At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

References

• Allison, P. D. (2009). Fixed effects regression models. Thousand Oaks, CA: Sage. doi: 10.4135/9781412993869
   Google Scholar
• Almeida, D. M., Wethington, E., & Chandler, A. L. (1999). Daily transmission of tension between marital dyads and parent-child dyads. Journal of Marriage and the Family, 61, 49–61. doi: 10.2307/353882
   Web of Science ®Google Scholar
• Asparouhov, T., Hamaker, E. L., & Muthén, B. (2017). Dynamic latent class analysis. Structural Equation Modeling, 24, 257–269. doi: 10.1080/10705511.2016.1253479
   Web of Science ®Google Scholar
• Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling, 25, 359–388. doi: 10.1080/10705511.2017.1406803
   Google Scholar
• Asparouhov, T., & Muthén, B. (2010). Bayesian analysis of latent variable models using Mplus (version 3)(Technical Report). Retrieved from http://statmodel.com/download/Bayes3.pdf
   Google Scholar
• Beltz, A. M., Wright, A. G. C., Sprague, B. N., & Molenaar, P. C. M. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23, 447–458. doi: 10.1177/1073191116648209
   PubMed Web of Science ®Google Scholar
• Berry, D., & Willoughby, M. T. (2017). On the practical interpretability of cross-lagged panel models: Rethinking a developmental workhorse. Child Development, 88, 1186–1206. doi: 10.1111/cdev.12660
   PubMed Web of Science ®Google Scholar
• Bisconti, T., Bergeman, C. S., & Boker, S. M. (2004). Emotional well-being in recently bereaved widows: A dynamical system approach. Journal of Gerontology, Series B: Psychological Sciences and Social Sciences, 59, 158–167. doi: 10.1093/geronb/59.4.P158
   PubMed Web of Science ®Google Scholar
• Blaauw, F. J., van der Krieke, L., Emerencia, A. C., Aiello, M., & de Jonge, P. (2017). Personalized advice for enhancing well-being using automated impulse response analysis — AIRA. Retrieved from https://arxiv.org/abs/1706.09268
   Google Scholar
• Boker, S. M., & Laurenceau, J.-P. (2006). Dynamic system modeling: An application to the regulation of intimacy and disclosure in marriage. In T. A. Walls & J. L. Schafer (Eds.), Models for intensive longitudinal data (p.195–218). New York, NY: Oxford University Press. doi: 10.1093/acprof:oso/9780195173444.003.0009
   Google Scholar
• Boker, S. M., & McArdle, J. J. (2005). Vector field plot. In P. Armitage & P. Colton (Eds.), Encyclopedia of biostatistics (Second Edition, pp. 5700–5704). Chichester, England: Wiley. doi: 10.1002/0470011815.b2a12068
   Google Scholar
• Boker, S. M., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., … J., F. (2011). OpenMx: An open source extended structural equation modeling framework. Psychometrika, 76, 306–317. doi: 10.1007/S11336-010-9200-6
   PubMed Web of Science ®Google Scholar
• Bolger, N., DeLongis, A., Kessler, R. C., & Wethington, E. (1989). The contagion of stress across multiple roles. Journal of Marriage and the Family, 51, 175–183. doi: 10.2307/352378
   Web of Science ®Google Scholar
• Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, NY: The Guilford Press.
   Google Scholar
• Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 203–219. doi: 10.1037/0033-295X.110.2.203
   PubMed Web of Science ®Google Scholar
• Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). Time series analysis: Forecasting and control (5 ed.). Hoboken, NJ: Holden-Day. doi: 10.1111/jtsa.12194
   Google Scholar
• Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, … Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE, 8, e60188, 1–13. doi: 10.1371/journal.pone.0060188
   Web of Science ®Google Scholar
• Brose, A., De Roover, K., Ceulemans, E., & Kuppens, P. (2015). Older adults’ affective experiences across 100 days are less variable and less complex than younger adults’. Psychology and Aging, 30, 194–208. doi: 10.1037/a0038690
   PubMed Web of Science ®Google Scholar
• Brose, A., Schmiedek, F., Koval, P., & Kuppens, P. (2015). Emotional inertia contributes to depressive symptoms beyond perseverative thinking. Cognition and Emotion, 29, 527–538. doi: 10.1080/02699931.2014.916252
   PubMed Web of Science ®Google Scholar
• Brose, A., Schmiedek, F., & Lindenberger, U. (2013). Affective states contribute to trait reports of affective well-being. Emotion, 13, 940–948. doi: 10.1037/a0032401
   PubMed Web of Science ®Google Scholar
• Brose, A., Voelkle, M. C., Lövdén, M., Lindenberger, U., & Schmiedek, F. (2015). Differences in the between-person and the within-person structures of affect are a matter of degree. European Journal of Personality, 29, 55–71. doi: 10.1002/per.1961
   Web of Science ®Google Scholar
• Cattell, R. B. (1952). The three basic factor-analytical research designs: Their interrelations and derivatives. Psychological Bulletin, 49, 499–520. doi: 10.1037/h0054245
   PubMed Web of Science ®Google Scholar
• Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Analysis, 1, 651–674. doi: 10.1214/06-BA122
   Web of Science ®Google Scholar
• Chatfield, C. (2004). The analysis of time series: An introduction (6th ed.). London, UK: Chapman and Hall. doi: 10.1007/978-1-4899-2923-5
   Google Scholar
• Chow, S.-M., Nesselroade, J. R., Shifren, K., & McArdle, J. J. (2004). Dynamic structure of emotions among individuals with Parkinson’s disease. Structural Equation Modeling: A Multidisciplinary Journal, 11, 560–582. doi: 10.1207/s15328007sem1104_4
   Web of Science ®Google Scholar
• Chow, S.-M., Ram, N., Boker, S. M., Fujita, F., & Clore, G. (2005). Emotion as a thermostat: Representing emotion regulation using a damped oscillator model. Emotion, 5, 208–225. doi: 10.1037/1528-3542.5.2.208
   PubMed Web of Science ®Google Scholar
• Conner, T. S., Tennen, H., Fleeson, W., & Feldman Barrett, L. (2009). Experience sampling methods: A modern idiographic approach to personality research. Social and Personality Psychology Compass, 3, 292–313. doi: 10.1111/j.1751-9004.2009.00170.x
   PubMedGoogle Scholar
• Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619. doi: 10.1146/annurev.psych.093008.100356
   PubMed Web of Science ®Google Scholar
• Deboeck, P., & Preacher, K. (2015). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling: A Multidisciplinary Journal, 23, 1–15. doi: 10.1080/10705511.2014.973960
   Web of Science ®Google Scholar
• de Haan-Rietdijk, S., Gottman, J. M., Bergeman, C. S., & Hamaker, E. L. (2016). Get over it! A multilevel threshold autoregressive model for state-dependent affect regulation. Psychometrika, 81, 217–241. doi: 10.1007/s11336-014-9417-x
   PubMed Web of Science ®Google Scholar
• de Haan-Rietdijk, S., Kuppens, P., & Hamaker, E. L. (2016). What’s in a day? A guide to decomposing the variance in intensive longitudinal data. Frontiers in Psychology, 7, 891. doi: 10.3389/fpsyg.2016.00891
   PubMed Web of Science ®Google Scholar
• de Haan-Rietdijk, S., Voelkle, M., Keijsers, L., & Hamaker, E. L. (2017). Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8, 1849. doi: 10.3389/fpsyg.2017.01849
   PubMed Web of Science ®Google Scholar
• Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77, 1–35. doi: 10.18637/jss.v077.i05
   Web of Science ®Google Scholar
• Durbin, J., & Koopman, S. J. (2012). Time series analysis by state space methods (2 ed.). New York, NY: Oxford University Press. doi: 10.1093/acprof:oso/9780199641178.001.0001
   Google Scholar
• Eichler, M. (2013). Causal inference with multiple time series: Principles and problems. Philosophical Transactions of the Royal Society A, 371, 2011.0613. doi: 10.1098/rsta.2011.0613
   Web of Science ®Google Scholar
• Eichler, M., & Didelez, V. (2010). On Granger-causality and the effect of interventions in time series. Life Time Data Analysis, 16, 3–32. doi: 10.1007/s10985-009-9143-3
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–18. doi: 10.18637/jss.v048.i04
   Web of Science ®Google Scholar
• Epskamp, S., Deserno, M. K., & Bringmann, L. F. (2017). mlVAR: Multi-level vector autoregression [computer software manual]. (R package version 0.4). Retrieved from https://cran.r-project.org/web/packages/mlVAR/mlVAR.pdf
   Google Scholar
• Epskamp, S., Waldorp, L. J., Mottus, R., & Borsboom, D. (2017). Discovering psychological dynamics: The Gaussian Graphical Model in cross-sectional and time-series data. Retrieved from https://arxiv.org/abs/1609.04156
   Google Scholar
• Gates, K. M., & Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 65, 310–319. doi: 10.1016/j.neuroimage.2012.06.026
   Web of Science ®Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. (2014). Bayesian data analysis (3 ed.). Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Gollob, H. F., & Reichardt, C. S. (1987). Taking account of time lags in causal models. Child Development, 58, 80–92. doi: 10.1111/j.1467-8624.1987.tb03492.x
   PubMed Web of Science ®Google Scholar
• Gottman, J. M., Murray, J. D., Swanson, C. C., Tyson, R., & Swanson, K. R. (2002). The mathematics of marriage: Dynamic nonlinear models. Cambridge, MA: MIT Press.
   Google Scholar
• Grice, J. W. (2004). Bridging the idiographic-nomothetic divide in ratings of self and others. Journal of Personality, 72, 203–241. doi: 10.1111/j.0022-3506.2004.00261.x
   PubMed Web of Science ®Google Scholar
• Halaby, C. N. (2004). Panel models in sociological research: Theory intro practice. Annual Review of Sociology, 30, 507–544. doi: 10.1146/annurev.soc.30.012703.110629
   Web of Science ®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, 404–418. doi: 10.1177/0049124104270220
   Web of Science ®Google Scholar
• Hamaker, E. L. (2012). Why researchers should think “within-person” a paradigmatic rationale. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (p.43–61). New York, NY: Guilford Publications. doi: 10.1177/1468794113484441
   Google Scholar
• Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2002). On the nature of SEM estimates of ARMA parameters. Structural Equation Modeling, 9, 347–368. doi: 10.1207/S15328007SEM0903_3
   Web of Science ®Google Scholar
• Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2003). ARMA-based SEM when the number of time points T exceeds the number of cases N: Raw data maximum likelihood. Structural Equation Modeling, 10, 352–379. doi: 10.1207/S15328007SEM1003_2
   Web of Science ®Google Scholar
• Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2005). Statistical modeling of the individual: Rationale and application of multivariate time series analysis. Multivariate Behavioral Research, 40, 207–233. doi: 10.1207/s15327906mbr4002_3
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Grasman, R. P. P. P., & Kamphuis, J. H. (2016). Modeling BAS dysregulation in bipolar disorder: Illustrating the potential of time series analysis. Assessment, 23, 436–446. doi: 10.1177/1073191116632339
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Kuiper, R., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20, 102–116. doi: 10.1037/a0038889
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Nesselroade, J. R., & Molenaar, P. C. M. (2007). The integrated trait-state model. Journal of Research in Personality, 41, 295–315. doi: 10.1016/j.jrp.2006.04.003
   Web of Science ®Google Scholar
• Hamaker, E. L., Schuurman, N. K., & Zijlmans, E. A. O. (2017). Using a few snapshots to distinguish mountains from waves: Weak factorial invariance in the context of trait-state research. Multivariate Behavioral Research, 52, 47–60 doi: 10.1080/00273171.2016.1251299
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26, 10–15. doi: 10.1177/0963721416666518
   Web of Science ®Google Scholar
• Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.
   Google Scholar
• Harvey, A. C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge, UK: University Press. doi: 10.1017/CBO9781107049994
   Google Scholar
• Houben, M., Noortgate, W. Van den, & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141, 901–930. doi: 10.1037/a0038822
   PubMed Web of Science ®Google Scholar
• Jongerling, J., Laurenceau, J.-P., & Hamaker, E. L. (2015). A multilevel AR(1) model: Allowing for inter-individual differences in trait-scores, inertia, and innovation variance. Multivariate Behavioral Research, 184, 334–349. doi: 10.1080/00273171.2014.1003772
   Web of Science ®Google Scholar
• Kim, C.-J., & Nelson, C. R. (1999). State-space models with regime switching: Classical and Gibbs-sampling approaches with applications. Cambridge, MA: The MIT Press. doi: 10.2307/2669796
   Google Scholar
• Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2015). Micro-randomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychology, 34, 1220–1228. doi: 10.1037/hea0000305
   PubMed Web of Science ®Google Scholar
• Koval, P., Kuppens, P., Allen, N. B., & Sheeber, L. (2012). Getting stuck in depression: The roles of rumination and emotional inertia. Cognition and Emotion, 26, 1412–1427. doi: 10.1080/02699931.2012.667392
   PubMed Web of Science ®Google Scholar
• Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21, 984–991. doi: 10.1177/0956797610372634
   PubMed Web of Science ®Google Scholar
• Kuppens, P., Sheeber, L. B., Yap, M. B. H., Whittle, S., Simmons, J., & Allen, N. B. (2012). Emotional inertia prospectively predicts the onset of depression in adolescence. Emotion, 12, 283–289. doi: 10.1037/a0025046
   PubMed Web of Science ®Google Scholar
• Lamiell, J. T. (1998). ‘Nomothetic’ and ‘idiographic’: Contrasting Windelband’s understanding with contemporary usage. Theory and Psychology, 8, 23–38. doi: 10.1177/0959354398081002
   Web of Science ®Google Scholar
• Liu, S. (2017). Person-specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels. British Journal of Mathematical and Statistical Psychology, 70, 480–498. doi: 10.1111/bmsp.12096
   PubMed Web of Science ®Google Scholar
• Liu, Y., & West, S. G. (2016). Weekly cycles in daily report data: An overlooked issue. Journal of Personality, 84, 560–579. doi: 10.1111/jopy.12182
   PubMed Web of Science ®Google Scholar
• Lynch, S. M. (2007). Introduction to applied Bayesian statistics and estimation for social scientists. New York, NY: Springer Science & Bussiness Media.
   Google Scholar
• Masten, A. S., & Cichetti, D. (2010). Developmental cascades. Development and Psychopathology, 22, 491–495. doi: 10.1017/S0954579410000222
   PubMed Web of Science ®Google Scholar
• Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series.Psychometrika, 50, 181–202. doi: 10.1007/bf02294246
   Web of Science ®Google Scholar
• Molenaar, P. C. M. (1987). Dynamic assessment and adaptive optimization of the psychotherapeutic process. Behavioral Assessment, 9, 389–416. doi: 10.1007/BF00959854
   Google Scholar
• Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology - this time forever. Measurement: Interdisciplinary Research and Perspectives, 2, 201–218. doi: 10.1207/s15366359mea0204_1
   Google Scholar
• Molenaar, P. C. M., Huizenga, H. M., & Nesselroade, J. R. (2003). The relationship between the structure of interindividual and intraindividual variability: A theoretical and empirical vindication of developmental systems theory. In U. M. Staudinger & U. Lindenberger (Eds.), Understanding human development: Dialogues with lifespan psychology (p.339–360). Norwell, MA: Kluwer Academic Publishers. doi: 10.1007/978-1-4615-0357-6_15
   Google Scholar
• Nesselroade, J. R. (2002). Elaborating on the differential in differential psychology. Multivariate Behavioral Research, 37, 543–561. doi: 10.1207/S15327906MBR3704_06
   PubMed Web of Science ®Google Scholar
• Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2011). A hierarchical latent stochastic difference equation model for affective dynamics. Psychological Methods, 16, 468–490. doi: 10.1037/a0024375
   PubMed Web of Science ®Google Scholar
• Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2016). Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behavioral Research, 51, 106–119. doi: 10.1080/00273171.2015.1110512
   PubMed Web of Science ®Google Scholar
• Ou, L., Hunter, M., & Chow, S.-M. (2018). dynr: Dynamic modeling in R. (R-package version 0.1.12-5). Retrieved from: https://cran.r-project.org/web/packages/dynr/
   Google Scholar
• Oud, J. H. L. (2007). Continuous time modeling of reciprocal relationships in the cross-lagged panel design. In S. M. Boker & M. J. Wenger (Eds.), Data analytic techniques for dynamic systems in the social and behavioral sciences (p.87–129). Mahwah, NJ: Lawrence Erlbaum Associates. doi: 10.4324/9780203936757
   Google Scholar
• Ousey, G. C., Wilcox, P., & Fisher, B. S. (2011). Something old, something new: Revisiting competing hypothesis of the victimization-offending relationship among adolescents. Journal of Quantitative Criminology, 27, 53–84. doi: 10.1007/s10940-010-9099-1
   Web of Science ®Google Scholar
• Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Retrieved from https://www.r-project.org/conferences/DSC-2003/Drafts/Plummer.pdf
   Google Scholar
• Ram, N., Chow, S.-M., Bowles, R. P., Wang, L., Grimm, K., Fujita, F., & Nesselroade, J. R. (2005). Examining interindividual differences in cyclicity of pleasant and unpleasant affects using spectral analysis and item response modeling. Psychometrika, 70, 773–790. doi: 10.1007/s11336-001-1270-5
   Web of Science ®Google Scholar
• Rovine, M. J., & Walls, T. A. (2006). Multilevel autoregressive modeling of interindividual differences in the stability of a process. In T. A. Walls & J. L. Schafer (Eds.), Models for intensive longitudinal data (pp.124–147). New York, NY: Oxford University Press. doi: 10.1093/acprof:oso/9780195173444.003.0006
   Google Scholar
• Sadler, P., Ethier, N., Gunn, G. R., Duong, D., & Woody, E. (2009). Are we on the same wavelength? Interpersonal complementarity as shared cyclical patterns during interactions. Journal of Personality and Social Psychology, 97, 1005–1020. doi: 10.1037/a0016232
   PubMed Web of Science ®Google Scholar
• Särkkä, S. (2013). Bayesian filtering and smoothing. Cambridge, UK: Cambridge University Press. doi: 10.1017/CBO9781139344203
   Google Scholar
• Schmitz, B. (2000). Auf der Suche nach dem verlorenen Individuum: Vier Theoreme zur Aggregation von Prozessen [In search of the lost individual: Four theorems regarding the aggregation of processes]. Psychologische Rundschau, 51, 83–92. doi: 10.1026//0033-3042.51.2.83
   Web of Science ®Google Scholar
• Schmitz, B., & Skinner, E. (1993). Perceived control, effort, and academic performance: Interindividual, intrainidividual, and multivariate time-series analyses. Journal of Personality and Social Psychology, 64, 1010–1028. doi: 10.1037/0022-3514.64.6.1010
   Web of Science ®Google Scholar
• Schultzberg, M., & Muthén, B. (2017). Number of subjects and time points needed for multilevel time series analysis: A simulation study of dynamic structural equation modeling. Structural Equation Modeling. doi: 10.1080/10705511.2017.1392862
   Web of Science ®Google Scholar
• Schuurman, N. K., Ferrer, E., Boer-Sonnenschein, M. de, & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21, 206–221. doi: 10.1037/met0000062
   PubMed Web of Science ®Google Scholar
• Schuurman, N. K., Houtveen, J. H., & Hamaker, E. L. (2015). Incorporating measurement error in n = 1 psychological autoregressive modeling. Frontiers in Psychology, 6, 1038. doi: 10.3389/fpsyg.2015.01038
   PubMed Web of Science ®Google Scholar
• Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: With R examples (4 ed.). New York, NY: Springer. doi: 10.1007/978-3-319-52452-8
   Google Scholar
• Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, 64, 1–34. doi: 10.1111/1467-9868.00353
   Google Scholar
• Spiegelhalter, D. J., Thomas, D., Best, N. G., & Lunn, D. (2003). WinBUGS version 1.4 user manual. Cambridge, UK: MRC Biostatistics Unit. Retrieved from http://www.mrc-bsu.cam.ac.uk/bugs/
   Google Scholar
• Stan Development Team. (2017). Stan modeling language: User’s guide and reference manual (version 2.16.0). Retrieved from http://mc-stan.org/users/documentation/
   Google Scholar
• Steyer, R., Ferring, D., & Schmitt, M. J. (1992). States and traits in psychological assessment. European Journal of Psychological Assessment, 8, 79–98.
   Google Scholar
• Suls, J., Green, P., & Hillis, S. (1998). Emotional reactivity to everyday problems, affective inertia, and neuroticism. Personality and Social Psychology Bulletin, 24, 127–136. doi: 10.1177/0146167298242002
   Web of Science ®Google Scholar
• Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176. doi: 10.1146/annurev-clinpsy-050212-185510
   PubMed Web of Science ®Google Scholar
• Van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113, 842–861. doi: 10.1037/0033-295X.113.4.842
   PubMed Web of Science ®Google Scholar
• van Borkulo, C. D., Boschloo, L., Borsboom, D., Penninx, B. W. J. H., & Schoevers, R. A. (2015). Association of symptom network structure and the course of depression. JAMA Psychiatry, 72, 1219–1226. doi: 10.1001/jamapsychiatry.2015.2079.
   PubMed Web of Science ®Google Scholar
• Voelkle, M., Brose, A., Schmiedek, F., & Lindenberger, U. (2014). Toward a unified framework for the study of between-person and within-person structures: Building a bridge between two research paradigms. Multivariate Behavioral Research, 49, 193–213. doi: 10.1080/00273171.2014.889593
   PubMed Web of Science ®Google Scholar
• Voelkle, M., Oud, J. H. L., Davidov, E., & Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia. Psychological Methods, 17, 176–192. doi: 10.1037/a0027543
   PubMed Web of Science ®Google Scholar
• Walls, T. A., & Schafer, J. L. (Eds.). (2006). Models for intensive longitudinal data. New York, NY: Oxford University Press.
   Google Scholar
• Wang, L., Hamaker, E. L., & Bergman, C. S. (2012). Investigating inter-individual difference in short-term intra-individual variability. Psychological Methods, 17, 567–581. doi: 10.1037/a0029317
   PubMed Web of Science ®Google Scholar
• Wang, L., & Maxwell, S. E. (2015). On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Multivariate Behavioral Research, 20, 63–83. doi: 10.1037/met0000030
   Web of Science ®Google Scholar
• 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, 1063–1070. doi: 10.1037//0022-3514.54.6.1063
   PubMed Web of Science ®Google Scholar
• Wichers, M. C., Barge-Schaapveld, D. G. C. M., Nicolson, N. A., Peeters, F., de Vries, M., Mengelers, R., & van Os, J. (2009). Reduced stress-sensitivity or increased reward experience: The psychological mechanism of response to antidepressant medication. Neuropsychopharmacology, 34, 923–931. doi: 10.1038/npp.2008.66
   PubMed Web of Science ®Google Scholar
• Zhang, Z., Hamaker, E. L., & Nesselroade, J. R. (2008). Comparisons of four methods for estimating a dynamic factor model. Structural Equation Modeling: A Multidisciplinary Journal, 15, 377–402. doi: 10.1080/10705510802154281
   Web of Science ®Google Scholar