1999 Fiscal Year Final Research Report Summary
Is a model accepted in a small sample really better than a model rejected in a large sample?
| Project/Area Number |
09680308
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Osaka University |
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Principal Investigator |
KANO Yutaka Osaka University, 人間科学部, 助教授 (20201436)
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| Co-Investigator(Kenkyū-buntansha) |
HARADA Akira Osaka University, 人間科学部, 助手 (10263336)
YOSHIDA Mitsuo Osaka University, 人間科学部, 教授 (10028334)
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| Project Period (FY) |
1997 – 1999
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| Keywords | factor analysis / variable selection / goodness of fit / augmentation method / elimination method / stepwise method / scaling |
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| Research Abstract |
1. In this project, exploratory factor analysis model is considered. A suitable model is often rejected in a large sample in factor analysis, and then one usually increases in the number of factors. As a result, an improper solution is obtained. The ultimate aim of this project is to discuss whether the model rejected should be adopted in case when such a problem arises. For this we need to study causes of the improper solution. One fruit of this project on this respect is to suggest how to identify the cause of an improper solution and to find it useful in many real examples. This result was presented as an invited lecture at the IFCS1998 conference at Rome.
2. One possible cause of rejecting a suitable model in factor analysis is to include an variable inconsistent with the model considered. We suggest a new way of identifying an inconsistent variable with respect to a measure of goodness-of-fit. The result will be published in Psychometrika, an international journal on psychometrics.
3. The new way of the variable selection was programmed using JAVA and was open to public as a Web Page. We refer to the program as SEFA.
4. We held a workshop on SEFA in the annual meeting of the Japanese Psychological Society in 1999 to discuss usefulness of the program through applications to various fields of statistical oriented studies. Both a variable augmentation method and variable elimination method are described. It was shown that the variable elimination method is useful for exploratory purpose while the variable augmentation method is important for the case where there are some important variables that can not be eliminated in the research.
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