1,721,031 research outputs found
Doorstep interactions and interviewer effects on the process leading to cooperation or refusal
Full text linkThis article presents an analysis of interviewer effects on the process leading to cooperation or refusal in face-to-face surveys. The focus is on the interaction between the householder and the interviewer on the doorstep, including initial reactions from the householder, and interviewer characteristics, behaviors, and skills. In contrast to most previous research on interviewer effects, which analyzed final response behavior, the focus here is on the analysis of the process that leads to cooperation or refusal. Multilevel multinomial discrete-time event history modeling is used to examine jointly the different outcomes at each call, taking account of the influence of interviewer characteristics, call histories, and sample member characteristics. The study benefits from a rich data set comprising call record data (paradata) from several face-to-face surveys linked to interviewer observations, detailed interviewer information, and census records. The models have implications for survey practice and may be used in responsive survey designs to inform effective interviewer calling strategies
Modelling call record data using multilevel modelling: examples from cross-sectional and longitudinal surveys
No full textAn investigation of interviewer effects on household nonresponse in six UK government surveys
No full textImputation methods in the social sciences: a methodological review
No full textMissing data are often a problem in social science data. Imputation methods fill in the missing responses and lead, under certain conditions, to valid inference. This article reviews several imputation methods used in the social sciences and discusses advantages and disadvantages of these methods in practice. Simpler imputation methods as well as more advanced methods, such as fractional and multiple imputation, are considered. The paper introduces the reader new to the imputation literature to key ideas and methods. For those already familiar with imputation methods the paper highlights some new developments and clarifies some recent misconceptions in the use of imputation methods. The emphasis is on efficient hot deck imputation methods, implemented in either multiple or fractional imputation approaches. Software packages for using imputationmethods in practice are reviewed highlighting newer developments. The paper discusses an example from the social sciences in detail, applying several imputation methods to a missing earnings variable. The objective is to illustrate how to choose between methods in a real data example. A simulation study evaluates various imputation methods, including predictive mean matching, fractional and multiple imputation. Certain forms of fractional and multiple hot deck methods are found to perform well with regards to bias and efficiency of a point estimator and robustness against model misspecifications. Standard parametric imputation methods are not found adequate for the application considered
Correcting for measurement error using missing data methods: an example from the UK Labour Force Survey
No full textImputation methods for handling item-nonresponse in practice: methodological issues and recent debates
Full text linkNonresponse is a major problem often faced by social scientists when analysing survey data. A range exists to impute the missing responses but the choice between these methods may be difficult. This paper reviews advantages and disadvantages of a range of imputation methods and provides guidance on how to use such methods in practice. The paper introduces the reader new to the imputation literature to key ideas and methods. For those already familiar with imputation, the paper highlights some new developments and recent debates. The paper discusses an example from the social sciences, applying several imputation methods to a missing earnings variable. The objective is to illustrate in a real data example basic considerations when choosing between methods and to advise practitioners in the use of such methods
Effects of interviewer attitudes and behaviors on refusal in household surveys
Full text linkInterviewers play a crucial role in gaining cooperation from a sample unit. This paper aims to identify the interviewer characteristics that influence survey cooperation. Of principal interest to survey practitioners are interviewer attributes associated with higher cooperation rates, particularly among sample members whose characteristics are traditionally associated with a lower probability of response. Our data source is unusually rich, in that it contains extensive information on interviewers, including their attitudes and behaviors, which are linked to detailed information on both responding and nonresponding sample units. An important value of the data is that they permit examining a host of as yet unanswered questions about whether some interviewer attributes stimulate cooperation among some respondents but not others. In short, we investigate whether some sample units react favorably to certain interviewer characteristics. A multilevel cross-classified logistic model with random interviewer effects is used to account for clustering of households within interviewers, due to unmeasured interviewer attributes, and for the cross-classification of interviewers within areas. The model allows for statistical interactions between interviewer and household characteristics.We find that interviewer confidence and attitudes toward persuading reluctant respondents play an important role in explaining between-interviewer variation in refusal rates. We also find evidence of interaction effects between the interviewer and householder, for example with respect to gender and educational level, supporting the notion of similarity between interviewers and respondents generating higher cooperation. The results are discussed with respect to potential implications for survey practice and desig
Multilevel modelling of refusal and noncontact nonresponse in household surveys: evidence from six UK government surveys
No full textThis paper analyses household unit nonresponse and interviewer effects in six major UK government surveys using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of survey dependent and independent effects of household and interviewer characteristics, providing an empirical exploration of the leverage-salience theory. The analysis is based on the 2001 UK Census Link Study, a unique data source containing an unusually rich set of auxiliary variables, linking the response outcome of six surveys to census data, interviewer observation data and interviewer information, available for respondents and nonrespondents
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