在多变量配对比较中建模随时间的变化:在窗口显示设计中的应用

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY Statistical Modelling Pub Date : 2021-03-31 DOI:10.1177/1471082X21995675
A. Grand, R. Dittrich
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引用次数: 0

摘要

本文提出了一种在多变量配对比较(PCs)中进行比较判断的替代方法,其中关于变化的判断是通过在两个时间点对一系列属性中的每个属性进行比较而直接做出的。该应用程序处理商店橱窗展示的设计,其中产品应由职业学生团队根据美学原则(属性)排列。比较时间1(反馈前)和时间2(反馈后)学生橱窗展示的照片,判断每个属性在时间1或时间2中实现得更好。与评分系统相比,这种个人电脑方法的一个优点是,它可以评估吸引力各个方面的细微变化,而这些变化是无法轻易用分数来衡量的。为了分析这些数据,我们使用了早期的工作,该工作开发了多属性数据的多元PC模式模型和随时间变化的PC模型,并定义了变化的多元PC模型(MPCC)。该模型可以拟合为非标准泊松对数线性模型,并提供时间2的三个属性的变化估计,我们能够检查这些属性之间可能的相互作用效应。
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Modelling changes over time in a multivariate paired comparison: An application to window display design
This article proposes an alternative method of making comparative judgements in multivariate paired comparisons (PCs) where judgements about change are made directly by comparing an object at two time points for each of a series of attributes. The application deals with the design of shop window displays where products should be arranged by teams of vocational students according to aesthetic principles (attributes). The photos of the students’ window displays at time 1 (before feedback) and at time 2 (after feedback) were compared by judging each attribute as to whether it was fulfilled better at time 1 or at time 2. An advantage of this PC approach over an alternative of a scoring system is the possibility to assess even subtle changes of various aspects of attractiveness, which cannot easily be measured using a score. To analyse these data, we used earlier work which developed both a multivariate PC pattern model for multi-attribute data and a PC model over time and defined a multivariate PC model of changes (MPCC). The model can be fitted as a non-standard Poisson log-linear model and provides estimates of change for the three attributes for time 2 and we were able to check for possible interaction effects between these attributes.
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来源期刊
Statistical Modelling
Statistical Modelling 数学-统计学与概率论
CiteScore
2.20
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: The primary aim of the journal is to publish original and high-quality articles that recognize statistical modelling as the general framework for the application of statistical ideas. Submissions must reflect important developments, extensions, and applications in statistical modelling. The journal also encourages submissions that describe scientifically interesting, complex or novel statistical modelling aspects from a wide diversity of disciplines, and submissions that embrace the diversity of applied statistical modelling.
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