政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/104748
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    政大典藏 > Journal of NCCU > Issue 72 > Journal Articles >  Item 140.119/104748


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/104748


    Title: Estimating the Latent Trait From Likert-Type Data: A Comparison of Factor Analysis, Item Response Theory, and Multidimensional Scaling
    Authors: 詹志禹
    Chan, Jason C.
    Contributors: 教育系
    Date: 1996-05
    Issue Date: 2016-12-09 11:19:02 (UTC+8)
    Abstract: 本研究比較三個統計模式從李克特式(Likert-Type)資料中估計單向度潛在特質的能力，這三個模式是「植基於多序類相關(polychoric correlations)的因素分析」(FA-PL)，「試題反應理論中的漸變反應模式」(IRT-GRM)，以及「加權多維展開法」(WMDU)。一般常用的方法(SSI)－－分派連續性整數給李克特式量尺中的一個反應類別（如「非常同意」），再將每一題得分加總－－則做為比較的基準線。本研究為電腦模擬研究，操弄了樣本大小、測驗長度，以及試題反應分配的偏態程度等三個自變項，依變項則為回復潛在特質的真傎的正確性，結果發現：IRT-GRM表現得最好，最不受偏態的影響；FA-PL只有在試題反應分配為常態時，才能表現與IRT-GRM一樣好，而在試題反應分配為高度偏態時，甚至表現得比SSI差；最後，WMDU只有在試題反應分配為常態或輕微偏態時，才能表現得與SSI一樣好。本文也討論了這些發現對模式選擇的涵意。
    Three statistical models were compared with one another in terms of the ability to recover a unidimensional latent trait from Likert-type data. They are factor analysis based on polychoric correlations (FA-PL), the graded response model in item response theory (IRT-GRM), and the weighted multidimensional unfolding (WMDU). The common procedure of summing up successive integers assigned to response categories (SSI) served as the base- line procedure. Sample size, test length, and skewness of item response distributions were manipulated in this simulation study. Generally speaking, IRT-GRM performed the best and was most robust against skewness. FA-PL were competitive with IRT-GRM only when item responses were normally distributed. It performed even worse than did SSI when item responses were highly skewed.WMDU might be a rival alternative to SSI only when item responses were normally distributed or moderately skewed and sample size was large for MDU models (e.g.. N=100).
    Relation: 國立政治大學學報,72 part 1 ,299-320
    Data Type: article
    Appears in Collections:[Issue 72] Journal Articles

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