Level: Advanced
This article introduces the new features of the Structural Equation Modeling platform in JMP Pro 17 for those who already have basic statistical knowledge such as linear regression analysis, maximum likelihood estimation, and multivariate normal distribution. The Structural Equation Modeling platform in JMP Pro allows you to estimate linear models that include latent variables based on full information maximum likelihood estimation, assuming a multivariate normal distribution. It can perform inference for a wide range of linear models, including path analysis and confirmatory factor analysis that include latent variables. Analysts can specify the intended model by drawing a path diagram, making it easy to intuitively understand the relationships between variables in the intended model. JMP Pro 17 also supports multi-population analysis. We will first briefly introduce several models that can be fitted using 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. (then) in 1996, working in the Technical Support Department, providing customer support related to statistics. After leaving the company in 2001, he worked as a biostatistician for a pharmaceutical company, a programmer for a manufacturing engineering company, and as a freelancer. In 2005, he rejoined SAS Institute Japan Co., Ltd. as a tester in the JMP Japan division, where 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 Best Performance Award). In 2012, he obtained the 1st-level Statistical Proficiency Test (Biostatistics). Co-translated works 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.
