References
- Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling: A Multidisciplinary Journal, 11(2), 272–300. https://doi.org/10.1207/s15328007sem1102_8
- Cham, H., Reshetnyak, E., Rosenfeld, B., & Breitbart, W. (2016). Full information maximum likelihood estimation for latent variable interactions with incomplete indicators. Multivariate Behavioral Research, 52(1), 12–30. https://doi.org/10.1080/00273171.2016.1245600
- Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling: A Multidisciplinary Journal, 8(1), 128–141. https://doi.org/10.1207/S15328007SEM0801_7
- Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 8(3), 430–457. https://doi.org/10.1207/S15328007SEM0803_5
- Evermann, J. (2010). Multiple-group analysis using the sem package in the R system. Structural Equation Modeling: A Multidisciplinary Journal, 17(4), 677–702. https://doi.org/10.1080/10705511.2010.510070
- Ferrer, E., Balluerka, N., & Widaman, K. F. (2008). Factorial invariance and the specification of second-order latent growth models. Methodology, 4(1), 22–36. https://doi.org/10.1027/1614-2241.4.1.22
- Ferrer, E., & Nesselroade, J. R. (2003). Modeling affective processes in dyadic relations via dynamic factor analysis. Emotion, 3(4), 344–360. https://doi.org/10.1037/1528-3542.3.4.344
- Finkbeiner, C. (1979). Estimation for the multiple factor model when data are missing. Psychometrika, 44(4), 409–420. https://doi.org/10.1007/BF02296204
- Gonzales, J. E., & Ferrer, E. (2016). Latent growth modeling for developmental research. In S. K. Whitbourne (Ed.), The encyclopedia of adulthood and aging (pp. 1–6). Wiley-Blackwell. https://doi.org/10.1002/9781118521373.wbeaa219
- Gonzales, J. E. (2020, August 1). JMP® Pro SEM review. https://doi.org/10.17605/OSF.IO/HDPF3
- Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 10(1), 80–100. https://doi.org/10.1207/S15328007SEM1001_4
- Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820–841. https://doi.org/10.1080/00273171.2018.1446849
- Lubke, G., & Muthén, B. (2005). Investigation population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21–39. https://doi.org/10.1037/1082-989X.10.1.21
- Lubke, G., & Muthén, B. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14(1), 26–47. https://doi.org/10.1207/s15328007sem1401_2
- 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. https://doi.org/10.1037/a0012869
- McArdle, J. J. (1988). Dynamic but structural equation modelling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), The handbook of multivariate experimental psychology (2nd ed., pp. 561–614). Plenum Press.
- McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model for moment structures. British Journal of Mathematical and Statistical Psychology, 37(2), 234–251. https://doi.org/10.1111/j.2044-8317.1984.tb00802.x
- Meredith, W., & Horn, J. L. (2001). The role of factorial invariance in modeling growth and change. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 203–240). American Psychological Association.
- Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50(2), 181–202. https://doi.org/10.1007/BF02294246
- Newman, D. A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6(3), 328–362. https://doi.org/10.1177/1094428103254673
- Ram, N., & Grimm, K. J. (2009). Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565–576. https://doi.org/10.1177/0165025409343765
- R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/
- Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315
- Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66(2), 201–223. https://doi.org/10.1111/j.2044-8317.2012.02049.x
- Selig, J. P., & Little, T. D. (2012). Autoregressive and cross-lagged panel analysis for longitudinal data. In B. Laursen, T. D. Little, & N. A. Card (Eds.), Handbook for developmental research methods (pp. 265–278). The Guilford Press.