Modernizing Measurement Models: 

How Sexual Scientists Can Get More Out of their Psychometric Data

John K. Sakaluk, Ph.D.

Western University

 Overview: 

Historically confined by a narrow “tried and true” selection of analytic techniques, the field of applied statistics for analyzing psychometric data has dramatically expanded in the last decade. As a multidisciplinary field, sexual science sometimes lags beyond adoption and understanding of emerging techniques generated in hub disciplines like psychology. The purpose of this day-long workshop is therefore to introduce attendees to new psychometric possibilities (and contrasting these against traditional practices) and highlight low-barrier open source analytic solutions. 

Analytic scripts (in R) of didactic examples using an open-access dataset will be provided; it is not necessary for attendees to bring their own data. While previous experience with R is not strictly necessary, some exposure/familiarity with it will benefit attendees. R for Data Science is an excellent Open Access resource for those needing a quick introduction/refresher. 

Schedule of Topics

• Situating measurement in the “big picture” of quantitative sexual science

• A Crash-Course Review of Traditional and Contemporary Psychometric Theory and Measurement Models

• Updated approaches to model selection in exploratory factor analysis

• Updated approaches to model selection in confirmatory factor analysis

• Updated approaches to testing measurement invariance

• Diagnosing and debugging problems when confirmatory factor analysis models go awry

• Conceptual considerations for alternatives to traditional factor analysis models

• Empirically testing alternatives to traditional factor analysis models

To Register

You can register for this post-conference workshop during your conference registration.

Preparing for the Workshop

Dr. Sakaluk will provide more information to registrants shortly before the workshop in regards to what they will need to get the most out of the session.