Last year, in 2017, The Behaviormetric Society conducted a symposium entitled “Behaviormetrika and Dr.Chikio Hayashi—Celebrating his 100 years from his birthday—.” This essay was revised and enlarged edition of my lecture at that symposium. In this essay, I tried to describe and analyze why Dr.Hayashi's methods, including “Idea of Quantification” and “Quantification theories” were so widely accepted and highly evaluated in many fields of academic societies, and influenced many areas of human societies. On group of “Quantification Theories”, the author has opinions that “Quantification Theory Family Ⅳ and Family Ⅵ” are specially unique and superb, hence contribute to policy decisions and social life in many ways. So, the author discussed on these two models, introducing several interesting research examples. On the naming for “Quantification theories” by myself, Use and Up-use of naming were discussed, which made clear Dr.Hayashi's intentions of developing his theories and methods. Finally, the author picking up especially MDA-OR and MDA-UO by Dr.Hayashi, tried to discuss the unique and useful models of MDS, Multi-Dimensional-Scaling.
Chikio Hayashi had paid his abiding respect to Kinji Mizuno with whom he had been endeavoring for over fourty years to elucidate mechanisms of complicated human behaviors by means of statistical methods. Investigation of research activities of Mizuno leads to the clarification of Science of Data by Hayashi as well as to further development of behaviormetrics. Contributions of Mizuno to behaviormetrics, however, has not sufficiently been known. His efforts are presented to survey researches including the Japanese national character by statistical surveys, disaster preparedness education for school children and other studies of human behaviors. The appreciations to Mizuno by Hayashi himself are also shown, which were addressed at the Memorial Party of late Professor Kinji Mizuno in 2000.
In the 1950s, Chikio Hayashi developed an election prediction model for Japan by using sampling theory and face-to-face surveys. This model is built on the following steps: (1) defining the support rate for each candidate; (2) estimating the ratio of votes garnered by each candidate; (3) using Hayashi’s quantification method type 1 and cor- recting the difference between the value estimated from the third order regression and the ratio of votes; and (4) calculating the winning probability for each candidate by us- ing quantification method type 2 or the margin of error for the estimated ratio of votes. In the 1990s, the telephone replaced the face-to-face survey mode, providing changes to Hayashi’s election prediction model. We discuss the reasons for these improvements.
Chikio HAYASHI has made great achievements in the development of marketing research and data analysis methods in Japan. After considering Chikio HAYASHI's human qualities (Logos and Pathos) and research attitude, describe the influence on sampling method and terminology.
Chikio Hayashi has conducted a variety of medical research throughout the wholecourse of his life and made many remarkable achievements in the field of medicine. Dur-ing his long carrier, he always noticed: “In clinical medicine we, the researchers, have tokeep in mind that we are not merely dealing with numbers or things, but we are dealingwith human life which is a part of society. They are people, patients who suffer fromdiseases. Therefore, in statistical analysis of clinical data, researchers have to considerthat these data were taken from human life.” In this paper, we have tried to followDr. Hayashi’s footprints and focus on his initial interest in clinical medical researchin the early 1950’s to the establishment of his methodology, “data science” in the late20th century. First, we reviewed the times he engaged in medical studies along withother specialists of medicine. Secondly, we reviewed the times he rebuilt the concepts of“risk factors” which has been used traditionally in clinical medicine from the patients’point of view. Lastly, we shed light on how Dr. Hayashi’s methodology, “data science”is suitable for dealing with complex problems in clinical medicine.
Quality of life (QOL) is a subjective assessment and is included among patient-re-ported outcome (PRO) studies. QOL is as important for cancer patients as objectiveassessments such as survival and response rates. However, PRO studies are more diffi-cult to conduct than are studies to assess objective data. Regarding QOL studies whichare important among PRO studies, first we describe QOL, which Dr. Hayashi promotedaiming to improve. Then we describe the methods of assessing QOL. Finally, we in-troduce a novel self-monitoring QOL intervention. In the methods of assessing QOL,Minimal clinically important difference (MCID) and response shift are major challengesamong the methods of assessing QOL. MCID enables recognition of the patient’s per-ceptions, as well as the clinical outcomes. It is important to consider response shift wheninterpreting actual change of QOL. In the introduction of novel self-monitoring QOLintervention, QOL assessments are often seen as being for research purposes, thoughQOL self-monitoring should also be implemented in daily clinical routines. QOL as-sessment is often used as an outcome measure in clinical trials, and cancer diagnosisand treatments affect patients’ QOL. However, medical personnel may not sufficientlyunderstand patients’ problems, including QOL. In advancing the study of QOL, it is afuture task to address how QOL assessment information is to be fed back to patients.
In this article, we focus on Saikaku’s posthumous works. Saikaku Ihara (c. 1642～93) is a fiction writer of the Genroku period (1688～1704) in Japan. His researchershave tried to identify his works but problems continue to exist. It remains unclearwhich works were really written by Saikaku especially his posthumous works. This pa-per examines the author of his posthumous works using Random Forests, Boosting andBagging. First, we examined Saikaku and Dansui’s works. A unigram of Japanese par-ticles is the best variable in our research. Among these algorithm, the preferred orderof classification accuracy rate is found to be Random Forests > AdaBoost > Bagging.Then, we examined his posthumous works using the usefulness 9 futures that we gotthe first analysis. It was found that from the result that most chapters are classifiedunder Saikaku, whereas some chapters are classified under Dansui. From what has beendiscussed above, we can conclude that Saikaku’s posthumous works are more likely tobe Saikaku’s than Dansui’s. The result supports the assumption that Dansui relativelyedit the Saikaku’s draft with sufficient accuracy rate however our result also providespotential to has become increasing Dansui’s editing throughout five years.