In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of Monte Carlo simulation studies (MCSS) in item response theory (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because they allow researchers to specify and manipulate an array of parameter values and experimental conditions (e.g., sample size, test length, and test characteristics). Dr. Leventhal and Dr. Ames review the conceptual foundation of MCSS in IRT and walk through the processes of simulating total scores as well as item responses using the two‐parameter logistic, graded response, and bifactor models. They provide guidance for how to implement MCSS using other item response models and best practices for efficient syntax and executing an MCSS. The digital module contains sample SAS code, diagnostic quiz questions, activities, curated resources, and a glossary.

中文翻译：

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数字模块13：项目响应理论中的蒙特卡洛模拟研究

在此数字ITEMS模块中，Brian Leventhal博士和Allison Ames博士概述了项目响应理论中的蒙特卡洛模拟研究（MCSS）（IRT）。使用MCSS的原因多种多样，其中最引人注目的是，当分析解决方案不切实际或不存在时，可以使用MCSS，因为它们使研究人员可以指定和操纵一系列参数值和实验条件（例如，样本量，测试长度和测试特性）。Leventhal博士和Ames博士回顾了IRT中MCSS的概念基础，并逐步通过使用两参数对数，分级响应和双因素模型来模拟总分以及项目响应的过程。它们提供了有关如何使用其他项目响应模型和最佳实践来实现MCSS的指南，以实现有效的语法和执行MCSS。该数字模块包含示例SAS代码，诊断测验问题，活动，精选资源和词汇表。