:::

詳目顯示

回上一頁
題名:以Hotdeck插補法推估成就測驗之不完整作答反應
作者:林曉芳 引用關係
作者(外文):Hsiao Fang, Lin
校院名稱:國立政治大學
系所名稱:教育學系
指導教授:余民寧
林邦傑
學位類別:博士
出版日期:2002
主題關鍵詞:熱卡插補法成就測驗不完整作答反應Hot Deck imputation methodAchievement testNon response
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(5) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:5
  • 共同引用共同引用:0
  • 點閱點閱:61
本研究之目的旨在探討成就測驗中,學生的不完整作答反應是否能利用插補法,對不完整作答反應資料進行彌補。研究者藉由試題參數與受試者能力參數的分析討論,期望能獲得支持插補技術應用於成就測驗的結論。研究欲探討的問題有三:(一)利用統計插補法所估算之替代值與實際作答反應之間是否有差異存在;(二)受試者之部分答題反應組型在經過插補後,與完全作答反應組型之分析結果是否有差異存在;(三)能否將統計插補技術應用於成就測驗模式中。
本研究程序包含兩部分,一為模擬資料(N=1000,3000,5000,10000;缺失比例為5%, 10%, 15%, 30%, 50%)的分析,模擬研究主要作為實證研究結果的驗證與推論;另一個則為實證資料的分析與討論。針對不完整作答反應,基於IRT的強假設前提,以及成就測驗作答反應的資料型態,研究者選擇熱卡插補法(Hot Deck imputation method)的統計插補技術,分別對於實證資料與模擬資料中之各類樣本數,與不同缺失比率下的作答反應作插補。另又以EM插補法作對照分析。
根據研究結果與討論,提出以下幾點歸納結論:(一)當缺失比例不大時,能符合原本的資料分佈假設,但隨著缺失比例愈高,高至30%以上時,已漸不符合原本假設;(二)當缺失比例愈高時,各項參數之估計標準差值幾乎是最大的;若忽略未作答反應之受試者的表現時,其分析所得的參數估計值亦並未是最佳的,反而是將所有受試者的作答反應進行插補估計後,所得的參數估計標準差值才是最小、最佳的;(三)本研究中,主要以熱卡法為插補方法,而EM插補法並不符合本研究資料之性質,故若採用此法進行插補,則所得的估計標準差會是最大的;(四)經過模擬研究與實證資料的分析後,證明熱卡法所推估的未作答反應,與直接刪除未作答反應或不處理未作答反應的確有差異存在,且經過插補所產生的替代值,對於受試者的能力表現能提供更穩定有效的解釋力。
This purpose of this study is to infer the feasibility if examinees’ non response could be made up, by using imputation method in non response or missing value of achievement test. The research design contains two procedures: one is simulation research (setting sample sizes are 1000, 3000, 5000, and 10000; percents of non response are 5%, 10%, 15%, 30%, and 50%), and the other is pragmatic research. Hot deck imputation method is the main concern method in this research. To test if this method fits to achievement test, EM method is used for comparison with the Hot deck imputation method.
The results are as follows: 1. The distribution of below 30% percent non response data after imputated is the same as the original data, but following the higher percents of non response, the distribution is not match what we expected. 2. Applying Hot Deck imputation method to the achievement test with different sample size and different percents of non response, the researcher found that following the higher percents of non response in any sample size, the higher standard deviation happened. Besides, ignoring or deleting these non responses is not a good way to deal with this test response pattern. Imputating an appropriate answer for the non response by Hot Deck imputation method, we could get the least standard deviation of the test and ability parameters estimation, and get largest test information for examinees. 3. We found the Hot Deck imputation method is suitable for the data pattern of achievement test than EM method. There are different outcomes between Hot deck imputation method and EM method. Hot Deck imputation method also has accuracy parameter estimation. 4. Based on above discussions, this study suggested that Hot deck imputation method could cope with non response in achievement test pretty well.
王文中(民86):測驗的建構:因素分析還是Rasch分析?調查研究,3期,129-66。new window
王寶墉(民84):現代測驗理論。台北:心理出版社。
余民寧(民80):試題反應理論的介紹(一):測驗理論的發展趨勢。研習資訊,8卷,6期,13-18。
余民寧(民86):教育測驗與評量:成就測驗與教學評量。台北 : 心理出版社。
李丕準(民85a):插補作業之芻議。主計月報,81卷,3期,42-45。
李丕準(民85b):不完整資料之處理─以二段式插補法為例。主計月報,81卷,6期,31-36。
周幼珍(民85):缺失值問題在分類上的應用。行政院國科會研究計畫NSC84-2121-M009-001。
林昆賢(民81):遺失資料分配函數估計方法的比較。國立中央大學統計研究所碩士論文。
姚穎吉(民86):選用共通題測驗等化於不完整數據。國立東華大學應用數學研究所碩士論文。
洪淑玲(民87):失去部分訊息的類別資料之貝氏分析。國立政治大學統計研究所博士論文。
翁彰佑、程爾觀(民80):隨機遺失資料插補法估計效用之比較。中國統計學報,29,2,111-130。new window
曹志弘(民88):遺漏值插補方法的比較。國立中央大學統計研究所碩士論文。
許禎元(民86):問卷調查資料的處理與統計分析--以SPSS for Windows 7.0的處理為例。復興岡學報,61期,76-91。new window
郭生玉(民74):心理與教育測驗。台北:精華出版社。
陳信木、林佳瑩(民86):調查資料之遺漏值的處置─以熱卡插補法為例。調查研究,3期,75-106。new window
陳政川(民86):續試資料之測驗等化。國立東華大學應用數學研究所碩士論文。
陳新豐(民88):多媒體線上適性測驗系統發展及其相關研究。國立台南師範學院國民教育研究所碩士論文。
陳麗如(民86):電腦化適性測驗題庫之品質管理策略。國立師範大學資訊教育研究所碩士論文。
葉瑞鈴(民89):統計調查中遺漏值處理之研究-以臺灣地區消費者動向調查為例。輔仁大學應用統計學研究所碩士論文
趙士儀(民89):以主成份分析法處理定量資料缺失值問題。元智大學資訊管理研究所碩士論文。
趙世倩(民87):問卷調查的不完整取樣設計與分析。國立清華大學統計學研究所碩士論文。
劉長萱(民90):第三十八屆中國心理學會年會主題演講:心理學研究的計量基礎。中國心理學刊,43卷,1期,1-9。new window
劉長萱、蔡政豐(民86):問卷調查的不完整取樣設計。調查研究,3期,107-27。new window
賴柔伶(民89):統計調查中插補法的研究。輔仁大學應用統計學研究所碩士論文。
Allen, M. J. & Yen, W. M.(1979). Introduction to measurement theory. Monterey, CA: Brooks/Cole.
Barton, M. A., & Lord, F. M. (1981). An upper asymptote for the three-parameter logistic item-response model. Research Bulletin, 81-20. Princeton, NJ: Educational Testing Service.
De Ayala, R. J., Plake, B. S., & Impara, J. C. (2001). The impact of omitted responses on the accuracy of ability estimation in item response theory. Journal of Educational Measurement, Vol. 38, No. 3, 213-234.
Dempster, A.P., Laird, N.M., & Rubin, D.B. (1976). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B39, 1-38.
Ford, N. L. (1976). Missing data procedures: A comparative study . American statistical Association: Proceedings of the Social Statistical Section 1976, Part 1324.
Gullikson, H. (1987). Theory of mental tests. Hillsdale, NJ: Lawrence Erlbaum Associates. (Originally published in 1950 by New York: John Wiley & Sons)
Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston, MA : kluwer Nijhoff.
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newburry Park, CA: SAGE.
Ho, R. G., & Hus, T. C. (1989). A comparison of three adaptive testing strategies using MicroCAT. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
Huisman, M. & Molenaar, I.W.(2001). Imputation of missing scale data with item response models.In A.Boomsma, M.A.J. van Duijn, & T.A.B. Snijder (Eds.), Essays on item response theory (pp. 222-244).New York: Springer-Verlag.
Kromrey, J. D., & Hines, C. V. (1994). Nonrandomly missing data in multiple regression: An empirical comparison of common missing data treatments. Educational and Psychological Measurement. 54, 3, 573-93.
Liou, M. & Cheng, P. E.(1995a). Asymptotic standard errors of equipercentile equating. Journal of Educational and Behavioral Statistics, v20, n3, 259-86.
Liou, M. & Cheng, P. E.(1995b). Equipercentile equating via data-imputation techniques. Psychometrika, v60, n1, 119-36.
Little, R. J. A.(1988). Missing data adjustments in large surveys(with discussion). Journal of Business and Economic Statistics,6,287-89.
Little, R. J. A., & Rubin, D. B.(1987). Statistical analysis with missing data. Wiley.
Lord, F. M. (1974). Estimation of latent ability and item parameters when there are omitted responses. Psychometrika, 39, 247-264.
Lord, F. M. (1980). Application of item response theory to practical testing problems. Hillsdale, NJ: Erlbaum.
Lord, F. M. (1983). Maximum likelihood estimation of item response parameters when some responses are omitted. Psychometrika, 48, 477-481.
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading. MA: Addison-Wesley.new window
Ludlow, L. H. & O’leary, M. (1999). Scoring omitted and not-reached items: practical data analysis implications. Educational and Psychological Measurement, Vol. 59, No. 4, 615-630.
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
Mislevy, R, J., & Bock, R. D. (1993). Bilog3: Item analysis and test scoring with binary logistic models. Chicago, IN: Scientific Software, INC.new window
Mislevy, R. J., & Stocking, M. L. (1989). A consumer’s guide to LOGIST and BILOG for Windows 3. Applied Psychological Measurement, 13, 57-75.
Mislevy, R. J., & Wu, P. (1988). Inferring examinee ability when some item responses are missing. ED395017.
Mislevy, R. J., & Wu, P. (1996). Missing response and IRT ability estimation: omits, choice, time limits, and adaptive testing. Princeton NJ: Educational Testing Service.
Oh, H. L. & Scheuren, F. J.(1980). Estimating the variance impact of missing CPS income data. American Statistical Association 1980 Proceedings of the Section on Survey Research Methods.
Oh, H. L., Scheuren, F. J., & Nisselson, H. (1980). Differential bias impacts of alternate census bureau Hot Deck procedures for imputing missing CPS income data. American Statistical Association 1980 Proceedings of the Section on Survey Research Methods.
Owen, R, J. (1975). A Bayesian sequential procedure for quanta response in the context of adaptive mental testing. Journal of the American Statistical Association, 70,351-356.
Patz, R. J., & Junker, B. W. (1999a). A straightforward approach to Markov chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics, Vol. 24, 146-178.
Patz, R. J., & Junker, B. W. (1999b). Applications and extensions of MCMC in IRT: multiple item types, missing data, and rated responses. Journal of Educational and Behavioral Statistics, Vol. 24, No.4, 342-336.
Pyle, D. (1999). Data preparation for data mining. Morgan Kaufmann Publishers.
Rubin, D. B.(1976). Inference and missing data. Biometrika, 63, 581-592.
Rubin, D. B.(1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business and Economic Statistics, 4, 87-94.
Rubin, D. B.(1987). Multiple imputation for nonresponse in Survey. Wiley.
Wang, X. B., Wainer, H., & Thissen’s, D. J. (1995). On the viability of some unstable assumptions in equating exams that allow examinee choice. Applied Measurement in Education, 8, 211-225.
Weiss,D. J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6, 473-492.
Welniak, E. J., & Coder, J. F.(1980). A measure of the bias in the March CPS earnings imputation system. American Statistical Association 1980 Proceedings of the Section on Survey Research Methods.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE