On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use
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Allen, 2007, Likert scales and data analyses, Quality Progress, 40, 64
Bandalos, 2009, Four common misconceptions in exploratory factor analysis, 61
Bartholomew, 2011
Bartlett, 1950, Tests of significance in factor analysis, British Journal of Statistical Psychology, 3, 77, 10.1111/j.2044-8317.1950.tb00285.x
Bartlett, 1951, A further note on tests of significance in factor analysis, British Journal of Statistical Psychology, 4, 1, 10.1111/j.2044-8317.1951.tb00299.x
Basto, 2012, An SPSS R-Menu for ordinal factor analysis, Journal of Statistical Software, 46, 10.18637/jss.v046.i04
Bentler, 1990, On the equivalence of factors and components, Multivariate Behavioral Research, 25, 67, 10.1207/s15327906mbr2501_8
Bernstein, 1989, Factoring items and factoring scales are different: spurious evidence for multidimensionality due to item categorization, Psychological Bulletin, 105, 467, 10.1037/0033-2909.105.3.467
Bock, 2007, Item response theory in a general framework, 469
Boomsma, 2001, The robustness of LISREL modeling revisited, 139
Briggs, 2003, Recovery of weak common factors by maximum likelihood and ordinary least squares estimation, Multivariate Behavioral Research, 38, 25, 10.1207/S15327906MBR3801_2
Browne, 1968, A comparison of factor analytic techniques, Psychometrika, 33, 267, 10.1007/BF02289327
Browne, 1973, Generalized least squares estimators in the analysis of covariance structures, 205
Browne, 2001, An overview of analytic rotation in exploratory factor analysis, Multivariate Behavioral Research, 36, 111, 10.1207/S15327906MBR3601_05
Carifio, 2007, Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes, Journal of Social Sciences, 3, 106, 10.3844/jssp.2007.106.116
Cattell, 1966, The scree test for the number of factors, Multivariate Behavioral Research, 1, 245, 10.1207/s15327906mbr0102_10
Cattell, 1978
Chen, 2012, A simulation study using EFA and CFA programs based the impact of missing data on test dimensionality, Expert Systems with Applications, 39, 4026, 10.1016/j.eswa.2011.09.085
Cho, 2009, Accuracy of the parallel analysis procedure with polychoric correlations, Educational & Psychological Measurement, 69, 748, 10.1177/0013164409332229
Conway, 2003, A review and evaluation of exploratory factor analysis practices in organizational research, Organizational Research Methods, 6, 147, 10.1177/1094428103251541
Courtney, 2013, 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, 18
Crawford, 1970, A general rotation criterion and its use in orthogonal rotation, Psychometrika, 35, 321, 10.1007/BF02310792
Crawford, 1979, Note: inter-rater reliability of scree test and mean square ratio test of number of factors, Perceptual and Motor Skills, 49, 223, 10.2466/pms.1979.49.1.223
Crawford, 2010, Evaluation of parallel analysis methods for determining the number of factors, Educational & Psychological Measurement, 70, 885, 10.1177/0013164410379332
Croasmun, 2011, Using Likert-type scales in the social sciences, Journal of Adult Education, 40, 19
Cudeck, 2007, Factor analysis in the year 2004: still spry at 100, 1
de Winter, 2012, Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size, Journal of Applied Statistics, 39, 695, 10.1080/02664763.2011.610445
de Winter, 2009, Exploratory factor analysis with small sample sizes, Multivariate Behavioral Research, 44, 147, 10.1080/00273170902794206
Dempster, 1977, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), 39, 1, 10.1111/j.2517-6161.1977.tb01600.x
Fabrigar, 2012
Fabrigar, 1999, Evaluating the use of exploratory factor analysis in psychological research, Psychological Methods, 4, 272, 10.1037/1082-989X.4.3.272
Fava, 1992, An empirical comparison of factor, image, component, and scale scores, Multivariate Behavioral Research, 27, 301, 10.1207/s15327906mbr2703_1
Forero, 2009, Factor analysis with ordinal indicators: a Monte Carlo study comparing DWLS and ULS estimation, Structural Equation Modeling: A Multidisciplinary Journal, 16, 625, 10.1080/10705510903203573
Fuller, 1966, Robustness of the maximum-likelihood estimation procedure in factor analysis, Psychometrika, 31, 255, 10.1007/BF02289512
Garrido, 2012, A new look at Horn's parallel analysis with ordinal variables, Psychological Methods
Gaskin, 2013, 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, 22, 69, 10.1111/j.1447-0349.2012.00845.x
Glorfeld, 1995, An improvement on Horn's parallel analysis methodology for selecting the correct number of factors to retain, Educational and Psychological Measurement, 55, 377, 10.1177/0013164495055003002
Gorsuch, 1983
Green, 2012, A proposed solution to the problem with using completely random data to assess the number of factors with parallel analysis, Educational and Psychological Measurement, 72, 357, 10.1177/0013164411422252
Guttman, 1953, Image theory for the structure of quantitative variates, Psychometrika, 18, 277, 10.1007/BF02289264
Hair, 2010
Harman, 1960
Harman, 1976
Hogarty, 2005, The quality of factor solutions in exploratory factor analysis: the influence of sample size, communality, and overdetermination, Educational and Psychological Measurement, 65, 202, 10.1177/0013164404267287
Holgado-Tello, 2010, Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables, Quality & Quantity, 44, 153, 10.1007/s11135-008-9190-y
Horn, 1965, A rationale and test for the number of factors in factor analysis, Psychometrika, 30, 179, 10.1007/BF02289447
Hubbard, 1987, An empirical comparison of alternative methods for principal component extraction, Journal of Business Research, 15, 173, 10.1016/0148-2963(84)90047-X
IBM Corp., 2012
IBM Corp., 2013
Jackson, 1980, Maximum-likelihood estimation in common factor analysis: a cautionary note, Psychological Bulletin, 88, 502, 10.1037/0033-2909.88.2.502
Jakobsson, 2004, Statistical presentation and analysis of ordinal data in nursing research, Scandinavian Journal of Caring Sciences, 18, 437, 10.1111/j.1471-6712.2004.00305.x
Jöreskog, 1962, On the statistical treatment of residuals in factor analysis, Psychometrika, 27, 335, 10.1007/BF02289643
Jöreskog, 1969, Efficient estimation in image factor analysis, Psychometrika, 34, 51, 10.1007/BF02290173
Jöreskog, 1994, On the estimation of polychoric correlations and their asymptotic covariance matrix, Psychometrika, 59, 381, 10.1007/BF02296131
Jöreskog, 2007, Factor analysis and its extensions, 47
Jöreskog, 1972, Factor analysis by generalized least squares, Psychometrika, 37, 243, 10.1007/BF02306782
Jöreskog, 2001, Factor analysis of ordinal variables: a comparison of three approaches, Multivariate Behavioral Research, 36, 347, 10.1207/S15327906347-387
Kaiser, 1958, The Varimax criterion for analytic rotation in factor analysis, Psychometrika, 23, 187, 10.1007/BF02289233
Kaiser, 1960, The application of electronic computers to factor analysis, Educational and Psychological Measurement, 20, 141, 10.1177/001316446002000116
Katsikatsou, 2012, Pairwise likelihood estimation for factor analysis models with ordinal data, Computational Statistics & Data Analysis, 56, 4243, 10.1016/j.csda.2012.04.010
Kline, 1986
Lawley, 1940, The estimation of factor loadings by the method of maximum likelihood, Proceedings of the Royal Society of Edinburgh, Section A, 50, 64, 10.1017/S037016460002006X
Lee, 2010, A review of CEFA Software: comprehensive exploratory factor analysis program, International Journal of Testing, 10, 95, 10.1080/15305050903537251
Lee, 2012, Ordinary least squares estimation of parameters in exploratory factor analysis with ordinal data, Multivariate Behavioral Research, 47, 314, 10.1080/00273171.2012.658340
Liu, 2008, A modified procedure for parallel analysis of ordered categorical data, Behavior Research Methods, 40, 556, 10.3758/BRM.40.2.556
Lorenzo-Seva, 2006, FACTOR: a computer program to fit the exploratory factor analysis model, Behavior Research Methods, 38, 88, 10.3758/BF03192753
Lorenzo-Seva, 2011, The Hull method for selecting the number of common factors, Multivariate Behavioral Research, 46, 340, 10.1080/00273171.2011.564527
Lozano, 2008, 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, 4, 73, 10.1027/1614-2241.4.2.73
MacCallum, 1991, Representing sources of error in the common-factor model: implications for theory and practice, Psychological Bulletin, 109, 502, 10.1037/0033-2909.109.3.502
MacCallum, 1999, Sample size in factor analysis, Psychological Methods, 4, 84, 10.1037/1082-989X.4.1.84
MacCallum, 2001, Sample size in factor analysis: the role of model error, Multivariate Behavioral Research, 36, 611, 10.1207/S15327906MBR3604_06
MacCallum, 2007, Factor analysis models as approximations, 153
McArdle, 1990, Principles versus principals of structural factor analyses, Multivariate Behavioral Research, 25, 81, 10.1207/s15327906mbr2501_10
Mundfrom, 2005, Minimum sample size recommendations for conducting factor analyses, International Journal of Testing, 5, 159, 10.1207/s15327574ijt0502_4
Neuhaus, 1954, The quartimax method: an analytic approach to orthogonal simple structure, British Journal of Statistical Psychology, 7, 81, 10.1111/j.2044-8317.1954.tb00147.x
Nunnally, 1978
O’Connor, 2000, SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test, Behavior Research Methods, Instruments, & Computers, 32, 396, 10.3758/BF03200807
Preacher, 2003, Repairing Tom Swift's electric factor analysis machine, Understanding Statistics, 2, 13, 10.1207/S15328031US0201_02
Rao, 1955, Estimation and tests of significance in factor analysis, Psychometrika, 20, 93, 10.1007/BF02288983
Ruscio, 2012, Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure, Psychological Assessment, 24, 282, 10.1037/a0025697
SAS Institute, 2013
Sass, 2010, A comparative investigation of rotation criteria within exploratory factor analysis, Multivariate Behavioral Research, 45, 73, 10.1080/00273170903504810
Schmitt, 2011, Current methodological considerations in exploratory and confirmatory factor analysis, Journal of Psychoeducational Assessment, 29, 304, 10.1177/0734282911406653
Schmitt, 2011, Rotation criteria and hypothesis testing for exploratory factor analysis: implications for factor pattern loadings and interfactor correlations, Educational and Psychological Measurement, 71, 95, 10.1177/0013164410387348
Schönemann, 1978, On the validity of indeterminate factor scores, Bulletin of the Psychonomic Society, 12, 287, 10.3758/BF03329685
Snook, 1989, Component analysis versus common factor analysis: a Monte Carlo study, Psychological Bulletin, 106, 148, 10.1037/0033-2909.106.1.148
Spearman, 1904, General intelligence, objectively determined and measured, American Journal of Psychology, 15, 201, 10.2307/1412107
Stevens, 1946, On the theory of scales of measurement, Science, 103, 677, 10.1126/science.103.2684.677
Tabachnick, 2012
Thomson, 1934, Hotelling's method modified to give Spearman's g, Journal of Educational Psychology, 25, 366, 10.1037/h0072648
Thomson, 1939, The factorial analysis of ability: I. The present position and problems confronting us, British Journal of Psychology. General Section, 30, 71, 10.1111/j.2044-8295.1939.tb00941.x
Thurstone, 1947
Velicer, 1976, Determining the number of components from the matrix of partial correlations, Psychometrika, 41, 321, 10.1007/BF02293557
Velicer, 1976, The relation between factor score estimates, image scores, and principal component scores, Educational and Psychological Measurement, 36, 149, 10.1177/001316447603600114
Velicer, 1977, An empirical comparison of the similarity of principal component, image, and factor patterns, Multivariate Behavioral Research, 12, 3, 10.1207/s15327906mbr1201_1
Velicer, 1998, Effects of variable and subject sampling on factor pattern recovery, Psychological Methods, 3, 231, 10.1037/1082-989X.3.2.231
Velicer, 1990, Component analysis versus common factor analysis: some further observations, Multivariate Behavioral Research, 25, 97, 10.1207/s15327906mbr2501_12
Velicer, 1990, Component analysis versus common factor analysis: some issues in selecting an appropriate procedure, Multivariate Behavioral Research, 25, 1, 10.1207/s15327906mbr2501_1
Velicer, 2000, Construct explication through factor or component analysis: a review and evaluation of alternative procedures for determining the number of factors or components, 41
Watson, 2006, Use of factor analysis in Journal of Advanced Nursing: literature review, Journal of Advanced Nursing, 55, 330, 10.1111/j.1365-2648.2006.03915.x
Widaman, 1990, Bias in pattern loadings represented by common factor analysis and component analysis, Multivariate Behavioral Research, 25, 89, 10.1207/s15327906mbr2501_11
Widaman, 1993, Common factor analysis versus principal component analysis: differential bias in representing model parameters?, Multivariate Behavioral Research, 28, 263, 10.1207/s15327906mbr2803_1
Widaman, 2007, Common factors versus components: principals and principles, errors and misconceptions, 177
Wirth, 2007, Item factor analysis: current approaches and future directions, Psychological Methods, 12, 58, 10.1037/1082-989X.12.1.58
Woltz, 2012, Relationship between perceived and actual frequency represented by common rating scale labels, Psychological Assessment, 24, 995, 10.1037/a0028693
Yates, 1987
Yuan, 2002, A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers, Psychometrika, 67, 95, 10.1007/BF02294711
Zwick, 1982, Factors influencing four rules for determining the number of components to retain, Multivariate Behavioral Research, 17, 253, 10.1207/s15327906mbr1702_5