Keith Markus' Urban Sprawl:

CRJ U80200
Advanced Quantitative Methods: 
Structural Equation Modeling in Criminal Justice Applications
Course Information
 Inter University Consortium for Political and Social Research
Statistical Package for the Social Sciences
Homework Assignments
Comprehensive R Archive Network
  Black Board 6

Site Map

Fall 2009

Time:  Thursdays 6:30-8:30 PM
Room:  2437N, John Jay Noth Hall, 445 W59th Street
Office Hours:  Tuesday 4 PM to 5 PM (It usually works best to email me).

Contact Information:
Dr. Keith A. Markus
Room 2127N
Psychology Department, John Jay College

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.

Text Book:
    Kline, R. B. (2005).  Principles and practice of structural equation modeling (2nd ed).  New York:  Guilford (Get the errata page here.)

Additional Reading: I will post additional resources on Blackboard
Required Software:

Mx GUI:  Mx software for structural equation modeling with a graphic user interface (GUI). You can download this software for free from the Mx Home Page (  You can also use Mx at some campus computer labs.

1. Point your web browser to the Mx Home Page listed above.
2. Click Download and then Windows Mx Gui (again, if you use a different operating system, choose accordingly, but you are on your own).
3. Choose the Windows installation file (currently Mx Win9x/2K/NT version 2.5MB).
4. Follow the instructions on the web page.
5. Mx Gui should install with a manual in pdf format. If you cannot find it in your Mx folder (I have one PC where it is there but the file manager window does not show it although save as windows do), you can download the manual directly by clicking Documentation on the Mx Gui page and then selecting Manual and then the PDF manual link from the manual page.
6. Note: If you use Windows Vista, the help files internal to MxGui will not work. However, the manual is more useful and sufficient.
7. Mx Gui should show on your start menu as Mx32.
8. If you click the tool button in Mx that looks like a little path diagram, it should open a new window. Inside that window, you should be able to use the circle, box, and arrow tool buttons to draw a path diagram.
9. To exit Mx, simply click Exit from the bottom of the File menu.

R with SEM 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. We will not make use of most of the facilities available in R and will primarily only use the SEM package. This does not come with the base installation and must be added after you install R.

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.


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.


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. Mx GUI closely resembles some aspects of AMOS's GUI and 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.

Optional Software:

SPSS:  You will find it helpful to have access to SPSS for examining the data sets used in the course.  You can access the full version on campus or purchase any version for use at home.  It does not matter which operating system (Windows, Mac, etc.) that you use.  You may want to avoid the student version because it cannot handle large data sets.  Consider instead the slightly more expensive but heavily discounted SPSS Graduate pack which offers all the functions of the full version but only for graduate students.  You can also use any other statistics program that reads SPSS files. You can also manage data and compute descriptive statistics using R, Excel, SAS, STATA, or another statistics package. However, this course is not designed to teach how to do these things using alternative software. So, if you are most familiar with SPSS, you may wish to stick with it.

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.  You are allowed one 8.5 x 11 inch hand-written page of notes and a calculator to be used during each examination.  Examinations will emphasize your ability to reason using statistical principles studied in the course.

Homework:  You will need to run examples using Mx or R and turn in printed output to demonstrate that you have done this.  As such, you need to have a PC capable of running Mx and 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.

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 well appear on Blackboard.

Grading:  Each of the two examinations is worth 30% of your total grade.  That leaves 40% for the homework assignments.  Letter grades will be assigned as indicated below.

Letter Grade
Percent Grade

Reading Assignments Due
Homework Assignments Due
Th 9/3
Chapter 1: Introduction.

Overview of SEM and course.  Theories and models.  Installing Mx Gui and R.

Th 9/10
Chapter 2:  Basic statistical concepts: I. Correlation and regression.

Th 9/17
Chapter 3:  Basic statistical concepts:  II. Data preparation and screening.

Homework Assignment 1 (HA1).
Th 9/24
(Classes cancelled due to bed bug infestation at John Jay College)

Th 10/1
Chapter 4:  Core SEM techniques and software.

Th 10/8
Chapter 5:  Introduction to path analysis. (Note: all readings include chapter appendices.)
Th 10/15
Chapter 6:  Details of path analysis. (Appendix 6a is out of date, use bootstrap instead.)
Th 10/22
Midterm Examination.

Th 10/29
Chapter 7:  Measurement models and confirmatory factor analysis. HA5
Th 11/5
Chapter 8:  Models with structural and measurement components. HA6
Th 11/12
Chapter 9:  Nonrecursive structural models. HA7
Th 11/19
Chapter 10:  Mean structures and latent growth models. HA8
Th 12/3
(11/26 College Closed)
Chapter 11:  Multiple-sample SEM. HA9
Th 12/10

Chapter 12: How to fool yourself with SEM. HA10
Th 12/17
Chapter 13:  Other horizons.
Tu 12/22 (Fr 12/18 early-bird special)
Final Examination.


Homework Assignments

Homework asignments will be posted on Blackboard along with the required data sets.

Created January 28, 2008
Updated December 2, 2009
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