دانلود کتاب رفتارسنجی: رویکردهای کمی به رفتار انسان – کمیت و تقارن بهینه

The major role of quantification theory is to transform qualitative data into quantitative data so that our familiar arithmetic operations can be used to analyze the data. The desideratum of this transformation is to introduce some optimal characteristics into the transforms so that we may hopefully grasp complex information contained in the original data in the most effective way. Quantification theory has existed for roughly 100 years and many researchers have been involved in its development. During these years, a large number of books and research papers have been published on the topic. Therefore, some readers may wonder why this book now? The main reason is to present a new perspective of quantification theory in terms of “symmetry” which appears throughout its mathematics and geometry, and most importantly in the context of symmetry combined with optimization. “Symmetry” appears inmathematics, biology, arts and our everyday life, all occupying some status of desirable, fine and supreme characters. Indeed, symmetry is also a paramount, underlying principle for quantification theory. This new look will help dissolve some ice surrounding the theory and hopefully bring its attractiveness closer to the readers. Be sure that symmetry and optimization will indeed play the role of an ironclad framework for multidimensional quantification. In the current book, we will startwith the reasonwhy optimal quantification should be pursued, using examples. Then, we discuss input data for quantification and the objectives of quantification. This is to make sure what kinds of data are appropriate for quantification and what is the goal of quantification. We will then proceed from simple steps for quantification to handling complex problems step by step. This book is geared for typical university courses on data analysis. With enough mathematics and numerical examples, the current book will attract the attention of not only eager students, but also many seasoned researchers. Recently I read a book written by my university classmate, Mr. Hideshi Seki, a noted historian in Japan, who published a book on the pioneer days of Hokkaido, Japan, based on his series of lectures. Through a cleaver arrangement of the questions from his students, the entire book became an excellent introduction to the history. I was so impressed with the style of Mr. Seki’s book which even an outsider could easily grasp and appreciate the history. His book motivated me to write this book, immediately after finishing a 2021 Springer book with three other friends,mentioned in the next paragraph. During the past few years, I felt uneasy about one topic of quantification, joint graphical display. Thus, this topic became the central issue of my contribution to Nishisato, Beh, Lombardo and Clavel (2021). When the book was completed, however, Mr. Seki’s book made me wonder if I had explained quantification theory clear enough to the readers. This prompted me to write the current book. To worry about a little thing like this may be a sign of senior moments, but I still hope that some readers will find the current book interesting, useful and appealing enough to read. The book is not a sequel to Nishisato, Beh, Lombardo and Clavel’s ModernQuantification Theory: Joint Graphical Display and Alternatives, Singapore: Springer Nature (2021). As compared with this book, the current book is introductory, but the object is to elucidate further a very basic symmetric framework flourished in the early days of quantification theory. As such, I hope the book will serve well as a good companion book to the book by Nishisato, Beh, Lombardo and Clavel. The topics were arranged so that the entire spectrum of quantification theory is covered in an understandable way. May the book serve as a valuable addition to the quantification theory literature. Finally, I have to apologize to the readers about two personal matters. First, since my 1960 paper on factor analysis, time has passed rather quickly. To write a book based on my chosen career, it is natural to cite my own work perhaps too often. Should I appear terribly conceited or if you feel slighted by my not referring to your work equally extensively, please do accept my sincere apologies. Second, I took the liberty of acknowledging the friendship of many friends, including those from my childhood. This may be unusual, but please excuse me for that since it was due to my selfish and sentimental urge to do so.