Logometrica AS, Bryne, Norway. ingjerd@logometrica
J Learn Disabil. 2012 Sep-Oct;45(5):467-79. doi: 10.1177/0022219411432688. Epub 2012 Jan 31.
The purpose of the present study was twofold: First, the authors investigated if an extended version of the component model of reading (CMR; Model 2), including decoding rate and oral vocabulary comprehension, accounted for more of the variance in reading comprehension than the commonly used measures of the cognitive factors in the CMR. Second, the authors investigated the fitness of a new model, titled the reading efficiency model (REM), which deviates from earlier models regarding how reading is defined. In the study, 780 Norwegian students from Grades 6 and 10 were recruited. Here, hierarchical regression analyses showed that the extended model did not account for more of the variance in reading comprehension than the traditional CMR model (Model 1). In the second part of the study the authors used structural equation modeling (SEM) to explore the REM. The results showed that the REM explained an overall larger amount of variance in reading ability, compared to Model 1 and Model 2. This result is probably the result of the new definition of reading applied in the REM. The authors believe their model will more fully reflects students' differentiated reading skills by including reading fluency in the definition of reading.
第一,作者调查扩展的阅读成分模型(CMR;模型 2),包括解码速度和口语词汇理解,是否比 CMR 中常用的认知因素测量指标能更好地解释阅读理解的差异。第二,作者调查新模型,即阅读效率模型(REM)的拟合程度,该模型在阅读定义方面与早期模型有所不同。在研究中,招募了来自 6 年级和 10 年级的 780 名挪威学生。这里,层次回归分析表明,扩展模型并不能比传统的 CMR 模型(模型 1)更好地解释阅读理解的差异。在研究的第二部分,作者使用结构方程建模(SEM)来探索 REM。结果表明,与模型 1 和模型 2 相比,REM 总体上能更好地解释阅读能力的差异。这一结果可能是由于 REM 中应用的新的阅读定义所致。作者认为,通过将阅读流畅性纳入阅读的定义,他们的模型将更充分地反映学生的差异化阅读技能。