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Combining Parallel and Exploratory Factor Analysis in Identifying Relationship Scales in Secondary Data.

Nathan D Wood1, Djidjoho C Akloubou Gnonhosou1, Justin Bowling1

  • 1Department of Family Sciences, University of Kentucky.

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Summary

This study introduces parallel analysis as a superior method for determining the number of factors in exploratory factor analysis (EFA). This approach increases confidence in identifying the correct factor structure for research scales.

Keywords:
Exploratory factor analysisParallel analysismarital qualitynational survey of households and familiesrelationship adjustment

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Area of Science:

  • Psychometrics
  • Statistical analysis
  • Social sciences research methodology

Background:

  • Traditional methods for factor identification in exploratory factor analysis (EFA) present potential challenges.
  • Accurate factor structure identification is crucial for valid scale development and interpretation.

Purpose of the Study:

  • To present and illustrate a state-of-the-art approach for identifying factor structure.
  • To demonstrate the utility of incorporating parallel analysis before conducting EFA.

Main Methods:

  • The study proposes adding parallel analysis as a preliminary step to exploratory factor analysis.
  • The methodology is exemplified using item data from the National Survey of Families and Households (NSFH).

Main Results:

  • Parallel analysis provides a robust method for determining the optimal number of factors to extract.
  • This technique enhances confidence in the identified factor structure compared to conventional approaches.

Conclusions:

  • Parallel analysis is a recommended procedure for improving the accuracy of factor structure identification in EFA.
  • Implementing parallel analysis leads to more reliable results in scale development and analysis.