Optimal Analysis of Qualitative Data with Illustrations
Sep 2024 · Cambridge Scholars Publishing
Ebook
274
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About this ebook
This is an introductory book on how to optimally analyze non-quantitative data, based on the author’s experiences over 60 years of research. The major message to the readers is that qualitative (non-quantitative) data are much more informative than quantitative data. This is good news for readers in applied areas of statistics such as those in the social sciences and marketing research, where qualitative data are everywhere. But how can one analyze qualitative data quantitatively and extract more information than from the sophisticated analysis of quantitative data? The key rests in illustrations of difficult topics in a way that anyone can understand. It is the author’s wish soon the use of AI will open a gate for simple means for optimal analysis of qualitative data, as illustrated throughout the book.
About the author
Shizuhiko Nishisato graduated from Hokkaido University (BA, MA) in Japan, and obtained his PhD from the University of North Carolina, USA. His first position was at McGill University, Montreal, Canada in 1966. After that, he moved to the Ontario Institute for Studies in Education, the University of Toronto, Canada, to teach, where he also served as Chair of the Department of Measurement and Evaluation from 1971-1976. In 2000, he retired as Professor Emeritus. He served as the Editor of Psychometrika (1991-95), President of the Psychometric Society, and a Fellow of both the American Statistical Association and the Japanese Classification Society. His numerous awards include that of the Distinguished Alumnus from the Psychology Alumni Association of the University of North Carolina. His specialty is quantification theory, in particular “dual scaling”. He published 9 books before his retirement and 9 books after retirement. In 2023, he published a Festschrift, entitled “Analysis of Categorical Data from Historical Perspectives: Essays in Honour of Shizuhiko Nishisato” (edited by Beh, Lombardo and Clavel).
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