Exploring latent class based on growth rates in number sense ability

Abstract

The purpose of this study was to explore latent class based on growth rates in number sense ability by using latent growth class modeling (LGCM). LGCM is one of the noteworthy methods for identifying growth patterns of the progress monitoring within the response to intervention framework in that it enables us to analyze latent sub-groups based not on an arbitrary cut-point but on each group’s growth pattern. Progress monitoring data for number sense were administered in four times for age of 4(n = 58), 5(n = 95), and 6(n = 58) children, by the measure named basic academic skill assessment: number sense developed to assess students’ number sense and includes Number identification, Missing number, Quantity discrimination and estimation. To perform LGCM analysis, M plus 5.0 was used, and Bayesian information criteria and entropy values were used as criteria to determine the number of sub-groups. Results showed that there were 2, 4, and 4 sub-groups according to each age group based on the growth patterns. Each group’s growth patterns were classified differently based on their initial performance and growth level. Advantages and limitations of using LGCM method to analyze latent groups’ growth patterns for screening and identifying children at risk were discussed.