Effect size variability in meta-analyses is often overlooked or misinterpreted. We describe two methods for making practical interpretations of credibility intervals and determining whether a particular SDρ represents a meaningful level of variability.
In this commentary, we address the critical distinction between systematic and random sampling components of measurement error. We describe how both forms can be addressed using psychometric meta-analysis to build cumulative scientific knowledge.
Conceptual and methodological complexity of narrow trait measures in personality-outcome research: Better knowledge by partitioning variance from multiple latent traits and measurement artifacts
Increased conceptual clarity and methodological rigor is needed in personality-outcome research. We describe the hierarchical nature of personality and the impact of multiple traits and errors on score interpretation.
Failure to properly model dominant general factors in data analysis can have dramatic impacts on observed results. We review the shortcomings of common analytic and provide recommendations for future studies.
We extend cluster analysis to compare of objects across the entirety of multiple score distributions, rather than merely distribution means. Potential applications include development of test norms across populations.
Performance ratings have been frequently criticized for low reliability. This article examines how ratings reliability can be improved using multi-scale composite performance measures.