Some recommended statistical analytic practices when reliability generalization studies are conducted.
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| Title: | Some recommended statistical analytic practices when reliability generalization studies are conducted. |
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| Authors: | Sánchez‐Meca, Julio, López‐López, José Antonio, López‐Pina, José Antonio |
| Source: | British Journal of Mathematical & Statistical Psychology. Nov2013, Vol. 66 Issue 3, p402-425. 24p. |
| Subjects: | Statistics, Generalization, Coefficients (Statistics), Meta-analysis, Comparative studies, Moderator variables, Least squares |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00071102 |
| DOI: | 10.1111/j.2044-8317.2012.02057.x |
