Model Selection for Complex Multilevel Latent Class Model

Abstract

Multilevel latent class analysis is conducive to providing more effective information on both individual and group typologies. However, model selection issues still need further investigation. Current study probed into issue of high-level class numeration for a more complex model using AIC, AIC3, BIC, and BIC*. Data simulation was conducted and its result was verified by empirical data. The result demonstrated that these criteria have a certain inclination relative to sample sizes. Sample size per group plays an evident role in improving accuracy of AIC3 and BIC. The complex model requires more sample size per group to ensure accurate class numeration.

Keywords:

• Fitness index
• Information evaluation criteria
• Model selection
• Multilevel latent class analysis

Mathematical Subject Classification:

• Primary 62J07
• Secondary 62H30

Acknowledgments

This research was funded by the National Education Science Key Project for 2011–2015 Planning (“全国教育科学‘十二五’规划重点课题” in Chinese, funding code: GFA111009), Project for Guangzhou Excellent Education Institution “Assessment of elementary and secondary school students’ academic level under cognitive diagnostic models” (“广州卓越教育项目:学生学业水平认知诊断评价” in Chinese), the Project for Quality Monitoring System of Basis Education in Guangzhou (“广州市基础教育学业质量监测系统项目” in Chinese, funding code: GZIT2012-ZB0292), and the 2012 Educational Ministry Foundation for Young Researchers in Humanity and Social Science (“2012年度教育部人文社会科学研究青年基金项目” in Chinese, funding code:12YJC190016).