歡迎來到全傑科技

今日訪客: 0
線上訪客:

本軟體最新開課活動

目前沒有相關活動!!!
本軟體之前的活動!!

下載專區 Download

    活動資訊

    • 目前尚無任何訓練課程!!

    聯絡我們

    姓名:
    Email:
    聯絡電話:
    單位:
    部門:
    附件:
    您的留言:

    提供專業軟體代購服務
    如有未列於網站之產品需求
    歡迎來電洽詢,感謝您!
    電話:(02)2507-8298

    IRTPRO 4.2
    Item Response Theory for Patient-Reported Outcomes
    軟體代號:201
    瀏覽次數:251
    WindowsXPWindowsVISTAWindows7Windows8
    商業版
    再啟動服務
    安裝序號
    網路啟動
    合法保證
    產品介紹!
    • SSI創建於1971年,旨在應用統計理論,開發新的統計軟件和完善已有的統計軟件。我們的產品被廣泛應用於統計、社會科學、醫藥健康、教育、經濟、工商管理、市場、環境科學、工程以及其他研究領域。每年,通過應用我們的軟件得出的結果而在國際性刊物上發表的論文不計其數。

     

    • Item Response Theory (IRT)項目反應理論軟件(稱簡IRT)包括一組分析軟件包: BILOG-MG, MULTILOG, PARSCALE,和TESTFACT。這些軟件作為題目分析、題庫建設以及分數估計等方面的重要工具在各個領域被廣泛應用。

    適用於Windows的IRTPRO 4.20,作者: Li Cai, David Thissen & Stephen du Toit

    IRTPRO(用於患者報告結果的項目響應理論)是使用IRT進行項目校準和測試評分的全新應用程序。

    在IRTPRO中實現了項目校準和評分的項目響應理論(IRT)模型基於以下廣泛使用的響應函數的一維和多維[確認因子分析(CFA)或探索性因子分析(EFA)]版本:

    • 兩參數對數邏輯(2PL)(Birnbaum,1968)[等式約束包括一參數對數邏輯(1PL)(Thissen,1982)]
    • 三參數邏輯(3PL)(Birnbaum,1968)
    • 分級(Samejima,1969; 1997)
    • 廣義部分信貸(Muraki,1992,1997)
    • 名義上的(Bock,1972,1997; Thissen,Cai,&Bock,2010)

     這些項目響應模型可以在測試或規模內以任何組合混合,並且可以指定參數之間的任何(可選)用戶指定的相等約束或參數的固定值。

     IRTPRO為項目參數估計(項目校準)實現最大似然(ML)方法,或者如果為項目參數指定了(可選)先驗分佈,則它會計算最大後驗(MAP)估計。話雖這麼說,也可以使用其他計算方法,每種方法都可以為維數和模型結構的某些組合提供最佳性能:

    • 博克·艾特金(BAEM)(博克和艾特金,1981年)
    • Bifactor EM(Gibbons&Hedeker,1992; Gibbons ,2007; Cai,Yang&Hansen(2011)
    • 廣義降維EM(Cai,2010-a)
    • 自適應正交(ADQEM)(Schilling&Bock,2005)
    • Metropolis-Hastings Robbins-Monro(MHRM)(Cai,2010-b,2010-c)
    • 馬爾可夫鏈蒙特卡洛(MCMC)帕茲-容克(1999-a,1999-b)

     可以使用以下任何一種方法來計算IRTPRO中的IRT量表分數:

    • 響應模式的最大後驗(MAP)
    • 期望後驗(EAP)用於響應模式(Bock&Mislevy,1982)
    • 期望後驗(EAP)的總分(Thissen&Orlando,2001; Thissen,Nelson,Rosa,&McLeod,2001)

     IRTPRO中的數據結構可以將受訪者分為幾類,並且可以針對多個組估計總體潛在變量均值和方差-協方差矩陣(Mislevy,1984,1985)。[通常,如果只有一組,則總體潛在變量均值和方差是固定的(通常為0和1)以指定規模;對於多個組,通常將一組標為具有潛在值的“參考組”。]

     為了檢測差異項目功能(DIF),IRTPRO使用Wald檢驗,該檢驗是根據Lord(1977)的建議建模的,但具有使用補充EM(SEM)算法計算的準確的項目參數誤差方差-協方差矩陣(Cai,2008)。

     根據項目的數量,響應類別和受訪者,IRTPRO在項目校准後報告適合度和診斷統計數據的多種變化。總是報告–2對數似然值,赤池信息準則(AIC)(Akaike,1974)和貝葉斯信息準則(BIC)(Schwarz,1978)。如果樣本數量充分超過了基於項目響應模式對受訪者進行完整交叉分類中的像元數,則會報告針對一般多項式選擇的總體似然比檢驗。對於某些型號,2統計數據(Maydeu-Olivares&Joe,2005,2006; Cai,Maydeu-Olivares,Coffman,&Thissen,2006)也已計算。診斷統計數據包括Chen&Thissen(1997)描述的局部依賴性(LD)統計量的多態反應的概括和Orlando&Thissen(2000,2003)建議SS-X 2項目擬合統計量。


    IRTPRO 4.20 for Windows by Li Cai, David Thissen & Stephen du Toit
     

    IRTPRO (Item Response Theory for Patient-Reported Outcomes) is an entirely new application for item calibration and test scoring using IRT.

    Item response theory (IRT) models for which item calibration and scoring are implemented in IRTPRO are based on unidimensional and multidimensional [confirmatory factor analysis (CFA) or exploratory factor analysis (EFA)] versions of the following widely used response functions:

    • Two-parameter logistic (2PL) (Birnbaum, 1968) [with which equality constraints includes the one-parameter logistic (1PL) (Thissen, 1982)]
    • Three-parameter logistic (3PL) (Birnbaum, 1968)
    • Graded (Samejima, 1969; 1997)
    • Generalized Partial Credit (Muraki, 1992, 1997)
    • Nominal (Bock, 1972, 1997; Thissen, Cai, & Bock, 2010)

     These item response models may be mixed in any combination within a test or scale, and any (optional) user-specified equality constraints among parameters, or fixed values for parameters, may be specified.

     IRTPRO implements the method of Maximum Likelihood (ML) for item parameter estimation (item calibration), or it computes Maximum a posteriori (MAP) estimates if (optional) prior distributions are specified for the item parameters. That being said, alternative computational methods may be used, each of which provides best performance for some combinations of dimensionality and model structure:

    • Bock-Aitkin (BAEM) (Bock & Aitkin, 1981)
    • Bifactor EM (Gibbons & Hedeker, 1992; Gibbons et al., 2007; Cai, Yang & Hansen (2011)
    • Generalized Dimension Reduction EM (Cai, 2010-a)
    • Adaptive Quadrature (ADQEM) (Schilling & Bock, 2005)
    • Metropolis-Hastings Robbins-Monro (MHRM) (Cai, 2010-b, 2010-c)
    • Markov Chain Monte Carlo (MCMC) Patz-Junker's (1999-a, 1999-b)

     The computation of IRT scale scores in IRTPRO may be done using any of the following methods:

    • Maximum a posteriori (MAP) for response patterns
    • Expected a posteriori (EAP) for response patterns (Bock & Mislevy, 1982)
    • Expected a posteriori (EAP) for summed scores (Thissen & Orlando, 2001; Thissen, Nelson, Rosa, & McLeod, 2001)

     Data structures in IRTPRO may categorize the item respondents into groups, and the population latent variable means and variance-covariance matrices may be estimated for multiple groups (Mislevy, 1984, 1985). [Most often, if there is only one group, the population latent variable mean(s) and variance(s) are fixed (usually at 0 and 1) to specify the scale; for multiple groups, one group is usually denoted the "reference group" with standardized latent values.]

     To detect differential item functioning (DIF), IRTPRO uses Wald tests, modeled after a proposal by Lord (1977), but with accurate item parameter error variance-covariance matrices computed using the Supplemented EM (SEM) algorithm (Cai, 2008).

     Depending on the number of items, response categories, and respondents, IRTPRO reports several varieties of goodness of fit and diagnostic statistics after item calibration. The values of –2 log likelihood, Akaike Information Criterion (AIC) (Akaike, 1974) and the Bayesian Information Criterion (BIC) (Schwarz, 1978) are always reported. If the sample size sufficiently exceeds the number of cells in the complete cross-classification of the respondents based on item response patterns, the overall likelihood ratio test against the general multinomial alternative is reported. For some models, the M2 statistic (Maydeu-Olivares & Joe, 2005, 2006; Cai, Maydeu-Olivares, Coffman, & Thissen, 2006) is also computed. Diagnostic statistics include generalizations for polytomous responses of the local dependence (LD) statistic described by Chen & Thissen (1997) and the SS-X2 item-fit statistic suggested by Orlando & Thissen (2000, 2003).

     

    Exporting IRTPRO spreadsheet files (*.ssig) to SPSS, SAS, STATA, etc. formats

    This feature is now available in IRTPRO 4 and is accomplished as follows:

    • Use the File, Open option to open an .ssig file
    • Use the File, Export option to export this file to a different format

    Example

    Assume we want to export Anxiety14.ssig to the SPSS .sav format. Use the File, Open option to locate and select this file.