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Manifest variable path analysis: potentially serious and misleading consequences due to uncorrected measurement error
David A Cole1, Kristopher J Preacher1
1Department of Psychology and Human Development, Vanderbilt University.
Psychological Methods
|October 2, 2013
Summary
Path analysis using unreliable measures leads to inaccurate results and reduced statistical power. Researchers should use reliable measures, multiple indicators, or latent variable models to ensure valid findings.
Area of Science:
- Psychometrics
- Statistical Modeling
Background:
- Manifest variable path analysis is widely used in research.
- Reliable measures are crucial for accurate path analysis, yet fallible measures are common.
Purpose of the Study:
- To highlight the detrimental effects of using fallible measures in path analysis.
- To review methods for preventing and correcting issues caused by measurement error.
Main Methods:
- The study reviews the consequences of measurement error in path analysis.
- It discusses strategies for mitigating these problems.
Main Results:
- Measurement error can lead to over/underestimated path coefficients and reduced statistical power.
- It can cause valid models to appear invalid and alter substantive conclusions.
Conclusions:
- Researchers must use more reliable measures or correct for error.
- Employing multiple indicators for latent variable modeling and simplifying models are recommended.
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