Online Program

Latent Trait Shared Parameter Mixed-Models For Ecological Momentary Assessment Data

*John Cursio, The University of Chicago Medicine 
Donald Hedeker, University of Illinois at Chicago 

Keywords: Latent trait, shared parameter, item response theory, positive affect, negative affect

Latent trait shared parameter mixed-models (LTSPMM) for Ecological Momentary Assessment (EMA) data are developed in which data are collected in an intermittent fashion. Using Item Response Theory (IRT) models, a latent trait is used to represent the missingness mechanism and shared jointly with a mixed-model for longitudinal outcomes. Both one- and two-parameter LTSPMM are presented. These new models offer a unique way to analyze EMA data with many unique response patterns that cannot easily formed into latent classes.

Data from an EMA study involving high-school students' positive and negative affect are presented. The proposed models will estimate a latent trait that corresponds to the students' "ability" to respond to the prompting device. The latent trait was a significant predictor of both positive affect and negative affect outcomes. One-thousand simulations are performed to test the proposed models across different simulation scenarios. The proposed models have lower bias and increased efficiency compared to standard approaches. The new models offer a viable alternative to latent class pattern-mixture models previously used with intermittent missing data.