The qualitative improvement of education is often discussed, and various approaches to realizing such improvements have been attempted in recent years. In the field of the qualitative improvement of commercial products, the statistical analysis of process data has a long history and questionnaire surveys for the qualitative improvement of such products are often performed. In this paper, we study the improvement of the quality of education from the standpoint of quality system engineering. However, the method for improving the quality of a commercial product cannot be easily applied to education. In the case of most products, it is comparatively easy to grasp the relationship between the product and the demand, because the persons who require the improvement of products and the improved products are not the same, and also products do not have the mind and heart. In the field of education, students who demand improved education are the same students who would benefit from this improvement, and they are intelligent, motivated, and have their own ways of working. Therefore, when the demand concerning the qualitative improvement of education is investigated, it is necessary to use a method that can clarify the relationship between the demands and characteristics of students. In this study, to analyze such a relationship, a questionnaire survey design is examined and enhanced, and two types of modeling are described and examined, and their effectiveness was confirmed.
Structural Equation Modeling has two problems : (1) the form of the structural part aRects the estimates of the measurement part and (2) when continuous and discrete variables are mixed, simultaneous estimates are difficult to obtain. To avoid these problems, many researchers use some type of stepwise estimation procedure involving estimated latent variable scores. This paper shows this type of estimation to be biased, proposes a modified estimation method using estimated latent variable scores, and demonstrates consistency for the new method. Simulation studies illustrate that existing methods are biased even when sample sizes are large while the proposed method results in the parameter estimates close to the true values with relatively small sample sizes. The proposed method (1) is easy to apply even when the model contains different levels of measurement, (2) rarely yields improper solutions, (3) prevents the structural part from affecting the measurement part, and (4) is able to estimate the structural part without raw data.
Working exchange design of sampling for RDD (random digit dialing) telephone survey uses a frame consisting of all possible telephone numbers in all the working exchanges and draws a telephone number sample in a single stage random sampling. This type of sample was believed to be practically useless, as the residential hit rate of such samples are very low. In RDD sampling of recent years, however, it has become standard practice to apply computer assisted screening of non-working numbers to telephone number samples. Unfortunately, we can hardly find any papers which take into consideration the effect of screening of non-working numbers in evaluating sampling designs. If samples drawn with working exchange design are executed with computer assisted screening, their residential hit rate can reach a practical executable level. The design should be re-evaluated since it has become suitable for practical use. The list-assisted design, which is recently used frequently because of its high household hit rate, has a problem of truncating telephone households in banks which contains only unlisted residential numbers. This paper tries to demonstrate that the working exchange design can attain a residential hit rate which is almost comparable with that of list-assisted design, as well as the full coverage of telephone household population.
This paper will overview the history of Hayashi's Quantification Methods (HQM), as a general statistical method and philosophy to obtain scientific information. The Institute of Statistical Mathematics (ISM) was established by the effort of Japanese statistical mathematicians during the Second World War. The postwar reformation of ISM, especially the establishment of the Department of Social Sciences, linked the traditional theories of statistics with practical social survey. As a result of this linkage, a new statistical approach and method known as HQM was formulated by C. Hayashi in 1950s. HQM was employed and extended to various fields, especially Marketing Research and Medical Engineering (ME). The education of HQM at the training school for statistical technicians of ISM also played an important role for HQM to be well received. Conducting a series of studies, statistical researchers gradually formed a new paradigm of statistical research, and it was crystallized as the foundation of Nihon Koudou-keiryo Gakkai (Behaviormetric Society of Japan) in 1973. The history of Hayashi's studies is the development of his statistical philosophy from “Statistical Mathematics” of 1950s to “Behaviormetrics”, “Multidimensional Data Analysis” of 1970s-1980s “Data Science” since 1990s. His approach and philosophy has been developed by his successors as “the Behaviormetric Studies of Civilization” as an exemplification of “Data Science”.