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IRTPRO 5—項目反應理論分析軟件包

軟件試用 獲取報價

IRTPRO 軟件簡介

IRTPRO 是一套全新的使用IRT進行項校準(item calibration)和檢驗計分(test scoring)的應用。
在IRTPRO中實現的項校準和計分的項目反應理論(IRT)模型是基于下列廣泛使用的響應功能的一維和多維(驗證性因子分析（CFA），或探索性因子分析（EFA)）版本：
◆ 2參數logisitic(2PL)(Birnbaum,1968) (帶等式約束，包括1參數logistic(1PL) (Thissen, 1982))
◆ 3參數logisitic(2PL)(Birnbaum,1968)
◆ Graded (Samejima, 1969; 1997)
◆ 分部評分模型(Generalized Partial Credit) (Muraki, 1992, 1997)
◆ Nominal (Bock, 1972, 1997; Thissen, Cai, & Bock, 2010)

這些項目反應模型可以在一個檢驗或測量中任意混合，以及在參數間的任意(可選)用戶指定等式約束，或參數的固定值，可以被指定。
IRTPRO采用極大似然(Maximum Likelihood(ML))方法用于項目參數估計(項校準)，或在(可選)用于項目參數的先驗分布指定的情況下，計算Maximum a posteriori (MAP)估計。

軟件簡介（英文）

Overview
For many years SSI has had a widely distributed suite of four IRT (Item Response Theory) programs called Bilog-MG, Multilog, Parscale and Testfact. However, with growth and success also come new users and new expectations. Despite their success, these IRT programs are far from perfect measured by today's software standards. Furthermore, they share a great deal of similarities and overlapping functionalities. Thus, it became evident that a single, state-of-the-art software product must be developed to replace these four programs.
In an effort to meet the growing demands of our user community, SSI has developed a new software product, called IRTPRO, which is on the cutting edge of current technology. The program has been tested extensively on the Microsoft Windows platform with Windows7, Vista and XP operating systems. Important sections of the IRTPRO numeric engine have been parallelized to run on multiple cores simultaneously.
IRTPRO supports both model-based and data-based graphical displays. Model-based graphs, currently available for unidimensional IRT models only, are trace lines, information curves, combined trace lines-information curves, total information, and test characteristic curves.
IRTPRO imports data from a variety of statistical software packages as well as importing data from fixed format data (.fixed), comma-separated (.csv), tab-delimited (usually .txt), and Excel (.xls) files. Whatever the original format, the imported data are saved to an IRTPRO data file with extension .ssig that is displayed visually as a spreadsheet, similar in appearance to an Excel spreadsheet.
IRTPRO 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
◆ 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)

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