2018 Fiscal Year Final Research Report
Developing a robust method to model misspecification in longitudinal data analysis: Applications for educational and developmental psychology.
| Project/Area Number |
16K17305
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Educational psychology
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| Research Institution | The University of Tokyo (2017-2018)
University of Tsukuba (2016) |
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Principal Investigator |
Usami Satoshi 東京大学, 高大接続研究開発センター, 准教授 (20735394)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | 縦断データ / 分類 / 因果推論 / 発達 / 成長 / 回帰木 / 決定木 / 潜在成長モデル |
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| Outline of Final Research Achievements |
Longitudinal design is useful because it enables researchers to effectively evaluate the trajectories of growths/changes in individuals and its individual differences. In this research project, we have proceeded the following research and published papers: (1) proposing a new statistical model that can account for time-specific effects, (2) proposing a unified framework to clarify the conceptual and mathematical relations among cross-lagged models to evaluate longitudinal associations between variables, (3) evaluating the performance of classification of individuals in SEM Trees through large scale simulation study, and (4) applying proposed methods to data in educational and developmental psychology.
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| Free Research Field |
心理統計学
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| Academic Significance and Societal Importance of the Research Achievements |
縦断データは仮説検証上の利点が多いことから，心理学，経済学・教育学・社会学・医学等多様な分野から注目を集めている．縦断データを分析するための方法論として，潜在成長モデル，クロスラグモデル，SEMTreeと呼ばれる手法等がこれまで広く利用されてきた．本研究は，実際の縦断データ分析においてその影響が生じる可能性が非常に高いにも拘わらず世界的に見ても検証が不十分であった，モデルの誤設定の問題に焦点を当てている．実践性が高い縦断データ分析手法における方法論的な問題を検証する本申請課題の遂行は，従来の理論・応用研究の双方に大きなインパクトを与えるものである．
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