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Parameter uncertainty in structural equation models: Confidence sets and fungible estimates
Jolynn Pek1, Hao Wu2
1Department of Psychology, The Ohio State University.
Psychological Methods
|January 5, 2018
Summary
This study introduces confidence sets (CSs) and fungible parameter estimates (FPEs) to enhance the dependability of psychological findings. These methods offer crucial insights into parameter uncertainty, strengthening scientific conclusions.
Area of Science:
- Psychological research methodology
- Statistical modeling in social sciences
Background:
- Concerns about the reliability of psychological findings necessitate methodological advancements.
- Parameter uncertainty is critical for evaluating the defensibility of scientific conclusions.
Purpose of the Study:
- To highlight the importance of confidence sets (CSs) and fungible parameter estimates (FPEs) in assessing scientific findings.
- To introduce a unified perturbation framework for understanding CSs and FPEs.
- To demonstrate how CSs and FPEs provide unique information for robust scientific interpretation.
Main Methods:
- Development of a general perturbation framework based on the likelihood function within structural equation modeling.
- Illustrating conceptual distinctions and differential influences on CSs and FPEs.
- Application of CSs and FPEs to empirical examples using OpenMx code.
Main Results:
- The perturbation framework unifies CSs and FPEs, clarifying their theoretical differences.
- Empirical examples demonstrate that CSs and FPEs offer distinct, valuable information.
- CSs and FPEs contribute to more informed scientific conclusions by quantifying parameter uncertainty.
Conclusions:
- Considering information from CSs and FPEs strengthens the interpretation of statistical results in psychological research.
- Methodological developments like CSs and FPEs are vital for dependable scientific conclusions.
- Future research should explore further applications and implications of these uncertainty measures.
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