A Multilevel Multinomial Analysis of Interviewer Effects on Various Components of Unit Nonresponse

This note demonstrates an application of a multilevel multinomial model. We use that model to analyse interviewer effects on various components of unit nonresponse to a face-to-face survey: refusals and noncontacts. The model allows for an analysis of these two interviewer effects and a possible connection between both at the same time. Our results show that both the chances for refusals as for noncontacts are subject to interviewer effects. We also find some evidence for a relation between both interviewer effects: interviewers who obtain more refusals are also more likely to report noncontacts. That result is however at least partly dependent on an outlier, an interviewer with a high number of refusals and noncontacts.     

A Method to Evaluate Mode Effects on the Mean and Variance of a Continuous Variable in Mixed‐Mode Surveys

Les enquêtes à mode de collecte mixte, dans lesquelles le choix du mode de réponse est laissé aux personnes interrogées, sont de plus en plus répandues. Cette possibilité du choix du mode de réponse peut induire une confusion entre deux sortes d'effets : les effets de sélection et les effets de mesure. L'estimation séparée de ces deux effets est impossible à partir des seules données d'une enquête à mode de collecte mixte. Dans cet article, nous montrons comment la même estimation devient possible à partir d'une comparaison entre les résultats d'une enquête à mode de collecte mixte et ceux d'un enquête à mode de collecte unique basées sur le même questionnaire. La méthode que nous proposons permet d'estimer l'effet du mode de réponse sur la moyenne et la variance d'une variable de réponse continue. Elle est illustrée par l'estimation des effets du mode de réponse sur six questions relatives aux opinions vis‐à‐vis des enquêtes.     

Evaluating,Comparing, Monitoring,and Improving Representativeness of Survey Response Through R‐Indicators and Partial R‐Indicators

Non‐response is a common source of error in many surveys. Because surveys often are costly instruments, quality‐cost trade‐offs play a continuing role in the design and analysis of surveys. The advances of telephone, computers, and Internet all had and still have considerable impact on the design of surveys. Recently, a strong focus on methods for survey data collection monitoring and tailoring has emerged as a new paradigm to efficiently reduce non‐response error. Paradata and adaptive survey designs are key words in these new developments. Prerequisites to evaluating, comparing, monitoring, and improving quality of survey response are a conceptual framework for representative survey response, indicators to measure deviations thereof, and indicators to identify subpopulations that need increased effort. In this paper, we present an overview of representativeness indicators or R‐indicators that are fit for these purposes. We give several examples and provide guidelines for their use in practice.     

The Impact of Respondent and Interviewer Characteristics on the Number of “No Opinion” Answers

In this article we analyse the number of no opinion answers to attitude items. We argue that that number can be considered as a count variable that should be analysed using Poisson regression or negative binomial regression. Since we're interested in the effect of both respondent and interviewer characteristics on the number of no opinion's we use multilevel analysis that takes into account the hierarchical structure of the data. As a consequence multilevel Poisson regression and multilevel negative binomial regression are applied. Our analysis shows that answering no opinion is related to some sociodemographic respondent characteristics. In addition we find a significant interviewer effect, but we are not able to explain that effect in terms of interviewer variables.     

The effect of the elapsed time between the initial refusal and conversion contact on conversion success: evidence from the 2nd round of the European social survey

The hold period between the initial refusal and the follow-up conversion attempt may be used as a strategic tool to improve conversion rates. We argue that longer hold periods result in better conversion rates, particularly among hard-to-convert refusals. In this article we will first investigate to what extend and in which manner survey authorities exert this elapsed time as an active survey instrument. Contact sheet data show that different national survey coordinators deploy different strategies with regard to this hold period. In the Netherlands, intentional survey tactics can be disentangled, whereas Swiss contact data show that the length of the hold period is a mere result other fieldwork conditions. Actual conversion success seems to be consistently dependent upon the hold period between the initial refusal and the follow-up contact.