Rival Hypotheses in Linear Structure Modeling: Factor Rotation in Confirmatory Factor Analysis and Latent Path...

Area of Science:

• Psychometrics
• Statistical Modeling

Background:

• Confirmatory Factor Analysis (CFA) is crucial for structural equation modeling.
• Factor identification is essential for valid CFA results.

Purpose of the Study:

• To present a procedure for identifying rotationally underidentified factors in CFA.
• To analyze the impact of measurement model rotation on latent path coefficients.

Main Methods:

• Summarizing Algina's (1980) factor identification criteria.
• Implementing additional restrictions to achieve rotational identification.
• Examining rotation effects within latent path analysis measurement models.

Main Results:

• A procedure is provided to identify underidentified factors by adding restrictions.
• The impact of rotation on latent path coefficients is analyzed.
• Illustrative examples demonstrate the practical application of the procedure.

Conclusions:

• The presented procedure aids in theory-based model development for identified models.
• It can be used exploratorily to generate alternative models when full identification is not initially possible.