Analysing Quantitative Data. Raymond A Kent
Analysing Quantitative Data
Variable-based and Case-based Approaches to Non-experimental Datasets
Raymond Kent
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IMB SPSS statistics screen images (Figures 2.2, 2.3, 2.6, 2.11 and 3.1): Reprinted courtesy of International Business Machines Corporation, © International Business Machines Corporation.
© Raymond Kent 2015
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Library of Congress Control Number: 2014947234
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ISBN 978-1-4462-7340-1
ISBN 978-1-4462-7341-8 (pbk)
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About the Author
Raymond Kentis now retired, but was Senior Lecturer in Marketing at the University of Stirling, UK. He has published on topics as diverse as product range policy, marketing communications with sensitive groups, private trading on the Internet, improving email responses and the use of fuzzy logic in data analysis. This is his eighth book; earlier books have been in the areas of the history of sociological research, survey data analysis and marketing research. After each book, he says it will be his last. We’ll see …
Companion Website
This book is supported by a brand new companion website (https://study.sagepub.com/kent). The website offers a wide range of free teaching and learning resources, including:
chapter summaries;
recommended reading lists;
free access to SAGE journal articles;
answers to exercises from the book;
PowerPoint slides for each chapter.
In addition, there is:
an overview of data analysis packages;
an introduction to SPSS;
weblinks to alternative datasets.
Preface
This text concentrates on the analysis of quantitative data rather than their generation, but takes a broad view of what ‘data analysis’ entails. It is more – much more – than simply ‘doing statistics’. It means understanding the data – how they were constructed in the first place, what kind of data they are and the ways in which they are structured. It includes the various processes involved in preparing and transforming the data ready for analysis, creating a data matrix, and then going through the processes of describing, interpreting, relating, evaluating, explaining, applying and presenting the results. Any dataset, furthermore, needs to be approached holistically, that is, as a complete, self-contained entity, set in the context of the objectives for which the research that generated the dataset was designed to achieve and with a well-rounded view of what all the evidence is saying. This may mean combining hard and soft research data with informal individual experience, knowledge and intuition and seeing this all within the context of the ‘bigger picture’.
Data analysis means getting the most out of a dataset, approaching it in several different ways so that the data tell the complete story. All too often, researchers go to a lot of trouble to construct their data, only to limit the analysis by throwing at such data a few statistics with which the researcher happens to be familiar or believes are appropriate to the data. Students in the social sciences normally undertake a course or module in research methods that includes an explanation of how a selected range of statistics is calculated. However, when approaching a dataset students will often limit themselves to asking ‘OK, so what statistics should I use here?’, instead of thinking about ‘What are my research objectives?’, ‘What do I want my analysis to show or investigate?’ or ‘What are the different ways in which the analysis could be approached?’
Analysing data, furthermore, is seldom a one-off enterprise accomplished in a single session. Analysis is a dialogue between ideas and evidence: researchers move backwards and forwards between constructed data and the objectives for which the research was undertaken, often on different occasions or at different phases of the research.
After studying this book, readers should be able to:
understand how datasets can be handled by being taken through, in a kind of ‘master class’, how a given dataset can be analysed in several different ways;
question basic statistical concepts and compare the results of using different techniques;
think about the choices they need to make in the analysis of non-experimental quantitative data by considering the dataset as a whole;
understand that quantitative case-based methods offer an alternative to or an addition to the more standard variable-based methods.
The subtitle of this book indicates that its focus is on non-experimental datasets.