10.6084/m9.figshare.7087064.v1
John Cursio
John
Cursio
Robin J. Mermelstein
Robin J.
Mermelstein
Donald Hedeker
Donald
Hedeker
Dataset for: Latent trait shared parameter mixed-models for missing ecological momentary assessment data
Wiley
2018
ecological momentary assessment
longitudinal data
latent trait
shared-parameter model
intermittent missing data
Statistics
Medicine
2018-10-31 11:37:20
Dataset
https://wiley.figshare.com/articles/dataset/Dataset_for_Latent_trait_shared_parameter_mixed-models_for_missing_ecological_momentary_assessment_data/7087064
Latent trait shared-parameter mixed-models (LTSPMM) for ecological momentary assessment (EMA) data containing missing values are developed in which data are collected in an intermittent manner. In such studies, data are often missing due to unanswered prompts. Using item response theory (IRT) models, a latent trait is used to represent the missing prompts and modeled jointly with a mixed-model for bivariate longitudinal outcomes. Both one- and two-parameter LTSPMMs are presented. These new models offer a unique way to analyze missing EMA data with many response patterns. Here, the proposed models represent missingness via a latent trait that corresponds to the students' "ability" to respond to the prompting device. Data containing more than 10,300 observations from an EMA study involving high-school students' positive and negative affect are presented. The latent trait representing missingness was a significant predictor of both positive affect and negative affect outcomes. The models are compared to a missing at random (MAR) mixed-model. A simulation study indicates that the proposed models can provide lower bias and increased efficiency compared to the standard MAR approach commonly used with intermittently missing longitudinal data.