AI Article Synopsis
- Advances in digital technology have improved the collection of intensive longitudinal data, like ecological momentary assessments (EMAs), useful for studying behavior changes.
- The study emphasizes the necessity of accounting for multilevel structures in data during multiple imputation to handle missing values effectively.
- Using empirical data from a tobacco cessation study, it compares a multilevel multiple imputation approach to other methods and highlights the importance of distinguishing between participant- and study-initiated EMAs in understanding individuals' emotional responses and urges.
Article Abstract
Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10676222 | PMC |
| http://dx.doi.org/10.3389/fdgth.2023.1099517 | DOI Listing |
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