Person-Centered Data Analysis With Covariates and the R-Package confreq

Stemmler M, Heine JH, Wallner S (2021)

Publication Type: Journal article

Publication year: 2021


Book Volume: 17

Pages Range: 149-167

Journal Issue: 2

DOI: 10.5964/meth.2865


Configural Frequency Analysis (CFA) is a useful statistical method for the analysis of multiway contingency tables and an appropriate tool for person-oriented or person-centered methods. In complex contingency tables, patterns or configurations are analyzed by comparing observed cell frequencies with expected frequencies. Significant differences between observed and expected frequencies lead to the emergence of Types and Antitypes. Types are patterns or configurations which are significantly more often observed than the expected frequencies; Antitypes represent configurations which are observed less frequently than expected. The R-package confreq is an easy-to-use software for conducting CFAs; another useful shareware to run CFAs was developed by Alexander von Eye. Here, CFA is presented based on the log-linear modeling approach. CFA may be used together with interval level variables which can be added as covariates into the design matrix. In this article, a real data example and the use of confreq are presented. In sum, the use of a covariate may bring the estimated cell frequencies closer to the observed cell frequencies. In those cases, the number of Types or Antitypes may decrease. However, in rare cases, the Type-Antitype pattern can change with new emerging Types or Antitypes.

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Stemmler, M., Heine, J.-H., & Wallner, S. (2021). Person-Centered Data Analysis With Covariates and the R-Package confreq. Methodology, 17(2), 149-167.


Stemmler, Mark, Joerg-Henrik Heine, and Susanne Wallner. "Person-Centered Data Analysis With Covariates and the R-Package confreq." Methodology 17.2 (2021): 149-167.

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