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Identification of indirect effects in the two-condition within-subject mediation model and its implementation using SEM

  • Eujin Park (Division of Humanities, Arts, and Social Sciences: Creative Technology Management, International College, Yonsei University) ;
  • Changsoon Park (Department of Applied Mathematics and Statistics, State University of New York)
  • 투고 : 2023.10.14
  • 심사 : 2023.10.30
  • 발행 : 2023.11.30

초록

In the two-condition within-subject mediation design, pairs of variables such as mediator and outcome are observed under two treatment conditions. The main objective of the design is to investigate the indirect effects of the condition difference (sum) on the outcome difference (sum) through the mediator difference (sum) for comparison of two treatment conditions. The natural condition variables mean the original variables, while the rotated condition variables mean the difference and the sum of two natural variables. The outcome difference (sum) is expressed as a linear model regressed on two natural (rotated) mediators as a parallel two-mediator design in two condition approaches: the natural condition approach uses regressors as the natural condition variables, while the rotated condition approach uses regressors as the rotated condition variables. In each condition approach, the total indirect effect on the outcome difference (sum) can be expressed as the sum of two individual indirect effects: within- and cross-condition indirect effects. The total indirect effects on the outcome difference (sum) for both condition approaches are the same. The invariance of the total indirect effect makes it possible to analyze the nature of two pairs of individual indirect effects induced from the natural conditions and the rotated conditions. The two-condition within-subject design is extended to the addition of a between-subject moderator. Probing of the conditional indirect effects given the moderator values is implemented by plotting the bootstrap confidence intervals of indirect effects against the moderator values. The expected indirect effect with respect to the moderator is derived to provide the overall effect of moderator on the indirect effect. The model coefficients are estimated by the structural equation modeling approach and their statistical significance is tested using the bias-corrected bootstrap confidence intervals. All procedures are evaluated using function lavaan() of package {lavaan} in R.

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참고문헌

  1. Aung MT, Song Y, Ferguson KK, Cantonwine DE, Zeng L, McElrath TF, and Mukherjee B (2020). Application of an analytical framework for multivariate mediation analysis of environmental data, Nature Communications, 11, 1-13, Available from: doi: 10.1038/s41467-020-19335-2
  2. Cheryan S, Plaut VC, Davies PG, and Steele CM (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science, Journal of Personality and Social Psychology, 97, 1045-1060, Available from: http://dx.doi.org/10.1037/a0016239
  3. Cole DA and Maxwell SE (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling, Journal of Abnormal Psychology, 112, 558-577, Available from: http://dx.doi.org/10.1037/0021-843X.112.4.558
  4. Converse BA and Fishbach A (2012). Instrumentality boosts appreciation: Helpers are more appreciated while they are useful, Psychological Science, 23, 560-566, Available from: http://dx.doi.org/10.1177/0956797611433334
  5. De Kwaadsteniet EW, Rijkhoff SAM, and van Dijk E (2013). Equality as a benchmark for third-party punishment and reward: The moderating role of uncertainty in social dilemmas, Organizational Behavior and Human Decision Processes, 120, 251-259, Available from: http://dx.doi.org/10.1016/j.obhdp.2012.06.007
  6. Edwards JR and Lambert LS (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis, Psychological Methods, 12, 1-22, Available from: http://dx.doi.org/10.1037/1082-989X.12.1.1
  7. Fairchild AJ and MacKinnon DP (2009). A general model for testing mediation and moderation effects, Prevention Science, 10, 87-99, Available from: http://dx.doi.org/10.1007/s11121-008-0109-6
  8. Ferguson KK, Chen YH, VanderWeele TJ, McElrath TF, Meeker JD, and Mukherjee B (2017). Mediation of the relationship between maternal phthalate exposure and preterm birth by oxidative stress with repeated measurements across pregnancy, Environmental Health Perspectives, 125, 488-494, Available from: https://dx.doi.org/10.1289%2FEHP282 https://doi.org/10.1289%2FEHP282
  9. Grant AM and Gino F (2010). A little thanks goes a long way: Explaining why gratitude expressions motivate prosocial behavior, Journal of Personality and Social Psychology, 98, 946-955, Available from: http://dx.doi.org/10.1037/a0017935
  10. Hayes AF (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis - A Regression-based Approach (2nd ed), Guilford Press, New York.
  11. Hayes AF, Montoya AK, and Rockwood NJ (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling, Australasian Marketing Journal, 25, 76-81, Available from: https://psycnet.apa.org/doi/10.1016/j.ausmj.2017.02.001
  12. Josephy H, Vansteelandt S, Vanderhasselt MA, and Loeys T (2015). Within-subjects mediation analysis in AB/BA crossover designs, International Journal of Biostatistics, 11, 1-22, Available from: https://doi.org/10.1515/ijb-2014-0057
  13. Judd CM, Kenny DA, and McClelland GH (2001). Estimating and testing mediation and moderation in within-subject designs, Psychological Methods, 6, 115-134, Available from: https://doi.org/10.1037/1082-989X.6.2.115
  14. Kraemer HC, Stice E, Kazdin A, Offord D, and Kupfer D (2001). How do risk factors work together? mediators, moderators, and independent, overlapping, and proxy risk factors, American Journal of Psychiatry, 158, 848-856, Available from: doi:10.1176/appi.ajp.158.6.848
  15. Montoya AK (2018). Conditional process analysis in two-instance repeated-measures designs (Ph.D. Thesis), The Ohio State University, Columbus, Ohio, Available from: https://etd.ohiolink.edu/apexprod/rwsetd/sendfile/send?accession=osu1530904232127584anddisposition=inline
  16. Montoya AK (2019). Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models, Behavior Research Methods, 51, 61-82, Available from: https://doi.org/10.3758/s13428-018-1088-6
  17. Montoya AK (2023). Selecting a within-or between-subject design for mediation: Validity, causality, and statistical power, Multivariate Behavioral Research, 58, 616-636, Available from: https://doi.org/10.1080/00273171.2022.2077287
  18. Montoya AK and Hayes AF (2017). Two-condition within-participant statistical mediation analysis: A path-analytic framework, Psychological Methods, 22, 6-27, Available from: http://dx.doi.org/10.1037/met0000086
  19. Morris MW, Sheldon OJ, Ames DR, and Young MJ (2007). Metaphors and the market: Consequences and preconditions of agent and object metaphors in stock market commentary, Organizational Behavior and Human Decision Processes, 102, 174-192, Available from: http://dx.doi.org/10.1016/j.obhdp.2006.03.001
  20. Muller D, Judd CM, and Yzerbyt VY (2005). When moderation is mediated and mediation is moderated, Journal of Personality and Social Psychology, 89, 852-863, Available from: http://dx.doi.org/10.1037/0022-3514.89.6.852
  21. Paladino MP, Mazzurega M, Pavani F, and Schubert TW (2010). Synchronous multisensory stimulation blurs self-other boundaries, Psychological Science, 21, 1202-1207, Available from: http://dx.doi.org/10.1177/0956797610379234
  22. Park E and Park C (2023). SEM approach to the mediation analysis of the two-condition within-subject design, Submitted to Structural Equation Modeling: A Multidisciplinary Journal (under review)
  23. Preacher KJ (2015). Advances in mediation analysis: A survey and synthesis of new developments, Annual Review of Psychology, 66, 825-852, Available from: https://doi.org/10.1146/annurev-psych-010814-015258
  24. Preacher KJ, Rucker DD, and Hayes AF (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescription, Multivariate Behavioral Research, 42, 185-227, Available from: http://dx.doi.org/10.1080/00273170701341316
  25. Rijnhart JJ, Lamp SJ, Valente MJ, MacKinnon DP, Twisk JW, and Heymans MW (2021). Mediation analysis methods used in observational research: A scoping review and recommendations, BMC Medical Research Methodology, 21, 1-17, Available from: https://doi.org/10.1186/s12874-021-01426-3
  26. Selig JP and Preacher KJ (2009). Mediation models for longitudinal data in developmental research, Research in Human Development, 6, 144-164, Available from: http://dx.doi.org/10.1080/15427600902911247
  27. Spiller SA (2011). Opportunity cost consideration, Journal of Consumer Behaviour, 38, 595-610, Available from: http://dx.doi.org/10.1086/660045
  28. Tingley D, Yamamoto T, Hirose K, Keele L, and Imai K (2014). Mediation: R package for causal mediation analysis, Journal of Statistical Software, 59, 1-38, Available from: http://hdl.handle.net/1721.1/91154
  29. Tofighi D and MacKinnon DP (2011). RMediation: An R package for mediation analysis confidence intervals, Behavior Research Methods, 43, 692-700, Available from: http://dx.doi.org/10.3758/s13428-011-0076-x
  30. Tofighi D (2021). Sensitivity analysis in nonrandomized longitudinal mediation analysis, Frontiers in Psychology, 12, 755102, Available from: https://doi.org/10.3389%2Ffpsyg.2021.755102 https://doi.org/10.3389%2Ffpsyg.2021.755102
  31. Vuorre M and Bolger N (2018). Within-subject mediation analysis for experimental data in cognitive psychology and neuroscience, Behavior Research Methods, 50, 2125-2143, Available from: https://doi.org/10.3758/s13428-017-0980-9
  32. Warren C and Campbell MC (2014). What makes things cool? how autonomy influences perceived coolness, Journal of Consumer Research, 41, 543-563, Available from: http://dx.doi.org/10.1086/676680
  33. Yu Q and Li B (2017). mma: An R package for mediation analysis with multiple mediators, Journal of Open Research Software, 5, 11, Available from: https://doi.org/10.5334/jors.160