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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃旻華 | |
dc.contributor.author | Tsung-Han Tsai | en |
dc.contributor.author | 蔡宗漢 | zh_TW |
dc.date.accessioned | 2021-06-08T06:05:49Z | - |
dc.date.copyright | 2007-07-25 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-20 | |
dc.identifier.citation | 一、中文部分
呂宜勵,2007,《多層線性模型的分層樣本數組合問題:對跨國政治文化研究的啟示》,台北市:政治大學政治學系碩士論文。 呂亞力,1997,《政治學》,台北市:三民書局。 吳怡銘,2001,〈台北市選民分裂投票之研究:民國八十七年市長市議員選舉之分析〉,《選舉研究》,第8卷第1期。 黃紀,2001,〈一致與分裂投票:方法論之探討〉,《人文及社會科學集刊》,第13卷第5期,頁541-574。 黃紀、張益超,2001,〈一致投票與分裂投票:嘉義市一九九七年市長與立委選舉之分析〉,載於徐永明、黃紀主編《政治分析的層次》,台北:韋伯文化。 二、英文部分 Achen, Christopher H. and W. Phillips Shively. 1995. Cross-Level Inference. Chicago: The University of Chicago Press. Anselin, Luc. 2000. “The Alchemy of Statistics, or Creating Data Where No Data Exist.” Annals of the Association of American Geographers, Vol. 90: 586-592. Anselin, Luc and Wendy K. Tam Cho. 2002. “Spatial Effects and Ecological Inference.” Political Analysis, Vol. 10, No. 3: 276-297. Burden, Barry C. and David C. Kimball. 1998. “A New Approach to the Study of Ticket Splitting.” The American Political Science Review, Vol. 92, No. 3: 533-544. Chambers R. L. and D. G. Steel. 2001. “Simple Methods for Ecological Inference in 2×2 Tables.” Journal of the Royal Statistical Society. Series A. (Statistics in Society) Vol. 164, Issue 1: 175-192. Cho, Wendy K. Tam. 1998. “Iff the Assumption Fits…: A Comment on the King Ecological Inference Solution.” Political Analysis, Vol. 7: 143-163. Cleave, N., P. J. Brown, and C. D. Payne. 1995. “Evaluation of Methods for Ecological Inference.” Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 158, No. 1: 55-72. Crawford, Carol A. Gotway and Linda J. Young. 2004. “A spatial View of the Ecological Inference Problem.” in Gary King et al. ed., Ecological Inference-New Methodological Strategies. Cambridge, UK ; New York: Cambridge University Press. Dixon, Philip and Aaron M. Ellison. 1996. “Introduction: Ecological Applications of Bayesian Inference.” Ecological Application, Vol. 6, No. 4: 1034-1035. Freedman, David A. et al. 1991. “Ecological Regression and Voting Right.” Evaluation Review, Vol. 15: 673-711. Freedman, David A., S. P. Klein, M. Ostland and M. R. Roberts. 1998. “Review of A Solution to the Ecological Inference Problem, by G. King.” Journal of the American Statistical Association, 93: 1518-22. Freedman, David A., S. P. Klein, M. Ostland and M. R. Roberts. 1999. “Response to King’s Comment.” Journal of the American Statistical Association, 94: 355-57. Fotheringham, A. Stewart. 2000. “A Bluffer’s Guide to A Solution to the Ecological Inference Problem.” Annals of the Association of American Geographers, Vol. 90: 582-586. Goodman, Leo. 1953. “Ecological Regressions and the Behavior of Individuals.” American Sociological Review, Vol. 18: 663-664. Goodman, Leo. 1959. “Some Alternatives to Ecological Correlation.” American Journal of Sociology, Vol. 64: 610-624. Imai, Kosuke, Gary King and Olivia Lau, 2006. Zelig: Everyone’s Statistical Software. Jackman, Simon. 2000. “Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo.” American Journal of Political Science, Vol. 44, No. 2: 375-404. King, Garry. 1997. A Solution to the Ecological Inference Problem. Princeton, NJ: Princeton University Press. King, Gary. 1999. “The Future of Ecological Inference Research: A Comment on Freedman et al..” Journal of the American Statistical Association, 94: 352-55. King, Gary, Ori Rosen, and Martin A. Tanner. 1999. “Binomial-beta Hierarchical Models for Ecological Inference.” Sociological Methods and Research, Vol. 28, No. 1: 61-90. Langbein, Laura Irwin, and Allen J. Lichtman. 1978. Ecological Inference. Beverly Hills, CA: Sage. Lee, Jack C. and Darius J. Sabavala. 1987. “Bayesian Estimation and Prediction for the Beta-Binomial Model.” Journal of Business & Economic Statistics, Vol. 5, No. 3: 357-367. Lee, Peter M. 2004. Bayesian Statics. 3rd ed. New York: John Wiley. Lewis, Jeffrey B. 2004. “Extending King’s Ecological Inference Model to Multiple Elections Using Markov Chain Monte Carlo.” in Gary King et al. ed., Ecological Inference-New Methodological Strategies. Cambridge, UK ; New York: Cambridge University Press. Mattos, Rogério Silva and Alvaro Veiga. 2002. “The Binomial-Beta Hierarchical Method for Ecological Inference: Methodological Issues and Fast Implementation via the ECM Algorithm.” Manuscript. http://web.polmeth.efl.edu. Mattos, Rogério Silva and Alvaro Veiga. 2004. “A Structured Comparison of Goodman Regression, the Truncated Normal, and Binomial-Beta Hierarchical Methods for Ecological Inference.” in Gary King et al. ed., Ecological Inference-New Methodological Strategies. Cambridge, UK ; New York: Cambridge University Press. O’Loughlin, John. 2000. “Can King’s Ecological Inference Method Answer a Social Scientific Puzzle: Who Voted for the Nazi Party in Weimar Germany.” Annals of the Association of American Geographers, Vol. 90: 592-601. Rivers, Douglas. 1998. “A Solution to the Ecological Inference Problem: Restructuring Individual Behavior form Aggregate Data.” American Political Science Review, 92: 442-43. Robinson, William S. 1950. “Ecological Correlation and the Behavior of Individual.” American Sociological Review, Vol. 15: 351-357. Schuessler, Alexander A. 1999. “Ecological Inference.” Proceedings of the National Academy of Science of the United States of America, Vol.96: 10578-10581. Selvin, Hanan C. 1958. “Durkheim’s Suicide and Problem of Empirical Research.” The American Journal of Sociology, Vol. 63, No.6: 607-619. Stoto, Michael A. 1998. “A Solution to the Ecological Inference Problem: Restructuring Individual Behavior form Aggregate Data.” Public Health Reports, 113: 182-83. Sui, Daniel. 2000. “On “A Solution to the Ecological Problem: Reconstructing Individual Behavior from Aggregate Data”, by Gary King.” Annals of the Association of American Geographers, Vol. 90: 579-582. Tanner, Martin A. 1996. Tools for Statistical Inference: Methods for the Exploration Posterior Distributions and Likelihood Functions. 3d ed. New York: Springer-Verlag. Wakefield, Jonathan. 2004a. “Prior and Likelihood Choices in the Analysis of Ecological Data.” in Gary King et al. ed., Ecological Inference-New Methodological Strategies. Cambridge, UK ; New York: Cambridge University Press. Wakefield, Jon. 2004b. “Ecological Inference for 2×2 Tables.” Journal of the Royal Statistical Society. Series A. (Statistics in Society) Vol. 167, Issue 3: 385-426. Wilcox, Rand R. 1981. “A Review of the Beta-Binomial Model and Its Extensions.” Journal of Educational Statistics, Vol. 6, No. 1: 3-32. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/25226 | - |
dc.description.abstract | 政治行為研究中,在個體資料不可得的情況下,從總體層次資料推論或得知個體層次行為的區位推論方法便有其必要性與實用性,但由於區位推論問題沒有正確的解答,所以本文的目的在藉由比較目前政治學領域中被應用的區位迴歸、EI模型與BBH模型三者,以期瞭解不同區位推論方法的適用性。本文首先界定政治學領域欲處理的區位推論問題,再以最簡化的2×2交叉列表形成的區位推論問題為核心,從統計模型的設立探究區位迴歸、EI模型和BBH模型如何排除區位謬誤的困難,最後以調查資料比較三個模型在不同面向所設定資料中的估計誤差表現,在執行上本文以BNH模型取代BBH模型。
根據本文的研究結果所示,當解釋變數與參數的相關關係顯著時,區位迴歸、EI模型與BNH模型會有較大的估計誤差。同樣地,當參數標準差減少時,三模型也會有較大的誤差。而參數平均數若改變,三個模型的估計誤差不會隨之增減。至於總體層次樣本數的減少,也不會使得三個模型在估計誤差有太大的變化。整體而言,區位迴歸的估計誤差高於EI模型和BNH模型,而EI模型和BNH模型的估計誤差表現大致相等。最後,本文根據上述研究結果討論以區位推論方法分析國內一致與分裂投票議題時,其應用上的限制。 | zh_TW |
dc.description.abstract | In political behavior study, it is necessary and practical to infer individual behavior from aggregate data, which is called “ecological inference”, when individual-level data is not available. There is no determinate solution to the ecological inference problem so far, and the purpose of this paper is to clarify the applications of different ecological inference methods by comparing Ecological Regression, EI Model and BBH model which are applied in political science recently. Firstly, this paper makes a definition of the ecological inference problem with which the researchers are concerned in political science. Then it explores how these three models solve the ecological fallacy from statistical setting of models, provided that the ecological inference problem is in terms of a 2×2 cross-table. Finally, it evaluates their performance, among which BBH model is replaced by BNH model, in different data which are set up based on different dimension from survey data.
The results show that Ecological Regression, EI Model and BNH model are all biased in the situation when the variable in the margin is correlated with the parameter in the cell. In the situation when the standard deviation of parameter decreases, all of the three models are biased as well. The performance of the three models would not be affected with the changes in the mean of parameter. As for the sample size of the aggregate level, it seems not to affect the performance of any model. Overall, Ecological Regression is worse than EI Model and BNH model and the performance of EI Model and BNH model is in a tie. According to the results mentioned above, this paper discusses the limit of ecological inference methods when they are applied to analyze split-ticket voting. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T06:05:49Z (GMT). No. of bitstreams: 1 ntu-96-R93322052-1.pdf: 4341396 bytes, checksum: 4f634fbb2ae6fe0013c2ebde219b2caf (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 口試委員審定書 ⅰ
致謝 ⅱ 中文摘要 ⅳ 英文摘要 ⅴ 第一章 緒論.………………………………………………………… 1 第一節 研究背景……………………………………………………… 1 第二節 研究動機與目的……………………………………………… 2 第三節 概念界定……………………………………………………… 5 壹、區位推論問題(ecological inference problem)……………… 5 貳、區位謬誤(ecological fallacy)………………………………… 9 第四節 章節安排…………………………………………………… 14 第二章 文獻探討…………………………………………………… 17 第一節 區位迴歸與相關模型……………………………………… 17 壹、區位迴歸(Ecological Regression)……………………… 17 貳、與區位迴歸相關之模型…………………………………… 21 第二節 EI模型(EI Model)………………………………………… 23 第三節 Binomial-beta Hierarchical Model………………………32 第四節 比較三種模型之研究……………………………………… 36 第五節 區位推論方法於國內政治學界之應用…………………… 38 第三章 研究設計…………………………………………………… 41 第一節 研究架構…………………………………………………… 41 第二節 研究假設…………………………………………………… 46 第三節 資料來源與變數說明……………………………………… 48 第四節 統計軟體…………………………………………………… 50 第五節 研究限制…………………………………………………… 51 第四章 資料設定與研究結果……………………………………… 53 第一節 資料處理與描述…………………………………………… 53 第二節 資料設定…………………………………………………… 54 壹、解釋變數( )與參數( )相關關係設定…………………… 55 貳、參數( )標準差設定………………………………………… 56 參、參數( )平均數設定………………………………………… 56 肆、總體層次樣本數控制……………………………………… 57 伍、小結………………………………………………………… 60 第三節 初步研究結果..…………………………………………… 62 壹、解釋變數( )與參數( )相關關係對參數估計的影響…… 62 貳、參數( )標準差減少對參數估計的影響…………………… 68 參、參數( )平均數改變對參數估計的影響…………………… 73 肆、樣本數改變對參數估計的影響…………………………… 78 第四節 研究結果討論……………………………………………… 87 第五章 結論………………………………………………………… 91 第一節 研究發現…………………………………………………… 91 第二節 區位推論應用上之限制…………………………………… 93 參考文獻……………………………………………………………… 95 附錄一 Zelig程式套件中區位推論層級模型說明……………… 101 附錄二 樣本數控制前所有設定資料的描述性統計表與相關係數…… 103 附錄三 樣本數控制後所有設定資料的描述性統計表與相關係數表…… 111 附錄四 區位迴歸、EI模型與層級模型在不同資料設下的參數估計誤差…… 125 | |
dc.language.iso | zh-TW | |
dc.title | 區位推論方法在政治學應用上之限制─區位迴歸、EI模型與BNH模型之比較研究 | zh_TW |
dc.title | The Limit of Application of Ecological Inference Methods in Political Science─A Study on the Comparison of Ecological Regression, EI Model and BNH Model | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 江瑞祥,劉正山 | |
dc.subject.keyword | 區位推論,區位謬誤,區位迴歸,EI模型,BBH模型,BNH模型, | zh_TW |
dc.subject.keyword | ecological inference,ecological fallacy,Ecological Regression,Goodman Regression,EI Model,Binomial-beta Hierarchical Model,Binomial-normal Hierarchical Model, | en |
dc.relation.page | 99 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2007-07-23 | |
dc.contributor.author-college | 社會科學院 | zh_TW |
dc.contributor.author-dept | 政治學研究所 | zh_TW |
顯示於系所單位: | 政治學系 |
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