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Alpha if item deleted: a note on loss of criterion validity in scale development if maximizing coefficient alpha
1Measurement and Quantitative Methods, Michigan State University, East Lansing, MI 48824, USA. raykov@msu.edu
Summary
The "alpha if item deleted" statistic may incorrectly suggest removing scale components, impacting measurement validity. Latent variable modeling offers a more robust approach for assessing composite criterion validity and reliability.
Area of Science:
- Psychometrics
- Measurement Theory
- Statistical Modeling
Background:
- The widely used 'alpha if item deleted' statistic is a common metric in developing multi-component measuring instruments.
- This statistic can lead to the removal of scale components that are crucial for criterion validity while artificially inflating coefficient alpha.
Purpose of the Study:
- To highlight a critical limitation of the 'alpha if item deleted' index in instrument development.
- To propose and discuss latent variable modeling as a superior alternative for assessing measurement quality.
Main Methods:
- A critique of the 'alpha if item deleted' statistic's validity-related shortcomings.
- Introduction and explanation of latent variable modeling for estimating composite criterion validity and reliability.
- Application of latent variable modeling for hypothesis testing regarding measurement quality indices.
Main Results:
- The 'alpha if item deleted' statistic can erroneously recommend discarding components essential for criterion validity.
- Latent variable modeling provides accurate point and interval estimations of composite criterion validity and reliability after component deletion.
- The proposed method allows for hypothesis testing on measurement quality changes.
Conclusions:
- The 'alpha if item deleted' statistic presents a significant limitation for psychometric scale construction.
- Latent variable modeling offers a more reliable and valid approach for evaluating and refining multi-component measuring instruments.
- This advanced methodology enhances the accuracy of validity and reliability assessments in instrument development.
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