Introduction to the special section: structural equation modeling in clinical research

Area of Science:

• Clinical Psychology
• Statistical Modeling

Background:

• Clinical research hypotheses and designs are increasingly complex and specific.
• Studies often involve multiple independent, intervening, and dependent variables.
• Traditional statistical models lag behind the complexity of modern clinical research.

Purpose of the Study:

• Introduce structural equation modeling (SEM) as a suitable statistical approach.
• Highlight SEM's utility for complex hypothesis testing in clinical research.
• Address the gap between research design complexity and analytical methods.

Main Methods:

• Review of statistical models used in clinical research over three decades.
• Introduction to structural equation modeling (SEM) principles.
• Discussion of SEM's application to multi-variable clinical studies.

Main Results:

• Identified a mismatch between complex clinical research designs and adopted statistical models.
• Highlighted the inadequacy of traditional methods for intricate hypotheses.
• Presented structural equation modeling as a powerful alternative.

Conclusions:

• Structural equation modeling is well-suited for the complexity of modern clinical research.
• SEM enables robust testing of specific hypotheses with multiple variables.
• Adoption of SEM can improve the analytical rigor in clinical psychology research.