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題名 潛在群體分析與對應分析關係之探討
作者 林錦鈺
貢獻者 江振東
林錦鈺
關鍵詞 潛在群體分析
對應分析
Latent class analysis
Correspondence analysis
日期 2001
上傳時間 15-Apr-2016 16:10:08 (UTC+8)
摘要 潛在群體分析及對應分析是在探討多個類別變數間關係常用的兩種分析方法，因其運用之領域不同，解說之方式不一，以致過去常被認為是兩種不相關的分析方法，然而實際卻非如此。本研究之目的係就雙變數及多變數的情況，針對這兩種分析方法間之關係作詳細的探討，並說明其對等及不對等的時機。
Latent class analysis and correspondence analysis are two well-known methods that can be used to study the relationship between categorical variables. Since the two were developed and applied in two different fields in the past, they were never thought to be related. In this study, we examine the relationship between the two in more details. We further point out the situations where they are equivalent, and where they are not.
參考文獻 Bartholomew, D. J. and Knott, M. (1999). Latent Variable Models and Factor Analysis, Oxford: Oxford Univ. Press.
     Benzecri, J. -P. et al. (1973). L’Analyse des Donnees. Tome (vol.) 1: La Taxinomie. Tome 2: L’Analyse des Correspondances. Dunod, Paris.
     Benzecri, J. -P. (1977). Histoire et prehistoire de l’analyse des donnees. 5-L’analyse des correspondances. Cahiers de l’Analyse des Donnees, 2, 9-40.
     Clausen, S.-E. (1998). Applied Correspondence Analysis, Sage Publications, Inc.
     Everitt, B. S. (1984). An Introduction to Latent Variable Models, Chapman & Hall, London.
     Fisher, R. A. (1940). The precision of discriminant functions. Ann. Eugen., 10, 422-429.
     Gifi, A. (1981). “Nonlinear Multivariate Analysis.” Department of Data Theory, University of Leiden, The Netherlands.
     Gilula, Z. (1979). Singular Value Decomposition of Probability Matrices: Probabilistic Aspects of Latent Dichotomous Variables, Biometrika, 66, 339-44.
     Gilula, Z. (1984). On Some Similarities Between Canonical Correlation Models and Latent Class Models for Two-way Contingency Tables, Biometrika, 71, 523-29.
     Goodman, L. A. (1974). Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models, Biometrika, 61, 215-31.
     Goodman, L. A. (1978). Analyzing qualitative/categorical data: Log-linear models and latent structure analysis. Cambridge, MA: Abt.
     Goodman, L. A. (1987). New Methods for Analyzing the Intrinsic Character of Qualitative Variables Using Cross-classified Data, American Journal of Sociology, 93, 529-83.
     Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis, Academic Press, London.
     Guttman, L. (1941). The quantification of a class of attributes: A theory and method of scale construction. In “The Prediction of Personal Adjustment” (Horst, P., ed), 319-348. Social Science Research Council, New York.
     Guttman, L. (1950). The principal components of scale analysis. In “Measurement and Prediction” (Stouffer, S. A., Guttman, L., Suchman, E. A., Lazarsfeld, P. F., Star, S. A. and Clausen, J. A., eds). Princeton University Press, Princeton.
     Guttman, L. (1959). Metricizing rank-ordered and ordered data for a linear factor analysis. Sankhy , 21, 257-268.
     Haberman, S. J. (1974). Log-linear models for frequency tables derived by indirect observation. Annals of Statistics, 2, 911-924.
     Haberman, S. J. (1976). Iterative scaling procedures for log-linear models for frequency data derived by indirect observation. Proceedings of the American Statistical Association 1975: Statistical Computing Section ,45-50.
     Haberman, S. J. (1979). Analysis of qualitative data. Vol 2. New developments. New York: Academic Press.
     Heinen, T. (1996). Latent Class and Discrete Latent Trait Models: Similarities and Differences, Advanced Quantitative Techniques in the Social Sciences, Sage Publications, Thousand Oaks, CA.
     Hirschfeld, H. O. (1935). A connection between correlation and contingency. Cambridge Philosophical Soc. Proc. (Math. Proc.) , 31, 520-524.
     Lazarsfeld, P. F. (1950a). The logical and mathematical foundation of latent structure analysis. In S. Stouffer (Ed.), Measurement and prediction, 362-412. Princeton, NJ: Princeton University Press.
     Lazarsfeld, P. F. (1950b). The interpretation and mathematical foundation of latent structure analysis. In S. Stouffer (Ed.), Measurement and prediction, 413-472. Princeton, NJ: Princeton University Press.
     Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
     McCutcheon, A. L. (1987). Latent Class Analysis, Sage Publications, Inc.
     Tenenhaus, M., and Young, F. W. (1985). An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis, and other methods for quantifying categorical multivariate data. Psychometrika, 50, 91-119.
     van der Heijden, P. G. M., Gilula, Z. and van der Ark, L. A. (1999). An Extended Study Into The Relationship Between Correspondence Analysis And Latent Class Analysis, Sociological Methodology.
描述 碩士
國立政治大學
統計學系
88354016
資料來源 http://thesis.lib.nccu.edu.tw/record/#A2002001351
資料類型 thesis
dc.contributor.advisor 江振東zh_TW
dc.contributor.author (Authors) 林錦鈺zh_TW
dc.creator (作者) 林錦鈺zh_TW
dc.date (日期) 2001en_US
dc.date.accessioned 15-Apr-2016 16:10:08 (UTC+8)-
dc.date.available 15-Apr-2016 16:10:08 (UTC+8)-
dc.date.issued (上傳時間) 15-Apr-2016 16:10:08 (UTC+8)-
dc.identifier (Other Identifiers) A2002001351en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/85138-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 88354016zh_TW
dc.description.abstract (摘要) 潛在群體分析及對應分析是在探討多個類別變數間關係常用的兩種分析方法，因其運用之領域不同，解說之方式不一，以致過去常被認為是兩種不相關的分析方法，然而實際卻非如此。本研究之目的係就雙變數及多變數的情況，針對這兩種分析方法間之關係作詳細的探討，並說明其對等及不對等的時機。zh_TW
dc.description.abstract (摘要) Latent class analysis and correspondence analysis are two well-known methods that can be used to study the relationship between categorical variables. Since the two were developed and applied in two different fields in the past, they were never thought to be related. In this study, we examine the relationship between the two in more details. We further point out the situations where they are equivalent, and where they are not.en_US
dc.description.tableofcontents 封面頁
     證明書
     致謝詞
     論文摘要
     目錄
     表目錄
     圖目錄
     第一章 緒論
     第一節 研究動機與目的
     第二節 研究架構
     第二章 文獻回顧
     第一節 潛在群體分析
     2.1.1 潛在群體分析之發展歷史
     2.1.2 潛在群體模型之介紹
     2.1.3 參數之確認問題
     2.1.4 模型適合度檢定
     第二節 對應分析
     2.2.1 對應分析之發展歷史
     2.2.2 對應分析模型之介紹
     2.2.3 模型適合度檢定
     第三章 雙變數時之關係
     第一節 模型關係探討
     3.1.1 秩R=1
     3.1.2 秩R=min(I,J)
     3.1.3 秩R=2且R
     3.1.4 秩2
     第二節 參數間之線性關係
     第三節 實例分析
     第四章 多變數時之關係
     第一節 多重對應分析
     第二節 聯合對應分析
     第三節 多元潛在群體分析
     第四節 關係探討
     4.4.1 模型間的關係
     4.4.2 參數間的線性關係
     第五章 結論與建議
     參考文獻
     附錄
     一、判斷潛在群體模型之參數是否為局部確認的方法
     二、對應分析模型的理論背景
     三、對應分析之應用程式
     四、潛在群體分析之應用程式
     五、秩R=3		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#A2002001351en_US
dc.subject (關鍵詞) 潛在群體分析zh_TW
dc.subject (關鍵詞) 對應分析zh_TW
dc.subject (關鍵詞) Latent class analysisen_US
dc.subject (關鍵詞) Correspondence analysisen_US
dc.title (題名) 潛在群體分析與對應分析關係之探討zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Bartholomew, D. J. and Knott, M. (1999). Latent Variable Models and Factor Analysis, Oxford: Oxford Univ. Press.
     Benzecri, J. -P. et al. (1973). L’Analyse des Donnees. Tome (vol.) 1: La Taxinomie. Tome 2: L’Analyse des Correspondances. Dunod, Paris.
     Benzecri, J. -P. (1977). Histoire et prehistoire de l’analyse des donnees. 5-L’analyse des correspondances. Cahiers de l’Analyse des Donnees, 2, 9-40.
     Clausen, S.-E. (1998). Applied Correspondence Analysis, Sage Publications, Inc.
     Everitt, B. S. (1984). An Introduction to Latent Variable Models, Chapman & Hall, London.
     Fisher, R. A. (1940). The precision of discriminant functions. Ann. Eugen., 10, 422-429.
     Gifi, A. (1981). “Nonlinear Multivariate Analysis.” Department of Data Theory, University of Leiden, The Netherlands.
     Gilula, Z. (1979). Singular Value Decomposition of Probability Matrices: Probabilistic Aspects of Latent Dichotomous Variables, Biometrika, 66, 339-44.
     Gilula, Z. (1984). On Some Similarities Between Canonical Correlation Models and Latent Class Models for Two-way Contingency Tables, Biometrika, 71, 523-29.
     Goodman, L. A. (1974). Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models, Biometrika, 61, 215-31.
     Goodman, L. A. (1978). Analyzing qualitative/categorical data: Log-linear models and latent structure analysis. Cambridge, MA: Abt.
     Goodman, L. A. (1987). New Methods for Analyzing the Intrinsic Character of Qualitative Variables Using Cross-classified Data, American Journal of Sociology, 93, 529-83.
     Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis, Academic Press, London.
     Guttman, L. (1941). The quantification of a class of attributes: A theory and method of scale construction. In “The Prediction of Personal Adjustment” (Horst, P., ed), 319-348. Social Science Research Council, New York.
     Guttman, L. (1950). The principal components of scale analysis. In “Measurement and Prediction” (Stouffer, S. A., Guttman, L., Suchman, E. A., Lazarsfeld, P. F., Star, S. A. and Clausen, J. A., eds). Princeton University Press, Princeton.
     Guttman, L. (1959). Metricizing rank-ordered and ordered data for a linear factor analysis. Sankhy , 21, 257-268.
     Haberman, S. J. (1974). Log-linear models for frequency tables derived by indirect observation. Annals of Statistics, 2, 911-924.
     Haberman, S. J. (1976). Iterative scaling procedures for log-linear models for frequency data derived by indirect observation. Proceedings of the American Statistical Association 1975: Statistical Computing Section ,45-50.
     Haberman, S. J. (1979). Analysis of qualitative data. Vol 2. New developments. New York: Academic Press.
     Heinen, T. (1996). Latent Class and Discrete Latent Trait Models: Similarities and Differences, Advanced Quantitative Techniques in the Social Sciences, Sage Publications, Thousand Oaks, CA.
     Hirschfeld, H. O. (1935). A connection between correlation and contingency. Cambridge Philosophical Soc. Proc. (Math. Proc.) , 31, 520-524.
     Lazarsfeld, P. F. (1950a). The logical and mathematical foundation of latent structure analysis. In S. Stouffer (Ed.), Measurement and prediction, 362-412. Princeton, NJ: Princeton University Press.
     Lazarsfeld, P. F. (1950b). The interpretation and mathematical foundation of latent structure analysis. In S. Stouffer (Ed.), Measurement and prediction, 413-472. Princeton, NJ: Princeton University Press.
     Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
     McCutcheon, A. L. (1987). Latent Class Analysis, Sage Publications, Inc.
     Tenenhaus, M., and Young, F. W. (1985). An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis, and other methods for quantifying categorical multivariate data. Psychometrika, 50, 91-119.
     van der Heijden, P. G. M., Gilula, Z. and van der Ark, L. A. (1999). An Extended Study Into The Relationship Between Correspondence Analysis And Latent Class Analysis, Sociological Methodology.		zh_TW