(*represents student or post-doc collaborator)

Under Review or Revision

  • Depaoli, S. & Visser, M.* (under review). Frequentist and Bayesian approaches to latent class modeling with a distal outcome: One- versus three-step approaches.
  • Winter, S. D.,* & Depaoli, S. (under revision). Bayesian approximate measurement invariance for second-order latent growth models: Assessing continuous and dichotomous indicators.


  • Depaoli, S. (2021). Bayesian structural equation modeling. The Guilford Press.

2021 and in press

  • Depaoli, S. (accepted). The specification and impact of prior distributions for categorical latent variable models. Structural Equation Modeling: A Multidisciplinary Journal.
  • Liu, H., Depaoli, S., & Marvin, L.* (accepted). Understanding the deviance information criterion for SEM: Cautions in prior specification. Structural Equation Modeling: A Multidisciplinary Journal.
  • Depaoli, S., Kaplan, D., & Winter, S. D.* (accepted). Foundations and extensions in Bayesian structural equation modeling. In Hoyle, R. (2nd Ed.), Handbook of structural equation modeling. The Guilford Press.
  • Bonifay, W. & Depaoli, S. (accepted) Model evaluation in the presence of categorical data. Prevention Science. [invited paper for special issue]
  • Arroyo, A.,* Winter, S. D.,* Depaoli, S., & Zawadzki, M. (2021). Illuminating differences in the psychological predictors of academic performance for first- and continuing-generation students. Educational & Psychological Research, 3, 234-246.
  • Kim, K. W.,* Wallander, J. L., Depaoli, S., Elliott, M. N., & Schuster, M. A. (2021). Longitudinal associations between parental SES and adolescents’ health-related quality of life: A growth curve model approach. Journal of Child and Family Studies, 30, 1463-1475.
  • Depaoli, S., Liu, H., & Marvin, L.* (2021). Parameter specification in Bayesian CFA: An exploration of multivariate and separation strategy priors. Structural Equation Modeling: A Multidisciplinary Journal. [online first]
  • Depaoli, S. (2021). Bayesian Statistical Methods in Psychology. In Oxford Bibliographies in Psychology. Ed. Dana S. Dunn. New York, NY: Oxford University Press.
  • van de Schoot, R., Depaoli, S., King, R., Kramer, B., M ̈artens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D.,† Willemsen, J., & Yau, C. (2021). Bayesian statistical modelling. Nature Reviews Methods Primers, 1, 1-26.
    • Commentary and a visual depiction of the points in this paper (which originated in Depaoli & van de Schoot, 2017; Psychological Methods): Morneau, D. (2021). PrimeView: Bayesian statistics and modelling. Nature Reviews Methods Primers, 1, 1-3.
  • Felt, J. M.,* Depaoli, S., & Tiemensma, J. (2021). Stress and information processing: Acute psychosocial stress affect levels of mental abstraction. Anxiety, Stress, & Coping, 34, 83-95.


  • 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, 1-18. [special issue on Quantitative Psychology and Measurement].
  • Depaoli, S., Lai, K, and Yang, Y.,*. (2020). Bayesian model averaging as an alternative to model selection for multilevel models. Multivariate Behavioral Research. [online first]
  • Martin-Gutierrez, G.*, Wallander, J.L., Yang, Y.*, Depaoli, S., Elliott, M.N., Coker, T.R., & Schuster, M.A. (2020). Racial/ethnic differences in the relationship between stressful life events and quality of life in adolescents. Journal of Adolescent Health, 68, 292-299.
  • Guerra-Peña, K., Garcia-Batista, Z. E., Depaoli, S., & Garrido, L. E. (2020). Class enumeration false positive in skew-t family of continuous growth mixture models. PLOS One, 1-19.
  • Zweers, I., van de Schoot, R., Tick, N. T., Depaoli, S.Clifton, J. P., Orobio de Castro, B., and Bijstra, J.O. (2020). Similar development in separate educational contexts? Development of social relationships and self-esteem in students with social-emotional and behavioral difficulties in inclusive classrooms and exclusive schools for special education. International Journal of Behavioral Development, 45, 59-68.
  • Smid, S.,* Depaoli, S., & van de Schoot, R. (2020). Predicting a distal outcome variable from a latent growth model: ML versus Bayesian estimation. Structural Equation Modeling: A Multidisciplinary Journal, 27, 169-191.
  • van de Schoot, R., Veen, D.,* Smeets, L.,* Winter, S.,* & Depaoli, S. (2020). A tutorial on using the WAMBS-checklist to avoid the misuse of Bayesian statistics. In van de Schoot & Miocevic (Eds.), Small sample size solutions (pp. 35-49). Taylor and Francis.


  • Winter, S. D.,* & Depaoli, S. (2019). An illustration of Bayesian approximate measurement invariance with longitudinal data and a small sample size. International Journal of Behavioral Development [Methods and Measures Section], 44, 371-382.
  • Epperson, A.,* Wallander, J., Song., A. V., Depaoli, S., Peskin, M. F., Elliot, M. N., & Schuster, M. A. (2019). Gender and racial/ethnic differences in adolescent intentions and willingness to smoke cigarettes: Evaluation of a structural equation model. Journal of Health Psychology, 26, 605-619.
  • Hansford, T. G., Depaoli, S., & Canelo, K. S.* (2019). Locating U.S. Solicitors General in the Supreme Court’s policy space. Presidential Studies Quarterly, 49, 855-869.
  • 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, 795-821.
  • Zondervan-Zwijnenburg, M. A. J.,* Depaoli, S., Peeters, M., and van de Schoot, R. (2019). Pushing the limits: The performance of ML and Bayesian estimation with small and unbalanced samples in a latent growth model. Methodology, 15, 31-43.


  • Depaoli, S., Agtarap, S.,* Choi, A. Y.,* Coburn, K. M.,* & Yu, J.* (2018). Advances in quantitative research within the psychological sciences. Translational Issues in Psychological Science, 4, 335-339. Special issue on Quantitative Methods in Psychology.
  • 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.
  • Tiemensma, J., Depaoli, S., Winter, S. D.,* Felt, J. M.,* Rus, H.,* and Arroyo, A.* (2018). The Performance of the IES-R for Latinos and non-Latinos: Assessing Measurement Invariance. PLOS One.
  • Depaoli, S., Tiemensma, J., and Felt, J.* (2018). Assessment of health surveys: Fitting a multidimensional graded response model. Psychology, Health, and Medicine (Methodology special issue), 23, 13-31.
  • Depaoli, S., and Liu, Y. (2018). Review: Bayesian Psychometric Modeling. Psychometrika, 83, 511-514. here
  • van de Schoot, R., Sijbrandij, M., Depaoli, S., Winter, S., Olff, M., & van Loey, N. (2018). Bayesian PTSD-trajectory analysis with informed priors based on a systematic literature search and expert elicitation. Multivariate Behavioral Research, 53, 267-291.


  • Zondervan-Zwijnenburg, M. A. J.,* Peeters, M., Depaoli, S., and van de Schoot, R. (2017). Where do priors come from? Applying guidelines to construct informative priors in small sample research. Research in Human Development, 14, 305-320. here
  • Depaoli, S., and van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. Psychological Methods, 22, 240-261. here
  • Felt, J.,* Depaoli, S., and Tiemensma, J. (2017). An overview of latent growth curve models for biological markers of stress, Frontiers in Neuroscience, 11, 1-17. here
  • Felt, J. M.,* Castaneda, R.,* Tiemensma, J., and Depaoli, S. (2017). Identifying “atypical” responses in the CushingQoL questionnaire: Using person fit statistics to detect outliers. Frontiers in Psychology, 8, 1-9. here
  • van de Schoot, R., Winter, S.,* Zondervan-Zwijnenburg, M.,* Ryan, O.,* and Depaoli, S. (2017). A systematic review of Bayesian applications in psychology: The last 25 years. Psychological Methods, 22, 217-239. here
  • Depaoli, S., Rus, H.,*  Clifton, J.,* van de Schoot, R., and Tiemensma, J. (2017).  An introduction to Bayesian statistics in health psychology. Health Psychology Review, 11, 248-264. here
    • Commentary published about this paper: Beard, E., & West, R. (2017). Using Bayesian statistics in health psychology: A comment on Depaoli et al. (2017), Health Psychology Review, 11, 298-301. here
  • Depaoli, S., Yang, Y.,* and Felt, J.* (2017). Using Bayesian statistics to model uncertainty in mixture models: A sensitivity analysis of priors. Structural Equation Modeling: A Multidisciplinary Journal, 24, 198-215. here
  • Epperson, A.,* Depaoli, S., Song, A. V., Wallander, J. L., Elliott, M., Cuccaro, P., Tortolero, S., and Schuster, M. (2017). Perceived physical appearance: Assessing measurement equivalence in Black, Latino, and White Adolescents. Journal of Pediatric Psychology, 42, 142-152. here
  • van de Schoot, R., Sijbrandij, M., Winter, S.,* Depaoli, S., and Vermunt, J. K. (2017). The development of the GRoLTS-checklist: A tool for assessing the quality of reporting on latent trajectory studies. Structural Equation Modeling: A Multidisciplinary Journal, 24, 451–467. here


  • Depaoli, S., Clifton, J. P.,* and Cobb, P. R.* (2016). Just Another Gibbs Sampler (JAGS): A Flexible Software for MCMC Implementation. Journal of Educational and Behavioral Statistics, 41, 628-649. here
  • Tiemensma, J., Depaoli, S., and Felt, J.* (2016). Using subscales when scoring the Cushing’s quality of life (CushingQoL) Questionnaire. European Journal of Endocrinology, 174, 33-40.
  • Refereed Conference Proceedings/Abstracts:
    • Felt, J.,* Depaoli, S., Andela, C., Pereira, A., Biermasz, N., Tiemensma, J. (2016). Using the Common sense model of illness representations to better understand the impaired quality of life of patients treated for neuroendocrine diseases. Endocrine Reviews, 37, SAT-502.


  • Felt, J.,* Depaoli, S., Pereira, A. M., Biermasz, N. R., and Tiemensma, J. (2015). Total score or subscales in scoring the Acromegaly Quality of Life Questionnaire: Using novel confirmatory methods to compare scoring options. European Journal of Endocrinology, 172, 37-42.
  • Moore, T. M., Reise, S. P., Depaoli, S., and Haviland, M. G. (2015). Iteration of partially specified target matrices in exploratory and Bayesian confirmatory factor analysis. Multivariate Behavioral Research, 50, 149-161.
  • Depaoli, S., and Clifton, J.* (2015). A Bayesian approach to multilevel structural equation modeling with continuous and dichotomous outcomes. Structural Equation Modeling, 22, 327-351here
  • Scott, S.,* Wallander, J., Depaoli, S., Grunbaum, J., Tortolero, S. R., Cuccaro, P. M., Elliott, M. N., and Schuster, M. A. (2015). Gender role orientation and health-related quality of life among African American, hispanic, and white youth. Quality of Life Research, 24, 2139-2149.
  • Depaoli, S., van de Schoot, R., van Loey, N., and Sijbrandij, M. (2015). Using Bayesian statistics for modeling PTSD through latent growth mixture modeling: Implementation and discussion. European Journal of Psychotraumatology, 6, 27516.
  • Refereed Conference Proceedings/Abstracts:
    • Felt, J.,* Depaoli, S., Pereira, A., Biermasz, N., & Tiemensma, J. (2015). Using novel confirmatory statistical methods to compare scoring options of the acromegaly quality of life (AcroQoL) questionnaire. Endocrine Reviews, 36, PP09-3.
    • Clifton, J.,* Depaoli, S., & Lai, K. (2015). Skewed within-class mixture distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration.  International Meeting of the Psychometric Society, 8.
    • Lai, K., Yang, Y.,* & Depaoli, S. (2015). Bayesian model averaging for near-equivalent path models.  International Meeting of the Psychometric Society, 19.
    • Yang, Y.,* & Depaoli, S. (2015). Autoregressive latent growth modeling: A Bayesian approach.  International Meeting of the Psychometric Society, 38.


  • Depaoli, S. (2014). The impact of inaccurate “informative” priors for growth parameters in Bayesian growth mixture modeling. Structural Equation Modeling, 21, 239-252.
  • Ortiz, R. M., Rodriguez, R.,* Depaoli, S., and Weffer, S. E. (2014). Increased physical activity reduces the odds of developing elevated systolic blood pressure independent of body mass category or ethnicity in rural adolescents. Journal of Hypertension: Open Access, 3, 1-8.
  • Depaoli, S., and Boyajian, J.* (2014). Linear and nonlinear growth models: Describing a new Bayesian perspective. Journal of Consulting and Clinical Psychology, 82, 784-802.
  • van de Schoot, R, and Depaoli, S. (2014). Bayesian analyses: Where to start and what to report. European Health Psychologist, 2, 75–84.


  • Depaoli, S. (2013). Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. Psychological Methods, 18, 186-219.
  • Kaplan, D., and Depaoli, S. (2013). Bayesian statistical methods. In Little, T. (Eds.), Handbook for quantitative methods (pp. 406-436). Oxford University Press.


  • Depaoli, S. (2012). The ability for posterior predictive checking to identify model mis-specification in Bayesian growth mixture modeling. Structural Equation Modeling, 19, 534-560.
  • Kaplan, D., and Depaoli, S. (2012). Bayesian structural equation modeling. In Hoyle, R. (Eds.), Handbook of structural equation modeling (pp. 650-673). Guilford Press.
  • Depaoli, S. (2012). Measurement and structural model class separation in mixture-CFA: ML/EM versus MCMC. Structural Equation Modeling, 19, 178-203.


  • Kaplan, D., and Depaoli, S. (2011). Two studies of specification error in models for categorical latent variables. Structural Equation Modeling, 18, 397-418.

2010 and earlier

  • Depaoli, S. (2010). Measurement and structural model class separation in mixture-CFA. Multivariate and Behavioral Research, 45, 1023.
  • Depaoli, S., and Meyers, L. S. (2007). A path model using esteem to predict health attitudes and exercise frequency. Contemporary Issues in Education Research, 1, 41-52.