THE EFFECTS OF UNREPRESENTED STUDIES ON THE ROBUSTNESS OF VALIDITY GENERALIZATION RESULTS

Autor:	Steven D. Ashworth, John C. Callender, H. G. Osburn, Kristin Ann Boyle
Rok vydání:	2006
Předmět:	Organizational Behavior and Human Resource Management Generalization business.industry Validity Test validity Machine learning computer.software_genre External validity Robustness (computer science) Econometrics Artificial intelligence business Psychology computer Applied Psychology
Zdroj:	Personnel Psychology. 45:341-361
ISSN:	0031-5826
Popis:	Researchers conducting meta-analyses such as validity generalization can never be certain that their review contains all studies relevant to the research domain. Indeed, several authors in the past have noted ways in which research reviews may be systematically biased. A few techniques have emerged for addressing the issue of “missing studies” including the use of Rosenthal's (1979) file-drawer equation. Noting that Rosenthal's technique is inappropriate when applied to validity generalization findings, this paper develops a new method for assessing the vulnerability of validity generalization results to unrepresented or missing studies. The results of this new procedure are compared to the results of file-drawer analyses for 103 findings from validity generalization studies. We illustrate that this procedure more appropriately estimates the robustness of validity generalization results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7b6213ad4cb609fc7e0ba20172f8bdd9 https://doi.org/10.1111/j.1744-6570.1992.tb00853.x Zobrazit plný text záznamu Plný text