Don Van Ravenzwaaij
Biographic Data
| ID | 1719629 |
|---|---|
| NAME | Don Van Ravenzwaaij |
| GIVEN NAMES | Don |
| FAMILY NAME | Van Ravenzwaaij |
| SIGNATURE | VAN RAVENZWAAIJ D |
| AFFILIATIONS | University of Groningen |
| ORCID | 0000-0002-5030-4091 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Sample-Size Planning for Frequentist and Bayesian 2 × 2 Analysis-of-Variance Designs
Sample-size justification is an essential aspect of rigorous research in the behavioral and social sciences and helps to ensure studies are adequately powered, minimize resource waste, and reduce participant burden. However, researchers often face challenges in navigating the array of sample-size-planning methods available, particularly when balancing inferential goals and statistical frameworks. The SampleSizePlanner (SSP), originally developed …
A many-analysts approach to the relation between religiosity and well-being
The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-…
Sample Size Justification
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable information given the inferential goals of the researcher. In this overview article six approaches are discussed to justify the sample size in a quantitative empirical study: 1) collecting data from (almost) the ent…
Worked-out examples of the adequacy of Bayesian optional stopping
Seven steps toward more transparency in statistical practice
Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in…
Discussion points for Bayesian inference
An evidence accumulation model of acoustic cue weighting in vowel perception
Discussion points for Bayesian inference
An evidence accumulation model of acoustic cue weighting in vowel perception
Seven steps toward more transparency in statistical practice
Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in…
An evidence accumulation model of acoustic cue weighting in vowel perception
Discussion points for Bayesian inference
Seven steps toward more transparency in statistical practice
Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in…
Sample Size Justification
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable information given the inferential goals of the researcher. In this overview article six approaches are discussed to justify the sample size in a quantitative empirical study: 1) collecting data from (almost) the ent…
Worked-out examples of the adequacy of Bayesian optional stopping
A many-analysts approach to the relation between religiosity and well-being
The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-…
Sample-Size Planning for Frequentist and Bayesian 2 × 2 Analysis-of-Variance Designs
Sample-size justification is an essential aspect of rigorous research in the behavioral and social sciences and helps to ensure studies are adequately powered, minimize resource waste, and reduce participant burden. However, researchers often face challenges in navigating the array of sample-size-planning methods available, particularly when balancing inferential goals and statistical frameworks. The SampleSizePlanner (SSP), originally developed …
Computer Science (6 works) · Psychology (5 works) · Artificial Intelligence (3 works) · Bayesian probability (3 works) · Statistical Methods and Bayesian Inference (3 works) · Advanced Causal Inference Techniques (2 works) · Bayesian inference (2 works) · Bayesian statistics (2 works) · Data mining (2 works) · Data science (2 works)