Keith Markus' Urban Sprawl: http://members.aol.com/kmarkus


PSYC U80103.JJ4
Advanced Quantitative Methods: 
Structural Equation Modeling
Course Information
Links
Syllabus
 Inter University Consortium for Political and Social Research
Schedule
Comprehensive R Archive Network
Homework Assignments
CUNY Blackboard Login

Site Map

Syllabus
 
Fall 2011
  

Time:  Wednesday 6:30-8:30 PM
Room:  2437N (2nd floor), John Jay North Hall, 445 W59th Street
Office Hours:  Tuesdays 2 PM to 3 PM and Wednesday 4 PM to 5 PM (It usually works best to email me).

Contact Information:
Dr. Keith A. Markus
kmarkus@aol.com
212-237-8784
Room 2127N
Psychology Department, John Jay College

Teaching Assistant:
Wen Gu
wgu@jjay.cuny.edu

Course Description: 

The course will provide a general introduction to the use of structural equation modeling in empirical research.  The course will pay special attention to criminal justice applications, although it will provide an appropriate introduction for applications in any social or behavioral science.  The course will cover path analysis, confirmatory factor analysis, and structural equation models with latent variables, including some useful special cases.  The coverage will include appropriate research design, model specification, parameter estimation, assessment of model fit, and model interpretation.  The treatment of these topics will emphasize practical application.  The course will also introduce the use of at least one software package.

Course Objectives:
1. Provide a survey of introductory topics in SEM.
2. Provide a foundation for advanced topics in SEM.
3. Provide experience with conducting SEM analyses.
4. Provide cognitive inoculation against the most common errors in applying SEM.

Text Book:
    Kline, R. B. (2011).  Principles and practice of structural equation modeling (3rd ed.).  New York:  Guilford. (The publisher's page links to electronic supplements to accompany the book.)

Additional Reading:
    Freedman, D. A. (1991). Statistical models and shoe leather. Sociological Methodology, 21, 291-313
    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.
    Markus, K. A. (2006) Structural Equation Modeling.  In S. G. Rogelberg (Ed.), The Encyclopedia of Industrial and Organizational Psychology, (pp 773 - 776).  Thousand Oaks, CA:  Sage Publications.
    Markus, K. A. (2010). Structural Equations and Causal Explanations: Some Challenges for Causal SEM. Structural Equation Modeling, 17, 654-676.
    Rogers, J. L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65, 1-12.

Handouts (posted on Blackboard):
    SEM glossary
    SEM matrix algebra basics

Required Software:

R with sem package and lavaan package: R is a powerful open-source free statistics package that runs very efficiently (even on a PDA) but requires a little adjustment for those accustomed to point and click statistical environments. The two packages do not come with the base installation and must be added after you install R. It will not be necessary to master R in order to use the packages for the class. See the package web pages for additional documentation beyond what is provided in R help files. If you are completely new to R, you may want to follow along with the sample session provided at the end of An Introduction to R just to get used to the R environment. This document offers a useful introduction to R, although it covers a great deal of material no needed for this course.

Installation:
1. Point your web browser to the Comprehensive R Archive Network (CRAN).
2. From the sidebar menu on the left, near the top, click Mirrors and select something geographically close (e.g., Pennsylvania). The same page will reload from a closer server.
3. Select Windows (if that is your operating system), if you use an Apple computer, your version of R differs somewhat and I am not familiar with it.
4. Click base. Then download and run the newest version installation file (currently R-2.9.1-win32.exe). Further installation instructions are provided on the CRAN web page.
5. Once installation is compete, start R. You will see a window with a '>' prompt. At the prompt you may type the following command to test the installation.

> demo(graphics)

You will be prompted to hit Enter several times as you move through the demo. A series of graphs should appear in a separate window inside the R window if R has been installed correctly.

6. On the Packages menu in R, select Install Packages. You will be prompted with a list of mirror sites that opens in a separate window. Again, pick something close (e.g., USA PA or USA PA2).
7. Momentarily, you will be prompted with a list of packages in a window similar to the mirror site window that you just used. Click sem. (Very close to the bottom of the list.)
8. After some brief chugging, you should have a message in your main R window indicating that the sem package installed correctly.
9. You can test the installation by typing the following command at the R prompt.

>library(sem)
>?sem

This should open a new window outside of the main R window with a help file on the sem() function. At the top, it should say General Structural Equation Models in large blue letters.
10. Return to the R console window where you type commands. At the prompt, enter the following command. When prompted, choose not to save the workspace image. This will close R.

>q()

Repeat steps 6-9 for the lavaan package. Use library(lavaan) and ?lavaan to test the installation.

Why, one might wonder, not use popular commercially available SEM programs such as LISREL, EQS, AMOS or Mplus? Because they are very expensive and free student versions typically only have the ability to run very limited models. Once you learn to use the free packages above, you will be in a better position to evaluate which SEM software you might want to purchase. You will also have a sufficient foundation to make the adjustment to alternative SEM software.

Blackboard Access: Access to Blackboard is an essential part of this course. Course materials will be distributed through Blackboard and I will use Blackboard to send you email. If you have any difficulty accessing the Graduate Center Blackboard system, please resolve those difficulties as soon as possible.

Examinations:  The examinations will not be cumulative but later material will always presuppose a familiarity with prior material.  Content of the examinations will reflect the reading. Examinations will emphasize your ability to reason using statistical principles studied in the course. Examinations comprise four online modules. Each student will receive a random sample of items to be completed before the due date on Blackboard. The course contains four examination modules,each covering roughly one quarter of the material.

Homework:  You will need to run examples using R and turn in printed output to demonstrate that you have done this.  As such, you need to have a PC capable of running R, access to the Internet, and a printer.  Homework will generally involve small tasks.  However, as with any other new skill, give yourself plenty of extra time to get confused, muck around by trial and error, and eventually figure out what you did wrong. (Running the Mac version of R is not recommended because it differs from the Windows version and I cannot answer questions about it.)

Turn in homework assignments at the beginning of class on the days noted on the schedule.  The assignments may not make sense to you until you cover the material to which they refer. The specific assignments will appear on Blackboard.

Grading:  Each of the four examination modules is worth 15% of your total grade.  That leaves 40% for the homework assignments.  Letter grades will be assigned as indicated below.
 
 

Letter Grade
Percent Grade
A
92-100
A-
84-91
B+
76-83
B
68-75
B-
60-67
C+
52-59
C
44-51
C-
36-43
F
0-35

Special Needs:
To request accommodations please contact the Office of the Vice President for Student Affairs (Room 7301 Graduate Center; (212) 817-7400). Information about accommodations can be found in the Graduate Center Student Handbook 05-06, pp. 51-52).

Academic Honesty:    
The Graduate Center of The City University of New York is committed to the highest standards of academic honesty. Acts of academic dishonesty include—but are not limited to—plagiarism, (in drafts, outlines, and examinations, as well as final papers), cheating, bribery, academic fraud, sabotage of research materials, the sale of academic papers, and the falsification of records. An individual who engages in these or related activities or who knowingly aids another who engages in them is acting in an academically dishonest manner and will be subject to disciplinary action in accordance with the bylaws and procedures of The Graduate Center and the Board of Trustees of The City University of New York.  

Each member of the academic community is expected to give full, fair, and formal credit to any and all sources that have contributed to the formulation of ideas, methods, interpretations, and findings. The absence of such formal credit is an affirmation representing that the work is fully the writer’s. The term “sources” includes, but is not limited to, published or unpublished materials, lectures and lecture notes, computer programs, mathematical and other symbolic formulations, course papers, examinations, theses, dissertations, and comments offered in class or informal discussions, and includes electronic media. The representation that such work of another person is the writer’s own is plagiarism.

Care must be taken to document the source of any ideas or arguments. If the actual words of a source are used, they must appear within quotation marks. In cases that are unclear, the writer must take due care to avoid plagiarism.

The source should be cited whenever:
(a) a text is quoted verbatim
(b) data gathered by another are presented in diagrams or tables
(c) the results of a study done by another are used
(d) the work or intellectual effort of another is paraphrased by the writer

    Because the intent to deceive is not a necessary element in plagiarism, careful note taking and record keeping are essential in order to avoid unintentional plagiarism.

    For additional information, please consult “Avoiding and Detecting Plagiarism,” available in the Office of the Vice President for Student Affairs, the Provost’s Office, or at http://web.gc.cuny.edu/provost/pdf/AvoidingPlagiarism.pdf.

(From The Graduate Center Student Handbook 05-06, pp. 36-37)


Top
Schedule
Date
Reading Assignments Due
Examination and Homework Assignments Due
Week1: W 8/31
Chapter 1: Introduction. Markus (2006).
Handout: SEM glossary.
Overview of SEM and course.  Theories and models.  Installing R.

Week 2: W 9/7

Chapter 2:  Fundamental Concepts.


Week 3: W 9/14

Chapter 3:  Data Preparation.

Homework Assignment 1 (HA1).
Week 4: W 9/21
Chapter 4: Computer Tools.
Handout: SEM Matrix Algebra Basics.
HA2
(No classes W 9/28)


Week 5: W 10/5
Chapter 5: Specification. Test Module 1 (weeks 1-4). HA3
Week 6: W 10/12
Chapter 6:  Identification (all readings include appendices). HA4
Week 7: W 10/19
Chapter 7:  Estimation.
Week 8: W 10/26
Chapter 8:  Hypothesis Testing. Test Module 2 (weeks 5-7). HA5
Week 9: W 11/2
Freedman (1991), Hayduk et al. (2003), Markus (2010). Rogers (2010).
HA6
Week 10: W 11/9
Chapter 9:  Measurement Models and Confirmatory
Factor Analysis.
HA7
Week 11: W 11/16
Chapter 10:  Structural Regression Models. Test Module 3 (weeks 8-10). HA8
Week 12: W 11/23
(Class will meet)
Chapter 11:  Mean structures and latent growth models. HA9
Week 13: W 11/30

Chapter 12: Interaction Effects and Multilevel SEM. HA10
Week 14: W 12/7
Chapter 13: How to fool yourself with SEM.
Finals Week: W 12/21
(W 12/14 is a reading day)

Test Module 4 (weeks 11-14).


Top
Created January 28, 2008
Migrated 17 August 2011
Updated 21 August 2011
This page was created using Mozilla SeaMonkey v.2.0 and is best viewed using a Mozilla web browser.