Publications

Bonifay W., Winter, S. D., Skoblow, H., & Watts. A. (revise & resubmit). Good fit is not a Sufficient Indicator of Replication Success. [Invited contribution to special issue of Assessment on “Assessment, Measurement, and Registered Replication.”]

Depaoli, S., Winter, S. D., & Liu, H. (in press). Under-Fitting and Over-Fitting: The Performance of Bayesian Model Selection and Fit Indices in SEM. Structural Equation Modeling: A Multidisciplinary Journal.

2022 - 2023

Parmenter, J. G. & Winter, S. D. (2023). Inequity within the LGBTQ+ Community as a Distal Stressor: An Extension of Minority Stress Theory. Psychology of Sexual Orientation and Gender Diversity. doi:10.1037/sgd0000674

Merkle, E., Ariyo, O., Winter, S. D., and Garnier-Villarreal, M. (2013). Opaque Prior Distributions in Bayesian SEM. Methodology, 19(3), 228-255. doi:10.5964/meth.11167

Winter, S. D., & Depaoli, S. (2023). Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal. doi:10.1080/10705511.2022.2164286 [OSF]

Winter, S. D., & Depaoli, S. (2022). Detecting Prior-Data Disagreement in Bayesian Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal. doi:10.1080/10705511.2022.2039066 [OSF]

Winter, S. D., & Depaoli, S. (2022). Sensitivity of Bayesian Model Fit Indices to the Prior Specification of Latent Growth Models. Structural Equation Modeling: A Multidisciplinary Journal, 29(5), 667-686. doi:10.1080/10705511.2022.2032078 [OSF]

Winter, S. D., & Depaoli, S. (2022). Performance of Model Fit and Selection Indices for Bayesian Structural Equation Modeling with Missing Data. Structural Equation Modeling: A Multidisciplinary Journal, 29(4), 531-549. doi:10.1080/10705511.2021.2018656 [OSF]

2020 - 2021

van de Schoot, R., Winter, S. D., Griffioen, E., Grimmelikhuijsen, S., Arts, I., Veen, D., Grand- field, E. M., & Tummers, L. G. (2021). The Use of Questionable Research Practices to Survive in Academia: Expert Elicitation, Prior-Data Conflicts, Bayes Factors for Replication Effects, and the Bayes Truth Serum. Frontiers in Psychology: Quantitative Psychology & Measurement, 12, 621547 doi:10.3389/fpsyg.2021.621547 [OSF]

Arroyo, A. C., Winter, S. D., Depaoli, S., & Zawadzki, M. J. (2021). Illuminating Differences in The Psychological Predictors of Academic Performance for First- And Continuing-Generation Students. Journal of Educational and Psychological Research, 3(2), 234-246.

Depaoli, S., Winter, S. D., & Visser, M. (2020). The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations using an Interactive Shiny App. Frontiers in Psychology: Quantitative Psychology & Measurement, 11, 3271. doi:10.3389/fpsyg.2020.608045 [OSF]

Smid, S. C., & Winter, S. D. (2020). Dangers of the Defaults: A Tutorial on the Impact of Default Priors when using Bayesian SEM with Small Samples. Frontiers in Psychology: Quantitative Psychology & Measurement, 11, 611963. doi:10.3389/fpsyg.2020.611963 [OSF]

Winter, S. D., & Depaoli, S. (2020). An Illustration of Bayesian Approximate Measurement Invariance with Longitudinal Data and a Small Sample Size. International Journal of Behavioral Development, 44(4), 371-382. doi:10.1177/0165025419880610

2018 - 2019

Depaoli, S., Winter, S. D., Lai, K., & Guerra-Peña, K. (2019). Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration. Multivariate Behavioral Research, 54(6), 795-821. doi:10.1080/00273171.2019.1593813

Hoorn, J. F., & Winter, S. D. (2018). Here comes the bad news: Doctor robot taking over. International Journal of Social Robotics, 10, 519-535. doi:10.1007/s12369-017-0455-2

Tiemensma, J., Depaoli, S., Winter, S. D., Felt, J. M., Rus, H., & Arroyo, A. C. (2018). The Performance of the IES-R for Latinos and non-Latinos: Assessing Measurement Invariance. PLoS ONE, 13(4), e0195229. doi:10.1371/journal.pone.0195229

van de Schoot, R., Sijbrandij, M., Depaoli, S., Winter, S. D., & van Loey, N. (2018). Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation. Multivariate Behavioral Research, 53(2), 267-291. doi:10.1080/00273171.2017.1412293 [OSF]

Winter, S. D., Depaoli, S., & Tiemensma, J. (2018). Assessing differences in how the CushingQoL is interpreted across countries: Comparing patients from the U.S. and the Netherlands. Frontiers in Endocrinology, 9, 368.doi:10.3389/fendo.2018.00368

Pre-2018

van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., & Vermunt, J. K. (2017). The GRoLTS- Checklist: Guidelines for reporting on latent trajectory studies. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 451-467. doi:10.1080/10705511.2016.1247646 [OSF]

van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M. & Depaoli, S. (2017). A systematic review of Bayesian articles in psychology: The last 25 years. Psychological Methods, 22(2), 217-239. doi:10.1037/met0000100

Konijn, E. A., van der Schoot, R., Winter, S. D., & Ferguson, C. J. (2015). Possible solution to publication bias through Bayesian statistics, including proper null hypothesis testing. Communication Methods and Measures, 9(4), 280-302. doi:10.1080/19312458.2015.1096332

Book Chapters

Depaoli, S., Kaplan, D., & Winter, S. D. (2023). Foundations and extensions in Bayesian structural equation modeling. In Hoyle, R. (2nd Ed.) Handbook of structural equation modeling. New York, NY: The Guilford Press. [OSF]

van de Schoot, R., Griffioen, E., & Winter, S. D. (2021). Dealing with imperfect elicitation results. In Hanea, A.M., Nane, G.F., Bedford, T., French, S. (Eds.), Expert Judgment in Risk and Decision Analysis. Basel: Springer International Publishing. [OSF]

van de Schoot, R., Veen, D., Smeets, L., Winter, S. D., & Depaoli, S. (2020). A Tutorial on Using The Wambs Checklist to Avoid The Misuse of Bayesian Statistics. In R. van de Schoot & M. Mio ̆cević (Eds.),Small Sample Size Solutions: A guide for applied researchers and practitioners (pp. 30–49). London: Routledge. doi:10.4324/9780429273872-4