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Researchers have become increasingly interested in better understanding the survey data collection process in interviewer-administered surveys. However, tools for analysing paradata capturing information about field processes, also called call record data, are still not yet fully explored. This paper introduces sequence analysis as a simple tool for investigating such data with the aim of better understanding and improving survey processes. A novel approach is to use sequence analysis within interviewers, which allows the identification of unusual interviewer calling behaviours, and may provide guidance on interviewer performance. Combining the technique with clustering, optimal matching and multidimensional scaling, the method offers a way of visualising, displaying and summarising complex call record data. The method is introduced to inform survey management and survey monitoring. Sequence analysis is applied to call record data from the UK Understanding Society survey. The findings inform further modelling of call record data to increase efficiency in call scheduling.

Using logistic and multilevel logistic modelling we examine non-response at the school and pupil level to the important educational achievement survey Programme for International Student Assessment (PISA) for England. The analysis exploits unusually rich auxiliary information on all schools and pupils sampled for PISA whether responding or not, including data from two large-scale administrative sources on pupils' results in national public exams, which correlate highly with the PISA target variable. Results show that characteristics associated with non-response differ between the school and pupil levels. The findings have important implications for the survey design of education data.

In 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.

Establishing contact is an important part of the response process and effective interviewer calling behaviours are critical in achieving contact and subsequent co-operation. The paper investigates best times of contact for different types of households and the influence of the interviewer on establishing contact. Recent developments in the survey data collection process have led to the collection of so-called field process data or paradata, which greatly extend the basic information on interviewer calls. The paper develops a multilevel discrete time event history model based on interviewer call record data to predict the likelihood of contact at each call. The results have implications for survey practice and inform the design of effective interviewer calling times, including responsive survey designs.

This 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 observations

Establishing contact is an important part of the survey response process and effective interviewer calling behaviours are critical in achieving contact and subsequent cooperation. This paper investigates best times of contact for different types of households and the influence of the interviewer on establishing contact. Recent developments in the survey data collection process have led to the collection of so-called field process or paradata, which greatly extend the basic information on interviewer calls. This paper develops a multilevel discrete time event history model based on interviewer call record data to predict the likelihood of contact at each call. The results have implications for survey practice and inform the design of effective interviewer calling times, including responsive survey designs.

Natural method use is often associated with high levels of unwanted births and induced abortions. This study investigates the correlates of natural method use in Moldova, a country with one of the highest proportions of natural contraceptive users in Europe. We hypothesize that economic and spatial disadvantage increase the reliance on natural methods whereas exposure to FP programs decreases the probability of natural method use. The analysis considers a sub-sample of 5860 sexually-active women from the 2005 Demographic and Health Survey. Results from multilevel multinomial models, controlling for relevant characteristics and data structure, show that economic disadvantage increases the probability of natural method use; but the overall effect is small. Higher FP media exposure reduces natural method use; however this effect attenuates with age. We conclude that FP efforts directed towards the poorest may have limited impact, but interventions targeted at older women could reduce the burden of unwanted pregnancies.

This 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.