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Breaking free from the limitations of classical test theory: Developing and measuring information systems scales using item response theory

机译:摆脱经典测试理论的局限性:使用项目响应理论开发和测量信息系统规模

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Information systems (IS) research frequently uses survey data to measure the interplay between technological systems and human beings. Researchers have developed sophisticated procedures to build and validate multi-item scales that measure latent constructs. The vast majority of IS studies uses classical test theory (CTT), but this approach suffers from three major theoretical shortcomings: (1) it assumes a linear relationship between the latent variable and observed scores, which rarely represents the empirical reality of behavioral constructs; (2) the true score can either not be estimated directly or only by making assumptions that are difficult to be met; and (3) parameters such as reliability, discrimination, location, or factor loadings depend on the sample being used. To address these issues, we present item response theory (IRT) as a collection of viable alternatives for measuring continuous latent variables by means of categorical indicators (i.e., measurement variables). IRT offers several advantages: (1) it assumes nonlinear relationships; (2) it allows more appropriate estimation of the true score; (3) it can estimate item parameters independently of the sample being used; (4) it allows the researcher to select items that are in accordance with a desired model; and (5) it applies and generalizes concepts such as reliability and internal consistency, and thus allows researchers to derive more information about the measurement process. We use a CTT approach as well as Rasch models (a special class of IRT models) to demonstrate how a scale for measuring hedonic aspects of websites is developed under both approaches. The results illustrate how IRT can be successfully applied in IS research and provide better scale results than CIT. We conclude by explaining the most appropriate circumstances for applying IRT, as well as the limitations" of IRT. (C) 2016 Elsevier B.V. All rights reserved.
机译:信息系统(IS)研究经常使用调查数据来衡量技术系统与人类之间的相互作用。研究人员已经开发出复杂的程序来构建和验证可测量潜在构造的多项目量表。绝大多数信息系统研究都使用经典测试理论(CTT),但是这种方法存在三个主要的理论缺陷:(1)假设潜在变量与观察到的分数之间存在线性关系,这很少代表行为构造的经验现实; (2)不能直接估计真实分数,或者只能通过做出难以满足的假设来估计真实分数; (3)可靠性,辨别力,位置或因素负荷等参数取决于所使用的样品。为了解决这些问题,我们提出项目响应理论(IRT),作为通过分类指标(即测量变量)测量连续潜在变量的可行替代方案的集合。 IRT具有以下优点:(1)假定非线性关系; (2)可以更适当地估计真实分数; (3)可以独立于所使用的样本估算项目参数; (4)允许研究人员选择符合期望模型的项目; (5)应用并概括了可靠性和内部一致性等概念,从而使研究人员可以得出有关测量过程的更多信息。我们使用CTT方法以及Rasch模型(IRT模型的特殊类)来说明在两种方法下如何开发用于测量网站享乐性的量表。结果说明了IRT如何可以成功地应用于IS研究中,并提供比CIT更好的规模结果。最后,我们解释了适用IRT的最合适情况以及IRT的局限性。(C)2016 Elsevier B.V.保留所有权利。

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