1EDF 7412
Structural Equation Modeling
Fall 2011
Instructor:

James Algina

Office:119D Norman Hall

Phone:273-4305

Email:

algina@ufl.edu

To help me keep track of email messages, please include EDF 7412 and your name in thesubject line of any email message you send to me.

If you have not sent me an email in the past, please send me an email today so I cancreate a distribution list for the class.

Course Web Page: http://online.education.ufl.edu/course/view.php?id=4278
Office Hours: Tuesday 5:15-6:15 PM, Thursday 4:15-6:15 PM, and by appointment. (To set anappointment, send me an email indicating several times when you are free.) I usually cannot
make appointments Tuesday or Thursday before class.
Textbook
Kline, R. B. (2010). Principles and practices of structural equation modeling. (3rd Ed.).
New York: Guilford Publications, Inc.
Program Manuals
Muthén, L.K. and Muthén, B.O. (1998-2010). Mplus User’s Guide. Sixth Edition. Los
Angeles, CA: Muthén & Muthén. Can be downloaded at
http://www.statmodel.com/ugexcerpts.shtml
Joreskog, K. G., and Sorbom, D. (1993). LISREL8: Structural equation modeling with
the SIMPLIS command language. Hillsdale, NJ: Erlbaum. EL †This book contains LISREL examples using the SIMPLIS command language and a reference tothe SIMPLIS command language. A copy has been placed on reserve in the Education library.
Other Readings

Bishop, R. M., & Bieschke, K. J. (1998). Applying social cognitive theory to interest in
research among counseling doctoral students: A path analysis. Journal of Counseling
Psychology, 45, 182-188. ACWS*

2Burch, M. A., Berko, E. H., & Haase, R. F. (1998). Shyness, masculine ideology, physicalattractiveness, and emotional inexpressiveness: testing a mediational model of men’sinterpersonal competence. Journal of Counseling Psychology, 45, 84-97. ACWS*Crocker, L. M. & Algina, J. (1986). Introduction to classical and modern test theory.
New York: Holt, Rinehart and Winston. EL †Cudek, R. (1988). Multiplicative models and MTMM matrices. Journal of EducationalStatistics, 13, 131-147. ACWS †Ewald, P. (1985). Developmental trends in the structure of self-concept. UF Dissertation.
LD1780 1985 .E94 EL*Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999).
Evaluating the use of exploratory factor analysis in psychological research. PsychologicalMethods, 4, 272-299. ACWS †Graham, J. W., & Hofer, S. M. (2000). Multiple imputation in multivariate research. In
Little, T. D., Schnabel, K. U., & Baumert, J. Modeling longitudinal and multilevel data.
Mahwah, NJ: Lawerence Erlbaum. AR †Grapentine, T. (2000). Path analysis vs. structural equation modeling. Marketing
Research, 12, 12-20. ACWS †Joreskog, K. G., & Yang, F. (1996). The Kenny-Judd model with interaction effects. In G.
A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues andtechniques. Mahwah, NJ: Erlbaum Associates. EL †MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor
analysis. Psychological Methods, 4, 84-89. ACWS †Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to thestudy of self-concept: first- and higher order factor models and their invariance across groups.
Psychological Bulletin, 97, 562-582. ACWS †Marsh, H. W. (1989). Confirmatory factor analyses of multitrait-multimethod data: many
problems and a few solutions. Applied Psychological Measurement, 13, 335-361. ACWS †Normandeau, S., & Guay, F. Preschool Behavior and first-Grade School Achievement:
The Mediational Role of Cognitive Self-Control. (1998). Journal of Educational Psychology,90, 111-121. ACWS*Ofir, C., Reddy, S.K., & Bechtel, G.G. (1987). Are semantic responses scales equivalent?
Multivariate Behavioral Research, 22, 21-38. ACWS*

3
Olson, V. & Bergman, L. R. (1977). A longitudinal factor model for studying change inability structure. Multivariate Behavioral Research, 12, 221-241. ACWS †Schneider, B., White, S. S., & Paul, M. C. (1998). Linking Service Climate and Customer
Perceptions of Service Quality: Test of a Causal Model. Journal of Applied Psychology, 83, 150163. ACWS*
Skowron, E. A., & Friedlander, M. L. (1998). The differentiation of self inventory:
development and initial validation. Journal of Counseling Psychology, 45, 235-246. ACWS*Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detectcorrelated and predictors of individual change over time. Psychological Bulletin, 116, 363-381.
ACWS †Wothke, W. (2000). Longitudinal and multiplegroup modeling with missing data. In Little,T. D., Schnabel, K. U., & Baumert, J. Modeling longitudinal and multilevel data. Mahwah, NJ:
Lawerence Erlbaum. AR †AR Available on ARES
ACWS Available on Course Web Site.

EL On Reserve in Education Library* These papers provide illustrations of methods presented in class. They are provided sostudents can find more complete presentations of substantive examples than is possible in class.
† Methodological paper or book.
SEMNETSEMNET is a listserve on structural equation modeling. The SEMNET archives(http://bama.ua.edu/archives/semnet.html) can provide useful information about structuralequation modeling.

4Topics and Readings
A. Confirmatory and exploratory factor analysis

A. Single-factor models
B. Scale indeterminancy
C. Two-factor models, rotational equivalence, and identification
D. S-factor models
E. Exploratory factor analysis
Kline, Chapter 5, pp. 112-118, Chapter 8, 189-210 and 214-222, Chapter 9, pp. 230-244
and 245-248, and Chapter 6, pp.124-131, 137-144, and pp.146-149; Joreskog and Sorbom,
Chapter 1, pp. 15-27, Chapters 3, 4, 5, and 6; Muthén and Muthén, Example 5.1 and Example
4.1; Skowron and Friedlander; Crocker and Algina; Grapentine; Fabrigar et al.; MacCallum etal.
B. Simultaneous equation models for observed variables
A. Recursive models
B. Goodness of fit tests, goodness of fit indices, and modification indicesC. Nonrecursive models
D. Identification
Kline, Chapter 5, pp.103-111, Chapter 7, pp. 160-169, and Chapter 6, pp.132-137 andpp.146-149; Joreskog and Sorbom, Chapter 1, pp. 1-12, Chapters 3, 4, 5, and 6; Muthén andMuthén, Example 3.11; Bishop and Bieschke.

C. Simultaneous equation models for latent variables (hybrid models)A. Effect of measurement error
B. Measurement models and structural models
C. Single indicators
D. Identification
Kline, Chapter 5, pp. 118-120, Chapter 10, pp. 265-280 and 289-284 and Chapter 6,pp.144-149 ; Joreskog and Sorbom, Chapter 1, pp. 28-43, Chapters 3, 4, 5, and 6; and Examples
5.11-5.13; Burch, M. A., Berko, E. H., & Haase, R. F.; Grapentine
D. Advanced topics

A. Dichotomous and ordinal variables
Joreskog and Sorbom, 44-50; Muthén and Muthén, Examples 5.2, 5.3. 4.2, 3.12, 3.13 and3.14
C. Missing Data
Graham and Hofer; Wothke

5
D. Alternative methods of estimation and robust analysis
B. Parallel measurements, essentially parallel measurements, and congeneric measurements
Ofir, Reddy, and Bechtel;
E. Multisample analysis of covariance matrices
Kline, Section 7.8; Joreskog and Sorbom, 51-66
F. Multitrait-multimethod analysis
Kline, Section 7.8; Cudek, Marsh
G. Second-order factor analysis
Marsh
H. Longitudinal methods
Olson and Bergman; Willet and Sayer
I. Multisample analysis of covariance matrices and means
Class AttendanceStudents are expected to attend class unless they have a valid excuse for missing a class.
Examples of valid excuses are illness, attendance at professional conferences, and the like. The
advanced topics are not covered by an exam. Nevertheless, students are expected to attend classwhen those topics are covered. Failure to do so without a valid excuse will result in a decreasein the final grade by one grade category (e.g., an A will become an A-; an A- will become a B+;
and so forth).
Requirements
Exams

There will be an exam after Topic 1 and after Topics 2 and 3. The exam will take place on theTuesday in the week after the topic was completed. As a result you will have between 4 and 7
days to prepare for each exam. A list of study questions will be distributed for each exam.
Data analysis problems

You will receive three data analysis problems, one for each of the first three topics. The problem
for the first topic will be due the second class period after the exam for the first topic is taken.
The problem for the second topic will be due the second class period after second topic isconcluded. The problem for the third topic will be due the second class period after the exam forthe second and third topics are taken. Note that the order of the problems you receive is notnecessarily the same as the order in which the topics are covered in class.
Paper

There are four options for the paper. If a student in the Program in Research and Evaluation
Methodology takes the first option, the study must use models that were not covered in class.
A. Design, conduct, and report a structural equation modeling analysis. With two
exceptions the data used in this project cannot come from a conference paper, dissertation,
thesis, unpublished, or published study. The exceptions are for data from a study in which (a)6structural equation modeling or regression analysis was not used or (b) structural equation
modeling structural equation modeling was used but you propose a radically different model
than the one used in the document. The report should be in the form of a paper prepared forsubmission to a journal and should be in APA style for (or in a style used in journals in yourfield).
I will evaluate the paper by using criteria typically used to judge papers submitted for
publication.
1. Is there a good rationale for the research? (Fitting models for no apparent purpose will countagainst the paper as will failure to fit models that are needed to answer the researchquestions).
2. Is the data collection clearly and adequately described?
3. Are the models appropriate models in light of the research questions?
4. Are the results presented clearly and in appropriate detail?
5. Are the results interpreted correctly?
6. Are the conclusions clearly justified by the results?
Students who elect option A should append to the paper the programs, and results thereof, used
to conduct analyses that are reported in the paper.
Here are two references that may be helpful in writing your paper:
Hoyle, R.H. & Panter, A.T. (1995). Writing about structural equation models. In R.H.
Hoyle (Ed.), Structural equation modeling. Conceptions, issues and application, (pp. 158-177).
London: Sage Publications.
Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation
Modeling, 7(3), 461-483.
Please note that using structural equation modeling methods is unlike using otherstatistical methods. There is no guarantee that the model you propose will fit the data. In
fact there is no guarantee that the estimation procedure will converge for the model you
propose. You may have to make changes to the model and this may take substantial timeand effort on your part. Do not leave the data analysis to the last few days before the paperis due.
If your model is a hybrid model, then by Thursday, November 3, 2011, you must
submit results showing that your measurement model fits adequately.

B. Write a literature review on a methodological issue in structural equation modeling.

7

The culminating section for a literature review must be either
1. A rationale for and design of a study to answer methodological problem that emerges
in the literature review; or
2. A guide to practice with regard to the methodological issue.
The latter would comprise decisions that need to be in an analysis, and criteria for making thosedecisions. Preparing a guide for practice should be undertaken only if the decisions and criteria
are sufficiently complex to make the guide a contribution to the literature on multivariateanalysis.
The criteria for evaluating the literature review section will be adequacy of coverage ofthe literature and the accuracy, clarity, and integration of the review. With regard to the last
three criteria, you must give sufficient detail to permit the reader to know what were thepurposes, methods and results of each study. Equally important, you must describe consistencies
and inconsistencies that emerge across the papers.
A guide to practice will be evaluated by the criteria of importance, clarity, andcorrectness.
A proposed research study will be evaluated in terms of clarity and quality of therationale and of the design.
C. Present and illustrate a structural equation modeling method that has not beenpresented in class. The presentation should, at a minimum, explain the models that are used andthe examples should include instruction about how to implement the models in a structural
equation modeling program.

Many articles and chapters have been published that present and illustrate SEM methods.
If you take option C, it is not sufficient to merely summarize an article or chapter. I expect thepresentation to be in your own words.
With three exceptions the data used in this project cannot come from a conference paper,
dissertation, thesis, unpublished, or published study. The exceptions are for data from a study inwhich (a) structural equation modeling or regression analysis was not used, (b) structural
equation modeling structural equation modeling was used but you propose a radically different
model than the one used in the document, or (c) the software code is not provided in thedocument and cannot be obtained from the authors or anyone else who subsequently used thedata.
Students who elect option C should append to the paper the programs, and results thereof, used
to conduct analyses that are reported in the paper.
D. Make a comparison of several (two or more) structural equation modeling methods

8that can be used to analyze a given data set. The presentation should, at a minimum, explain themodels that are used and the examples should include instruction about how to implement themodels in a structural equation modeling program.
The didactic presentation should explain the models that are used and the example should
include instruction as to how to implement the methods in appropriate software.
There are published articles and chapters have that present and compare SEM methods.
If you take option D, it is not sufficient to merely summarize an article or chapter. I expect thedidactic presentation to be in your own words.
With three exceptions the data used in this project cannot come from a conference paper,
dissertation, thesis, unpublished, or published study. The exceptions are for data from a study inwhich (a) structural equation modeling or regression analysis was not used, (b) structural
equation modeling structural equation modeling was used but you propose a radically different
model than the one used in the document, or (c) the software code is not provided in thedocument and cannot be obtained from the authors or anyone else who subsequently used thedata.
Students who elect option D should append to the paper the programs, and results
thereof, used to conduct analyses that are reported in the paper.
The paper is due on Friday, December 9, 2011 by 5:00 PM.
Paper Proposal

You must submit a brief proposal for your paper by Thursday, October 6, 2011.
Because the proposal is due before I complete coverage of the first three topics, doing thefollowing develop may help you understand the applications of SEM that are covered in the first
three topics:
1. Read Chapter 5 in Kline.
2. Read the papers on which your exercises are based.
It may be helpful for you to talk with me about your paper before you submit the proposal.
Proposals for a structural equation modeling analysis (Option A) must include (a) the samplesize, (b) for each instrument used in data collection, report the number of items and the numberof scale points on the items (c), for other methods of data collection (e.g., a brain scan), briefly
describe the nature of the data produced by the method, (d) a path diagram for the model ormodels to be estimated in the study, and (e) a rationale for the model. If you propose using datafrom a conference paper, dissertation, thesis, unpublished, or published study, submit a copy of
the relevant document with your proposal.
I will respond to your proposal by (a) approving it, (b) asking for clarification, or (c)asking for a new proposal. In the event of (b) I will expect your response by the class period

9following the date on which I return the proposal. In the event of (c) I will expect your responseby the fourth class period following the data on which I return the proposal. Getting a proposal
approved sometimes takes several cycles.
Late proposals will be accepted as will revisions that do not meet the time lines presented above.
However, late proposals and resubmissions may delay your work on the project and as a result
have a negative impact on the grade awarded to your paper.
Late Data Analysis Problems and Papers

Papers submitted after Friday, December 9, 2011 will be graded last and may result in an I
grade for the course.
Late data analysis problems and papers will receive grade penalty according to thefollowing table:

Number ofDays Late(or partthereof)
1
2
3
4
5
6
7
8
9
10
11

A

A-

B+

AB+
B
BC+
C
CD+
D
DE

B+
B
BC+
C
CD+
DDE

B
BC+
C
CD+
DDEGrade for Work submitted on Time
BBC+
C
C-BC+
C
CD+
DDEC+
C
CD+
DDECCD+
DDECD+
DDE

D+
DDE

D+

D

D-DDE

DE

E

Submission of Work

Data analysis problems, the paper proposal, and papers must be submitted electronically at
http://online.education.ufl.edu/course/view.php?id=4278. A data analysis problem must be
received by 3:00 on the due date or it will be considered late.

10Grading

All work will be scored by using the following 23-point scale:
Score Grade
21-22
A
19-20
A17-18 B+
15-16
B
13-14
B11-12 C+9-10C
7-8
C5-6
D+
3-4
D
1-2
D0E
An overall exam score will be obtained by averaging the individual exam scores. An overall dataanalysis score obtained by averaging the individual data analysis exercise score. A total scorewill be obtained by averaging the overall exam score, the data analysis score, and the score on
your paper.
Academic Honesty
Students are expected to do their own work on exercises that are submitted for a grade.
You should not solicit help with your data analysis problems from other people (includingstudents and faculty), nor should you provide help to other students. Soliciting help
includes discussing with other people (including students and faculty) what the appropriate
approach to an exercise might be.
If you need to discuss a data analysis problem you should talk with me. Be advised
however that if your question is about the correct analysis for an exercise I will usually require
you to phrase your question about analysis in general and not about the specific data analysis
problem. If you know the correct analysis and have a question about a computer program orformula to implement the analysis, I will usually answer the question directly.
You are expected not to cheat on examinations.
In the event a violation of standards of academic honesty occurs, I will contact the Officeof Student Conduct and Conflict Resolution to determine if it is the first violation. If not, I willturn the matter over to the Student Conduct and Conflict Resolution office. For a first offense
the exercise or the exam will receive a grade of zero.

11
Students with Disabilities

Students with disabilities who need reasonable modifications to complete assignments
successfully and otherwise satisfy course criteria are encouraged to meet with the instructor as
early in the course as possible to identify and plan specific accommodations. Students must
supply a letter from the UF Office for Students with Disabilities to assist in planning
modifications.
7412.11.doc