Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear

Multivariate Behavioral Research

Volumn 45, Issue 4, 2010, Pages 627-660

  Hayes, Andrew F ^a   Preacher, Kristopher J ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF KANSAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77955913760     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1080/00273171.2010.498290     Document Type: Article

References (63)

1. Abuhamdeh, S., & Csikszentmihalyi, M. (2009). Intrinsic and extrinsic motivational orientations in the competitive context: An examination of person-situation interactions. Journal of Personality, 77, 1615-1635.
2. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage.
3. Ames, D. R., & Flynn, F. J. (2007). What breaks a leader: The curvilinear relation between assertiveness and leadership. Journal of Personality and Social Psychology, 92, 307-324.
4. Aroian, L. A. (1944). The probability function of the product of two normally distributed variables. Annals of Mathematical Statistics, 18, 265-271.
5. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
6. Berry, W. D. (1993). Understanding regression assumptions. Newbury Park, CA: Sage.
7. Bollen, K. A., & Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability. Sociological Methodology, 20, 115-140.
8. Borkenau, P., Zaltauskas, K., & Leising, D. (2009). More may be better, but there may be too much: Optimal trait level and self-enhancement bias. Journal of Personality, 77, 825-858.
9. Busemeyer, J. R., & Jones, L. E. (1983). Analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93, 549-562.
10. Chernick, M. R. (2008). Bootstrap methods: A guide for practitioners and researchers (2nd ed.). New York: Wiley.
11. Cheung, M. W. L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling, 14, 227-246.
12. Chi, N.-W., Huang, Y. M., & Lin, S. C. (2009). A double-edged sword? Exploring the curvilinear relationship between organizational tenure diversity and team innovation: The moderating role of team-oriented HR practices. Group and Organizational Management, 34, 698-726.
13. Church, T. (2009). The low-fitness phenotype as a risk factor: More than just being sedentary? Obesity, 17, 539-542.
14. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.
15. Cortese, S., Falissard, B., Angirman, M., et al. (2009). The relationship between body size and depression in adolescents. Journal of Pediatrics, 154, 86-90.
16. Davidson, R., & MacKinnon, J. G. (2006). Bootstrap methods in econometrics. In T. C. Mills & K. Patterson (Eds.), Palgrave handbook of econometrics (Vol. 1, pp. 812-838). New York: Palgrave Macmillan.
17. Davis, C., & Fox, J. (2007). Sensitivity to reward and body mass index (BMI): Evidence for a nonlinear relationship. Appetite, 50, 43-49.
18. De Dreu, C. K. W. (2006).When too little or too much hurts: Evidence for a curvilinear relationship between task conflict and innovation in teams. Journal of Management, 32, 83-107.
19. Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171-185.
20. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall.
21. Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233-239.
22. Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408-420.
23. Hipp, J. R., Bauer, D. J., Curran, P. J., & Bollen, K. A. (2004). Crimes of opportunity or crimes of emotion? Testing two explanations of seasonal change in crime. Social Forces, 82, 1333-1372.
24. Huang, B., Sivaganesan, S., Succop, P., & Goodman, E. (2004). Statistical assessment of mediational effects for logistic mediation models. Statistics in Medicine, 23, 2713-2728.
25. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.
26. Kiesner, J. (2009). Physical characteristics of the menstrual cycle and premenstrual depressive symptoms. Psychological Science, 20, 763-770.
27. Kleim, B., & Ehlers, A. (2009). Evidence for a curvilinear relationship between posttraumatic growth and posttrauma depression and PTSD in assault survivors. Journal of Traumatic Stress, 22, 45-52.
28. Knobloch, L. K. (2007). Perceptions of turmoil within courtship: Associations with intimacy, relational uncertainty, and interference from partners. Journal of Social and Personal Relationships, 24, 363-384.
29. Knobloch, L. K., Miller, L. E., & Carpenter, K. E. (2007). Using the relational turbulence model to understand negative emotion within courtship. Personal Relationships, 14, 91-112.
30. Li, Y., Schneider, J. A., & Bennett, D. A. (2007). Estimation of the mediation effect with a binary mediator. Statistics in Medicine, 26, 3398-3414.
31. Lockwood, C., & MacKinnon, D. P. (1998). Bootstrapping the standard error of the mediated effect. Proceedings of the 23rd annual meeting of SAS Users Group International (pp. 997-1002). Cary, NC: SAS Institute, Inc.
32. Lubinski, D., & Humphreys, L. G. (1990). Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability. Psychological Bulletin, 107, 385-393.
33. Lunneborg, C. A. (2000). Data analysis by resampling: Concepts and applications. Pacific Grove, CA: Duxbury.
34. MacCallum, R. C., & Mar, C. M. (1995). Distinguishing between moderator and quadratic effects in multiple regression. Psychological Bulletin, 118, 405-421.
35. MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19-40.
36. MacKinnon, D. P. (2008). Introduction to statistical mediation analysis: New York: Routledge.
37. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.
38. MacKinnon,D. P., Lockwood,C. M., Hoffman, J.M.,West, S. G., & Sheets, V. (2002).A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.
39. MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99-128.
40. Markey, P. M., & Markey, C. N. (2007). The interpersonal meaning of promiscuity. Journal of Research in Personality, 41, 1199-1212.
41. Marquardt, D. W. (1980). You should standardize the predictor variables in your regression models. Journal of the American Statistical Association, 75, 87-91.
42. Moosbrugger, H., Schermelleh-Engel, K., Kelava, A., & Klein, A. G. (2009). Testing multiple nonlinear effects in structural equation modeling: A Comparison of alternative estimation approaches. In T. Teo & M. S. Khine (Eds.), Structural equation modelling in educational research: Concepts and applications. Rotterdam, The Netherlands: Sense Publishers.
43. Neale, M. C., & McArdle, J. J. (2000). Structured latent growth curves for twin data. Twin Research, 3, 165-177.
44. Newsom, J. T., Prigerson, H. G., Schultz, R., & Reynolds, C. F. (2003). Investigating moderator hypotheses in aging research: Statistical, methodological, and conceptual difficulties with comparing separate regressions: International Journal of Aging and Human Development, 57, 119-150.
45. Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in multiple mediator models. Behavior ResearchMethods, Instruments, and Computers, 36, 717-731.
46. Preacher, K. J., & Hayes, A. F. (2008a). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior ResearchMethods, 40, 879-891.
47. Preacher, K. J., & Hayes, A. F. (2008b). Contemporary approaches to assessing mediation in communication research. In A. F. Hayes,M. D. Slater, & L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage.
48. Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227.
49. Raykov,T., & Marcoulides,G. A. (2004). Using the delta method for approximate interval estimation of parametric functions in covariance structure models. Structural Equation Modeling, 11, 659- 675.
50. Rodgers, J. L. (1999). The bootstrap, the jackknife, and the randomization test: A sampling taxonomy. Multivariate Behavioral Research, 34, 441-456.
51. Ruppert, D., & Carroll, R. J. (1988). Transformation and weighting in regression. New York: Chapman & Hall.
52. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445.
53. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-313.
54. Stine, R. (1989). An introduction to bootstrap methods. Sociological Methods and Research, 18, 243-291.
55. Stolzenberg, R. M. (1980). The measurement and decomposition of causal effects in nonlinear and nonadditive models. Sociological Methodology, 11, 459-488.
56. Stolzenberg, R. M., & Land, K. C. (1983). Causal modeling and survey research. In P. Rossi, J. Wright, & A. Anderson (Eds.), Handbook of survey research (pp. 613-675).New York: Academic Press.
57. Tabatabai, M., Williams, D. K., & Bursac, Z. (2005). Hyperbolastic growth models: Theory and application. Theoretical Biology and Medical Modelling, 2. Retrieved June 27, 2001, from http://www.tbiomed.com/
58. Tom Tong, S., Van Der Heide, B., Langwell, L., & Walther, J. B. (2008). Curvilinear associations between number of friends on Facebook and perceived social attractiveness and extraversion. Journal of Computer-Mediated Communication, 13, 531-549.
59. Van de Vliert, E., Schwartz, S. H., Sipke, P. E., Hofstede, G., & Daan, S. (1999). Temperature, cultural masculinity, and domestic political violence: A cross-national study. Journal of Cross Cultural Psychology, 30, 291-314.
60. Walcott, C. M., Upton, A., Bolen, L. M., & Brown, M. B. (2008). Associations between perceived status and aggression in young adolescents. Psychology in the Schools, 45, 550-561.
61. Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit formation. Journal of Comparative Neurology and Psychology, 18, 459-482.
62. Yung, Y.-F., & Chan,W. (1999). Statistical analyses using bootstrapping: Concepts and implementation. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 81-105). Thousand Oaks, CA: Sage.
63. Zhou, J., Shin, S. J., Brass, D. J., Choi, J., & Zhang, Z. X. (2009). Social networks, personal values, and creativity: Evidence for curvilinear and interaction effects. Journal of Applied Psychology, 94, 1544-1552.