| Autor: |
Liang M; Department of Statistics, Rice University., Koslovsky MD; Department of Statistics, Colorado State University., Hébert ET; Department of Health Promotion and Behavioral Sciences, University of Texas Health Science Center at Austin (UTHealth), School of Public Health., Kendzor DE; Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center., Businelle MS; Department of Statistics, Rice University., Vannucci M |
| Jazyk: |
angličtina |
| Zdroj: |
Psychological methods [Psychol Methods] 2023 Aug; Vol. 28 (4), pp. 880-894. Date of Electronic Publication: 2021 Dec 20. |
| DOI: |
10.1037/met0000433 |
| Abstrakt: |
Intensive longitudinal data collected with ecological momentary assessment methods capture information on participants' behaviors, feelings, and environment in near real-time. While these methods can reduce recall biases typically present in survey data, they may still suffer from other biases commonly found in self-reported data (e.g., measurement error and social desirability bias). To accommodate potential biases, we develop a Bayesian hidden Markov model to simultaneously identify risk factors for subjects transitioning between discrete latent states as well as risk factors potentially associated with them misreporting their true behaviors. We use simulated data to demonstrate how ignoring potential measurement error can negatively affect variable selection performance and estimation accuracy. We apply our proposed model to smartphone-based ecological momentary assessment data collected within a randomized controlled trial that evaluated the impact of incentivizing abstinence from cigarette smoking among socioeconomically disadvantaged adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved). |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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