On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use

International Journal of Nursing Studies

Volumn 51, Issue 3, 2014, Pages 511-521

  Gaskin, Cadeyrn J ^a,b   Happell, Brenda ^c  

^a Gaskin Research   (Australia)

^b CENTRAL QUEENSLAND UNIVERSITY   (Australia)

^c CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

Factor analysis; Measurement; Nursing research; Principal components analysis; Research design; Review literature as topic; Statistics; Statistics as topic

Indexed keywords

EVIDENCE BASED PRACTICE; FACTORIAL ANALYSIS; LITERATURE; MEASUREMENT; METHODOLOGY; NURSING RESEARCH; PRINCIPAL COMPONENT ANALYSIS; REVIEW; STATISTICS;

FACTOR ANALYSIS; MEASUREMENT; NURSING RESEARCH; PRINCIPAL COMPONENTS ANALYSIS; RESEARCH DESIGN; REVIEW LITERATURE AS TOPIC; STATISTICS; STATISTICS AS TOPIC;

EVIDENCE-BASED PRACTICE; FACTOR ANALYSIS, STATISTICAL; PRINCIPAL COMPONENT ANALYSIS;

EID: 84894257976     PISSN: 00207489     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijnurstu.2013.10.005     Document Type: Review

References (112)

1. Allen I.E., Seaman C.A. Likert scales and data analyses. Quality Progress 2007, 40(7):64-65.
2. Bandalos D.L., Boehm-Kaufman M.R. Four common misconceptions in exploratory factor analysis. Statistical and Methodological Myths and Urban Legends: Doctrine, Verity, and Fable in the Organizational and Social Sciences 2009, 61-87. Taylor & Francis, New York, NY. C.E. Lance, R.J. Vandenberg (Eds.).
3. Bartholomew D., Knott M., Moustaki I. Latent Variable Models and Factor Analysis: A Unified Approach 2011, Wiley, Hoboken, NJ.
4. Bartlett M.S. Tests of significance in factor analysis. British Journal of Statistical Psychology 1950, 3:77-85.
5. Bartlett M.S. A further note on tests of significance in factor analysis. British Journal of Statistical Psychology 1951, 4:1-2.
6. Basto M., Pereira J.P. An SPSS R-Menu for ordinal factor analysis. Journal of Statistical Software 2012, 46(4). Retrieved from:. http://www.jstatsoft.org/.
7. Bentler P.M., Kano Y. On the equivalence of factors and components. Multivariate Behavioral Research 1990, 25:67-74.
8. Bernstein I.H., Teng G. Factoring items and factoring scales are different: spurious evidence for multidimensionality due to item categorization. Psychological Bulletin 1989, 105:467-477.
9. Bock R.D., Moustaki I. Item response theory in a general framework. Handbook of Statistics 2007, 469-514. North-Holland, Amsterdam, Netherlands. C.R. Rao, S. Sinharay (Eds.).
10. Boomsma A., Hoogland J.J. The robustness of LISREL modeling revisited. Structural Equation Models: Present and Future. A Festschrift in Honor of Karl Jöreskog 2001, 139-168. Scientific Software International, Lincolnwood, IL. R. Cudeck, S.H.C. Du Toit, D. Sörbom (Eds.).
11. Briggs N.E., MacCallum R.C. Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research 2003, 38:25-56.
12. Browne M.W. A comparison of factor analytic techniques. Psychometrika 1968, 33:267-334.
13. Browne M.W. Generalized least squares estimators in the analysis of covariance structures. Latent Variables in Socio-economic Models 1973, 205-226. North-Holland, Amsterdam, Netherlands. D.J. Aigner, A.S. Goldberger (Eds.).
14. Browne M.W. An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research 2001, 36:111-150.
15. Carifio J., Perla R.J. Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences 2007, 3:106-116.
16. Cattell R.B. The scree test for the number of factors. Multivariate Behavioral Research 1966, 1:245-276.
17. Cattell R.B. The Scientific use of Factor Analysis in Behavioral and Life Sciences 1978, Plenum, New York, NY.
18. Chen S.-F., Wang S., Chen C.-Y. A simulation study using EFA and CFA programs based the impact of missing data on test dimensionality. Expert Systems with Applications 2012, 39:4026-4031.
19. Cho S.-J., Feiming L., Bandalos D.L. Accuracy of the parallel analysis procedure with polychoric correlations. Educational & Psychological Measurement 2009, 69:748-759.
20. Conway J.M., Huffcutt A.I. A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods 2003, 6:147-168.
21. Courtney M.G.R. Determining the number of factors to retain in EFA: using the SPSS R-Menu v2.0 to make more judicious estimations. Practical Assessment, Research & Evaluation 2013, 18(8). Retrieved from: http://pareonline.net/.
22. Crawford C.B., Ferguson G.A. A general rotation criterion and its use in orthogonal rotation. Psychometrika 1970, 35:321-332.
23. Crawford C.B., Koopman P. Note: inter-rater reliability of scree test and mean square ratio test of number of factors. Perceptual and Motor Skills 1979, 49:223-226.
24. Crawford A.V., Green S.B., Levy R., Lo W.-J., Scott L., Svetina D., Thompson M.S. Evaluation of parallel analysis methods for determining the number of factors. Educational & Psychological Measurement 2010, 70:885-901.
25. Croasmun J.T., Ostrom L. Using Likert-type scales in the social sciences. Journal of Adult Education 2011, 40(1):19-22.
26. Cudeck R. Factor analysis in the year 2004: still spry at 100. Factor Analysis at 100: Historical Developments and Future Directions 2007, 1-7. Lawrence Erlbaum, Mahwah, NJ. R. Cudeck, R.C. MacCallum (Eds.).
27. de Winter J.C.F., Dodou D. Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics 2012, 39:695-710.
28. de Winter J.C.F., Dodou D., Wieringa P.A. Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research 2009, 44:147-181.
29. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 1977, 39:1-38.
30. Fabrigar L.R., Wegener D.T. Exploratory Factor Analysis 2012, Oxford University Press, New York, NY.
31. Fabrigar L.R., Wegener D.T., MacCallum R.C., Strahan E.J. Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods 1999, 4:272-299.
32. Fava J.L., Velicer W.F. An empirical comparison of factor, image, component, and scale scores. Multivariate Behavioral Research 1992, 27:301-322.
33. Forero C.G., Maydeu-Olivares A., Gallardo-Pujol D. Factor analysis with ordinal indicators: a Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling: A Multidisciplinary Journal 2009, 16:625-641.
34. Fuller E.L., Hemmerle W.J. Robustness of the maximum-likelihood estimation procedure in factor analysis. Psychometrika 1966, 31:255-266.
35. Garrido L.E., Abad F.J., Ponsoda V. A new look at Horn's parallel analysis with ordinal variables. Psychological Methods 2012, (Advance online publication).
36. Gaskin C.J., Happell B. Power of mental health nursing research: a statistical analysis of studies in the International Journal of Mental Health Nursing. International Journal of Mental Health Nursing 2013, 22:69-75.
37. Glorfeld L.W. An improvement on Horn's parallel analysis methodology for selecting the correct number of factors to retain. Educational and Psychological Measurement 1995, 55:377-393.
38. Gorsuch R.L. Factor Analysis 1983, Erlbaum, Hillsdale, NJ.
39. Green S.B., Levy R., Thompson M.S., Lu M., Lo W.-J. A proposed solution to the problem with using completely random data to assess the number of factors with parallel analysis. Educational and Psychological Measurement 2012, 72:357-374.
40. Guttman L. Image theory for the structure of quantitative variates. Psychometrika 1953, 18:277-296.
41. Hair J.F., Black W.C., Babin B.J., Anderson R.E. Multivariate Data Analysis: A Global Perspective 2010, Pearson, Upper Saddle River, NJ.
42. Harman H.H. Modern Factor Analysis 1960, University of Chicago Press, Chicago, IL.
43. Harman H.H. Modern Factor Analysis 1976, University of Chicago Press, Chicago, IL. 3rd ed. rev.
44. Hogarty K.Y., Hines C.V., Kromrey J.D., Ferron J.M., Mumford K.R. The quality of factor solutions in exploratory factor analysis: the influence of sample size, communality, and overdetermination. Educational and Psychological Measurement 2005, 65:202-226.
45. Holgado-Tello F., Chacón-Moscoso S., Barbero-García I., Vila-Abad E. Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Quality & Quantity 2010, 44:153-166.
46. Horn J.L. A rationale and test for the number of factors in factor analysis. Psychometrika 1965, 30:179-185.
47. Hubbard R., Allen S.J. An empirical comparison of alternative methods for principal component extraction. Journal of Business Research 1987, 15:173-190.
48. IBM Corp. IBM SPSS Statistics for Windows, Version 21.0 2012, IBM Corp., Armonk, NY.
49. IBM Corp. Extension Bundles from IBM SPSS 2013, IBM Corp., Armonk, NY.
50. Jackson D.N., Chan D.W. Maximum-likelihood estimation in common factor analysis: a cautionary note. Psychological Bulletin 1980, 88:502-508.
51. Jakobsson U. Statistical presentation and analysis of ordinal data in nursing research. Scandinavian Journal of Caring Sciences 2004, 18:437-440.
52. Jennrich R.I., Sampson P.F. Rotation for simple loadings. Psychometrika 1966, 31:313-323.
53. Jöreskog K.G. On the statistical treatment of residuals in factor analysis. Psychometrika 1962, 27:335-354.
54. Jöreskog K.G. Efficient estimation in image factor analysis. Psychometrika 1969, 34:51-75.
55. Jöreskog K.G. On the estimation of polychoric correlations and their asymptotic covariance matrix. Psychometrika 1994, 59:381-389.
56. Jöreskog K.G. Factor analysis and its extensions. Factor Analysis at 100: Historical Developments and Future Directions 2007, 47-77. Lawrence Erlbaum, Mahwah, NJ. R. Cudeck, R.C. MacCallum (Eds.).
57. Jöreskog K.G., Goldberger A.S. Factor analysis by generalized least squares. Psychometrika 1972, 37:243-260.
58. Jöreskog K.G., Moustaki I. Factor analysis of ordinal variables: a comparison of three approaches. Multivariate Behavioral Research 2001, 36:347-387.
59. Kaiser H.F. The Varimax criterion for analytic rotation in factor analysis. Psychometrika 1958, 23:187-200.
60. Kaiser H.F. The application of electronic computers to factor analysis. Educational and Psychological Measurement 1960, 20:141-151.
61. Kaiser H.F. A second generation Little Jiffy. Psychometrika 1970, 35:401-415.
62. Kaiser H.F., Caffrey J. Alpha factor analysis. Psychometrika 1965, 30:1-14.
63. Katsikatsou M., Moustaki I., Yang-Wallentin F., Jöreskog K.G. Pairwise likelihood estimation for factor analysis models with ordinal data. Computational Statistics & Data Analysis 2012, 56:4243-4258.
64. Kline P. A Handbook of Test Construction: Introduction to Psychometric Design 1986, Methuen, London, UK.
65. Lawley D.N. The estimation of factor loadings by the method of maximum likelihood. Proceedings of the Royal Society of Edinburgh, Section A 1940, 50:64-82.
66. Lee S.-M. A review of CEFA Software: comprehensive exploratory factor analysis program. International Journal of Testing 2010, 10:95-103.
67. Lee C.-T., Zhang G., Edwards M.C. Ordinary least squares estimation of parameters in exploratory factor analysis with ordinal data. Multivariate Behavioral Research 2012, 47:314-339.
68. Liu O.L., Rijmen F. A modified procedure for parallel analysis of ordered categorical data. Behavior Research Methods 2008, 40:556-562.
69. Lorenzo-Seva U., Ferrando P.J. FACTOR: a computer program to fit the exploratory factor analysis model. Behavior Research Methods 2006, 38:88-91.
70. Lorenzo-Seva U., Timmerman M.E., Kiers H.A.L. The Hull method for selecting the number of common factors. Multivariate Behavioral Research 2011, 46:340-364.
71. Lozano L.M., García-Cueto E., Muñiz J. Effect of the number of response categories on the reliability and validity of rating scales. Method: European Journal of Research Methods for the Behavioral and Social Sciences 2008, 4:73-79.
72. MacCallum R.C., Tucker L.R. Representing sources of error in the common-factor model: implications for theory and practice. Psychological Bulletin 1991, 109:502-511.
73. MacCallum R.C., Widaman K.F., Zhang S., Hong S. Sample size in factor analysis. Psychological Methods 1999, 4:84-99.
74. MacCallum R.C., Widaman K.F., Preacher K.J., Hong S. Sample size in factor analysis: the role of model error. Multivariate Behavioral Research 2001, 36:611-637.
75. MacCallum R.C., Browne M.W., Cai L. Factor analysis models as approximations. Factor Analysis at 100: Historical Developments and Future Directions 2007, 153-175. Lawrence Erlbaum, Mahwah, NJ. R. Cudeck, R.C. MacCallum (Eds.).
76. McArdle J.J. Principles versus principals of structural factor analyses. Multivariate Behavioral Research 1990, 25:81-87.
77. Mundfrom D.J., Shaw D.G., Tian Lu K. Minimum sample size recommendations for conducting factor analyses. International Journal of Testing 2005, 5:159-168.
78. Neuhaus J.O., Wrigley C. The quartimax method: an analytic approach to orthogonal simple structure. British Journal of Statistical Psychology 1954, 7:81-91.
79. Nunnally J.C. Psychometric Theory 1978, McGraw-Hill, New York, NY.
80. O'Connor B.P. SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behavior Research Methods, Instruments, & Computers 2000, 32:396-402.
81. Preacher K.J., MacCallum R.C. Repairing Tom Swift's electric factor analysis machine. Understanding Statistics 2003, 2(1):13-43.
82. Rao C.R. Estimation and tests of significance in factor analysis. Psychometrika 1955, 20:93-111.
83. Ruscio J., Roche B. Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychological Assessment 2012, 24:282-292.
84. ® 9.4 Procedures Guide: Statistical Procedures 2013, SAS Institute, Cary, NC.
85. Sass D.A., Schmitt T.A. A comparative investigation of rotation criteria within exploratory factor analysis. Multivariate Behavioral Research 2010, 45:73-103.
86. Schmitt T.A. Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment 2011, 29:304-321.
87. Schmitt T.A., Sass D.A. Rotation criteria and hypothesis testing for exploratory factor analysis: implications for factor pattern loadings and interfactor correlations. Educational and Psychological Measurement 2011, 71:95-113.
88. Schönemann P.H., Steiger J.H. On the validity of indeterminate factor scores. Bulletin of the Psychonomic Society 1978, 12:287-290.
89. Snook S.C., Gorsuch R.L. Component analysis versus common factor analysis: a Monte Carlo study. Psychological Bulletin 1989, 106:148-154.
90. Spearman C. General intelligence, objectively determined and measured. American Journal of Psychology 1904, 15:201-292.
91. Stevens S.S. On the theory of scales of measurement. Science 1946, 103:677-680.
92. Tabachnick B.G., Fidell L.S. Using Multivariate Statistics 2012, Pearson, Boston, MA.
93. Thomson G.H. Hotelling's method modified to give Spearman's g. Journal of Educational Psychology 1934, 25:366-374.
94. Thomson G.H. The factorial analysis of ability: I. The present position and problems confronting us. British Journal of Psychology. General Section 1939, 30:71-77.
95. Thurstone L.L. Multiple Factor Analysis 1947, University of Chicago Press, Chicago, IL.
96. Velicer W.F. Determining the number of components from the matrix of partial correlations. Psychometrika 1976, 41:321-327.
97. Velicer W.F. The relation between factor score estimates, image scores, and principal component scores. Educational and Psychological Measurement 1976, 36:149-159.
98. Velicer W.F. An empirical comparison of the similarity of principal component, image, and factor patterns. Multivariate Behavioral Research 1977, 12:3-22.
99. Velicer W.F., Fava J.L. Effects of variable and subject sampling on factor pattern recovery. Psychological Methods 1998, 3:231-251.
100. Velicer W.F., Jackson D.N. Component analysis versus common factor analysis: some further observations. Multivariate Behavioral Research 1990, 25:97-114.
101. Velicer W.F., Jackson D.N. Component analysis versus common factor analysis: some issues in selecting an appropriate procedure. Multivariate Behavioral Research 1990, 25:1-28.
102. Velicer W.F., Eaton C.A., Fava J.L. Construct explication through factor or component analysis: a review and evaluation of alternative procedures for determining the number of factors or components. Problems and Solutions in Human Assessment: Honoring Douglas N. Jackson at Seventy 2000, 41-71. Kluwer Academic, New York, NY. R.D. Goffin, E. Helmes (Eds.).
103. Watson R., Thompson D.R. Use of factor analysis in Journal of Advanced Nursing: literature review. Journal of Advanced Nursing 2006, 55:330-341.
104. Widaman K.F. Bias in pattern loadings represented by common factor analysis and component analysis. Multivariate Behavioral Research 1990, 25:89-95.
105. Widaman K.F. Common factor analysis versus principal component analysis: differential bias in representing model parameters?. Multivariate Behavioral Research 1993, 28:263-311.
106. Widaman K.F. Common factors versus components: principals and principles, errors and misconceptions. Factor Analysis at 100: Historical Developments and Future Directions 2007, 177-203. Lawrence Erlbaum, Mahwah, NJ. R. Cudeck, R.C. MacCallum (Eds.).
107. Wirth R.J., Edwards M.C. Item factor analysis: current approaches and future directions. Psychological Methods 2007, 12:58-79.
108. Woltz D.J., Gardner M.K., Kircher J.C., Burrow-Sanchez J.J. Relationship between perceived and actual frequency represented by common rating scale labels. Psychological Assessment 2012, 24:995-1007.
109. Yates A. Multivariate Exploratory Data Analysis: A Perspective on Exploratory Factor Analysis 1987, State University of New York, Albany, NY.
110. Yuan K.-H., Marshall L.L., Bentler P.M. A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers. Psychometrika 2002, 67:95-122.
111. Zwick W.R., Velicer W.F. Factors influencing four rules for determining the number of components to retain. Multivariate Behavioral Research 1982, 17:253-269.
112. Zwick W.R., Velicer W.F. Comparison of five rules for determining the number of components to retain. Psychological Bulletin 1986, 99:432-442.