Arbuckle, James L. (2006). Amos 7.0 User's Guide. Chicago, IL: SPSS Inc.
G. Arminger, C.C. Clogg and M.E. Sobel [Eds.] (1995) Handbook of statistical modeling for the social and behavioral sciences. NY: Plenum Press.
Bentler, P. M. and C. P. Chou (1987). Practical issues in structural modeling. Sociological Methods and Research. 16(1): 78-117.
Bollen, K. A. (1989). Structural equations with latent variables. NY: Wiley. A leading, readable text on structural modeling.
Bollen, K. A. (1990). Overall fit in covariance structure models: Two types of sample size effects. Psychological Bulletin 107(2): 256-259.
Burnham, K, P., and D. R. Anderson (1998). Model selection and inference: A practical information-theoretic approach. New York: Springer-Verlag. Discusses AIC and related measures of model fit.
Byrne, Barbara (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS. Hillsdale, NJ: Lawrence Erlbaum. Mainly covers LISREL 8, but also PRELIS 2, and SIMPLIS. Some emphasis on but not limited to psychology.
Barbara N. Byrne (2001). Structural Equation Modeling with AMOS.Rahwah, NJ: Lawrence Erlbaum Associates, 2001. Datasets downloadable from the publisher.
Carmines, Edward G. and John P. McIver (1981). Analyzing models with unobserved variables: Analysis of covariance structures. Pp. 65-115 in George W. Bohmstedt and Edward F. Borgatta, eds., Social Measurement. Thousand Oaks, CA: Sage Publications. Carmines and McIver provided an early overview of SEM and advocated use of relative chi-square as a significance test, and recommended the acceptable range (in the 2:1 or 3:1 range) within which this test should lie for the researcher not to accept the null hypothesis.
Chen, F., K. A. Bollen, P. Paxton, P. Curran, and J. Kirby (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods and Research 29: 468-508. Covers causes and handlin of negative error variances.
Curran, Patrick J.; Kenneth A. Bollen, Pamela Paxton, James Kirby, & Feinian Chen (2002), The noncentral chi-square distribution in misspecified structural equation models: Finite sample results from a Monte Carlo simulation. Multivariate Behavioral Research 37(1), 1-36.
Fan, X., B. Thompson, and L. Wang (1999). Effects of sample size, estimation method, and model specification on structural equation modeling fit indexes. Structural Equation Modeling (6): 56-83.
Graham, James M. (2006). Congeneric and (essentially) tau-equivalent.estimates of score reliability: What they are and how to use them: Educational and Psychological Measurement 66; 930-944.
Hatcher, Larry (1994). A step-by-step approach to using the SAS system for factor analysis and atructural equation modeling. Cary, NC: SAS Institute. Focuses on CALIS under SAS. Chapter 6 covers SEM.
Hershberger, S. L. (1994). The specification of equivalent models before the collection of data. Pp. 68-105 in A. von Eye and C. C. clogg, eds., Latent variables analysis. Thousand Oaks, CA: Sage Publications.
Hocking, R.R. (1984), The Analysis of Linear Models. Monterey, CA: Brooks-Cole Publishing Co.
Hoyle, Rick H., ed. (1995). Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage Publications. An introduction focusing on AMOS.
Hu, L. and P. M. Bentler (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling 6(1): 1-55.
Jaccard, James and Choi K. Wan (1996). LISREL approaches to interaction effects in multiple regression. Thousand Oaks, CA: Sage Publications. In spite of the title, this monograph deals directly with SEM using LISREL.
Jonsson, F. (1998). Modeling interaction and non-linear effects: A step-by-step LISREL example. Pp. 17-42 in R. E. Schumacker & G. A. Marcoulides, eds., Interaction and nonlinear effects in structural equation modeling. Mahwahm ?NJ: Lawrence Erlbaum Associates.
Jöreskog, Karl G. (1973). "A general method for estimating a linear structural equation system," in Arthur S. Goldberger and Otis Dudley Duncan, eds., Structural equation models in the social sciences. New York: Seminar Press/Harcourt Brace, 1973. Generally accepted as the earliest citation pertaining to LISREL models.
Jöreskog, K. G., and D. Sörbom (1988). PRELIS. A program for multivariate data screening and data summarization. User’s Guide (2nd Ed.). Chicago: Scientific Software International.
Joreskog, K. and F. Yang (1996). Non-linear structural equation models: The Kenny-Judd model with interaction effects. Pp. 57-88 in G. Marcoulides and R. Schumacker, eds., Advanced structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage Publications. Argues that it is justifiable to use only one of several indicator interactions to test the interaction effect of two latent variables in a structural model.
Kenny, D. A. & Judd, C. M. (1984). Estimating the non-linear and interactive effects of latent variables. Psychological Bulletin 96, 201-210.
Kline, Rex B. (1998). Principles and practice of structural equation modeling. NY: Guilford Press. A very readable introduction to the subject, with good coverage of assumptions and SEM's relation to underlying regression, factor, and other techniques.
Lee, S. and S. Hershberger (1990). A simple rule for generating equivalent models in covariance structure modeling. Multivariate Behavioral Research, Vol. 25: 313-334.
T.D. Little, K.U. Schnabel and J. Baumert [Eds.] (2000) Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples. Mahwah, NJ: Lawrence Erlbaum Associates
Loehlin, J. C. (1987, 1992). Latent variable models: An introduction to factor, path, and structural analysis.. Hillsdale, NJ: Lawrence Erlbaum. Second edition 1992.
Long, J. S. (1997). Regression models for categorical and limied dependent variables. Thousand Oaks, CA: Sage Publications. Long discusses use of categorical variables as endogenous or dependent variables.
Madigan, D. & Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal of the American Statistical Association 89: 1535-1546.
Marsh, H. W., Balla, J. R., & Hau, K. T. (1996). An evaluation of incremental fit indexes: A clarification of mathematical and empirical properties. Pp. 315-353 in G. A. Marcoulides & R. E. Schumacker, eds, Advanced structural equation modeling techniques. Mahwah , NJ : Lawrence Erlbaum.
Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness of fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin 103, 391-410.
Maruyama, Geoffrey M. (1997). Basics of structual equation modeling. Thousand Oaks, CA: Sage Publications. Covers path analysis, confirmatory factor analysis, and SEM using a single data set for an example. Comes with LISREL control statements for exercises.
McDonald, R.P., and Ring Ho, M. H. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods 7: 64-82.
Mikkelson, Aslaug, Torvald Ogaard, and Nicholas Lovrich (1997). Impact of an integrative performance appraisal experience on perceptions of management quality and working environment. Review of Public Personnel Administration, Vol. 17, No. 3 (Summer): 82-98.
Mitchell, R. J. (1993). Path analysis: Pollination. Pp. 211-231 in Schneider, S. M. & Gurevitch, J., eds,m Design and analaysis of ecological experiements. NY: Chapman and Hall.
Mueller, R. O. (1996). Basic Principles of structural equation modeling: An introduction to LISREL and EQS. Secaucus, NJ: Springer A good intro if you are using these packages. Oriented toward psychology, social sciences, education, and health sciences.
Mulaik, S. A. & Millsap, R. E. (2000). Doing the fouyr-step right. Structural Equation Modeling 7, 36-73.
Olsson, Ulf Henning, Tron Foss, Sigurd V. Troye, and Roy D. Howell (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling 7(4): 557 -- 595 .
Pampel, Fred C. (2000). Logistic regression: A primer. Sage Quantitative Applications in the Social Sciences Series #132. Thousand Oaks, CA: Sage Publications. Pp. 40-48 provide a good discussion of maximum likelihood estimation.
Penev, S., & Raykov, T. (2006). Maximal reliability and power in covariance structure models. British Journal of Mathematical and Statistical Psychology, 59, 75-87.
Perry, James L. and Theodore K. Miller (1991). The Senior Execuitve Service: Is it improving managerial performance? Public Administration Review, Vol. 51, No. 6 (Nov./Dec.): 554-563.
Raftery, Adraian E. (1995). Bayesian model selection in social research. In Adrian E. Raftery, ed. Sociological Methodology 1995, pp. 111-164. Oxford: Blackwell. Covers the derivation of BIC.
Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement. 21, 173-184.
Raykov, Tenko (1998). Coefficient alpha and composite reliability with interrelated nonhomogeneous items Applied Psychological Measurement, 22(4), 375-385.
Raykov, Tenko (2000). On the large-sample bias, variance, and mean squared error of the conventional noncentrality parameter estimator of covariance structure models. Structural Equation Modeling, 7, 431-441.
Raykov, Tenko (2005). Bias-corrected estimation of noncentrality parameters of covariance structure models. Structural Equation Modeling, 12, 120-129.
Schumacker, Randall E. and R. G. Lomax (1996). A beginners guide to structural equation modeling. Hillsdale, NJ: Erlbaum. Readable introduction to use of EQS 5.0 or LISREL8-SIMPLIS
Schumacker, Randall E. and Richard G. Lomax (2004). A beginner's guide to structural equation modeling, Second edition. Mahwah, NJ: Lawrence Erlbaum Associates.
Bill Shipley, Bill (2000). Cause and correlation in Biology: A user's guide to path analysis, structural equations and causal inference. Cambridge, UK: Cambridge University Press.
Silvia, E. S. M. and R. C. MacCallum (1988). Some factors affecting the success of specification searches in covariance structure modeling. Mutlivariate Behavioral Research 23: 297-326. Discusses need for theory-based utilization of modification index based adjustments to models.
Spirtes, Peter, Thomas Richardson, Christopher Meek, Richard Scheines, and Clark Glymour (1998). Using path diagrams as a structural equation modeling tool. Sociological Methods & Research, Vol. 27, No. 2 (Nov.): 182-225.
Steenkamp, J-B E. M. and H. Baumgartner (1998), Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research 25: 78-90. A good discussion of the problem of lack of measurement invariance across groups or across time.
Stevens, J. (1996). Applied multivariate statistics for the social sciences.
Thompson, B. (2000). Ten commandments of structural equation modeling. Pp. 261-284 in L. Grimm & P. Yarnell, eds. Reading and understanding more multivariate statistics. Washington, DC: American Psychological Association.
Ullman, J. B. (2001). Structural equation modeling. In Tabachnick, B.G., & Fidell, L.S. (2001). Using Multivariate Statistics (4th ed.): 653- 771. Needham Heights, MA: Allyn & Bacon.
Winship, Christopher, ed. (1999). Special issue on the Bayesian Information Criterion. Sociological Methods and Research 27(3): 355-443. A collection of seven articles on BIC.
