Vertaile menetelmiä

	Psychometric Meta-Analysis	Event History Turnover Analysis
Tieteenala	Organisaatiokäyttäytyminen	Organisaatiokäyttäytyminen
Menetelmäperhe	Process / pipeline	Survival analysis
Syntyvuosi	1977	1993
Kehittäjä	Frank L. Schmidt & John E. Hunter	Paul D. Allison; June G. Morita, Thomas W. Lee & Richard T. Mowday
Tyyppi	Artifact-corrected meta-analytic estimation pipeline	Time-to-event modeling of employee turnover
Alkuperäislähde	Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Sage Publications. ISBN: 9781412904797	Morita, J. G., Lee, T. W., & Mowday, R. T. (1993). The regression-analog to survival analysis: A selected application to turnover research. Academy of Management Journal, 36(6), 1430-1464.
Rinnakkaisnimet	Hunter-Schmidt Meta-Analysis, Validity Generalization, Artifact-Corrected Meta-Analysis, VG	Survival Analysis of Turnover, Hazard Modeling of Employee Turnover, Time-to-Turnover Analysis, Employee Tenure Survival Models
Liittyvät	3	3
Tiivistelmä	Psychometric meta-analysis is the Hunter-Schmidt approach to cumulating research findings while correcting for the statistical artifacts that distort individual studies. Frank Schmidt and John Hunter developed it to solve the problem of validity generalization: across many studies the observed validity of a selection test varied widely, leading people to conclude that validity was situationally specific, when in fact most of the variation was an illusion produced by small samples, unreliable measures, and restricted ranges. Their 1977 Journal of Applied Psychology paper showed that once these artifacts are removed, the apparent variability shrinks and a stable true validity emerges that generalizes across settings. The full method, codified in their book Methods of Meta-Analysis, pools effect sizes, subtracts the variance due to sampling error, and corrects the mean and remaining variance for measurement unreliability and range restriction. It estimates not only the average true effect but how much it really varies and whether it generalizes.	Event history turnover analysis models not just whether employees leave but when they leave, treating tenure as a duration and the act of quitting as an event whose timing carries information. Paul Allison's 1984 monograph brought event history methods — survival and hazard models — into the social sciences with a regression-oriented treatment that handles the censoring inherent in longitudinal data. Morita, Lee, and Mowday's 1993 Academy of Management Journal paper applied these techniques to turnover research, showing organizational scholars how to model the hazard of leaving and why time-to-event methods are superior to simple stayed-versus-left comparisons. The core object is the hazard function, the instantaneous risk of quitting given that one has stayed so far, which can depend on tenure and on employee and job characteristics. Because some employees are still present when the study ends, the analysis must correctly handle censored observations rather than discarding or mis-coding them. The result is a model that explains and predicts the timing of turnover.