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Research on the effectiveness of intervention strategies for students with special needs is an important research topic in the field of special education. Until now, the majority of special education research on this topic has been based on the use of frequentist statistics and pre- and post-test designs. However, recent research in the natural and social sciences has criticised the limitations of frequentist statistics and emphasised the importance of reliable longitudinal data. Therefore, the purpose of this study is to introduce a Bayesian longitudinal data analysis model that can overcome the limitations of frequentist statistics in special education situations where longitudinal data are collected. According to the results of this study, when using Bayesian longitudinal data analysis model to test the effectiveness of intervention strategies, it is not only possible to reflect previous studies in parameter estimation, but also to continuously update the effect size of existing intervention strategies. In addition, it is possible to estimate effect sizes with good reliability even in research conditions with a small number of cases, such as in the field of special education. Finally, the limitations of Bayesian theory and future research topics are discussed.
Seungsoo Yeo (Sun,) studied this question.
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