Who Stays and Who Leaves? Examining Sample-Source Effect on Attrition in a Longitudinal Panel Survey

Term Start:

June 1, 2026

Term End:

May 31, 2027

Budget:

$200,000

Keywords:

Longitudinal studies, Surveys, Travel Behavior

Thrust Area(s):

Data Collection Mechanisms, Data Modeling and Analytic Tools

University Lead:

Arizona State University

Researcher(s):

Ram Pendyala; Irfan Batur

Longitudinal panel surveys are a cornerstone of travel behavior research, enabling measurement of behavioral change over time and facilitating before-and-after studies of policy interventions. Despite their scientific value, panel surveys suffer from a well-documented challenge: attrition. When respondents exit the panel non-randomly between different waves of longitudinal surveys, the resulting stayer sample becomes biased, thereby undermining both sample size and representativeness. While prior literature has examined how individual socio-demographic characteristics influence panel retention, little attention has been paid to whether the initial recruitment channel through which respondents enter a panel survey shapes their propensity to remain across multiple waves. This gap limits the ability of transportation researchers and survey practitioners to design longitudinal studies that yield reliable, representative data. This project aims to examine whether recruitment strategy significantly influences panel survey retention, even after controlling for socio-economic, demographic, and attitudinal factors, and to identify which recruitment approach yields the highest retention rates. The study will use data from the COVID Future Survey, a three-wave nationwide longitudinal panel survey conducted between April 2020 and November 2021. Wave 1 recruited 8,385 valid respondents through three distinct channels: convenience sampling, mass email outreach, and a commercial online survey panel. Of the original sample, 22.5% responded to all three waves, 11.1% responded to Waves 1 and 2 only, and 66.4% responded only to Wave 1. The project will formulate and estimate a Generalized Heterogeneous Data Model (GHDM) that jointly treats recruitment strategy membership and panel survey retention as endogenous outcomes, accounting for correlated latent attitudinal constructs and socio-demographic characteristics. Latent constructs to be incorporated include Risk Perception, Work-from-Home Propensity, and Virtual Activity Perception, specified through exploratory and confirmatory factor analyses to capture shared unobserved heterogeneity that simultaneously influences recruitment channel membership and panel retention propensity. The insights derived from this project are expected to provide actionable guidance for transportation survey practitioners on recruitment strategy selection, improve the reliability and representativeness of longitudinal travel behavior data, and strengthen the data foundations underlying travel demand models and transportation planning decisions.

Project scope

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