Personality as a Predictor of Unit Nonresponse in Panel Data: An Analysis of an Internet-based Survey

General Information

Personality as a Predictor of Unit Nonresponse in Panel Data: An Analysis of an Internet-based Survey
Albert Cheng, Gema Zamarro, and Bart Orriens
Publication Type
Working paper
CESR-Schaeffer Working Paper
Unit nonresponse or attrition in panel data sets is often a source of nonrandom measurement error. Why certain individuals attrite from longitudinal studies and how to minimize this phenomenon have been examined by researchers. However, this research has typically focused on data sets collected via telephone, postal mail, or face-to-face interviews. Moreover, this research usually focuses on using demographic characteristics such as educational attainment or income to explain variation in the incidence of unit nonresponse. We make two contributions to the existing literature. First, we examine the incidence of unit nonresponse in an internet panel, a relatively new, and hence understudied, approach to gathering longitudinal data. Second, we hypothesize that personality traits, which typically remain unobserved and unmeasured in many data sets, affect the likelihood of unit nonresponse. Using data from an internet panel that includes self-reported measures of personality in its baseline survey, we find that conscientiousness and openness to experience predict the incidence of unit nonresponse in subsequent survey waves, even after controlling for cognitive ability and demographic characteristics that are usually available and used by researchers to correct for panel attrition. We also test the potential to use paradata as proxies for personality traits. Although we show that these proxies predict panel attrition in the same way as self-reported measures of personality traits, it is unclear to what extent they capture particular personality traits versus other individual circumstances related to future survey completion. Our results suggest that obtaining explicit measures of personality traits or finding better proxies for them are crucial to more fully address the potential bias that may arise as a result of panel attrition.