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On Studying Common Factor Dominance and Approximate Unidimensionality in Multicomponent Measuring Instruments With
Tenko Raykov1, George A Marcoulides2
1Michigan State University, East Lansing, MI, USA.
Educational and Psychological Measurement
|August 25, 2018
Summary
This study presents a latent variable modeling method to quantify common factor dominance in binary and ordinal item measures. It estimates variance proportions attributable to global versus local factors, aiding in assessing unidimensionality.
Area of Science:
- Psychometrics
- Statistical modeling
Background:
- Multicomponent measuring instruments with binary items are common.
- Determining the dominance of a common factor over local factors is crucial for accurate interpretation.
- Existing methods may not adequately account for the discrete nature of item responses.
Purpose of the Study:
- To introduce a procedure for quantifying the degree of common factor dominance in multicomponent measures.
- To provide a method for estimating variance explained by common (global) and other (local) factors.
- To offer a tool for assessing approximate unidimensionality in item response modeling.
Main Methods:
- Application of latent variable modeling methodology.
- Accounting for the discrete nature of manifest indicators (binary and ordinal items).
- Providing point and interval estimates of variance proportions.
Main Results:
- The procedure quantifies the proportion of variance due to the common factor.
- It also quantifies the proportion of variance due to local factors.
- The method is applicable to binary and Likert-type ordinal items.
Conclusions:
- The proposed method effectively examines common factor dominance in various measurement instruments.
- It serves as a valuable tool for assessing unidimensionality in psychometric analyses.
- The approach enhances the interpretation of results from multicomponent measures.
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