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Factor comparison across studies.

C Teel1, J A Verran

  • 1College of Nursing, University of Arizona, Tucson 85721.

Research in Nursing & Health
|February 1, 1991
PubMed
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Comparing factor analysis results across studies is crucial. This review covers four methods using factor loadings: Salient Variable Similarity Index (S), Coefficient of Congruence (CC), Pearson Correlation Coefficient (r), and Confirmatory Factor Analysis (EQS).

Area of Science:

  • Psychometrics
  • Statistical Analysis

Background:

  • Replication of substantive and instrumentation studies across populations necessitates comparing factor analysis results.
  • Consistent methodology is key for valid cross-study comparisons.

Purpose of the Study:

  • To review four factor loading-based procedures for comparing factors across studies.
  • To highlight methods applicable when identical designs and variables are used with different populations.

Main Methods:

  • The review examines the Salient Variable Similarity Index (S).
  • It analyzes the Coefficient of Congruence (CC).
  • It includes the Pearson Correlation Coefficient (r) and Confirmatory Factor Analysis (EQS) using latent variable modeling.

Main Results:

Related Experiment Videos

  • The article details four distinct techniques for cross-study factor comparison.
  • It acknowledges the ongoing debate regarding factor loading versus factor score comparison methods.

Conclusions:

  • The reviewed methods offer quantitative approaches to assess factor similarity across populations.
  • Understanding these techniques is vital for researchers conducting cross-study factor analyses.