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Base Models for Configural Frequency Analysis - Data Generation Processes.

Alexander von Eye1,2, Wolfgang Wiedermann3, Stefan von Weber4

  • 1Michigan State University, East Lansing, MI, USA. voneye@msu.edu.

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Summary
This summary is machine-generated.

This study expands Configural Frequency Analysis (CFA) by defining base models using data generation processes (DGPs). This approach broadens the scope of interpretable base models beyond traditional probability constraints.

Keywords:
Base modelConfigural Frequency AnalysisData generation processTaxonomy of base models

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Data Analysis

Background:

  • Configural Frequency Analysis (CFA) interprets model-data discrepancies using predefined base models.
  • Current CFA base models are limited to probability models constraining variable relations.

Purpose of the Study:

  • To propose an extended definition of CFA base models.
  • To incorporate data generation processes (DGPs) into the specification of CFA base models.

Main Methods:

  • The study defines base models with reference to data generation processes (DGPs).
  • DGPs encompass uni- or multivariate distributions reflecting variable relations, probability distributions, or change processes.
  • Four classes of DGP-based base models are introduced and illustrated with data examples.

Main Results:

  • The proposed DGP-based definition broadens the types of base models available in CFA.
  • This extension maintains the unique interpretability of CFA results.
  • The new framework allows for a wider range of statistical models to serve as CFA base models.

Conclusions:

  • Specifying CFA base models via DGPs enhances the flexibility and applicability of Configural Frequency Analysis.
  • This approach offers a more comprehensive framework for analyzing statistical data and model-data fit.