ArticleJournal for person-oriented research2026
Control of Type 1 and Type 2 Errors in Configural Frequency Analysis.
Article in Journal for person-oriented research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
In this article, standard configural frequency analysis (CFA) is compared with combinatorial Victor CFA with respect to Type I errors and power. Victor CFA is most important and suitable when phantom types or phantom antitypes are suspected to exist. These can emerge when types and antitypes contain cases from populations other than the sample under study, and, thus, distort marginal probabilities. Simulation results are presented that suggest that, when samples are comparatively small, standard CFA has more power. In contrast, Victor CFA has more power when sample sizes increase. The concept of type strength is introduced. It can be used as a supplement to statistical CFA testing. Data examples illustrate that the two variants of CFA can result in dramatically different data evaluations. In the article, it is discussed that Victor CFA can also be used when base models are more complex than the main effect model that was used in Lienert's standard CFA and Victor's original alternative CFA.
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Registered trials
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