On the Differences between General Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model: Interpretation of Cross-Lagged Parameters and Model Choice

References

• Asparouhov, T., Hamaker, E. L., & Muthen, B. (2018). Dynamic structural equation models. Structural Equation Modeling, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803
   Web of Science ®Google Scholar
• Bianconcini, S., & Bollen, K. A. (2018). The latent variable autoregressive latent trajectory (LV-ALT) Model: A general framework for longitudinal data analysis. Structural Equation Modeling, 25(5), 791–808. https://doi.org/10.1080/10705511.2018.1426467
   PubMed Web of Science ®Google Scholar
• Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models a synthesis of two traditions. Sociological Methods and Research, 32(3), 336–383. https://doi.org/10.1177/0049124103260222
   Web of Science ®Google Scholar
• Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation approach. Hoboken, NJ: Wiley.
   Google Scholar
• Bollmann, G., Rouzinov, S., Berchtold, A., & Rossier, J. (2019). Illustrating instrumental variable regressions using the career adaptability – Job satisfaction relationship. Frontiers in Psychology, 10, 1481. https://doi.org/10.3389/fpsyg.2019.01481
   Google Scholar
• Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2008). Time series analysis: Forecasting and control (4th ed.). Wiley.
   Google Scholar
• Browne, M., & Nesselroade, J. R. (2005). Representing psychological processes with dynamic factor models: Some promepsilong uses and extensions of autoregressive moving average time series models. In A. Madeau & J. J. McArdle (Eds.), Advances in psychometrics: A festschrift for R. P. McDonald (pp. 415–452). Erlbaum.
   Google Scholar
• Choi, K., Forster, J., Erickson, D., Lazovich, D., & Southwell, B. G. (2012). The reciprocal relationships between changes in adolescent perceived prevalence of smoking in movies and progression of smoking status. Tobacco Control, 21, 492– 496. https://doi.org/10.1136/tc.2011.044099
   PubMed Web of Science ®Google Scholar
• 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. 105–136). American Psychological Association.
   Google Scholar
• 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). Erlbaum Associations.
   Google Scholar
• Hamaker, E. L. (2005). Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances. Sociological Methods and Research, 33(3), 404–416. https://doi.org/10.1177/0049124104270220
   Web of Science ®Google Scholar
• Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-laggedpanel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
   PubMed Web of Science ®Google Scholar
• Hong, G. (2015). Causality in a social world: Moderation, mediation and spill-over. Wiley.
   Google Scholar
• Imai, K., & Ratkovic, M. (2015). Robust estimation of inverse probability weights for marginal structural models. Journal of the American Statistical Association, 110(511), 1013–1023. https://doi.org/10.1080/01621459.2014.956872
   Web of Science ®Google Scholar
• Kenny, D. A., & Zautra, A. (2001). Trait-state models for longitudinal data. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 243–263)). American Psychological Association.
   Google Scholar
• Kenny, D. A., & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63(1), 52–59. https://doi.org/10.1037/0022-006X.63.1.52
   PubMed Web of Science ®Google Scholar
• Lefebvre, G., Delaney, J. A. C., & Platt, R. W. (2008). Impact of mis-specification of the treatment model on estimates from a marginal structural model. Statistics in Medicine, 27(18), 3629–3642. https://doi.org/10.1002/sim.3200
   PubMed Web of Science ®Google Scholar
• Lüdtke, O., Robitzsch, A., & Wagner, J. (2018). More stable estimation of the STARTS model: A bayesian approach using Markov Chain Monte Carlo techniques. Psychological Methods, 23(3), 570–593. https://doi.org/http://dx.doi.10.1037/met0000155
   PubMed Web of Science ®Google Scholar
• McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 137–175). American Psychological Association.
   Google Scholar
• McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605. https://doi.org/10.1146/annurev.psych.60.110707.163612
   PubMed Web of Science ®Google Scholar
• McArdle, J. J., & Nesselroade, J. R. (2014). Longitudinal data analysis using structural equation models. American Psychological Association.
   Google Scholar
• Meredith, W., & Tisak, J. (1984). On “Tuckerizing” curves. Presented at the annual meeting of the psychometric society.
   Google Scholar
• Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55(1), 107–122. https://doi.org/10.1007/BF02294746
   Web of Science ®Google Scholar
• Orth, U. D., Clark, A. M., Donnellan, B., & Robins, R. W. (in press). Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. Journal of Personality and Social Psychology. http://dx.doi.org/10.1037/pspp0000358
   Web of Science ®Google Scholar
• Oswald, F. L. (2019). Measuring and modeling cognitive ability: Some comments on process overlap theory. Journal of Applied Research in Memory and Cognition, 8(3), 296–300. https://doi.org/10.1016/j.jarmac.2019.06.009
   Web of Science ®Google Scholar
• Robins, J. (1999). Marginal structural models versus structural nested models as tools for causal inference. In M. E. Halloran & D. Berry (Eds.), Statistical models in epidemiology: The environment and clinical trials (pp. 95–134). Springer.
   Google Scholar
• Robins, J. M. (1994). Correcting for non-compliance in randomized trials using structural nested mean models. Communications in Statistics, 23(8), 2379–2412. https://doi.org/10.1080/03610929408831393
   Google Scholar
• Robins, J. M. (1998). Marginal structural models. In 1997 proceedings of the section on bayesian statistical science, Alexandria, VA: American Statistical Association, 1–10.
   Google Scholar
• Robins, J. M., & Hernán, M. A. (2009). Estimation of the causal effect of time-varying exposures. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Longitudinal data analysis, (pp. 553–599). CRC Press.
   Google Scholar
• Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560. https://doi.org/10.1097/00001648-200009000-00011
   PubMed Web of Science ®Google Scholar
• Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
   Web of Science ®Google Scholar
• Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701. https://doi.org/10.1037/h0037350
   Web of Science ®Google Scholar
• Schisterman, E. F., Cole, S. R., & Platt, R. W. (2009). Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology, 20(4), 488–495. https://doi.org/10.1097/EDE.0b013e3181a819a1
   PubMed Web of Science ®Google Scholar
• Usami, S. (2020). Within-person variability score-based causal inference: A two-step semiparametric estimation for joint effects of time-varying treatments. arXiv:2007.03973 [stat.ME].
   Google Scholar
• Usami, S., Hayes, T., & McArdle, J. J. (2015). On the mathematical relationship between latent change score model and autoregressive cross-lagged factor approaches: Cautions for inferring causal relationship between variables. Multivariate Behavioral Research, 50(6), 676–687. https://doi.org/10.1080/00273171.2015.1079696
   PubMed Web of Science ®Google Scholar
• Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24(5), 637–657. https://doi.org/10.1037/met0000210
   PubMed Web of Science ®Google Scholar
• Usami, S., Todo, N., & Murayama, K. (2019). Modeling reciprocal effects in medical research: Critical discussion on the current practices and potential alternative models. Plos One, 14(9), e0209133. https://doi.org/10.1371/journal.pone.0209133
   PubMed Web of Science ®Google Scholar
• Vansteelandt, S., & Joffe, M. (2014). Structural nested models and g-estimation: The partially realized promise. Statistical Science, 29(4), 707–731. https://doi.org/10.1214/14-STS493
   Web of Science ®Google Scholar
• Wallace, M. P., Moodie, E. E., & Stephens, D. A. (2017). An R package for G-estimation of structural nested mean models. Epidemiology, 28(2), e18–e20. https://doi.org/10.1097/EDE.0000000000000586
   PubMed Web of Science ®Google Scholar
• Zhang, Y., Liu, X., & Chen, W. (2019). Fight and flight: A contingency model of third parties’ approach-avoidance reactions to peer abusive supervision. Journal of Business and Psychology. https://doi.org/10.1007/s10869-019-09650-x
   Web of Science ®Google Scholar
• Zyphur, M. J., Allison, P. D., Tay, L., Voelkle, M. C., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P., & Diener, E. (2020a). From data to causes I: Building a general cross-lagged panel model (GCLM). Organizational Research Methods, 23(4), 651–687. https://doi.org/10.1177/1094428119847278
   Google Scholar
• Zyphur, M. J., Voelkle, M. C., Tay, L., Allison, P. D., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P., & Diener, E. (2020b). From data to causes II: Comparing approaches to panel data analysis. Organizational Research Methods, 23(4), 688–716. https://doi.org/10.1177/1094428119847280
   Google Scholar