Applied Measurement Modeling

Our motivating goal is to provide an interactive and engaging instructional experience that focuses on a large number of topics in the theory and application of contemporary measurement models. We strive to strike an equal balance between core concepts of the underlying statistical models along with the practical application and interpretation of measurement models and scoring fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to develop a real understanding of both foundational and advanced measurement models and to be able to thoughtfully and knowledgeably apply these models to their own data.

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, educational, and health sciences and other related fields. It is recommended, but not required, that participants have a working knowledge of the linear regression model. Participants who would like a more thorough refresher on linear regression prior to attending may wish to watch our (no cost) Linear Regression Playlist on YouTube.

Chapter 1: Introduction & History of Factor Analysis
1.1 Introduction to Workshop & Goals for the Week
1.2 Background and History of Factor Analysis
1.3 Latent Variables

Chapter 2: Principal Components Analysis
2.1 Motivation
2.2 Eigenvalues and Eigenvectors
2.3 Factor Enumeration & Rotation

Chapter 3: The Common Factor Model
3.1 Motivation
3.2 Maximum Likelihood Estimation
3.3 Extensions and Limitations

Chapter 4: Confirmatory Factor Analysis
4.1 Moving from EFA to CFA
4.2 Identification, Estimation, and Model Fit
4.3 Model Re-Specification and Non-Normality
4.4 CFA Approach to Classical Test Theory and Reliability
4.5 Scoring and Alternative Factor Structures

Chapter 5: Factor Analysis with Discrete Items
5.1 Discrete Item Responses
5.2 The Underlying Latent Variable Approach
5.3 Full Information Maximum Likelihood Estimation

Livestream lectures will begin at 10:00 a.m. and finish at approximately 3:30 p.m. Eastern Time (US) each day. There will be 10-minute morning and afternoon breaks and a one-hour lunch break, the exact times of which are determined during lecture. Local time zones within which the participant is connected must be adjusted to correspond to Eastern Time (US).

Pre-recorded software demonstrations for all measurement models are provided for self-paced learning. Software demonstrations vary depending on the model under consideration (not all software packages can estimate all models). We provide demonstrations in SPSS, SAS, and R for principal components analysis, SPSS, SAS, R, and Mplus for exploratory factor analysis, and R and Mplus for confirmatory factor analysis. Comprehensive demonstration notes, script, and data files are provided for all four programs.

While it is helpful to have some familiarity with one or more of these programs, this is not necessary and all necessary code will be provided assuming no prior knowledge. Note that R can be downloaded free-of-charge.

Patrick Curran and Gregory Hancock co-teach the workshop via Zoom and will alternate lecturing throughout the day, with recordings posted at the end of each day for those who might miss or wish to review any of the material. The lectures for the workshop are software-independent but demonstrations are provided in Mplus, R, SAS, and SPSS via pre-recorded videos.

Participants receive access to the livestream, one-year of access to recorded lecture and demonstration videos, downloadable complete PDF copies of the course notes and software demonstrations, as well as data and code for all examples. The PDFs are not time-limited and may be retained indefinitely but should not be distributed to others without obtaining prior permission.