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Comparing two approaches for studying symptom clusters: factor analysis and structural equation modeling

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Abstract

Purpose

We investigated alternative ways of understanding the relationships among co-occurring symptoms in individuals with advanced cancer. While factor analysis has been increasingly used to identify symptom clusters, we argue that structural equation modeling is more appropriate because it permits investigating and testing of a greater variety of potential causal interconnections among symptoms.

Methods

The sample included 82 palliative patients whose symptom scores were obtained from a database of the Capital Health Regional Palliative Care Program in Alberta, Canada, from 1995 to 2000. Data were analyzed using exploratory factor analysis (SPSS PASW 18.0.0, 2009) and compared to previous results obtained using structural equation modeling (LISREL 8.8, 2009).

Results

Factor models failed to fit the covariance data, even though a single factor “explained” nearly half the variance. Structural equation models fit the data and explained an average of 66 % of the variance in the dependent latent variables. The factor analytic estimates were not clinically useful because they failed to correspond to the reasonable underlying common causes of the symptoms. Structural equation models, on the other hand, incorporated and tested specific clinically anticipated causal relationships among the symptoms and changes in those symptoms over time.

Conclusion

We used factor analysis to reanalyze data previously investigated with structural equation modeling and found that the structural equation models fit the data better and were more interpretable from a clinical perspective. We caution that factor models should be tested for consistency with the data and critically examined for inconsistencies with clinical understandings of the causal foundations of coordinated symptoms.

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Acknowledgments

The authors acknowledge the financial support of the Killam Cornerstone Fund at the University of Alberta and the analytic assistance provided by Dr. Y. Cui in the Department of Educational Psychology at the University of Alberta. The authors also acknowledge Mr. Hue Quan, currently the data manager for the Edmonton Zone Palliative Care Program, who provided the original data set.

Conflict of interest

The University of Alberta provided 4 months of graduate student support for Ms. Thomas and employs both Drs. Olson and Hayduk, but had no control over the data or analysis. The authors agree to allow the journal to review their data if requested.

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Authors and Affiliations

  1. Faculty of Nursing, University of Alberta, Edmonton, AB, Canada

    Karin Olson

  2. Department of Sociology, University of Alberta, Edmonton, AB, Canada

    Leslie Hayduk & Jasmine Thomas

Authors
  1. Karin Olson
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  2. Leslie Hayduk
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  3. Jasmine Thomas
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Corresponding author

Correspondence to Karin Olson.

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Olson, K., Hayduk, L. & Thomas, J. Comparing two approaches for studying symptom clusters: factor analysis and structural equation modeling. Support Care Cancer 22, 153–161 (2014). https://doi.org/10.1007/s00520-013-1965-6

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  • DOI: https://doi.org/10.1007/s00520-013-1965-6

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