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Sarah Depaoli
Sarah Depaoli
Associate Professor of Quantitative Psychology, University of California, Merced
Bestätigte E-Mail-Adresse bei ucmerced.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Bayesian statistics and modelling
R van de Schoot, S Depaoli, R King, B Kramer, K Märtens, MG Tadesse, ...
Nature Reviews Methods Primers 1 (1), 1-26, 2021
6122021
The GRoLTS-checklist: guidelines for reporting on latent trajectory studies
R Van De Schoot, M Sijbrandij, SD Winter, S Depaoli, JK Vermunt
Structural Equation Modeling: A Multidisciplinary Journal 24 (3), 451-467, 2017
4972017
A systematic review of Bayesian articles in psychology: The last 25 years.
R Van De Schoot, SD Winter, O Ryan, M Zondervan-Zwijnenburg, ...
Psychological Methods 22 (2), 217, 2017
3942017
Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist.
S Depaoli, R Van de Schoot
Psychological methods 22 (2), 240, 2017
3812017
Bayesian structural equation modeling.
D Kaplan, S Depaoli
The Guilford Press, 2012
2842012
Bayesian analyses: Where to start and what to report
R van de Schoot, S Depaoli
The European Health Psychologist 16 (2), 75-84, 2014
2142014
A Bayesian approach to multilevel structural equation modeling with continuous and dichotomous outcomes
S Depaoli, JP Clifton
Structural Equation Modeling: A Multidisciplinary Journal 22 (3), 327-351, 2015
1992015
Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation.
S Depaoli
Psychological methods 18 (2), 186, 2013
1512013
Where do priors come from? Applying guidelines to construct informative priors in small sample research
M Zondervan-Zwijnenburg, M Peeters, S Depaoli, R Van de Schoot
Research in Human Development 14 (4), 305-320, 2017
1062017
Just another Gibbs sampler (JAGS) flexible software for MCMC implementation
S Depaoli, JP Clifton, PR Cobb
Journal of Educational and Behavioral Statistics 41 (6), 628-649, 2016
982016
The impact of inaccurate “informative” priors for growth parameters in Bayesian growth mixture modeling
S Depaoli
Structural Equation Modeling: A Multidisciplinary Journal 21 (2), 239-252, 2014
922014
Bayesian statistical methods
D Kaplan, S Depaoli
Oxford handbook of quantitative methods, 407-437, 2013
912013
The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App
S Depaoli, SD Winter, M Visser
Frontiers in Psychology 11, 2020
902020
Latent growth curve models for biomarkers of the stress response
JM Felt, S Depaoli, J Tiemensma
Frontiers in neuroscience 11, 315, 2017
712017
Assessment of health surveys: fitting a multidimensional graded response model
S Depaoli, J Tiemensma, JM Felt
Psychology, health & medicine 23 (sup1), 1299-1317, 2018
602018
Bayesian PTSD-trajectory analysis with informed priors based on a systematic literature search and expert elicitation
R van de Schoot, M Sijbrandij, S Depaoli, SD Winter, M Olff, NE Van Loey
Multivariate behavioral research 53 (2), 267-291, 2018
592018
An introduction to Bayesian statistics in health psychology
S Depaoli, HM Rus, JP Clifton, R van de Schoot, J Tiemensma
Health Psychology Review 11 (3), 248-264, 2017
502017
Using person fit statistics to detect outliers in survey research
JM Felt, R Castaneda, J Tiemensma, S Depaoli
Frontiers in psychology 8, 863, 2017
492017
Iteration of partially specified target matrices: Applications in exploratory and Bayesian confirmatory factor analysis
TM Moore, SP Reise, S Depaoli, MG Haviland
Multivariate behavioral research 50 (2), 149-161, 2015
422015
Measurement and structural model class separation in mixture CFA: ML/EM versus MCMC
S Depaoli
Structural Equation Modeling: A Multidisciplinary Journal 19 (2), 178-203, 2012
362012
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