Rehabilitating the Lagged Dependent Variable With Structural Equation Modeling

References

• Achen, C. H. (2000). Why lagged dependent variables can suppress the explanatory power of other independent variables. Prepared for Annual Meeting of the Political Methodology Section of the APSA. http://www-personal.umich.edu/∼franzese/Achen.2000.LDVstealingExplanPower.pdf
   Google Scholar
• Allison, P. D. (2011). Fixed effects regression models. Sage Publications.
   Google Scholar
• Allison, P. D., Williams, R., & Moral-Benito, E. (2017). Maximum likelihood for cross-lagged panel models with fixed effects. Socius: Sociological Research for a Dynamic World, 3, 1–7. https://doi.org/10.1177/2378023117710578
   Google Scholar
• Altman, C. E. (2015). Age, Period, and Cohort Effects. In F. Bean, S. Brown (Eds.), Encyclopedia of Migration, (pp. 1–4). Springer. https://doi.org/10.1007/978-94-007-6179-7_88-1
   Google Scholar
• Andersen, H. K. (2021). Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models? Investigating the benefits and drawbacks of the latent curve model with structured residuals and the random intercept cross-lagged panel model. Psychological Methods. Advanced online publication. https://doi.org/10.1037/met0000285
   Google Scholar
• Andersen, H. K. (2022). A closer look at random and fixed effects panel regression in structural equation modeling using lavaan. Structural Equation Modeling: A Multidisciplinary Journal, 29, 476–486. https://doi.org/10.1080/10705511.2021.1963255
   Web of Science ®Google Scholar
• Bianconcini, S., & Bollen, K. A. (2018). The latent variable-autoregressive latent trajectory model: A general framework for longitudinal data analysis. Structural Equation Modeling : A Multidisciplinary Journal, 25, 791–808. https://doi.org/10.1080/10705511.2018.1426467.
   PubMed Web of Science ®Google Scholar
• Bock, R. D. (1975). Multivariate statistical methods in behavioral research. McGraw-Hill.
   Google Scholar
• Bollen, K. A., & Brand, J. E. (2010). A general panel model with random and fixed effects: A structural equations approach. Social Forces; A Scientific Medium of Social Study and Interpretation, 89, 1–34. https://doi.org/10.1353/sof.2010.0072.
   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 & Research, 32, 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 perspective. John Wiley; Sons.
   Google Scholar
• Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 301–328). Springer Science + Business Media.
   Google Scholar
• Brüderl, J., & Ludwig, V. (2014). Fixed-effects panel regression. In H. Best & C. Wolf (Eds.), The SAGE handbook of regression analysis and causal inference (pp. 327–358). SAGE Publications Ltd. https://doi.org/10.4135/9781446288146.n15
   Google Scholar
• Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships, 35, 32–58. https://doi.org/10.1177/0265407517718387
   Web of Science ®Google Scholar
• Cinelli, C., Forney, A., & Pearl, J. (2022, June 3). A crash course in good and bad controls. https://www.researchgate.net/publication/340082755_A_Crash_Course_in_Good_and_Bad_Controls.
   Google Scholar
• Coleman, J. S., Hoffer, T., & Kilgore, S. (1982). High school achievement: Public. Catholic, and Private Schools Compared. Basic Books.
   Google Scholar
• Collischon, M., & Eberl, A. (2020). Let’s talk about fixed effects: Let’s talk about all the good things and the bad things. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 72, 289–299. https://doi.org/10.1007/s11577-020-00699-8
   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 of the analysis of change (pp. 107–135). American Psychological Association. https://doi.org/10.1037/10409-004
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Dafoe, A. (2014). Prescriptions for temporal dependence: First do no harm. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2422871
   Google Scholar
• Dafoe, A. (2018). Nonparametric identification of causal effects under temporal dependence. Sociological Methods & Research, 47, 136–168. https://doi.org/10.1177/0049124115613784
   Web of Science ®Google Scholar
• Foster, E. M. (2010). Causal inference and developmental psychology. Developmental Psychology, 46, 1454–1480. https://doi.org/10.1037/a0020204.
   PubMed Web of Science ®Google Scholar
• Gattig, A., & Minkus, L. (2021). Does marriage increase couples’ life satisfaction? Evidence using panel data and fixed-effects individual slopes. Comparative Population Studies, 46, 123–148. https://doi.org/10.12765/CPoS-2021-05
   Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Jamieson, J. (1999). Dealing with baseline differences: Two principles and two dilemmas. International Journal of Psychophysiology, 31, 155–161. https://doi.org/10.1016/S0167-8760(98)00048-8
   PubMed Web of Science ®Google Scholar
• Keele, L., & Kelly, N. J. (2006). Dynamic models for dynamic theories: The ins and outs of lagged dependent variables. Political Analysis, 14, 186–205. https://doi.org/10.1093/pan/mpj006
   Web of Science ®Google Scholar
• Kühnel, S., & Mays, A. (2018). Probleme von Cross-Lagged Panelmodellen Zur Analyse Gegenseitiger Beeinflussung von Einstellung Und Verhalten. In J. Mayerl, T. Krause, A. Wahl, & M. Wuketich (Eds.), Einstellungen Und Verhalten in Der Empirischen Sozialforschung (pp. 359–386). Springer Fachmedien. https://doi.org/10.1007/978-3-658-16348-8_15
   Google Scholar
• Leszczensky, L., & Wolbring, T. (2022). How to deal with reverse causality using panel data? Recommendations for researchers based on a simulation study. Sociological Methods & Research, 51, 837–865. https://doi.org/10.1177/0049124119882473
   Google Scholar
• Lord, F. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304–305. https://doi.org/10.1037/h0025105.
   PubMed Web of Science ®Google Scholar
• Moral-Benito, E., Allison, P., & Williams, R. (2019). Dynamic panel data modelling using maximum likelihood: An alternative to Arellano-Bond. Applied Economics, 51, 2221–2232. https://doi.org/10.1080/00036846.2018.1540854
   Web of Science ®Google Scholar
• Morgan, S. L., & Winship, C. (2014). Counterfactuals and causal inference: Methods and principles for social research. Cambridge University Press. https://doi.org/10.1017/cbo9781107587991
   Google Scholar
• Mouw, T. (2006). Estimating the causal effect of social capital: A review of recent research. Annual Review of Sociology, 32, 79–102. https://doi.org/10.1146/annurev.soc.32.061604.123150
   Web of Science ®Google Scholar
• Mulder, J. D., & Hamaker, E. L. (2021). Three extensions of the random intercept cross-lagged panel model. Structural Equation Modeling: A Multidisciplinary Journal, 28, 638–648. https://doi.org/10.1080/10705511.2020.1784738
   Web of Science ®Google Scholar
• Ou, L., Chow, S.-M., Ji, L., & Molenaar, P. C. M. (2017). (Re)evaluating the implications of the autoregressive latent trajectory model through likelihood ratio tests of its initial conditions. Multivariate Behavioral Research, 52, 178–199. https://doi.org/10.1080/00273171.2016.1259980.
   PubMed Web of Science ®Google Scholar
• Pearl, J. (2016). Lord’s paradox revisited—(Oh Lord! Kumbaya!). Journal of Causal Inference, 4. https://doi.org/10.1515/jci-2016-0021
   Google Scholar
• Rogosa, D. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245–258. https://doi.org/10.1037/0033-2909.88.2.245
   Web of Science ®Google Scholar
• Senn, S. (2006). Change from baseline and analysis of covariance revisited. Statistics in Medicine, 25, 4334–4344. https://doi.org/10.1002/sim.2682.
   PubMed Web of Science ®Google Scholar
• Teachman, J., Duncan, G. J., Yeung, J., & Levy, D. (2001). Covariance structure models for fixed and random effects. Sociological Methods & Research, 30, 271–288. https://doi.org/10.1177/0049124101030002005
   Web of Science ®Google Scholar
• Thompson, A. J., & Pickett, J. T. (2021). Asymmetry in process-based modelrelationships: A longitudinal study of adjudicated adolescents. Criminology, 59, 585–609. https://doi.org/10.1111/1745-9125.12279
   Web of Science ®Google Scholar
• Tu, Y.-K., Gunnell, D., & Gilthorpe, M. S. (2008). Simpson’s paradox, lord’s paradox, and supression effects are the same phenomenon - the reversal paradox. Emerging Themes in Epidemiology, 5. https://doi.org/10.1186/1742-7622-5-2
   Google Scholar
• Vaisey, S., & Miles, A. (2017). What you can—and can’t—do with three-wave panel data. Sociological Methods & Research, 46, 44–67. https://doi.org/10.1177/0049124114547769
   Web of Science ®Google Scholar
• Van Breukelen, G. J. P. (2013). ANCOVA versus CHANGE from baseline in nonrandomized studies: The difference. Multivariate Behavioral Research, 48, 895–922. https://doi.org/10.1080/00273171.2013.831743.
   PubMed Web of Science ®Google Scholar
• Walters, G. D. (2020). Criminal thinking as a moderator of the perceived certaintyoffending relationship: Age variations. Psychology, Crime & Law, 26, 267–286. https://doi.org/10.1080/1068316X.2019.1652749
   Web of Science ®Google Scholar
• Wilkins, A. S. (2018). To lag or not to lag? Re-evaluating the use of lagged dependent variables in regression analysis. Political Science Research and Methods, 6, 393–411. https://doi.org/10.1017/psrm.2017.4
   Web of Science ®Google Scholar
• Williams, R., Allison, P. D., & Moral-Benito, E. (2018). Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling. The Stata Journal: Promoting Communications on Statistics and Stata, 18, 293–326. https://doi.org/10.1177/1536867X1801800201
   Google Scholar
• Wooldridge, J. M. (2012). Introductory econometrics: A modern approach (5th ed.). South-Western.
   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. (2020). From data to causes I: Building a general cross-lagged panel model (GCLM). Organizational Research Methods, 23, 651–687. https://doi.org/10.1177/1094428119847278
   Web of Science ®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. (2020). From data to causes II: Comparing approaches to panel data analysis. Organizational Research Methods, 23, 688–716. https://doi.org/10.1177/1094428119847280
   Web of Science ®Google Scholar