Simulation Study on Bifactor and Multilevel Factor Models in Psychometrics

Christian L. L. Strauss authored a study exploring the overlap between bifactor and multilevel confirmatory factor analysis models. The 28.4 KB document, last updated on May 1, 2026, uses simulation and empirical analysis to demonstrate that bifactor solutions can emerge as artifacts of unmodeled data clustering. The work encourages researchers to consider multilevel measurement models as alternative explanations for bifactor structures.

Use Cases

• Testing the sensitivity of bifactor model selection to unmodeled clustering based on the described simulation framework.
• Comparing model fit between bifactor and multilevel confirmatory factor analysis as discussed in the study.
• Investigating the inflation of general factor loadings in clustered data scenarios described in the document.
• Developing methodological guidelines for interpreting factors at the correct level of analysis as proposed by the author.

Strengths

• The document is openly licensed under CC-BY-4.0, permitting broad reuse.
• The study is based on both simulation and empirical data analysis, as stated in the description.

Limitations

• The dataset is a 28.4 KB DOCX file, indicating a very limited scope, likely a manuscript or supplementary material.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for direct data analysis.

Provenance

Source

Christian L. L. Strauss via figshare

Collection Method

Likely contains results from a simulation study and empirical analysis, as described.

Time Range

Publication date is implied as 2026.

Freshness

Last updated 2026-05-01 08:14:59; freshness should be verified.

Geography

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