Spring Quarter Professor Ross L. Matsueda
2018 227 Savery Hall
Office Hours: Mon, Tue 2-3pm
(& Thur 7:30-8pm Savery 409)
SOCIOLOGY 529/CS&SS 526
Structural Equation Models for the Social Sciences
This course introduces covariance structure analysis, focusing on Jöreskog and Sörbom's LISREL approach.
It begins with the notion of a causal structure underlying a set of observable moments (covariances). This
notion is illustrated briefly with path analysis applied to a multiple equation recursive model of observable
variables. We will discuss the implications of random measurement error in linear regression models,
discuss the concept of unobservable variables, and review some elementary principles of classical test
theory. We then introduce the LISREL model, describing the model in matrix form, and then briefly present
the estimation issue and use of maximum likelihood estimation and likelihood ratio testing. We will discuss
the LISREL and PRELIS software programs, and briefly discuss R package’s SEM and lavaan, Stata’s SEM
package and Muthén’s Mplus Program. We will then examine the identification issue and examine specific
classes of models, such as confirmatory factor models, MIMIC models, regression models with latent
variables, and non-recursive models. We will also cover estimation when observed data are not multivariate
normal, and observed variables are ordinal, dichotomous, or censored. Time permitting, we will survey other
models used in recent research in the social sciences, such as models for panel data, growth curve models,
models for nested data, mixture models for latent class and trajectory analysis, and models for data missing
1. Introduce students to the fundamentals of structural equation modeling, including specification, estimation,
2. Provide students with a critical understanding of SEM, including the assumptions required to use them.
3. Provide students with the tools to use SEM to address important social scientific problems when the
problems and data are appropriate to the method.
4. Provide students the opportunity for specifying, estimating, and testing a simple SEM and writing up the results
in a short paper structured on the model of a research article.
5. Provide students with a solid foundation in SEM with which to pick up more advanced topics not covered
Bollen, Kenneth A. 1989. Introduction to Structural Equation Models with Latent Variables. New York: Wiley.
Hayduk, Leslie. 1987. Structural Equation Modeling with LISREL: Essentials and Advances. Baltimore:
Johns Hopkins Press.
Jöreskog, Karl G., Ulf H. Olsson, and Fan Y. Wallentin. 2016. Multivariate Analysis with LISREL.
Switzerland: Springer International Publishing.
Byrne, Barbara M. 1998. Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts,
Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum.
Time & Location Thursday 5:30-7:20pm Savery 409
Instructors Ross L. Matsueda
Office Hours Mon, 2-3pm,
227 Savery Hall
(& Thu 7:30-8pm
409 Savery Hall)
Students should have a sound background in intermediate statistics for social scientists, including a basic
course in statistical inference and the general linear model or multiple regression, as presented in Sociology
506. Also desirable is a knowledge of (or facility to learn independently) elementary tools of matrix algebra.
Students will be expected to complete semi-weekly exercises. Several computer assignments will allow
students to analyze data provided by the instructor and write a brief (no more than 5 pages) report.
Students will complete a short (10 pages of text) seminar paper using the methods presented in the course
using data provided by the instructor or data from the student. Alternatively, students may opt to take a final
exam. The paper will be due on Thur June 7, 5pm. A paper proposal along with a path diagram will be due
Thur, May 3 in lecture.
All assignments must be completed on time. A grade of incomplete will not be given except under
unusual circumstances, such as a family emergency.
Grades will be based on homework assignments, the seminar paper (or final exam), and possibly an
unannounced (pop) exam.
Lecture Notes: Introduction to the Course
Lecture 10: Panel Models & Sibling Models
Lecture 11: Latent Growth Curve Models
This memo describes how to import an SPSS save file into PRELIS and then create a PRELIS system file (*.psf) in LISREL 8.8 (or equivalently, create a LISREL system file (*.lsf) in LISREL 9.3). The system file can then be read directly into PRELIS and LISREL.
This memo describes how to input data (including raw data in ascii format with blanks separating variables, raw data in ascii format with commas separating variables, and an SPSS save file. Use the files, Informal.dat, Informal.csv, and Informal.sav to practice reading in data into PRELIS and creating a .psf file (in LISREL 8.8) or a .lsf file in (LISREL 9.3).
This memo provides examples of PRELIS runs that create a covariance matrix to input into LISREL for continuous variable models and a polychoric correlation matrix and associated asymptotic covariance matrix to be input into LISREL for ordinal variable models. It also includes LISREL command files to estimate models based on the PRELIS saved files. At the end is annotated output for the two PRELIS runs.
PRELIS Command Files
This file reads in a raw data file and computes and saves a covariance matrix to be input into LISREL (below), which assumes continuous indicators and normal distributions.
This file reads raw data into LISREL and computes polychoric correlations and associated asymptotic covariance matrix. It saves the two matrices to disk to be input into a LISREL run that assumes ordinal indicators (see below).
LISREL Command Files
This file reads in the covariance matrix from the PRELIS run Preexch1.pr2 (above) and estimates a one factor confirmatory factor model using ML under the assumption of continuous and normally distributed indicators.
This file reads in the polychoric correlation matrix and associated asymptotic covariance matrix (of the polychoric correlations) and estimates a one-factor confirmatory factor model using WLS under the assumption of ordinal indicators and non-normality.
This is an ascii file with blanks between variables. It consists of four variables taken from the Seattle Neighborhoods and Crime Survey, which sampled 4,670 residents from 123 neighborhoods (see Matsueda and Drakulich 2016). The variables are measures of reciprocated exchange, measured on an ordinal scale 1=never, 2=sometimes, 3=often:
How often have you watched your neighbor's property when they were out of town?
How often have you borrowed tools or small food items from your neighbors?
How often have you helped a neighbor with a problem?
How often have you asked neighbors about personal things like child rearing or jobs?
This is an ascii file containing raw data with blanks between variables from the study discussed above. It includes four measures of observed deviance in the neighborhood (dichotomous measures), plus five measures of child-centered social control (measured on a four-category ordinal scale (see Matsueda and Drakulich 2016).
Same as above, but the ascii file contains commas between variables.
Same as above, but this file is an SPSS save file.
Matsueda, Ross L. 2012. “Key Advances in the History of Structural Equation Modeling.”
Pp. 17-42 in Handbook on Structural Equation Modeling. Edited by Rick H. Hoyle. Guilford Press.
Duncan, Otis Dudley. 1975. Introduction to Structural Equation Models. New York: Academic Press,
Chapters 1 & 2.
Bielby, William T., and Robert M. Hauser. 1977. Introduction to Structural Equation Models. New York:
Academic Press, Chapter 1.
Duncan, Otis Dudley. 1975. Introduction to Structural Equation Models. New York: Academic Press,
Chapters 3 & 4.
Alwin, Duane F. and Robert M. Hauser. 1975. "The Decomposition of Effects in Path Analysis.” American
Sociological Review 40:37-47.
Paxton, Pamela, John R. Hipp, and Sandra Marquart-Pyatt. 2011. Nonrecursive Models: Endogeneity,
Reciprocal Relationships, and Feedback Loops. Beverly Hills: Sage Publications. (Chapter 2.
“Specification in Simultaneous Equation Models,” pages 4-22.)
Bielby, William T., and Ross L. Matsueda. 1991. "Statistical Power in Non-Recursive Linear Models." In
Sociological Methodology 1991, Vol. 20, edited by P. Marsden. Oxford: Basil Blackwell.
John Robert Warren. 2009. “Socioeconomic Status and Health across the Life Course: A Test of the Social
Causation and Health Selection Hypotheses.” Social Forces 87: 2125-2154.
Bielby, William T., Robert M. Hauser, and David L. Featherman. 1977. “Response Errors of Black and
Nonblack Males in Models of the Intergenerational Transmission of Socioeconomic Status.” American
Journal of Sociology 82:1242-88.
Matsueda, Ross L. 1982. “Testing Control Theory and Differential Association: A Causal Modeling
Approach. American Sociological Review.” American Sociological Review 47:489-504.
Paxton, Pamela. 1999. “Is Social Capital Declining in the United States? A Multiple Indicator
Assessment.” American Journal of Sociology 105:88-127.
Bartusch, Dawn Jeglum, and Ross L. Matsueda. 1996. “Gender, Reflected Appraisals, and Labeling: A
Cross-Group Test of an Interactionist Theory of Delinquency.” Social Forces 75:145-176.
Lei, Pui-Wa Lei, and Qiong Wu. 2012. “Estimation in Structural Equation Modeling.” Pp. 164-180 in
Handbook on Structural Equation Modeling. Edited by Rick H. Hoyle. Guilford Press.
Matsueda, Ross L., and Kathleen Anderson. 1998. “The Dynamics of Delinquent Peers and Delinquency.”
Paxton, Pamela. 2002. “Social Capital and Democracy: An Interdependent Relationship.” American
Sociological Review 67:254-277.
Hauser, Robert M., and Peter A. Mossel. 1985. “Fraternal Resemblance in Educational Attainment and
Occupational Status.” American Journal of Sociology 91:650-673.
Matsueda, Ross L. and William T. Bielby. 1986. "Statistical Power in Covariance Structure Models." Pp.
120-58 in Sociological Methodology 1986, edited by N.B. Tuma. Washington, D.C.: American