Understanding and dealing with unit nonresponse during and post survey data collection

Abstract

Nonresponse in sample surveys is a longstanding concern among social researchers and survey methodologists. In addition to potential biases in point estimates, nonresponse can result in inflation of the variances of such estimates. This thesis focuses on understanding and dealing with unit nonresponse in sample surveys during and post data collection. In particular it looks at modelling the process leading to nonresponse using call record data; developing weighting adjustments for clustered nonresponse; and investigating variance estimation methods in the presence of nonresponse. During data collection, effective interviewer calling behaviours are critical in achieving contact and subsequent cooperation. Recent developments in the survey data collection process have led to the collection of so-called paradata, which greatly extend the basic information on interviewer calls. The first part of the thesis develops multilevel models based on a particular type of paradata, call record data and interviewer observations, to predict the likelihood of contact and cooperation conditioning on household and interviewer characteristics. The research is based on the UK 2001 Census Link Study dataset. The results have implications for survey practice and, among others, inform the design of effective interviewer calling strategies, including responsive survey designs. Post-survey estimation methods to adjust and account for nonresponse, such as weighting methods, include inverse probability weighting and generalized raking estimation. The second part of the thesis investigates alternative inverse probability weighted estimators for clustered nonresponse through a simulation study. Results from an empirical application using data from the Expenditure and Food Survey 2001 are presented. It also discusses three forms of generalized raking estimator in the presence of nonresponse. Weighting methods might result in increased variability in the weights and thereby lower the precision of the survey estimates. This thesis explores alternative forms of linearization and replication variance estimators for generalized raking estimators under nonresponse that allow for variation in the weights.

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