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Underlined names indicate collaborators that were students or postdoctoral fellows at the time of publication.

Books

  • Koenig, C., Depaoli, S., Liu, H., Van De Schoot, R., eds. (2022). Moving beyond non-informative prior distributions-achieving the full potential of Bayesian methods for psychological research. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88974-214-1

  • Depaoli, S. (2021). Bayesian structural equation modeling. New York, NY: The Guilford Press. ISBN: 9781462547746

Digital Media

  • Depaoli, S. (2023). Bayesian for beginners. QuantFish. [link here]
    • [22 part video series for statistical online streaming service.]
  • Depaoli, S. (2022). Bayesian structural equation modeling with Mplus. QuantFish. [link here]
    • [24 part video series for statistical online streaming service.]

Peer Reviewed Journal Publications

2023 and in press

  • Marvin, L., Liu, H., & Depaoli, S. (in press). Using Bayesian piecewise growth curve models to handle complex nonlinear trajectories. Journal of Behavioral Data Science.

  • Depaoli, S. & Liu, R. (forthcoming book chapter). Selecting, developing, and analyzing measures in health psychology. In Hagger, M. et al. Sage handbook of health psychology. Newbury Park, CA.

  • Winter, S. D. & Depaoli, S. (2023). Illustrating the value of prior predictive checking for Bayesian structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal. [online first]

2022

  • Heo, I., Jia, F.,& Depaoli, S. (2022). Book Review of Longitudinal structural equation modeling with Mplus: A latent-state trait perspective. Psychometrika. [online first]

  • Depaoli, S., Jia, F., & Heo, I. (2022). Detecting model misspecification in Bayesian piecewise growth models. Structural Equation Modeling: A Multidisciplinary Journal. [online first]

  • Hansford, T. Depaoli, S., & Canelo, K. (2022). Estimating the ideal points of organized interests in legal policy space. Justice System Journal, 43, 564-575.

  • Winter, S. D., & Depaoli, S. (2022). Detecting prior-data disagreement in Bayesian structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 29, 821-838.

  • Visser, M. & Depaoli, S. (2022). A guide to detecting and modeling local dependence in latent class analysis models. Structural Equation Modeling: A Multidisciplinary Journal, 29, 971-982.

  • 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, 667-686.

  • Depaoli, S., Kaplan, D., & Winter, S. D. (2022). Foundations and extensions in Bayesian structural equation modeling. In Hoyle, R. (2nd Ed.), Handbook of structural equation modeling. New York, NY: The Guilford Press.

  • Koenig, C., Depaoli, S., Liu, H., & van de Schoot, R. (2022). Editorial: Moving beyond non-informative prior distributions--achieving the full potential of Bayesian methods for psychological research. Frontiers in Psychology: Quantitative Psychology and Measurement.

  • 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, 531-549.

  • Depaoli, S. (2022). The specification and impact of prior distributions for categorical latent variable models. Structural Equation Modeling: A Multidisciplinary Journal, 29, 350-367.

  • Liu, H., Depaoli, S., & Marvin, L. (2022). Understanding the deviance information criterion for SEM: Cautions in prior specification. Structural Equation Modeling: A Multidisciplinary Journal, 29, 278-294.

2021

  • Bonifay, W., & Depaoli, S. (2021). Model evaluation in the presence of categorical data. Prevention Science. [online first]

  • Arroyo, A. C., Winter, S. D., Depaoli, S., & Zawadzki, M. (2021). Illuminating differences in the psychological predictors of academic performance for first- and continuing-generation students. Journal of 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 using growth curve modeling. Journal of Pediatric Psychology, 30, 1463-1475. doi: 10.1007/s10826-021-01970-y

  • 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. doi: 10.1080/10705511.2021.1894154 [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. doi: 10.1093/OBO/9780199828340-0277

  • van de Schoot, R., Depaoli, S., King, R., Kramer, B., Martens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., & Yau, C (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1, 1-26. doi: 10.1038/s43586-020-00001-2

    • 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. doi: 10.1038/s43586-020-00003-0

  • Felt, J. M., Depaoli, S., & Tiemensma, J. (2021). Stress and information processing: Acute psychosocial stress affects levels of mental abstraction. Anxiety, Stress & Coping, 34, 83-95. doi: 10.1080/10615806.2020.1839646

2020

  • 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. doi: 10.3389/fpsyg.2020.608045 [Special issue about Bayesian priors]

  • Depaoli, S., Lai, K, & Yang, Y. (2020). Bayesian model averaging as an alternative to model selection for multilevel models. Multivariate Behavioral Research. doi: 10.1080/00273171.2020.1778439 [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. doi: 10.1016/j.jadohealth.2020.05.055

  • Guerra-Pena, 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. doi: 10.1371/journal.pone.0231525

  • Zweers, I., van de Schoot, R., Tick, N. T., Depaoli, S., Clifton, J. P., Orobio de Castro, B., & Bijstra, J.O. (2020). Social-emotional development of students with social-emotional and behavioral difficulties in inclusive regular and exclusive special education. International Journal of Behavioral Development, 45, 59-68. doi: 10.1177/0165025420915527

  • 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. doi: 10.1080/10705511.2019.1604140

  • 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 priors of Bayesian statistics. In van de Schoot & Miocevic (Eds.), Small sample size solutions: A guide for applied researchers and practitioners (pp. 30-49). New York, NY: Taylor and Francis. eBook ISBN: 9780429273872

2019

  • 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. doi: 10.1177/0165025419880610

  • Epperson, A., Wallander, J. L., 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. doi: 10.1177/1359105319829536

  • 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. doi: 10.1111/psq.12593

  • Depaoli, S., Winter, S. D., Lai, K., & Guerra-Pena, 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. doi: 10.1080/00273171.2019.1593813

  • Zondervan-Zwijnenburg, M. A. J., Depaoli, S., Peeters, M., & 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. doi: 10.1027/1614-2241/a000162

2018

  • Depaoli, S., Agtarap, S., Choi, A., Coburn, K., & Yu, J. (2018). Translating quantitative methodology within psychological research. Translational Issues in Psychological Science, 4, 335-339. doi: 10.1037/tps0000183 [Special issue I edited 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, 1-7. doi: 10.3389/fendo.2018.00368/

  • Tiemensma, J., Depaoli, S., Winter, S. D., Felt, J. M., Rus, H., & Arroyo, A. (2018). The performance of the IES-R for Latinos and non-Latinos: Assessing measurement invariance. PLOS One, 1-14. doi:10.1371/journal.pone.0195229

  • Depaoli, S., Tiemensma, J., & Felt, J. M. (2018). Assessment of health surveys: Fitting a multidimensional graded response model. Psychology, Health, & Medicine, 23, 1299-1317. doi: 10.1080/13548506.2018.1447136 [Special issue on methodology]

    • Top 10 downloaded article in 2018, 2019, and 2020

  • Depaoli, S., & Liu, Y. (2018). Book review: Bayesian psychometric modeling. Psychometrika, 83, 511-514. doi: 10.1007/s11336-017-9567-8 [invited review] van de Schoot, R, Sijbrandij, M. Depaoli, S., Winter, S. D.,y Ol, M., & van Loey, N. E. (2018). Bayesian PTSD-trajectory analysis with informed priors based on a systematic literature search and expert elicitation. Multivariate Behavioral Research, 53, 267-291. doi: 10.1080/00273171.2017.1412293

2017

  • Depaoli, S., Rus, H., Clifton, J. P., van de Schoot, R., & Tiemensma, J. (2017). An introduction to Bayesian statistics in health psychology. Health Psychology Review, 11, 248-264. doi: 10.1080/17437199.2017.1343676 [Special Issue on Advanced Analytic and Statistical Methods in Health Psychology]

    • 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.

  • 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, 451-467. doi: 10.1080/10705511.2016.1247646

  • Zondervan-Zwijnenburg, M. A. J., Peeters, M., Depaoli, S., & 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. doi: 10.1080/15427609.2017.1370966

  • Felt, J. M., Depaoli, S., & Tiemensma, J. (2017). Latent growth curve models for biomarkers of the stress response. Frontiers in Neuroscience, 11, 1-17. doi: 10.3389/fnins.2017.00315

  • Felt, J. M., Castaneda, R., Tiemensma, J., & Depaoli, S. (2017). Using person t statistics to detect outliers in survey research. Frontiers in Psychology, 8, 1-9. doi: 10.3389/fpsyg.2017.00863

  • Depaoli, S., Yang, Y., & Felt, J. M. (2017). Using Bayesian statistics to model uncertainty in mixture models: A sensitivity analysis of priors. Structural Equation Modeling: A Multidisciplinary Journal, 24, 198-215. doi: 10.1080/10705511.2016.1250640

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

  • Epperson, A., Depaoli, S., Song, A. V., Wallander, J. L., Elliott, M. N., Cuccaro, P., Tortolero, S., & Schuster, M. A. (2017). Perceived physical appearance: Assessing measurement equivalence in Black, Latino, and White adolescents. Journal of Pediatric Psychology, 42, 142-152. doi:10.1093/jpepsy/jsw047

  • Depaoli, S., & van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. Psychological Methods, 22, 240-261. doi: 10.1037/met0000065

2016

  • Depaoli, S., Clifton, J. P., & Cobb, P. R. (2016). Just Another Gibbs Sampler (JAGS): Flexible software for MCMC implementation. Journal of Educational and Behavioral Statistics, 41, 628-649. doi: 10.3102/1076998616664876

  • Tiemensma, J., Depaoli, S., & Felt, J. M. (2016). Using subscales when scoring the Cushing's Quality of Life Questionnaire. European Journal of Endocrinology, 174, 33-40. doi: 10.1530/EJE-15-0640

  • Felt, J. M., Depaoli, S., Andela, C., Pereira, A. M., Biermasz, N. R., Kaptein, A. A., & 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. [refereed abstract]

2015

  • Scott, S., Wallander, J. L., Depaoli, S., Grunbaum, J., Tortolero, S. R., Cuccaro, P. M., Elliott, M. N., & Schuster, M. A. (2015). Gender role orientation is associated with health-related quality of life differently among African American, Hispanic, and White youth. Quality of Life Research, 24, 2139-2149. doi: 10.1007/s11136-015-0951-5

  • Depaoli, S., & Clifton, J. P. (2015). A Bayesian approach to multilevel structural equation modeling with continuous and dichotomous outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 22, 327-351. doi: 10.1080/10705511.2014.937849

  • Felt, J. M., Depaoli, S., Pereira, A. M., Biermasz, N. R., & 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, 173, 37-42. doi: 10.1530/EJE-15-0228

  • Moore, T. M., Reise, S. P., Depaoli, S., & Haviland, M. G. (2015). Iteration of partially specified target matrices: Applications in exploratory and Bayesian confirmatory factor analysis. Multivariate Behavioral Research, 50, 149-161. doi: 10.1080/00273171.2014.973990

  • Depaoli, S., van de Schoot, R., van Loey, N., & Sijbrandij, M. (2015). Using Bayesian statistics for modeling PTSD through latent growth mixture modeling: Implementation and discussion. European Journal of Psychotraumatology, 6, 27516. doi: 10.3402/ejpt.v6.27516

  • Yang, Y., & Depaoli, S. (2015). Autoregressive latent growth modeling: A Bayesian approach. International Meeting of the Psychometric Society, p38. [refereed abstract]

  • Lai, K., Yang, Y., & Depaoli, S. (2015). Bayesian model averaging for near-equivalent path models. International Meeting of the Psychometric Society, p19. [refereed abstract]

  • Clifton, J. P., 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, p8. [refereed abstract]

  • Felt, J. M., Depaoli, S., Pereira, A. M., Biermasz, N. R., & Tiemensma, J. (2015). Using novel confirmatory statistical methods to compare scoring options of the Acromegaly Quality of Life (AcroQoL) Questionnaire. Endocrinology Review, PP09-3. [refereed abstract]

2014

  • Ortiz, R. M., Rodriguez, R., Depaoli, S., & Weer, S. E. (2014). Increased physical activity reduces the odds of developing elevated systolic blood pressure independent of body mass or ethnicity in rural adolescents. Journal of Hypertension: Open Access, 3, 1-8. doi: 10.4172/2167-1095.1000150

  • van de Schoot, R, & Depaoli, S. (2014). Bayesian analyses: Where to start and what to report. European Health Psychologist, 16, 75-84.

  • Depaoli, S. (2014). The impact of inaccurate \informative" priors for growth parameters in Bayesian growth mixture modeling. Structural Equation Modeling: A Multidisciplinary Journal, 21, 239-252. doi: 10.1080/10705511.2014.882686

  • Depaoli, S., & Boyajian, J. (2014). Linear and nonlinear growth models: Describing a Bayesian perspective. Journal of Consulting and Clinical Psychology, 82, 784-802. doi: 10.1037/a0035147

2013

  • Depaoli, S. (2013). Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. Psychological Methods, 18, 186-219. doi: 10.1037/a0031609

  • Kaplan, D., & Depaoli, S. (2013). Bayesian statistical methods. In Little, T. (Eds.), The Oxford handbook of quantitative methods (pp. 406-436). New York, NY: Oxford University Press.

2012

  • Depaoli, S. (2012). The ability for posterior predictive checking to identify model mis-specification in Bayesian growth mixture modeling. Structural Equation Modeling: A Multidisciplinary Journal, 19, 534-560. doi: 10.1080/10705511.2012.713251

  • Kaplan, D., & Depaoli, S. (2012). Bayesian structural equation modeling. In Hoyle, R. (Eds.), Handbook of structural equation modeling (pp. 650-673). New York, NY: The Guilford Press.

  • Depaoli, S. (2012). Measurement and structural model class separation in mixture-CFA: ML/EM versus MCMC. Structural Equation Modeling: A Multidisciplinary Journal, 19, 178-203. doi: 10.1080/10705511.2012.659614

2011 and before

  • Kaplan, D., & Depaoli, S. (2011). Two studies of specification error in models for categorical latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 18, 397-418. doi: 10.1080/10705511.2011.582016

  • Depaoli, S. (2010). Measurement and structural model class separation in mixture-CFA. Multivariate and Behavioral Research, 45, 1023. doi: 10.1080/00273171.2010.534376 [refereed abstract]

  • Depaoli, S., & Meyers, L. S. (2007). A path model using esteem to predict health attitudes and exercise frequency. Contemporary Issues in Education Research, 1, 41-52.