Measurement in Marketing (original) (raw)
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This research note briefly describes the levels of measurement of variables and their applications in the quantitative analysis of survey data. It first presents the concept of the measurement of variables. Second, the four levels of measurements, namely, nominal, ordinal, interval, and ratio, with examples are offered. Then, the application of these measurement levels to the statistical analysis of data at the univariate (descriptive statistics), bivariate, and multivariate (e.g., binary logistic and multiple linear regression) levels are discussed. This note is expected to be useful to the beginning (naïve) scholars for real-world application of statistical tools to analyze survey data.
Formative and Reflective Measurement Models
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We compare formative and reflective measurement models in the context of structural equation models (SEM). The formative model is expressed as part of the structural regression equation. The identification status of the models is different, although they differ only in the direction of some arrows. It is shown, that the reflective model permits the identification of more parameters and requires less restrictions. Formative measurement models are recommended only under a strict theoretical necessity.
Formative Versus Reflective Measurement Models: Two Applications of Formative Measurement
Journal of Business …, 2008
This paper presents a framework that helps researchers to design and validate both formative and reflective measurement models. The framework draws from the existing literature and includes both theoretical and empirical considerations. Two important examples, one from international business and one from marketing, illustrate the use of the framework. Both examples concern constructs that are fundamental to theory-building in these disciplines, and constructs that most scholars measure reflectively. In contrast, applying the framework suggests that a formative measurement model may be more appropriate. These results reinforce the need for all researchers to justify, both theoretically and empirically, their choice of measurement model. Use of an incorrect measurement model undermines the content validity of constructs, misrepresents the structural relationships between them, and ultimately lowers the usefulness of management theories for business researchers and practitioners. The main contribution of this paper is to question the unthinking assumption of reflective measurement seen in much of the business literature.
Interest in measurement issues remains unabated, as evidenced by published research in the area of reliability, validity, and, in particular, scale development. At the same time, psychometricians have continued to generate alternative measurement approaches and models at an explosive pace. Surprisingly, these alternative measurement approaches have been slow to diffuse into the marketing literature despite marketers' inherent interest in measurement issues. This paper discusses one such measurement approach, item response theory (IRT), which can potentially address critical measurement concerns. My focus is on identifying basic principles and key characteristics and on providing an assessment for applied researchers. Toward this end, an empirical example of role conflict (RC) and role ambiguity (RA) concepts is included to illustrate IRT principles and amplify the theory's relevance to resolving measurement dilemmas. In addition, I provide a comparison with the current paradigm of measurement—classical test theory (CTT)—to afford a balanced appreciation of the payoffs of adopting the IRT approach.