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Common factor analysis versus principal component analysis: choice for symptom cluster research.

¹Full-time Instructor, Department of Nursing, University of Ulsan, Ulsan, South Korea.

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Summary

Common factor analysis (CFA) is more accurate for explaining correlations and examining data structure in symptom cluster research. Principal component analysis (PCA) is better for summarizing data or as an initial step in CFA.

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Area of Science:

Psychometrics
Statistical analysis
Symptom cluster research

Background:

Factor analysis is crucial for understanding complex data structures.
Distinguishing between common factor analysis (CFA) and principal component analysis (PCA) is vital for appropriate application.

Purpose of the Study:

To compare common factor analysis (CFA) and principal component analysis (PCA).
To evaluate the relevance of CFA and PCA for symptom cluster research.

Main Methods:

Critical literature review to identify differences between CFA and PCA.
Secondary data analysis (N=84) to demonstrate practical differences in results.

Main Results:

CFA focuses on reliable common variance and underlying constructs.
PCA analyzes all data variance without assuming underlying constructs.
PCA can inflate factor loadings, making it less suitable for data structure examination.

Conclusions:

CFA is preferred for explaining correlations and examining data structure in symptom research.
PCA is suitable for data summarization or as a preliminary step in CFA.
Consider sample size, subjectivity, and symptom selection when applying factor analysis.