Methods Center

Associate Professor Sarah Depaoli

Distinguished Visiting Professor in Quantitative Methods


Haußerstr. 11
72076 Tübingen


Office hours
By appointment via E-Mail

Dr. Sarah Depaoli is an Associate Professor of Quantitative Psychology at the University of California, Merced. She has been appointed as a Visiting Distinguished Professor at the Faculty of Economics and Social Sciences, Methods Center, at the University of Tübingen.

She is a proven expert in the field of quantitative methods in the social and behavioural sciences, especially psychometry. She has had a broad impact in the past, e.g. in the field of Bayesian modelling and the modelling of longitudinal data. She has made both generic method developments of statistical-quantitative methods as well as content applications the object of her work (e.g. in the investigation of clinical phenomena).

Since 2016, she has been an Elected Member of the Society of Multivariate Experimental Psychology (SMEP), a highly selective and renowned professional association with a limited number of 65 members worldwide. She is also an associate editor in the most influential journals of psychometry (e.g. Psychological Methods, Multivariate Behavioral Research).

Main research

  • Bayesian estimation of latent variable models
  • Estimation issues arising from nonlinear growth patterns over time

Curriculm Vitae

since 10/2019
Visting Professor at the Methods Center

University of Tübingen

since 07/2017
Associate Professor for Quantitative Psychology

University of California, Merced

since 2016
Elected Member of the Society of Multivariate Experimental Psychology (SMEP)
2011 - 2017
Assistant Professor

for Quantitative Psychology, University of California, Merced

Ph.D. in Quantitative Methods

minor in mathematical society, University of Wisconsin

M.A. in Quanitative Psychology

California State University – Sacramento

B.A. in Psychology

California State University – Sacramento

Professional Affiliations


  • 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

Editorial Work

  • 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

Review Experience

  • 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.

Contributions to Handbooks

  • 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.

Contributions to Edited Volumes

* 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.