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Summer 2009
T & R 1-3:20PM
Professor
Name: Dr. Marcus Butts
Office: 212 COBA
Office hours: T & R 11am-12pm
Phone: (817)272-3855
E-mail: mbutts@uta.edu
Course Materials
Prerequisite: Multivariate Statistics (BSAD-6314)
Required
Textbook:
Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User’s reference guide
.
Chicago: Scientific Software, Inc.
Software: I will be using LISREL 8.8 for teaching and demonstration purposes in this class.
You will need to have access to full versions of LISREL. I know that the costs of the full versions of LISREL are sometimes outside of Ph.D. students’ means.
There are some alternatives, though, that you should consider. First, many departments have already purchased a full version of an SEM program for one or more faculty members. You (or the department) can purchase an add-on user license agreement (a site license with one additional user) to the original departmental license agreement for $120. Details as to how this works may be found at http://www.ssicentral.com/ordering/index.html
for LISREL.
Second, you may rent LISREL from SSI Central for $75 for 6 months or $130 for
1 year. Please see http://www.ssicentral.com/rental/index.html
. You can order rental editions by using the online PDF form which is available at http://www.ssicentral.com/rental/SSIRentalOrderForm.pdf
. The form instructions are at http://www.ssicentral.com/rental/CSRentalInstructions.pdf
.
I am purposefully not requiring any additional introductory textbooks for the class because I require all students to purchase LISREL and at least have shared access to a user’s reference guide. It is by far the most important tool needed for the class. All of the readings you will need will be provided in PDF form on WebCT.

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Course Objectives
The major purpose of this course is to provide a foundation into structural equation modeling
(SEM) techniques and issues. Underlying this purpose is my sincere hope to demystify SEM.
That is, SEM is largely a “glorified” regression procedure, and in that vein, if you have a good grasp of multiple regression, you already have an understanding of some of the basics underlying
SEM. Unlike ordinary regression, though, SEM: (a) doesn’t assume that measurement error is zero; and (b) can simultaneously estimate parameters representing the whole model rather than just pieces of the model. The use of the term “foundation” in the first sentence is purposeful in that this is an introductory course in SEM. The outcome that I wish to achieve is to give you the knowledge and skills to test ordinary models. Testing more complex models, such as multilevel ones, would be part of an advanced SEM course or further reading on your own.
Course Requirements
Readings.
Each class, there are readings assigned. You are required to read the assigned readings before the day they are assigned. The class is very discussion-based. Thus, it’s important that you have a grasp of the readings before coming to class. For empirical articles, it is not important that you know (or read) the conceptual information in the article; just read for discussion of the method or statistical technique. Readings that are assigned as “suggested readings” are solely to supplement your knowledge of the topic. I don’t expect you to read those articles for class. They are primarily suggested as a guide for future reference on the subject.
Examinations.
There is one examination (see schedule below) worth 30% of your final grade.
The exam will primarily be of the essay variety, but could vary from this format.
Homework.
There are 5 homework assignments throughout the course accounting for 40% of your total grade. Typically, homework will be assigned on Thursday and due on Monday at
10am to give me time to review. Then, we’ll discuss them the following Tuesday class. The intent is to get you to conduct the analyses yourselves and stimulate discussion about any problems. Homework will be graded on a scale of 1-10, depending on a) the level of effort put forth to conduct the analyses, b) the level of knowledge exhibited, and c) the correctness of the results. The former two components will be weighed much more heavily than the latter.
Course Project.
This is the remaining 30% of your course grade. Given the diversity of students taking this course, I am willing to negotiate project structure with each student individually.
Alternatives include applying SEM methods to a data set (Option 1) or an in-depth examination of a particular technique related to SEM (Option 2). The latter option is to choose a published paper which employed "structural equation modeling" (SEM) methods and conduct a thorough, detailed critique . The published study need not have employed the LISREL software, although that may well simplify the task.
Regardless of whether you actually conduct an analysis or undertake the critique, the project involves much more than simply doing an SEM analysis. The student's report must begin with a short review and discussion of the theoretical rationale of the focal paper in the context of other work in the field. The report will examine the extent to which the use of SEM to test that theoretical model is either appropriate or inappropriate. Students will examine the properties and development of the measures used in the paper, in light of standards and methods discussed in

3 this course. In the case of Option 1, students will undertake the analyses by themselves. In the case of Option 2, students will attempt to reproduce the SEM analysis undertaken in the article, will try to understand why a particular approach was chosen, will look at weaknesses and limitations of the analysis, and will suggest and evaluate alternatives to the published analysis. If students can find a basis for supporting alternative models, then they should test those models as well. By no means should students take statements from published work at face value--big mistakes can be found even in premier journals. In choosing between the options, I will say that
I will expect a much more thorough written critique for Option 2, while I expect Option 1 to focus on the steps involved in the analyses and proper presentation of results as well as discussion of those results. If students wish to choose Option 1 but have no data, you can come see me about possible data sources. Also, please note that no “Incomplete” grades will be given for this course.
Schedule of Events ( Please note that the following schedule is subject to change.)
Topic 1 – Introduction to SEM
R 6/4 – Class overview
T 6/9 & R 6/11
Readings
Schumacker, R.E., & Lomax, R.G. (2004).
A beginner’s guide to structural equation modeling
(2 nd
Edition). Mahwah, NJ: Lawrence Erlbaum. Chapter 3 (pp. 37- 60).
Rigdon, E. E. (1998). Structural equation modeling. In G.A. Marcoulides (ed.), Modern Methods for
Business Research Chapter 9 (pp. 251-294). Mahwah, NJ: Lawrence Erlbaum.
Weston, R. & Gore, P. A. (2006). A brief guide to structural equation modeling. The Counseling
Psychologist, 34 , 719 – 751.
Topic 2 – LISRELeese
T 6/16 & R 6/18
Readings
Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User’s reference guide . Chicago: Scientific Software,
Inc. Chapter 1 (pp. 1-36).
Schumacker, R.E., & Lomax, R.G. (2004). A beginner’s guide to structural equation modeling (2 nd
Edition). Mahwah, NJ: Lawrence Erlbaum. Chapter 15 (pp. 406- 469).
Homework 1:
You will be given some matrices and models to diagram using LISREL syntax
Topic 3 – SEM Basics
T 6/23
Readings:
Schumacker, R.E., & Lomax, R.G. (2004). A beginner’s guide to structural equation modeling (2 nd
Edition). Mahwah, NJ: Lawrence Erlbaum. Chapter 4 (pp. 61- 78).
Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin , 103 , 411-423.

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Topic 4 – Model Fit
R 6/25
Readings:
Schumacker, R.E., & Lomax, R.G. (2004).
A beginner’s guide to structural equation modeling
(2 nd
Edition). Mahwah, NJ: Lawrence Erlbaum. Chapter 5 (pp. 79- 123).
Hu, L.T., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling , 6 , 1-55.
Suggested Readings:
Hu, L. H., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3 , 424-453.
Homework 2:
You will be given a series of model fit indices and parameter estimates from real data. You will write a short paragraph telling me which models fit the best from a statistical and practical perspective and any potential problems you see.
Topic 5 – Measurement Models and Confirmatory Factor Analysis
T 6/30 & R 7/2
Readings:
Kline, R. B. (2004). Principles and Practice of Structural Equation Modeling (2 nd Ed.). New York:
Guilford Publications. Chapter 7 (pp. 165-208).
Lance, C.E., & Vandenberg, R.J. (2001). Confirmatory factor analysis. In F. Drasgow & N.
Schmitt (Eds.), Measuring and Analyzing Behavior in Organizations: Advances in
Measurement and Data Analysis (pp. 221-256, Volume in the Organizational Frontier Series ). San
Francisco: Jossey-Bass.
Gerbing, D. W., & Hamilton, J. G. (1996). The viability of exploratory factor analysis as a precursor to confirmatory factor analysis. Structural Equation Modeling , 3, 62-72.
Homework 3:
You will be given data to conduct an EFA, multiple 1 st order CFAs, and a 2 nd order CFA – compare and contrast results.
T 7/7 – Discussion of homework
R 7/9 – MIDTERM EXAM
Topic 6 – Path Models
T 7/14
Readings:
Kline, R. B. (2004). Principles and Practice of Structural Equation Modeling (2 nd Ed.). New York:
Guilford Publications. Chapter 5 & 6 (pp. 123-164).
Rigdon, E.E. (1994). Calculating degrees of freedom for a structural equation model. Structural Equation
Modeling , 1 , 274-278.
Suggested Readings:
Material at http://www2.chass.ncsu.edu/garson/pa765/path.htm

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Topic 7 – Models with Structural and Measurement Components (Full Structural Models)
R 7/16
Readings:
Kline, R. B. (2004). Principles and Practice of Structural Equation Modeling (2 nd Ed.). New York:
Guilford Publications. Chapter 8 (pp. 209-234).
Homework 4:
You will run 3 to 4 SEMs with interpretation.
Topic 8 – Testing for Mediation and Moderation in SEM
T 7/21 & R 7/23
Readings:
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.
Cortina, J.E., Chen, G., & Dunlap, W.P. (2001). Testing interaction effects in LISREL: Examination and illustration of available procedures. Organizational Research Methods , 4 , 324-360.
Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation
Modeling, 13, 497-519.
Suggested Readings:
Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1-22.
Homework 5:
You will run a series of SEMs to test for mediation/moderation and interpret the results.
Topic 9 – Issues and Problems with SEM & New Horizons
T 7/28 – T 8/4
Readings:
Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods & Research, 29, 468-508.
McDonald, R., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses.
Psychological Methods, 7 , 64-82.
Suggested Readings:
Williams, L.J., Edwards, J. & Vandenberg, R.J. (2003). A review of advanced applications of structural equation techniques in organizational behavior and human resources management research. Journal of Management , 29 , 903-936.
W 8/12 – COURSE PROJECT DUE IN MY BOX (9AM)
ADDITIONAL TOPICS NOT COVERED
Testing for Common Method Bias using SEM
Suggested Readings:
Podsakoff, P.M., MacKenzie, S. B., & Lee, J. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879-903.

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Richardson, H. A., Simmering, M. J., & Sturman, M. C. (in press). A tale of three perspectives:
Examining post hoc statistical techniques for detection and correction of common method variance.
Organizational Research Methods.
Nonrecursive Path Models
Suggested Readings:
Kline, R. B. (2004). Principles and Practice of Structural Equation Modeling (2 nd Ed.). New York:
Guilford Publications. Chapter 9 (pp. 237-262).
Multisample Analyses, Tests of Mean Structures, & Measurement Invariance/Equivalence
Suggested Readings:
Vandenberg, R.J., & Lance, C.E. (2000). A Review and synthesis of the measurement invariance literature: Suggestions, practices and recommendations for organizational research.
Organizational Research Methods , 3 , 4-70.
Ployhart, R.E., & Oswald, F.L. (2004). Applications of mean and covariance structure analysis:
Integrating correlational and experimental approaches. Organizational Research Methods , 7 , 27-65.
Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93, 568-592.
Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User’s reference guide . Chicago: Scientific Software,
Inc. (Chapter 9 & 10).
French, B. F., & Finch, W. H. (2008). Multigroup confirmatory factor analysis: Locating the invariant referent sets. Structural Equation Modeling, 15, 96-113.
Latent Growth Modeling
Suggested Readings:
Duncan, T.E., Duncan, S.C., Strycker, L.A., Li, F, & Alpert, A. (1999). An introduction to latent variable growth modeling: Concepts, issues and applications (Chapters 1 and 2, pp. 1-32). Mahwah, NJ:
Lawrence Erlbaum.
Kaplan, D. (2000). Structural equation modeling: Foundations and extensions . Thousand Oaks, CA:
Sage (Chapter 8, pp. 149-170).
Lance, C.E, Vandenberg, R.J., & Self, R.M. (2000). Latent growth model of individual change: The case of newcomer adjustment. Organizational Behavior and Human Decision Processes , 83 , 107-140.