This course will examine the interface between the philosophy of science underlying theory testing and statistical methodologies for testing theories. We will explore measurement issues (classical test theory, exploratory and confirmatory factor analysis, and item response theory), statistical inference and hypothesis testing, path analysis and structural equations modeling from both an econometric perspective (that is, issues of exogeneity and instrumental variables) and the latent variable structural equations modeling perspective (LISREL-type models). Special attention will be given to the assessment of causality. Issues regarding experimental and quasi-experimental designs and analysis will be included. The course is designed to provide the student with a sound basis for conducting original research and with the background for studying more specialized and advanced techniques.
The course will consist of both lecture and discussion of assigned readings. A paper describing the analysis of new data or a re-analysis of existing data constitutes the primary requirement for the course. Some of the readings are available at http://rhowell.ba.ttu.edu under the heading “Readings for theory testing”. For further information, contact email@example.com.
Upon completion of the course, the students will be able to:
– recognize situations in which structural equation modeling may be useful,
– demonstrate a general understanding of structural equation modeling techniques,
– use structural equation modeling techniques in their research
– explain the limitations of structural equation modeling techniques, and
– critique articles that use structural equation modeling techniques.
Project – Students will critique a published article that uses structural equation modeling. The critique will focus on the analyses used in the article. It should discuss whether the use of structural equation modeling is appropriate/inappropriate. The critique should also include a discussion of the properties and development of the measures used in the study. In addition, the student will try to reproduce the analysis undertaken in the article. (Therefore, the article needs to include either a correlation matrix or a covariance matrix.)
Alternatively, students may perform structural equation modeling on a data set of their choice. Briefly summarize the rationale for the model, and write-up the results and conclusions in journal format.
Class participation and discussion 40%
Tentative Class Schedule CHECK WEEKLY FOR UPDATES!!
Week 1 Introduction
Exploring SEM Bring an article employing SEM for discussion
Week 3 Path Analysis
Models imply covariances
Testing fit Excel example
Week 4 Adding the measurement model Anderson and Gerbing article
Week 5 LISREL programming and output run ex56.ls8 from LISREL manual and bring output
Fit indices Week 6 Model modification
Week 8 Mean Structures
Ordinal Data Ordinal
Search for ‘Heckman selection bias’ and ‘propensity score matching’ & prepare to discuss
Revisit the article you discussed the second week of class. How do you feel about it now?