2019
Hepworth, A., Brick, T., & Small, M. (). Exploring Parents’ Satisfaction with Infant and Toddler Feeding Information: A Repeated-Measures Analysis of Information Need and Acquisition Characteristics (P17-006-19). Current developments in nutrition, 3(0), nzz038--P17. https://doi.org/10.1093/cdn/nzz038.P17-006-19
Girault, J., Arizmendi, C., Fisher, Z., Urban, C., Piven, J., & Gates, K. (). Identifying Age-Related Functional Connectivity Features Across Different Levels of Spatial Resolution: An Application of Multi-Scale GIMME: Brain Initiative. .
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
Schneider, J., & Brick, T. (). plsR: Partial Least Squares Analysis for Dummies. .
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.
Li, Y., Ji, L., Oravecz, Z., Hunter, M., & Chow, S. (). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling: Proceedings of the International Conference on Computational Social Science (ICCSS 2019). , 305-314.
2018
Bollen, K., Gates, K., & Fisher, Z. (). Robustness Conditions for MIIV-2SLS When the Latent Variable or Measurement Model is Structurally Misspecified. Structural Equation Modeling, 25(6), 848-859. https://doi.org/10.1080/10705511.2018.1456341
Santos-Lozada, A., & Howard, J. (). Use of death counts from vital statistics to calculate excess deaths in Puerto Rico following Hurricane Maria. JAMA, 320(14), 1491-1493. https://doi.org/10.1001/jama.2018.10929
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
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
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
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
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.
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. .
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
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
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