• Elected member: Society of Multivariate Experimental Psychology (only 65 active members in the world)
• American Educational Research Association, Division D: Measurement and Research Methodology
• American Psychological Association, Division 5: Quantitative and Qualitative Methods
• Association for Psychological Science
• National Council on Measurement in Education
• Psychometric Society

• Association for Psychological Science, Rising Star (Early Career) Award, Quantitative Psychology, 2015
• University of California, Hellman Fellow, 2013
• American Psychological Association, Division 5 Distinguished Dissertation
• Award, 2011
• Society of Multivariate Experimental Psychology Travel Award, 2010
• International Meeting of the Psychometric Society Travel Award, 2010
• American Educational Research Association, Division D Travel Award, 2010
• CSU, Sacramento Competitive Research Travel Award, 2007
• CSUS, Albert M. Swanson Memorial Scholarship Award, 2007

• Associate Editor for Psychological Methods, 2019-present
• Associate Editor for Multivariate Behavioral Research, 2017-present
• Guest Editor for Special Issue on Quantitative Methods in Psychology for Translational Issues in Psychological Science, 2017-2018

• American Psychologist
• Behavior Research Methods
• Child Development Perspectives Computational Statistics Evaluation Review
• Int. Journal of Systems Science
• International Journal of Testing
• Journal of Behavioral Medicine
• Journal of Counseling Psychology
• Journal of Experimental Education
• Journal of Multivariate Analysis
• Journal of Personality Assessment
• Journal of the Royal Statistical Society
• J. of Statistical Theory and Practice Learning and Individual Differences Methodology
• Multivariate Behavioral Research Psychological Methods Psychometrika
• Statistics in Medicine
• Structural Equation Modeling
• Research Synthesis Methods
• Psychological Methods
• Multivariate Behavioral Research
• Psychological Methods
• Methods in Psychology (Quantitative section of Journal)
• Society of Multivariate Experimental Psychology
• Health Psychology Review (Research Methods and Statistical Analysis section
• American Psychologist
• Advances in Methods and Practices in Psychological Science
• VENI grant reviewer for the Dutch government
• Reviewer for American Psychological Association, Div 5 conference
• Reviewer for American Educational Research Association, Div D conf.
• Reviewer for Society of Multivariate Experimental Psychology student proposals
• What Works Clearinghouse

* denotes a student or post-doc collaborator

• Winter, S. D.,* & Depaoli, S. (accepted). An illustration of Bayesian approximate measurement invariance with longitudinal data and a small sample size. International Journal of Behavioral Development [Methods and Measures Section].
• Epperson, A.,* Wallander, J., Song., A. V., Depaoli, S., Peskin, M. F., Elliot, M. N., & Schuster, M. A. (in press). Gender and racial/ethnic differences in adolescent intentions and willingness to smoke cigarettes: Evaluation of a structural equation model. Journal of Health Psychology.
• Smid, S.,* Depaoli, S., & van de Schoot, R. (in press). Predicting a distal outcome variable from a latent growth model: ML versus Bayesian estimation. Structural Equation Modeling: A Multidisciplinary Journal.
• Hansford, T. G., Depaoli, S., & Canelo, K. S.* (in press). Locating U.S. Solicitors General in the Supreme Court’s policy space. Presidential Studies Quarterly.
• Depaoli, S., Winter, S. D.,* Lai, K., & Guerra-Peña, K. (in press). Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration. Multivariate Behavioral Research.
• 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.
• Zondervan-Zwijnenburg, M. A. J.,* Depaoli, S., Peeters, M., and van de Schoot, R. (2018). Pushing the limits: The performance of ML and Bayesian estimation with small and unbalanced samples in a latent growth model. Methodology.
• 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.
• 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. 
• 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.  
• Depaoli, S., and van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. Psychological Methods, 22, 240-261.  
• 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.  
• 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. 
• 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. 
• 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.  
• 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. 
• 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. 
• 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. 
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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, 327-351. 
• 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.

• Depaoli, S. (under contract). Bayesian Structural Equation Modeling. Guilford Press.

• van de Schoot, R., Veen, D.,* Smeets, L.,* Winter, S.,* & Depaoli, S. (in press). A tutorial on using the WAMBS-checklist to avoid the misuse of Bayesian statistics.

* denotes a student or post-doc collaborator

• van de Schoot, R., Veen, D.,* Smeets, L.,* Winter, S.,* & Depaoli, S. (in press). A tutorial on using the WAMBS-checklist to avoid the misuse of Bayesian statistics.