Associate Professor Sarah Depaoli

Visiting Distinguished Professor / Gastprofessorin für quantitative Methoden

Haußerstr. 11
72076 Tübingen


Termine nach Vereinbarung per E-Mail

Dr. Sarah Depaoli ist Associate Professor für Quantitative Psychologie an der University of California, Merced und Visiting Distinguished Professor am Methodenzentrum der Eberhard Karls Universität Tübingen.


  • Bayesische Schätzung latenter Variablenmodelle  in den Sozial- und Verhaltenswissenschaften
  • Wahl von Priori-Verteilungen bei Wachstumskurvenmodellen


seit Oktober 2019
Gastprofessorin am Methodenzentrum

Universität Tübingen

seit Juli 2017
Associate Professor in Quantitativer Psychologie

University of California, Merced

Aufnahme in die Society of Multivariate Experimental Psychology (SMEP)
2011 - 2017
Assistant Professor

der Quantitativen Psychologie, University of California, Merced

Ph.D. in quantitativen Methoden

mit Nebenfach mathematische Statistik, University of Wisconsin

M.A. quanitative Psychologie

California State University – Sacramento

B.A. der Psychologie

California State University – Sacramento

Mitgliedschaften in Fachorganisationen

  • Gewähltes Mitglied: Society of Multivariate Experimental Psychology (weltweit nur 65 aktive Mitglieder)
  • 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

Herausgeberische Tätigkeiten

  • Mitherausgeberin von Psychological Methods, 2019-heute
  • Mitherausgeberin von Multivariate Behavioral Research, 2017-heute
  • Gastredakteurin für die Sonderausgabe über Quantitative Methoden in Psychology for Translational Issues in Psychological Science, 2017-2018

Gutachterliche Tätigkeiten

  • 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 für die Niederländische Regierung
  • 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



* bezeichnet Studierende oder Post-Doc-Mitarbeitende

  • Winter, S. D.,* & Depaoli, S. (angenommen). 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. (im Erscheinen). 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. (im Erscheinen). 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.* (im Erscheinen). 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. (im Erscheinen). 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. (im Erscheinen). Bayesian Structural Equation Modeling. Guilford Press.


  • Kaplan, D., and Depaoli, S. (2013). Bayesian statistical methods. In Little, T. (Eds.), Handbook for quantitative methods (pp. 406-436). Oxford University Press.
  • Kaplan, D., and Depaoli, S. (2012). Bayesian structural equation modeling. In Hoyle, R. (Eds.), Handbook of structural equation modeling (pp. 650-673). Guilford Press.

Beiträge zu Sammelbänden

* bezeichnet Studierende oder Post-Doc-Mitarbeitende

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