"Holiday Goodies" for Latent Class Analysis
Podcast: Latent Class Analysis (LCA) Part 1 - Common Questions about LCA
In our latest podcast, Methodology Center scientists Stephanie Lanza and Bethany Bray discuss common, practical issues that arise in latent class analysis (LCA). Issues discussed include selecting indicator variables, selecting a model, determining minimum sample size, finding LCA software, and getting started in LCA. This is the first in a two-part podcast; the next podcast will address some of our recent research on LCA.
Download the podcast.
Tutorial Video: Basic Features of PROC LCA
Are you new to PROC LCA? The SAS procedure for latent class analysis (LCA) developed by the Methodology Center is available for free. This video demonstrates the main features of the procedure including running an LCA model, using a grouping variable, specifying measurement invariance, running LCA with covariates, and specifying a reference class. The example in the video is based on Lanza, Collins, Lemmon, and Schafer (2007). Future videos will address other features of PROC LCA. If there are features you would like to see in a video, email email@example.com with your suggestions.
Watch the video.
Interest Group: Optimizing Behavioral Interventions
For those at Penn State, Linda Collins is organizing a discussion group on optimizing behavioral interventions that will meet in the spring semester. The purpose of the group is to bring intervention scientists and methodologists together to promote substantive and methodological research on optimizing behavioral interventions using approaches such as the multiphase optimization strategy (MOST), the sequential, multiple assignment, randomized trial (SMART), and related methods. The group will meet on a monthly basis (dates and times TBA).
Interested? Email firstname.lastname@example.org and ask to be added to the group’s email list.
Featured Articles: Building Interventions That Adapt to Meet Individuals' Needs
In adaptive interventions, the intervention options (such as the type, dosage, intensity, or content of the intervention) are individualized and adapted over time in response to the ongoing performance and changing needs of the participant. In recent years, investigators have become increasingly interested in obtaining empirical evidence that informs the construction of high-quality adaptive interventions, especially adaptive interventions that optimize long-term outcomes. Methodology Center scientists recently published two related papers in Psychological Methods in which they discuss experimental designs and data analysis methods specifically developed to help investigators construct adaptive interventions based on empirical evidence.
In the first, "Experimental Design and Primary Data Analysis Methods for Comparing Adaptive Interventions," Inbal Nahum-Shani, Daniel Almirall, Susan Murphy, and their colleagues describe the sequential, multiple assignment, randomized trial (SMART), an innovative experimental design that enables investigators to address critical questions concerning the sequencing and adaptation of an intervention over time. In the second article, "Q-Learning: A Data Analysis Method for Constructing Adaptive Interventions," the same authors discuss using Q-learning, a method drawn from computer science, to analyze data from SMART designs and to assess the quality (in terms of optimizing long-term outcome) of adaptive interventions.