RESPONDENT RECRUITMENT TO CONSECUTIVE TRAVEL SURVEYS: EXPLORING SAMPLE REPRESENTATIVENESS AND TRAVEL BEHAVIOR MODEL QUALITY USING SAMPLE SELECTION MODELS
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Declining survey response rates have increased the costs of travel survey recruitment. Recruiting respondents based on their expressed willingness to participate in future surveys, obtained from a preceding survey, is a potential solution but may exacerbate sample biases. In this thesis, we analyze self-selection biases of survey respondents recruited from the 2017 U.S. National Household Travel Survey (NHTS), who had agreed to be contacted again for follow-up surveys. We apply a probit with sample selection (PSS) model to analyze respondents’ willingness to participate in a follow-up survey and their actual response behavior once contacted. Results verify the existence of self-selection biases, which are related to survey burden, sociodemographic characteristics, travel behavior, and item non-response to sensitive variables. The PSS model is then validated using a hold-out sample and applied to the NHTS samples from various geographic regions to predict follow-up survey participation. Effect size indicators suggest that resulting samples may be most biased along age and education dimensions. We further summarized six model performance measures based on the PSS model structure. Lastly, we analyze the consequence of self-selection biases by assessing their influence on travel behavior models developed on the sample recruited through the proposed method. We recommend applying the sample selection model to correct for such biases when the data are available. Otherwise, sample weights should be applied when the unweighted sample would produce inconsistent coefficient estimates. However, if the Hausman test supports the consistency of the estimated parameters, unweighted regression models should be preferred to avoid inefficient estimates.