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Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note.

Edward E Rigdon1, Jan-Michael Becker2, Marko Sarstedt3,4

  • 1Georgia State University, P.O. Box 3991, Atlanta, GA, 30302-3991, USA. erigdon@gsu.edu.

Psychometrika
|July 12, 2019
PubMed
Summary
This summary is machine-generated.

Parceling strengthens factor loadings but can worsen factor indeterminacy, potentially endangering valid statistical inference. This method should be used cautiously in factor analysis.

Keywords:
factor analysisfactor indeterminacyparcelinguncertainty

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Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Parceling, a technique in factor analysis, combines observed variables into composite indicators for a common factor.
  • While parceling can strengthen factor loadings, it reduces the number of indicators, impacting model properties.

Purpose of the Study:

  • To investigate the effects of parceling on factor indeterminacy in factor analysis.
  • To determine the conditions under which parceling influences factor indeterminacy.

Main Methods:

  • Theoretical analysis of factor analysis models.
  • Mathematical proofs regarding factor indeterminacy and parceling.

Main Results:

  • Parceling cannot reduce factor indeterminacy.
  • Factor indeterminacy is worsened by parceling unless the ratio of loading to residual variance is constant across all items within a parcel.
  • Parceling does not affect parameter estimates, standard errors, or fit indices, but increases uncertainty.

Conclusions:

  • Parceling generally increases factor indeterminacy, posing a risk to the validity of statistical inference.
  • Researchers should carefully consider the implications of parceling on factor indeterminacy in their analyses.