From Cross-Sectional to Longitudinal: The Impact of Sampling Strategies on Measuring Mobility Choices

Term Start:

June 1, 2024

Term End:

May 31, 2026

Budget:

$200,000

Keywords:

Equity, Sampling Strategy, Survey Data, Weighting

Thrust Area(s):

Data Collection Mechanisms, Equity and Understanding User Needs

University Lead:

Arizona State University

Researcher(s):

Ram Pendyala; Irfan Batur

The transportation profession has long relied on surveys as a main source of data. These surveys are used across a broad range of applications, including but not limited to, travel demand forecasting, travel behavior analysis, policy evaluation, environmental impact assessment, equity analysis, and economic evaluation. Despite the increasing accessibility of passive data collection methods, such as cell phone tracking and location-based services, surveys remain invaluable sources for gaining deep insights into the transportation landscape. These comprehensive data sets form the backbone of various models, evaluations, and planning efforts in transportation. The efficacy and credibility of these models and planning efforts are dependent on the assumption that survey data are reasonably representative of the population under study. However, the representativeness of a survey is intrinsically linked to which sampling strategies were used in recruiting and targeting respondents.

Employing effective sampling strategies is not just about ensuring data quality; it is also a matter of cost-effectiveness and efficiency. For example, a targeted web-based survey of students may be combined with a traditional phone-based household survey to better represent younger age groups. Different sampling strategies serve different purposes. While convenience sampling may be quick and economical, it can introduce biases. Purchasing email lists targets specific segments but may lack broader representativeness. Similarly, online panels, such as those offered by Qualtrics, often yield samples of professional survey-takers, which might not adequately mirror the larger population as they miss less frequent survey participants. These complexities raise important questions: Do these varied sampling methods introduce biases that result in fundamentally different samples, affecting both the socio-demographic profiles of respondents and their mobility patterns? Even more so, do respondents recruited through different sampling strategies exhibit similar evolutions in their mobility choices over time?

This study addresses these questions by leveraging a longitudinal data set collected through three different sampling methods across three waves during the COVID-19 pandemic. While existing studies have occasionally touched on this issue, they have mostly relied on cross-sectional surveys, limiting their findings to a single point in time. By employing a longitudinal data set collected over three waves during the COVID-19 pandemic, this study not only assesses the representativeness and biases associated with different sampling methods but also investigates how these samples evolve over time in terms of mobility choices. This dual focus provides a more comprehensive understanding of the role of sampling strategies, thereby enriching both current research and shaping future directions in data collection methods within the realm of travel behavior research.

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