Annotated Factor Analysis Bibliography Michael Friendly

This bibliography, prepared for my Multivariate Data Analysis graduate course and Statistical Consulting Service short course lists over 130 references dealing with factor analysis and related methods, including path analysis and structural equations modelling. Most (but not all) have brief descriptive or evaluative comments to help you determine if they are worth pursuing.

Contents

You can jump to a particular topic by selecting the link in the contents list here.

• 1. Theory and Methods Part 1 is concerned with theory and methods and includes texts, from basic to advanced, and tutorials as well as technical papers and methodological critiques.

o General references & texts o Historical milestones o Interpretation of factors o Principal components & Singular value decomposition o Specialized factor analysis models & estimation methods o Communalities o Number of factors o Rotation methods o Factorial invariance, Multimethod factor analysis o Scale construction, factoring item-correlations o Structural equations models, causal models o Confirmatory factor analysis o Unclassified

• 2. Research Applications Part 2 gives examples of research applications of these methods in a variety of disciplines, though the greatest concentration is in subfields of psychology.

o Path analysis, Causal models for observable variables o Structural equation models, Causal models for latent

variables o Measurement models for validating instruments and

constructs o Multi-trait, Multi-method studies, Higher-order factor

models o Factorial invariance, Multi-sample CFA models

1. Theory and Methods General references & texts

• Bentler, P. M. & Chou, C. P. (1987). Practical issues in structural modeling Sociological Methods and Research, 16(1), 78-117.

• Bollen, K. A. (1989). Structural equations with latent variables, New York: Wiley.

[A comprehensive introduction to LISREL models, including path analysis, structural equations, and confirmatory factor analysis, with many worked examples using the LISREL and EQS programs. This is a moderately difficult, graduate level text, but it is the most complete treatment of LISREL models available. The discussion of rules and methods for identifying parameters of structural equation and factor analysis models is excellent.]

• Byrne, B.M. (1990). A Primer of LISREL: Basic Applications and Programming for Confirmatory Factor Analytic Models. New York: Springer-Verlag Inc.

[ This book was designed specifically as a starting point for people who always wanted to use LISREL, but were too afraid to try. Byrne provides input and output for 6 different types of applications - 3 using

single samples and 3 using multiple samples. The data are taken from her research, so each is accompanied by the referenced article for those who may want to follow up on the substantive aspects of the topic. The examples are somewhat marred by the use of poor quality printouts for the input and output.]

• Byrne, B.M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS. Hillsdale, NJ: Lawrence Erlbaum. Mainly covers LISREL 8, but also PRELIS 2, and SIMPLIS. Emphasis on psychological applications.

• Comrey, A. L. (1973). A first course in factor analysis. New York: Academic Press.

[As the title indicates, a relatively elementary text, at the graduate or advanced undergraduate level. The last few chapters contain a good discussion of designing a factor analytic study, with a case study of Comrey's personality scales.]

• Cuttance, P. and Ecob, R. (1987). Structural Modelling by Example: Applications in Educational, Behavioural and Social Research. Cambridge University Press.

[A nicely integrated collection of papers by various authors illustrating the application of CFA and SEM models in psychology and behavioural science. An appendix includes the LISREL statements for the analyses in all the chapters.]

• Everitt, B. S. (1984). An Introduction to Latent Variable Models. New York: Chapman and Hall.

[If brevity is the soul of wit, Everitt gets the prize for this compact, readable treatment of exploratory and confirmatory factor analysis in under 70 pages, of which half is devoted to a variety of LISREL models. Numerical examples are provided for most of the

methods discussed and an additional chapter describes latent variable models for categorical data.]

• Fruchter, B. (1954). Introduction to factor analysis. New York: Van Nostrand.

[A simplified introduction to the history and basic ideas of factor analysis from Spearman through Thurstone.]

• Gorsuch, R. L. (1974). Factor analysis. Toronto: W.B. Saunders.

[A graduate-level text, but not too difficult mathematically. Contains many worked examples, and excellent coverage of the practical issues in conducting factor analytic research.]

• Gorsuch, R. L. (1983). Factor analysis (2nd. ed.) Hillsdale, N.J.: Erlbaum.

[A substantial revision of Gorsuch (1973), with extensive comments on using computer programs and a new chapter on confirmatory factor analysis.]

• Kim, J.-O. and Mueller, C. W. (1978). Factor analysis: Statistical methods and practical issues. Beverly Hills, CA: Sage Publications. (Sage University Paper series on quantitative applications in the social sciences).

[A simple non-technical introduction to the ideas of factor analysis for social scientists. The "Coles Notes" of factor analysis.]

• Harmon, H. H. (1967). Modern factor analysis. (2nd ed.). Chicago: University of Chicago Press.

[Long considered "the bible" of factor analysis methods, Harmon covers all the classic methods of extracting and rotating factors, with a great many

worked examples. No coverage of CFA or developments post 1966.]

• Hatcher, Larry (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute.

Focuses on using CALIS for CFA and SEMs under SAS. Each chapter describes a plausible research context, input and output for PROC CALIS, and interpretation of results. Chapter 6 covers SEM.

• Hoyle, Rick H., ed. (1995). Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage Publications.

An introduction focusing on AMOS.

• Hayduk, L. A. (1987). Structural equation modelling with LISREL: Essentials and advances. Baltimore: The Johns Hopkins University Press.

[A non-technical introductory text for LISREL. The major focus of the book is on causal submodels. Discusses estimation problems.]

• Jackson, J. E. (1991). A user's guide to principal components. NY: Wiley.

[A practical, detailed treatment of principal components analysis and related methods. In addition to the usual topics one would expect, the book covers multidimensional scaling and preference analysis, correspondence analysis, applications of PCA to regression and MANOVA, robust PCA, with a brief treatment of factor analysis.]

• Loehlin, J. C. (1987). Latent variable models: An introduction to factor, path, and structural analysis. Hillsdale, NJ: Erlbaum Associates.

[An excellent introductory-level presentation of modern methods for analysis of covariance structures and structural equation modelling. A particularly readable, non-technical (less than Everitt or Long) introduction to recent developments in the field including path diagrams, confirmatory factor models, simplex, test theory, multitrait-multimethod models and some others.]

• Long, J. S. (1983). Confirmatory factor analysis: A preface to LISREL Beverly Hills, CA: Sage.

[A non-technical introduction to the aims of confirmatory factor analysis.]

• Long, J.S. (1988). Covariance Structure Models: An Introduction to LISREL. Sage University Series, 07-034. Beverly Hills: Sage.

• McDonald, R. (1985). Factor Analysis and Related Methods. Hillsdale, NJ: Erlbaum.

[Discusses test/measurement theory ideas, e.g., reliability in the context of factor models and item response theory (i.e., latent trait models).]

• Muliak, S. (1972). The foundations of factor analysis. New York: McGraw Hill.

[A comprehensive graduate-level text which covers the mathematical basis for methods of factor analysis from the early Thurstonian methods up through Joreskog's development of confirmatory methods.]

Historical milestones

• Holzinger, K. J. & Swineford, F. (1939). A study in Factor Analysis: The stability of a Bi-Factor solution, University of Chicago: Supplementary Educational Monorgraphs, Number 48.

• Spearman, C. (1904). General intelligence objectively determined and measured. American Journal of Psychology, 15, 201-293.

• Thurstone, L. L. (1935). Vectors of the mind. Chicago: University of Chicago Press.

• Thurstone, L. L. (1947). Multiple factor analysis. Chicago: University of Chicago Press.

Interpretation of factors

• Brogden, H. E. (1971). Further comments on the interpretation of factors. Psychological Bulletin, 75, 362-363.

• Eysenck, H. J. (1952). The uses and abuses of factor analysis. Applied Statistics, 1, 45-49.

• Eysenck, H. J. (1953). The logical basis of factor analysis. American Psychologist, 8, 105-114.

• Gould, S. J. (1981). The mismeasure of man. NY: W. Norton & Co.

[Chapter 6 of this book is a fascinating account of the role played by the early history of factor analysis in Cyril Burt's attempt to establish a hereditary, causal explanation of intelligence in terms of Spearman's g factor. In Gould's description he gives a clear non- technical account of the aims of factor analysis and the dangers of giving factors a wealth of theoretical meaning only because they came out of some mathematical process. Gould's treatment is not particularly balanced, but his arguments are generally sound.]

• Harris, C. W. (1971). On Brogden's interpretation of factors. Psychological Bulletin, 75, 360-361.

• Jensen, A. R. (1985). The nature of the black-white difference on various psychometric tests: Spearman's hypothesis (with commentary). Behavioral and Brain Sciences, 8, 193-263.

• Nesselroade, J. R., & Cable, D. G. (1974). "Sometimes, it's okay to factor difference scores"--the separation of state and trait anxiety. Multivariate Behavioral Research, 9, 273-281.

• Overall, J. E. (1964). Note on the scientific status of factors. Psychological Bulletin, 61, 270-276.

• Schonemann, P. H. (1981). Factorial definitions of intelligence: Dubious legacy of dogma in data analysis. In Borg, I. (Ed.). Multidimensional data representations: When and why (pp. 325-374). Ann Arbor, MI: Mathesis.

• Schonemann, P. H. (1983). Do IQ tests really measure intelligence (with commentary). Behavioral and Brain Sciences, 6, 311-315.

Principal components & Singular value decomposition

• Eckart, C. & Young, G. (1936). Approximation of one matrix by another of lower rank. Psychometrika, 1, 211-218.

• Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441, 498-520.

Specialized factor analysis models & estimation methods

• Gibson, W. A. (1963). Factoring the circumplex. Psychometrika, 28, 87-92.

• Guttman, L. (1944). General theory and methods of matric factoring. Psychometrika, 9, 1-16.

• Guttman, L. (1953). Image theory for the structure of quantitative variates. Psychometrika, 18, 277-296.

• Guttman, L. (1955). A generalized simplex for factor analysis. Psychometrika, 20, 173-192.

• Kendall, M. G. and Lawley, D. N. (1952). The principles of factor analysis. Journal of the Royal Statistical Society A, 5, 1-6.

Communalities

• Guttman, L. (1956). Best possible systematic estimates of communalities. Psychometrika, 21, 273-285.

• Guttman, L. (1957). Simple proofs of relations between communality problem and multiple correlation. Psychometrika, 22, 147-157.

• Guttman, L. (1958). To what extent can communalities reduce rank? Psychometrika, 23, 297-308.

Number of factors

• Cattell, R. B. (1958). Extracting the correct number of factors in factor analysis. Educational Researcher, 18, 791-838.

• Cliff, N. & Hamburger, C. D. (1967). The study of sampling errors in factor analysis by means of artificial experiments. Psychological Bulletin, 68, 430-???.

• Cliff, N., & Pennell, R. (1967). The influence of communality, factor strength, and loading size on the sampling characteristics of factor loadings. Psychometrika, 32, 309-326.

• Crawford, C. B. (1975). Determining the number of interpretable factors. Psychological Bulletin, 82, 226-237.

• Crawford, C. B., & Koopman, P. (1973). A note on Horn's test for the number of factors in factor in analysis. Multivariate Behavioral Research, 8, 117-125.

• Horn, J. L. (1965). A rational and test for the number of factors in factor analysis. Psychometrika, 30, 179-185.

• Hakstian, A. R., & Muller, V. J. (1973). Some notes on the number of factors problem. Multivariate Behavioral Research, 8, 461-475.

• Lawley, D. N. (1956). Tests of significance for the latent roots of covariance and correlation matrices. Biometrika, 43, 128-136.

• McNemar, Q. (1942). On the number of factors. Psychometrika, 7, 9-18.

• Veldman, D. J. (1974). Simple structure and the number of factors problem. Multivariate Behavioral Research, 9, 191-200.

• Wold, S. (1978). Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20, 397-405.

• Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442.

[Compared Horn's method, Scree test, Bartlett's chi², Kaiser's eigenvalue > 1 and Velicer's minimum average partial correlation method. Kaiser's method tended to severely overestimate the number of components. Horn's and Velicer's methods generally performed quite well.]

Rotation methods

• Arbuckle, J., & Friendly, M. (1977). On rotating to smooth functions. Psychometrika, 42, 127-140.

• Carroll, J. B. (1953). An analytical solution for approximating simple structure in factor analysis. Psychometrika, 18, 23.

• Harris, W. and Kaiser, H. F. (1964). Oblique factor analytic solutions by orthogonal transformations. Psychometrika, 29, 347-362.

• Hendrickson, A. E. and White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Mathematical and Statistical Psychology, 17, 65-70.

• Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200.

• Joreskog, K. G. (1966). Testing a simple structure hypothesis in factor analysis. Psychometrika, 31, 165-178.

• Neuhaus, J. O. and Wrigley, C. (1954). The quartimax method: An analytical approach to orthogonal simple structure. British Journal of Mathematical and Statistical Psychology, 7, 81-91.

• Tucker, L. R. (1955). The objective definition of simple structure in linear factor analysis. Psychometrika, 20, 209-225.

Factorial invariance, Multimethod factor analysis

• Alwin, D. F. & Jackson, D. J. (1980). Measurement models for response errors in surveys: Issues and applications. Sociological Methodology, 68-119. San Francisco: Jossey-Bass.

• Alwin, D. F. & Jackson, D. J. (1981). Applications of simultaneous factor analysis to issues of factorial invariance. In D. D. Jackson & E. P. Borgotta (Eds.) Factor analysis and measurement in sociological research: A multidimensional perspective. Beverly Hills, CA: Sage.

• Byrne, B. M., Shavelson, R. J., & Muthen, B. (1989). Testing the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456-466.

[Illustrates the use of LISREL for testing various forms of factorial invariance across groups. They demonstrate procedures for identifying noninvariant measurement parameters and testing differences in latent factor means. When groups are found to differ in some parameters (e.g., factor loadings), they demonstrate the use of LISREL methods to pinpoint the source of the differences.]

• Bechtoldt, H. P. (1961). An empirical study of the factor analysis stability hypothesis. Psychometrika, 26, 405-432.

• Conger, A. J. (1971). Evaluation of multimethod factor analysis. Psychological Bulletin, 75, 416-420.

• Jackson, D. N. (1971). Comments on "Evaluation of multimethod factor analysis". Psychological Bulletin, 75, 421-423.

• Lomax, R. G. (1983). A guide to multiple-sample structural equation modelling. Behavior Research Methods and Instrumentation, 15, 580-584.

• Meredith, W. (1964). Rotation to achieve factorial invariance. Psychometrika, 29, 187-206.

• Meredith, W. (1964). Notes on factorial invariance. Psychometrika, 29, 177-185.

• Gibson, W. A. (1960). Remarks on Tucker's inter-battery method of factor analysis. Psychometrika, 25, 19-25.

• Horst, Paul (1961). Relations among m sets of measures. Psychometrika, 26, 129-149.

• Kenny, D. A., & Kashy, D. A. (1992). Analysis of the multitrait-multimethod matrix by confirmatory factor analysis. Psychological Bulletin, 112, 165-172.

[Describes difficulties (negative variance estimates, failure to converge) encountered with estimation of some models for MTMM data -- those which postulate equal loadings of variables on the trait and method factors -- which they ascribe to the model being unidentified. Several alternative ways to model MTMM data by CFA are suggested.]

• Marsh, H. W., Byrne, B. M., & Craven, R. (1992). Overcoming problems in confirmatory factor analysis of MTMM data: The correlated uniqueness model and factorial invariance. Multivariate Behavioral Research, 27, 489-507.

• Schmitt, N., & Stults, D. M. (1986). Methodological review: Multitrait- multimethod matrices. Applied Psychological Measurement, 10, 1-22.

• Tucker, L. R. (1958). An inter-battery method of factor analysis. Psychometrika, 23, 111-136.

Scale construction, factoring item-correlations

• McDonald, R. P. (1968). A unified treatment of the weighting problem. Psychometrika, 33, 351-381.

• McDonald, R. P. (1981). The dimensionality of tests and items. British Journal of Mathematical and Statistical Psychology, 34, 100-117.

• Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement invariance. Psychological Bulletin, 114, 552-566.

[How can you establish whether a given test measures the same trait dimension, in exactly the

same way, in two or more distinct groups of individuals? This paper compares the utility of CFA and item response models used to investigate whether mood ratings collected in Minnesota and China were comparable.]

• ten Berge, J. M. F., & Knol, D. L. (1985). Scale construction on the basis of components analysis: A comparison of three strategies. Multivariate Behavioral Research, 20, 45-55.

Structural equations models, causal models

• Bentler, P.M. (1980). Multivariate analysis with latent variables: causal modeling. Annual Review of Psychology, 31, 419-456.

• Breckler, S.J. (1990). Applications of covariance structure modeling in psychology: Cause for concern? Psychological Bulletin, 107, 260-273.

[Surveys over 70 applied studies using structural equation models.]

• Duncan, O. D. (1975). Introduction to structural equation models. New York: Academic Press.

[Considered a "classic" nontechnical introductory text on the topic.]

• James, L. R., Muliak, S. A., & Brett, J. (1982). Causal analysis: Models, assumptions and data. Beverly Hills, CA: Sage.

• Joreskog, K.G. (1973). A general method for estimating a linear structural equation system. In A.S. Goldberger and O.D. Duncan (Eds). Structural Equation Models in the Social Sciences, New York: Seminar Press.

• Joreskog, K.G. & Sorbom, D. (1982). Recent developments in structural equation modeling. Journal of Marketing Research, 19, 404-416.

• Maruyama, G., & McGarvey, B. (1980). Evaluating causal models: An application of maximum likelihood analysis of structural equations. Psychological Bulletin, 87, 502-512.

Confirmatory factor analysis

• Arbuckle, J.A. (1988). AMOS User's Guide. Department of Psychology, Temple University.

[AMOS is an IBM/PC program for Analysis of Moment Structures, including all the models handled by LISREL, EQS and SAS PROC CALIS. The latest DOS/Windows version has a menu-driven front end which makes it quite easy to use; a Windows version provides a graphical interface (AMOS Draw) which allows you to specify a model by drawing the path diagram rather than using matrices or linear equations. When you estimate the model, the coefficients are shown on the path diagram.

The AMOS program has the best facilities for testing and comparing multiple models for the same set of data, and for multi-sample analyses: much easier to set up, and the output provides all the model-comparison statistics described by Bollen (1989) and Marsh, Balla, & McDonald (1988). The program is also unique in providing facilities for bootstrapped estimates of standard errors. The User's Guide contains an extensive set of worked examples, which provide an excellent tutorial introduction to the use and interpretation of structural equation and CFA models. Further information is contained on the Amos Home Page]

• Bentler, P.M. (1989). EQS Structural Equations Program Manual. Los Angeles: BMDP Statiscal Software.

• Bentler, P. M. & Bonnet, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.

[Discusses the use of the chi² test in ACOVS and LISREL, as well as goodness of fit indices used to compare models, including the Tucker-Lewis index.]

• Bock, R. D. & Bargmann, R. E. (1966). Analysis of covariance structures. Psychometrika, 31, 507-534.

• Brown, R. L. (1986). A comparison of the LISREL and EQS programs for obtaining parameter estimates in confirmatory factor analysis studies. Behavior Research Methods, Instruments, & Computers, 18(4), 382-388.

[A CFA example, using data from Wheaton (1978) on psychological disorders of patients over two time periods. Shows the setup and results from the LISREL and EQS programs.]

• Dillon, W.R., Kumar, A., & Mulani, N. (1987). Offending estimates in covariance structure analysis: Comments on the causes of and solution to Heywood cases. Psychological Bulletin, 101, 126-135.

• Fornell, C. (1983). Issues in the application of covariance structure analysis: A comment. Journal of Consumer Research, 9, 443-448.

[Describes some major problems in applying and interpreting covariance structure analysis.]

• Hartmann, W. M. (1992). The CALIS Procedure Extended User's Guide Cary, N.C.: SAS Institute.

[The CALIS Procedure (Covariance Analysis and LInear Structural equations) is the SAS answer to LISREL. CALIS is far more flexible than LISREL (it provides 5 different ways to specify models), but is, in some ways, somewhat more complex. This extended user's guide is now largely incorporated in the SAS/STAT manual, but contains some additional technical details and an extensive list of sample applications, which are all available in the SAS/STAT Sample Library.]

• O'Grady, K. E. & Medoff, D. R. (1991). Rater reliability: A maximum likelihood confirmatory factor-analytic approach. Multivariate Behavioral Research, 26, 363-387.

[Describes the formulation of assessing inter- rater reliability in terms of test-theory constructs of parallel tests and shows how these may be estimated and tested as confirmatory factor models. Standard methods based on the intraclass correlation coefficient assume compound symmetry, while these models do not. Two worked examples are included.]

• Joreskog, K.G. (1969). A general approach to confirmatory maximum liklihood factor analysis. Psychometrika, 34, 183-202.

• Joreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239-251.

• Joreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109-132.

[Joreskog's first paper applying the ACOVS model to test-theory questions and multitrait-multimethod matrices. Most of the examples are reprinted in Joreskog (1974).]

• Joreskog, K. G. (1974). Analyzing psychological data by structural analysis of covariance matrices. In R.C. Atkinson, D.H. Krantz, R.D. Luce and P. Suppes (Eds.), Contemporary developments in mathematical psychology - Vol. II (pp. 1-56). San Francisco: W.H. Freeman.

• Joreskog, K. G. (1980). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443-477.

• Joreskog, K. G. and Sorbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, MA: Abt Books.

[A somewhat technical collection of papers that introduced the LISREL framework. Discusses all LISREL submodels including factor mean comparisons across populations.]

• Joreskog, K.G. & Sorbom, D. (1989). LISREL 7 (2nd ed.). A Guide to the Program and Applications. Chicago: SPSS.

[This manual for the LISREL program contains a wide variety of illustrative examples, with background, input and output, for the various special cases of the LISREL model, including structural equations (path) models, confirmatory factor analysis, multi-sample models, and models with means structures. See SPSS (1990) for details specific to SPSS.]

• MacCallum, R. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107-120.

• Marsh, H.W. & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psychological Bulletin, 97, 562-582.

[Presents a comprehensive tutorial on the use of LISREL to test models of first- and second-order factors and factorial invariance across groups. The data analyzed comes from 28 subscales of the Self-Description Questionnaire used to examine components of self-concept in 658 children in grades 2 - 5. LISREL specifications are given for a number of models tested.]

• Marsh, H.W., Balla, J.R., & McDonald, R. P. (1988). Goodness of fit indices in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410.

[Examines more than 30 indices which have been proposed for testing the goodness of fit of LISREL & ACOVS modesls, using real and simulated data. The Tuker-Lewis (1973) index was the only widely-used index that was relatively uninfluenced by sample size. Contrary to claims by Joreskog & Sorbom (1981), their GFI and AGFI indices were influenced by sample size.]

• Muliak, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwill, C. D. (1989). Evaluation of goodness-of-fit indices for

structural equation models. Psychological Bulletin, 105, 430-445.

[A sensitive discussion of the trade-off between goodness-of-fit and parsimony in structural equation models. Describes how to adjust the LISREL GFI index by a measure of parsimony for a model.]

• SPSS, Inc. (1990). SPSS LISREL 7 and PRELIS User's Guide and Reference Chicago: SPSS.

[With version 7, LISREL has become more like a regular SPSS procedure, and the syntax more SPSS-like. PRELIS is a pre-processor for LISREL. Among other things, it computes polyserial and polychoric correlations necessary for a proper analysis of categorical or discrete data, as in item analysis.]

• Tuker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1-10.

Unclassified

• Davison, M. L. (1985). Multidimensional scaling versus components analysis of test intercorrelations. Psychological Bulletin, 97, 94-105.

• Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Psychological Bulletin, 81, 358-361.

• MacCullum, R. (1983). A comparison of factor analysis programs in SPSS, BMDP, and SAS. Psychometrika, 48, 223-231.

• Pruzek, R. M., & Frederick, B. C. (1978). Weighting predictors in linear models: Alternatives to least squares and limitations of equal weights. Psychological Bulletin, 85, 254-266.

• Ross, J. (1964). Mean performance and the factor analysis of learning data. Psychometrika, 29, 67-73.

• Steiger, J. H., & Schonemann, P. H. (1978). A history of factor indeterminacy. In Shye, S. (Ed.). Theory construction and data

analysis in the behavioral sciences (pp. 136-178). San Francisco: Jossey-Bass.

• Thorndike, R. L. (1985). The central role of general ability in prediction. Multivariate Behavioral Research, 20, 241-254.

2. Research Applications Path analysis, Causal models for observable variables

• deJong- Gierveld, J. (1987). Developing and testing a model of loneliness. Journal of Personality and Social Psychology, 53, 119-128.

• Earley, P.C., & Lituchy, T.R. (1991). Delineating goal and efficacy effects: A test of three models. Journal of Applied Psychology, 76, 81-98.

• Felson, R.B. (1984). The effect of self appraisals of ability on academic performance. Journal of Personality and Social Psychology, 47, 944-952.

• Forehand, R., McCombs, T., Wierson, M., Brody, G., & Fauber, R. (1990). Role of maternal functioning and parenting skills in adolescent functioning following parent divorce. Journal of Abnormal Psychology, 99, 278-283.

• Gass, K.A., & Chang, A.S. (1989). Appraisals of bereavement coping, resources, and psychosocial health dysfunction in widows and widowers. Nursing Research, 38, 31-36.

• Leidy, N.K. (1990). A structural model of stress, psychosocial resources, and symptomatic experience in chronic physical illness. Nursing Research, 39, 230-236.

Structural equation models, Causal models for latent variables

• Aneshensel, C.S., & Yokopenic, P.A. (1985). Tests for comparability of a causal model of depression under two conditions of interviewing. Journal of Personality and Social Psychology, 49, 1337-1348.

• Bachman, J.G. & O'Malley, P.M. (1986). Self concepts, self-esteem and educational experiences: The hog pond revisited (again). Journal of Personality and Social Psychology, 50, 35-46.

• Cochran, S.D., & Hammen, C.L. (1985). Perceptions of stressful life events and depression: A test of attributional models. Journal of Personality and Social Psychology, 48, 1562-1571.

• Davis, M.H., & Franzoi, S.L. (1986). Adolescent loneliness, self-disclosure, and private self- consciousness: A longitudinal investigation. Journal of Personality and Social Psychology, 51, 595-608.

• Hays, R.D., Widaman, K.F., DiMatteo, M.R., & Stacy, A.W. (1987). Structural-equation models of current drug use: Are appropriate models so simple (x)? Journal of Personality and Social Psychology, 52, 134-144.

• Holahan, C.J., & Moos, R.H. (1991). Life stressors, personal and social resources, and depression: A 4-year structural model. Journal of Abnormal Psychology, 100, 31-38.

• Hom, P.W., & Griffeth, R.W. (1991). Structural equations modeling test of a turnover theory: Crosssectional and longitudinal analyses. Journal of Applied Psychology, 76, 250-366.

• Hull, J.G., & Mendolia, M. (1991). Modeling the relations of attributional style, expectations, and depression. Journal of Personality and Social Psychology, 61,85-97.

• Jaccard, J., & Turris, R. (1987). Cognitive processes and individual differences in judgments relevant to drunk driving. Journal of Personality and Social Psychology, 53, 135-145.

• Maruyama, G., Miller, N., & Holtz, R. (1986). The relation between popularity and achievement: a longitudinal test of the lateral transmission of value hypothesis. Journal of Personality and Social Psychology, 51, 730-741.

• Reisenzein, R. (1986). A structural equation analysis of weiner's attribution-affect model of helping behavior. Journal of Personality and Social Psychology, 50, 1123-1133.

• Stacy, A.W., Newcomb, M.D., & Bentler, P.M. (1991). Personality, problem drinking, and drunk driving: mediating, moderating,

and direct-effect models. Journal of Personality and Social Psychology, 60, 795-818.

• Stein, J.A., Newcomb, M.D., & Bentler, P.M. (1989). An 8-year study of multiple influences on drug use and drug use consequences. Journal of Personality and Social Psychology, 53, 1094-1105.

• Vinokur, A., Schul, Y., & Caplan, R.D. (1987). Determinants of perceived social support: interpersonal transactions, person outlook, and transient affective states. Journal of Personality and Social Psychology, 5, 1137-1145.

• White, J.D., Tashchiar, A., & Ohanian, R. (1991). An exploration into the scaling of consumer confidence: Dimensions, antecedents, and consequences. Journal of Social Behaviour and Personality, 6, 509-528.

Measurement models for validating instruments and constructs

• Ashkanasy, N. M. (1985). Rotter's Internal-External scale: Confirmatory factor analysis and correlation with social desireability for alternative scale forms. Journal of Personality and Social Psychology, 48, 1328-1341.

• Bagozzi, R.P. (1991). Further thoughts on the validity of measures of elation, gladness, and joy. Journal of Personality and Social Psychology, 61, 98-104.

• Breckler, S.J. (1984). Empirical validation of affect, behavior, and cognition as distinct components of attitude. Journal of Personality and Social Psychology,, 47, 1191-1205.

• Byrne, B.M., & Shavelson, R.J. (1986). An the structure of adolescent self-concept. Journal of Educational Psychology, 78, 474-481.

• Calsyn, R. J., & Kenny, D. A. (1977). Self-concept of ability and perceived evaluation of others: Cause or effect of academic achievement. Journal of Educational Psychology, 69, 136-145.

• Marshall, G.N. (1991). A multidimensional analysis of internal health locus of control beliefs: Separating the wheat from the chaff? Journal of Personality and Social Psychology, 61, 483-491.

• Melnyk, K.A. (1990). Barriers to care: Operationalizing the variable. Nursing Research, 39, 108-112.

• Moore, M.K., & Neimeyer, R.A. (1991). A confirmatory factor analysis of the threat index. Journal of Personality and Social Psychology, 60, 122-129.

Multi-trait, Multi-method studies, Higher-order factor models

• Cudeck, R. (1985). A structural comparison of conventional and adaptive versions of the ASVAB. Multivariate Behavioral Research, 20, 305-322.

[Examines 12 factor models for a battery of conventional tests computerized adaptive versions designed to measure the same aptitudes, using a double cross- validation design. A good example of analysis of multi- trait, multi-method data. The factor model provides an estimate of the (disattentuated) method correlation between conventional and adaptive testing.]

• Marsh, H.W., Byrne, B.M., & Shavelson, R.J. (1988). A multifaceted academic self concept: Its hierarchical structure and its relation to academic achievement. Journal of Educational Psychology, 80, 366-380.

• Marsh, H. W. & Richards, G. E. (1987). The multidimensionality of the Rotter I-E scale and its higher-order structure: An application of confirmatory factor analysis. Multivariate Behavioral Research, 22, 39-69.

[Rotter believed his Internal-External scale was unidimensional, i.e., that a single general factor could explain most of the correlations among the items. Marsh & Richards summarize 20 published factor analysis studies of the scale which show that 4-6 factors are required. They carry out a confirmatory analysis of a five-factor model, with

the factors allowed to be correlated. A second-order model tested whether the correlations among the first-order factors could be accounted for in terms of a single second order factor; this model provided a good fit to the data, and the second-order factor was interpreted as a generalized I-E construct. The paper contains a good tutorial discussion of the use of goodness-of-fit indices in comparing different models for covariance structure.]

• Newcomb, M.D. (1986). Nuclear attitudes and reactions: Associations with depression, drug use and quality of life. Journal of Personality and Social Psychology, 50, 906-920.

• Stacy, A. W., Widaman, K. F., Hays, R., & DiMatteo, M. R. (1985). Validity of self-reports of alcohol and other drug use: A multitrait-multimethod assessment. Journal of Personality and Social Psychology, 49, 219-232.

[A nice example of the use of CFA for studying convergent and divergent validity via analysis of the MTMM matrix.]

• Tanaka, J.S., & Huba, G.J. (1984). Confirmatory hierarchical factor analysis of psychological distress measures. Journal of Personality and Social Psychology, 46, 621-635.

Factorial invariance, Multi-sample CFA models

• Byrne, B.M. (1988). The Self Description Questionnaire III: Testing for equivalent factorial validity across ability. Educational and Psychological Measurement, 48, 397-406.

• Byrne, B.M., & Shavelson, R.J. (1987). Adolescent self-concept: Testing the assumption of equivalent structure across gender. American Educational Research Association, 24, 365-385.

© 1995 Michael Friendly

Author: Michael Friendly

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