Program

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Predicting Panel Attrition in Longitudinal HRQoL Surveys during the COVID-19 Pandemic in the US

Speaker(s)

Yu T1, Chen J2, Gu NY3, Hay JW1, Gong CL4
1University of Southern California, Los Angeles, CA, USA, 2University of Southern California, LOS ANGELES, CA, USA, 3NYG Technologies, Santa Clarita, CA, USA, 4Children's Hospital Los Angeles, Los Angeles, CA, USA

Presentation Documents

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 online during the pandemic using Amazon MTurk. Variables included respondents’ demographics, medical history, socioeconomic status, COVID-19 experience, behavioral change, employment change, financial spending change, and health-related quality of life (HRQoL). Results were compared to US pre-pandemic norms. Measures that predicted attrition in the following wave were identified via logistic regression with stepwise selection.

Results: 1,467 out of 2,734 wave 1 respondents participated in wave 2 and, 964 out of 2,454 wave 2 respondents participated in wave 3. Younger age group, Hispanic origin (wave 1: p<0.001; wave 2: p=0.001), and higher self-rated survey difficulty (wave 1: p=0.002; wave 2: p<0.001) at waves 1 and 2 consistently predicted attrition in the following wave, respectively. COVID-19 experience, employment status, and limited physical activities were commonly observed factors contributed to attrition while specific measures change to reflect the most concerning matter at the time. In addition, mental health, average hours worked per day (p=0.004), and COVID-19 impact on work productivity (p<0.001) at wave 1 were found to be correlated with a higher attrition rate at wave 2. Support of social distancing (p=0.032), being Republican (p<0.001), and having just enough money to make ends meet (p=0.003) were some of the remaining key characteristics that predicted attrition at wave 3.

Conclusions: Attritions found in this longitudinal COVID-19 panel survey was not at random. Besides commonly identified demographic factors that contribute to panel attrition, COVID-19 presented new challenges to address sample biases by correlating attrition with additional factors in a constantly evolving environment.

Code

MSR29

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference, Missing Data, PRO & Related Methods, Survey Methods

Disease

No Additional Disease & Conditions/Specialized Treatment Areas