Note on differential weight averaging models in functional measurement
Note on differential weight averaging models in functional measurement
复制标题
功能测量中微分权重平均模型的注意事项
DOI：
10.1007/s11135-011-9567-1
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发表时间：
2013
影响因子：
--
通讯作者：
G. Vidotto
中科院分区：
文献类型：
--
作者：
G. Vidotto
平均模型在心理和社会研究的几个不同领域有很大的应用。它们由两个参数表示组成：刺激在反应维度上的主观位置的尺度值和其在综合反应中的重要性的权重。在本文中，我们建议对用于表示模型的传统公式进行轻微但重要的修改，以明确模型的一些相关性质，这些性质对于在考虑微分权平均模型时获得唯一和无偏的参数估计是必不可少的。这种表示有利于更好地理解尺度值和重要性权重之间的区别，以便在我们分析与平均模型一致的经验数据时认识到权重的差异可能意味着什么。
Averaging Models have a large diffusion in several different areas of psychological and social research. They consist of a two parameters representation: a scale value for the subjective location of the stimulus on the response dimension and a weight for its importance in the integrated response. In the present paper we suggest a light but significant modification of the traditional formula used to represent the model in order to make clear some relevant properties of the model, which are essential to obtain unique and unbiased parameter estimations when a differential-weight averaging model is considered. This representation favors a superior understanding of the distinction between scale-values and importance-weights in order to realize what differences in weight could mean when we analyze empirical data coherent with an Averaging Model.