References
- Baker, G. A. (1954). Factor analysis of relative growth. Growth, 18(3), 137–143.
- Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley.
- Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101(1), 147–158. doi:https://doi.org/10.1037/0033-2909.101.1.147
- Cheong, J., MacKinnon, D. P., & Khoo, S. T. (2003). Investigation of meditational processes using parallel process latent growth curve modeling. Structural Equation Modeling: A Multidisciplinary Journal, 10(2), 238–262. doi:https://doi.org/10.1207/S15328007SEM1002_5
- Chou, C.-P., Bentler, P. M., & Pentz, M. A. (1998). Comparisons of two statistical approaches to study growth curves: The multilevel model and the latent curve analysis. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 247–266. doi:https://doi.org/10.1080/10705519809540104
- Crawford, A. (2014). Posterior predictive model checking in Bayesian networks (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. Arizona State University, Tempe, AZ.
- Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. doi:https://doi.org/10.1037/1082-989X.1.1.16
- Depaoli, S. (2010). Specification issues in Bayesian growth mixture modeling (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. University of Wisconsin-Madison, WI.
- Depaoli, S. (2012). The ability of posterior predictive checking to identify model misspecification in Bayesian growth mixture modeling. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 534–560. doi:https://doi.org/10.1080/10705511.2012.713251
- Duncan, S. C., & Duncan, T. E. (1996). A multivariate latent growth curve analysis of adolescent substance use. Structural Equation Modeling: A Multidisciplinary Journal, 3(4), 323–347. doi:https://doi.org/10.1080/10705519609540050
- Gelman, A., Meng, X., & Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistical Sinica, 6, 733–807. Retrieved from www.jstor.org/stable/24306036
- Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov chain Monte Carlo in practice. London, England: Chapman & Hall.
- Gill, J. (2014). Bayesian methods: A social and behavioral sciences approach (3rd ed.). Boca Raton, FL: Chapman & Hall/CRC.
- Goldstein, H. (2003). Multilevel statistical models (3rd ed.). London, England: Arnold.
- Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
- Levy, R. (2011). Bayesian data-model fit assessment for structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 18(4), 663–685. doi:https://doi.org/10.1080/10705511.2011.607723
- Levy, R., Crawford, A. V., Fay, D. M., & Poole, K. L. (2011). Data-model fit assessment for Bayesian networks for simulation-based assessments. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA, April, 2011.
- Levy, R., & Mislevy, R. J. (2016). Bayesian psychometric modeling. Boca Raton, FL: Chapman and Hall/CRC.
- Levy, R., Xu, Y., Yel, N., & Svetina, D. (2015). A standardized generalized dimensionality discrepancy measure and a standardized model-based covariance for dimensionality assessment for multidimensional models. Journal of Educational Measurement, 52(2), 144–158. doi:https://doi.org/10.1111/jedm.12070
- Lin, L. A. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255–268. doi:https://doi.org/10.2307/2532051
- Mäkikangas, A., Bakker, A. B., Aunola, K., & Demerouti, E. (2010). Job resources and flow at work: Modelling the relationship via latent growth curve and mixture model methodology. Journal of Occupational and Organizational Psychology, 83(3), 795–814. doi:https://doi.org/10.1348/096317909X476333
- McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nessroade & R. B. Cattell (Eds.), The handbook of multivariate experimental psychology (2nd ed., pp. 561–614). New York, NY: Plenum Press.
- McArdle, J. J. (1989). A Structural modeling experiment with multiple growth functions. In R. Kanfer, P. L. Ackerman, & R. Cudeck (Eds.), Abilities, motivation, and methodology: The Minneapolis symposium on learning and individual differences (pp. 71–117). Hillsdale, NJ: Lawrence Erlbaum.
- Meng, X.-L. (1994). Posterior predictive p-values. The Annals of Statistics, 22(3), 1142–1160.
- Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55(1), 107–122. doi:https://doi.org/10.1007/BF02294746
- Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55(2), 463–469. doi:https://doi.org/10.1111/j.0006-341X.1999.00463.x
- Nagin, D. (1999). Analyzing development trajectories: A semi-parametric, group-based approach. Psychological Methods, 4(2), 139–177. doi:https://doi.org/10.1037/1082-989X.4.2.139
- Plummer, M. (2013). JAGS Version 3.4.0 user manual. Retrieved from http://www.stats.ox.ac.uk/∼nicholls/MScMCMC14/jags_user_manual.pdf
- R Development Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
- Revuelta, J., & Ximénez, C. (2017). Bayesian dimensionality assessment for the multidimensional nominal response model. Frontiers in Psychology, 8, 961. doi:https://doi.org/10.3389/fpsyg.2017.00961
- Rupp, A. A., Levy, R., DiCerbo, K. E., Sweet, S., Crawford, A. V., Calico, T., … Behrens, J. T. (2012). Putting ECD into practice: The interplay of theory and data in evidence models within a digital learning environment. Journal of Educational Data Mining, 4(1), 49–110. doi:https://doi.org/10.5281/zenodo.3554643
- Satorra, A. (1992). Asymptotic robust inferences in the analysis of mean and covariance structures. Sociological Methodology, 22, 249–278. doi:https://doi.org/10.2307/270998
- Schaeffer, C. M., Petras, H., Ialongo, N., Poduska, J., & Kellam, S. (2003). Modeling growth in boys’ aggressive behavior across elementary school: Links to later criminal involvement, conduct disorder, and antisocial personality disorder. Developmental Psychology, 39(6), 1020–1035. doi:https://doi.org/10.1037/0012-1649.39.6.1020
- Scheines, R., Hoijtink, H., & Boomsma, A. (1999). Bayesian estimation and testing of structural equation models. Psychometrika, 64(1), 37–52. doi:https://doi.org/10.1007/BF02294318
- Shevlin, M., & Millar, R. (2006). Career education: An application of latent curve modeling to career information-seeking behavior of school pupils. British Journal of Educational Psychology, 76(1), 141–153. doi:https://doi.org/10.1348/000709904x22386
- Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.
- Verbeke, G., & Molenberghs, G. (2003). The use of score tests for inference on variance components. Biometrics, 59(2), 254–262. doi:https://doi.org/10.1111/1541-0420.00032
- Vonesh, E. F. (1992). Nonlinear models for the analysis of longitudinal data. Statistics in Medicine, 11(14–15), 1929–1954. doi:https://doi.org/10.1002/sim.4780111413
- Vonesh, E. F., Chinchilli, V. M., & Pu, K. (1996). Goodness-of-fit in generalized nonlinear mixed-effects models. Biometrics, 52(2), 572–587. doi:https://doi.org/10.2307/2532896
- Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates of predictors of individual change over time. Psychological Bulletin, 116(2), 363–381. doi:https://doi.org/10.1037/0033-2909.116.2.363
- Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 58–79. doi:https://doi.org/10.1037/1082-989X.12.1.58
- Wu, W., & West, S. G. (2010). Sensitivity of fit indices to misspecification in growth curve models. Multivariate Behavioral Research, 45(3), 420–452. doi:https://doi.org/10.1080/00273171.2010.483378
- Wu, W., & West, S. G. (2013). Detecting misspecifications in mean structures for growth curve models: Performance of Pseudo R2s and concordance correlation coefficients. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 455–478. doi:https://doi.org/10.1080/10705511.2013.797829
- Wu, W., West, S. G., & Taylor, A. B. (2009). Evaluating model fit for growth curve models: Integration of fit indices from SEM and MLM frameworks. Psychological Methods, 14(3), 183–201. doi:https://doi.org/10.1037/a0015858
- You, S., & Sharkey, J. (2009). Testing developmental-ecological model of student engagement: A multilevel latent growth curve analysis. Educational Psychology, 29(6), 659–684. doi:https://doi.org/10.1080/01443410903206815
- Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes (Unpublished doctoral dissertation). University of California, Los Angeles.