More Precise Estimation of Lower-Level Interaction Effects in Multilevel Models

References

• Allison, P. D. (2015). Don’t Put Lagged Dependent Variables in Mixed Models. retrieved from https://statisticalhorizons.com/lagged-dependent-variables.
   Google Scholar
• Baird, M. & Maxwell, S. (2016). Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects. Psychological Methods, 21(2), 175–188.
   PubMed Web of Science ®Google Scholar
• Bates, M. D., Castellano, K. E., Rabe-Hesketh, S., & Skrondal, A. (2014). Handling correlations between covariates and random slopes in multilevel models. Journal of Educational and Behavioral Statistics, 39(6), 524–549.
   Web of Science ®Google Scholar
• Brumback, B. a., Dailey, A. B., Brumback, L. C., Livingston, M. D., & He, Z. (2010). Adjusting for confounding by cluster using generalized linear mixed models. Statistics and Probability Letters, 80(21-22), 1650–1654.
   Web of Science ®Google Scholar
• Castellano, K. E., Rabe-Hesketh, S., & Skrondal, a. (2014). Composition, context, and endogeneity in school and teacher comparisons. Journal of Educational and Behavioral Statistics, 39(5), 333–367.
   Web of Science ®Google Scholar
• Croissant, Y. & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of Statistical Software, 27(2), 1–43.
   Web of Science ®Google Scholar
• Curran, P. J. & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619.
   PubMed Web of Science ®Google Scholar
• Dewitte, M., Van Lankveld, J., Vandenberghe, S., & Loeys, T. (2015). Sex in its daily relational context. Journal of Sexual Medicine, 12(12), 2436–2450.
   PubMed Web of Science ®Google Scholar
• Dodge, Y. & Rousson, V. (2012). The complications of the fourth central moment. The American Statistician, 1305(May), 2–5.
   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(2), 121–138.
   PubMed Web of Science ®Google Scholar
• Goetgeluk, S. & Vansteelandt, S. (2008). Conditional generalized estimating equations for the analysis of clustered and longitudinal data. Biometrics, 64(3), 772–780.
   PubMed Web of Science ®Google Scholar
• Greenland, S. (2002). A review of multilevel theory for ecologic analyses. Statistics in Medicine, 21, 389–395.
   PubMed Web of Science ®Google Scholar
• Hofmann, D. a. & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623–641.
   Web of Science ®Google Scholar
• Josephy, H., Vansteelandt, S., Vanderhasselt, M.-A., & Loeys, T. (2015). Within-subject mediation analysis in AB/BA crossover designs. The International Journal of Biostatistics, 11(1), 1–22.
   PubMedGoogle 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(30), 1–21.
   PubMedGoogle Scholar
• Loeys, T., Talloen, W., Goubert, L., Moerkerke, B., & Vansteelandt, S. (2016). Assessing moderated mediation in linear models requires fewer confounding assumptions than assessing mediation. British Journal of Mathematical and Statistical Psychology, 69(3), 352–374.
   PubMed Web of Science ®Google Scholar
• Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological methods, 13(3), 203–229.
   PubMed Web of Science ®Google Scholar
• McNeish, D., Stapleton, L., & Silverman, R. (2016). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, page Advance online publication.
   Google Scholar
• Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology—this time forever. Measurement: Interdisciplinary Research & Perspective, 2(4), 201–218.
   Google Scholar
• Molenaar, P. C. M. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18, 112–117.
   Web of Science ®Google Scholar
• Mundlak, Y. (1978). Pooling of time series and cross-section data. Econometrica, 46, 69–86.
   Web of Science ®Google Scholar
• Muthén, B. O. (1990). Mean and Covariance Structure Analysis of Hierarchical Data, volume 62. Los Angeles, CA, ucla stati edition.
   Google Scholar
• Nesselroade, J. R. & Molenaar, P. C. M. (2016). Some behaviorial science measurement concerns and proposals. Multivariate Behavioral Research, 51(2–3), 396–412.
   PubMed Web of Science ®Google Scholar
• Neuhaus, J. & Kalbfleisch, J. (1998). Between- and within-cluster covariate effects in the analysis of clustered data. Biometrics, 54(2), 638–645.
   PubMed Web of Science ®Google Scholar
• Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2016). Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychological Methods, 21(2), 189–205.
   PubMed Web of Science ®Google Scholar
• Raudenbush, S. & Bryk, A. (2002). Hierarchical Linear Models. Applications and Data Analysis Methods. (Second ed.). Thousand Oaks, CA: Sage.
   Google Scholar
• Ryu, E. (2015). The role of centering for interaction of level 1 variables in multilevel structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 22(4), 617–630.
   Web of Science ®Google Scholar
• Wang, L. P. & Maxwell, S. E. (2015). On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychological Methods, 20(1), 63–83.
   PubMed Web of Science ®Google Scholar
• Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. The MIT Press: Cambridge, MA.
   Google Scholar