Abstract
This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using radio buttons). Data are analyzed by means of multitrait–multimethod models estimated as structural equation models with a mean and covariance structure. For the first time in such research, the results include bias, valid variance, method variance, and random error variance. The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers. Neither of the measurement instruments was found to be biased with respect to the other, meaning that their scores are comparable. While both instruments achieve valid and reliable measurements, the reliability and validity are higher for the endecanary scale. This study contributes to the still scarce literature on fuzzy measurement instruments and on the comparability and relative merits of graphic and discrete rating scales on computer-assisted questionnaires.
Highlights
Given a set of objects or individuals, the assumption that each of them may partially fulfill a certain property lies at the grounds of fuzzy set theory
The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers
Much research has been conducted on the degree to which an object fulfills a property [5,6], and using rating response scales may become essential in determining said degree of fulfillment, for instance in the definition of fuzzy numbers [7,8,9,10] or linguistic variables [11,12], in graph theory [13,14,15], in determining the intervals resulting in an experton [16,17], or in determining the levels of truth in the forgotten effects theory [18]
Summary
Given a set of objects or individuals, the assumption that each of them may partially fulfill a certain property lies at the grounds of fuzzy set theory. Much research has been conducted on the degree to which an object fulfills a property [5,6], and using rating response scales may become essential in determining said degree of fulfillment, for instance in the definition of fuzzy numbers [7,8,9,10] or linguistic variables [11,12], in graph theory [13,14,15], in determining the intervals resulting in an experton [16,17], or in determining the levels of truth in the forgotten effects theory [18]. There is no consensus on the ideal number of response alternatives that a response scale should have, it has been demonstrated that solutions to certain problems may depend on this number, which is used to measure the input variables [19]. It must be taken into account that answering questions on a screen, as opposed to using a paper and pencil, tends to change the rules of the game, and that computer-assisted questionnaires are extremely diverse in nature [25,26]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
