Sarah Depaoli

4.7k total citations · 3 hit papers
67 papers, 3.0k citations indexed

About

Sarah Depaoli is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Sarah Depaoli has authored 67 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Statistics and Probability, 21 papers in Artificial Intelligence and 13 papers in Management Science and Operations Research. Recurrent topics in Sarah Depaoli's work include Statistical Methods and Bayesian Inference (33 papers), Statistical Methods and Inference (12 papers) and Bayesian Methods and Mixture Models (11 papers). Sarah Depaoli is often cited by papers focused on Statistical Methods and Bayesian Inference (33 papers), Statistical Methods and Inference (12 papers) and Bayesian Methods and Mixture Models (11 papers). Sarah Depaoli collaborates with scholars based in United States, Netherlands and South Africa. Sarah Depaoli's co-authors include Rens van de Schoot, Sonja D. Winter, Marit Sijbrandij, Jeroen K. Vermunt, Mariëlle Zondervan‐Zwijnenburg, David Kaplan, Andrew Gelman, Marina Vannucci, Joukje E. Willemsen and Mahlet G. Tadesse and has published in prestigious journals such as PLoS ONE, Journal of Consulting and Clinical Psychology and Frontiers in Psychology.

In The Last Decade

Sarah Depaoli

63 papers receiving 2.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Bayesian statistics and modelling

2021 616 citations Rens van de Schoot, Sarah Depaoli et al. Nature Reviews Methods Primers profile →
The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

2016 433 citations Rens van de Schoot, Marit Sijbrandij et al. Structural Equation Modeling A Multidisciplinary Journal profile →
Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist.

2015 292 citations Sarah Depaoli, Rens van de Schoot Psychological Methods profile →

Peers

Countries citing papers authored by Sarah Depaoli

Since Specialization

Citations

This map shows the geographic impact of Sarah Depaoli's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sarah Depaoli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarah Depaoli more than expected).

Fields of papers citing papers by Sarah Depaoli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sarah Depaoli. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sarah Depaoli. The network helps show where Sarah Depaoli may publish in the future.

Co-authorship network of co-authors of Sarah Depaoli

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Depaoli. A scholar is included among the top collaborators of Sarah Depaoli based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sarah Depaoli. Sarah Depaoli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Parameter Recovery for Misspecified Latent Mediation Models in the Bayesian Framework 2025 ·Structural Equation Modeling A Multidisciplinary Journal ·Ren Liu, (unknown), Sarah Depaoli, (unknown)	1
2	Recovering knot placements in Bayesian piecewise growth models with missing data 2025 ·Behavior Research Methods ·(unknown), Fan Jia, Sarah Depaoli	0
3	Bayesian approach to piecewise growth mixture modeling: Issues and applications in school psychology 2024 ·Journal of School Psychology ·(unknown), Sarah Depaoli, Fan Jia, (unknown)	1
4	Under-Fitting and Over-Fitting: The Performance of Bayesian Model Selection and Fit Indices in SEM 2023 ·Structural Equation Modeling A Multidisciplinary Journal ·Sarah Depaoli, Sonja D. Winter, Haiyan Liu	5
5	Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data 2023 ·Structural Equation Modeling A Multidisciplinary Journal ·(unknown), Fan Jia, Sarah Depaoli	5
6	Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling 2023 ·Structural Equation Modeling A Multidisciplinary Journal ·Sonja D. Winter, Sarah Depaoli	9
7	Performance of Model Fit and Selection Indices for Bayesian Structural Equation Modeling with Missing Data 2022 ·Structural Equation Modeling A Multidisciplinary Journal ·Sonja D. Winter, Sarah Depaoli	9
8	Detecting Model Misspecification in Bayesian Piecewise Growth Models 2022 ·Structural Equation Modeling A Multidisciplinary Journal ·Sarah Depaoli, Fan Jia, (unknown)	4
9	Detecting Prior-Data Disagreement in Bayesian Structural Equation Modeling 2022 ·Structural Equation Modeling A Multidisciplinary Journal ·Sonja D. Winter, Sarah Depaoli	2
10	Sensitivity of Bayesian Model Fit Indices to the Prior Specification of Latent Growth Models 2022 ·Structural Equation Modeling A Multidisciplinary Journal ·Sonja D. Winter, Sarah Depaoli	13
11	Bayesian statistics and modelling breakdown → 2021 ·Nature Reviews Methods Primers ·Rens van de Schoot, Sarah Depaoli, Ruth King, Bianca Kramer, Kaspar Märtens, Mahlet G. Tadesse, Marina Vannucci, Andrew Gelman, Duco Veen, Joukje E. Willemsen, Christopher Yau	616
12	Longitudinal Associations Between Parental SES and Adolescent Health-Related Quality of Life Using Growth Curve Modeling 2021 ·Journal of Child and Family Studies ·(unknown), Jan L. Wallander, Sarah Depaoli, Marc N. Elliott, Mark A. Schuster	9
13	An illustration of Bayesian approximate measurement invariance with longitudinal data and a small sample size 2019 ·International Journal of Behavioral Development ·Sonja D. Winter, Sarah Depaoli	20
14	Predicting a Distal Outcome Variable From a Latent Growth Model: ML versus Bayesian Estimation 2019 ·Structural Equation Modeling A Multidisciplinary Journal ·Sanne C. Smid, Sarah Depaoli, Rens van de Schoot	14
15	Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration 2019 ·Multivariate Behavioral Research ·Sarah Depaoli, Sonja D. Winter, Keke Lai, Kiero Guerra-Peña	19
16	Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation 2018 ·Multivariate Behavioral Research ·Rens van de Schoot, Marit Sijbrandij, Sarah Depaoli, Sonja D. Winter, Miranda Olff, Nancy E. E. Van Loey	51
17	A systematic review of Bayesian articles in psychology: The last 25 years. 2017 ·Psychological Methods ·Rens van de Schoot, Sonja D. Winter, Oisín Ryan, Mariëlle Zondervan‐Zwijnenburg, Sarah Depaoli	238
18	Where Do Priors Come From? Applying Guidelines to Construct Informative Priors in Small Sample Research 2017 ·Research in Human Development ·Mariëlle Zondervan‐Zwijnenburg, Margot Peeters, Sarah Depaoli, Rens van de Schoot	75
19	A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes 2015 ·Structural Equation Modeling A Multidisciplinary Journal ·Sarah Depaoli, (unknown)	115
20	Bayesian analyses: Where to start and what to report 2014 ·Utrecht University Repository (Utrecht University) ·Rens van de Schoot, Sarah Depaoli	112

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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