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Some recommended statistical analytic practices when reliability generalization studies are conducted
Author(s) -
SánchezMeca Julio,
LópezLópez José Antonio,
LópezPina José Antonio
Publication year - 2013
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.2012.02057.x
Subject(s) - generalization , moderation , reliability (semiconductor) , statistics , mathematics , random effects model , population , statistical model , econometrics , meta analysis , computer science , medicine , mathematical analysis , power (physics) , physics , environmental health , quantum mechanics
Precursors of the reliability generalization (RG) meta‐analytic approach have not established a single preferred analytic method. By means of five real RG examples, we examine how using different statistical methods to integrate coefficients alpha can influence results in RG studies. Specifically, we compare thirteen different statistical models for averaging reliability coefficients and searching for moderator variables that differ in terms of: (a) whether to transform or not the coefficients alpha, and (b) the statistical model assumed, distinguishing between ordinary least squares methods, the fixed‐effect (FE) model, the varying coefficient (VC) model, and several versions of the random‐effects (RE) model. The results obtained with the different methods exhibited important discrepancies, especially regarding moderator analyses. The main criterion for the model choice should be the extent to which the meta‐analyst intends to generalize the results. RE models are the most appropriate when the meta‐analyst aims to generalize to a hypothetical population of past or future studies, while FE and VC models are the most appropriate when the interest focuses on generalizing the results to a population of studies identical to those included in the meta‐analysis. Finally, some guidelines are proposed for selecting the statistical model when conducting an RG study.