O'Reilly logo

Meta-Analysis: A Structural Equation Modeling Approach by Mike W.-L. Cheung

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Chapter 4Univariate meta-analysis

This chapter begins by introducing the basic ideas of the fixed-effects model. The extension to the random-effects model is then introduced. Conceptual and statistical differences between the fixed-effects and the random-effects models are discussed. By including study characteristics as moderators, we extend the random-effects model to the mixed-effects model. Key concepts in a meta-analysis are introduced, such as testing the homogeneity of effect sizes, estimating heterogeneity variance, quantifying the degree of heterogeneity in the random-effects model, and quantifying the explained variance in the mixed-effects model. These models are then formulated under the structural equation modeling (SEM) framework. This SEM-based meta-analysis provides the foundation for more advanced analyses such as the multivariate and the three-level meta-analyses introduced in later chapters. Graphical models are proposed to represent the meta-analytic models. Several applications are used to illustrate the procedures in the R statistical environment.

4.1 Introduction

We begin this chapter by considering a model with only one variable c04-math-0001 that is normally distributed with a mean of c04-math-0002 and a variance of , that is, . As we are going to show later, this simple model has ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required