Applying guidelines to construct informative priors in small sample research
The current paper demonstrates the usefulness of Bayesian estimation with small samples. In Bayesian estimation, prior information can be included …
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist
Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons:
Bayesian statistics in educational research: a look at the current state of affairs
A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline …
An Introduction to Bayesian Statistics in Health Psychology
Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve models, and hierarchical linear models.
A Systematic Review of Bayesian Papers in Psychology: The Last 25 Years
Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity.
Possible Solution to Publication Bias Through Bayesian Statistics
The present paper argues that an important cause of publication bias resides in traditional frequentist statistics forcing binary decisions. An alternative approach through Bayesian statistics provides various degrees of support for any hypothesis allowing balanced decisions and proper null hypothesis testing, which may prevent publication bias.
How to handle missing data: A comparison of different approaches
Many researchers face the problem of missing data in longitudinal research. Especially, high risk samples are characterized by missing data which can complicate analyses and the interpretation of results.
Effectiveness, Mediators, and Effect Predictors of Internet Interventions for Chronic Cancer-Related Fatigue
This paper describes the design and analysis plan that will be used to study 2 Internet interventions aimed at reducing severe fatigue in cancer survivors: a mobile ambulant activity feedback therapy supported through a weekly email by a physiotherapist and a weekly Web- and mindfulness-based cognitive therapy supported online by a psychologist.
Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors
The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions.
Social Influence Interpretation of Interpersonal Processes and Team Performance Over Time Using Bayesian Model Selection
The team behavior literature is ambiguous about the relations between members’ interpersonal processes—task debate and task conflict—and team performance. From a social influence perspective, we show why members’ interpersonal processes determine team performance over time in small groups.