Illustrating psychometric tests, scales, and constructs: An R package for Item Pool Visualization.

Researchers assessing psychological constructs have to understand and choose between several competing measures. Item Pool Visualization (IPV, Dantlgraber et al., 2019) was developed to offer a systematic and detailed portrayal of the actual content and internal balance of competing measures. To enable the use of IPV, we developed and present here the IPV R package. Its aim is to allow researchers to add IPV to their repertoire with minimal effort. Creating IPV charts from raw data requires two simple function calls, because the package streamlines model specification, model estimation, and chart creation. It improves IPV conceptually by introducing the aggregate center distance and the item overview chart. It provides many customization options and generates high-quality, vector-based PDF output. The workflow of the package is explained using a reproducible open data example from a personality assessment.

Problems of Domain Factors with Small Factor Loadings in Bi-Factor Models.

Many measurement designs produce domain factors with small variances and factor loadings. The current study investigates the cause, prevalence, and problematic consequences of such domain factors. We collected a meta-analytic sample of empirical applications, conducted a simulation study on statistical power and estimation precision, and provide a reanalysis of an empirical example. The meta-analysis shows that about a quarter of all standardized domain factor loadings is in the range of -.2<λ<.2 and about a third of all domains is measured by five or fewer indicators, resulting in small factor variances. The simulation study examines the associated difficulties concerning statistical power, trait recovery, irregular estimates, and estimation precision for a range of such realistic cases. The empirical example illustrates the challenge to develop measures that produce clearly interpretable domain factors. Study planning and interpretation need to take the (expected) sum of squared factor loadings per domain factor into account. This is relevant even if influences of domain factors are desired to be small, and equally applies to different model variants. We propose several strategies for how researchers may better unlock the bifactor model's full potential and clarify its interpretation.