2024
Losardo, D., Chow, S., Panter, A., Burkley, M., & Burkley, E. (). Ecological momentary assessment (EMA) designs with planned missingness. Springer Proceedings in Mathematics & Statistics: Dependent Data in Social Sciences Research. , 2, 657-698.
Chow, S., Hamaker, E., & Ram, N. (). From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar. Multivariate Behavioral Research, 59(6), 1107-1110. https://doi.org/10.1080/00273171.2024.2394054
Fatimah, H., Hunter, M., & Bornovalova, M. (). Modeling the Dynamics of Addiction Relapse via the Double-Well Potential System. Journal of Psychopathology and Clinical Science. https://doi.org/10.1037/abn0000960
Heshmati, S., Muth, C., Roeser, R., Smyth, J., Jamalabadi, H., & Oravecz, Z. (). Conceptualizing psychological well-being as a dynamic process: Implications for research on mobile health interventions. Social and Personality Psychology Compass, 18(1). https://doi.org/10.1111/spc3.12933
Henry, T., Fisher, Z., & Bollen, K. (). Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators. Structural Equation Modeling. https://doi.org/10.1080/10705511.2024.2343926
Park, J., Fisher, Z., Hunter, M., Shenk, C., Russell, M., Molenaar, P., & Chow, S. (). Unsupervised Model Construction in Continuous-Time. Structural Equation Modeling, 32(3), 377-399. https://doi.org/10.1080/10705511.2024.2429544
Felt, J., Chimed-Ochir, U., Shores, K., Olson, A., Yanling, Y., Li, Y., Fisher, Z., Ram, N., & Shenk, C. (). Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems. Journal of Child Psychology and Psychiatry and Allied Disciplines, 10. https://doi.org/10.1111/jcpp.13990
2023
Rhubart, D., & Santos, A. (). Research Note Showing That the Rural Mortality Penalty Varies by Region, Race, and Ethnicity in the United States, 1999–2016. Demography, 60(6), 1699-1709. https://doi.org/10.1215/00703370-11078239
Lipton, R., de la Garza, A., Wang, C., Derby, C., Katz, M., Oravecz, Z., Hakun, J., Sliwinski, M., & Ezzati, A. (). The Association of Blood-based biomarkers and Cognitive Change as Measured by Ecologically Sensitive Digital Biomarkers. Alzheimer's & Dementia, 19(0). https://doi.org/10.1002/alz.077999
Heshmati, S., Kibrislioglu Uysal, N., Kim, S., Oravecz, Z., & Donaldson, S. (). Momentary PERMA: An Adapted Measurement Tool for Studying Well-Being in Daily Life. Journal of Happiness Studies, 24(8), 2441-2472. https://doi.org/10.1007/s10902-023-00684-w
Ou, L., Hunter, M., Lu, Z., Stifter, C., & Chow, S. (). Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter. British Journal of Statistical Psychology, 76(3), 462-490. https://doi.org/10.1111/bmsp.12318
Long, J., Pritschet, S., Keller, K., Cheah, C., Boot, L., Klippel, A., Brick, T., Edwards, C., Rolls, B., & Masterson, T. (). Portion size affects food selection in an immersive virtual reality buffet and is related to measured intake in laboratory meals varying in portion size. Appetite, 191(107052). https://doi.org/10.1016/j.appet.2023.107052
Ahn, Y., Önal Ertuğrul, I., Chow, S., Cohn, J., & Messinger, D. (). Automated measurement of infant and mother Duchenne facial expressions in the Face-to-Face/Still-Face. Infancy, 28(5), 910-929. https://doi.org/10.1111/infa.12556
Luong, G., Miller, J., Kirkland, D., Morse, J., Wrzus, C., Diehl, M., Chow, S., & Riediger, M. (). Valuing negative affect weakens affect-health linkages: similarities and differences across affect valuation measures. Motivation and Emotion, 47(3), 347-363. https://doi.org/10.1007/s11031-023-10012-7
Fisher, Z., Parsons, J., Gates, K., & Hopfinger, J. (). Blind Subgrouping of Task-based fMRI. Psychometrika, 88(2), 434-455. https://doi.org/10.1007/s11336-023-09907-8
Luo, L., Fisher, Z., Molenaar, P., Beltz, A., & Kathleen, G. (). Estimating Both Directed and Bidirectional Contemporaneous Relations in Time Series Data Using Hybrid-GIMME.. Psychological Methods.
Santos-Lozada, A. (). Trends in deaths from falls among adults aged 65 years or older in the US, 1999-2020. JAMA, 329(18), 1605-1607. https://doi.org/10.1001%2Fjama.2023.3054
Hunter, M., Pritikin, J., Kirkpatrick, R., & Neale, M. (). Rethinking Ordinal Variable Identification in Weighted Least Squares Structural Equation Modeling. PsyArXiv, 1-81. https://doi.org/10.31234/osf.io/mnc7q
Van Doren, N., Oravecz, Z., Soto, J., & Roeser, R. (). Correction to: Examining the Cultural Consensus on Beliefs About Mindfulness Among US College-Attending Young Adults (Mindfulness, (2022), 13, 10, (2420-2433), 10.1007/s12671-022-01956-x). Mindfulness, 14(3), 761. https://doi.org/10.1007/s12671-023-02081-z
Olson, A., Chow, S., Jones, D., & Shenk, C. (). Child maltreatment, parent-child relationship quality, and parental monitoring in relation to adolescent behavior problems: Disaggregating between and within person effects. Child Abuse and Neglect, 136, 106003. https://doi.org/10.1016/j.chiabu.2022.106003
Fisher, Z. (). Machine Learning for Multiple-Subject Time Series Analysis. Annual Meeting of the Society for Multivariate Experimental Psychology.
Chow, S., Losardo, D., Park, J., & Molenaar, P. (). Continuous-Time Dynamic Models: Connections to Structural Equation Models and Other Discrete-Time Models: Structural Equation Modeling: Concepts, Issues, and Applications. , 2.
Chen, M., Chen, M., Chow, S., Oravecz, Z., & Ferrer, E. (). Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach. Multivariate Behavioral Research, 58(5), 1014-1038. https://doi.org/10.1080/00273171.2023.2171354
Fisher, Z., Kim, Y., Pipiras, V., Crawford, C., Petrie, D., Hunter, M., & Geier, C. (). Structured Estimation of Heterogeneous Time Series. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2023.2283837
Pearce, A., & Brick, T. (). Validation of computational models to characterize cumulative intake curves from video-coded meals. Frontiers in Nutrition, 10(1088053). https://doi.org/10.3389/fnut.2023.1088053