Mean-Centered Moderated Regression Models: A Reassessment of Their Role in Moderation Analysis
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Moderated regression models (MRMs) are widely used in business research to investigate how the relationship between a predictor and an outcome changes across different levels of a moderator. A common issue in MRM is multicollinearity, and mean-centering is often applied to address this problem, though its effectiveness is debated. This study reveals that the t-test for the main effect of the focal predictor at the mean of a moderator in a mean-centered MRM is equivalent to testing the overall simple effect of the predictor as a population-level effect, independent of specific moderator levels. This insight provides a fresh perspective on the utility of mean-centering in MRMs. Beyond mitigating multicollinearity, mean-centering offers a practical and efficient approach for understanding the average impact of a predictor across varying contextual conditions. By assessing both main effects and interaction effects within a single model, researchers can derive more nuanced insights into complex business phenomena, ultimately supporting more informed strategic decision-making.
Abstract
Moderated regression models (MRMs) are widely used in business research to investigate how the relationship between a predictor and an outcome changes across different levels of a moderator. A common issue in MRM is multicollinearity, and mean-centering is often applied to address this problem, though its effectiveness is debated. This study reveals that the t-test for the main effect of the focal predictor at the mean of a moderator in a mean-centered MRM is equivalent to testing the overall simple effect of the predictor as a population-level effect, independent of specific moderator levels. This insight provides a fresh perspective on the utility of mean-centering in MRMs. Beyond mitigating multicollinearity, mean-centering offers a practical and efficient approach for understanding the average impact of a predictor across varying contextual conditions. By assessing both main effects and interaction effects within a single model, researchers can derive more nuanced insights into complex business phenomena, ultimately supporting more informed strategic decision-making.
참고문헌 (41)
- Aguinis, H., "Effect Size and Power in Assessing Moderating Effects of Categorical Variables Using Multiple Regression: A 30-year Review", Journal of Applied Psychology, 90(1), pp.94~107 (2005)
- Aiken, L. S., "Multiple Regression: Testing and Interpreting Interactions" (1991), Sage Publications
- Brambor, T., "Understanding Interaction Models: Improving Empirical Analysis", Political Analysis, 14, pp.63~82 (2006)
- Correlation Analysis for the Behavioral Sciences, "Applied Multiple Regression", pp.1975, Cohen, J.
- Correlation Analysis for the Behavioral Sciences, "Applied Multiple Regression", pp.2003, Cohen, J.
- Cronbach, L. J., "Statistical Tests for Moderator Variables: Flaws in Analyses Recently Proposed", Psychological Bulletin, 102(3), pp.414~417 (1987)
- Dalal, D. K., "Some Common Myths about Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression", Organizational Research Methods, 15(3), pp.339~362 (2012)
- Darlington, R. B., "Regression Analysis and Linear Models: Concepts, Applications, and Implementation" (2017), The Guilford Press
- Dawson, J. F., "Moderation in Management Research : What, Why, When, and How", Journal of Business Psychology, 29(1), pp.1~19 (2014)
- Echambadi, R., "Mean-centering Does Not Alleviate Collinearity Problems in Moderated Multiple Regression Models", Marketing Science, 26(3), pp.438~445 (2007)
- Edwards, J. R., "Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences", pp.2009, Routledge
- Friedrich, R. J., "In Defense of Multiplicative Terms in Multiple Regression Equations", American Journal of Political Science, 26(4), pp.797~833 (1982)
- PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling, "Hayes, A. F.", ~ (2012)
- Hayes, A. F., "Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach" (2022), The Guilford Press
- Hayes, A. F., "A Tutorial on Testing, Visualizing, and Probing an Interaction Involving a Multicategorical Variable in Linear Regression Analysis", Communication Methods and Measures, 11(1), pp.1~30 (2017)
- Helm, R., "Analysis and Evaluation of Moderator Effects in Regression Models : State of Art, Alternatives and Empirical Example", Review of Managerial Science, 6, pp.307~332 (2012)
- Iacobucci, D., "Mean Centering Helps Alleviate"Micro"But Not"Macro"Multicollinearity", Behavior Research Methods, 48, pp.1308~1317 (2016)
- Igartua, J. J., "Mediation, Moderation, and Conditional Process Analysis : Concepts, Computations, and Some Common Confusions", The Spanish Journal of Psychology, 24, pp.1~23 (2021)
- Irwin, J. R., "Misleading Heuristics and Moderated Multiple Regression Models", Journal of Marketing Research, 38(1), pp.100~109 (2001)
- Jaccard, J., "Interaction Effects in Multiple Regression" (2003), Sage Publications
- Jaccard, J., "Interaction Effects in Multiple Regression" (1990), Sage Publications
- Kam, C. D., "Modeling and Interpreting Interactive Hypotheses in Regression Analysis: A Refresher and Some Practical Advice" (2005), University of Michigan Press
- Krishna, A., "A Clearer Spotlight on Spotlight : Understanding, Conducting and Reporting", Journal of Consumer Psychology, 26(3), pp.314~324 (2016)
- Kromrey, J. D., "Mean Centering in Moderated Multiple Regression: Much Ado about Nothing", Educational and Psychological Measurement, 58(1), pp.42~67 (1998)
- MacCallum, R. C., "Distinguishing between Moderator and Quadratic Effects in Multiple Regression", Psychological Bulletin, 118, pp.405~421 (1995)
- MacCallum, R. C., "On the Practice of Dichotomization of Quantitative Variables", Psychological Methods, 7(1), pp.19~40 (2002)
- MacKinnon, D. P., "Confidence Limits for the Indirect Effect : Distribution of the Product and Resampling Methods", Multivariate Behavioral Research, 39(1), pp.99~128 (2004)
- McClelland, G. H., "Multicollinearity is a Red Herring in the Search for Moderator Variables : A Guide to Interpreting Moderated Multiple Regression Models and a Critique of Iacobucci, Schneider, Popovich, and Bakmitsos(2016)", Behavioral Research Methods, 49(1), pp.394~402 (2017)
- Park, S. -J., "Assessing Moderator Effects, Main Effects, and Simple Effects without Collinearity Problems in Moderated Regression Analysis", Journal of Business Research, 145, pp.905~919 (2022)
- Park, S. -J., "Decomposing Main Effects in Moderated Regression Models", Journal of Business Research, pp.113577~ (2023)
- Park, S. -J., "A Simple Technique Assessing Ordinal and Disordinal Interaction Effects", Journal of Educational and Behavioral Statistics (2024)
- Preacher, K. J., "SPSS and SAS Procedures for Estimating Indirect Effects in Simple Mediation Models", Behavior Research Methods, Instruments, & Computers, 36, pp.717~731 (2004)
- Preacher, K. J., "Addressing Moderated Mediation Hypotheses : Theory, Methods, and Prescriptions", Multivariate Behavioral Research, 42(1), pp.185~227 (2007)
- Rogosa, D., "Comparing Nonparallel Regression Lines", Psychological Bulletin, 88, pp.307~321 (1980)
- Rogosa, D., "On the Relationship between the Johnson-Neyman Region of Significance and Statistical Tests of Parallel within Group Regressions", Educational and Psychological Measurement, 41, pp.73~84 (1981)
- Sharma, S., "Identification and Analysis of Moderator Variables", Journal of Marketing Research, 18(3), pp.291~300 (1981)
- Shieh, G., "Clarifying the Role of Mean Centering in Multicollinearity of Interaction Effects", British Journal Mathematical and Statistical Psychology, 64(3), pp.462~477 (2011)
- Spiller, S. A., "Spotlights, Floodlights, and the Magic Number Zero : Simple Effects Tests in Moderated Regression", Journal of Marketing Research, 50(2), pp.277~288 (2013)
- Whisman, M. A., "Designing, Testing, and Interpreting Interactions and Moderator Effects in Family Research", Journal of Family Psychology, 19(1), pp.111~120 (2005)
- Widaman, K. F., "Distinguishing Ordinal and Disordinal Interactions", Psychological Methods, 17(4), pp.615~622 (2012)
- Yi, Y., "On the Evaluation of Main Effects in Multiplicative Regression Models", Journal of the Market Research Society, 31(1), pp.133~138 (1989)