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Research Articles

Latent Curve Detrending for Disaggregating Between-Person Effect and Within-Person Effect

Pages 192-213 | Received 21 Jan 2022, Accepted 19 Apr 2022, Published online: 20 Sep 2022

References

  • Allison, P. D. (2009). Fixed effects regression models. Sage.
  • Asparouhov, T., & Muthén, B. (2019). Latent variable centering of predictors and mediators in multilevel and time-series models. Structural Equation Modeling: A Multidisciplinary Journal, 26, 119–142. https://doi.org/10.1080/10705511.2018.1511375
  • Bainter, S. A., & Howard, A. L. (2016). Comparing within-person effects from multivariate longitudinal models. Developmental Psychology, 52, 1955–1968. https://doi.org/10.1037/dev0000215
  • Baird, R., & Maxwell, S. E. (2016). Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects. Psychological Methods, 21, 175–188. https://doi.org/10.1037/met0000070
  • Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3, 133–153. https://doi.org/10.1017/psrm.2014.7
  • Bollen, K. A., & Brand, J. E. (2010). A general panel model with random and fixed effects: A structural equations approach. Social Forces, 89, 1–34. https://doi.org/10.1353/sof.2010.0072
  • Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Wiley.
  • 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, 333–367. https://doi.org/10.3102/1076998614547576
  • Cronbach, L. J. (1976). Research on classrooms and schools: Formulation of questions, design, and analysis. Stanford University Evaluation Consortium.
  • Croon, M. A., & van Veldhoven, M. J. P. M. (2007). Predicting group-level outcome variables from variables measured at the individual level: A latent variable multilevel model. Psychological Methods, 12, 45–57. https://doi.org/10.1037/1082-989X.12.1.45
  • 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. https://doi.org/10.1146/annurev.psych.093008.100356
  • Curran, P. J., & Bollen, K. A. (2001). The best of both worlds: Combining autoregressive and latent curve models. In L. M. Collins & A. G. Sayer (Eds.), New Methods for the Analysis of Change (pp. 107–135). American Psychological Association.
  • Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82, 879–894. https://doi.org/10.1037/a0035297
  • Curran, P. J., Lee, T., Howard, A. L., Lane, S., & MacCallum, R. (2012). Disaggregating within-person and between-person effects in multilevel and structural equation growth models. In J. R. Harring & G. R. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 217–253). Information Age Publishing.
  • 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. https://doi.org/10.1037/1082-989X.12.2.121
  • Falkenström, F., Finkel, S., Sandell, R., Rubel, J. A., & Holmqvist, R. (2017). Dynamic models of individual change in psychotherapy process research. Journal of Consulting and Clinical Psychology, 85, 537–549. https://doi.org/10.1037/ccp0000203
  • Frisch, R., & Waugh, F. V. (1933). Partial time regressions as compared with individual trends. Econometrica, 1, 387–401. https://doi.org/10.2307/1907330
  • Hallquist, M. N., & Wiley, J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 25, 621–638. https://doi.org/10.1080/10705511.2017.1402334
  • Hamagami, F., & McArdle, J. J. (2001). Advanced studies of individual differences: Linear dynamic models for longitudinal data analysis. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 203–246). Lawrence Erlbaum Associates Publishers.
  • Hamaker, E. L. (2012). Why researchers should think “within-person”: A paradigmatic rationale. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 43–61). The Guilford Press.
  • Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20, 102–116. https://doi.org/10.1037/a0038889
  • Hamaker, E. L., & Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25, 365–379. https://doi.org/10.1037/met0000239
  • Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge.
  • Hoffman, L. (2019). On the interpretation of parameters in multivariate multilevel models across different combinations of model specification and estimation. Advances in Methods and Practices in Psychological Science, 2, 288–311. https://doi.org/10.1177/2515245919842770
  • Hoffman, L., & Stawski, R. S. (2009). Persons as contexts: Evaluating between-person and within-person effects in longitudinal analysis. Research in Human Development, 6, 97–120. https://doi.org/10.1080/15427600902911189
  • Hoffman, L., & Walters, R. W. (2022). Catching up on multilevel modeling. Annual Review of Psychology, 73, 659–689. https://doi.org/10.1146/annurev-psych-020821-103525
  • Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24, 623–641. https://doi.org/10.1177/014920639802400504
  • 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. https://doi.org/10.1207/s15327906mbr3001_1
  • Lovell, M. C. (1963). Seasonal adjustment of economic time series and multiple regression analysis. Journal of the American Statistical Association, 58, 993–1010. https://doi.org/10.1080/01621459.1963.10480682
  • Lüdtke, O., Marsh, H. W., Robitzsch, A., & Trautwein, U. (2011). A 2 × 2 taxonomy of multilevel latent contextual models: Accuracybias trade-offs in full and partial error correction models. Psychological Methods, 16, 444–467. https://doi.org/10.1037/a0024376
  • 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, 203–229. https://doi.org/10.1037/a0012869
  • Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122. https://doi.org/10.1007/BF02294746
  • Murayama, K., Goetz, T., Malmberg, L.-E., Pekrun, R., Tanaka, A., & Martin, A. J. (2017). Within-person analysis in educational psychology: Importance and illustrations. British Journal of Educational Psychology Monograph Series II, 12, 71–87.
  • Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22, 376–398. https://doi.org/10.1177/0049124194022003006
  • Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus users guide (8th ed.). Muthén & Muthén.
  • Podsakoff, N. P., Spoelma, T. M., Chawla, N., & Gabriel, A. S. (2019). What predicts within-person variance in applied psychology constructs? An empirical examination. Journal of Applied Psychology, 104, 727–754. https://doi.org/10.1037/apl0000374
  • Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209–233. https://doi.org/10.1037/a0020141
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.
  • Rights, J. D., Preacher, K. J., & Cole, D. A. (2020). The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects. British Journal of Mathematical and Statistical Psychology, 73, 194–211. https://doi.org/10.1111/bmsp.12194
  • Rüttenauer, T., & Ludwig, V. (2020). Fixed effects individual slopes: Accounting and testing for heterogeneous effects in panel data or other multilevel models. Sociological Methods & Research, Advance online publication. https://doi.org/10.1177/0049124120926211
  • Shin, Y., & Raudenbush, S. W. (2010). A latent cluster-mean approach to the contextual effects model with missing data. Journal of Educational and Behavioral Statistics, 35, 26–53. https://doi.org/10.3102/1076998609345252
  • Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage.
  • Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24, 637–657. https://doi.org/10.1037/met0000210
  • Wang, L. P., & Maxwell, S. E. (2015). On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychological Methods, 20, 63–83. https://doi.org/10.1037/met0000030
  • Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press.
  • Yaremych, H. E., Preacher, K. J., & Hedeker, D. (2021). Centering categorical predictors in multilevel models: Best practices and interpretation. Psychological Methods, Advance Online Publication. https://doi.org/10.1037/met0000434
  • Zitzmann, S. (2018). A computationally more efficient and more accurate stepwise approach for correcting for sampling error and measurement error. Multivariate Behavioral Research, 53, 612–632. https://doi.org/10.1080/00273171.2018.1469086
  • Zitzmann, S., & Helm, C. (2021). Multilevel analysis of mediation, moderation, and nonlinear effects in small samples, using expected a posteriori estimates of factor scores. Structural Equation Modeling: A Multidisciplinary Journal, 28, 529–546. https://doi.org/10.1080/10705511.2020.1855076
  • Zitzmann, S., Helm, C., & Hecht, M. (2020). Prior specification for more stable Bayesian estimation of multilevel latent variable models in small samples: A comparative investigation of two different approaches. Frontiers in Psychology, 11, 611267. https://doi.org/10.3389/fpsyg.2020.611267.
  • Zitzmann, S., Lüdtke, O., & Robitzsch, A. (2015). A Bayesian approach to more stable estimates of group-level effects in contextual studies. Multivariate Behavioral Research, 50, 688–705. https://doi.org/10.1080/00273171.2015.1090899

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