2021
Li, Y., Wood, J., Wood, , Ji, L., Ji, , Chow, S., Chow, S., & Oravecz, Z. (). Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus. Structural Equation Modeling, 29(3), 452-475. https://doi.org/10.1080/10705511.2021.1911657
2020
Park, J., Park, J., Chow, S., Chow, S., Fisher, Z., & Molenaar, P. (). Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models. European Journal of Psychological Assessment, 36(6), 1009-1023. https://doi.org/10.1027/1015-5759/a000612
Chapman, A., & Santos-Lozada, A. (). Racial and ethnic differences in the associations between social integration, C-reactive protein and depressive symptoms. SSM-Population Health, 12, 100663. https://doi.org/10.1016/j.ssmph.2020.100663
Oravecz, Z., & Vandekerckhove, J. (). A joint process model of consensus and longitudinal dynamics. Journal of Mathematical Psychology, 98. https://doi.org/10.1016/j.jmp.2020.102386
Fisher, Z., & Bollen, K. (). An Instrumental Variable Estimator for Mixed Indicators: Analytic Derivatives and Alternative Parameterizations. Psychometrika, 85(3), 660-683. https://doi.org/10.1007/s11336-020-09721-6
Oravecz, Z., & Vandekerckhove, J. (). A joint model of consensus and change over time. Journal of Mathematical Psychology, 98.
Liu, C., Ji, L., Chow, S., Kang, B., Leve, L., Shaw, D., Ganiban, J., Natsuaki, M., Reiss, D., & Neiderhiser, J. (). Child Effects on Parental Negativity: The Role of Heritable and Prenatal Factors. Child Development, 91(5), e1064-e1081. https://doi.org/10.1111/cdev.13404
Dopp, A., Mundey, P., Silovsky, J., Hunter, M., & Slemaker, A. (). Economic value of community-based services for problematic sexual behaviors in youth: A mixed-method cost-effectiveness analysis. Child Abuse and Neglect, 105. https://doi.org/10.1016/j.chiabu.2019.104043
McKee, K., Hunter, M., & Neale, M. (). A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering. Multivariate Behavioral Research, 55(3), 405-424. https://doi.org/10.1080/00273171.2019.1642730
Ji, L., Chen, M., Oravecz, Z., Cummings, E., Lu, Z., & Chow, S. (). A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes. Structural Equation Modeling, 27(3), 442-467. https://doi.org/10.1080/10705511.2019.1623681
Booij, S., Wigman, J., Jacobs, N., Thiery, E., Derom, C., Wichers, M., & Oravecz, Z. (). Cortisol dynamics in depression: Application of a continuous-time process model. Psychoneuroendocrinology, 115. https://doi.org/10.1016/j.psyneuen.2020.104598
Gates, K., Fisher, Z., & Bollen, K. (). Latent variable GIMME using model implied instrumental variables (MIIVs). Psychological Methods, 25(2), 227-242. https://doi.org/10.1037/met0000229
Lei, Z., Hung, H., Lee, W., Yang, D., Shen, C., & Chow, S. (). Efficient Algorithms towards Network Intervention. The Web Conference 2020 (WWW ’20), 2021, 2021-2031. https://doi.org/10.1145/3366423.3380269
Fisher, Z., Chow, S., Molenaar, P., Molenaar, Peter C. M., , Fredrickson, B., Pipiras, V., & Gates, K. (). A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters. Multivariate Behavioral Research, 57(1), 134-152. https://doi.org/10.1080/00273171.2020.1815513
Leonard, K., Evans, B., Evans, M., Oravecz, Z., Smyth, J., & Downs, D. (). Effect of Technology-Supported Interventions on Prenatal Gestational Weight Gain, Physical Activity, and Healthy Eating Behaviors: a Systematic Review and Meta-analysis. Journal of Technology in Behavioral Science, 6(1), 25-41. https://doi.org/10.1007/s41347-020-00155-6
You, D., Hunter, M., Chen, M., & Chow, S. (). A Diagnostic Procedure for Detecting Outliers in Linear State-Space Models. MULTIVARIATE BEHAVIORAL RESEARCH, 55(2), 231-255. https://doi.org/10.1080/00273171.2019.1627659
Cole, P., Lougheed, J., Chow, S., & Ram, N. (). Development of Emotion Regulation Dynamics Across Early Childhood: a Multiple Time-Scale Approach. Affective Science, 1(1), 28-41. https://doi.org/10.1007/s42761-020-00004-y
Chow, S., & Chen, M. (). Nonparametric models for longitudinal data: With implementation in R. JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, 45(3), 369-373. https://doi.org/10.3102/1076998620915291
Chow, S., You, D., & Clouthier, T. (). A regime-switching framework for formulating multi-phase linear and nonlinear growth curves: MARCES Series: Applications of Artificial Intelligence to Assessment. , 193-234.
Ji, L., Chow, S., Crosby, B., & Teti, D. (). Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model.. Multivariate Behavioral Research, 55(1), 150-151. https://doi.org/10.1080/00273171.2019.1697863
Bastiaansen, J., Kunkels, Y., Blaauw, F., Boker, S., Ceulemans, E., Chen, M., Cheng, M., Chow, S., Jonge, P., de Jonge, Peter, , de Jonge, P., Emerencia, A., Epskamp, S., Fisher, A., Hamaker, E., Kuppens, P., Lutz, W., Meyer, M., Moulder, R., Oravecz, Z., et al (). Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology. Journal of Psychosomatic Research, 137, 110211. https://doi.org/10.1016/j.jpsychores.2020.110211
Chen, M., Chow, S., Hammal, Z., Messinger, D., Cohn, J., Cohn, J., & Messinger, D. (). A Person- and Time-Varying Vector Autoregressive Model to Capture Interactive Infant-Mother Head Movement Dynamics. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2020.1762065
Dunton, G., Wang, S., Ponnada, A., Campo, R., Chow, S., & Intille, S. (). INNOVATIVE ECOLOGICAL MOMENTARY ASSESSMENT STRATEGIES TO CAPTURE MICRO-TEMPORAL PROCESSES UNDERLYING LONG-TERM CHANGES IN PHYSICAL ACTIVITY, SEDENTARY BEHAVIOR, AND SLEEP. ANNALS OF BEHAVIORAL MEDICINE, 54, S146-S146.
Chow, S., You, D., & Clouthier, T. (). A REGIME-SWITCHING (RS) FRAMEWORK FOR FORMULATING MULTI-PHASE LINEAR AND NONLINEAR GROWTH CURVE MODELS. , 193-234.
2019
Gates, K., Fisher, Z., Arizmendi, C., Henry, T., Duffy, K., & Mucha, P. (). Assessing the robustness of cluster solutions obtained from sparse count matrices. Psychological Methods, 24(6), 675-689. https://doi.org/10.1037/met0000204