Measurement, Scaling, and Dimensional Analysis

Instructor(s):

  • Adam Enders, University of Louisville

This workshop will be offered in an online video format.

This workshop will focus on several strategies for producing geometric representations of structure in data. These methods tend to be used for three main reasons:

(1) Data reduction. Typically, multiple indicators are combined to improve the quality (e.g., reliability) of measurement. For example, a researcher may want to combine individual responses across a set of survey questions which ask about a common topic. In this case, the objective may be to obtain more fine-grained resolution of respondents' attitudes on the topic than can be obtained from any single survey item.

(2) Evaluating dimensionality. How many distinct sources of variability underlie a set of empirical indicators? For example, a researcher may want to determine the evaluative criteria that respondents bring to bear on a given stimulus object. The objective may be to recover the "mental maps" that give rise to individual beliefs and attitudes.

(3) Measurement. Scaling methods are often used to assign numerical scores to aspects of empirical objects (which may be qualitative in nature). Here, the objective often is to obtain reliable interval-level variables that can be employed in subsequent statistical analyses.

Specific scaling methods to be covered in the course include summated rating scales, item response theory models, unfolding models, principal components analysis, factor analysis, multidimensional scaling, and correspondence analysis. In-class examples will rely on the R statistical computing environment. However, the scaling techniques covered in this course are also largely available in the other major statistical packages (e.g., Stata, SPSS, and SAS). No prior exposure to, or experience with, scaling methods is necessary; however, course participants should be familiar (and comfortable) with multiple regression analysis.

Fee and Registration: This course is part of the first four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: scaling, measurement theory, factor analysis, principal components analysis, cluster analysis, Likert, Guttman, unfolding techniques, Mokken scaling, biplot

Course Sections

Section 1

Location: Online -- Video format,

Date(s): June 22 - July 17

Time: 1:00 PM - 3:00 PM

Instructor(s):

  • Adam Enders, University of Louisville

Syllabus: