查詢結果分析
相關文獻
- A Comparison of the Standardization and IRT Methods of Adjusting Pretest Item Statistics Using Realistic Data
- Rasch模式概率比法的差異試題功能分析
- 無參數試題反應理論的能力群組之模糊分割
- 利用試題反應理論編製國中壘球擲準測驗
- 破壞性檢驗最適抽樣個數之研究
- 生活品質量表的發展
- Applications of the Asymptotic Standard Errors of multidimensional IRT Parameter Estimates
- 利用試題反應理論分析標準舞比賽裁判之判決
- 貝氏估計下破壞性檢驗最適抽樣個數之研究--以農產品批購為例
- 考慮損失成本下破壞性檢驗最適抽樣個數之研究--以農產品批購為例
頁籤選單縮合
題名 | A Comparison of the Standardization and IRT Methods of Adjusting Pretest Item Statistics Using Realistic Data=標準化法與IRT法於校正預試試題統計值之比較--真實資料研究 |
---|---|
作者 | 章舜雯; Chang, Shun-wen; Hanson,Bradley A.; Harris,Deborah J.; |
期刊 | 測驗年刊 |
出版日期 | 20010700 |
卷期 | 48:2 2001.07[民90.07] |
頁次 | 頁109-129 |
分類號 | 521.32 |
語文 | eng |
關鍵詞 | 試題預試; 標準化法; 試題反應理論; 樣本大小; Item pretesting; Standardization; Item response theory; Sample size; |
中文摘要 | 在實際的預試情況下,常常無法滿足IRT模式估計試題參數所要求的大本條件,雖然傳統的試題分析方法所需的樣本這較IRT法逼小,但是不同梃本所得的傳統試題統計值非屬同一量尺,因此無法直接比較。本研究承續Cheng, Hanson, and Harris (2000)的研究,使用比該研究更似真實的資料,進一步探討在小樣本的情況之下,標準化法(the standardization method)在調整預試試題統計值(即估計母群試題參數)的功能,並與1PL以及3PL的表現進行比較。研究結果顯示,使用MIRT 50向度所模擬的真實資料時,3PL在估計母群時試題難易度與鑑吸度的表現比1PL或標準化法來得好。就估計母群試題鑑別度而言,標巫化法比1PL好,但就估計母群試題難易度而言,1PL卻比標準化法好。另外,本研究亦使用一大型測驗的預試資料進行比較,結果顯示1PL的表現最好。就估計難易度而言,3PL的表現最差;就估計鑑別度而言,標準化法的表現最差。茲因研究考慮的變項有限,標準化之於1PL與3PL表現之結論應該有所保留。雖然標準化法的結果不比IRT法來得精確,但就方法的簡便性而言,使用標準化法代替IRT法似乎是可行的。 |
英文摘要 | The requirement of large sample sizes for calibrating items based on IRT models is not easily met in many practical pretesting situations. Although classical item statistics could be estimated with much smaller samples, the values may not be comparable across different groups of examinees. This study extended Chang, Hanson, and Harris (2000) by further exploring the standardization method and comparing its effectiveness with the one-parameter (1PL) and three-parameter (3PL)logistic IRT models in adjusting pretest item statistics with small sample sizes, using more realistic data than the previous study. Based on the realistic data generated from a 50-dimensional MIRT model, the 3PL model performed better than 1PL or standardization method in recovering both the population p-values and point biserial correlations. The standardization method outperformed the 1PL model in recovering the population point biserial correlations, but not in recovering the population p-values. The performance of the methods was also evaluated using the real pretest data of a high-stakes test. In terms of recovering the p-values and point biserial correlations for the real data, the 1PL model produced the most satisfactory results. The 3PL model performed worst in terms of recovering the p-values for the real data, and the standardization method performed worst in recovering the point biserial correlations for the real data. Due to the very limited number or conditions studied, one must be cautious about making conclusions about the standardization method relative to IRT methods based on these studies. The standardization method appears t be a viable alternative to IRT methods that may be simpler to implement, although these results do not suggest that it will produce more accurate results. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。