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Related Experiment Videos

Common method variance and specification errors: a practical approach to detection.

T J Kline1, L M Sulsky, S D Rever-Moriyama

  • 1Department of Psychology, The University of Calgary, Alberta, Canada.

The Journal of Psychology
|July 25, 2000
PubMed
Summary
This summary is machine-generated.

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Examining correlations between self-report items helps distinguish common method variance from model specification errors. This research shows how social desirability influences structural models, impacting results when omitted.

Area of Science:

  • Psychological research methodology
  • Quantitative psychology

Background:

  • Self-report measures are susceptible to common method variance.
  • Distinguishing method variance from theoretical misspecification is crucial for valid findings.

Purpose of the Study:

  • To demonstrate how bivariate correlations aid in differentiating common method variance from model specification errors.
  • To investigate the role of social desirability as either a source of common method variance or a theoretically relevant construct.

Main Methods:

  • Utilized LISREL (Linear Structural Relations) modeling.
  • Manipulated correlations between social desirability measures and other constructs.
  • Analyzed structural model fit under different conditions.

Main Results:

Related Experiment Videos

  • Provided insights into when common variance effects may impact structural models.
  • Illustrated the point at which omitting social desirability leads to poor model fit.

Conclusions:

  • Bivariate correlation analysis is valuable for diagnosing method variance issues.
  • Social desirability's inclusion as a theoretical variable is critical for accurate structural modeling.