PDC18: A Didactic Introduction to Latent Variable Modeling in R

Session Type: Professional Development Course

Abstract

Latent variable modeling for the social and behavioral sciences has been expanding quickly to address diverse research questions about the growth of student skills, outcome differences in randomized control trials, and building better assessments. This course will introduce participants to latent variable modeling, the use of R for analyses, and models commonly used to explore education research–related questions. The main objective of the course is for participants to gain a working knowledge of several analyses using latent variable models, including factor analysis (exploratory and confirmatory), structural equation models, item response theory models, and latent growth and profile models. Instruction will be didactic, using a combination of lecture and hands-on examples to encourage understanding of the material and to prepare the learner to continue acquiring technical skills with R. Real data examples will be used to assist understanding. The target audience is graduate students or researchers new to R and/or to latent variable modeling. The prerequisite knowledge is a foundational understanding of multiple regression techniques. Participants are encouraged to bring a laptop as data sets and codes will be provided.

Sub Unit

• Professional Development and Training Committee

Instructors

• Brian F. French, Washington State University

• William Holmes Finch, Ball State University