초록

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Abstract ▼ AI-Helper

The main purpose of this study was to identify students at risk for reading disabilities using latent growth class analysis that is referred to as cutting-edge skill in education. Recently, statistical techniques for longitudinal data have received a great attention in the areas of social science. H...

The main purpose of this study was to identify students at risk for reading disabilities using latent growth class analysis that is referred to as cutting-edge skill in education. Recently, statistical techniques for longitudinal data have received a great attention in the areas of social science. However, these techniques has one weakness: it assumes that a single growth rate is estimated to capture the pattern of growth rate for an entire population. In reality, it is common case that individuals can be categorized into distinct subgroups. The use of Latent Growth Class Analysis (LGCA) that is the relatively newer technique allows for modeling heterogeneity in distinct patterns of development. In this study, students at risk for reading disabilities were divided into subgroups on the basis of developmental characteristics. This study showed that the LGCA was a useful tool to identify subgroups of students who are at risk for reading disabilities. Future research and implications are discussed.

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