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

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.

Combining Old and New for Better Understanding and Predicting Dyslexia

“…To get the best estimate of the correlation between listening comprehension and reading comprehension, we carried out a model‐based meta‐analysis (Quinn & Wagner, ). The meta‐analysis included 155 studies and over one million students.…”

Meta‐analysis and Individual Participant Data Synthesis in Child Development: Introduction to the Special Section

“…One clear message of the articles presented here is that the quantitative synthesis of developmental data is a domain‐general enterprise . Indeed, the articles featured in this special section cover most of the full range of modern developmental science, including developmental cognitive neuroscience (Yaple & Arsalidou, ), language development (Bergmann et al., ; Quinn & Wagner, ), numerical cognition (Hornburg, Wang, & McNeil, ), social and emotional development (Larzelere et al., ; Verhage et al., ), intervention and prevention science (Gardner et al., ; Leijten et al., ), and even generational‐ and age‐related change in children's stereotypes and behavior (Miller et al., ). In short, it would be a mistake to claim that meta‐analysis is only appropriate for select domains of developmental science.…”

“…Third, the work in this special section highlights potentially fruitful methodological tools by which the current practice of meta‐analysis in the field might be improved upon. As the work of Quinn and Wagner () illustrates, quantitative reviews need no longer be exclusively focused on bivariate associations. More specifically, meta‐analytic structural equation modeling (Cheung, ) combines structural equation modeling with meta‐analysis by fitting structural models to meta‐analyzed covariance matrices.…”

“…One primary emphasis was on methods of meta‐analysis as applied to developmental questions and we thus welcomed articles illustrating these methods with substantive developmental examples (alongside the publication of extensive electronic supplements to maximize reproducibility and future extension to other domains). In the developmental field specific questions arise, for example, about how to conduct meta‐analyses on cross‐sectional and longitudinal studies (e.g., Larzelere, Gunnoe, & Ferguson, ; Leijten et al., ; Miller et al., ), maximize the quality of causal inferences (Gardner et al., ; Larzelere et al., ; Leijten et al., ), and go beyond bivariate associations to testing more complete and realistic developmental models (Quinn & Wagner, ). Articles in this special section touch on these and related topics.…”

The Multivariate Genome-wide Architecture of Interrelated Literacy, Language and Working Memory Skills Reveals Distinct Etiologies

“…Recent meta-analytic structural equation modeling studies have reported strong to moderate interrelations between language, literacy and related traits, such as working memory, spanning childhood and adolescence [12]. A considerable part of these relationships can be attributed to shared genetic factors as shown by twin research [13,14] and by studies of unrelated individuals using genome-wide genotyping information [15].…”