Florida State University, 2010 Levy Ave, Suite 100, Tallahassee, FL, 32310, USA.
Ann Dyslexia. 2020 Jul;70(2):160-179. doi: 10.1007/s11881-020-00204-y. Epub 2020 Jul 29.
Models of word reading that simultaneously take into account item-level and person-level fixed and random effects are broadly known as explanatory item response models (EIRM). Although many variants of the EIRM are available, the field has generally focused on the doubly explanatory model for modeling individual differences on item responses. Moreover, the historical application of the EIRM has been a Rasch version of the model where the item discrimination values are fixed at 1.0 and the random or fixed item effects only pertain to the item difficulties. The statistical literature has advanced to allow for more robust testing of observed or latent outcomes, as well as more flexible parameterizations of the EIRM. The purpose of the present study was to compare four types of Rasch-based EIRMs (i.e., doubly descriptive, person explanatory, item explanatory, doubly explanatory) and more broadly compare Rasch and 2PL EIRM when including person-level and item-level predictors. Results showed that not only was the error variance smaller in the unconditional 2PL EIRM compared to the Rasch EIRM due to including the item discrimination random effect, but that patterns of unique item-level explanatory variables differ between the two approaches. Results are interpreted within the context of what each statistical model affords to the opportunity for describing and explaining differences in word-level performance.
同时考虑项目水平和个体水平固定和随机效应的单词阅读模型通常被称为解释性项目反应模型（EIRM）。尽管有许多 EIRM 的变体，但该领域通常侧重于对项目反应中的个体差异进行建模的双重解释模型。此外，EIRM 的历史应用一直是模型的 Rasch 版本，其中项目区分值固定为 1.0，并且随机或固定的项目效应仅与项目难度有关。统计文献已经发展到允许对观察到的或潜在的结果进行更稳健的测试，以及对 EIRM 进行更灵活的参数化。本研究的目的是比较四种基于 Rasch 的 EIRM（即双重描述性、个体解释性、项目解释性、双重解释性），并在包括个体水平和项目水平预测因子时更广泛地比较 Rasch 和 2PL EIRM。结果表明，由于包含项目区分随机效应，无条件 2PL EIRM 的误差方差不仅比 Rasch EIRM 小，而且两种方法之间的独特项目水平解释变量的模式也不同。结果在每个统计模型为描述和解释单词水平表现差异提供的机会的背景下进行解释。
