References
- Almeida, D. M., Piazza, J. R., & Stawski, R. S. (2009). Interindividual differences and intraindividual variability in the cortisol awakening response: An examination of age and gender. Psychology and Aging, 24, 819–827. https://doi.org/https://doi.org/10.1037/a0017910
- Alvarez, I., Niemi, J., & Simpson, M. (2014). Bayesian inference for a covariance matrix. arXiv preprint arXiv:1408.4050.
- Asparouhov, T., Hamaker, E. L., & Muthén, B. O. (2018). Dynamic structural equation models. Structural Equation Modeling, 25, 359–388. https://doi.org/https://doi.org/10.1080/10705511.2017.1406803
- Asparouhov, T., & Muthén, B. O. (2010). Plausible values for latent variables using Mplus. [Unpublished]. http://www.statmodel.com/download/Plausible.pdf.
- Beck, E. D., & Jackson, J. J. (2020). Consistency and change in idiographic personality: A longitudinal ESM network study. Journal of Personality and Social Psychology, 118, 1080. https://doi.org/https://doi.org/10.1037/pspp0000249
- Betancourt, M. (2016). Diagnosing suboptimal cotangent disintegrations in Hamiltonian Monte Carlo. arXiv preprint arXiv:1604.00695.
- Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434.
- Bradburn, N. M. (1969). The structure of psychological well-being. Aldine.
- Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53, 293–314. https://doi.org/https://doi.org/10.1080/00273171.2018.1439722
- Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE, 8, e60188. https://doi.org/https://doi.org/10.1371/journal.pone.0060188
- Brose, A., Schmiedek, F., Koval, P., & Kuppens, P. (2015). Emotional inertia contributes to depressive symptoms beyond perseverative thinking. Cognition & Emotion, 29, 527–538. https://doi.org/https://doi.org/10.1080/02699931.2014.916252
- Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods. Sage Publications, Inc.
- Candel, M. J. (2004). Performance of empirical bayes estimators of random coefficients in multilevel analysis: Some results for the random intercept-only model. Statistica Neerlandica, 58, 197–219. https://doi.org/https://doi.org/10.1046/j.0039-0402.2003.00256.x
- Canova, F. (2011). Methods for applied macroeconomic research. Princeton university press.
- Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker M., Guo J., Li P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76. https://doi.org/https://doi.org/10.18637/jss.v076.i01
- Chow, S.-M., Haltigan, J. D., & Messinger, D. S. (2010a). Dynamic infant-parent affect coupling during the face-to-face/still-face. Emotion, 10, 101–114.
- Chow, S.-M., Haltigan, J. D., & Messinger, D. S. (2010b). Dynamic patterns of infant-parent interactions during face-to-face and still-face episodes. Emotion, 10, 101–114. https://doi.org/https://doi.org/10.1037/a0017824
- Chow, S.-M., Tang, N., Yuan, Y., Song, X., & Zhu, H. (2011). Bayesian estimation of semiparametric dynamic latent variable models using the Dirichlet process prior. British Journal of Mathematical and Statistical Psychology, 64, 69–106. https://doi.org/https://doi.org/10.1348/000711010X497262
- Chow, S.-M., & Zhang, G. (2013). Nonlinear regime-switching state-space (RSSS) models. Psychometrika: Application Reviews and Case Studies, 78, 740–768. https://doi.org/https://doi.org/10.1007/s11336-013-9330-8
- Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396. https://doi.org/https://doi.org/10.2307/2136404
- Costa, P. T., Jr., McCrae, R. R., & Dye, D. A. (1991). Facet scales for agreeableness and conscientiousness: A revision of the NEO personality inventory. Personality and Individual Differences, 12, 887–898. https://doi.org/https://doi.org/10.1016/0191-8869(91)90177-D
- Daniels, M. J., & Hogan, J. W. (2008). Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. Chapman and Hall.
- Denwood, M. J. (2016). Runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software, 71, 1–25. https://doi.org/https://doi.org/10.18637/jss.v071.i09
- Diener, E., & Emmons, R. A. (1984). The independence of positive and negative affect. Journal of Personality and Social Psychology, 47, 1105. https://doi.org/https://doi.org/10.1037/0022-3514.47.5.1105
- Diggle, P., & Kenward, M. G. (1994). Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 43, 49–73.
- Du Toit, S. H., & Browne, M. W. (2007). Structural equation modeling of multivariate time series. Multivariate Behavioral Research, 42, 67–101. https://doi.org/https://doi.org/10.1080/00273170701340953
- Egloff, B. (1998). The independence of positive and negative affect depends on the affect measure. Personality and Individual Differences, 25, 1101–1109. https://doi.org/https://doi.org/10.1016/S0191-8869(98)00105-6
- Emotions and Dynamic Systems Laboratory. (2010). The affective dynamics and individual differences (ADID) study: Developing non-stationary and network-based methods for modeling the perception and physiology of emotions. [Unpublished manual]. University of North Carolina at Chapel Hill.
- Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53, 453–480. https://doi.org/https://doi.org/10.1080/00273171.2018.1454823
- Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10, 507–521.
- Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310–319. https://doi.org/https://doi.org/10.1016/j.neuroimage.2012.06.026
- Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1, 515–534. https://doi.org/https://doi.org/10.1214/06-BA117A
- Gelman, A., Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–472. https://doi.org/https://doi.org/10.1214/ss/1177011136
- Goldstein, M. D., & Strube, M. J. (1994). Independence revisited: The relation between positive and negative affect in a naturalistic setting. Personality & Social Psychology Bulletin, 20, 57–64. https://doi.org/https://doi.org/10.1177/0146167294201005
- Hallquist, M. N., & Wiley, J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling, 25, 621–638. https://doi.org/https://doi.org/10.1080/10705511.2017.1402334
- Hamaker, E., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. O. (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, 820–841. https://doi.org/https://doi.org/10.1080/00273171.2018.1446819
- Hamaker, E., Asparouhov, T., & Muthén, B. O. (2017). Dynamic structural equation modeling of intensive longitudinal data using Mplus version 8 (parts 1 and 2). In Workshop at Johns Hopkins University.
- Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton University Press.
- Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter. Cambridge university press.
- Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109. https://doi.org/https://doi.org/10.1093/biomet/57.1.97
- Hedeker, D., & Mermelstein, R. J. (2007). Mixed-effects regression models with heterogeneous variance: Analyzing ecological momentary assessment (EMA) data of smoking. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 183–206). Lawrence Erlbaum Associates Publishers.
- Hedeker, D., Mermelstein, R. J., Berbaum, M. L., & Campbell, R. T. (2009). Modeling mood variation associated with smoking: An application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data. Addiction, 104, 297–307. https://doi.org/https://doi.org/10.1111/j.1360-0443.2008.02435.x
- Huber, F., & Feldkircher, M. (2019). Adaptive shrinkage in bayesian vector autoregressive models. Journal of Business and Economic Statistics, 37, 27–39. https://doi.org/https://doi.org/10.1080/07350015.2016.1256217
- Ji, L., Chen, M., Oravecz, Z., Cummings, E. M., Lu, Z.-H., & Chow, S.-M. (2020). A Bayesian vector autoregressive model with nonignorable missingness in dependent variables and covariates: Development, evaluation, and application to family processes. Structural Equation Modeling, 27, 442–467. https://doi.org/https://doi.org/10.1080/10705511.2019.1623681
- Kernighan, B. W., & Ritchie, D. M. (2006). The C programming language. Prentice-Hall, Englewood Cliffs, NJ.
- Korner-Nievergelt, F., Roth, T., Von Felten, S., Guélat, J., Almasi, B., & Korner-Nievergelt, P. (2015). Bayesian data analysis in ecology using linear models with R, BUGS, and Stan. Academic Press.
- Koval, P., Kuppens, P., Allen, N. B., & Sheeber, L. (2012). Getting stuck in depression: The roles of rumination and emotional inertia. Cognition & Emotion, 26, 1412–1427. https://doi.org/https://doi.org/10.1080/02699931.2012.667392
- Kruschke, J. (2014). Doing bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
- Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21, 984–991. https://doi.org/https://doi.org/10.1177/0956797610372634
- Lee, S.-Y., & Song, X.-Y. (2004). Evaluation of the Bayesian and maximum likelihood approaches in analyzing structural equation models with small sample sizes. Multivariate Behavioral Research, 39, 653–686. https://doi.org/https://doi.org/10.1207/s15327906mbr3904_4
- Lee, S.-Y., & Tang, N.-S. (2006). Analysis of nonlinear structural equation models with nonignorable missing covariates and ordered categorical data. Statistica Sinica, 16, 1117–1141.
- Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100, 1989–2001. https://doi.org/https://doi.org/10.1016/j.jmva.2009.04.008.
- Lipsitz, S. R., & Ibrahim, J. G. (1996). A conditional model for incomplete covariates in parametric regression models. Biometrika, 83, 916–922. https://doi.org/https://doi.org/10.1093/biomet/83.4.916.
- Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. Wiley.
- Li, Y., Ji, L., Oravecz, Z., Brick, T.R., Hunter, M.D., & Chow, S-M. (2019). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 13(5), 302–311.
- Lucas, R. E., Le, K., & Dyrenforth, P. S. (2008). Explaining the extraversion/positive affect relation: Sociability cannot account for extraverts’ greater happiness. Journal of Personality, 76, 385–414. https://doi.org/https://doi.org/10.1111/j.1467-6494.2008.00490.x
- Lütkepohl, H. (2005). Introduction to multiple time series analysis (2nd ed.). Springer-Verlag.
- McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.
- Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087–1092. https://doi.org/https://doi.org/10.1063/1.1699114
- Monnahan, C. C., Thorson, J. T., & Branch, T. A. (2017). Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods in Ecology and Evolution, 8, 339–348. https://doi.org/https://doi.org/10.1111/2041-210X.12681
- Muthén, B. O., Asparouhov, T., Hunter, A. M., & Leuchter, A. F. (2011). Growth modeling with nonignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17. https://doi.org/https://doi.org/10.1037/a0022634
- Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus user’s guide (8th ed.). Los Angeles, CA: Muthén & Muthén.
- Neal, R. M. (2003). Slice sampling. Annals of Statistics, 31, 705–741. https://doi.org/https://doi.org/10.1214/aos/1056562461
- Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, 2, 2.
- Oravecz, Z., & Muth, C. (2018). Fitting growth curve models in the Bayesian framework. Psychonomic Bulletin & Review, 25, 235–255. https://doi.org/https://doi.org/10.3758/s13423-017-1281-0
- Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2011). A hierarchical latent stochastic differential equation model for affective dynamics. Psychological Methods, 16, 468. https://doi.org/https://doi.org/10.1037/a0024375
- Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2016). Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behavioral Research, 51, 106–119. https://doi.org/https://doi.org/10.1080/00273171.2015.1110512
- Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing (Vol. 124, pp. 1–10).
- Plummer, M., Stukalov, A., & Denwood, M. (2019). rjags: Bayesian graphical models using MCMC. R package version 4-10.
- Raghunathan, T., Lepkowski, J., van Hoewyk, J., & Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27, 85–95.
- Robert, C., & Casella, G. (2013). Monte Carlo statistical methods. Springer Science & Business Media.
- Schuurman, N. K. (2018). Intensive longitudinal data analysis and DSEM. Invited workshop at the Tilburg Experience Sampling Center.
- Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21, 206. https://doi.org/https://doi.org/10.1037/met0000062
- Schuurman, N. K., Grasman, R., & Hamaker, E. (2016). A comparison of inverse-wishart prior specifications for covariance matrices in multilevel autoregressive models. Multivariate Behavioral Research, 51, 185–206. https://doi.org/https://doi.org/10.1080/00273171.2015.1065398
- Snijders, T. A., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.
- Song, H., & Zhang, Z. (2014). Analyzing multiple multivariate time series data using multilevel dynamic factor models. Multivariate Behavioral Research, 49, 67–77. PMID: 26745674. https://doi.org/https://doi.org/10.1080/00273171.2013.851018
- Sorensen, T., & Vasishth, S. (2015). Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists. arXiv preprint arXiv:1506.06201.
- Stan Development Team. (2018). Stan modeling language users guide and reference manual. Version 2.18.0. http://mc-stan.org/
- Stan Development Team. (2020). RStan: The R interface to Stan. http://mc-stan.org/(R package version 2.19.3)
- Su, Y.-S., & Yajima, M. (2020). R2jags: Using R to run JAGS. R Package Version 0.6-1.
- Tang, N., Chow, S.-M., Ibrahim, J. G., & Zhu, H. (2017). Bayesian sensitivity analysis of a nonlinear dynamic factor analysis model with nonparametric prior and possible nonignorable missingness. Psychometrika, 82, 875–903. https://doi.org/https://doi.org/10.1007/s11336-017-9587-4 Retrieved from
- Van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods and Medical Research, 16, 219–242. https://doi.org/https://doi.org/10.1177/0962280206074463
- Van Buuren, S., Brand, J., Groothius-Oudshoorn, C., & Rubin, D. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76, 1049–1064. https://doi.org/https://doi.org/10.1080/10629360600810434
- Wabersich, D., & Vandekerckhove, J. (2014). Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example). Behavior Research Methods, 46, 15–28. https://doi.org/https://doi.org/10.3758/s13428-013-0369-3
- Wang, L. P., Hamaker, E. L., & Bergeman, C. S. (2012). Investigating inter-individual differences in short-term intra-individual variability. Psychological Methods, 17, 567. https://doi.org/https://doi.org/10.1037/a0029317
- Watson, D. (1988). Intraindividual and interindividual analyses of positive and negative affect: Their relation to health complaints, perceived stress, and daily activities. Journal of Personality and Social Psychology, 54, 1020. https://doi.org/https://doi.org/10.1037/0022–3514.54.6.1020
- Watson, D., & Clark, L. A. (1997). Extraversion and its positive emotional core. In R. Hogan, J. Johnson, & S. Briggs (Eds.), Handbook of personality psychology (pp. 767–793). Elsevier. San Diego: Academic Press.
- Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063. https://doi.org/https://doi.org/10.1037/0022-3514.54.6.1063
- Wright, A. G., Gates, K. M., Arizmendi, C., Lane, S. T., Woods, W. C., & Edershile, E. A. (2019). Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology. Psychological Assessment, 31, 502. https://doi.org/https://doi.org/10.1037/pas0000617
- You, D., Hunter, M., Chen, M., & Chow, S.-M. 2019. A diagnostic procedure for detecting outliers in linear state-space models. Multivariate Behavioral Research, 1–25. ( PMID: 31264463). https://doi.org/https://doi.org/10.1080/00273171.2019.1627659.
- Zhang, Z., & Nesselroade, J. R. (2007). Bayesian estimation of categorical dynamic factor models. Multivariate Behavioral Research, 42, 729–756. https://doi.org/https://doi.org/10.1080/00273170701715998