The third Japanese Stata Users Group meeting was Saturday, 16 September 2017 at Kyoto Research Park, but you can view the program and presentation slides below.
Analysis of social network in developing countries: From cases of adopting new agricultural technology in Madagascar
Abstract: Regarding agricultural productivity in developing countries, even in the same natural environment, it is common for a large disparity to arise among households of each farm. It is certain that these differences are derived from farm household individuals, characteristics of households, etc., however, in recent research, connection with neighbors and relatives, that is, that difference in relationships within social networks, is one factor of disparity that has been pointed out. Social networks in rural developing countries are thought to affect agricultural production through three paths: (1) provision of new technical information, (2) mutual insurance function, and (3) relaxation of labor market imperfections and credit constraints. However, because of the difficulty of accumulation of research in the field, this is also not fully done. Therefore, based on household survey data of a rural Madagascar village collected independently, we focus on the provision of new technical information and how the social network of rural communities influences the adoption of agricultural new technology.
Kwansei Gakuin University
Development of Japan school climate scale using item response theory (IRT)
Abstract: Item response theory (IRT) simultaneously estimates potential characteristics (ability, psychological characteristics, etc.) of each respondent and difficulty and discrimination of each item from respondents' answer patterns for each item of the scale. It is a test theory that can be done. IRT has many advantages in estimating reliability and validity for classical test theory, such as being able to study the measurement accuracy of each item in detail without depending on the characteristics of the sample group, such as psychological characteristics, as well as in the development of a scale for measuring the size of a sample. In this study, I used Stata to develop a scale to measure school atmosphere (school culture) and to examine the reliability and validity of the scale by IRT. Because the answer was multivalued data of five methods, a multilevel response model (graded response model) was used.
Hamamatsu University School of Medicine Research Center for Child Mental Development
Luncheon seminar: Comparison of commands for probability score analysis
Abstract: The propensity to estimate the treatment effect introduces the basic idea of score analysis and explains the functional difference between the Stata command teffects psmatch and the conventional ado-file psmatch2, pscore command. Using these commands, you can avoid unnecessary confusion when referring to previous research.
Lightstone Co., Ltd.
Estimation of treatment effects by Stata
Abstract: In the field of social science, analysis by survey observation data is the majority and, unlike experimental data, it is not easy to measure the effect of a specific treatment. For example, in the field of labor economics, the effects of treatments such as university admission and vocational training participation on subsequent wages will be analyzed, but becausee such treatments are not randomly assigned, bias on wages is biased. Several analytical methods have been developed to deal with these problems, one of which is treatment estimation. I will introduce analysis examples, especially when there are multiple treatments.
Nanzan University, Faculty of Policy Studies
Spatial quantitative analysis by Stata: Program development using Mata
Abstract: A programming language called Mata is available in Stata. In this presentation, I will show that flexible program development can be performed according to the purpose of each researcher using Mata. I will introduce examples of spatial statistics and spatial econometric applications that have been actively performed in recent years at Stata. The advantage of Mata is that it handles matrix operations intuitively. Because geographical space is treated as a matrix, one can perform flexible spatial analysis in Stata by linking it with Mata. The range of statistical analysis broadens widely by using the spgen command to calculate the space lag variables developed by the author; the getisord command can perform hotspot analysis and mutually uses the advantages of Stata and Mata. I will also introduce examples of using the Stata command related to the geographic information system, such as the method of creating maps in Stata.
Research Institute of Economy, Trade and Industry
Disparity by socioeconomic indicator of cancer: Cancer registry, analysis using population dynamics statistics
Abstract: Cancer is a disease whose cause is clear and preventable: it can be overcome by early diagnosis and treatment. While Japan is under the national health insurance system, it has become apparent in recent years that disparities in health condition and life span are occurring because of socioeconomic reasons. In this presentation, I analyzed the survival rate, morbidity rate (incidence), and social economic disparity of mortality rates of cancer patients by using all public survey data such as cancer registration data and demographic statistics. Using socioeconomic indicators based on the residential area of cancer patients using Osaka prefecture cancer registration data, we found that there is a disparity in the survival rate of cancer patients. Also, looking at the prevalence rate by degree of progress, men living in rich areas suffered from early cancer and the prevalence rate of advanced cancer was low. Social and economic disparities also occurred in the mortality rate of cancer in the statistics of population dynamics throughout the country. I introduce statistical analysis and graphical expression by Stata, including survival analysis and various regression analysis used in this research.
Osaka International Cancer Institute Cancer Control Center
Introduction to the bayes prefix in Stata 15
Abstract: Bayesian analysis has become a popular tool for many statistical applications. Yet many statisticians have little training in the theory of Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models using Stata. No prior knowledge of Bayesian analysis is necessary and specific topics will include the relationship between likelihood functions, prior, and posterior distributions, Markov Chain Monte Carlo (MCMC) using the Metropolis–Hastings algorithm, and how to use the new Bayes prefix in Stata 15 to fit Bayesian models.