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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 attrition at different times of the pandemic were identified via logistic regression with stepwise selection. Results 1467 of 2734 wave 1 respondents participated in wave 2 and, 964 of 2454 wave 2 respondents participated in wave 3. Younger age group, Hispanic origin (p ≤ 0.001) and higher self-rated survey difficulty (p ≤ 0.002) consistently predicted attrition in the following wave. COVID-19 experience, employment, productivity, and limited physical activities were commonly observed variables correlated with attrition with specific measures varying by time periods. From wave 1, mental health conditions, average daily hours worked (p = 0.004), and COVID-19 impact on work productivity (p |
