Using Paradata to Improve Survey Data Quality 2
|Convenor||Professor Volker Stocké (University of Kassel, Germany )|
|Coordinator 1||Professor Jochen Mayerl (TU Kaiserslautern, Germany)|
|Coordinator 2||Dr Oliver Lipps (Swiss Centre of Expertise in the Social Sciences (FORS), Lausanne, Switzerland)|
Although survey length was shown to have an impact on data quality and costs, researchers are often uncertain about the length of their survey. The present study investigates the influence of question properties and respondent characteristics on item-level response times in web surveys. By using client-side response times of the GESIS Online Panel Pilot, a probability-based online panel, the majority of findings from previous research can be replicated. Beyond replication, the analysis indicates that respondents speed up across the waves of the panel survey and that questionnaire navigation paradata can explain variation in response times.
We propose an approach that uses response times as prior information about the distribution of errors when answers to sensitive question are the subject of interest. By applying different prior distributions about the nature of errors while answering sensitive item questions, we are able to shed light on the open questions about the cognitive answering process while avoiding the use of complex and reactive randomization strategies to investigate sensitive topics.
The aim of the paper is to test moderation effects of cognitive accessibility of the respective target information in respondents’ memory concerning a) moderation of attitude-behavior consistency, and b) moderation of the occurrence of response effects. The paper compares two alternative indicators of cognitive accessibility: self-reported response certainty and the time needed to answer the question.
A second issue addressed in this paper is how response certainties and response latencies should be transformed prior to data analysis.
Statistical analyses of data of two surveys include multiple group comparison and interaction effects in linear as well as logistic regression.
We study bias from nonobservation in a survey with a sample drawn from an address-based population register. While the primary survey mode was the landline, households with no matched telephone number received a personal visit. We distinguish bias from (telephone) undercoverage, noncontact, and noncooperation.
The strongest composition bias of the telephone sample is due to undercoverage. In the combined telephone/face-to-face sample, bias from noncooperation reduces the advantage of adding the face-to-face mode. We give recommendations on the prioritization of person groups or who in the telephone sample should rather receive a personal visit.