查詢結果分析
相關文獻
- On the Fractional Calculus(e礼戸(z-a)[fef5])β)α)and (((z-a)[fef5])β.e )α
- Establishing Score Comparability in Heterogeneous Populations
- A Fractional Derivative Model of HDR Bearings with Temperature Effect
- 新進國中教師主要工作內容之分析
- 什麼是分數量子霍爾效應﹖
- 國小分數比大小概念實作評量的發展及應用
- 分數量子霍爾效應
- 國小教師分數教學之相關知識研究
- Particular Solutions of Riemann's Differential Equations by N-fractional Calculus Operator N广
- Landmark-based Morphometric Analysis in Selectecd Species of Serranid Fishes(Perciformes: Teleostei)
頁籤選單縮合
題 名 | Establishing Score Comparability in Heterogeneous Populations |
---|---|
作 者 | Liou,Michelle; | 書刊名 | Statistica Sinica |
卷 期 | 8:3 1998.07[民87.07] |
頁 次 | 頁669-690 |
分類號 | 521.3 |
關鍵詞 | 分數; Bayesian methods; Categorical data; Data-imputation; Equipercentile equating; EM algorithm; Log-linear smoothing; |
語 文 | 英文(English) |
英文摘要 | In educational testing contexts, the relative comparability of scores on two tests is commonly established using the equipercentile method, which equates scores base don the corresponding percentile ranks in test score distributions. Because of security or disclosure considerations, data collection for a comparability study is often conducted using an incomplete-data collection for a comparability study is often conducted using an incomplete-data design, that is, the two tests are given two non-random groups at slightly different time points, and a set of common items is included in the test administration to allow some statistical adjustments for possible sample-selection bias. In the literature, researchers have made the missing-at-random assumption when estimating population score distributions using the common-item scores. This assumption can be violated in various ways, especially when the groups differ in ages or when the tests are administered a few months apart. In this study a general model is proposed for estimating score distributions using incomplete data; the model considers background information (e.g., gender, ethnicity) together with common-item scores as possible predictors of sample-selection bias, and allows nonresponse to depend on missing scores, the model parameters are estimated suing the maximum-likelihood method and a Bayesian procedure. The standard errors of comparable scores are also derived under the proposed model. The use of the model is illustrated in two applications. |
本系統中英文摘要資訊取自各篇刊載內容。