期刊目錄列表 - 60卷(2015) - 【教育科學研究期刊】60(1) 三月刊

追蹤資料分析中隨時間變動解釋變項平減之研究 作者:
溫福星(東吳大學國際經營與貿易學系)

卷期:60卷第1期
日期:2015年3月
頁碼:73-97
DOI:10.6209/JORIES.2015.60(1).03

摘要:

利用多層次模式或是階層線性模式進行重複觀測資料的分析,如果個體層次解釋變項包含隨時間變動解釋變項時,在個體層次方程式對它不平減或是總平減所獲得的迴歸係數是一個偏誤的結果,因為這個隨時間變動的解釋變項具有追蹤與橫斷面的資料特性,對個體層次結果變項的影響可以拆解為互斥的組間迴歸係數與組內迴歸係數,因此,必須利用組平減並將組平均數置回截距項方程式方能獲得正確的估計結果。但在不平減、總平減與組平減三種方法下都加上組平均數置回截距項方程式,在隨機截距模型下則會獲得等價的估計結果。本研究整理出這些平減方法之間的統計關係,並利用實徵資料示範分析各種模式,說明之間的差異與等價關係,最後提出研究的結論與建議。

關鍵詞:追蹤資料、組平減、等價、隨機截距模型、總平減

《詳全文》 檔名

參考文獻:
  1. 中央研究院人社中心調查研究專題中心(2014)。學術調查研究資料庫:台灣高等教育資料庫。取自https://srda.sinica.edu.tw/group/scigview/3/10【Center for Survey Research, RCHSS, Academic Sinica. (2014). SRDA: Taiwan Higher Education Database. Retrieved from https://srda.sinica.edu.tw/group/scigview/3/10】
  2. 巫博瀚、陸偉明、賴英娟(2012)。台灣青少年快樂發展之縱貫性研究:二階層線性成長模式的發現。中華輔導與諮商學報,34,1-18。【Wu, P.-H., Luh, W.-M., & Lai, Y.-C. (2012). A longitudinal study of teenagers’ development of happiness in Taiwan: An analysis of hierarchical linear growth model. Chinese Journal of Guidance and Counseling, 34, 1-18.】
  3. 李靜芳、溫福星(2008)。階層線性模式於追蹤研究之應用-以子宮切除婦女之術後初期症狀困擾為例。護理雜誌,55(4),63-72。doi:10.6224/JN.55.4.63【Lee, C.-F., & Wen, F.-H. (2008). Applying the hierarchical linear model in longitudinal studies: An example of symptom distress in women who had undergone a hysterectomy. The Journal of Nursing, 55(4), 63-72. doi:10. 6224/JN.55.4.63】
  4. 周玉慧(2011)。夫妻間衝突因應策略之類型變遷及其長期影響。中華心理學刊,53(2),229- 253。【Jou, Y.-H. (2011). Longitudinal transmission and longitudinal effects of conflict-coping strategies styles on Taiwanese married couples’ martial quality. Chinese Journal of Psychology, 53(2), 229-253.】
  5. 林清山(1992)。心理與教育統計學。臺北市:東華。【Lin, C.-S. (1992). Statistics for psychology and education. Taipei, Taiwan: Tung Hua.】
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中文APA引文格式
溫福星(2015)。追蹤資料分析中隨時間變動解釋變項平減之研究。教育科學研究期刊60(1),73-97。doi:10.6209/JORIES.2015.60(1).03
APA Format
Wen, F.-H. (2015). Centering on the Time-Varying Independent Variables in Longitudinal Data Analysis. Journal of Research in Education Sciences, 60(1), 73-97. doi:10.6209/JORIES.2015.60(1).03

Journal directory listing - Volume 60 (2015) - Journal of Research in Education Sciences【60(1)】March

Centering on the Time-Varying Independent Variables in Longitudinal Data Analysis Author:
Fur-Hsing Wen(Department of International Business, Soochow University)

Vol.&No.:Vol. 60, No. 1
Date:March 2015
Pages:73-97
DOI:10.6209/JORIES.2015.60(1).03

Abstract:

When analyzing repeated measures by using multilevel modeling (MLM) or hierarchical linear modeling (HLM), if the individual-level independent variables include a time-varying variable and it is modeled as uncentered or grand-mean centered in a level-one equation, then this regression coefficient is a biased estimate. Because repeated measures data comprise longitudinal and cross-sectional parts, the total effect of the time-varying independent variable on the individual outcomes can be decomposed into within- and between-subject regression coefficients. Therefore, the optimal approach is to use group-mean centered in a level-one equation and group means replaced in the intercept equation. In some cases (e.g., the random intercepts model), the three methods, namely uncentered, grand-mean centered, and group-mean centered time-varying variable approaches with group means replacement, are equivalent in MLM and HLM. We adopted a statistical model and empirical data analysis to determine the equivalent relationships and differences among the three centered methods and present a conclusion and recommendations.

Keywords:longitudinal data, grand-mean centering, equivalence, random intercepts model, group-mean centering