Research in Developmental Disabilities 40 (2015) 51–62

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Research in Developmental Disabilities

Reading disabilities in children: A selective meta-analysis of the cognitive literature§ Milagros F. Kudo *, Cathy M. Lussier, H. Lee Swanson * University of California, Riverside, United States

A R T I C L E I N F O

A B S T R A C T

Article history: Received 10 April 2014 Received in revised form 29 December 2014 Accepted 16 January 2015 Available online

This article synthesizes literature that compares the academic, cognitive, and behavioral performance of children with and without reading disabilities (RD). Forty-eight studies met the criteria for the meta-analysis, yielding 735 effect sizes (ESs) with an overall weighted ES of 0.98. Small to high ESs in favor of children without RD emerged on measures of cognition (rapid naming [ES = 0.89], phonological awareness [ES = 1.00], verbal working memory [ES = 0.79], short-term memory [ES = 0.56], visual–spatial memory [ES = 0.48], and executive processing [ES = 0.67]), academic achievement (pseudoword reading [ES = 1.85], math [ES = 1.20], vocabulary [ES = 0.83], spelling [ES = 1.25], and writing [ES = 1.20]), and behavior skills (ES = 0.80). Hierarchical linear modeling indicated that specific cognitive process measures (verbal working memory, visual–spatial memory, executive processing, and shortterm memory) and intelligence measures (general and verbal intelligence) significantly moderated overall group effect size differences. Overall, the results supported the assumption that cognitive deficits in children with RD are persistent. ß 2015 Published by Elsevier Ltd.

Keywords: Reading disabilities Cognition Meta-analysis

1. Introduction A popular assumption is that children with reading disabilities (RD) have specific localized low-order processing deficits. A component consistently implicated in reading disabilities is phonological awareness. Phonological awareness is ‘‘the ability to attend explicitly to the phonological structure of spoken words’’ (Scarborough, 1998, p. 95). Abundant evidence shows that children with RD have problems in processing phonological information (e.g., Gottardo, Collins, Baciu, & Gebotys, 2008; MelbyLerva˚g, Lyster, & Hulme, 2012; Nelson, Lindstrom, & Lindstrom, 2012; Scarborough, 2009; Stanovich & Siegel, 1994; Vellutino, Fletcher, Snowling, & Scanlon, 2004; Wagner & Torgesen, 1987; Waterman & Lewandowski, 1993). Recently, some studies have suggested other processes may be involved in reading acquisition that are as important as phonological awareness (e.g., Swanson, Harris, & Graham, 2003; Swanson & Jerman, 2007). Although several studies show that reading deficiencies are related to phonological awareness (e.g., Badian, 2001, 2005; Bus & van Ijzendoorn, 1999; Catts, Gillispie, Leonard, Kail, & Miller, 2002; Morris et al., 1998; Stanovich, 1988), additional studies suggest other processes such as those related to rapid naming (e.g., Bonifacci & Snowling, 2008; Compton, 2003; Cronin, 2013; Kirby, Parrila, & Pfeiffer, 2003; Schatschneider, Fletcher, Francis, Carlson, & Foorman, 2004), orthography (e.g., Cunningham & Stanovich, 1990; McBride-Chang, Manis, Seidenberg,

§ This research was supported by an Institute of Education Science (IES), Cognition and Student Learning, grant numbers R324B080002 and R324A090002 awarded to H. Lee Swanson. * Corresponding author at: University of California, Riverside, 900 University Avenue, Riverside, CA 9252, United States. Tel.: +19518274734. E-mail addresses: [email protected] (M.F. Kudo), [email protected] (H.L. Swanson).

http://dx.doi.org/10.1016/j.ridd.2015.01.002 0891-4222/ß 2015 Published by Elsevier Ltd.

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Custodio, & Doi, 1993; Shany & Share, 2011; Spencer & Hanley, 2004; Zaretsky, Kraljevic, Core, & Lencek, 2009), semantics (e.g., Crisp & Lambon Ralph, 2006; Nation & Snowling, 1998), and memory span (e.g., Das & Mishra, 1991; Nevo & Breznitz, 2013; Parrila, Kirby, & McQuarrie, 2004) contribute statistically significant amounts of variance to reading. The current literature weighs heavily on the side of phonological deficits as the major sources of reading difficulties. Nevertheless, an understanding of the interplay between multiple processes is necessary before one has an adequate account on the major information processing variables that contribute to RD. More important, little is known about potential moderator variables (e.g., age of the sample, severity of reading problems) that influence the magnitude of the effect sizes between children with and without RD on cognitive measures. In the present study, we sought to investigate the evidence on cognitive differences between children with and without RD. Thus, our interest in reading ability was narrowly confined to word reading and those variables (e.g., phonological awareness, rapid naming speed) that have been identified in the literature as critically related to reading disabilities (see Siegel, 2003, for a comprehensive review). We are also interested in investigating potential cognitive processes (e.g., spelling, orthography, vocabulary, memory) that may also play an important role in predicting reading disabilities. The study used meta-analytic procedures to aggregate the research literature. The three primary purposes of this synthesis were to (a) conduct a meta-analysis of differences between children with and without RD, (b) identify some of the variables that moderate effect sizes between children with and without RD (e.g., age groups, socioeconomic status [SES], and types of criterion reading measures used to classify skilled and readers at-risk), and (c) to see if interactions between variables moderated the effect sizes between the two groups of children. Two main research questions directed this synthesis: 1. Which performance domains (i.e., intellectual, academic, cognitive) make the largest contribution to the differences between children with RD and their average-achieving counterparts? In other words, which array of measures show the largest magnitude of effect sizes that explain the similarities and differences between the two groups? 2. What performance similarities or differences among children with and without RD are a function of variations in age, intelligence quotient (IQ), ethnicity, and gender? For example, we determine if some of the same deficits (as reflected in the magnitude of effect size) that emerge in studies that include older participants with RD in secondary school also occur when the sample is early elementary school age. To answer these questions, the present synthesis uses hierarchical linear modeling (HLM) procedure to identify key constructs (e.g., IQ, reading, math, memory, and phonological processing) that contribute unique (independent) variance to defining differences and similarities between children with and without RD. 2. Method 2.1. Identification of studies (literature search) 2.1.1. Data gathering Several approaches were used to locate the relevant studies published in peer-reviewed journals. First, a computer search located studies comparing children with reading disabilities and without reading disabilities on psychological, occupational, and vocational variables using the Web of Knowledge, PsycINFO, and ERIC databases. The computer search used the following terms: ‘‘cognitive processes, cognition, memory, speed, phonological’’ coupled with ‘‘learning disabilities, dyslexia, reading disorders, orthographic, and specific reading disabilities.’’ Entry of these terms yielded 11,432 references. Additional terms were entered into the search such as ‘‘IQ’’ and ‘‘assessment,’’ but these results were found to produce results that overlapped with the earlier terms. A refinement of the search focused only on empirical studies and journal articles published in English. The sample search obtained articles using the above descriptors that ranged in publication dates from 1957 (the earliest year of the earliest article found using the descriptors) and March 2013. Second, published articles by primary researchers (i.e., Badian, Berninger, Bowers, Bull, Chiappe, Das, De Jong, Fletcher, Johnson, Naglieri, Pennington, Siegel, Stanovich, Swanson, Vellutino, Willcutt, and Wolf) were also analyzed for possible inclusion. Finally, a manual search of journals where the majority of reading disability articles is published was conducted (e.g., Journal of Educational Psychology, Journal of Learning Disabilities, and Learning Disability Quarterly). From this pool of literature, articles were eliminated that focused on children with below average intelligence (mild mental retardation range, 88% agreement initially and, eventually, 100% after clarification). 3. Results 3.1. Study characteristics The meta-analysis results generated 48 studies with a total of 735 ESs across categories between children with RD and average readers, yielding a mean ES of 0.98. These studies were most frequently published in Journal of Educational

Table 2 Weighted effect sizes, standard errors, confidence intervals, and homogeneity of Q. Comparisons between reading disabled and average readers

K

ES

SE

Lower

Upper

Homogeneity Q

I2

Total across categories Reading comprehension Reading recognition General IQ Verbal IQ Rapid naming Phonological awareness Pseudoword reading Math vocabulary Spelling writing Problem solving/reasoning Verbal working memory Visual–spatial memory Executive processing Short-term memory Perceptual motor skills Auditory processing General information Behavior

735 52 52 72 33 55 66 19 28 39 17 6 7 68 35 46 98 5 8 9 19

0.98 1.63 2.20 0.54 0.87 0.89 1.00 1.85 1.20 0.83 1.25 1.20 0.43 0.79 0.48 0.67 0.56 0.25 0.63 0.69 0.80

0.05 0.04 0.04 0.02 0.04 0.03 0.04 0.07 0.05 0.04 0.06 0.11 0.1 0.03 0.04 0.04 0.03 0.09 0.07 0.08 0.05

0.88 1.56 2.12 0.49 0.80 0.84 0.93 1.71 1.10 0.76 1.13 0.99 0.24 0.72 0.40 0.60 0.51 0.08 0.50 0.53 0.70

1.09 1.71 2.28 0.59 0.94 0.95 1.07 1.98 1.29 0.91 1.37 1.41 0.62 0.85 0.57 0.75 0.61 0.42 0.76 0.86 0.90

205.31*** 553.83*** 251.07*** 399.60*** 120.88*** 181.27*** 296.29*** 83.67* 157.17*** 229.17*** 328.00*** 22.30** 56.16** 230.67*** 111.54*** 146.78*** 533.02*** 28.53*** 27.69** 184.99*** 115.91***

0.91 0.80 0.82 0.74 0.70 0.78 0.78 0.83 0.83 0.95 0.78 0.89 0.71 0.70 0.69 0.82 0.86 0.75 0.96 0.84

Note: K = number of effect sizes. * p < 0.05. ** p < 0.01. *** p < 0.001.

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Psychology, Journal of Experimental Child Psychology, Reading and Writing, Journal of Learning Disabilities, and Learning Disability Quarterly. Publication dates for the included studies ranged from 1988 to 2012, with the average year of publication in 2003. The number of authors ranged from 1 to 9, with 30 of the included studies conducted in the United States, 9 in Canada, 6 in England, 2 in Australia, and 1 in both the US and Canada. The average age of students across studies was 10 years old (M = 130.03 months), and the majority of students were male. There were 425 effect sizes that were associated with a male to female gender ratio of participants, and 13 studies reported ethnicity within each sample of RD. Initial testing of the gender and ethnicity ratio resulted in non-significant differences in ESs, so they were removed from subsequent analyses. In addition, all studies failed to report performance outcomes as a function of gender or ethnicity. Further, the majority of studies either did not report SES or did not separate it between the children with and without RD, so these variables were not considered further. Table 1 shows the overall performance of children with and without RD on norm-referenced psychometric information (e.g., IQ and reading). Results showed that on standardized classification measures of reading comprehension and word recognition, children with RD were approximately 1 standard deviation below the mean (where M = 100 and SD = 15). Some of the comparative measures, such as phonological awareness, word attack (pseudoword reading), and spelling skills were particular points of weaknesses for children with RD when compared to children without RD, many scoring at or below the 25th percentile on standard scores. 3.2. Domain categories The weighted means and standard errors for the ESs within each category are reported in Table 2, along with the upper and lower bounds for the 95% confidence interval and the homogeneity Q. Using criteria provided by Cohen (1988), results indicated large effect sizes (>0.80) for the following categories: reading comprehension, word recognition, verbal IQ, rapid naming, phonological awareness, pseudoword reading, math, vocabulary, spelling, writing, and behavior. There were also moderate ESs (0.50–0.80) for the categories of general IQ, verbal working memory, executive processing/inhibition, shortterm memory, auditory processing (auditory perception skills), and general information. The remaining categories, problem solving/reasoning, visual–spatial memory, and perceptual motor skills, yielded relatively low ESs (16th percentile) and severe ( 15). The full model accounted for 53% of the explainable variance when compared to the unconditional (0.55 + 0.02)  (0.05 + 0.24)/(0.55 + 0.02)] and provided a significantly better fit than the unconditional model (Dx2(15) = 192.90 (1558.5–1365.6, p < 0.0001). The intercept for the full model was 1.21 (SE = 0.05). In addition, several cognitive measures (i.e., verbal working memory, visual–spatial memory, executive processing, and short-term memory) were significant moderators of the intercept.

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Table 3 Hierarchical linear modeling predicting effect sizes between reading disabled and average readers. Unconditional model Estimate Random effects Study Study * word ID Residual s2 Fit statistics 2 log likelihood AIC BIC Fixed effects Intercept Word Id Intelligence Chronological age Rapid naming Phonological awareness Pseudoword reading Mathematics Vocabulary Spelling Problem solving Verbal WM Visual–spatial memory Executive processing Short-term memory Behavior

0.07** 0.55*** 0.22**

Full model SE 0.03 0.06 0.02

1558.5 1566.5 1558.5 1.23***

Estimate 0.05* 0.24** 0.22***

Reduced model SE 0.03 0.03 0.02

1365.6 1405.6 1365.6

0.06

1.21*** 1.12*** 0.87*** 0.003 0.29 0.008 0.76*** 0.26 0.50*** 0.68*** 0.54 0.52** 0.70** 0.60** 0.68* 0.41

Estimate 0.06*** 0.26*** 0.22**

SE 0.03 0.04 0.02

1379.3 1405.3 1379.3

0.05 0.13 0.13 0.002 0.15 0.14 0.18 0.18 0.14 0.24 0.44 0.14 0.17 0.16 0.30 0.28

1.22*** 1.25*** 0.78***

0.05 0.13 0.13

0.84***

0.18

***

0.40 0.80**

0.14 0.24

0.40** 0.59*** 0.47*** 0.42***

0.14 0.18 0.17 0.16

* p < 0.05. ** p < 0.01. *** p < 0.001.

To provide a parsimonious account of the data, however, a reduced model was tested that removed all the nonsignificant parameters from the nested model. The reduced model was not significantly different from the full (nested) model (Dx2(6) = 13.70 (BIC: 1379.3–1365.6), p > 0.05. Thus, the reduced model provided a more parsimonious fit to the data. When compared to the unconditional model, the reduced model accounted for 48% of the explainable variance between studies (0.62  0.32/0.62). The reduced model revealed a significant relationship between the overall ES between children with and without RD and various moderators related to cognition (verbal working memory, visual–spatial memory, executive processing, and short-term memory) and academic domains (pseudoword reading, vocabulary, and spelling). 3.4. Summary In summary, this synthesis showed that large effect sizes emerged when comparing children with and without RD for reading comprehension, word recognition, verbal IQ, rapid naming, phonological awareness, pseudoword reading, math, vocabulary, spelling, writing, and behavior. Moderate effect sizes were also found for general IQ, verbal working memory, executive processing, short-term memory, auditory processing (auditory perception skills), and general information. Hierarchical linear modeling, that examined the various cognitive and academic domains, found that several cognitive (verbal working memory, visual–spatial memory, executive processing, and short-term memory) and some academic (pseudoword reading, vocabulary, and spelling) variables significantly moderated the overall ESs, when accounting for the initial reading levels and age of students reported in the studies. The reduced model accounted for approximately 48% of the explainable variance across the studies. 4. Discussion This study synthesized the cognitive process differences between children with and without RD. The study focused on two issues. The first focused on identifying those domains that contributed to the largest to differences between children with and without RD. As shown in Table 2, moderate to high ESs in favor of children without RD emerged on measures of cognition (rapid naming speed [ES = 0.89], phonological awareness [ES = 1.00], verbal working memory [ES = 0.68], shortterm memory [ES = 0.64], and perceptual motor skills [ES = 0.79]), academic achievement (pseudoword reading [ES = 1.84], math [ES = 1.20], vocabulary [ES = 0.83], spelling [ES = 1.25], and writing [ES = 1.20]), and behavior skills (ES = 0.80). Hierarchical linear modeling indicated that both cognitive (verbal working memory, visual–spatial memory, executive

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processing, short-term memory, and perceptual motor skills) and academic (pseudoword reading, vocabulary, spelling) measures contributed unique and significant variance to the overall group effect size differences. The second issue focused on whether performance differences between children with and without RD was moderated differences in IQ levels, age, ethnicity, or gender. During our investigation we found that few eligible studies reported on the specific effects of ethnicity and gender and therefore, the influence of these variables on performance outcomes could not be pursued further. As shown in Table 3, chronological age was not a significant moderator of the overall ES between children with and without RD. In contrast, IQ moderated the overall outcomes, even when competing measures (e.g., the cognitive and academic measures that had been found to be significant) were entered into the HLM analysis. The present synthesis extends the literature on the assessment of children with RD in three major ways. First, this metaanalysis showed that RD students have numerous cognitive lags in several areas compared to average reading students, and specifically identifies the areas in need of intervention. Those areas found specifically that contributed to understanding the differences between RD children and average readers were in phonological processing skills, including word attack, as well as cognitive and academic areas including spelling and vocabulary, verbal working memory, visual–spatial memory, executive processing, and short-term memory. Furthermore, while this and previous studies (e.g., Johnson, Humphrey, Mellard, Woods, & Swanson, 2010) found large ESs for processing speed (i.e., rapid naming) and math ability, our HLM analysis did not find processing speed or math ability to be predictors of the ES between children with and without RD. It should be noted, however, that processing speed and math ability are predictors of effect size between adults without and without RD (Swanson, 2012; Swanson & Hsieh, 2009). These results suggest that understanding the differences between children with and without RD among children should not include processing speed or math ability, but that these areas should be included when studying adult populations. Future research is needed to examine the differential effects of processing speed and math ability on reading ability among children and adult populations. Second, direct comparisons were made across studies in terms of variations in IQ and reading level. We found support for the notion that IQ was a valid component in the assessment of RD. Several researchers have suggested eliminating IQ from the classification of RD. Our results found that general IQ significantly moderated ES differences across a broad array of measures. That is, the HLM analysis showed that variations in reading did not partial out the influence of general IQ in predicting ES differences between children with and without RD. Further, we found variables in the cognitive and academic domains that there were significant moderators and independently predicted ESs between RD and NRD children after the influence of all other variables had been entered into the analysis. These results are consistent with assessment models emphasizing both IQ and various cognitive measures (i.e., verbal working memory, visual–spatial memory, executive processing, short-term memory, and perceptual motor skills) in the assessment of RD. Finally, we found support for the notion that problems in RD extend beyond a phonological core deficit. Although initially the analyses found clear indications of weaknesses in comparative processing between RD and skilled readers on measures of phonological awareness (as well as pseudoword reading and spelling), our results did not maintain this significance upon closer examination. Instead the outcomes were also more in line with those studies indicating that other cognitive processes, independent of phonological awareness, are related to differences between RD and NRD children, such as those on memory span (Swanson & Jerman, 2007) that indicate cognitive processes contribute significant amounts of variance toward reading capability. No doubt, the above finding creates a conceptual problem when one attempts to link RD in children to a specific or core phonological processing deficit. Perhaps one obvious means of reconciling this conceptual problem is to suggest that relationships among cognitive processes reflect ‘‘bootstrapping effects’’ (see Stanovich, 1986, p. 364, for an earlier discussion of this concept). As stated by Stanovich (1986), ‘‘Many things that facilitate further growth in reading. . .general knowledge, vocabulary. . .are developed by reading itself’’ (p. 364). Thus, due to the mutual facilitation between reading and cognitive processing, such interrelationships would be expected to increase with skill improvement. The implicit assumption is that the deficits in word recognition skills (e.g., phonological skills) underlie such bootstrapping effects. Another means of reconciling the phonological core issue is to suggest that high-order cognitive processing problems can exist in children with RD, independent of their specific problems in low-order processes, such as phonological processing. Children with RD may be viewed as having difficulty accessing high-level information (as reflected in their reading comprehension and vocabulary scores) and/or lower-order skills (phonological codes), or switching between the two levels of processing. Thus, one may speculate that the processing problems in children with RD reflect a system that fails to compensate for (or effectively coordinate) deficiencies in lower-order specialized processes. This lack of compensatory processing may be characterized by a processing system either not contributing enough information to a specialized system or failing to provide an adequate capacity of processing resources (i.e., because of verbal memory deficiencies), given that there are problems in a specialized system. Future research will have to focus on the interaction between higher and lower order processing during the act of reading to disentangle these issues. 4.1. Limitations There are several caveats in our synthesis that limit our generalizations. Three are considered. First, it is important to note we selected studies that classified children with RD performing at various levels on either a word recognition and/or reading comprehension continuum. Each of these measures draws upon different processes and therefore, may have obscured the results. It is also important to note in our studies that one of the most frequent identifiers of children with RD provided by the

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primary authors was an existing discrepancy between the targeted sample’s IQ and his/her current reading achievement. Thus, our use of aggregated scores related to IQ and reading may not reflect variables that underlie how subjects were selected in the first place. The second was that although we selected studies that included only samples with at least average IQ score above 80 in the analysis (and no indication that the majority were below 80), no cap was placed on the upper limits of IQ scores. Some studies had mean IQ scores that fell within a level that might be referred to as ‘‘high,’’ and therefore, these children may have experienced more specific deficits in cognitive and language areas than children closer to an IQ of 80. It is possible that if the parameters for defining the non-discrepancy groups focused on higher and lower IQ groups, as well as different types of IQ measures (e.g., nonverbal, performance), different outcomes may have emerged. The final issue was that our selection of studies was biased toward those that included samples with designated labels of RD (or a related term) as well as samples that had reported both intelligence and reading scores. Given these restrictions, however, this meta-analysis supports previous syntheses showing that IQ is important in predicting effect size differences across language, behavioral and cognitive variables (Fuchs, Fuchs, Mathes, & Lipsey, 2000; Hoskyn & Swanson, 2000). 4.2. Summary In general, several cognitive measures yielded a unique contribution to the overall ES differences between groups. For example, verbal working memory, visual–spatial memory, executive processing, short-term memory, and perceptual motor skills) as well as academic (pseudoword reading, vocabulary, and spelling) domains were found to be significant contributors to group differences in ESs. 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1

Asterisk denoting that articles were part of the meta analysis.

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