Exploring item and higher order factor structure with the Schmid-Leiman solution: syntax codes for SPSS and SAS.

Area of Science:

• Psychometrics
• Statistical Analysis
• Psychological Measurement

Background:

• Higher-order factor analysis can be complex to interpret.
• Direct relationships between variables and higher-order factors are often needed.
• Existing statistical software typically lacks direct implementation of the Schmid-Leiman solution.

Purpose of the Study:

• To present the Schmid-Leiman solution (SLS) for simplifying higher-order factor analysis.
• To demonstrate how SLS orthogonalizes factor levels for clearer interpretation.
• To facilitate theorizing and scale development using factor analysis.

Main Methods:

• The study details the Schmid-Leiman solution (SLS) transformation.
• Syntax codes for SPSS and SAS are provided for implementing SLS.
• The procedure involves orthogonalizing first-order and higher-order factors.

Main Results:

• The Schmid-Leiman solution (SLS) allows for the interpretation of the relative impact of factor levels on variables.
• SLS provides a clear method for understanding complex factor structures.
• The transformation facilitates a more intuitive understanding of factor analysis results.

Conclusions:

• The Schmid-Leiman solution (SLS) is a valuable tool for researchers using higher-order factor analysis.
• Implementing SLS aids in the interpretation of factor structures and supports scale development.
• Availability of syntax codes promotes wider adoption and application of the method.