Selecting relevant moderators with Bayesian regularized meta-regression.

When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence.

The adaptive ability performance test (ADAPT): A factor analytic study in clients with intellectual disabilities.

Background: The adaptive ability performance test (ADAPT) was developed to assess adaptive skills in individuals with intellectual disabilities and borderline intellectual functioning, with or without mental disorders. As a follow-up to earlier research on the ADAPT, a factor analytic study was conducted.

Method: One thousand and sixty six ADAPTs from clients with (suspected) intellectual disabilities or borderline intellectual functioning and 129 ADAPTs from participants from the general population were collected along with other characteristics (e.

Prior sensitivity analysis in default Bayesian structural equation modeling.

Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information.