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Autor(en): 
  • Mike W.-L. Cheung
  • Meta-Analysis: A Structural Equation Modeling Approach 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Mai 2015  
    Genre:  Schulbücher 
     
    Angew. Wahrscheinlichkeitsrechn. u. Statistik / Modelle / Applied Probability & Statistics - Models / Metaanalyse / Statistics / Statistics for Social Sciences / Statistik / Statistik in den Sozialwissenschaften / Strukturgleichungsmodell
    ISBN:  9781119993438 
    EAN-Code: 
    9781119993438 
    Verlag:  Wiley 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 229 mm / B 152 mm / D 37 mm 
    Gewicht:  634 gr 
    Seiten:  400 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta-analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

      
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