期刊專區/ Periodical Section

非線性結構方程模式：以試題反應模式

Nonlinear Structural Equation Models: An Item Response

所屬期刊：	第6卷第4期 「測驗與評量」 主編：國立臺灣師範大學教育心理與輔導學系 林世華
系統編號：	vol023_01
主題：	測驗與評量
出版年份：	2010
作者：	蘇啟明;王文中
作者(英文)：	Chi-Ming Su;Wen-Chung Wang
論文名稱：	非線性結構方程模式：以試題反應模式
論文名稱(英文)：	Nonlinear Structural Equation Models: An Item Response
共同作者：
最高學歷：
校院名稱：
系所名稱：
語文別：
論文頁數：	46
中文關鍵字：	試題反應理論;潛在反應;非線性結構模式;貝氏統計;可能值
英文關鍵字：	item response theory;latent response;nonlinear structural equation modeling;Bayesian statistics;plausible value
服務單位：	國立中正大學心理學研究所博士候選人;香港教育學院心理研究學系講座教授
稿件字數：	11976
作者專長：	測驗與評量
投稿日期：	2010/11/7
論文下載：
摘要(中文)：	人類學科裡的反應變項通常是二元或順序的，而不是等距。由於類別 的試題反應跟其所欲測量的潛在特質的關係不會是線性的，於是就發展了 試題反應理論來描述它們之間的非線性關係。另一種類別資料分析方法，試 圖建立潛在連續反應與所欲測量的潛在特質的線性關係，然後透過閾值模式 將潛在連續反應轉化為觀察的類別反應。當結構方程模式裡的測量模式中的 試題反應與潛在特質之間的關係是非線性時，就可稱為非線性結構模式。其 參數可用貝氏（WinBUGS軟體）或非貝氏（Mplus軟體）來估計。從模擬研 究裡，我們發現雖然這兩種軟體都可以很精確的估計參數，但當測驗短時， WinBUGS 的估計效果比Mplus好。如果原始試題反應不可得的話，使用可 能值的作法可以有效估計內衍變項和外衍變項間的結構參數，但是最大概似 估計的作法嚴重低估結構參數，因為它完全沒有考慮測量誤差。兩個實證的 例子說明了非線性結構模式和可能值作法的意涵與應用。
摘要(英文)：	Response variables in the human sciences are often binary or ordinal rather than interval. Because the relationship between categorical item responses and their underlying latent traits cannot be linear, item response theory (IRT) models have been developed to describe the nonlinear relationship between them. Another approach to categorical data is to establish linear relationship between latent continuous responses and their underlying latent traits and convert latent continuous responses to observed categorical item responses via threshold models. When the relationships between item responses and their underlying latent traits are nonlinear in the measurement part of structural equation modeling (SEM), the resulting SEM can be called nonlinear SEM (NSEM), to emphasize the nonlinear relationships between item responses and the underlying latent traits. Parameters in NSEM can be estimated using the Bayesian (WinBUGS) or non-Bayesian (Mplus) approaches. In a series of simulations, it was found that although both WinBUGS and Mplus can recover parameters in NSEM very well, WinBUGS slightly out-performs Mplus when tests are short. When original item responses are not accessible, the use of the plausible-value approach can recover the structural parameters for exogenous and endogenous variables as satisfactorily as WinBUGS and Mplus can when original responses are accessible. However, the use of the maximum likelihood estimate approach underestimates the structural parameters substantially because the measurement error is ignored completely. Depression and resource planning are two empirical examples that illustrate the implications and applications of NSEM and the plausible-value approach.
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