Indiana University Bloomington

Sociology S651
Multivariate Analysis Of Social Science Data

Contact: Scott Long (
Offered: Fall, 2015
Class Time: 9:30-10:45AM
Class Days: Tu, Th
Capacity: 20
Pre-Requisites: Course in linear regression; course in regression for categorical outcomes
Algebra Required: yes
Calculus Required: concepts used
Contact Person for Authorization: Scott Long (
Instructor: Scott Long
Days Per Week Offered: MW
Syllabus: No Syllabus Avaliable
Keywords: measurement; multivariate; factors analysis; IRT; LCA
Description: This class deals with techniques referred to broadly as multivariate methods. We focuses on how these methods can be used to transform a set of related variables into a smaller number of more fundamental measures. This is sometimes referred to as "scaling". Examples of how these methods might be used include: multiple tests scores used to create a measure of ability; using variables for exposure to cultural events to create a scale of cultural capital; using questions about interactions with people having a mental illness to create a measure of social distance. Creating scales is often a critical first step in data analysis. Too often a simple summated scale, presented along with Crohnbach's alpha, is all that is done, possibly obscuring as much as it reveals. After reviewing methods such as multidimensional scaling, principal components, and cluster analysis, we focus on latent structure analysis (LSA). LSA includes exploratory factor analysis, confirmatory factor analysis, latent class analysis, item response models, and structural equation modeling. Assignment will involve exercises applying these models to real data.
Books: Analysis of Multivariate Social Science Data, Second Edition by Bartholomew, Steele, Moustaki, and Galbraith
Substantive Orientation: social science
Statistical Orientation: applied
Applied/Theoretical: Applied
Formal Computing Lab: Yes
Software: Stata, MPlus
How Software is Used: data analysis
Problem Sets: yes
Data Analysis: yes
Presentation: no
Exams: no