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Introduction to Structural Equation Modeling



May 16, 2014 to June 13, 2014 November 07, 2014 to December 05, 2014

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Introduction to Structural Equation Modeling

taught by Randall Schumacker

Aim of Course:

Structural Equation Modeling (SEM) is a general statistical modeling technique to establish relationships among variables. A key feature of SEM is that observed variables are understood to represent a small number of "latent constructs" that cannot be directly measured, only inferred from the observed measured variables. This course covers the theory of SEM, and includes practical work with computer software and real data. It covers the key concepts in SEM - at the conclusion of the course students will be able to specify different forms of models, using observed, latent, dependent and independent variables. Student will be able to conduct confirmatory factor analysis, and diagram SEM models.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Preliminaries

  • LISREL software installation
  • Data entry and Data Edit issues
  • Correlation and Covariance Data Files

WEEK 2: Modeling

  • SEM Basics
  • Regression models
  • Diagramming Models
  • Path Analysis Models

WEEK 3: Measurement Models

  • Exploratory vs. Confirmatory factor analysis
  • Latent Variables
  • CFA models

WEEK 4: Developing Structural Equation Models

  • Combining Path and Factor Models
  • 5 Basic SEM steps
    • Model Specification
    • Model Identification
    • Model Estimation
    • Model Testing
    • Model Modification
  • Amos Audio/Video Presentation


Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

In addition to assigned readings, this course also has supplemental readings available online, and an exam.

Introduction to Structural Equation Modeling

Be sure you meet all of the minimum requirements before you register, click here to learn more.


May 16, 2014 to June 13, 2014 November 07, 2014 to December 05, 2014

Course Fee: $629

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)


Have you reviewed the REQUIREMENTS for this course?

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Introduction to Structural Equation Modeling

taught by Randall Schumacker

Who Should Take This Course:

Market researchers, educational researchers, sociologists and psychologists, political scientists, economists, and survey researchers.



These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

If you are unclear as to whether you have mastered the above requirements, try these placement tests.

Participants should have some familiarity with statistical modeling (e.g. regression) and the basics of educational measurement and assessment.

Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:
The course text is A Beginner's Guide to Structural Equation Modeling, 3rd edition, published April 2010, by Randall E. Schumacker and Richard Lomax.


The course will provide illustrations in LISREL, a programming environment. You can download a free student version of LISREL from the textbook website. The Introductory guide and user's manual are also available here. TA's can provide LISREL support if needed.

Course staff will not be available to illustrate or help with examples other than those included in the course. Students are encouraged to visit each software website to obtain the latest student versions of the software. Some, but not all software can be obtained on vendor websites. For LISREL a limited student edition is available at http://www.ssicentral.com/lisrel/student.html.

MAC users note: LISREL software is not available for Macintosh. Mac end users run the Windows editions of the software products using Virtual PC or VM on Power PC Macs (G series) or the Windows OS on Intel-based Macs.

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