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A general approach to testing multifaceted personality constructs.

J G Hull1, D A Lehn, J C Tedlie

  • 1Department of Psychology, Dartmouth College, Hanover, New Hampshire 03755.

Journal of Personality and Social Psychology
|December 1, 1991
PubMed
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This study reviews methods for testing complex personality traits. Structural equation modeling offers advantages for analyzing multifaceted constructs and measurement error, despite statistical complexity.

Area of Science:

  • Psychology
  • Personality Psychology
  • Psychometrics

Background:

  • Many personality characteristics are multifaceted, comprising distinct subcomponents.
  • Existing methods for testing these complex constructs have limitations.
  • Examples of multifaceted constructs include Type A behavior, hardiness, attributional style, and self-monitoring.

Purpose of the Study:

  • To review the advantages and disadvantages of typical approaches for testing multifaceted personality constructs.
  • To propose structural equation modeling (SEM) as an alternative approach.
  • To highlight the benefits of SEM for analyzing complex personality structures.

Main Methods:

  • Review of three typical approaches for testing multifaceted personality constructs.

Related Experiment Videos

  • Introduction and discussion of structural equation modeling (SEM) as an alternative.
  • Comparison of SEM with traditional methods regarding construct structure, subcomponent analysis, and measurement error.

    • Main Results:

    • SEM provides an explicit test of the construct's underlying structure.
    • SEM allows for simultaneous testing of the general construct's effects and unique subcomponent effects.
    • SEM explicitly accounts for measurement error, unlike many traditional approaches.

    Conclusions:

    • Structural equation modeling offers significant advantages for studying multifaceted personality constructs.
    • Key benefits include testing construct structure, analyzing general and specific effects, and handling measurement error.
    • Limitations of SEM, such as statistical complexity and training needs, are comparable to or shared with alternative methods.