Ji Hoon Ryoo^1,*, Seohee Park², Seongeun Kim³, Heungsun Hwang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 801-822, 2022, DOI:10.32604/cmes.2022.019708

Abstract Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become increasingly popular along with the popularity of machine learning techniques. Although there are many software packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural equation modeling framework. The package, gscaLCA which is implemented in the R statistical computing environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling. More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis (GSCA), the package gscaLCA computes membership prevalence and item response probabilities… More >

Chunyang Zhang¹, Yijun Lin¹, Yuyang Zhou², Wei Xu^1,3,*

International Journal of Mental Health Promotion, Vol.23, No.4, pp. 443-454, 2021, DOI:10.32604/IJMHP.2021.017229

Abstract Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places. College students with left-behind experience showed higher aggression levels. To further explore the relationship between left-behind experience and aggression, the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression. One thousand twenty-eight Chinese college students with left-behind experience were recruited, and their aggression levels were assessed. The results showed that there were four categories of left-behind experience: “starting… More >