2009; Cooper & Hedges, 1994) to be included in the analysis. CMAadjusts the effect size estimations and standard errors according to the methods specified inHedges and Olkin (1985), detailed in the formulas and their applications next. The majorityof studies (59%) included means and standard deviations for pre- and posttests for theexperimental and control groups; 22% included the same data but only for the posttests;10% included mean differences between experimental and control groups and a p or Fvalue and the remaining 9% included p or F values and sample sizes only. All effect sizeswere expressed as Hedges’s g (i.e., standardized mean difference, or d, corrected for biasusing J) to take sample size into account as follows:

g = J × d

where: d = X1−X2Swithin

and: J = 1 − 34df −1

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In the preceding formulas, X1 and X2 refer to the sample means of the groups, SWITHIN,the within-groups standard deviation pooled across groups, and df , the degrees of freedomused to estimate SWITHIN, (i.e., NTOTAL-1).

The following formulas were applied to the most prevalent study design: treatment andcontrol group, pre- and posttest:3

d = X1 − X2

Swithin

where : X1 = XTREATMENTPOST − XTREATMENTPRE

and :X2 = XCONTROLPOST − XCONTROLPRE

and :Swithin =√

(n1 − 1)S21 + (n2 − 1)S2

2

n1 + n2 − 2

corrected for bias : g = J × d; J = 1 − 3

4df − 1

where : df = n1 + n2 − 2

The related standard errors for the data from these designs were calculated as follows:

Vd = n1 + n2

n1n2+ d2

2 (n1 + n2)

SEd =√

Vd

corrected for bias: Vg = J 2 × V d

SEg = √Vg

Adjustments were made for study designs such as matched groups or within-subjectdesigns where the unit of deviation most often reported was the standard deviation ofthe difference scores. In these cases, we converted the reported standard deviation tothe appropriate pooled within-condition standard deviation using the following formula(Borenstein et al., 2009):

Swithin = Sdiff√2(1 − r)

Where r is the correlation between pairs of observations (e.g., pre–post test correlations).To estimate the variance, the following adjustments were made:

Vd =(

1

n+ d2

2n

)2 (1 − r)

and: SEd = √Vd

After making this adjustment for the type of study design, the same formulas used forindependent groups were applied to compute d and g.

3Formulas for all study designs are available upon request from the first author.

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For nearly 90% of studies that included data for both pre-and posttests, we were ableto obtain these pre–post correlations. For the remaining studies, we used .70, a conservativeestimate that was also empirically indicated by our data as the lowest pre–post correlationcoefficient obtained was .65 and the mean was .84 (.65–.98).

To factor in the proportionate reliability of each study to the overall analysis, weweighted the effect sizes by the inverse of their error variances and calculated a mean effectsize per study (Shadish & Haddock, 1994).

To calculate the overall mean effect size for our sample and 95% confidence intervalsto address our first question, we employed a random effects model to increase generaliz-ability. Furthermore, based on our previous meta-analysis (Marulis & Neuman, 2010), weanticipated that variability would not be limited to sampling error and did not expect that thetrue estimate would be the same across studies due to the diversity in methods, participants,and instruction. Instead, we expected to have a large dispersion of effect sizes making up aheterogeneous sample in which the variability would most likely reflect both within-studysampling error as well as true variation (heterogeneity) in the effect sizes across studiesindicating the use of a random-effects model (Borenstein, Hedges, & Rothstein, 2007;Lipsey & Wilson, 2001).

Moderator Analyses

Accordingly, we planned subgroup and multivariate meta-regression moderator analysesaround the characteristics we believed would moderate the effects for at-risk children basedon previous meta-analyses and vocabulary intervention studies (e.g., Elleman et al., 2009;Mol et al., 2009; Mol et al., 2008). These characteristics fell within 13 categories (10 relatedto the intervention itself and three related to the specific risk factors of the participants). Toaddress statistical power and properly conduct moderator analyses using contrasts (i.e., theQbetween statistic with df = number of subgroups – 1), we required that there were at least fourstudies in each subgroup (Bakermans-Kranenburg, van Ijzendoorn, & Juffer, 2003). Whenstudies had missing data in our variable of interest, we removed them from analysis. How-ever, each of the subgroups contained a sufficient number of studies even after this removal;therefore, we were able to carry out all moderator analyses as planned. Specifically, for thesubgroup analyses we used a mixed-effects model so that we were able to partition the vari-ance and examine the considerable heterogeneity we found (Borenstein et al., 2009; Wood &Eagly, 2009). For the multivariate meta-regression analyses, we used a method-of-momentsmodel.

Because the focus of this study was to elucidate the factors most strongly—andindependently—associated with vocabulary growth for young children at risk, we em-phasized the multivariate meta-regression analysis to allow for the inclusion of multiplemoderators (while controlling for shared variance and reducing the probability of Type Ierrors) and examine both the combined and unique predictive ability of the moderators(DeCoster, 2009; Lipsey & Wilson, 2001). Specifically, we included the Hedges’s g effectsizes (obtained through CMA) of all potential moderators in successive multivariate meta-regression models using a SPSS macros developed by D. B. Wilson (2005; METAREG.SPS)that was further specified by Hofmann (2009). The macro included specification of the nec-essary parameters for the analyses conducted such as an outlier threshold, which we set at3 standard deviations above the mean effect size; minimal number of categories per group,which we set at 4 as described above; and the type of model, which we specified as method-of-moment (Hofmann, 2009). The vocabulary effect size was included as the dependentvariable while the moderators were the independent variables. This meta-analytic multiple

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regression was examined using the Q statistic with k (number of studies)-p (number ofmoderators) degrees of freedom. All mean effect sizes were calculated using CMA; allother descriptive statistics as well as all moderator analyses were calculated in SPSS 19.

RESULTS

Descriptive Results

Our sample comprised 43 individual papers targeting vocabulary training to at-risk childrenin educational or home-based settings, yielding 51 studies and 138 effect sizes (7,403 chil-dren; N exp = 4,093; N control = 3,310). As anticipated, the sample was largely heterogeneous:Qwithin(50) = 506.02, I2 = 90.32; 90% of the variance was attributable to true heterogeneityor between-studies variance and 10% to within-studies based on random error. For the rangeof associated effect sizes and the precision of each estimate, see Figure 1.

Table 1 depicts the key characteristics and effect sizes of the meta-analyzed studies.As can be seen, studies included children with multiple risk factors, with a median of fourfactors. The most prevalent risk factors were poverty and language delays; 76% involved

Figure 1. Forest plot of effect sizes from smallest to largest. Note. The circles indicate the meaneffect size per study, whereas the confidence interval bars illustrate the estimated precision of eachstudy (k = 51). The diamond depicts the mean Hedges’s g effect size (.87).

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Tabl

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239

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children with language delays (either explicitly stated by the authors or determined basedon standard scores of 85 or below) and 75% of the studies involved low-SES children. Otherkey risk factors included marginalized racial groups (71%) and urban and rural communitysettings (71%). Almost one fourth of the studies included ELLs (26%) and more than50% of the studies involved children with low academic achievement (again determinedby author specification or standard scores on achievement tests). Not surprisingly, giventhe ages of children in the studies and their likelihood of being diagnosed prior to formalschooling, there were few studies targeted to students with identified special needs in speechand language (8%).

The typical study included a variant of shared book reading or dialogic reading asthe vocabulary intervention and used author-created or standardized measures to examinechildren’s outcomes. Two thirds of the studies were published in peer-reviewed journals,with the remaining third not published.

Overall Effect Sizes

To examine the benefit of vocabulary training on word learning for at-risk children, we firstcalculated an overall effect size using CMA. The overall effect size was g = 0.87, SE =0.08, 95% CI [0.71, 1.04], p < .001. According to Cohen’s categorization of effect sizes(1988), as well as Lipsey and Wilson’s (1993) synthesis of 302 meta-analyses examiningthe efficacy of psychological, behavioral, and educational interventions, these vocabularyinterventions resulted in gains that were both large and educationally meaningful. However,there was considerable variability: the effect sizes spanned from –.10 to 2.13 with varyingdegrees of precision as shown in our forest plot and descriptive chart (see Figure 1 andTable 1). Although we searched for interventions with children beginning at birth, theyoungest age group in the studies we found (involved in interventions using real Englishwords) was a 36-month old prekindergarten sample. In contrast to previous meta-analyses(Marulis & Neuman, 2010; Mol et al., 2008), we found no difference in the vocabularygain experience by prekindergarten (k = 29) and kindergarten (k = 22) children, Qb(1) =1.17, p = .28.

Outliers and Publication Bias

We examined publication bias through a trim and fill procedure (Duval & Tweedie, 2000)and found that our results were robust to the threat of publication bias. Our funnel plot wassymmetric and only one study was deemed missing due to publication bias, resulting in anonly slightly attenuated effect size when the trim and fill estimate was imputed: from g =0.87, 95% CI [0.71, 1.04], p < .001 to g = 0.85, 95% CI [0.68, 1.01], p < .001. Alongwith our search for unpublished studies, these calculations provided reassurance that ourresults would not be distorted by publication bias. Surprisingly, in this sample, unpublishedstudies did not have significantly lower effect sizes than published studies, Qb(1) = .67,p = .42. Last, none of our effect sizes qualified as outliers (i.e., 3 standard deviations abovethe sample mean; SD = .62).

Methodological Characteristics

Only one methodological characteristic was associated with effect size (see Table 2). Effectsizes for studies employing random assignment to conditions did not differ from those not

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Table 2. Mean effect sizes for methodological characteristics

Characteristic k g SE 95% CI Qbetween

Design .09Random assignment 10 .82∗∗∗ .19 .44, 1.19Non random assignment 41 .89∗∗∗ .10 .70, 1.08

Type of control group 6.99Received nothing (includes waitlist) 5 .69∗ .29 .14, 1.25Alternate treatment 18 .71∗∗∗ .15 .43, 1.00Business as usual 14 .78∗∗∗ .17 .45, 1.10Within-subject 6 1.35∗∗∗ .23 .90, 1.81

Type of assessment 8.96∗∗

Author-created 8 1.26∗∗∗ .16 .95, 1.57Standardized 31 .69∗∗∗ .10 .52, .91

Note. CI = confidence interval; Qbetween = the moderator contrasts (df = number of subgroups – 1).∗p < .05. ∗∗p < .01. ∗∗∗p < .001.

employing random assignment to conditions, Qb(1) = .09, p = .76. However, this should beinterpreted with caution due to the much smaller number of studies that employed randomassignment (20%). Furthermore, the nature of the control group did not relate to outcomes,Qb(4) = 6.99, p = .14; there was no significant difference between studies with controlgroups who received no treatment, alternate treatment control groups, business as usualcontrols or within-subject controls (see Table 2). Thus, any differences in effect sizes forour sample cannot be attributed to the type of control group or assignment to conditionemployed.

However, interventions assessed with distal standardized measures (k = 31, g = .69,SE = .10), 95% CI [.52, .91] had significantly lower effect sizes than those assessed withproximal author-created tests (k = 8, g = 1.26, SE = .16), 95% CI [.95, 1.57], Qb(1) =8.96, p = .003. Again, this analysis should be interpreted with caution due to the smallernumber of studies that used solely author-created outcome measures (16%).

The majority of studies (73%) measured the success of their interventions throughstandardized assessment. Chi-square tests indicated that this distribution was similar acrossall moderators (e.g., 71% of the studies focused on low-SES participants used standardizedoutcome measures; 75% for child care providers; 69% in combined explicit and implicittraining); ps = .36–.90. Further, distributions of standardized measures within moderatorcategories did not differ substantially (e.g., 71% for low SES; 73% for mid- to high SES).

Examining the distribution of the author-created and standardized measures allowed usto determine the relation between these factors, which could be obscured through statisticalcontrol alone. In doing so, we could closely examine whether and how the type of assess-ment was related to the effect size within each important moderator category. Consequently,we did not conduct separate analyses for the proximal author-created outcomes comparedto the distal standardized outcome or control for the type of dependent measure in subse-quent analyses. Further, due to the lack of an association between other methodologicalcharacteristics and effect size, we did not control for these factors in subsequent analysesallowing enough variance left to explain within our categories of interest (e.g., risk factors)and preserving the focus of our meta-analysis: examining the magnitude of word learninggains across diverse risk factors and related to various instructional characteristics.

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Last, even when examining our results at the most conservative level (studies thatemployed standardized assessment measures only), the overall effect size is still consideredin the moderate to large range and educationally significant (g = .69; Cohen, 1988; Lipsey &Wilson, 1993). These results indicate that vocabulary interventions can significantly impactword learning outcomes for children at risk prior to conventional reading even under themost stringent methodological conditions.

Moderator Analyses4

As we did not find strong relations to the effect size for background (e.g., grade) ormethodological factors, we did not control for these factors in our subsequent analyses.

Risk Factors

Our first set of moderator questions was designed to examine whether gains resultingfrom vocabulary interventions would differ by the type of risk factor. In addition, previousresearch has indicated that the number of risk factors (Denton et al., 2003; Halle et al.,2009) rather than type, might account for differences in the magnitude of gains. Because thestudies themselves were our units of analysis, our study variable coding was based on theinformation provided by the authors. Therefore, although we were able to comprehensivelycode for many risk factors identified as being potential moderators (e.g., low SES, ELL),there were a number of factors identified in the research that we were unable to codeor analyze (e.g., neighborhoods of concentrated poverty; W. J. Wilson, 1987). In somesituations, we were able to use a variable associated with a risk factor (e.g., percentage offree lunch for poverty levels); however, in other cases, information on the potential factorswas missing and not available for analysis. The number of studies in which participantswere coded within each risk factor category, the mean effect sizes, their standard errors andconfidence intervals, and the moderator contrasts are presented in Table 3.

We conducted multivariate meta-regression, therefore, on the factors we were able tocode associated with our three categorical distinctions (1 = yes the risk factor was present;0 = no, the risk factor was not present): (a) marginalized race, ELL, language impairments,language delays, and low academic achievement (i.e., risk factors intrinsic to the individualchild); (b) low-SES (i.e., risk factors related to the family environment); and (c) ruraland urban versus suburban communities (i.e., risk factors associated with neighborhoodand community; Bronfenbrenner, 1979) to examine whether these factors significantlyrelated to vocabulary growth. Because the risk factors were coded dichotomously, weexamined distributions between them using a chi-square test; we found that none of therisk factors subgroups highly overlapped with one another, χ2 ≤ 3.47, ps = .09–.74.

The overall risk factor meta-regression model accounted for 32% of the variabilityin the vocabulary effect sizes and, though as a whole, was only marginally significant,Qmodel(7) = 12.86, p = .076, R2 = .32, one risk factor—low SES—was related to the effectsize while controlling for all other risk factors (see Table 4).

No other categories of risk were significantly related to the effect size (e.g., whetherchildren were coded as having low academic achievement through Title I qualification,teacher reports, or standardized tests was not significantly related to their magnitude of

4Substantial heterogeneity remained within many of the moderator subgroups signified by Q-withinand I-squared values.

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Table 3. Mean effect sizes for risk factors

Coefficient k g SE 95% CI Qbetween

Intrinsic to the individual childMarginalized race 2.23

Yes (90% of sample) 36 .81∗∗∗ .10 .61, 1.01No 14 1.04∗∗∗ .18 .73, 1.34

English language learners .94Yes (90% of sample) 12 .73∗∗∗ .20 .33, 1.13No 39 .95∗∗∗ .11 .74, 1.16

Language impairment .59Yes (90% of sample) 4 1.05∗∗∗ .11 .84, 1.26No 47 .93∗∗∗ .11 .72, 1.14

Language Delay .54Yes (90% of sample) 38 .81∗∗∗ .10 .60, 1.01No 11 .98∗∗∗ .21 .56, 1.40

Low academic achievement 1.33Yes (90% of sample) 29 .99∗∗∗ .12 .75, 1.22No 22 .75∗∗∗ .16 .43, 1.07

Family environmentSES 7.44∗∗

Low SES (90% of sample) 38 .76∗∗∗ .10 .57, .96Middle to high SES 10 1.49∗∗∗ .25 1.01,1.98

Larger environmentType of community (urban or rural) 3.42

Yes (90% of sample) 36 .92∗∗∗ .11 .66, 1.17No 6 1.30∗∗∗ .26 .80, 1.82

Note. CI= confidence interval; Qbetween = the moderator contrasts (df = number of subgroups – 1);SES = socioeconomic status.

∗p < .05. ∗∗p < .01. ∗∗∗p < .001.

vocabulary growth). In other words, factors other than poverty did not appear to additionallyimpact the magnitude of gains associated with vocabulary interventions. Furthermore, whenwe excluded all nonsignificant risk factors, the model with only low-SES as a predictorwas significant, Qmodel(1) = 6.95, p = .008, R2 = .14; low SES negatively predicted effectsize (β = –.38, p = .008). Last, a subgroup contrast analysis indicated that children at riskwith low-SES status (k = 38, g = .76, SE = .10), 95% CI [.57, .96] demonstrated gainsthat were significantly lower compared to middle- to high-SES at-risk children (k = 10,g = 1.49, SE = .25), 95% CI [1.01, 1.98], Qb(1) = 7.44, p = .006 (see Table 3).

In addition, to determine whether (a) the total number or risk factors and/or (b) cumu-lative risks above and beyond low SES affected the amount of word learning gained, wefirst examined the number of risk factors in a meta-regression model. Across all studies,the number of risk factors ranged from two to seven with a median of 4.00 and mean of3.83. We found no significant relation to the effect size for the number of participant riskfactors present, Qb(1) = 1.40, p = .31, regardless of the type of risk factor. Next, we exam-ined cumulative risk similarly to Halle et al. (2009) by coding the number of risk factorsadditional to low SES. In a meta-regression model where 0 = middle to high SES; 1 = lowSES only; 2 = low SES plus one additional risk factor, and so on, we found that cumulativerisk was a significant negative predictor of vocabulary gain, Qmodel(1) = 4.57, p = .03,

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Table 4. Multivariate meta-regression for risk factors and dosage

Coefficient β p

Risk factorsa Intrinsic to the individual childMarginalized race −.14 .43

Yes (90% of sample)No

English language learners −.03 .89Yes (90% of sample)No

Language impairment −.13 .49Yes (90% of sample)No

Language delay .06 .75Yes (90% of sample)No

Low academic achievement .13 .46Yes (90% of sample)No

Family environmentSocioeconomic status −.44 .01

Low SES (90% of sample)Middle to high SES

Larger environmentType of community (urban or rural) −.31 .06

Yes (90% of sample)No

Dosage factorsb

Intensity (length of each session) −.29 .24Frequency (how often sessions) –.02 .97Duration (entire length of intervention) −.13 .80

Note. SES = socioeconomic status.aQmodel(7) = 12.86, p = .076, R2 = .32. bQmodel(3) = 3.80, p = .28, R2 = .15.

β = –.32, R2 = .10. Therefore, our projections were partially supported; although the totalnumber of risk factors was not related to effect size, cumulative risk factors in addition topoverty compounded the disadvantage in vocabulary gain. Thus, it appears that, althoughlow SES is the strongest negative predictor of effect size, intervention effects continue todecrease for children living in poverty with additional risk factors.

Pedagogical Characteristics

Dosage of Instruction. Often considered one of the most important pedagogical featuresof intervention is the “dosage” of instruction. Taken from the field of medicine (Shonkoff& Phillips, 2000), the term dosage refers to the amount of intervention that is delivered tothe recipient. Many investigators have argued that interventions with greater dosage, oftendefined as “longer,” are better or more effective than those of lesser dosage, particularlyfor children who may be at risk (e.g., Ramey & Ramey, 2006). Others, however, havechallenged such conclusions, arguing that shorter programs can also be effective in certain

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Table 5. Mean effect sizes for pedagogical characteristics


DosageFrequency of training .05

30 sessions or less 12 1.00∗∗∗ .21 .59, 1.42More than 30 sessions 11 .94∗∗∗ .18 .58, 1.30

Duration of training .92Less than 56 days 14 .82∗∗∗ .17 .48, 1.16More than 56 days 22 1.03∗∗∗ .14 .76, 1.30

Intensity of training .0320 min or less 11 1.16∗∗∗ .20 .76, 1.56More than 20 min 11 1.12∗∗∗ .21 .70, 1.53

Note. CI = confidence interval; Qbetween = the moderator contrasts (df = number of subgroups – 1).∗∗∗p < .001.

circumstances where the objectives of instruction have been clearly defined (Halle, 2010).Even brief doses of instruction, for example, (e.g., 10 hr in total) can be associated withpositive child outcomes (Neuman, 1999; Whitehurst, Epstein, et al., 1994).

Dosage of instruction, however, goes beyond answering the question, “How muchvocabulary intervention is delivered?” Rather, it involves duration (e.g., how long theinterventions lasts from start to finish), frequency (e.g., how many sessions are delivered),and intensity (e.g., the amount of time within each session). To be more concrete, if anintervention was provided to participants for 30 min, five times a week, for 6 weeks, wecoded dosage as follows: frequency would be 30 (i.e., 5 × 6), duration would be 42 days(6 × 7 days a week), and intensity would be 30.

As shown in Table 5, dosage of instruction and the characteristics within it variedconsiderably across interventions. Therefore, we examined each aspect of intervention todetermine how these characteristics might influence word learning outcomes.

Frequency. Number of sessions for interventions varied widely, ranging from three to 180sessions; the median was 30 instructional sessions. Splitting our sample at the median forcomparison, we found no difference in effect sizes for studies with the median 30 sessionsor less or those with more than 30 sessions, Qb(1) = .05, p = .83.

Duration. Similar to the number of sessions, the duration of interventions in our samplevaried largely, ranging from 7 to 270 days; the median was 56 days of instruction. We foundno significant difference between studies with durations shorter than the median 56 daysand durations longer than the median, Qb(1) = .92, p = .34. These results indicate that,like frequency, the duration of the intervention was not significantly associated with effectsize.

Intensity. We calculated the length of each individual training session as our final measureof the intensity of instruction. Amount of time per session ranged from 7 min to a highof 60 min, an intensive period particularly for prekindergarten and kindergarten children,with a median of 20 min per instruction session. Again, there was no significant differencebetween interventions lasting longer or shorter than the median 20 min, Qb(1) = .03, p = .87.

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Moreover, we grouped studies by those in which all three components of interventiondosage fell above the medians and compared them with studies whose dosage componentsfell below the medians. We found no significant differences. Interventions with all threeaspects of dosage above the medians were equally as effective as those below the medians,Qb(3) = 4.07, p = .25. Further, no combination of the three aspects of dosage (e.g., highfrequency and longer duration compared to high intensity only) was significantly related tothe magnitude of word learning gain.

To disentangle possible confounds between the three aspects of dosage and ensurewe were not retesting the same factor (e.g., shorter interventions may have also been moreintense), we conducted a multivariate meta-regression including all three aspects of dosage.The overall model was not significant, Qmodel(3) = 3.80, p = .28, R2 = .15; none of thedosage characteristics predicted effect sizes while controlling for all other dosage factors(see Table 4).

Taken together, these results indicate that “more” instruction was not synonymous withbetter outcomes. Our analyses indicate that even smaller dosages of intervention yieldedlarge effects. Rather, as Halle (2010) reported, intensity/duration/frequency of interventionmay be related to the goals of instruction. Interventions that target a discrete set of skillsmay only require short-term and brief “dosages,” whereas programs that have a broad focusor that cover more complex content may require the provision of more instructional time.

Instructional Characteristics

The potential moderator variables related to instructional characteristics of the interventionswere categorical and thus our analyses were conducted as subgroup (analysis of variance)comparison analyses.

Context of Instruction. Previous studies (Mol et al., 2009; Powell et al., 2008) have shownthat contextual features—the person who delivers the instruction and the organizationof instruction—influence effect sizes. Studies (Vaughn & Linan-Thompson, 2003) haveindicated that small-group or tutorial-like instruction may be particularly appropriate forat-risk students, who may benefit from the greater interaction and attention than what istypically available in whole group instruction.

As shown in Table 6, more than 80% of the studies provided information on thegroup configuration of instruction. Of these studies, the majority of the interventions, aspredicted, occurred in small groups, though only a small number of interventions employedone-on-one instruction. However, in contrast to previous research and our predictions,the moderator analysis indicated no significant difference between interventions providedindividually, in small groups of five or less, in large groups of six or more or in interventionsthat provided instruction first to the large group and then to small groups, Qb(3) = 1.24,p = .74. These results indicate that all group sizes appeared equally beneficial for at-riskchildren’s word learning gains; group size was not a significant mechanism through whichinstruction affected child outcomes for children at risk.

In contrast, the person who provided instruction mattered. Of the 86% of studies that in-dicated the type of intervener, school teachers conducted the largest number of interventions(46%), followed by the experimenter him- or herself (30%). Fewer studies involved childcare providers (14%) and/or parents (11%) as instructors. Moderator analyses indicatedthat interventions conducted by child care providers who were noncertified/nondegreed(k = 6, g = .21, SE = .11), 95% CI [–.001, .44] were significantly less effective than thoseconducted by parents (k = 5, g = .71, SE = .26), 95% CI [.31, 1.22]; experimenters (k = 13,

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Table 6. Mean effect sizes for context of instruction


Group size 1.24Individual 8 .96∗∗∗ .21 .55, 1.38Small (5 or less) 17 .90∗∗∗ .15 .61, 1.18Large (6 or more) 12 .85∗∗∗ .17 .52, 1.18Large + Small 5 1.19∗∗∗ .26 .67, 1.70

Intervener 25.91∗∗∗

Experimenter 13 .95∗∗∗ .24 .48, 1.42Teacher 20 1.25∗∗∗ .19 .89, 1.62Parent 5 .71∗∗∗ .26 .31, 1.22Child care provider 6 .21∗ .11 -.001, .44

Note. CI = confidence interval; Qbetween = the moderator contrasts (df = number of subgroups – 1).∗p < .05. ∗∗∗p < .001.

g = .95, SE = .24), 95% CI [.48, 1.42]; or certified teachers (k = 20, g = 1.25, SE = .19),95% CI [.89, 1.62], Qb(3) = 25.91, p < .001 (see Table 6). However, there was no signif-icant difference between gains associated with interventions conducted by experimenters,teachers, or parents, Qb(2) = 3.06, p = .22. Given that at-risk children are likely to spend asubstantial amount of time with child care providers during their early word learning years(Shonkoff & Phillips, 2000), these results are particularly relevant. Our results suggest thatthe type of intervener may be an important mechanism related to vocabulary outcomes anda potential source for exacerbating already-existing vocabulary gaps between children atrisk for word learning difficulties and their more advantaged peers.

Instructional Design Features

Our last set of moderator analyses focused on instructional design features. Vocabularyinterventions, although varied in type and resources used, are designed to teach wordmeanings using certain instructional design features. Perhaps most important, some inter-ventions will target specific words to be taught prior to the treatment and will focus theinstructional session to emphasis these words. In contrast, other interventions might bedesigned to engage children in rich language instruction, with no particular words targetedprior to instruction, with the goal of improving children’s expressive language more gener-ally. Relatedly, some intervention programs focus on explicit instruction. For example, anintervention would be coded as explicit if detailed definitions and examples were discussedbefore, during, or after a storybook reading intervention, whereas an intervention wouldbe coded as implicit if words were taught within the context of an activity (e.g., storybookreading without intentional stopping or deliberate focus on target word meanings). In somecases, interventions might include both approaches (explicit and implicit instruction). Anintervention might target particular words (prior to instruction), teach them explicitly be-fore reading, embed them in storybook reading (implicit instruction), and assess whetherstudents have learned the target words and whether students have gains in overall receptiveand expressive language.

Previous research (Gunn, Smolkowski, & Vadasy, 2011) has shown that children at riskbenefit most from explicit instruction. Therefore, we predicted that interventions, which

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Table 7. Mean effect sizes for instructional design features


Type of training 7.41∗∗∗

Explicit 14 .91∗∗∗ .17 .58, 1.24Implicit 18 .61∗∗∗ .15 .32, .89Combination 17 1.18∗∗∗ .15 .88, 1.47

Storybook intervention .05Yes 28 .86∗∗∗ .11 .64, 1.08No 23 .89∗∗∗ .13 .64, 1.15

Note. CI = confidence interval; Qbetween = the moderator contrasts (df = number of subgroups – 1).∗∗∗p < .001.

employed instructional design features focused on target words that engaged children inexplicit instruction, would be more effective than interventions that were not targetedand exclusively employed an implicit exposure to words. Our prediction was generallysupported.

The overall moderator analysis was significant for type of instruction, Qb(2) = 7.41,p = .006 (see Table 7). Further, pairwise comparisons indicated that interventions withcombined (explicit and implicit) word learning instruction targeted to specific words (k =17, g = 1.18, SE = .15), 95% CI [.88, 1.47], were significantly more effective than implicitinstruction alone (k = 18, g = .61, SE = .15) 95% CI [.32, .89], Qb(1) = 6.96, p = .008.Pairwise analyses comparing explicit to implicit instruction as well as explicit to combinedinstruction did not reach statistical significance, Qb(1) = 3.00, p = .08, Qb(1) = 1.25, p =.26, respectively. These findings suggest that providing explicit information about wordsbeing taught and giving children opportunities to engage in word learning in the contextof storybook reading or other meaningful activities may be the most effective approach forenhancing word knowledge and word meanings for at-risk young children.

Of importance, the majority (84%) of these studies employed at least one standardizedmeasurement of word learning (e.g., Peabody Picture Vocabulary Test), indicating that thisfinding was not limited to learning specific words that were then tested. Rather, it indicateda generative growth in learning word meanings; in other words, a combination of explicitand implicit vocabulary instruction was a mechanism for greater word learning on bothglobal and proximal measures.

Given the variety of intervention types (see Table 1), we partitioned storybook readinginterventions (e.g., shared book reading/interactive/dialogic reading; k = 28) and comparedthem to all other interventions (k = 23; there were too few studies within most categoriesof interventions). We found no differences in the amount of gain received related to thisdistinction, Qb(1) = .05, p = .83 (see Table 7). Rather, it appeared that it was the instruc-tional design features of instruction, not the intervention type that was most significantlyassociated with word learning growth.

DISCUSSION

This meta-analysis examined the effects of vocabulary interventions and the factors thatmay moderate outcomes for young children at risk for word learning difficulties. It wasdesigned to build on our previous meta-analysis (Marulis & Neuman, 2010) and to provide

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a more thorough and critical analysis of risk factors associated with language outcomeswith the inclusion of additional studies targeted to at-risk young children and the exam-ination of the unique contribution of important moderating variables. In contrast to ourprevious research, we sought to conduct a more fine-tuned analysis of risk factors (e.g.,rather than dichotomous coding) and to use multivariate meta-regression that allowed tous to investigate these factors with greater precision and specificity than in our previouswork.

The results of our comprehensive review revealed strong support for the efficacy ofvocabulary intervention for at-risk learners prior to conventional reading. Effect sizes werelarge and educationally meaningful. For example, expressing the .87 overall effect size asa z score, if the control groups in our sample of studies were to receive similar vocabularyinterventions, their scores would improve by roughly 31% and 81% of the students shouldexpect to improve. Even if we were to assume the most conservative estimate—includingonly studies that employed standardized outcome measures—the overall effect size wouldstill be .69, reflecting a near large word learning gain. Taken together, these results indicatethat vocabulary interventions can effectively impact word learning gains for children whomay be at risk for failure to learn to read.

This good news, however, must be tempered by the fact that the average pretest standardscores were 79.89 on receptive vocabulary tests and 82.79 on expressive vocabulary tests (ofthe 71% of studies with reported pretest scores), indicating that the majority of participantsscored well below the 16th percentile prior to the start of the studies. Consequently, althoughthe gains in these studies might be educationally meaningful, they may not be substantialenough to close or sufficiently narrow the vocabulary gap for children with various riskfactors. Given the enormous discrepancy between the estimated number of words knownfor children at risk and their more advantaged peers prior to school entry (Hart & Risley,1995), it is doubtful that these interventions were able to accelerate word knowledge to thedegree it would be necessary to close the gap. Biemiller (2006), for example, estimatedthat the average early intervention may teach a total of 50 words. Therefore, although theseresults suggested that interventions can make a difference in word learning, whether theycan make a profound difference in children’s longer term achievement is a matter that mustbe further investigated. Few of the studies examined delayed effects or measured gainsbeyond those immediately following the interventions.

There were, however, a number of interventions that achieved high-enough effectsizes to substantially narrow the achievement gap. For example, a study by Coyne and hiscolleagues (Coyne et al., 2007) that produced the largest effect size (Study 1; g = 2.13)appeared to exemplify instructional design features such as target words, explicit instructiongiven by extensively trained experimenters, using author-created measures that measuredproximal vocabulary growth. Other studies, employing a combination of these instructionalfeatures were similarly successful (Loftus, 2008; Pullen, Tuckwiller, Konold, Maynard, &Coyne, 2010), suggesting an instructional regime that may prove to be especially effectivefor at-risk children.

Our multivariate meta-regression analysis also allowed us to examine the independentcontribution of multiple risk factors that in our previous work, which relied on subgroupmoderator analyses, could not disentangle. Using this method, we were able to isolateindividual risk factors by controlling for shared variance among these factors in the analysis.As a result, this is the first meta-analysis to our knowledge that has attempted to unravel theeffects of intrinsic family and environmental risk factors on young children’s developingword learning.

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This analysis revealed that one factor trumped all others in moderating word learninggains: poverty. This was the only risk factor found to significantly and negatively to predictlower word learning outcomes. Children with other types of risk factors apart from povertyreceived similar gains from vocabulary interventions. In fact, there was a double disadvan-tage: Children from high-poverty families not only received the smallest gains, they alsotended to have the lowest baseline scores to begin with. These results have been replicatedin other meta-analyses as well as surveys (e.g., Lee & Burkham, 2002). Moreover, becausethe achievement gap between low- and middle-income children related to language hasbeen shown to be nearly 1 standard deviation (e.g., Noble, McCandliss, & Farah, 2007),current interventions may not be sufficiently powerful and targeted to meet these children’sneeds. For example, the mean effect size for children from low-SES families in our samplewas .76, which, although a large and meaningful effect size, would still be inadequate inovercoming this gap.

In fact, economists (Hanushek, 1996; Mayer, 1998) have suggested that it is the con-comitants of poverty, not poverty in and of itself, that explain its corrosive effects onachievement. They have argued that two major pathways, material resources and psycho-logical resources, may account for these effects. Material resources relate to cognitionstimulation and the ability of families to purchase books, lessons, and stimulating learn-ing materials that engage children in learning about reading and about their worlds. Withlimited access to print materials and opportunities for learning, a second pathway may besignificantly curtailed. This pathway relates to the quality of the home environment andmother–child interactions over stimulating activities and learning opportunities. Studies(Hart & Risley, 1995; Hoff, 2003) have reported profound differences in the quantity andquality of interactions. Hart and Risley (2003), for example, argued that the accumulatedexperiences with words for children who come from poor circumstances compared withchildren from professional families may constitute a 30-million-word differential. Withoutopportunities to be read to, children have less experience with new, different, and moresophisticated vocabulary outside of their day-to-day encounters. They are less likely tolearn about their world and to hear decontextualized language, the beginnings of abstract-ing information from print. And as Walberg and Tsai (1983) and Stanovich (1986) found intheir now-classic model of the Matthew Effect, differences in cognitive, motivational, andeducational experiences in the early years become magnified during the process of readingacquisition.

Consequently, particularly for vocabulary instruction, it may be that the earlier theintervention, the better. Most of the studies identified for our analysis engaged childrenin vocabulary interventions in the 4 through 6 age range. However, recent research hasshown that children undergo a “vocabulary spurt” (McMurray, 2007), a point in de-velopment in which the pace of word learning increases rapidly, much earlier on, at18 to 24 months. Therefore, considerations about the developmental timing of vocabu-lary interventions, especially for children who live in poverty circumstances, are clearlywarranted.

Further, our analysis builds on previous research that has shown evidence of thecumulative risk factors on children’s achievement (Denton et al., 2003). Our meta-regressionanalyses indicated that cumulative risk factors—in general—did not negatively predictword-learning growth. However, when cumulative risk factors occurred in addition topoverty, there was a significant and detrimental effect on word learning. Therefore, theseresults indicate that poverty further compounds the effects of other risk factors, and mayadd substantially to our understanding of the environmental factors that influence children’svocabulary outcomes.

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However, studies in our analysis frequently suffered from a lack of specification relatedto risk factors. A sizeable number of studies, for example, identified children as at risk yetdid not fully delineate the particular risk factor or factors. Although our comprehensivecoding scheme allowed us to further distinguish how children were at risk, there remainedinstances in which we were unable to parse author-identified risk status. This phenomenoncould be a result of the age in which our vocabulary interventions occurred. With theexception of Head Start, which has been at the forefront of efforts in early identificationof difficulties, many early education programs lack the resources or personnel to diagnosehealth-related difficulties, speech and language delays, and other potential risk factors.Particularly in these early years, therefore, poverty may the most easily identified riskfactor resulting in greater attention to these children than others.

Despite their characterization of targeting at-risk children, interventions in our analy-ses generally did not seem well specified or aligned to specific risk factors. For example,children with diagnosed speech delays in a number of studies received shared book-readinginterventions, or dialogic reading, similar interventions as implemented with those whomay have low academic achievement. Although this is not to suggest that those with speechdelays might not also benefit from such interventions, they do not address the particularcharacteristic associated with risk. Previous research has demonstrated that when childrenreceive targeted intervention specific to their needs, gains are likely to be maintained overtime (Neuman, 2009). In the future, rather than adopt generic interventions, researchersmight consider adapting instructional features and pedagogical characteristics to ensurebetter alignment with children’s needs and the particular risk factors that have been identi-fied.

This lack of specification in these interventions could explain why group size, oneof our important contextual moderators, did not account for differences in word-learninggains. Presumably, teachers group children in smaller configurations such as one-to-oneinstruction or small-group instruction to better meet their individual needs, and to tailorinstruction on the basis of their progress. However, if group size is merely an organiza-tional framework, and instructional interventions are merely delivered in a one-size-fits-allapproach, then there is no reason to expect that group size configurations would make adifference. We found no significant differences in word-learning gains as a result of groupsize for at-risk children. Nevertheless, it could be that small-group instruction was not usedoptimally. Therefore, these findings should be viewed cautiously, considering the body ofextant evidence (Vaughn & Linan-Thompson, 2003) that indicates the benefits of small-group instruction for at-risk children. Small-group or one-to-one instruction should takeadvantage of what the group configuration may offer.

The person who delivered the vocabulary intervention clearly made a difference interms of the magnitude of word learning gains. This factor could be related to the fidelity oftreatment. Conceivably, if the experimenter is delivering the treatment, then the fidelity ishigh. Unfortunately, we were not able to test this thesis because most studies did not reportspecific information regarding the level of fidelity to treatment. However, our findings mayalso be related to previous training and the expertise of the intervener in working with at-riskchildren. Word-learning gains were negligible in cases in which the child care provider wasresponsible for the treatment, suggesting that they may have been ill-equipped to addressthe special needs of children at risk. Traditionally, neither Child Development Associateprograms, a primary certification for child care providers, nor associate degree programshave adequately addressed the needs of at-risk populations through training or coursework(Early & Winton, 2001). Given the changing demographics in U.S. population (Yoshikawa,2011), this is an important issue that should be addressed in the future. Recognizing the

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benefits of starting intervention early, we need to maximize the quality of treatment forchildren before they enter formal schooling with highly trained caregivers sensitive to theneeds of special populations.

Certain instructional design features were associated with word-learning gains forchildren at risk. In contrast to our previous meta-analysis focused generally on youngchildren, we found only a marginally significant difference between explicit and implicitinstructional strategies for teaching words to at-risk children. However, similar to what wefound for all children, combining explicit and implicit instruction was superior to implicitinstruction alone for at-risk children. What seems to be most important to vocabularygains is, at the very least, providing explicit instruction embedded in meaningful contexts.Further, providing multiple opportunities to learn words in both isolated and in meaningfulcontexts may be a proxy for a type of dosage different than which we have previously coded.Gunn and colleagues (2011), for example, examined the benefits of contextualized practicefollowing explicit instruction for at-risk kindergartners. Consistent with our findings, theyreported significant gains based on the rate of independent opportunities to practice. Inshort, therefore, explicit instruction along with embedded meaningful practice—ratherthan intervention type—is associated with magnitude of word learning gains in at-riskchildren.

Perhaps the most complex and inconclusive aspect of the vocabulary interventionknowledge base relates to the issue of dosage. Dosage of intervention has found to beassociated with measurable impacts in a number of studies, particularly those associatedwith children at risk of learning difficulties (Shonkoff & Phillips, 2000). For example,the Abecedarian intervention (Campbell & Ramey, 1995) reported greater effect sizes forchildren who participated from infancy into elementary years compared to those whoparticipated in the preschool intervention alone. Others have challenged that “more isnecessarily better,” as advocacy-driven research (Shonkoff & Phillips, 2000). Lower dosageprograms, for example, have been found to be highly effective in certain circumstances.Even brief doses of professional development (e.g., 10 hr in one case, 30 min in another) areassociated with positive child outcomes (Neuman, 1999; Whitehurst et al., 1999). Examplesinclude interventions targeting language and literacy, mathematics, and social-emotionaldomains (Zaslow, Martinez-Beck, Tout, & Halle, 2011).

In our case, we operationally defined dosage as frequency, intensity, and duration, ex-amining each of these components separately, as well as a total “dosage” amount. Althoughthe variability in service dosage was considerable, we found no relation to outcome gainsfor these at-risk children. Halle (2010) argued that the frequency/intensity/duration of anintervention depends on its goals; interventions that attempt to improve a large corpus ofvocabulary and other language-related skills may require a more extensive dosage, perhapsspread over time; those with more discrete goals, a lesser dosage. Further, different riskfactors may account for differential dosages. Differences in both amount and duration ofintervention may be related to the age of referral, the nature, and severity of the child’s dif-ficulty. For example, in a study of 190 children enrolled in 29 community-based programs,the strongest predictor of service dosage in the 1st year was the child’s pretest score atthe time of program entry (Shonkoff, Hauser-Cram, Krauss, & Upshur, 1992). Therefore,questions related to the dosage need to be considered in the context of the interventionand the at-risk children who are receiving services. A clear example of the complexity ofdosage was raised in a recent analysis of a print referencing intervention for preschoolers(McGinty, Breit-Smith, Fan, Justice, & Kaderavek, 2011): Researchers manipulated twotypes of dosage: amount and frequency of the intervention. Their results indicated a benefitfor increasing the dose amount or dose frequency, but not both, which together appeared

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to have a diminishing benefit to children’s learning. However, our findings did not indicatethat any aspect (or combination) of dosage (controlling for all other aspects) was predictiveof at-risk children’s word learning growth. Further, isolating the effects of dosage factors(e.g., frequency, duration, and intensity) through our multivariate metaregression analysisprovided more robust evidence than previous research that dosage does not seem to relateto the amount of gain received by vocabulary interventions. Our analysis of explicit plusimplicit instruction, however, might lead to a different definition of dosage—one that isassociated with the depth of processing rather than frequency, duration, or amount. Con-sequently, further research where different characteristics of dosage are experimentallymanipulated is clearly needed to provide definitive answers.

Last, the only methodological characteristic associated with word learning gain washow the vocabulary growth was measured, though surprisingly, only 16% of the studieswe found used author-created assessment. As demonstrated in previous meta-analyses(Elleman et al., 2009; Marulis & Neuman, 2010) as well as a meta-analysis of 11 meta-analyses (Rosenshine, 2010), studies that employed author-created measures had largereffect sizes. These results could be due to their greater sensitivity to detect differences dueto treatment effects (e.g., the National Reading Panel Report, 2000; Senechal & Cornell,1993; Silverman, 2007b). They might also reflect greater content validity, with measuresthat accurately address what is taught throughout the intervention. On the other hand,however, they may inflate effect sizes by “teaching to the test” and are less likely to providean indication of global language proficiency. Consequently, it would be most beneficial forresearchers to use author-created and standardized measures to examine both proximal anddistal outcomes. Examining correlations between vocabulary growth on author-created andstandardized measures within studies may also provide empirical evidence to address thisissue. Further, using both types of measures may enable researchers to assess the content ofintervention and, at the same time, address the issue of consequential validity by examininghow these gains affect overall language proficiency.

There are a number of important limitations in our endeavor to better understand howvocabulary interventions might benefit young children at risk for word learning difficulties.As previously mentioned, there was often limited detail regarding the nature and depth ofchildren’s risk factors. In fact, several of the studies identified children as being at riskwithout fully specifying delineating features. Further, there were many missing details ininterventions, known to relate to principles of quality instruction in vocabulary precludingmoderator analyses related to these previously identified features. For example, we werenot able to reliably code the number of words taught, the difficulty level of words provided,the number of repetitions, and review of words in many of the interventions. In the future,it is essential that these factors be thoroughly described in primary research so that futuremeta-analyses are better able to assess them as well. Unfortunately, therefore, much of theactive ingredients of these interventions remain opaque to the researcher or practitioner,who might wish to extend or apply the findings.

Taken together, our meta-analysis provides some promising recommendations forclassroom settings for at-risk children. However, the moderator analyses should not beinterpreted as testing causal relationships (Cooper, 1998; Viechtbauer, 2007). Rather, ourresults should be verified through experimental manipulations that vary these factors sys-tematically. This research, together with the findings from our meta-analysis, would bestelucidate the educational mechanisms through which practices and policies affect word-learning outcomes and differentially affect at-risk children. Understanding that povertypresents a unique disadvantage to word learning—and that cumulative risks in addition topoverty exacerbates this disadvantage for young children in their earliest years—should

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hopefully spur additional, more powerful interventions to support children’s developingword knowledge.

ACKNOWLEDGMENTS

We thank Suzanne E. Mol for her insightful feedback and considerable assistance to thisresearch and earlier draft of this article; anonymous reviewers for their comments andsuggestions which substantially improved our article; and the research assistants at theReady to Learn project for their important contributions to this study. Portions of thedata in this study were presented at the annual conference of the Society for Research onEducational Effectiveness, March 2011, in Washington, DC., and the European Associationfor Research on Learning and Instruction Biennial Conference, September 2011, in Exeter,United Kingdom.

We gratefully acknowledge funding from the Institute of Education Sciences (grantR305A090013).

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How vocabulary interventions affect young children at risk: A meta-analytic review

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