J Korean Acad Nurs.  2013 Oct;43(5):587-594. 10.4040/jkan.2013.43.5.587.

A Guide on the Use of Factor Analysis in the Assessment of Construct Validity

• Kang H

• KMID: 1707160
• DOI: http://doi.org/10.4040/jkan.2013.43.5.587

Abstract

PURPOSE
The purpose of this study is to provide researchers with a simplified approach to undertaking exploratory factor analysis for the assessment of construct validity.
METHODS
All articles published in 2010, 2011, and 2012 in Journal of Korean Academy of Nursing were reviewed and other relevant books and articles were chosen for the review.
RESULTS
In this paper, the following were discussed: preliminary analysis process of exploratory factor analysis to examine the sample size, distribution of measured variables, correlation coefficient, and results of KMO measure and Bartlett's test of sphericity. In addition, other areas to be considered in using factor analysis are discussed, including determination of the number of factors, the choice of rotation method or extraction method of the factor structure, and the interpretation of the factor loadings and explained variance.
CONCLUSION
Content validity is the degree to which elements of an assessment instrument are relevant to and representative of the targeted construct for a particular assessment purpose. This measurement is difficult and challenging and takes a lot of time. Factor analysis is considered one of the strongest approaches to establishing construct validity and is the most commonly used method for establishing construct validity measured by an instrument.

Keyword

Factor analysis; Construct validity; Sample size; Communality; Factor loading

MeSH Terms

Analysis of Variance
Factor Analysis, Statistical
Nursing Research/*standards
Publishing

Reference

1. Arrindel WA, van der Ende J. An empirical test of the utility of the observations-to-variables ratio in factor and components analysis. Appl Psychol Meas. 1985; 9(2):165–178. http://dx.doi.org/10.1177/014662168500900205.

2. Barrett PT, Kline P. The observation to variable ratio in factor analysis. Personal Study Group Behav. 1981; 1:23–33.

3. Bollen K, Lennox R. Conventional wisdom on measurement:A structural equation perspective. Psychol Bull. 1991; 110(2):305–314.

4. Cartell L, Harman A. The scree test for the number of factors. Multivar Behav Res. 1966; 1:245–276.

5. Chin WW. Issue and opinion on structural equation modeling. MIS Q. 1998; 22(1):1–10.

6. Cho HD. A study on issues of using structure equation modeling in education study. Seoul: Korea University;2011. Unpublished master's thesis.

7. Comrey AL, Lee HB. A first course in factor analysis. 2nd ed. Hillsdale, NJ: Erlbaum;1992.

8. Guilford JP. Psychometric methods. 2nd ed. New York, NY: McGraw-Hill;1956.

9. Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. 4th ed. Upper Saddle River, NJ: Prentice Hall;1995.

10. Hogarty KY, Hines CV, Kromrey JD, Ferron JM, Mumford KR. The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination. Educ Psychol Meas. 2005; 65(2):202–226. http://dx.doi.org/10.1177/0013164404267287.

11. Jarvis CB, MacKenzie SB, Podsakoff PM. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J Consum Res. 2003; 30(2):199–218. http://dx.doi.org/10.1086/376806.

12. Kaiser HF. An index of factorial simplicity. Psychometrika. 1974; 39(1):31–36.

13. Kang H. Discussions on the suitable interpretation of model fit indices and the strategies to fit model in structural equation modeling. J Korean Data Anal Soc. 2013; 15(2):653–668.

14. Kang H, Han ST, Kim K, Jhun M. Multivariate data analysis using SAS by examples. Paju: Freedom Academy;2005.

15. Lawley DN, Maxwell AE. Factor analysis as a statistical method. 2nd ed. New York, NY: American Elsevier Pub. Co.;1971.

16. Lee H, Kim JH. Structural equation modeling and AMOS 20.0. Seoul: JypHyunJae Publishing Co.;2013.

17. MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods. 1996; 1(2):130–149.

18. MacCallum RC, Widaman KF, Zhang S, Hong S. Sample size in factor analysis. Psychol Methods. 1999; 4(1):84–99.

19. Munro BH. Statistical methods for health care research. 5th ed. Philadelphia, PA: Lippincott Williams & Wilkins;2005.

20. Nunnally JC. Psychometric theory. 2nd ed. New York, NY: McGraw-Hill;1978.

21. Seong TJ. Validity and reliability. 2nd ed. Seoul: Hakjisa Publisher;2002.

22. Shin HS, Hyun MS, Ku MO, Cho MO, Kim SY, Jeong JS, et al. Analysis of research papers published in the Journal of the Korean Academy of Nursing-focused on research trends, intervention studies, and level of evidence in the research. J Korean Acad Nurs. 2010; 40(1):139–149. http://dx.doi.org/10.4040/jkan.2010.40.1.139.

23. Snook SC, Gorsuch RL. Component analysis versus common factor analysis: A Monte Carlo study. Psychol Bull. 1989; 106(1):148–154. http://dx.doi.org/10.1037/0033-2909.106.1.148.

24. Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Boston, MA: Pearson/Allyn & Bacon;2007.

25. Tafreshi MZ, Yaghmaei F. Factor analysis of construct validity: A review of nursing articles. J Med Educ. 2006; 10(1):19–26.

26. Tak JK. Psychological testing: An understanding of development and evaluation method. 2nd ed. Seoul: Hakjisa Publisher;2007.

27. Williams B, Brown T, Onsman A. Exploratory factor analysis: A five-step guide for novices. J Emerg Prim Health Care. 2012; 8(3):Article 1.