Level: Advanced
This article introduces the new features of the Structural Equation Modeling platform in JMP Pro 17 for those who have already acquired basic statistical knowledge such as linear regression analysis, maximum likelihood estimation, and multivariate normal distribution. The Structural Equation Modeling platform in JMP Pro can estimate linear models including latent variables based on full information maximum likelihood estimation assuming multivariate normal distribution. It can perform inferences for a wide range of linear models, such as path analysis and confirmatory factor analysis including latent variables. The analyst can specify the model he or she is assuming by drawing a path diagram, so the relationship between variables in the model he or she is assuming is intuitively easy to understand. JMP Pro 17 also supports multi-population analysis. First, we will briefly introduce some models that can be fitted with structural equation models, and then introduce the new features of JMP Pro 17, focusing on multi-population analysis.
Graduated from the Faculty of Letters, Hokkaido University in 1994. Joined SAS Institute Japan Co., Ltd. (at the time) in 1996, where he worked in the Technical Support Department, providing customer support related to statistics. After leaving the company in 2001, he worked in biostatistics at a pharmaceutical company, as a programmer at a manufacturing engineering company, and as a freelancer, before rejoining SAS Institute Japan Co., Ltd. in 2005. As a tester in the JMP Japan division, he was involved in software testing and translation proofreading. In 2014, he received the RSS/JSS Graduate Diploma (Passing with Distinction and the Japan Statistical Society Statistical Proficiency Test RSS/JSS Exam Best Performance Award). In 2012, he received the 1st-level Statistical Proficiency Test (Biostatistics). Co-translated books include Tourangeau, R., Conrad, FG, and Couper (authors), translated by Osumi, Noboru, Nio, Makiko, Ida, Junji, and Ono, Yusuke (2013), The Science of Web Research, Asakura Publishing; and Le Roux, B. and Rouanet, H. (authors), translated by Osumi, Noboru, Ono, Yusuke, and Nio, Makiko (2010), Multiple Correspondence Analysis, Ohm Publishing.
