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Using LISREL analysis for drug research.

P H Kennedy, S L Starrfield, C Baffi

The Journal of School Health

|April 1, 1983

Summary

LISREL Analysis offers a powerful way to model complex relationships and determine causality in health behaviors. This statistical method, using structural equations, proved more effective than ANOVA for understanding antecedent variables.

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Area of Science:

Behavioral Science
Psychometrics
Health Education Research

Background:

Understanding causality in human health behaviors is complex.
Traditional statistical methods may not fully capture intricate relationships between variables.

Purpose of the Study:

To introduce and apply LISREL (Linear Structural Relations) Analysis for modeling causal relationships.
To compare the explanatory power of LISREL with ANOVA in drug education research.

Main Methods:

LISREL Analysis, a statistical technique utilizing maximum-likelihood estimation for structural equation modeling.
Application of LISREL to existing drug education research data.
Comparative analysis against an ANOVA-based model.

Main Results:

LISREL successfully generated a model for analyzing relationships among observed and latent variables.
The LISREL model demonstrated superior explanatory value compared to the ANOVA-based model.
LISREL effectively addressed antecedent variables in complex health behaviors.

Conclusions:

LISREL Analysis is a valuable tool for determining causality in behavioral and health research.
LISREL provides a more comprehensive approach than ANOVA for complex human health behaviors.
The study highlights the utility of structural equation modeling in advancing health education research.

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