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Original Articles

Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models

References

  • Allison, P. D. (2005). Fixed effects regression methods for longitudinal data using SAS (1st ed.). Cary, NC: SAS Publishing.
  • Alvarez, A. N., & Juang, L. P. (2010). Filipino Americans and racism: A multiple mediation model of coping. Journal of Counseling Psychology, 57, 167–178. doi:10.1037/a0019091
  • Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48, 5–37. doi:10.1016/j.jsp.2009.10.001
  • Bartholomew, D. J., Knott, M., & Moustaki, I. (2011). Latent variable models and factor analysis: A unified approach (3rd ed.). Chichester, UK: Wiley.
  • Bind, M.-A. C., VanderWeele, T. J., Coull, B. A., & Schwartz, J. D. (2016). Causal mediation analysis for longitudinal data with exogenous exposure. Biostatistics, 17, 122–134. doi:10.1093/biostatistics/kxv029
  • Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods & Research, 29(4), 468-508. doi:10.1177/0049124101029004003
  • Chen, Z., & Wang, H. (2017). Abusive supervision and employees’ job performance: A multiple mediation model. Social Behavior and Personality, 45(5), 845–858. doi:10.2224/sbp.5657
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  • Cole, D. A., Ciesla, J. A., & Steiger, J. H. (2007). The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis. Psychological Methods, 12, 381–398. doi:10.1037/1082-989X.12.4.381
  • 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
  • Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38, 529–569. doi:10.1207/s15327906mbr3804_5
  • Daniel, R. M., De Stavola, B. L., Cousens, S. N., & Vansteelandt, S. (2015). Causal mediation analysis with multiple mediators. Biometrics, 71(1), 1–14. doi:10.1111/biom.12248
  • Demiray, B., & Janssen, S. M. J. (2015). The self‐enhancement function of autobiographical memory. Applied Cognitive Psychology, 29, 49–60. doi:10.1002/acp.3074
  • Dillon, W. R., Kumar, A., & Mulani, N. (1987). Offending estimates in covariance structure analysis: Comments on the causes of and solutions to Heywood cases. Psychological Bulletin, 101, 126–135. doi:10.1037/0033–2909.101.1.126
  • Flannery, B. A., Volpicelli, J. R., & Pettinati, H. M. (1999). Psychometric properties of the Penn Alcohol Craving Scale. Alcoholism: Clinical and Experimental Research, 23(8), 1289–1295. doi:10.1111/acer.1999.23.issue-8
  • Fritz, M. S., Kenny, D. A., & MacKinnon, D. P. (2016). The combined effects of measurement error and omitting confounders in the single-mediator model. Multivariate Behavioral Research, 51(5), 681–697. doi:10.1080/00273171.1224154.2016
  • Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233–239. doi:10.1111/j.1467-9280.01882.x.2007
  • Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. New York, NY: Guilford Press.
  • Hallgren, K. A., Wilson, A. D., & Witkiewitz, K. (2018). Advancing analytic approaches to address key questions in mechanisms of behavior change research. Journal of Studies on Alcohol and Drugs, 79, 182–189. doi:10.15288/jsad.79.1822018.
  • Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods, 22, 616–631. doi:10.1037/met0000103
  • Holland, P. W. (1988). Causal Inference, path analysis, and recursive structural equations models. Sociological Methodology, 18, 449–484. doi:10.2307/271055
  • Hoyle, R. H. (Ed). (2012). Handbook of structural equation modeling. New York, NY: Guilford Press.
  • Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334. doi:10.1037/a0020761
  • Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25, 51–71. doi:10.1214/10-STS321
  • Imai, K., & Yamamoto, T. (2013). Identification and sensitivity analysis for multiple causal mechanisms: Revisiting evidence from framing experiments. Political Analysis, 21, 141–171. doi:10.1093/pan/mps040
  • 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
  • Judd, C. M., & Kenny, D. A. (1981). Process analysis. Evaluation Review, 5, 602–619. doi:10.1177/0193841X8100500502
  • Kenny, D. A. (2004). Correlation and causality. New York, NY: Wiley. Retrieved from http://davidakenny.net/doc/cc_v1.pdf.
  • Kenny, D. A., & Milan, S. (2012). Identification: A non-technical discussion of a technical issue. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 145–163). New York, NY: Guilford Press.
  • Kershaw, K. N., Mezuk, B., Abdou, C. M., Rafferty, J. A., & Jackson, J. S. (2010). Socioeconomic position, health behaviors, and C-reactive protein: A moderated-mediation analysis. Health Psychology, 29, 307–316. doi:10.1037/a0019286
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford.
  • Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification?Sociological Methods & Research, 41(1), 124–167. doi:10.1177/0049124112442138
  • Kranzler, H. R., Armeli, S., Covault, J., & Tennen, H. (2013). Variation in OPRM1 moderates the effect of desire to drink on subsequent drinking and its attenuation by naltrexone treatment: Naltrexone pharmacogenetics. Addiction Biology, 18, 193–201. doi:10.1111/j.1369-1600.00471.x2012.
  • 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
  • Leung, C.-K., & Lam, K. (1975). A note on the geometric representation of the correlation coefficients. The American Statistician, 29, 128–130. doi:10.2307/2683440
  • Ma, Y., Cheng, W., Ribbens, B. A., & Zhou, J. (2013). Linking ethical leadership to employee creativity: Knowledge sharing and self efficacy as mediators. Social Behavior and Personality, 41, 1409–1419. doi:10.2224/sbp.41.9.14092013.
  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Erlbaum.
  • MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. doi:10.1146/annurev085542.psych.58.110405.
  • 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
  • Mann, K., Roos, C. R., Hoffmann, S., Nakovics, H., Leménager, T., Heinz, A., & Witkiewitz, K. (2018). Precision medicine in alcohol dependence: A controlled trial testing pharmacotherapy response among reward and relief drinking phenotypes. Neuropsychopharmacology, 43, 891–899. doi:10.1038/npp.2822017
  • McNeish, D., & Kelley, K. (2018). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods. doi:10.1037/met0000182
  • Miller, W. R. (1996). Form 90: A structured assessment interview for drinking and related behaviors. In M. E. Mattson (Ed.), NIAAA Project MATCH Monograph Series (Vol. 5). Bethesda, MD: U.S. Department of Health and Human Services.
  • Muthén, B., & Asparouhov, T. (2015). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modeling, 22, 12–23. doi:10.1080/10705511.9358432014
  • Muthén, L. K., & Muthén, B. O. (2017). Mplus User’s Guide (Version 8). Los Angeles, CA: Muthén & Muthén.
  • Pearl, J. (2011). The mediation formula: A guide to the assessment of causal pathways in nonlinear models. In C. Berzuini, P. Dawid, & L. Bernardinell (Eds.), Causality: Statistical Perspectives and Applications (pp. 151–179). Hoboken, NJ: Wiley.
  • Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19, 459–481. doi:10.1037/a0036434
  • 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
  • Priesemuth, M., Schminke, M., Ambrose, M. L., & Folger, R. (2014). Abusive supervision climate: A multiple-mediation model of its impact on group outcomes. Academy of Management Journal, 57, 1513–1534. doi:10.5465/amj.2011.0237
  • R Development Core Team. (2017). R: A Language and Environment for Statistical Computing (Version 3.4.3). Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
  • Rindskopf, D. (1983). Parameterizing inequality constraints on unique variances in linear structural models. Psychometrika, 48(1), 73–83. doi:10.1007/BF02314677
  • Rindskopf, D. (1984). Structural equation models: Empirical identification, Heywood cases, and related problems. Sociological Methods & Research, 13, 109–119. doi:10.1177/0049124184013001004.
  • Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155. doi:10.1097/00001648-199203000-00013
  • Rousseeuw, P. J., & Molenberghs, G. (1994). The shape of correlation matrices. The American Statistician, 48, 276–279. doi:10.2307/2684832.
  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701. doi:10.1037/h0037350.
  • Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6, 34–58. doi:10.1214/aos/1176344064
  • Schacht, J. P., Randall, P. K., Latham, P. K., Voronin, K. E., Book, S. W., Myrick, H., & Anton, R. F. (2017). Predictors of naltrexone response in a randomized trial: Reward-related rrain activation, OPRM1 genotype, and smoking status. Neuropsychopharmacology, 42(13), 2640–2653. doi:10.1038/npp74.2017.
  • Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144–164. doi:10.1080/15427600902911247.
  • Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis modeling change and event occurrence. Oxford, MA: Oxford University Press.
  • Snijders, T. A. B., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: SAGE.
  • Strelan, P., Karremans, J. C., & Krieg, J. (2017). What determines forgiveness in close relationships? The role of post‐transgression trust. British Journal of Social Psychology, 56, 161–180. doi:10.1111/bjso.12173
  • Talloen, W., Moerkerke, B., Loeys, T., De Naeghel, J., Van Keer, H., & Vansteelandt, S. (2016). Estimation of indirect effects in the presence of unmeasured confounding for the mediator–Outcome relationship in a multilevel 2-1-1 mediation model. Journal of Educational and Behavioral Statistics, 41, 359–391. doi:10.3102/1076998616636855.
  • Tofighi, D., & Kelley, K. (2016). Assessing omitted confounder bias in multilevel mediation models. Multivariate Behavioral Research, 51, 86–105. doi:10.1080/00273171.11057362015
  • 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
  • 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
  • Valente, M. J., Pelham, W. E. I., Smyth, H., & MacKinnon, D. P. (2017). Confounding in statistical mediation analysis: What it is and how to address it. Journal of Counseling Psychology, 64(6), 659–671. doi:10.1037/cou0000242
  • VanderWeele, T. J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21, 540–551. doi:10.1097/EDE.0b013e3181df191c
  • VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. New York, NY: Oxford University Press.
  • Villegas-Gold, R., & Yoo, H. C. (2014). Coping with discrimination among Mexican American college students. Journal of Counseling Psychology, 61(3), 404–413. doi:10.1037/a0036591.
  • Von Soest, T., & Hagtvet, K. A. (2011). Mediation analysis in a latent growth curve modeling framework. Structural Equation Modeling: A Multidisciplinary Journal, 18, 289–314. doi:10.1080/10705511.5573442011
  • West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Thousand Oaks, CA, US: Sage.
  • Willett, J. B., & Bub, K. L. (2005). Structural equation modeling: Latent growth curve analysis. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of Statistics in Behavioral Science. Hoboken, NJ: John Wiley & Sons. doi:10.1002/0470013192.bsa599