2021
Le, Y., Le, Y., Fredman, S., Chow, S., Chow, S., Marshall, A., Chow, S., McDaniel, B., Laurenceau, J., & Feinberg, M. (). Relational Impacts of Capitalization in Early Parenthood. Journal of Family Psychology, 36(1), 69-79. https://doi.org/10.1037/fam0000847
Lane, S., Gates, K., Fisher, Z., Arizmendi, C., Molenaar, P., Hallquist, M., Pike, H., Henry, T., Duffy, K., Luo, L., & others, (). Package ‘gimme’. .
Zhou, S., Li, Y., Li, Y., Chi, G., Oravecz, Z., Yin, J., Oravecz, Z., Bodovski, Y., Friedman, N., Bodovski, Y., Vrieze, S., Friedman, N., Chow, S., Vrieze, S., & Chow, S. (). GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data. Journal of Behavioral Data Science, 1(2), 130-158. https://doi.org/10.35566/jbds/v1n2/p5
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
Oravecz, Z., & Vandekerckhove, J. (). A joint model of consensus and change over time. Journal of Mathematical Psychology, 98.
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
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
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
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
Brick, T., Mundie, J., Weaver, J., Fraleigh, R., & Oravecz, Z. (). Low-burden mobile monitoring, intervention, and real-time analysis using the wear-IT framework: example and usability study. JMIR Formative Research, 4(6). https://doi.org/10.2196/16072
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
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
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
Oravecz, Z., Dirsmith, J., Heshmati, S., Vandekerckhove, J., & Brick, T. (). Psychological well-being and personality traits are associated with experiencing love in everyday life. Personality and Individual Differences, 153. https://doi.org/10.1016/j.paid.2019.109620
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
Fisher, Z. (). Robust Estimation and Dynamic Models. .
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.
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
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
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
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
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