Application of diffuse measurement to the evaluation of psychological structures

Costas, Concepción San Luis; Marañón, Pedro Prieto; Cabrera, Juan Andrés Hernández

doi:10.1007/bf01098946

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…On this research line, for instance, [21] adopted a system which includes biometric measures of cognitive response process (e.g., response time, computer-mouse trajectories) in the construction of fuzzy responses. Despite their differences, both the approaches have successfully been adopted to measure psychological constructs [27], to evaluate students' perceptions and feelings [28], to measure gendered beliefs [29], to inspect experience of perplexity [30], to evaluate the quality of linguistic descriptions [31], to explore physicals' perception of mental patients [32], to evaluate service quality [33] as well as the quality of products [34]. Unlike direct or indirect fuzzy rating, fuzzy conversion scales adopt stochastic or deterministic procedures (e.g., fuzzy systems) to convert crisp rating data -usually collected by means of traditional rating tools (e.g., Likert-type scales) -into fuzzy sets with the aim of obtaining an improvement of the scaling procedure.…”

Section: Currently Used Methods In Fuzzy Ratingmentioning

confidence: 99%

A psychometric modeling approach to fuzzy rating data

Calcagnì,

Cao,

Rubaltelli

et al. 2021

Preprint

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Modeling fuzziness and imprecision in human rating data is a crucial problem in many research areas, including applied statistics, behavioral, social, and health sciences. Because of the interplay between cognitive, affective, and contextual factors, the process of answering survey questions is a complex task, which can barely be captured by standard (crisp) rating responses.Fuzzy rating scales have progressively been adopted to overcome some of the limitations of standard rating scales, including their inability to disentangle decision uncertainty from individual responses. The aim of this article is to provide a novel fuzzy scaling procedure which uses Item Response Theory trees (IRTrees) as a psychometric model for the stage-wise latent response process. In so doing, fuzziness of rating data is modeled using the overall rater's pattern of responses instead of being computed using a single-item based approach. This offers a consistent system for interpreting fuzziness in terms of individual-based decision uncertainty. A simulation study and two empirical applications are adopted to assess the characteristics of the proposed model and provide converging results about its effectiveness in modeling fuzziness and imprecision in rating data.

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Section: Currently Used Methods In Fuzzy Ratingmentioning

confidence: 99%

A psychometric modeling approach to fuzzy rating data

Calcagnì,

Cao,

Rubaltelli

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…3.1 Questionnaire with fuzzy set theory After the research of Fuzzy Graphic Rating Scale (FGRS) presented by Hesketh et al (1988), Costas et al (1994) furthered to choose 100 university students as a sample of 1332 MD 52,7 the research, they found that FGRS fits to the feature of human psychology. Herrera and Herrera-Viedma (2000) presented the steps of linguistic decision analysis under linguistic information.…”

Section: Statistical Analysis With Soft Computingmentioning

confidence: 99%

New statistical analysis on the marketing research and efficiency evaluation with fuzzy data

Nguyen

2014

Management Decision

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Purpose – The purpose of this paper is to use soft computing technique and fuzzy statistical tool to evaluate people's performance on the marketing research and time management. Design/methodology/approach – Through standardized measurement system, the authors come up with real-value data to satisfy not the current needs but data itself. This is when fuzzy classification stands out and highlights the area of in-between and undefined. Findings – The proposed metric system helps the authors to assess the distance among trapezoidal fuzzy data. The index of efficiency between observed time and ideal time is also presented. Originality/value – With the ranking of fuzzy sample, the authors can examine the decision process by non-parametric testing hypothesis.

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“…While the former asks raters to provide their response by means of a stage-wise methodology, which is in turn supposed to elicit the subjective components of the rating response (e.g., see [5,6,7]), the latter aims at turning standard rating data into fuzzy numbers by means of expert-based or statistical-based procedures (e.g., see [8,9,10]). Despite the differences on the way of mapping fuzzy numbers to the rating process, both provide a valuable strategy to avoid the loss of subjective information entailed by standard rating response formats [11,12].…”

Section: Introductionmentioning

confidence: 99%

Jointly modeling rating responses and times with fuzzy numbers: An application to psychometric data

Cao,

Calcagnì

2022

Preprint

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In several research areas, ratings data and response times have been successfully used to unfold the stage-wise process through which human raters provide their responses to questionnaires and social surveys. A limitation of the standard approach to analyze this type of data is that it requires the use of independent statistical models. Although this provides an effective way to simplify the data analysis, it could potentially involve difficulties with regards to statistical inference and interpretation. In this sense, a joint analysis could be more effective. In this research article, we describe a way to jointly analyze ratings and response times by means of fuzzy numbers. A probabilistic tree model framework has been adopted to fuzzify ratings data and four-parameter triangular fuzzy numbers have been used in order to integrate crisp responses and times. Finally, a real case study on psychometric data is discussed in order to illustrate the proposed methodology. Overall, we provide initial findings to the problem of using fuzzy numbers as abstract models for representing ratings data with additional information (i.e., response times). The results indicate that using fuzzy numbers lead to theoretically sound and more parsimonious data analysis methods, which limit some statistical issues that may occur with standard data analysis procedures.

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Application of diffuse measurement to the evaluation of psychological structures

Cited by 14 publications

References 7 publications

A psychometric modeling approach to fuzzy rating data

A psychometric modeling approach to fuzzy rating data

New statistical analysis on the marketing research and efficiency evaluation with fuzzy data

Jointly modeling rating responses and times with fuzzy numbers: An application to psychometric data

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