2018
Wilburne, J., Polly, D., Franz, D., & Wagstaff, D. (). Mathematics teachers' implementation of high-leverage teaching practices: A Q-sort study. School Science and Mathematics, 118(6), 232-243. https://doi.org/10.1111/ssm.12293
Graham-Engeland, J., Song, S., Mathur, A., Wagstaff, D., Klein, L., Whetzel, C., & Ayoub, W. (). Emotional State Can Affect Inflammatory Responses to Pain Among Rheumatoid Arthritis Patients: Preliminary Findings. Psychological Reports, 122(6), 2026-2049. https://doi.org/10.1177/0033294118796655
Ji, L., Chow, S., Schermerhorn, A., Jacobson, N., & Cummings, E. (). Handling Missing Data in the Modeling of Intensive Longitudinal Data. Structural Equation Modeling, 25(5), 715-736. https://doi.org/10.1080/10705511.2017.1417046
Benson, L., Ram, N., & Stifter, C. (). Using Fishery Models to Examine Self- and Co-Regulation Processes Across Multiple Timescales. Structural Equation Modeling: A Multidisciplinary Journal, 25(6), 906-923. http://doi.org/10.1080/10705511.2018.1491313
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
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
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
Pritikin, J., Brick, T., & Neale, M. (). Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random. Behavior Research Methods, 50(2), 490-500. https://doi.org/10.3758/s13428-017-1011-6
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
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
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
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
Brinberg, M., Ram, N., Hülür, G., Brick, T., & Gerstorf, D. (). Analyzing dyadic data using grid-sequence analysis: Interdyad differences in intradyad dynamics. Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 73(1), 5-18. https://doi.org/10.1093/geronb/gbw160
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. .
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
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
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.
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
Henry, T., Fisher, Z., & Bollen, K. (). Bayesian model averaging for model implied instrumental variable two stage least squares estimators. arXiv preprint arXiv:1808.10522.
Brick, T., Koffer, R., Gerstorf, D., & Ram, N. (). Feature selection methods for optimal design of studies for developmental inquiry. Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 73(1), 113-123. https://doi.org/10.1093/geronb/gbx008
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
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