Literature Resources for Advanced Topics in Factor Analysis

Transcription

1 Literature Resources for Advanced Topics in Factor Analysis Confirmatory Factor Analysis Byrne, B. M. (2005). Factor analytic models: viewing the structure of an assessment instrument from three perspectives. J.Pers.Assess., 85, Crowley, S. L. & Fan, X. (1997). Structural equation modeling: basic concepts and applications in personality assessment research. J.Pers.Assess., 68, Francis, D. J. (1988). An introduction to structural equation models. J.Clin.Exp.Neuropsychol., 10, Gagne, P. & Hancock, G. R. (2006). Measurement Model Quality, Sample Size, and Solution Propriety in Confirmatory Factor Models. Multivariate Behavioral Research, 41, Hays, R. D. & White, K. (1987). The importance of considering alternative structural equation models in evaluation research. Eval.Health Prof., 10, MacCallum, R. C. & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annu.Rev.Psychol., 51, McQuitty, S. & Bishop, J. W. (2006). Issues in multi-item scale testing and development using structural equation models. J.Appl.Meas., 7, Musil, C. M., Jones, S. L., & Warner, C. D. (1998). Structural equation modeling and its relationship to multiple regression and factor analysis. Res.Nurs.Health, 21, Shevlin, M. & Adamson, G. (2005). Alternative Factor Models and Factorial Invariance of the GHQ-12: A Large Sample Analysis Using Confirmatory Factor Analysis. Psychol.Assess., 17, Thompson, B. (1997). The importance of structure coefficients in structural equation modeling confirmatory factor analysis. Educational and Psychological Measurement, 57, Van Prooijen, J. W. & Van Der Kloot, W. A. (2001). Confirmatory analysis of exploratively obtained factor structures. Educational and Psychological Measurement, 61, Ward, L. C. (2006). Comparison of Factor Structure Models for the Beck Depression Inventory--II. Psychol.Assess., 18, Number of Factors to Retain (Advanced topics): Coovert, M. D. & McNelis, K. (1988). Determining the number of common factors in factor analysis: A review and program. Educational and Psychological Measurement, 48, Glorfeld, L. W. (1995). An improvement on Horn's parallel analysis methodology for selecting the correct number of factors to retain. Educational and Psychological Measurement, 55, Lee, S. Y. & Song, X. Y. (2003). Bayesian model selection for mixtures of structural equation models with an unknown number of components. Br.J.Math.Stat.Psychol., 56, Nasser, F., Benson, J., & Wisenbaker, J. (2002). The performance of regression-based variations of the visual scree for determining the number of common factors. Educational and Psychological Measurement, 62, Walkey, F. H. & McCormick, I. A. (1985). Multiple replication of factor structure: A logical solution for a number of factors problem. Multivariate Behavioral Research, 20, Wood, J. M., Tataryn, D. J., & Gorsuch, R. L. (1996). Effects of under- and overextraction on principal axis factor analysis with varimax rotation. Psychol.Methods, 1, Yuan, K. H., Fung, W. K., & Reise, S. P. (2004). Three Mahalanobis distances and their role in assessing unidimensionality. Br.J.Math.Stat.Psychol., 57, Zoski, K. W. & Jurs, S. (1996). An objective counterpart to the visual scree test for factor analysis: The standard error scree. Educational and Psychological Measurement, 56,

2 Extraction Methods Bookstein, F. L. (1990). Least squares and latent variables. Multivariate Behavioral Research, 25, Briggs, N. E. & MacCallum, R. C. (2003). Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38, Cliff, N. & Caruso, J. C. (1998). Reliable component analysis through maximizing composite reliability. Psychological Methods, 3, Gerbing, D. W. & Anderson, J. C. (1985). The effects of sampling error and model characteristics on parameter estimation for maximum likelihood confirmatory factor analysis. Multivariate Behavioral Research, 20, Lee, S. Y. & Song, X. Y. (2004). Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes. Multivariate Behavioral Research, 39, Schneeweiss, H. (1997). Factors and principal components in the near spherical case. Multivariate Behavioral Research, 32, Velicer, W. F. & Fava, J. L. (1987). An evaluation of the effects of variable sampling on component, image, and factor analysis. Multivariate Behavioral Research, 22, Wolins, L. (1995). A Monte Carlo study of constrained factor analysis using maximum likelihood and unweighted least squares. Educational and Psychological Measurement, 55, Rotation Methods Finch, H. (2006). Comparison of the Performance of Varimax and Promax Rotations: Factor Structure Recovery for Dichotomous Items. Journal of Educational Measurement, 43, Jennrich, R. I. & Trendafilov, N. T. (2005). Independent component analysis as a rotation method: a very different solution to Thurstone's box problem. Br.J.Math.Stat.Psychol., 58, Lorenzo-Seva, U. (1999). Promin: A method for oblique factor rotation. Multivariate Behavioral Research, 34, Lorenzo-Seva, U., Kiers, H. A. L., & ten Berge, J. M. F. (2002). Techniques for oblique factor rotation of two or more loading matrices to a mixture of simple structure and optimal agreement. Br.J.Math.Stat.Psychol., 55, Ogasawara, H. (1998). Standard errors for rotation matrices with an application to the promax solution. Br.J.Math.Stat.Psychol., 51, Raykov, T. & Little, T. D. (1999). A note on Procrustean rotation in exploratory factor analysis: A computer intensive approach to goodness-of-fit evaluation. Educational and Psychological Measurement, 59, Schonemann, P. H. (1966). Varism: a new machine method for orthogonal rotation. Psychometrika., 31, Stelzl, I. (1991). Rival hypotheses in linear structure modeling: Factor rotation in confirmatory factor analysis and latent path analysis. Multivariate Behavioral Research, 26, Trendafilov, N. T. (1994). A simple method for Procrustean rotation in factor analysis using majorization theory. Multivariate Behavioral Research, 29, Trendafilov, N. T. (1996). Iterative majorizing rotation to orthogonal simple structure solution. Multivariate Behavioral Research, 31,

3 Factor Scores Grice, J. W. & Harris, R. J. (1998). A comparison of regression and loading weights for the computation of factor scores. Multivariate Behavioral Research, 33, Grice, J. W. (2001). A comparison of factor scores under conditions of factor obliquity. Psychol.Methods, 6, Standard Errors of Factor Loadings Cudeck, R. & O'Dell, L. L. (1994). Applications of standard error estimates in unrestricted factor analysis: significance tests for factor loadings and correlations. Psychol.Bull., 115, Lambert, Z. V., Wildt, A. R., & Durand, R. M. (1991). Approximating confidence intervals for factor loadings. Multivariate Behavioral Research, 26, Ogasawara, H. (2000). Standard errors of the principal component loadings for unstandardized and standardized variables. Br.J.Math.Stat.Psychol., 53, Ogasawara, H. (2002). Asymptotic standard errors of estimated standard errors in structural equation modelling. Br.J.Math.Stat.Psychol. 55[2], Factor Indeterminacy MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychol.Bull., 114, Goodness of Fit Indices Kwan, C. W. & Fung, W. K. (2005). Influence curves for factor loadings. Br.J.Math.Stat.Psychol., 58, Botha, J. D., Shapiro, A., & Steiger, J. H. (1988). Uniform indices-of-fit for factor analysis models. Multivariate Behavioral Research, 23, Browne, M. W., MacCallum, R. C., Kim, C. T., Andersen, B. L., & Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological Methods, 7, Kwan, C. W. & Fung, W. K. (2005). Influence curves for factor loadings. Br.J.Math.Stat.Psychol., 58, Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychol.Bull., 103, McDonald, R. P. & Mok, M. M. C. (1995). Goodness of fit in item response models. Multivariate Behavioral Research, 30, Effects of Item Value Distribution: Bernstein, I. H. & Teng, G. (1989). Factoring items and factoring scales are different: Spurious evidence for multidimensionality due to item categorization. Psychological Bulletin, 105, Browne, M. W. & Shapiro, A. (1988). Robustness of normal theory methods in the analysis of linear latent variate models. British Journal of Mathematical and Statistical Psychology, 41, Comrey, A. L. (1985). A method for removing outliers to improve factor analytic results. Multivariate Behavioral Research., 20, Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1,

4 Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychol.Methods, 9, Joreskog, K. G. & Moustaki, I. (2001). Factor analysis of ordinal variables: A comparison of three approaches. Multivariate Behavioral Research, 36, Lee, S. Y. & Song, X. Y. (2003). Bayesian analysis of structural equation models with dichotomous variables. Stat.Med., 22, Maydeu-Olivares, A. & Bockenholt, U. (2005). Structural Equation Modeling of Paired-Comparison and Ranking Data. Psychol.Methods, 10, Potthast, M. J. (1993). Confirmatory factor analysis of ordered categorical variables with large models. Br.J.Math.Stat.Psychol., 46, Shi, J. Q. & Lee, S. Y. (1997). Estimation of factor scores with polytomous data by the EM algorithm. Br.J.Math.Stat.Psychol., 50, Shi, J. Q. & Lee, S. Y. (1998). Bayesian sampling-based approach for factor analysis models with continuous and polytomous data. Br.J.Math.Stat.Psychol., 51, Test Statistics in Confirmatory Factor Analysis Gonzalez, R. & Griffin, D. (2001). Testing parameters in structural equation modeling: every "one" matters. Psychol.Methods, 6, Hu, L. t., Bentler, P. M., & Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted? Psychological Bulletin, 112, Kaplan, D. (1989). Power of the likelihood ratio test in multiple group confirmatory factor analysis under partial measurement invariance. Educational and Psychological Measurement, 49, Labouvie, E. & Ruetsch, C. (1995). Testing for equivalence of measurement scales: Simple structure and metric invariance reconsidered. Multivariate Behavioral Research, 30, MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: the problem of capitalization on chance. Psychol.Bull., 111, MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychol.Methods, 11, Millsap, R. E. & Everson, H. (1991). Confirmatory measurement model comparisons using latent means. Multivariate Behavioral Research, 26, Nevitt, J. & Hancock, G. R. (2004). Evaluating Small Sample Approaches for Model Test Statistics in Structural Equation Modeling. Multivariate Behavioral Research, 39, Reuterberg, S. E. & Gustafsson, J. E. (1992). Confirmatory factor analysis and reliability: Testing measurement model assumptions. Educational and Psychological Measurement, 52, Factor Analysis of Family Data Lee, S. Y. & Tang, N. S. (2006). Bayesian analysis of structural equation models with mixed exponential family and ordered categorical data. Br.J.Math.Stat.Psychol., 59, Posthuma, D. & Boomsma, D. I. (2005). Mx scripts library: structural equation modeling scripts for twin and family data. Behav.Genet., 35,

5 Missing Data in Factor Analysis Allison, P. D. (2003). Missing data techniques for structural equation modeling. J.Abnorm.Psychol., 112, Bernaards, C. A. & Sijtsma, K. (1999). Factor analysis of multidimensional polytomous item response data suffering from ignorable item nonresponse. Multivariate Behavioral Research, 34, Furlow, C. F. & Beretvas, S. N. (2005). Meta-analytic methods of pooling correlation matrices for structural equation modeling under different patterns of missing data. Psychol.Methods, 10, Lee, S. Y. & Song, X. Y. (2004). Bayesian model comparison of nonlinear structural equation models with missing continuous and ordinal categorical data. Br.J.Math.Stat.Psychol., 57, McArdle, J. J. (1994). Structural factor analysis experiments with incomplete data. Multivariate Behavioral Research, 29, Multilevel Factor Analysis Goffin, R. D. & Jackson, D. N. (1992). Analysis of multitrait-multirater performance appraisal data: Composite Direct Product method versus Confirmatory Factor Analysis. Multivariate Behavioral Research, 27, Graham, J. W. & Collins, N. L. (1992). Controlling correlational bias via confirmatory factor analysis of MTMM data. Multivariate Behavioral Research, 26, Kenny, D. A. & Kashy, D. A. (1992). Analysis of the multitrait-multimethod matrix by confirmatory factor analysis. Psychological Bulletin, 112, Marsh, H. W., Byrne, B. M., & Craven, R. (1992). Overcoming problems in confirmatory factor analyses of MTMM data: The correlated uniqueness model and factorial invariance. Multivariate Behavioral Research, 27, Mehta, P. D. & Neale, M. C. (2005). People Are Variables Too: Multilevel Structural Equations Modeling. Psychol.Methods, 10, Reise, S. P., Ventura, J., Nuechterlein, K. H., & Kim, K. H. (2005). An illustration of multilevel factor analysis. J.Pers.Assess., 84, Toland, M. D. & De Ayala, R. J. (2005). A Multilevel Factor Analysis of Students' Evaluations of Teaching. Educational and Psychological Measurement, 65, Tomas, J. M., Hontangas, P. M., & Oliver, A. (2000). Linear confirmatory factor models to evaluate multitrait-multimethod matrices: The effects of number of indicators and correlation among methods. Multivariate Behavioral Research, 65,