103,188 research outputs found
Alternative approaches to multilevel modelling of survey non-contact and refusal
No full textWe review three alternative approaches to modelling survey non-contact and refusal: multinomial, sequential, and sample selection (bivariate probit) models. We then propose a multilevel extension of the sample selection model to allow for both interviewer effects and dependency between non-contact and refusal rates at the household and interviewer level. All methods are applied and compared in an analysis of household non-response in the United Kingdom, using a data set with unusually rich information on both respondents and non-respondents from six major surveys. After controlling for household characteristics, there is little evidence of residual correlation between the unobserved characteristics affecting non-contact and refusal propensities at either the household or the interviewer level. We also find that the estimated coefficients of the multinomial and sequential models are surprisingly similar, which further investigation via a simulation study suggests is due to non-contact and refusal having largely different predictor
Doorstep interactions and interviewer effects on the process leading to cooperation or refusal
No full textThis 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
Alternative approaches to multilevel modelling of survey noncontact and refusal
No full textWe review three alternative approaches to modelling survey noncontact and refusal: multinomial, sequential and sample selection (bivariate probit) models. We then propose a multilevel extension of the sample selection model to allow for both interviewer effects and dependency between noncontact and refusal rates at the household and interviewer level. All methods are applied and compared in an analysis of household nonresponse in the UK, using a dataset with unusually rich information on both respondents and nonrespondents from six major surveys. After controlling for household characteristics, there is little evidence of residual correlation between the unobserved characteristics affecting noncontact and refusal propensities at either the household or the interviewer level. We also find that the estimated coefficients of the multinomial and sequential models are surprisingly similar, which further investigation via a simulation study suggests is due to there being little overlap between the predictors of noncontact and refusal
Imputing nonignorable missing data using a data augmentation approach: an application to the estimation of hourly pay distributions
No full textAnalysing the probability of attrition in a longitudinal survey
No full textThis paper aims to analyse predictors of attrition in a major UK longitudinal survey, the Family and Children Study, and thus to contribute to a deeper understanding ofthe process and reasons for attrition as a social phenomenon. Multilevel modelling techniques are used to analyse attrition across several waves accounting for clustering of sample members within interviewers. The models are guided by current conceptual frameworks and theories of survey participation. The analysis also explores the role of the interviewer in gaining cooperation in a longitudinal study, in particular investigating effects of changes of interviewers across waves. An advantage of the data is that relatively rich information on both respondents and non-respondents is available from early waves and from interviewer observation
Analysing interviewer call record data to understand the process leading to cooperation or refusal
No full textAnalysing the process leading to cooperation or refusal using call record data: A multilevel multinomial modelling approach
No full textIn recent years, survey agencies have started to collect detailed call record data, including information on the timing and outcome of each interviewer call to a household. In interviewbased household surveys, effective interviewer calling behaviours are critical in achieving cooperation and reducing the likelihood of refusal. This paper aims to analyze interviewer call record data to inform the process leading to cooperation or refusal in face-to-face surveys. Of particular interest are the influences on the outcome of a call of interactions between the interviewer and householder and of time-varying characteristics of the call. A multilevel multinomial logistic regression approach is used in which the different possible outcomes at each call are modelled jointly
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