Assessing Omitted Confounder Bias in Multilevel Mediation Models

References

• Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142–163. doi:10.1037/1082-989X.11.2.142
   PubMed Web of Science ®Google Scholar
• Blakely, T. (2002). Commentary: Estimating direct and indirect effects—fallible in theory, but in the real world? International Journal of Epidemiology, 31, 166–167. doi:10.1093/ije/31.1.166
   PubMed Web of Science ®Google Scholar
• Blalock, H. M. (1984). Contextual-effects models: Theoretical and methodological issues. Annual Review of Sociology, 10, 353–372. doi:10.1146/annurev.so.10.080184.002033
   Web of Science ®Google Scholar
• Brewer, M. B., Campbell, D. T., & Crano, W. D. (1970). Testing a single-factor model as an alternative to the misuse of partial correlations in hypothesis-testing research. Sociometry, 33, 1–11. doi:10.2307/2786268
   Google Scholar
• Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105. doi:10.1037/h0046016
   PubMed Web of Science ®Google Scholar
• Cohen, J. (1978). Partialed products are interactions; partialed powers are curve components. Psychological Bulletin, 85, 858–866. doi:10.1037/0033-2909.85.4.858
   Web of Science ®Google Scholar
• Cox, M. G., Kisbu-Sakarya, Y., Miočević, M., & MacKinnon, D. P. (2013). Sensitivity plots for confounder bias in the single mediator model. Evaluation Review, 37, 405–431. doi:10.1177/0193841X14524576
   PubMed Web of Science ®Google Scholar
• Davis, J. A. (1985). The logic of causal order. Beverly Hills, CA: SAGE.
   Google Scholar
• Enders, C. K. (2013). Centering predictors and contextual effects. In M. A. Scott, J. S. Simonoff, & B. P. Marx (Eds.), The SAGE handbook of multilevel modeling (pp. 89--107). Los Angeles, CA: SAGE.
   Google Scholar
• Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138. doi: 10.1037/1082-989X.12.2.121
   PubMed Web of Science ®Google Scholar
• Gill, J. (2015). Bayesian methods: A social and behavioral sciences approach (3rd ed.). Boca Raton, FL: CRC Press.
   Google Scholar
• Hafeman, D. M. (2011). Confounding of indirect effects: A sensitivity analysis exploring the range of bias due to a cause common to both the mediator and the outcome. American Journal of Epidemiology, 174, 710–717. doi:10.1093/aje/kwr173
   PubMed Web of Science ®Google Scholar
• Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. Sociological Methodology, 18, 449–484. doi:10.2307/271055
   Google Scholar
• Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334. doi:10.1037/a0020761
   PubMed Web of Science ®Google Scholar
• James, L. R. (1980). The unmeasured variables problem in path analysis. Journal of Applied Psychology, 65, 415–421. doi:10.1037/0021-9010.65.4.415
   Web of Science ®Google Scholar
• James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for mediation. Journal of Applied Psychology, 69, 307–321. doi:10.1037/0021-9010.69.2.307
   Web of Science ®Google Scholar
• Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5, 602–619. doi:10.1177/0193841X8100500502
   Web of Science ®Google Scholar
• Kenny, D. A. (2011, September). Terminology and basics of SEM. Retrieved 7/11/2015, from http://davidakenny.net/cm/basics.htm
   Google Scholar
• Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8, 115–128. doi:10.1037/1082-989X.8.2.115
   PubMed Web of Science ®Google Scholar
• Kreft, I. G. G., de Leeuw, J., & Aiken, L. S. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research, 30, 1–21. doi: 10.1207/s15327906mbr3001_1
   PubMed Web of Science ®Google Scholar
• Krull, J. L., & MacKinnon, D. P. (1999). Multilevel mediation modeling in group-based intervention studies. Evaluation Review, 23, 418–444. doi:10.1177/0193841X9902300404
   PubMed Web of Science ®Google Scholar
• Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research, 36, 249–277. doi:10.1207/S15327906MBR3602_06
   PubMed Web of Science ®Google Scholar
• Lanaj, K., Johnson, R. E., & Barnes, C. M. (2014). Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organizational Behavior and Human Decision Processes, 124, 11–23. doi:10.1016/j.obhdp.2014.01.001
   Web of Science ®Google Scholar
• Lash, T. L., Fox, M. P., & Fink, A. K. (2009). Applying quantitative bias analysis to epidemiologic data. New York, NY: Springer.
   Google Scholar
• Lüdtke, O., Marsh, H., Robitzsch, A., Trautwein, U., Asparouhov, T., & Müthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203–229. doi: 10.1037/a0012869
   Web of Science ®Google Scholar
• MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Erlbaum.
   Google Scholar
• 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. doi:10.1037//1082-989X.7.1.83
   PubMed Web of Science ®Google Scholar
• MacKinnon, D. P., & Pirlott, A. G. (2015). Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19, 30–43. doi:10.1177/1088868314542878
   PubMed Web of Science ®Google Scholar
• Marsh, H. W. (1987). The big-fish-little-pond effect on academic self-concept. Journal of Educational Psychology, 79, 280–295. doi:10.1037/0022-0663.79.3.280
   Web of Science ®Google Scholar
• Marsh, H. W., Kuyper, H., Morin, A. J. S., Parker, P. D., & Seaton, M. (2014). Big-fish-little-pond social comparison and local dominance effects: Integrating new statistical models, methodology, design, theory and substantive implications. Learning and Instruction, 33, 50–66. doi:10.1016/j.learninstruc.2014.04.002
   Web of Science ®Google Scholar
• Marsh, H. W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B., & Nagengast, B. (2009). Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling error. Multivariate Behavioral Research, 44, 764. doi:10.1080/00273170903333665
   PubMed Web of Science ®Google Scholar
• Marsh, H. W., Trautwein, U., Lüdtke, O., Köller, O., & Baumert, J. (2005). Academic self-concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering. Child Development, 76, 397–416. doi:10.1111/j.1467-8624.2005.00853.x
   PubMed Web of Science ®Google Scholar
• Mauro, R. (1990). Understanding L.O.V.E. (left out variables error): A method for estimating the effects of omitted variables. Psychological Bulletin, 108, 314–329. doi:10.1037/0033-2909.108.2.314
   Web of Science ®Google Scholar
• Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12, 23–44. doi:10.1037/1082-989X.12.1.23
   PubMed Web of Science ®Google Scholar
• Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46, 816–841. doi:10.1080/00273171.2011.606716
   PubMed Web of Science ®Google Scholar
• McDonald, R. P. (1997). Haldane's lungs: A case study in path analysis. Multivariate Behavioral Research, 32, 1–38. doi:10.1207/s15327906mbr3201_1
   PubMed Web of Science ®Google Scholar
• Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user's guide (7th ed.) [Computer software manual]. Los Angeles, CA: Muthén & Muthén.
   Google Scholar
• Nagengast, B., & Marsh, H. W. (2012). Big fish in little ponds aspire more: Mediation and cross–cultural generalizability of school-average ability effects on self-concept and career aspirations in science. Journal of Educational Psychology, 104, 1033–1053. doi:10.1037/a0027697
   Web of Science ®Google Scholar
• Pearl, J. (2001). Direct and indirect effects. In J. Breese & D. Koller (Eds.), Uncertainty in artificial intelligence, proceedings of the seventeenth conference (pp. 411–420). San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Pearl, J. (2012). The causal mediation formula—a guide to the assessment of pathways and mechanisms. Prevention Science, 13, 426–436. doi:10.1007/s11121-011-0270-1
   PubMed Web of Science ®Google Scholar
• Pituch, K. A., & Stapleton, L. M. (2012). Distinguishing between cross- and cluster-level mediation processes in the cluster randomized trial. Sociological Methods & Research, 41, 630–670. doi:10.1177/0049124112460380
   Web of Science ®Google Scholar
• 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, 879–903. doi:10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. doi:10.1146/annurev-psych-120710-100452
   PubMed Web of Science ®Google Scholar
• Preacher, K. J. (2011). Multilevel SEM strategies for evaluating mediation in three-level data. Multivariate Behavioral Research, 46, 691–731. doi:10.1080/00273171.2011.589280
   PubMed Web of Science ®Google Scholar
• Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825–852. doi:10.1146/annurev-psych-010814-015258
   PubMed Web of Science ®Google Scholar
• Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209–233. doi:10.1037/a0020141
   PubMed Web of Science ®Google Scholar
• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: SAGE.
   Google Scholar
• Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12, 762–800. doi:10.1177/1094428109332834
   Web of Science ®Google Scholar
• Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: SAGE.
   Google Scholar
• Sturgeon, J. A., Zautra, A. J., & Arewasikporn, A. (2014). A multilevel structural equation modeling analysis of vulnerabilities and resilience resources influencing affective adaptation to chronic pain. Pain, 155, 292–298. doi:10.1016/j.pain.2013.10.007
   PubMed Web of Science ®Google Scholar
• Tofighi, D., & MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x
   PubMed Web of Science ®Google Scholar
• Tofighi, D., & Thoemmes, F. (2014). Single-level and multilevel mediation analysis. The Journal of Early Adolescence, 34, 93–119. doi:10.1177/0272431613511331
   Web of Science ®Google Scholar
• Tofighi, D., West, S. G., & MacKinnon, D. P. (2013). Multilevel mediation analysis: The effects of omitted variables in the 1–1–1 model. British Journal of Mathematical and Statistical Psychology, 66, 290–307. doi:10.1111/j.2044-8317.2012.02051.x
   PubMed Web of Science ®Google Scholar
• Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18, 137–150. doi:10.1037/a0031034
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21, 540–551. doi:10.1097/EDE.0b013e3181df191c
   PubMed Web of Science ®Google Scholar
• Wang, M., Liu, S., Liao, H., Gong, Y., Kammeyer-Mueller, J., & Shi, J. (2013). Can't get it out of my mind: Employee rumination after customer mistreatment and negative mood in the next morning. Journal of Applied Psychology, 98, 989–1004. doi:10.1037/a0033656
   PubMed Web of Science ®Google Scholar
• Zhang, Z., Zyphur, M., & Preacher, K. (2009). Testing multilevel mediation using hierarchical linear models. Organizational Research Methods, 2009, 1–25. doi:10.1177/1094428108327450
   Google Scholar