[Updated on Apr. 18] Integration of CiNii Articles into CiNii Research

Consistency Between Theoretical Interests in Collaborative Learning Studies and Methods of Statistical Analysis : A Review of Statistics for Hierarchical Data(<Special Issue>Collaborative Learning and Networked Communities)

  • KITAMURA Satoshi
    Interfaculty Initiative in Information Studies, The University of Tokyo

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  • 協調学習研究における理論的関心と分析方法の整合性 : 階層的データを扱う統計的分析手法の整理(<特集>協調学習とネットワーク・コミュニティ)
  • 協調学習研究における理論的関心と分析方法の整合性--階層的データを扱う統計的分析手法の整理
  • キョウチョウ ガクシュウ ケンキュウ ニ オケル リロンテキ カンシン ト ブンセキ ホウホウ ノ セイゴウセイ カイソウテキ データ オ アツカウ トウケイテキ ブンセキ シュホウ ノ セイリ

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Abstract

Statistical methods are used in studies of collaborative learning to analyze questionnaire and achievement test data. In collaborative group learning studies, the theoretical assumptions of collaborative learning, however, conflicts with the assumption made in the tests, namely that the samples are independent. In this paper, statistical methods for analyzing hierarchical data and clustered samples, which are consistent with the theoretical assumption of collaborative learning, are identified. Such methods are particularly suitable for analyzing aggregated data, and include regression analysis with robust standard error, hierarchical linear model (HLM), and multilevel covariance structural analysis (MCA). The special features of these methods are discussed.

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