CAML—Maximum likelihood consensus analysis
CAML—Maximum likelihood consensus analysis
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CAML—最大似然一致性分析
DOI：
10.3758/s13428-011-0138-0
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发表时间：
2011
影响因子：
5.4
通讯作者：
E. Erdfelder
中科院分区：
文献类型：
--
作者：
André Aßfalg;E. Erdfelder
共识分析，使估计的能力和反应倾向的个体差异时，答案的二分法强迫选择问题是未知的。CAML是一组用R语言编写的函数，它实现了作为共识分析基础的一般孔多塞模型的最大似然估计。CAML避免了替代方法的问题，这些方法在过去常常使共识分析变得不切实际或不可行。它提供（1）模型拟合的度量，（2）共识的度量，（3）能力和响应趋势的点和区间估计，以及（4）未知答案的估计。本文介绍了通用孔多塞模型、CAML算法和软件处理。此外，CAML结果的有效性进行了测试，在识别记忆的研究，使用选择性的实验操作的参数。结果表明，CAML在实践中工作得很好，并提供了有效的估计能力，反应倾向，和答案的关键。
Consensus analysis enables estimation of individual differences in competencies and response tendencies when answer keys to dichotomous forced-choice questions are unknown. CAML, a set of functions written in R, implements maximum likelihood estimation for the general Condorcet model that underlies consensus analysis. CAML avoids problems of alternative approaches that have often rendered consensus analysis impractical or unfeasible in the past. It provides (1) measures of model fit, (2) a measure of consensus, (3) point and interval estimates of competencies and response tendencies, and (4) an estimate of the unknown answer key. The present article describes the general Condorcet model, the CAML algorithms, and the handling of the software. In addition, the validity of CAML results is tested in a recognition memory study using selective experimental manipulations of the parameters. The results show that CAML works very well in practice and provides valid estimates of competencies, response tendencies, and answer keys.