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

Published online by Cambridge University Press:  01 January 2025

Edward E. Rigdon Open the ORCID record for Edward E. Rigdon [Opens in a new window] ,
Jan-Michael Becker Open the ORCID record for Jan-Michael Becker [Opens in a new window]  and
Marko Sarstedt
Edward E. Rigdon*
Affiliation:
Georgia State University
Jan-Michael Becker
Affiliation:
University of Cologne
Marko Sarstedt
Affiliation:
Otto-von-Guericke-University Magdeburg Monash University Malaysia
*
Correspondence should bemade to Edward E. Rigdon, Georgia State University, P.O. Box 3991, Atlanta, GA30302-3991, USA. Email: erigdon@gsu.edu
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Abstract

Parceling—using composites of observed variables as indicators for a common factor—strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference.

Keywords

factor analysisparcelingfactor indeterminacyuncertainty

Information

Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 3 , September 2019 , pp. 772 - 780
Copyright
Copyright © 2019 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-019-09677-2) contains supplementary material, which is available to authorized users.

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