Kulas John T, Smith Jeffrey A, Xu Hui
John T. Kulas, 1046 Voyageur St., St. Cloud, MN 56303, USA,
J Appl Meas. 2017;18(4):393-407.
Lord (1980) presented a purely conceptual equation to approximate the nonlinear functional relationship between classical test theory (CTT; aka true score theory) and item response theory (IRT) item discrimination indices. The current project proposes a modification to his equation that makes it useful in practice. The suggested modification acknowledges the more common contemporary CTT discrimination index of a corrected item-total correlation and incorporates item difficulty. We simulated slightly over 768 trillion individual item responses to uncover a best-fitting empirical function relating the IRT and CTT discrimination indices. To evaluate the effectiveness of the function, we applied it to real-world test data from 16 workforce and educational tests. Our modification results in shifted functional asymptotes, slopes, and points of inflection across item difficulties. Validation with the workforce and educational tests suggests good prediction under common assumption testing conditions (approximately normal distribution of abilities and moderate item difficulties) and greater precision than Lord's (1980) formula.
洛德(1980年)提出了一个纯概念性方程,用于近似经典测验理论(CTT;又称真分数理论)与项目反应理论(IRT)项目区分度指标之间的非线性函数关系。当前项目对他的方程提出了一种修正,使其在实际应用中有用。建议的修正承认了更常见的当代CTT区分度指标——校正后的项目总分相关,并纳入了项目难度。我们模拟了略超过768万亿个单个项目反应,以发现IRT和CTT区分度指标之间的最佳拟合经验函数。为了评估该函数的有效性,我们将其应用于来自16项劳动力和教育测试的实际测试数据。我们的修正导致了在不同项目难度下函数渐近线、斜率和拐点的变化。通过劳动力和教育测试进行的验证表明,在常见的假设测试条件下(能力近似正态分布且项目难度适中)预测效果良好,并且比洛德(1980年)的公式更精确。