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Meta-Analysis Using Structural Equation Modeling
Meta-Analysis Using Structural Equation Modeling
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143,28 €
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Filling a gap in the contemporary literature, this practical guide shows how structural equation modeling (SEM) can be used to analyze results from multiple different studies on the same subject. Randall E. Schumacker presents programs using LISREL, Mplus, and R software, complete with worked-through examples, procedural steps, and sample annotated code. He provides an overview of meta-analysis and the basic SEM modeling steps before delving into meta-analysis in path models, confirmatory facto…

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Filling a gap in the contemporary literature, this practical guide shows how structural equation modeling (SEM) can be used to analyze results from multiple different studies on the same subject. Randall E. Schumacker presents programs using LISREL, Mplus, and R software, complete with worked-through examples, procedural steps, and sample annotated code. He provides an overview of meta-analysis and the basic SEM modeling steps before delving into meta-analysis in path models, confirmatory factor models, and structural equation models (MASEM). The book offers insights into why meta-analysis should be considered a multi-level method since data is collected over several years, making time a nested effect. It discusses key MASEM issues, such as publication bias, sample size differences between studies, and the heterogeneity of effect sizes across studies, and reviews PRISMA reporting guidelines aligned with APA style. Full code and output for the book's examples can be accessed at the companion website.

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Filling a gap in the contemporary literature, this practical guide shows how structural equation modeling (SEM) can be used to analyze results from multiple different studies on the same subject. Randall E. Schumacker presents programs using LISREL, Mplus, and R software, complete with worked-through examples, procedural steps, and sample annotated code. He provides an overview of meta-analysis and the basic SEM modeling steps before delving into meta-analysis in path models, confirmatory factor models, and structural equation models (MASEM). The book offers insights into why meta-analysis should be considered a multi-level method since data is collected over several years, making time a nested effect. It discusses key MASEM issues, such as publication bias, sample size differences between studies, and the heterogeneity of effect sizes across studies, and reviews PRISMA reporting guidelines aligned with APA style. Full code and output for the book's examples can be accessed at the companion website.

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