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Review
. 2018 Nov;89(6):1956-1969.
doi: 10.1111/cdev.13049. Epub 2018 Feb 27.

Using Meta-analytic Structural Equation Modeling to Study Developmental Change in Relations Between Language and Literacy

Jamie M Quinn  1 , Richard K Wagner  2
Affiliations
Review

Using Meta-analytic Structural Equation Modeling to Study Developmental Change in Relations Between Language and Literacy

Jamie M Quinn et al. Child Dev. 2018 Nov.

Abstract

The purpose of this review was to introduce readers of Child Development to the meta-analytic structural equation modeling (MASEM) technique. Provided are a background to the MASEM approach, a discussion of its utility in the study of child development, and an application of this technique in the study of reading comprehension (RC) development. MASEM uses a two-stage approach: first, it provides a composite correlation matrix across included variables, and second, it fits hypothesized a priori models. The provided MASEM application used a large sample (N = 1,205,581) of students (ages 3.5-46.225) from 155 studies to investigate the factor structure and relations among components of RC. The practical implications of using this technique to study development are discussed.

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Figures

Figure 1
Figure 1
Flow chart of the record screening process. Reasons for exclusion are included in brackets to the right of the excluded boxes.
Figure 2
Figure 2
Meta-SEM path diagram for the three-factor model. * p < .001. Dashed, grey pathway is not significant. WRF = word reading fluency; DEC = decoding accuracy; TRF = text reading fluency; VOC = vocabulary knowledge; LC = listening comprehension; BGK = background knowledge; WM = working memory; R/I = reasoning and inference. e = residual variance error terms, d = disturbance term.
Figure 3
Figure 3
Meta-SEM path diagram for the younger sample. *p < .001. Dashed, grey pathways are not significant. WRF = word reading fluency; DEC = decoding accuracy; TRF = text reading fluency; VOC = vocabulary and morphological knowledge; LC = listening comprehension; WM = working memory; R/I = reasoning and inference. e = residual variance error terms, d = disturbance term.
Figure 4
Figure 4
Meta-SEM path diagram of Model 2 for the older sample. *p < .001. Dashed, grey pathways are not significant. WM = working memory; R/I = reasoning and inference; BGK = background knowledge; WRF = word reading fluency; DEC = decoding accuracy; TRF = text reading fluency; VOC = vocabulary knowledge; LC = listening comprehension. e = residual variance error terms, d = disturbance term.
See this image and copyright information in PMC

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