臺灣博碩士論文加值系統

社會大眾在心理學或教育學測驗上相當關注於其測驗的公平性,然而在專家編製測驗試卷的過程中,可能會因為其本身的專業素養、文化認知或主觀偏見等因素進而影響編製出的試卷內容,使的某部分族群在回答某些試題上較其他的族群有利,而於此試題上產生的這種現象稱之為DIF現象。在此篇論文中,我們主要研究目的是比較Strobl et al.(2013)提出的RT方法及我們提出的LR方法在偵測具有二元反應變數資料上的DIF現象的能力。藉由模擬實驗後,我們發現LR方法的表現普遍優於RT方法,而且當我們針對一組實際資料分析時,發現LR方法的表現結果也優於RT方法。

1 導論
2 模型介紹
2.1 單參數對數模式(1PL 模式)
2.2 二參數對數模式(2PL 模式)
2.3 三參數對數模式(3PL 模式) .
2.4 試題相關模型
3 研究方法
3.1 變數選取
3.1.1 RT 方法
3.1.2 LR 方法
3.2 切割點選取
3.2.1 RT 方法
3.2.2 LR 方法
4 模擬實驗與分析
4.1 變數選取
4.1.1 利用SPISA資料的模擬
4.1.2 利用VerbalAggression資料的模擬
4.1.3 利用試題相關模型的模
4.2 型I 誤差機率與檢定力
4.2.1 第一種模擬
4.2.2 第二種模擬
5 實際資料分析
6 未來展望
參考文獻

Andersen, E. B. (1973). ”A goodness of fit test for the Rasch model”. Psychometrika, 38. 40-123.

Andrich, D. (1978). ”A rating formulation for ordered response categories”. Psychometrika, 43. 561-573.

Birnbaum, A. (1968). ”Some latent trait models and their use in inferring an examinee’s ability”. In F. M. Lord and M. R. Novick(Eds.), Statistical Theories Of Mental Test Scores, 397-479, Reading, MA:Addison-Wesley.

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). ”Classification and regression trees”.
Belmont, California:Wadsworth.

Dorans, N. J. and Holland, P. W. (1993). ”DIF detection and description: Mantel-Haenszel and Standardization”. In Holland P. W. and Wainer, H.(Eds), Differential Item Functioning, 35-36, Hillsdale, NJ:Lawrence Erlbaum Associates.

Hothorn, T., Hornik, K., and Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3):651–674.

Kim, H. and Loh, W.-Y. (2001). ”Classification trees with unbiased multiway splits”. Journal of the American Statistica Association, 96. 589-604.

Loh, W.-Y. (2002). ”Regression trees with unbiased variable selection and interaction detection”. Statistica Sinica, 12. 361-386.

Loh, W.-Y. and Shih, Y.-S. (1997). ”Split selection methods for classification trees”. Statistica Sinica, 7. 815-840.

Lord, F. M. (1952). ”A theory of test scores”. Iowa City, IA: Psychometric Society.

Lord, F. M. and Novick, M. (1968). ”Statistical theories of mental test scores”. Reading, Mass.: Addison-Wesley.

Mair, P., Hatzinger, R., and Maier, M. (2014). ”eRm : Extended Rasch Modeling”. R package. version 0.15-4, 58-59.

Marais, I. and Andrich, D. (2008). ”Formalising dimension and response violations of local independence in the unidimensional Rasch model”. Journal of applied measurement, 9. 200-215.

Masters, G. N. (1982). ”A Rasch model for partial credit scoring”. Psychometrika, 47. 149-174.

Partchev, I. (2014). ”irtoys : Simple interace to estimation and plotting of IRT models”. R package. version 0.1.7, 6-9.

Radloff, L. S. (1977). ”The CES-D scale: A self-report depression scale for research in the general population”. Psychological Measurement, 1. 340-385.

Rasch, G. (1960). ”Probabilistic Models for some Intelligence and Attainment Tests”. Danish Institute for Educationl Research, Copenhagen.

Samejima, F. (1969). ”Estimation of a latent ability using a response pattern of graded scores”. Psychometrika Monographs, 34(Suppl.4).

Strobl, C., Zeileis, A., and Kopf, J. (2013). ”Rasch Trees: A new method for detecting differential item
functioning in the Rasch model”. Psychometrika. Forthcoming.

Wickelmaier, F., Zeileis, A., and Strobl, C. (2014). ”psychtools : Infrastructure for Psychometric Modeling”. R package. version 0.2-0, 34-35.

Zeileis, A. and Hornik, K. (2007). ”Generalized M-fluctuation tests for parameter instability”. Statistica Neerlandica, 61(4). 488-508.

Zeileis, A., Hothorn, T., and Hornik, K. (2008). ”Model-based recursive partitioning”. Journal of Computational and Graphical Statistics, 17. 492-514.