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題名 單一總體尺度及多項評估的合意度分析
Agreement analysis between a single global scale and multi‐item assessments
作者 顏柏魁
貢獻者 鄭宗記
顏柏魁
關鍵詞 期望最大演算法
隨機差異
位置分布系統差異
位置集中系統差異
單調合意度係數
加總尺度
因素分析
多序類相關係數
非線性主成分分析
日期 2013
上傳時間 29-Jul-2014 16:03:07 (UTC+8)
摘要 實務上，不完整資料為常見的問題，對於遺漏值的處理方式，分成刪除法或填補法這兩種方法，而面對問卷類型的資料，通常採用順序尺度變數當作問卷的評分標準，本篇使用EM方法填補遺漏值，由於順序尺度變數時常發生樣本數可能沒有遠大於問卷之題目組成的列聯表格子數，導致EM無法執行，因此逐次對資料執行EM填補遺漏值。藉由EM填補後的完整資料使用加總尺度、因素分析和非線性主成分分析整合為單一總體尺度，應用等級轉換法將單一總體尺度轉換為順序尺度，接著評估兩順序尺度變數之間合意度。
參考文獻 Carroll, J. B. (1961). The nature of the data, or how to choose a correlation coefficient. Psychometrika, 26(4), 347-372.
Chen, T., & Fienberg, S. E. (1974). Two-dimensional contingency tables with both completely and partially cross-classified data. Biometrics, 629-642.
3104-3117.
De Leeuw, J., & Mair, P. (2009). Gifi methods for optimal scaling in R: The package homals. Journal of Statistical Software, forthcoming, 1-30.
DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research & Evaluation, 14(20), 1-11.
Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Springer.
Fuchs, C. (1982). Maximum likelihood estimation and model selection in contingency tables with missing data. Journal of the American Statistical Association, 77(378), 270-278.
Fayers, P., & Machin, D. (2007). Quality of life: the assessment, analysis and interpretation of patient-reported outcomes. John Wiley & Sons.
Gifi, A. (1990). Homogeneity analysis. Nonlinear multivariate analysis. Chichester: John Wiley & Sons.
Hochberg, Y. (1977). On the use of double sampling schemes in analyzing categorical data with misclassification errors. Journal of the American Statistical Association, 72(360a), 914-921.
Kuroda, M., Mori, Y., Masaya, I., & Sakakihara, M. Alternating least squares in nonlinear prin-cipal components.
Little, R. J., & Rubin, D. B. (1989). The analysis of social science data with missing values. Sociological Methods & Research, 18(2-3), 292-326.
Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44(4), 443-460.
Svensson, E. (2000). Comparison of the quality of assessments using continuous and discrete ordinal rating scales. Biometrical Journal, 42(4), 417-434.
Svensson, E., & Holm, S. (1994). Separation of systematic and random differences in ordinal rating scales. Statistics in medicine, 13(23‐24), 2437-2453.
Schafer, J. L. (2010). Analysis of incomplete multivariate data. CRC press.
Svensson, E. (2012). Different ranking approaches defining association and agreement measures of paired ordinal data. Statistics in medicine, 31(26), 3104-3117.
Tallis, G. M. (1962). The maximum likelihood estimation of correlation from contingency tables. Biometrics, 18(3), 342-353.
Uebersax, J. S. (2006). The tetrachoric and polychoric correlation coefficients.Statistical methods for rater agreement web site, 2006.
描述 碩士
國立政治大學
統計研究所
101354020
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101354020
資料類型 thesis
dc.contributor.advisor 鄭宗記zh_TW
dc.contributor.author (Authors) 顏柏魁zh_TW
dc.creator (作者) 顏柏魁zh_TW
dc.date (日期) 2013en_US
dc.date.accessioned 29-Jul-2014 16:03:07 (UTC+8)-
dc.date.available 29-Jul-2014 16:03:07 (UTC+8)-
dc.date.issued (上傳時間) 29-Jul-2014 16:03:07 (UTC+8)-
dc.identifier (Other Identifiers) G0101354020en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/67859-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 101354020zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 實務上，不完整資料為常見的問題，對於遺漏值的處理方式，分成刪除法或填補法這兩種方法，而面對問卷類型的資料，通常採用順序尺度變數當作問卷的評分標準，本篇使用EM方法填補遺漏值，由於順序尺度變數時常發生樣本數可能沒有遠大於問卷之題目組成的列聯表格子數，導致EM無法執行，因此逐次對資料執行EM填補遺漏值。藉由EM填補後的完整資料使用加總尺度、因素分析和非線性主成分分析整合為單一總體尺度，應用等級轉換法將單一總體尺度轉換為順序尺度，接著評估兩順序尺度變數之間合意度。zh_TW
dc.description.tableofcontents 第一章 研究動機 7
第二章 文獻回顧 9
第一節 不完整類別變數資料處理 9
第二節 單一總體尺度 13
第三節 評估順序尺度的變數合意度 18
第三章 實證分析 29
第一節 不完整資料的敘述統計 29
第二節 EM填補遺漏值 32
第三節 單一總體尺度與合意度評估 36
第四章 模擬研究 49
第一節 5%遺漏比例 50
第二節 10%遺漏比例 52
第三節 15%遺漏比例 54
第五章 結論 57		zh_TW
dc.format.extent 1146808 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101354020en_US
dc.subject (關鍵詞) 期望最大演算法zh_TW
dc.subject (關鍵詞) 隨機差異zh_TW
dc.subject (關鍵詞) 位置分布系統差異zh_TW
dc.subject (關鍵詞) 位置集中系統差異zh_TW
dc.subject (關鍵詞) 單調合意度係數zh_TW
dc.subject (關鍵詞) 加總尺度zh_TW
dc.subject (關鍵詞) 因素分析zh_TW
dc.subject (關鍵詞) 多序類相關係數zh_TW
dc.subject (關鍵詞) 非線性主成分分析zh_TW
dc.title (題名) 單一總體尺度及多項評估的合意度分析zh_TW
dc.title (題名) Agreement analysis between a single global scale and multi‐item assessmentsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Carroll, J. B. (1961). The nature of the data, or how to choose a correlation coefficient. Psychometrika, 26(4), 347-372.
Chen, T., & Fienberg, S. E. (1974). Two-dimensional contingency tables with both completely and partially cross-classified data. Biometrics, 629-642.
3104-3117.
De Leeuw, J., & Mair, P. (2009). Gifi methods for optimal scaling in R: The package homals. Journal of Statistical Software, forthcoming, 1-30.
DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research & Evaluation, 14(20), 1-11.
Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Springer.
Fuchs, C. (1982). Maximum likelihood estimation and model selection in contingency tables with missing data. Journal of the American Statistical Association, 77(378), 270-278.
Fayers, P., & Machin, D. (2007). Quality of life: the assessment, analysis and interpretation of patient-reported outcomes. John Wiley & Sons.
Gifi, A. (1990). Homogeneity analysis. Nonlinear multivariate analysis. Chichester: John Wiley & Sons.
Hochberg, Y. (1977). On the use of double sampling schemes in analyzing categorical data with misclassification errors. Journal of the American Statistical Association, 72(360a), 914-921.
Kuroda, M., Mori, Y., Masaya, I., & Sakakihara, M. Alternating least squares in nonlinear prin-cipal components.
Little, R. J., & Rubin, D. B. (1989). The analysis of social science data with missing values. Sociological Methods & Research, 18(2-3), 292-326.
Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44(4), 443-460.
Svensson, E. (2000). Comparison of the quality of assessments using continuous and discrete ordinal rating scales. Biometrical Journal, 42(4), 417-434.
Svensson, E., & Holm, S. (1994). Separation of systematic and random differences in ordinal rating scales. Statistics in medicine, 13(23‐24), 2437-2453.
Schafer, J. L. (2010). Analysis of incomplete multivariate data. CRC press.
Svensson, E. (2012). Different ranking approaches defining association and agreement measures of paired ordinal data. Statistics in medicine, 31(26), 3104-3117.
Tallis, G. M. (1962). The maximum likelihood estimation of correlation from contingency tables. Biometrics, 18(3), 342-353.
Uebersax, J. S. (2006). The tetrachoric and polychoric correlation coefficients.Statistical methods for rater agreement web site, 2006.		zh_TW