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Towards a new paradigm of measurement in marketing

机译：迈向营销计量的新范式

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In measuring latent variables, marketing research currently defines measurement as the assignment of numerals to objects. On this basis, marketing researchers utilize a multitude of avenues to measurement. However, the scientific concept of measurement requires the discovery of a specific structure in the data allowing for the inference of a quantitative latent variable. A review of the quite diverse approaches used to measure marketing constructs reveals serious limitations in terms of their suitability as measurement models. Adhering to a revised definition of measurement, this paper finds that the Rasch model is the most adequate available. An empirical example illustrates its application to a marketing scale. Furthermore, this study investigates how instructions about response speed and the direction of an agree-disagree response scale impact the fit of the data to the Rasch model and to confirmatory factor analysis. The findings are diametrically opposed, with Rasch suggesting more plausible conclusions. Rasch favors well-considered responses and the agree-disagree scale, while factor analysis supports spontaneous responses and the disagree-agree format. The adoption of the Rasch model as the foundation of measurement in marketing promises to promote more advanced and substantial construct theories, is likely to deliver better-substantiated measures, and will enhance the crucial link between content and construct validity.
机译：在测量潜在变量时，市场研究当前将测量定义为对对象的数字分配。在此基础上，市场研究人员利用多种途径进行评估。但是，科学的测量概念要求发现数据中的特定结构，以便推断出定量的潜在变量。回顾用于衡量营销结构的多种方法，发现它们在作为衡量模型的适用性方面存在严重限制。遵循修订的测量定义，本文发现Rasch模型是最合适的模型。一个经验例子说明了它在市场规模上的应用。此外，本研究调查了有关响应速度和同意－不同意见规模的方向的指示如何影响数据对Rasch模型和验证性因子分析的拟合。该发现截然相反，Rasch提出了更合理的结论。 Rasch赞成考虑周全的回答和同意－不同意的量表，而因子分析则支持自发的回答和不同意－同意的格式。采用Rasch模型作为市场营销的度量基础有望促进更高级和更实质的构建理论，可能会提供更好的依据，并会增强内容与构建效度之间的关键联系。

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