2015 Fiscal Year Final Research Report
Methodology for classification of subjects through longitudinal data and its applications for educational and developmental psychology
| Project/Area Number |
26885007
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Educational psychology
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| Research Institution | University of Tsukuba |
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Principal Investigator |
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| Project Period (FY) |
2014-08-29 – 2016-03-31
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| Keywords | 縦断データ / 決定木 / 構造方程式モデル / 分類 / ソフトウェア / モデルの誤設定 |
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| Outline of Final Research Achievements |
When conducting longitudinal research, investigation of between-individual differences in patterns of within-individual change can provide important insights into a variety of typical developmental and growth patterns. We focused on a model-based exploratory data mining technique called structural equation model trees (SEMTrees). SEMTrees is a technique that classifies participants into subsets (classes) by recursively partitioning data based on the values of the observed covariates such as age, sex and personality of individuals. In the present research we investigated the performances of SEMTrees under various research designs including an influence of possible model misspecifications, and also investigated a relation between the sample size and precision of classification under various template models specified.
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| Free Research Field |
心理統計学
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