Manipulating the alpha level cannot cure significance testing
We argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of cumulative science.
Bayes with Informed Priors Based on Literature and Expert Elicitation
Bayesian Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation in the field of Post Traumatic Stress.
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 …
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 …
A General procedure for Testing Inequality Constrained Hypotheses in SEM
Researchers in the social and behavioral sciences often have clear expectations about the order and/or the sign of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than β2 and β3.
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:
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.
The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies
Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported.