This dissertation discusses three separate but related applications of paradata for survey design and analysis. Chapter 1 expands on the use of call record data for nonresponse investigation in face-to-face surveys. By focusing on the relatively underexplored analysis of longitudinal call record data in household surveys, it considers association between Wave 1 call record sequences and response outcomes in proceeding Waves (2, 3 and 4) of the UK Household Longitudinal Study (or Understanding Society). This chapter addresses the predictive power of predefined call record sequences observed in the baseline wave of this survey by comparing model estimates that employ not just this type of paradata but also more conventional predictors of nonresponse (like sociodemographic characteristics of issued households as well as auxiliary geographic information). Beyond finding associations and comparing model specifications, this analysis is primarily interested in informing response retention strategies for panel surveys based on the calling patterns of earlier waves. Chapter 2 is similarly concerned with field effort optimization. However, while Chapter 1 uses call record data as predictors of a given outcome of interest (namely future wave contact and cooperation), Chapter 2 proposes models to predict the calling effort inherent in the processes of contact and cooperation conditional on household and aggregate individual-level data (as well as lagged contact record data). Given the onerous fieldwork demands of household longitudinal surveys, the analysis of this chapter aims to inform data collection optimization by identifying predictors (especially those derived from paradata) of differential contactability, cooperation and overall field effort requirements in longitudinal context. The third and final chapter analyses different types of CAWI generated paradata to assess progress indicator (PI) effects on survey response quality. The data used for this analysis comes from an experiment designed by the author. This chapter seeks to further examine and develop standing theories of progress indicator effects on surveys by focusing on their impact on response quality while also expanding on the uses of web survey paradata and their applications for response quality assessment and respondent behaviour (including satisficing, as well as time and effort management).