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Local Effects of Intervention: a Configural Analysis.

Alexander von Eye1, Wolfgang Wiedermann2, Keith C Herman3

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|May 13, 2021
PubMed
Summary

Configural frequency analysis (CFA) offers a person-oriented approach to evaluating intervention effectiveness, complementing traditional variable-focused methods. Integrating both perspectives provides a more comprehensive understanding of prevention success for specific individual profiles.

Keywords:
Configural frequency analysisIntervention effectivenessLocal effectLog-linear modelPerson-oriented research

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Area of Science:

  • Statistics
  • Psychology
  • Public Health

Background:

  • Standard statistical analysis often uses variable relations to assess intervention effects.
  • Regression-type methods indicate overall intervention success but may miss nuanced outcomes.
  • A person-oriented perspective is valuable for understanding intervention impact on specific individual profiles.

Purpose of the Study:

  • To introduce Configural Frequency Analysis (CFA) as a person-oriented alternative or complement to variable-oriented methods.
  • To explore the application of CFA in evaluating intervention and prevention efforts.
  • To compare CFA with standard log-linear modeling in real-world intervention data.

Main Methods:

  • Configural Frequency Analysis (CFA) was employed to identify individuals with specific profiles.
  • CFA was applied alongside or independently of regression-type methods.
  • The study included three real-world data examples from both observational and randomized intervention settings.

Main Results:

  • CFA and standard variable-oriented methods address different research questions.
  • Person-oriented CFA identifies specific profiles for whom interventions are successful.
  • Integrating person- and variable-oriented analyses enhances the understanding of intervention effectiveness.

Conclusions:

  • Configural Frequency Analysis (CFA) provides a valuable person-oriented perspective in intervention research.
  • Combining CFA with traditional methods offers a more complete evaluation of intervention success.
  • Further extensions of the CFA approach can enrich statistical data analysis.