COURSE SYLLABUS
Cpse 790R (Section 2): Structural Equation Modeling
Spring 2013
Instructors:
Lane Fischer
340-L MCKB
801-422-8293
Email: lane_fischer@byu.edu
Office Hours:
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Richard Sudweeks
150-M MCKB
801-422-7078
Email: richard_sudweeks@byu.edu
Office Hours:
T, Th, 10 a.m.–-11. a.m.
Wed., 10 a.m.—3 p.m.
Class Meeting Schedule:
T & Th, 12:00—2:50 p.m.
102 SWKT
Required Textbooks
Geiser, C. (2010). Data analysis with Mplus. New York: Guilford.
Kline, R.B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York:
Guilford.
EXPECTED LEARNING OUTCOMES
As a result of completing this course, students should be able to:
1. Explain the similarities and differences among (a) path analysis, (b) confirmatory factor analysis,
(c) analysis of structural models, and (d) longitudinal growth curve analysis.
2. Use confirmatory factor analysis procedures to assess alternative measurement models
describing hypothesized relationships between latent variables and their respective indicator
variables.
3. Use confirmatory factor analysis to investigate questions related to reliability of measurement,
construct validity, and factor invariance either across multiple groups of persons or with a single
group across multiple occasions.
4. Test alternative structural models describing hypothesized patterns of relationships among
endogenous and exogenous variables including direct effects as well as mediating and
moderating effects.
5. Demonstrate proficiency in using SEM software (e.g., AMOS, Mplus, & LISREL) and
interpreting the resulting output.
6. Prepare written reports of completed SEM studies in accordance with commonly accepted
guidelines and recommendations.
7. Critique published reports of completed research and evaluation studies that utilized a SEM
approach.
Overview and Rationale:
SEM is a family of methods that collectively provide a comprehensive data analytic framework
for testing models that describe hypothesized relationships among sets of observed and latent
variables. SEM is an extension of multiple regression, path analysis, and exploratory factor analysis
that subsumes these narrower approaches as well as analysis of variance into a broader, more general
framework. SEM can be used to test hypotheses about differences in group means as well as
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hypothesized associations among pairs of variables, but in SEM the focus is on testing alternative
models that represent theories describing hypothesized patterns of relationships among sets of
variables.
SEM can be used to analyze survey data, archival data, and other forms of nonexperimental data
as well as data from experimental designs. SEM improves upon path analysis by allowing for the
inclusion of latent variables as well as observed variables. In addition, SEM improves upon multiple
regression by providing explicit estimates of measurement error and producing parameter estimates
that describe relationships among latent variables after they have been adjusted for the estimated
measurement error.
The roots of SEM can be traced back to developments in path analysis during the 1920s and
1960s, factor analysis in the 1920s and multiple regression during the 1950s and 60s. But the
unifying framework and thee software that made SEM feasible were developed by Karl Joreskog and
his colleagues in the 1960s and 1970s. Since that time SEM has been used for a wide variety of
purposes in the social and natural sciences. During the last 20 years its use has grown exponentially.
One reason for this growth has been the development of more user-friendly software such as EQS,
AMOS, and Mplus. Another reason has been the ongoing development of refinements in SEM
concepts and procedures.
SEM includes two main types of models that are used sequentially. The first is the measurement
model which has come to be known as Confirmatory Factor Analysis (CFA). It provides a way of
testing whether a designated set of observed variables can be interpreted as one or more valid and
reliable latent variables. The second type is the structural model that is used after a measurement
model has been found to fit the data and which provides a way of testing hypothesized relationships
among the latent variables defined by the measurement model.
SEM provides a language and a set of concepts for thinking about and designing research. It also
provides a set of procedures and conventions for analyzing, interpreting, and reporting the results of
research. Research journals in education as well as in psychology, sociology, and other related
disciplines now regularly publish reports of studies based on SEM.
Course Outline:
Session Date
1
4/30
2
5/2
3
5/7
4
5/9
Lead Readings
LF
Lei & Wu (pdf)
Keith 17
(Keith 10,11,13)
LF
LF
Topic
Overview
Keith Appendix D & E
Kline 1-3, Keith 9
Fundamentals &
Data Screening
Geiser 1-2
Intro to Mplus
Geiser 3, pp 24-61
Manifest Regression,
Latent Regression,
CFA
Geiser 3, pp 62-80
Path Modeling:
Manifest
Latent
In Class Exercises
In Class Exercises
2
Homework
5
5/14
RS
Kline 5 & 6
Specification &
Identification
6
5/16
RS
Kline 7
Estimation
7
5/21
RS
Kline 8
Hypothesis Testing
8
5/23
RS
Kline 9
Keith 14
Measurement Models,
CFA
9
5/28
RS
10
5/30
LF
Kline 10
Keith 15-16
SR Models
11
6/4
LF
Thompson
SR Models
12
6/6
RS
Geiser 4
Kline 11
Longitudinal Models
13
6/11
RS
14
6/13
Final
6/20
Measurement Models,
CFA
Longitudinal Models
Geiser 5
Kline 12
3:00-5:00
Multilevel Models
Supplementary Readings
Keith, T.Z. (2006). Multiple regression and beyond. Boston, MA: Pearson.
Lei, P.-W. & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical
considerations. Educational Measurement: Issues and Practice, 26(3), 33-43.
Thompson, B. (2000). Ten commandments of structural equation modeling. In L.G. Grimm & P.R.
Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 261-283).
Washington, DC: American Psychological Association.
Additional Reference Materials
Baldwin, B. (1989). A primer in the use and interpretation of structural equation models.
Measurement and Evaluation in Counseling and Development, 22, 100-112.
Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley.
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Bollen, K.A. (2002). Latent variables in psychology and the social sciences. Annual Review of
Psychology, 53, 605-634.
Bollen, K.A. & Curran, P.J. (2006). Latent curve models: A structural equation perspective.
Hoboken, NJ: Wiley.
Brown, T.A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
Byrne, B.M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and
programming. New York: Routledge.
Fassinger, R.E. (1987). Use of structural equation modeling in counseling psychology research.
Journal of Counseling Psychology, 34, 425-436.
Fox, J. (2006). Structural equation modeling with the sem package in R. Structural Equation
Modeling, 13, 465-486.
Graham, J.M. (2008). The general linear model as structural equation modeling. Journal of
Educational and Behavioral Statistics, 33, 485-506.
Hancock, G.R. (1987). Structural equation modeling methods of hypothesis testing of latent variable
means. Measurement Evaluation in Counseling and Development, 30, 91-105.
Hancock, G.R. & Mueller, R.O. (Eds.) (2006). Structural equation modeling; A second course.
Greenwich, CT: Age Publishing.
Hoyle, R.H. (1995). Structural equation modeling: Concepts, issues, and applications. Thousand
Oaks, CA: Sage.
Hoyle, R.H. (Ed.) (2012). Handbook of structural equation modeling. New York: Guilford.
Kaplan, D. (2009). Structural equation modeling: Foundations and extensions (2nd ed.). Thousand
Oaks, CA; Sage.
Klem, L. (2000). Structural equation modeling. In L.G. Grimm & P.R. Yarnold (Eds.), Reading and
understanding more multivariate statistics (pp. 227-260). Washington, DC: American
Psychological Association.
MacCullum, R.C., & Austin, J.T. (2000). Applications of structural equation modeling in
psychological research. Annual Review of Psychology, 51, 201-226.
MacKinnon, D.P. (2008). Introduction to statistical mediation analysis. New York: Psychology
Press.
Mueller, R.O. (1997). Structural equation modeling: Back to basics. Structural Equation Modeling,
4, 353-369.
Mulaik, S.A. (2009). Linear causal modeling with structural equations. Boca Raton, FL: CRC Press.
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Raykov, R. & Marcoulides, G.A. (2006). A first course in structural equation modeling (2nd ed.).
Mahwah, NJ: Erlbaum.
Schumacker, R.E. & Lomax, R.G. (2010). A beginner’s guide to structural equation modeling (3rd
ed.). New York: Routledge.
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