Suchergebnisse

"The use of mobile devices such as smartphones and tablets for survey completion is growing rapidly, raising concerns regarding data quality in general, and nonresponse and measurement error in particular. We use the data from six online waves of the GESIS Panel, a probability-based mixed-mode panel representative of the German population to study whether the responses provided using tablets or smartphones differ on indicators of measurement and nonresponse errors from responses provided via personal computers or laptops. We follow an approach chosen by Lugtig and Toepoel (2015), using the following indicators of nonresponse error: item nonresponse, providing an answer to an open question; and the following indicators of measurement error: straightlining, number of characters in open questions, choice of left-aligned options in horizontal scales, and survey duration. Moreover, we extend the scope of past research by exploring whether data quality is a function of device-type or respondent-type characteristics using multilevel models. Overall, we find that responding with mobile devices is associated with a higher likelihood of measurement discrepancies compared to PC/laptop survey completion. For smartphone survey completion, the indicators of measurement and nonresponse error tend to be higher than for tablet completion. We find that most indicators of nonresponse and measurement error used in our analysis cannot be attributed to the respondent characteristics but are rather effects of mobile devices." (author's abstract)

In this article, we examine two types of item nonresponse in a face-to-face population survey: 'don't know' (DK) and 'item refusal' (REF). Based on the cognitive model of survey response, the theory of survey satisficing and previous research, we derive explanatory variables on three levels: question, respondent and interviewer characteristics. The results of our cross-classified model show that while the two levels question and respondents' characteristics affected both types of item nonresponse, interviewer characteristics affected only DK answers. Our results also confirm that DK and REF are substantially different item nonresponse types resulting from distinguishable disruptions of the cognitive response process. Since most results are in line with prior theoretical predictions, they suggest that survey practitioners are well-advised by continuing to follow the large body of practical guidance derived from the theories tested here.

Detailed and precise documentation is the key to scientific research and of particular importance for high-quality data collection. An important parameter for each survey is the response rate. It reflects how many persons initially invited to participate finally took part in a survey interview. In any case, every survey based on a probability sample should report its response rate, since it provides first and easily assessable information about the data collection process. The American Association for Public Opinion Research (AAPOR) has proposed Standard Definitions that include final disposition codes as guidelines for reporting of response rates. These definitions take various modes of data collection into account. However, the final disposition codes proposed by AAPOR are based on sampling and data collection practices in the context of the United States. To date, there is no clear guidance on how to adapt these definitions in a comparable manner for the German context. This survey guideline aims to close this information gap and offers operational definitions for the AAPOR final disposition codes that are applicable in the German context.