विधियों की तुलना करें

	Common Method Bias Remedies	Psychometric Meta-Analysis
क्षेत्र	संगठनात्मक व्यवहार	संगठनात्मक व्यवहार
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2003	1977
प्रवर्तक	Philip Podsakoff, Scott MacKenzie, Jeong-Yeon Lee & Nathan Podsakoff; Michael Lindell & David Whitney	Frank L. Schmidt & John E. Hunter
प्रकार	Procedural and statistical remedies for method-induced bias	Artifact-corrected meta-analytic estimation pipeline
मौलिक स्रोत	Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.	Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Sage Publications. ISBN: 9781412904797
उपनाम	Common Method Variance Remedies, CMV Controls, Harman's Single-Factor Test, Marker Variable Technique	Hunter-Schmidt Meta-Analysis, Validity Generalization, Artifact-Corrected Meta-Analysis, VG
संबंधित	3	3
सारांश	Common method bias remedies are the procedural and statistical tools researchers use to detect and reduce the spurious covariance that arises when constructs are measured with the same method — typically a single self-report survey. Podsakoff, MacKenzie, Lee, and Podsakoff's 2003 review crystallized the problem, cataloguing the many sources of method bias and the design and analysis safeguards available, and it became the field's reference point. Because the same respondent, rating scale, and occasion can inflate correlations among unrelated constructs, method variance can manufacture or distort relationships that researchers then mistake for substance. The remedies fall into two families: procedural design choices that prevent method variance from entering the data, and statistical techniques that diagnose or partial it out afterward. Lindell and Whitney's marker-variable approach and Williams, Hartman, and Cavazotte's confirmatory-factor-analysis marker technique are the leading statistical correctives. Used together, these remedies make method bias a problem to be designed against and tested for rather than assumed away.	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.