2018
Anders, R., Oravecz, Z., & Alario, F. (). Improved information pooling for hierarchical cognitive models through multiple and covaried regression. Behavior Research Methods, 50(3), 989-1010. https://doi.org/10.3758/s13428-017-0921-7
Chow, S., Ou, L., Ciptadi, A., Prince, E., You, D., Hunter, M., Rehg, J., Rozga, A., & Messinger, D. (). Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching. Psychometrika, 83(2), 476-510. https://doi.org/10.1007/s11336-018-9605-1
Forgeard, M., & Benson, L. (). Extracurricular involvement and psychological adjustment in the transition from adolescence to emerging adulthood: The role of mastery and creative self-efficacy. Applied Developmental Science, 201, 1-18. http://doi.org/10.1080/10888691.2017.1288124
Baribault, B., Donkin, C., Little, D., Trueblood, J., Oravecz, Z., Van Ravenzwaaij, D., White, C., De Boeck, P., & Vandekerckhove, J. (). Metastudies for robust tests of theory. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 2607-2612. https://doi.org/10.1073/pnas.1708285114
Hunter, M. (). State Space Modeling in an Open Source, Modular, Structural Equation Modeling Environment. Structural Equation Modeling, 25(2), 307-324. https://doi.org/10.1080/10705511.2017.1369354
Lieberman, J., De Souza, M., Wagstaff, D., & Williams, N. (). Menstrual Disruption with Exercise Is Not Linked to an Energy Availability Threshold. Medicine & Science in Sports & Exercise, 50(3), 551-561. http://doi.org/10.1249/MSS.0000000000001451
Batchelder, W., Anders, R., & Oravecz, Z. (). Cultural Consensus Theory: The Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Volume V: Methodology. , 221-264. https://doi.org/10.1002/9781119170174.epcn506
Oravecz, Z., & Muth, C. (). Fitting growth curve models in the Bayesian framework. Psychonomic Bulletin and Review, 25(1), 235-255. https://doi.org/10.3758/s13423-017-1281-0
Helm, J., Castro-Schilo, L., Zavala-Rojas, D., DeCastellarnau, A., & Oravecz, Z. (). Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design. Structural Equation Modeling, 25(1), 71-85. https://doi.org/10.1080/10705511.2017.1378103
Chen, M., Chow, S., & Hunter, M. (). Stochastic Differential Equation Models with Time-Varying Parameters. Continuous Time Modeling in the Behavioral and Related Sciences, 205--238. https://doi.org/10.1007/978-3-319-77219-6_9
Snoke, J., Brick, T., Slavković, A., & Hunte, M. (). Providing accurate models across private partitioned data: Secure maximum likelihood estimation. Annals of Applied Statistics, 12(2), 877-914. https://doi.org/10.1214/18-AOAS1171
Wood, J., Oravecz, Z., Vogel, N., Benson, L., Chow, S., Cole, P., Conroy, D., Pincus, A., & Ram, N. (). Modeling intraindividual dynamics using stochastic differential equations:age differences inaffect regulation. Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 73(1), 171-184. https://doi.org/10.1093/geronb/gbx013
Baribault, B., Donkin, C., Little, D., Trueblood, J., Oravecz, Z., van Ravenzwaaij, D., While, C., de Boeck, P., & Vandekerckhove, J. (). Meta-studies for robust tests of theory: Proceedings of the National Academy of Sciences. Proceedings of the National Academy of Sciences, 115(11), 2607-2612. https://doi.org/10.1073/pnas.1708285114
Gates, K., Fisher, Z., Bollen, K., & Henry, T. (). Identifying and Estimating Relations Within and Between Functional Brain Networks using MIIVsem and GIMME: Modern Modeling Methods. .
Oravecz, Z., Wood, J., & Ram, N. (). Fitting continuous time stochastic process models in the Bayesian framework.. Continuous Time Modeling in the Behavioral and Related Sciences, 55-78. https://doi.org/10.1007/978-3-319-77219-6_3
Garrison, S., O'Keefe, P., Hunter, M., Beasley, W., Bard, D., & Rodgers, J. (). AC'RE model: Estimating rearing effects without twins raised apart: Behavior Genetics. , 48(6), 472--472.
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(1), 126-135. https://doi.org/10.1093/geronb/gby014
Henry, T., Fisher, Z., & Bollen, K. (). Bayesian model averaging for model implied instrumental variable two stage least squares estimators. arXiv preprint arXiv:1808.10522.
Benson, L., Ram, N., Almeida, D., Zautra, A., & Ong, A. (). Fusing Biodiversity Metrics into Investigations of Daily Life: Illustrations and Recommendations With Emodiversity. The Journals of Gerontology: Series B, 73(1), 75–86. http://doi.org/10.1093/geronb/gbx025
Koffer, R., Ram, N., & Almeida, D. (). More than Counting: An Intraindividual Variability Approach to Categorical Repeated Measures. The Journals of Gerontology: Series B, 73(1), 87-99. http://doi.org/10.1093/geronb/gbx086
Varangis, E., Razlighi, Q., Habeck, C., Fisher, Z., & Stern, Y. (). Between-network functional connectivity is modified by age and cognitive task domain. Journal of Cognitive Neuroscience, 31(4), 607-622. https://doi.org/10.1162/jocn_a_01368
Muth, C., Oravecz, Z., & Gabry, J. (). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan: Quantitative Methods for Psychology. Quantitative Methods for Psychology, 14(2), 99-119. https://doi.org/10.20982/tqmp.14.2.p099
Chang, B., Fisher, Z., Bollen, K., Mintz, A., Flotron, S., Borde, A., Swor, R., Peak, D., Rathlev, N., & McLean, S. (). Contributions of Minor Traumatic Brain Injury to the Development of Posttraumatic Stress Following Motor Vehicle Accident. Biological Psychiatry, 83(9).
2017
Wood, J., Oravecz, Z., Vogel, N., Benson, L., Chow, S., Cole, P., Conroy, D., Pincus, A., & Ram, N. (). Modeling Intraindividual Dynamics Using Stochastic Differential Equations: Age Differences in Affect Regulation.. The journals of gerontology. Series B, Psychological sciences and social sciences, 73(1), 171-184. https://doi.org/10.1093/geronb/gbx013
Tang, N., Chow, S., Ibrahim, J., & Zhu, H. (). Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness. Psychometrika, 82(4), 875-903. https://doi.org/10.1007/s11336-017-9587-4