2023
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
Ji, L., Li, Y., Potter, L., Lam, C., Nahum-Shani, I., Wetter, D., & Chow, S. (). Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data. Frontiers in Digital Health, 5. https://doi.org/10.3389/fdgth.2023.1099517
Jin, C., Osotsi, A., & Oravecz, Z. (). Predicting Adolescent Female Stress with Wearable Device Data Using Machine and Deep Learning. . https://doi.org/10.1109/BSN58485.2023.10331414
Fisher, Z. (). Machine Learning for Multiple-Subject Time Series Analysis. Annual Meeting of the Society for Multivariate Experimental Psychology.
Shenk, C., Olson, A., Olson, A., Dunning, E., Dunning, E., Shores, K., Ram, N., Fisher, Z., Felt, J., Chimed-Ochir, U., & Chimed-Ochir, U. (). Addressing Contamination Bias in Child Maltreatment Research: Innovative Methods for Enhancing the Accuracy of Causal Estimates: Innovative Methods in Child Maltreatment Research and Practice. . https://doi.org/10.1007/978-3-031-33739-0_2
Chow, S., Nahum-Shani, I., Baker, J., Spruijt-Metz, D., Allen, N., Auerbach, R., Dunton, G., Friedman, N., Intille, S., Klasnja, P., Marlin, B., Nock, M., Rauch, S., Pavel, M., Vrieze, S., Wetter, D., Kleiman, E., Brick, T., Perry, H., Wolff-Hughes, D., et al (). The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols. Translational Behavioral Medicine, 13(1), 7-16. https://doi.org/10.1093/tbm/ibac069
Ollero, M., Estrada, E., Hunter, M., & Cáncer, P. (). Characterizing Affect Dynamics With a Damped Linear Oscillator Model: Theoretical Considerations and Recommendations for Individual-Level Applications. Psychological Methods. https://doi.org/10.1037/met0000615
Park, J., Fisher, Z., Chow, S., P., Molenaar PCM, , & Molenaar, P. (). Evaluating Discrete Time Methods for Subgrouping Continuous Processes. Multivariate Behavioral Research, 1-13. https://doi.org/10.1080/00273171.2023.2235685
2022
Petrie, D., Fisher, Z., & Geier, C. (). Examining the Influence of Training on the Balance Between Goal-Directed and Habitual Control using Time-varying GAM Models in Adolescents and Young Adults.: The International Congress for Integrative Developmental Cognitive Neuroscience (Flux). .
Bollen, K., Fisher, Z., Brehm, C., Lilly, A., Luo, L., & Ye, A. (). Fifty Years of Structural Equation Models (SEMs): A History of Generalization, Unification, and Diffusion.. Social Science Review, 107, 40.
Lee, T., Fisher, Z., & Gatkze-Kopp, L. (). Performance Monitoring and its Development – A Cognitive Systems Approach.. Psychophysiology, 58(51).
Bollen, K., Fisher, Z., Lilly, A., Brehm, C., Luo, L., Martinez, A., & Ye, A. (). Fifty years of structural equation modeling: A history of generalization, unification, and diffusion. Social Science Research, 107. https://doi.org/10.1016/j.ssresearch.2022.102769
Ciston, A., Forster, C., Brick, T., Kühn, S., Verrel, J., & Filevich, E. (). Do I look like I'm sure?: Partial metacognitive access to the low-level aspects of one's own facial expressions. Cognition, 225. https://doi.org/10.1016/j.cognition.2022.105155
Hunter, M., Fatimah, H., & Bornovalova, M. (). Erratum to: Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data (Psychometrika, (2022), 87, 2, (477-505), 10.1007/s11336-021-09827-5). Psychometrika, 87(2), 797. https://doi.org/10.1007/s11336-022-09862-w
Fisher, Z., Kim, Y., Fredrickson, B., & Pipiras, V. (). Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data. Psychometrika, 87(2), 1-29. https://doi.org/10.1007/s11336-021-09825-7
Schneider, J., Matyjek, M., Weigand, A., Dziobek, I., & Brick, T. (). Subjective and objective difficulty of emotional facial expression perception from dynamic stimuli. PLoS One, 17(6). https://doi.org/10.1371/journal.pone.0269156
Hunter, M., Fatimah, H., & Bornovalova, M. (). Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data. Psychometrika, 87(2), 477-505. https://doi.org/10.1007/s11336-021-09827-5
Luo, L., Fisher, Z., Arizmendi, C., Molenaar, P., Beltz, A., & Gates, K. (). Estimating Both Directed and Undirected Contemporaneous Relations in Time Series Data Using Hybrid-Group Iterative Multiple Model Estimation. Psychological Methods, 28(1), 189-206. https://doi.org/10.1037/met0000485
Bustamante, D., Amstadter, A., Pritikin, J., Brick, T., & Neale, M. (). Associations Between Traumatic Stress, Brain Volumes and Post-traumatic Stress Disorder Symptoms in Children: Data from the ABCD Study. Behavior Genetics, 52(2), 75-91. https://doi.org/10.1007/s10519-021-10092-6
Mascherek, A., Weber, S., Riebandt, K., Cassanello, C., Leicht, G., Brick, T., Gallinat, J., & Kühn, S. (). On the relation between a green and bright window view and length of hospital stay in affective disorders. Psychiatrie et Psychobiologie, 65(1). https://doi.org/10.1192/j.eurpsy.2022.9
Heshmati, S., & Oravecz, Z. (). I feel loved when other people feel loved: Cultural congruence in beliefs on love is related to well-being. Journal of Social and Personal Relationships, 39(2), 347-371. https://doi.org/10.1177/02654075211036510
Wan, S., Brick, T., Alvarez-Vargas, D., & Bailey, D. (). Triangulating on Developmental Models With a Combination of Experimental and Nonexperimental Estimates. Developmental Psychology. https://doi.org/10.1037/dev0001490
Li, Y., Li, , Oravecz, Z., Zhou, S., Barnett, I., Bodovski, Y., Zhou, S., Chow, S., Bodovski, Y., Barnett, I., Friedman, N., Chi, G., Barnett, I., Zhou, Y., Chow, S., S., Zhou, Y., Friedman, N., Vrieze, Scott, I, , Vrieze, S., et al (). Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates. Psychometrika, 87(2), 376-402. https://doi.org/10.1007/s11336-021-09831-9
Fredman, S., Fischer, M., Baucom, D., Le, Y., Taverna, E., Chow, S., Ram, N., & Marshall, A. (). PTSD Symptom Cluster Severity Predicts Momentary Emotion Dynamics During Couple Conversations. Behavior Therapy, 54(2), 330-345. https://doi.org/10.1016/j.beth.2022.09.004