Department of Cognitive Sciences, University of California, Irvine, CA, USA.
Behav Res Methods. 2012 Mar;44(1):57-66. doi: 10.3758/s13428-011-0134-4.
Number-knower levels are a series of stages of number concept development in early childhood. A child's number-knower level is typically assessed using the give-N task. Although the task procedure has been highly refined, the standard ways of analyzing give-N data remain somewhat crude. Lee and Sarnecka (Cogn Sci 34:51-67, 2010, in press) have developed a Bayesian model of children's performance on the give-N task that allows knower level to be inferred in a more principled way. However, this model requires considerable expertise and computational effort to implement and apply to data. Here, we present an approximation to the model's inference that can be computed with Microsoft Excel. We demonstrate the accuracy of the approximation and provide instructions for its use. This makes the powerful inferential capabilities of the Bayesian model accessible to developmental researchers interested in estimating knower levels from give-N data.
数感水平是儿童早期数概念发展的一系列阶段。儿童的数感水平通常使用“给 N”任务进行评估。尽管任务程序已经得到了高度完善，但标准的给 N 数据分析方法仍然有些粗糙。Lee 和 Sarnecka（Cogn Sci 34:51-67, 2010, in press）开发了一种贝叶斯模型，可以更有原则地推断儿童在给 N 任务上的表现，该模型允许以更有原则的方式推断出数感水平。然而，该模型需要相当多的专业知识和计算能力来实现和应用于数据。在这里，我们提出了一种对模型推断的近似，该近似可以用 Microsoft Excel 进行计算。我们演示了该近似的准确性，并提供了使用说明。这使得对数感水平感兴趣的发展研究人员可以从给 N 数据中使用贝叶斯模型的强大推断能力。
