Predicting panel attrition in longitudinal HRQoL surveys during the COVID-19 pandemic in the US.

ABSTRACT:

Background

Online longitudinal surveys may be subject to potential biases due to sample attrition. This study was designed to identify potential predictors of attrition using a longitudinal panel survey collected during the COVID-19 pandemic.

Methods

Three waves of data were collected using Amazon Mechanical Turk (MTurk), an online crowd-sourced platform. For each wave, the study sample was collected by referencing a US national representative sample distribution of age, gender, and race, based on US census data. Variables included respondents' demographics, medical history, socioeconomic status, COVID-19 experience, changes of health behavior, productivity, and health-related quality of life (HRQoL). Results were compared to pre-pandemic US norms. Measures that predicted at

SUBMITTER: Yu T 

PROVIDER: S-EPMC9258760 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature