The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Mediation Analysis in Social Psychology: Current Practices and New Recommendations
Abstract
A key aim of social psychology is to understand the psychological processes through which independent variables affect dependent variables in the social domain. This objective has given rise to statistical methods for mediation analysis. In mediation analysis, the significance of the relationship between the independent and dependent variables has been integral in theory testing, being used as a basis to determine (1) whether to proceed with analyses of mediation and (2) whether one or several proposed mediator(s) fully or partially accounts for an effect. Synthesizing past research and offering new arguments, we suggest that the collective evidence raises considerable concern that the focus on the significance between the independent and dependent variables, both before and after mediation tests, is unjustified and can impair theory development and testing. To expand theory involving social psychological processes, we argue that attention in mediation analysis should be shifted towards assessing the magnitude and significance of indirect effects.
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“In the structural model, the unstandardized regression coefficient between psychological resilience and embitterment was β=-0.185, whereas in the mediation model, this coefficient decreased to β=-0.154. This reduction suggests a partial mediation effect [26,27].”
In the citing paper
“Note that a statistically significant total effect of X on Y is not necessary for mediation to occur and that mediation analysis does not require evidence of a total effect prior to investigating direct and indirect effects [63–66].”
From Acceptance as a Mediator for Change in Acceptance and Commitment Therapy for Persons with Chronic Pain? (2016) · cites this paper as [64]
Citations
Cites
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Advances In Experimental Social Psychology
(2022) · Advances in experimental social psychology · doi:10.1016/s0065-2601(22)x0002-6 · Cited by 22,559
SPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383
Self-citationAsymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Sobel, M. E. (1982) · Sociological Methodology · doi:10.2307/270723 · Cited by 12,810
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583
Mediation in experimental and nonexperimental studies: New procedures and recommendations.
Shrout, P. E., & Bolger, N. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.4.422 · Cited by 11,335
Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007) · Multivariate Behavioral Research · doi:10.1080/00273170701341316 · Cited by 9,973
Self-citationBeyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium
Hayes, A. F. (2009) · Communication Monographs · doi:10.1080/03637750903310360 · Cited by 9,757
A comparison of methods to test mediation and other intervening variable effects
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.83 · Cited by 9,406
The Elaboration Likelihood Model of Persuasion
Petty, R. E., & Cacioppo, J. T. (1986) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60214-2 · Cited by 8,822
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Statistical mediation analysis with a multicategorical independent variable
Hayes, A. F., & Preacher, K. J. (2014) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.12028 · Cited by 3,403
Self-citationHayes, A. F., & Rockwood, N. J. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2016.11.001 · Cited by 2,350
Mediation analysis in partial least squares path modeling
Nitzl, C., Roldán, J. L., & Cepeda‐Carrión, G. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-07-2015-0302 · Cited by 2,818
Determining power and sample size for simple and complex mediation models
Schoemann, A. M., Boulton, A. J., & Short, S. D. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550617715068 · Cited by 1,837
Gu, J., Strauss, C., Bond, R., & Cavanagh, K. (2015) · Clinical Psychology Review · doi:10.1016/j.cpr.2015.01.006 · Cited by 1,712
Metadata
- Authors
- Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E.
- Affiliations
- Northwestern University (US)
- University of Kansas (US)
- Stanford University (US)
- The Ohio State University (US)
- Venue
- Social and Personality Psychology Compass
- Publisher
- Wiley
- Published
- 2011-06
- Volume
- 5
- Issue
- 6
- Pages
- 359-371
- ISSN
- 1751-9004 (ISSN-L)
- OpenAlex
- W2160326831
- Cited by
- 2,430
- References
- 29
- Citations by year
- 2026: 142 · 2025: 166 · 2024: 152 · 2023: 159 · 2022: 193 · 2021: 248 · 2020: 199 · 2019: 190 · 2018: 170 · 2017: 182 · 2016: 193 · 2015: 149 · 2014: 138 · 2013: 96 · 2012: 42
- FWCI
- 79.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Social and Personality Psychology Compass (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- mediation analysis, indirect effects, significance testing, theory testing, theory development, social psychological processes, statistical power, effect size
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