Abstract
Fixed effects models assume that all differences between correlation coefficients are due to sampling fluctuations, and do not allow inference beyond the studies included in the meta-analysis. Random effects models are more appropriate when researchers wish to make more general statements. Differences between studies’ coefficients may occur for other reasons than sampling, for example because other measurement instruments were used or because characteristics of the samples are different. Random effects meta-analytic structural equation modeling takes the study level variance into account. This chapter shows how one can test for heterogeneity of correlation coefficients, and how to quantify the size of the heterogeneity. If heterogeneity is present, the fixed effects model is not appropriate. One option is to explain all heterogeneity with study level variables, for example using subgroup analysis. Random effects analysis can also be combined with subgroup analysis, by fitting a random effects model to subgroups of studies.
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Jak, S. (2015). Heterogeneity. In: Meta-Analytic Structural Equation Modelling. SpringerBriefs in Research Synthesis and Meta-Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-27174-3_3
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DOI: https://doi.org/10.1007/978-3-319-27174-3_3
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