GeneralizIT is a Python package designed to streamline the application of Generalizability Theory (G-Theory) in research and practice. G-Theory extends classical test theory by estimating multiple sources of error variance, providing a more flexible and detailed approach to reliability assessment. Despite its advantages, G-Theory's complexity can present a significant barrier to researchers. GeneralizIT addresses this challenge by offering an intuitive, user-friendly mechanism to calculate variance components, generalizability coefficients E*rho^2 and dependability Phi and to perform decision (D) studies. D-Studies allow users to make decisions about potential study designs and target improvements in the reliability of certain facets. The package supports both fully crossed and nested designs, enabling users to perform in-depth reliability analysis with minimal coding effort. With built-in visualization tools and detailed reporting functions, GeneralizIT empowers researchers across disciplines, such as education, psychology, healthcare, and the social sciences, to harness the power of G-Theory for robust evidence-based insights. Whether applied to small or large datasets, GeneralizIT offers an accessible and computationally efficient solution to improve measurement reliability in complex data environments.

翻译：GeneralizIT是一个专为简化和促进概化理论（G-Theory）在研究与实践中应用的Python软件包。概化理论通过估计多个误差方差来源，扩展了经典测验理论，为信度评估提供了更灵活和精细的方法。尽管具有这些优势，概化理论的复杂性仍可能对研究者构成显著障碍。GeneralizIT通过提供直观、用户友好的机制来计算方差分量、概化系数E*rho^2和可靠性系数Phi，以及执行决策（D）研究，有效应对了这一挑战。D研究使用户能够针对潜在的研究设计方案做出决策，并针对特定层面的信度改进目标进行优化。该软件包支持完全交叉设计和嵌套设计，使用户能够以最少的编码工作量进行深入的信度分析。凭借内置的可视化工具和详细的报告功能，GeneralizIT赋能教育、心理学、医疗保健和社会科学等多学科研究者，充分利用概化理论的力量获得稳健的循证见解。无论应用于小型或大型数据集，GeneralizIT都提供了一种易于使用且计算高效的解决方案，以提升复杂数据环境中的测量信度。