Engaging and informative, this book provides students and researchers with a pragmatic, new perspective on the process of collecting survey data. By proposing a post-positivist, interviewee-centred approach, it improves the quality and impact of survey data by emphasising the interaction between interviewer and interviewee. Extending the conventional methodology with contributions from linguistics, anthropology, cognitive studies and ethnomethodology, Gobo and Mauceri analyse the answering process in structured interviews built around questionnaires.

The following key areas are explored in detail: An historical overview of survey research; The process of preparing the survey and designing data collection; The methods of detecting bias and improving data quality; The strategies for combining quantitative and qualitative approaches; The survey within global and local contexts

Incorporating the work of experts in interpersonal and intercultural relations, this book offers readers an intriguing critical perspective on survey research.

Giampietro Gobo, Ph.D., is Professor of Methodology of Social Research and Evaluation Methods at the Department of Social and Political Studies - University of Milan. He has published over fifty articles in the areas of qualitative and quantitative methods. His books include Doing Ethnography (Sage 2008) and Qualitative Research Practice (Sage 2004, co-edited with C. Seale, J.F. Gubrium and D. Silverman). He is currently engaged in projects in the area of workplace studies.

Sergio Mauceri, Ph.D., is Lecturer in Methodology of Social Sciences and teaches Quantitative and Qualitative Strategies of Social Research at the Department of Communication and Social Research - University of Rome ‘La Sapienza’. He has published several books and articles on data quality in survey research, mixed strategies, ethnic prejudice, multicultural cohabitation, delay in the transition to adulthood, worker well-being in call centres and homophobia.

Deviant Case Analysis: Improving (A Posteriori) Data Quality

Deviant case analysis (DCA) is a research strategy originally proposed by Lazarsfeld and promoted within the Columbia School from the 1940s to the 1960s; unfortunately without subsequently achieving full acceptance within the area of survey research in general. Unexpectedly however, the importance of DCA in the fine-tuning of interpretive models has been acknowledged in contemporary qualitative research (Lincoln and Guba 1985; Creswell 1998; Patton 1999, 2001; Silverman 2000; Corbin and Strauss 2008; Gobo 2008).

DCA refers to searching outside the data matrix for clues able to shed light on why statistical analysis of the data has revealed anomalous responses that either deviate from research hypotheses or give rise to contradictory classificatory results. Unlike procedures for checking the validity ...

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