2020
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
Brick, T., & Bailey, D. (). Rock the MIC: The Matrix of Implied Causation for Design and Model Checking. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920922775
Hilpert, P., Brick, T., Flueckiger, C., Vowels, M., Ceuleman, E., Kuppens, P., & Sels, L. (). What Can Be Learned From Couple Research: Examining Emotional Co-Regulation Processes in Face-to-Face Interactions. Journal of Counselling Psychology, 67(4), 475-487. https://doi.org/10.1037/cou0000416
Heshmati, S., Oravecz, Z., Brick, T., & Roeser, R. (). Momentary PERMA as a correlational network: Empirical evidence for daily measures of well-being via network analysis. Applied Developmental Science. https://doi.org/10.1080/10888691.2020.1766356
Shewark, E., Brick, T., & Buss, K. (). Capturing Temporal Dynamics of Fear Behaviors on a Moment to Moment Basis. Infancy. https://doi.org/10.1017/S0954579419001743
Brick, T., Mundie, J., Weaver, J., Fraleigh, R., & Oravecz, Z. (). Wear-IT: Low-burden Mobile Monitoring and Intervention through Real-time Analysis. Journal of Medical Internet Research.
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), e16072. https://doi.org/10.2196/16072
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.
2019
Adams, E., Marini, M., Brick, T., Paul, I., Birch, L., & Savage, J. (). Ecological momentary assessment of using food to soothe during infancy in the INSIGHT trial. International Journal of Behavioral Nutrition and Physical Activity, 16(1). https://doi.org/10.1186/s12966-019-0837-y
Chow, S. (). Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models. Multivariate Behavioral Research, 54(5), 690-718. https://doi.org/10.1080/00273171.2019.1566050
Oravecz, Z., & Brick, T. (). Associations Between Slow- and Fast-Timescale Indicators of Emotional Functioning. Social Psychological and Personality Science, 10(7), 864-873. https://doi.org/10.1177/1948550618797128
Jacobson, N., Chow, S., & Newman, M. (). The differential time-varying effect model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data. Behavior research methods, 51(1), 295-315. https://doi.org/10.3758/s13428-018-1101-0
Jenkins, A., Fredman, S., Le, Y., Sun, X., Brick, T., Skinner, O., & McHale, S. (). Prospective associations between depressive symptoms and marital satisfaction in Black couples. Journal of Family Psychology. https://doi.org/10.1037/fam0000573
Roberts, N., Oravecz, Z., Sprague, B., & Geier, C. (). A novel hierarchical later process model: Evaluating latent sources of variation in reaction times of adult daily smokers. Frontiers in Psychiatry, 10(0). https://doi.org/10.3389/fpsyt.2019.00474
Kolanowski, A., Behrens, L., Lehman, E., Oravecz, Z., Resnick, B., Boltz, M., & Van Haitsma, K. (). Living well with dementia: Factors associated with nursing home residents’ affect balance. Research in Gerontological Nursing. https://doi.org/10.3928/19404921-20190823-01
Ou, L., Hunter, M., & Chow, S. (). What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R. R JOURNAL, 11(1), 91-111.
Ji, L., Chen, M., Oravecz, Z., 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, 1-26. https://doi.org/10.1080/10705511.2019.1623681
Lu, Z., Chow, S., Ram, N., & Cole, P. (). Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data. Psychometrika. https://doi.org/10.1007/s11336-019-09664-7
Surachman, A., Wardecker, B., Chow, S., & Almeida, D. (). Life Course Socioeconomic Status, Daily Stressors, and Daily Well-Being: Examining Chain of Risk Models. Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 74(1), 126-135. https://doi.org/10.1093/geronb/gby014
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. https://doi.org/10.1080/00273171.2019.1697863
You, D., Hunter, M., Chen, M., & Chow, S. (). A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1627659
Li, Y., Ji, L., Oravecz, Z., Hunter, M., Brick, T., & Chow, S. (). 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.
Li, Y., Ji, L., Oravecz, Z., Brick, T., Hunter, M., & Chow, S. (). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling. International Journal of Information, Control and Computer Sciences, 13(5), 302-311. https://doi.org/10.5281/zenodo.3298841
Heshmati, S., Oravecz, Z., Pressman, S., Batchelder, W., Muth, C., & Vandekerckhove, J. (). What does it mean to feel loved: Cultural consensus and individual differences in felt love. Journal of Social and Personal Relationships, 36(1), 214-243. https://doi.org/10.1177/0265407517724600
2018
Yang, F., Zhong, B., Kumar, A., Chow, S., & Ouyang, A. (). Exchanging Social Support Online: A Longitudinal Social Network Analysis of Irritable Bowel Syndrome Patients’ Interactions on a Health Forum. Journalism and Mass Communication Quarterly, 95(4), 1033-1057. https://doi.org/10.1177/1077699017729815