Application of Random Matrix Theory to sociological data analysis
| Project/Area Number |
23530630
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Sociology
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
NAKAI MIKI 立命館大学, 産業社会学部, 教授 (00241282)
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| Research Collaborator |
GRAZIANO Vernizzi Siena College, Department of Physics and Astronomy, Associate Professor
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2011: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 社会調査データ / 共分散行列 / ランダム行列理論 / カテゴリカル変数 / 社会学的データ / 欠損データ / 多変量解析 / 共分散 / 行列理論 |
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| Research Abstract |
We have been applying the novel geometrical framework for computing variance-covariance matrices and linear correlation matrices for sets of heterogenous variables (meaning for system of variables that can be continuous or categorical), introduced first in our paper (forthcoming). We have tested the efficiency and limitations of the approach when applied to statistical analysis of sociological data. In particular, we are advancing a variance-covariance analysis of the 2005 Japanese national survey on social stratification and mobility (SSM2005). Such an analysis gives us the opportunity to apply our recent mathematical tools in conjunction with the application of Random Matrix Theory (RMT) for filtering the covariance matrix of the data, and eliminating the statistical noise due to the complex structure of the questionnaire, and to the finite size of the respondents. Moreover, we apply the analysis to the SSP2010 data and comment on a number of different RMT techniques and tools.
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Report
(4 results)
Research Products
(11 results)
