Direction of effects in categorical variables: Looking inside the table

Area of Science:

• Social Sciences
• Statistics
• Psychology

Background:

• Traditional variable-oriented analysis defines dependence by changes in univariate distributions.
• Existing methods often overlook nuanced relationships within categorical data.
• A person-oriented perspective offers a more detailed examination of individual-level data patterns.

Purpose of the Study:

• To extend direction of dependence analysis for categorical variables using a person-oriented perspective.
• To define direction dependence based on specific interactions within data tables, moving beyond marginal distributions.
• To introduce an event-based perspective, defining effects at the individual category level.

Main Methods:

• Developed a person-oriented approach for analyzing direction dependence in categorical variables.
• Utilized log-linear models to test hypotheses about these specific interactions and event-based effects.
• Conducted simulation studies to evaluate the performance of the proposed models.

Main Results:

• Demonstrated that direction dependence can be effectively analyzed through specific interactions within data tables.
• Showcased the utility of an event-based perspective for understanding category-specific effects.
• An empirical example explored the relationship between financial status and happiness.

Conclusions:

• The proposed person-oriented, event-based approach provides a novel framework for analyzing categorical variable dependence.
• Log-linear models are effective tools for testing hypotheses within this framework.
• This method offers deeper insights into complex relationships, applicable to various social science research questions.