Psych 533 Structural Equation Models                                                                 Spring 2007

Prof. Roger E. Millsap

Office: Rm 286, Psychology Bldg.                                                                        Page 1

Phone: 5-2584; Fax: 5-8544

email: millsap@asu.edu

homepage: http://www.public.asu.edu/~millsap/

 

                                                             Course Syllabus

 

 

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

 

 

 

Topics to be covered:

 

1) Basic Concepts: Confirmatory modeling; fitting covariance structures; latent vs. measured variables; path models and path diagrams; causal models; LISREL notation. Reading: Chapters 1-3.

 

2) Path Models with Measured Variables: Model specification and translation from theory; model identification; estimation in path models; fit evaluation (part 1); total, direct, and indirect effects; metric considerations; recursive and nonrecursive models.  Reading: Chapter 4.

 

3) Measurement Models: Measurement error; effects of measurement error on regression results; reliability; validity and latent variables.  Reading: Chapters 5-6.

 

4) Confirmatory Factor Analysis: Common factor model specification; model identification; estimation in factor analysis; relations with exploratory factor analysis; fit evaluation (part 2); model modification; estimation problems in factor analysis; special applications.  Reading: Chapter 7.

 

5) Structural Equations with Latent and Measured Variables: Model specification and translation from theory; model identification; estimation;  fit evaluation (part 3); total, direct, and indirect effects; metric considerations.  Reading: Chapter 8.

 

6) Extensions and Advanced Applications: Multiple group models; models with mean structures; interactions in structural models; longitudinal and growth curve models; problems with discrete indicators.  Reading: Chapter 9.

 

About the course:

 

This course will provide you with an introduction to the theory and application of structural equation models.  The course assumes that you have already completed a course in multivariate statistics.  Readings in the required text will be supplemented with outside readings, especially examples of applications in psychology.  We will be using the LISREL software program on the mainframe computer to perform the statistical analyses.  Coursework will include fairly frequent homework assignments, a midterm exam, and a final project.  Details about the final project assignment will be given in class.  The project will be an independent application of the methods learned in class to data chosen by the student, and will include a written report of the results with interpretation.  There will be no final exam.

 

                          REFERENCES

 

Bartholomew, D.J. (1987).  Latent variable models and factor analysis.  London: Charles Griffin.

 

Bentler, P.M. & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis of covariance structures.  Psychological Bulletin, 88, 588‑600.

 

Bollen, K.A. & Long, J.S. (Eds.) (1993). Testing structural equation models. Newbury Park, CA: Sage.

 

Byrne, B.M. (1989). A primer of LISREL: Basic applications and programming for the confirmatory factor analytic model. New York: Springer‑Verlag.

 

Byrne, B.M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS.  Mahwah, N.J.: Erlbaum.

 

Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18, 115‑126.

 

Cohen, J., West, S.G., Aiken, L., & Cohen, P. (2002). Applied multiple regression/correlation analysis for the behavioral sciences, Mahwah, NJ: Erlbaum.

 

Crocker, L. & Algina, J. (1986). Introduction to classical and modern test theory. New York: Holt, Rinehart and Winston.

 

Draper, N. & Smith, H. (1981). Applied regression analysis, 2nd Ed., New York: John Wiley & Sons.

 

Duncan, T.E., Duncan, S.C., Strycker, L.A., Li, F., & Alpert, A. (1999).  An introduction to latent variable growth curve modeling.  Mahwah, NJ: Erlbaum.

 

Dwyer, J.H. (1983). Statistical models for the social and behavioral sciences. New York: Oxford University Press.

 

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

 

Graybill, F.A. (1983). Matrices with applications in statistics, 2nd Ed., Belmont, CA: Wadsworth.

 

Hayduk, L.A. (1987). Structural equation modeling with LISREL. Baltimore: The Johns Hopkins University Press.

 

James, L.R., Mulaik, S.A., & Brett, J.M. (1982). Causal analysis: Assumptions, models, data. Beverly Hills: Sage.

 

Jöreskog, K.G. & Sörbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, M.A.: Abt Books.

 

Jöreskog, K.G. & Sörbom, D. (1996). LISREL VIII user's reference guide. Chicago: Scientific Software.

 

Hoyle, R.H. (Ed.) (1995).  Structural equation modeling: Concepts, Issues, and Applications.  Thousand Oaks, CA: Sage.

 

Kline, R.B. (2005).  Principles and practice of structural equation modeling.  New York: Guilford Press.

 

Loehlin, J.C. (1992).  Latent variable models. Hillsdale, NJ: Lawrence Erlbaum.

 

Lord, F.M. & Novick, M.R. (1968). Statistical theories of mental test scores.  Menlo Park, CA: Addison-Wesley.

 

Kenny, D.A. (1979). Correlation and causality, New York: Wiley.

 

Marcoulides, G.A. & Schumacker, R.E. (1996). Advanced structural equation modeling: Issues and techniques. Hillsdale, NJ: Erlbaum.

 

McDonald, R.P. (1985). Factor analysis and related methods. Hillsdale, N.J.: Erlbaum.

 

McDonald, R.P. (1999).  Test Theory: A Unified Treatment. Mahwah, NJ: Erlbaum.

 

Millsap, R.E. (2002). Structural equation modeling: A users guide.  In F. Drasgow & N. Schmitt (Eds.) Measuring and Analyzing Behavior in Organizations. (pp. 257-301) San Francisco: Jossey-Bass.

 

Millsap, R.E. & Everson, H. (1991). Confirmatory measurement model comparisons using latent means.  Multivariate Behavioral  Research, 26, 479-497.

 

Millsap, R.E. & Taylor, R. (1996).  Latent variable models in the investigation of salary discrimination: Theory and practice. Journal of Management, 23(4), 653-673.

 

Mueller, R.O. (1996) Basic Principles of Structural Equation Modeling. New York: Springer-Verlag.

 

Mulaik, S.A. (1972). The foundations of factor analysis. New York: McGraw‑Hill.

 

Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.

 

Raykov, T. & Marcoulides, G.A. (2000). A First Course in Structural Equation Modeling. Mahwah, NJ: Erlbaum.

 

Schumacker, R.E. & Marcoulides, G.A. (Eds.) (1998).  Interaction and nonlinear effects in structural equation modeling.  Mahwah, N.J. : Erlbaum.