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Area of Science:

  • Psychology
  • Statistics
  • Social Sciences

Background:

  • Moderator variables influence the relationship between other variables.
  • Traditional analyses often overlook individual differences in these relationships.
  • A person-oriented approach is needed to understand variable interactions.

Purpose of the Study:

  • To propose and demonstrate the use of individuals as moderators in statistical analysis.
  • To investigate whether variable relationships differ across individuals.
  • To introduce Configural Frequency Analysis (CFA) from a person-oriented perspective.

Main Methods:

  • Configural Frequency Analysis (CFA) was employed to identify individuals who differ in variable relationships.
  • Base models were specified within CFA to detect these individual differences.
  • A data example involving alcoholics, subjective stress, and beer consumption was analyzed.

Main Results:

  • The study found that not all individuals in the sample exhibited the overall sample-level association between subjective stress and beer consumption.
  • This highlights significant heterogeneity in variable relationships at the individual level.
  • Configural moderator models revealed distinct patterns for specific individuals.

Conclusions:

  • Individuals can effectively serve as moderators, offering a more nuanced understanding of variable interactions.
  • A person-oriented approach using CFA is valuable for identifying individuals with unique variable relationship patterns.
  • Future research should explore extensions of the configural moderator model.