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Related Concept Videos

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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A primer on method effects on observed correlations with examples.

Charles E Lance1

  • 1Organizational Research & Development, University of the Western Cape, Lawrenceville, GA, USA.

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PubMed
Summary
This summary is machine-generated.

This study clarifies how measurement error and method variance impact observed measures. It reinterprets validity criteria, showing some common method effect interpretations are correct, while others need nuance.

Keywords:
MTMMMethod variancebias

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Area of Science:

  • Psychological Measurement
  • Quantitative Psychology
  • Structural Equation Modeling

Background:

  • Measurement error and method variance can distort observed measures.
  • Understanding their impact is crucial for accurate psychological research.
  • Existing interpretations of method effects may require refinement.

Purpose of the Study:

  • To provide an in-depth analysis of measurement error and method variance effects.
  • To re-evaluate Campbell and Fiske's criteria for construct validity and method bias.
  • To clarify the nuanced nature of common method effects in research.

Main Methods:

  • Path analysis and confirmatory factor analysis framework.
  • Integration of results from a primary study and four meta-analyses.
  • Analytical examination of trait and method factor influences.

Main Results:

  • Demonstrates the impact of measurement error and method variance on observed data.
  • Provides empirical examples illustrating theoretical points.
  • Confirms some, refutes others, and nuances common interpretations of method effects.

Conclusions:

  • Common method bias interpretations require careful consideration.
  • The study offers a refined understanding of convergent and discriminant validity.
  • Suggests avenues for future research on measurement quality.