CJT 765: SPECIAL TOPICS IN QUANTITATIVE COMMUNICATION
RESEARCH METHODS: STRUCTURAL EQUATION MODELING
Spring, 2007
Professor:
Office:
Phone:
Office hours:
Classroom:
Class meeting hours:
Rick Zimmerman
245 Grehan
257-4099
Mon. 11:45-12:45 and by appointment
B35 W.T. Young Library
Mon. 1-3:30; labs 3:45 – 4:45 p.m. Monday, and TBA
B35 W.T. Young Library
Teaching Assistant: Olga Dekhtyar
TA’s office:
308 Breckinridge Hall
TA’s phone number: 257-8133
TA’s office hours:
TBA
Course Philosophy
While for most students, some of the material will be review, the course will be taught at the
graduate student level. That is, students are expected to have a level of commitment to the course well
beyond that expected for undergraduates in their courses, as the material covered in this course may be of
use throughout the students' careers. It is expected that multiple readings of material will have been
undertaken before class, and that an all-out effort will be made to understand the material and to work on
assignments for the class.
The instructor will do his best to make sure that the information presented is understandable, but
expects that students will have first spent some time trying to assimilate the material on their own.
Quizzes in this class will be completed in-class, will be designed to be difficult, and will be graded on a
curve, so that students who have superior ability and/or have expended much effort will be able to
demonstrate these on the exam. Late assignments will not be accepted and make-up exams will not be
given, except for extenuating circumstances.
Each student must keep up with the material as we go along; statistical methods is typically not a
subject for which several weeks of material can be crammed into one's brain in several hours.
Laboratories to become familiar with computer programming, model testing, and completing exercises
will be required. Students are strongly urged to stop in during office hours with Dr. Zimmerman or Ms.
Dekhtyar if they have any questions.
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Primary Course Objectives
to familiarize graduate students in the social and behavioral sciences with the language, logic, and
implementation of structural equation modeling;
to compare and contrast structural equation modeling with more commonly used statistical
strategies in the social and behavioral sciences such as multiple regression analysis and factor
analysis;
to teach the criteria associated with the decisions that must be made at each phase of a structural
equation modeling analysis;
to consider the philosophical and statistical criticisms of structural equation modeling as an
approach to research design and data analysis;
to provide firsthand experience reviewing research reports that feature structural equation
modeling and writing up and presenting orally the results of structural equation modeling
analyses.
Elements of the Course
Readings
There are three required books:
1. Kline, R.B. (2005). Principles of Structural Equation Modeling (2nd edition). New York:
Guilford.
2. Hoyle, R.H. (ed.) (1995). Structural Equation Modeling: Concepts, Issues, and Applications.
Thousand Oaks, CA: Sage.
3. Byrne, B. M. (2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications,
and Programming. Mahwah, NJ: Erlbaum.
Most weeks, additional readings are also required. More advanced topics are occasionally covered
in suggested (but not required) readings, marked by an asterisk (*). All additional readings (generally as
pdf files) will be posted on the course website by the first day of class.
Quizzes
Two quizzes will comprise the testing in the course. While we hope you will read and learn the
material just for learning sake, sometimes in the mix of other activities and coursework, it is easy to let
readings and mastery of the material go by the wayside. So, I think some grades related to mastery of the
material may help students keep on the top of the material, and have decided to include 2 quizzes as part
of your grade. Each will occupy about 30 minutes of class time on Feb. 26 and Apr. 2. Most will be
short answer or short essay but some writing of computer programming, simple calculations, path
diagrams, and /or interpreting output may also be included.
Homework Assignments
Three homework assignments will also be required. All will use the dataset we are using for the
course, a 3-wave, longitudinal sample of about 5000 rural high school students from the beginning of 9th
to the end of 10th grade. We will discuss the dataset and the codebook for the dataset in the first
laboratory section of the course, to be held the week of Jan. 29. Laboratory sessions will focus on
preparing students for these homework assignments, including practice questions.
Research Project
The major product of the course will be a written report of a structural equation modeling analysis
you conduct on data of your choosing. On Feb. 19 I will ask you to specify a dataset that you will
analyze and write up for the course. On Mar. 26 I will ask you to prepare a document in which you
specify the names and characteristics of the variables your analysis will include and the nature of the
model you plan to fit. All models should include both measurement and structural components for this
assignment. About two-thirds of the way through the course I will ask you to meet outside of class with
another member of the class to discuss your data and plan of analysis and to exchange feedback on your
projects. The final draft of the research project is due by May 3th at 10 a.m. An 8-10-minute oral
presentation will also be given on either April 23 or April 30.
Attendance and Participation
Students are expected to attend all class sessions, as both hearing about statistics material and
reading it as important elements to learning it. Attendance is also required at laboratory sessions (1 per
week), as doing statistics is probably the most important learning component of all. I also expect
students’ participation in class; both the quality and quantity of student’s participation will be considered
in their evaluation.
Published Article Presentation
Each student will give a 2-minute presentation in which he/she describes and evaluates a published
study in which the data are analyzed using structural equation modeling. Students can choose from a list
of recently published articles in top-tier journals in their field of study; references and abstracts for
psychology, communication, and business/economics/marketing will be available on the course website
by January 31, over two months before the presentations begin. Presentations will take place on April 9th.
Details about the selection of an article and the contents of the presentation will be provided around
January 31st as well.
Grading
Three computer assignments, an oral presentation about a published article using structural
equation modeling, a research project (both an oral presentation and a written paper), and a midterm exam
will comprise the grading in this course. The total grade will be distributed as follows:
Homework Assignments
24% (3 @8% each)
Published Article Presentation
10%
Research Project—written component
26%
Research Project—oral presentation
10%
Quizzes (2 @ 10%)
20%
Attendance/participation
10%
Everyone should receive an “A” or a “B” barring poor attendance or not doing the work, so that
students can spend more time and energy on learning the material rather than on their grade.
Course Website
The website for the course is at www.uky.edu/centers/hiv/cjt765/cjt765.html. The course
syllabus, assignments, dataset to be used throughout the course (in SPSS format), additional readings (in
PDF files), articles for the published article presentation, datasets, and a variety of other materials will be
available on the course website.
Acknowledgments
I would like to acknowledge the following faculty members, whose syllabi helped provide some
suggestions for assignments, readings, or course organization. I either spoke to these faculty members
and/or their syllabi were available through publicly accessible websites. Copies of my syllabus have been
shared with them.
Rick Hoyle, Duke University, Psychology/Sociology 779, Structural Equation Modeling, taught Fall,
2000 at UK. (I have especially drawn on this syllabus for readings and assignments.)
Robert Hauser, University of Wisconsin, Sociology 952, Mathematical and Statistical Applications in
Sociology, Topic: Path Analysis and Structural Equation Models, taught Spring, 2004.
Stephen West, Cathy Cottrell, & Oi-Man Kwok, Arizona State University, Psychology 533,
Structural Equation Modeling, taught Spring, 2004.
Gregory Elliott, Brown University, Sociology 226, Structural Equation Models in the Social
Sciences, taught Fall, 2004.
Course Outline
Jan. 22
Introduction to Structural Equation Modeling:
Ancestry, History, and Philosophy of Science
Kline: 1; Hoyle: 7
Blalock (1991)
Berk (1988)
Hershberger (2003)
Freedman (1991)
Jan. 29
Review of correlation and regression
Kline: 2; Byrne: 1
Cohen et al. (2003): Ch. 2-3
Feb. 5
Review of data preparation, screening,
measurement issues
Kline: 3; Byrne: 2
Allison (2003)
Cohen et al. (2003) pp.225-251
DeVellis (1991), pp. 1-41
Schaefer & Graham (2002)
Feb . 12
Overview of SEM notation, path diagrams,
programs; Homework 1 due
Kline: 4; Hoyle: 1, 2, 8
Byrne: 3
*Byrne (1994), Chapters 1 & 2
*Kelloway (1998),Ch. 4-7
Feb. 19
Path Analysis 1: Basic theorems, mediation, coefficients, Kline: 5 pp. 93-105; Hoyle: 3
Choose dataset
Kenny (1979), Chapters 3-4
Baron & Kenny (1986)
MacKinnon et al. (2002)
Cole & Maxwell (2003)
Gionta et al. (2005)
*Shrout & Bolger (2002)
Feb. 26
Path Analysis 2: decomposing a correlation, direct and
indirect effects, identification; Quiz 1
Kline: 5 pp. 105-122
Alwin & Hauser (1975)
Holbert & Stephenson (2001)
Pedhazur (1982), pp. 614-628
Fox (1980)
Mar. 5
Path Analysis 3: fitting a model, fit indices, comparing
models, statistical power; Homework 2 due
Kline: 6; Hoyle 3, 5
Hayduk et al., (2003)
Bollen & Long (1993)
Tanaka (1993)
Marsh et al. (2004)
Fan & Sivo (2005)
*Reichardt (2002)
*Dormann, 2001
*Muthén & Muthén (2002)
Course Outline (cont.)
Mar. 19
Measurement Models and Confirmatory
Factory Analysis; item parcels
Kline: 7; Hoyle: 10, 12
Byrne: 4
Lance et al., 2002
Noar, 2003
Quilty et al. (2006)
Hagtvet & Nasser (2004)
Mar. 26
Putting it all together: Structural and measurement
components in SEM; Turn in dataset description;
Homework 3 due
Kline: 8; Byrne: 6
Anderson & Gerbing (1988)
Holbert & Stephenson (2002)
McDonald & Ho (2002)
Stephenson & Holbert (2003)
MacCallum & Austin (2000)
*Hayduk & Glaser (2000)
Apr. 2
Nonrecursive structural models; Quiz 2
Kline: 9
James & Singh, 1978
*Berry, 1984
Apr. 9
Advanced topics: Multi-group SEM,
Latent Growth Models
Published article presentations
Kline: 10, 11; Hoyle: 11, 13
Bentler & Dudgeon (1996)
McCallum et al. (1993)
Byrne (2004)
Kim (2005)
*Gonzalez & Griffin (2001)
*Raykov (2005)
Apr. 16
Pitfalls in using SEM; Critique of SEM
and future directions
Kline: 12, 13
Tomarken & Waller (2005)
Raykov & Marcoulides (2001)
Schumacker (2002)
deJong (1999)
Apr. 23
Research report presentations 1
Apr. 30
Research report presentations 2
May 3
Research reports due, 10 a.m.
Additional Readings
January 22
Berk, R.A. (1988). Causal inference for sociological data. In N.J. Smelser (Ed.), Handbook of
Sociology (pp. 155-172). Newbury Park: Sage.
Hershberger, S. L. (2003). The growth of structural equation modeling: 1994-2001. Structural
Equation Modeling, 10, 35-46.
Blalock, H. M., Jr. (1991). Are there really any constructive alternatives to casual modeling? P. V.
Marsden (Editor), Sociological Methodology, 21, 325-335. Cambridge, MA: Basil Blackwell.
Freedman, D. A. (1991). Statistical models and shoe leather. P. Marsden (Editor), Sociological
Methodology, 21, 291-313. Cambridge, MA: Basil Blackwell.
January 29
Cohen, J., Cohen, P., West, S. G., & Aiken, L.S. (2003). Applied multiple regression/correlation analysis
for the behavioral sciences (3rd Ed.). Hillsdale, NJ: Erlbaum.
February 5
Cohen, J., Cohen, P., West, S. G., & Aiken, L.S. (2003). Applied multiple regression/correlation analysis
for the behavioral sciences (3rd Ed.). Hillsdale, NJ: Erlbaum.
DeVellis, R. F. (1991). Scale Development: Theory and Applications. Newbury Park, CA: Sage.
Schafer, J.L., & Graham, J.W. (2002). Missing data: our view of the state of the art. Psychological
Methods, 7, 147-177.
Allison, P. D. (2003). Missing data techniques for Structural Equation Modeling. Journal of Abnormal
Psychology, 112(4), 545-557.
February 12
*Byrne, B. M. (1994). Structural Equation Modeling with EQS and EQS/Windows: Basic Concepts,
Applications, and Programming. Thousand Oaks, CA: Sage.
*Kelloway, E. K. (1998). Using LISREL for Structural Equation Modeling: A Researcher’s Guide.
Thousand Oaks, CA: Sage.
February 19
Kenny, D. A. (1979). Correlation and Causality. New York, NY: John Wiley.
Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51, 173-182.
MacKinnon, D. P., Lockwook, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison
of methods to test the significance of the mediation and intervening variable effects.
Psychological Methods, 7, 83-104.
Cole, D. A., & Maxwell, S. (2003). Testing mediational models with longitudinal data: Questions and
tips in the use of Structural Equation Modeling. Journal of Abnormal Psychology, 112(4),
558-577.
Gionta, D. A., Harlow, L. L., Loitman, J. E., & Leeman, J. M. (2005). Testing a mediational model of
communication among medical staff and families of cancer patients. Structural Equation
Modeling, 12(3), 454–470.
*Shrout, P.E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New
procedures and recommendations. Psychological Methods, 7, 422-445.
Additional Readings (cont.)
February 26
Alwin, D.F., and Hauser, R.M. (1975). The decomposition of effects in path analysis. American
Sociological Review, 40, 37-47.
Pedhazur, E.J. (1982). Multiple Regression in Behavioral Research, Chapter 15, pp. 577-588. New
York, NY: Holt.
Fox, J. (1980). Effect analysis in structural equation models. Sociological Methods and Research, 9,
3-26.
Holbert, R. L., & Stephenson, M. T. (2001). The importance of indirect effects in media effects research:
Testing for mediation in structural equation modeling. Journal of Broadcasting and Electronic
Media, 47, 556-572.
March 5
Hayduk, L., Cummings, G., Stratkotter, R., Nimmo, M., Grygoryev, K., Dosman, D., Gillespie, M.,
Pazderka-Robinson, H., & Boadu, K. (2003). Pearl’s D-separation: One more step into causal
thinking. Structural Equation Modeling, 10, 289-311.
Bollen, K.A., & Long, J.S. (1993). Introduction. In K.A. Bollen & J.S. Long (Eds.), Testing Structural
Equation Models (pp.1-9). Newbury Park, CA: Sage.
Tanaka, J.S. (1993). Multifaceted conceptions of fit in structural equation models. In K.A. Bollen & J.S.
Long (Eds.), Testing Structural Equation Models (pp. 10-39). Newbury Park, CA: Sage.
Marsh, H. W., Hau, K., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing
approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and
Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320-341.
Fan, X., & Sivo, S. A. (2005). Sensitivity of fit indexes to misspecified structural or measurement model
components: Rationale of two-index strategy revisited. Structural Equation Modeling, 12(3),
343-367.
*Reichardt, C. S. (2002). The priority of just-identified, recursive models. Psychological Methods,
7, 307-315.
*Dormann, C. (2001). Modeling unmeasured third variables in longitudinal studies. Structural
Equation Modeling, 8, 575-598.
*Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and
determine power. Structural Equation Modeling, 9, 599-620.
March 19
Lance, C. E., & Noble, C. L. (2002). A critique of the correlated trait-correlated method and correlated
Uniqueness models for multitrait-multimethod data. Psychological Methods, 7, 228-244.
Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation
Modeling, 10, 622-647.
Quilty, L. C., Oakman, J. M., & Risko, E. (2006). Correlates of the Rosenberg Self-Esteem Scale
method effects. Structural Equation Modeling, 13(1), 99-117.
Hagtvet, K. A., & Nasser, F. M. (2004). How well do item parcels represent conceptually defined latent
constructs? A two-facet approach. Structural Equation Modeling, 11(2), 168-193.
Additional Readings (cont.)
March 26
Holbert, R. L., & Stephenson, M. T. (2002). Structural equation modeling in the communication
sciences, 1995-2000. Human Communication Research, 28, 351-551.
McDonald, R.P., & Ho, M-H. R. (2002). Principles and practice in reporting structural equation analysis.
Psychological Methods, 7, 64-82.
Stephenson, M. T., & Holbert, R. L. (2003). A Monte Carlo simulation of observable latent variable
structural equation modeling techniques. Communication Research, 30, 332-354.
MacCallum, R.C., & Austin, J.T. (2000). Applications of structural equation modeling in psychological
research. Annual Review of Psychology, 51, 201-22.
Anderson, J. & Gerbing, D. (1988). Structural equation modeling in practice: A review and
recommended two-step procedure. Psychological Bulletin, 103, 411-423.
*Hayduk, L. A., & Glaser, D. N. (2000). Jiving the four-step, waltzing around factor analysis, and other
serious fun. Structural Equation Modeling, 7, 1-35.
April 2
James, L.R., and B.K. Singh. (1978). An introduction to the logic, assumption, and basic analytical
procedures of two-stage least squares. Psychological Bulletin, 85, 1104-1122.
*Berry, W. D. (1984). Nonrecursive Causal Models. Newbury Park, CA: Sage.
April 9
Bentler, P.M., & Dudgeon, P. (1996). Covariance structure analysis: Statistical practice, theory,
directions. Annual Review of Psychology, 47, 563-592.
MacCallum, R.C., Wegener, D.T., Uchino, B.N., & Fabrigar, L.R. (1993). The problem of equivalent
models in applications of covariance structure analysis. Psychological Bulletin, 114, 185-191.
Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled.
Structural Equation Modeling, 11(2), 272-300.
Kim, K. H. (2005). The relation among fit indexes, power, and sample size in structural equation
modeling. Structural Equation Modeling, 12(3), 368-390.
*Gonzalez, R., & Griffin, D. (2001). Testing parameters in structural equation modeling: Every “one”
matters. Psychological Methods, 6, 258-269.
*Raykov, T. (2005). Analysis of longitudinal studies with missing data using covariance structure:
Modeling with full-information maximum likelihood. Structural Equation Modeling, 12(3),
493–505.
April 16
Raykov, T., & Marcoulides, G. A. (2001) Can there be infinitely many models equivalent to a given
covariance structure model? Structural Equation Modeling, 8, 142-149.
Schumacker, R. E. (2002). Latent variable interaction modeling. Structural Equation Modeling, 9, 4054.
deJong, P. F. (1999). Hierarchical regression analysis in structural equation modeling. Structural
Equation Modeling, 6, 187-211.
Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling: Strengths, limitations, and
misconceptions. Annual Review of Clinical Psychology, 1, 31-65.