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| Title: | 迴歸分析中Suppression與Enhancement現象之探討
research suppression and enhancement phenomenon in regression |
| Authors: | 劉家齊 |
| Contributors: | 江振東
劉家齊 |
| Keywords: | 迴歸分析
抑制變數
Suppression
Enhancement |
| Date: | 2015 |
| Issue Date: | 2015-08-03 13:19:18 (UTC+8) |
| Abstract: | 自Horst (1941)提出suppressor變數一詞起，由於後續許多研究採用不盡相同思維之著眼點，也就衍生出許多不同定義的suppressor變數。Horst (1941)著重在判定係數的變化，Darlington (1968)、Conger (1974) 及 Cohen and Cohen (1975)則著重在迴歸係數的變化， Velicer (1978)則改用semipartial correlation coefficient來定義suppressor變數，再度將焦點轉回Horst (1941)的思維。Currie and Korabinski (1984)引進enhancement一詞，以便與suppression有所區分。
為了釐清這些紛擾的名詞定義，第二章、三章中，我們分別回顧enhancement與suppression兩現象。第四章中，我們針對enhancement、suppression兩種現象的關聯性，依據四種不同的面向進行比較。第五章，我們針對suppressor變數存在的情況下，藉由模擬實驗的方式，探討stepwise regression、forward selection、backward elimination三種變數選取方式的可能缺憾。第六章為總結。
Since Horst (1941) introduced the term of suppressor variable, many different definitions of suppressor variables have appeared in literature. Originally, Horst (1941) based the definition on the coefficient of determination. Darlington (1968), Conger (1974) and Cohen and Cohen (1975) paid more attention on the regression coefficients instead. On the other hand, Velicer (1978) used semipartial correlation coefficient to define a suppressor variable, and directed the focus back to that of Horst (1941). In order to differentiate the two similarly related ideas, Currie and Korabinski (1984) proposed the term of enhancement to describe exclusively the situations reflected by the definition of Horst (1941) or Velicer (1978).
In order to clarify the ambiguities resulting from various definitions of suppressor variable in literature, we first reviewed enhancement and suppression respectively in Chapters 2 and 3. In Chapter 4, we investigated their relationships from four different perspectives. In Chapter 5, we studied the possible drawbacks on using stepwise regression, forward selection, and backward elimination these three variable selection procedures on the presence of a suppressor variable. Conclusions are provided in Chapter 6.
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| Reference: | Bertrand, P.V., and Holder, R.L. (1988). “A quirk in multiple regression: The whole regression can be greater than the sum of its parts.” The Statistician, 37, 371-374.
Currie, I., and Korabinski, A. (1984). “Some comments on bivariate regression.” The Statistician, 33, 283–292.
Cohen, J., and Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioral sciences, New Jersey: Lawrence Erlbaum Associates.
Conger, A.J. (1974). “A revised definition for suppressor variables: a guide to their identification and interpretation.” Educational and Psychological Measurement, 34, 35-46
Darlington, R.B. (1968). “Multiple regression in psychological research and practice.” Psychological Bulletin, 69,161-182
Dayton, M. (1972). “A method for constructing data which illustrate a suppressor variable.” The American Statistician, 26, 36.
Feldman, B. (2005), “Relative Importance and Value.” Unpublished manuscript(Version 1.1, March 19 2005).
Friedman, L., and Wall, M. (2005). “Graphical views of suppression and multicollinearity in multiple linear regression.” The Educational and Psycgological Measurment, 2005, 59, 127-137.
Hamilton, D. (1987). “Sometimes : Correlated variables are not always redundant.” The American Statistician, 41, 129-132.
Holling, H. (1983). “Suppressor structures in the general linear model.” Educational and Psychological Measurement, 43, 1-9.
Horst, P. (1941). “The role of prediction variables which are independent of the criterion.” In Horst, P.(Ed.): The prediction of personal adjustment . Social Science Research Bulletin, 48, 431-436.
Lindeman, R. H., Merenda, P. F., and Gold, R. Z. (1980). Introduction to Bivariate and Multivariate Analysis. Glenview, IL: Scott, Foresman.
Lynn, H.S. (2003). “Suppression and confounding in action.” The American Statistician, 57, 58-61.
Lutz, G. (1983). “A method for construction data which illustrate there types of suppressor variable.” Educational and Psychological Measurement, 43, 373-377.
Newton, R. G. and Spurrel, D. J. (1967). “Examples of the use of elements for clarifying regression analysis.” Applied Statistics, 16, 165-172.
Pratt, J. W. (1987). “Dividing the indivisible: Using simple symmetry to partitionvariance explained”, in T. Pukkila and S. Puntanen (eds.), Proceedings of the Second International Conference in Statistics (University of Tampere, Tampere,Finland) pp. 245–260.
Schey, H.M. (1993). “The relationship between the magnitudes of SSR(x2) and SSR(x2|x1): A geometric description.” The American Statistician, 47, 26-30.
Shieh, G. (2001). “The inequality between the coefficient of determination and the sum of squared simple correlation coefficients.” The American Statistician, 55, 121–124.
Shieh, G. (2006). “Suppression situation in multiple linear regression.” The Educational and Psychological Measurement, 66,435-447.
Smith, R.L., Ager, J.W., and Williams, D. L. (1992). “Suppressor variables in multiple regression/correlation.” Educational and Psychological Measurement, 52, 17-29.
Velicer, W. (1978). “Suppressor variables and the semipartial correlation coefficient.” Educational and Psychological Measurement, 38, 953-958. |
| Description: | 碩士
國立政治大學
統計研究所
102354025 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0102354025 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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