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The Perilous Use of Proxy Variables.

Ryan G N Seltzer1

  • 1Department of Psychology, University of Arizona, Tucson, AZ, USA.

Evaluation & the Health Professions
|February 7, 2020
PubMed
Summary
This summary is machine-generated.

Proxy variables must accurately represent intended constructs to ensure reliable research findings. Poor construct validity in proxy variables can lead to misleading results in statistical modeling and analysis.

Keywords:
confoundsconstruct validityproxy variablesstructural equation modeling (SEM)

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Area of Science:

  • Social Sciences
  • Psychometrics
  • Statistical Modeling

Background:

  • Proxy variables are frequently used in research to measure complex latent constructs.
  • The degree to which proxy variables accurately represent these constructs is often not explicitly stated or quantified.
  • This lack of clarity can impact the validity of research findings.

Purpose of the Study:

  • To investigate how the representational accuracy of proxy variables affects statistical model estimates.
  • To quantify the impact of varying levels of construct validity in proxy variables.
  • To emphasize the importance of selecting appropriate proxy variables in research.

Main Methods:

  • A sensitivity analysis was conducted using data from the Survey of Health, Ageing and Retirement in Europe (SHARE).
  • Statistical models were estimated with varying degrees of construct validity for proxy variables.
  • The analysis focused on how parameter estimates change with different levels of variable representation.

Main Results:

  • Parameter estimates in statistical models varied substantially depending on the representational accuracy of the proxy variables.
  • When proxy variables exhibited poor construct validity, they failed to adequately account for the variance in the intended latent construct.
  • This resulted in an insufficient removal of spurious relationships between design variables and outcomes.

Conclusions:

  • The accuracy with which proxy variables represent underlying constructs is critical for valid research.
  • Using proxy variables with poor construct validity can introduce bias and obscure true relationships.
  • Researchers must prioritize the selection of proxy variables with high construct validity for robust and reliable findings.