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

Author

Listed:
  • Edward E. Rigdon

    (Georgia State University)

  • Jan-Michael Becker

    (University of Cologne)

  • Marko Sarstedt

    (Otto-von-Guericke-University Magdeburg
    Monash University Malaysia)

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.

Suggested Citation

  • Edward E. Rigdon & Jan-Michael Becker & Marko Sarstedt, 2019. "Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 772-780, September.
    • Handle: RePEc:spr:psycho:v:84:y:2019:i:3:d:10.1007_s11336-019-09677-2
    DOI: 10.1007/s11336-019-09677-2
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    References listed on IDEAS

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    1. Peter Schönemann & Ming-Mei Wang, 1972. "Some new results on factor indeterminacy," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 61-91, March.
    2. James Steiger, 1979. "The relationship between external variables and common factors," Psychometrika, Springer;The Psychometric Society, vol. 44(1), pages 93-97, March.
    3. Anders Skrondal & Petter Laake, 2001. "Regression among factor scores," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 563-575, December.
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