An aircraft seat discomfort scale using item response theory

Menegon, Lizandra da Silva; Vincenzi, Silvana Ligia; Andrade, Dalton Francisco de; Barbetta, Pedro Alberto; Vink, P.; Merino, Eugênio Andrés Díaz

doi:10.1016/j.apergo.2018.12.015

Cited by 21 publications

(17 citation statements)

References 36 publications

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“…The anchoring analysis was performed following guidelines set by Vincenzi et al (2018) [45], and it is one of the best advantages of IRT models compared to CTT [50]. Anchoring analysis has identified three levels, namely lower, moderate, and highest levels of agreement on the domains of the entrepreneurial ecosystem, i.e., general finance, capital finance, general support, support professions, human resources, culture, market, and The information and expected total score curves, Figure 2 indicate that the latent trait fits well to the cumulative model and that the information covers the different latent trait values, validating the instruments.…”

Section: Discussionmentioning

confidence: 99%

“…Table 5 shows the results of the anchoring process. The IRT parameters were linearly transformed with (100 ± 10), as described by author [44] and author [45]. After the linear transformation, which showed the cumulative probability by using two parameter logistics (2PL), the (2PL)-IRT method was applied for all items, this resulted in three categories (<0.50, <0.65, and ≥0.65).…”

Section: Anchoring Of Itemsmentioning

confidence: 99%

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Application of Item Response Theory (IRT)-Graded Response Model (GRM) to Entrepreneurial Ecosystem Scale

et al. 2022

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The scale of entrepreneurial ecosystems (EE) assesses the perceptions about entrepreneurial ecosystem domains, finances, capital finances, support, support professions, policies, markets, human resources, and culture. The scales are always error-prone—these scales must possess properties that enable them it to provide maximum information and validity reliability. Convenient sampling data from (n = 474) founders, co-founders, and entrepreneurs were collected. The IRT-GRM model is used to validate and test the instrument-based on polytomous scales. IRT yields discriminating power—the level of difficulty of the items of the scale. The scale consists of 48 items. The item Pol5 (4.13) was found to have the highest discriminating value (4.13), the item mar5 had the lowest discriminating value (1.57), and all items had discriminating values greater than the threshold value of 0.60. The EE Scale showed good reliability based on McDonald’s omega and Cronbach’s alpha (0.80 and 0.88). The parallel and factor analysis showed good agreement of the one-dimesnionality of the scale. The model goodness of fit statistics based on the comparative fit index (CFI) and the Tucker–Lewis index, (TLI) and the standardized root mean square residual (SRMR) showed a satisfactory level of fit; however, the root mean square error of approximation (RMSE) showed a poor fit. The item characteristic curves showed that the all item responses were properly ordered. The items of the scale showed a satisfactory level of discrimination power and level of difficulty, and it was found to have three levels of agreement about entrepreneurial ecosystem scale. It is concluded that the EE scale possesses good psychometric properties and that it is reliable and valid instrument to measure the entrepreneurial ecosystem of the given region.

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Section: Discussionmentioning

confidence: 99%

Section: Anchoring Of Itemsmentioning

confidence: 99%

Application of Item Response Theory (IRT)-Graded Response Model (GRM) to Entrepreneurial Ecosystem Scale

et al. 2022

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“…In the application of comfort grading, Menegon et al (2019) divided the indicators affecting comfort into five levels, from the lowest discomfort to the highest discomfort. J. Wang et al (2021) divided the indicators affecting comfort in the aircraft cabin into three levels according to the degree of dissatisfaction and explained that these levels also reflect the priority of improving the comfort indicators.…”

Section: Discussionmentioning

confidence: 99%

A comfort evaluation method based on an intelligent car cockpit

Yang

Chen

Xing

et al. 2022

Hum Ftrs & Erg Mfg Svc

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With the rapid development of automobiles, car cockpits are becoming more and more intelligent and advanced, and the intelligent requirements of automobile cockpits are gradually increasing. However, the real value of intelligence can only be realized when it makes passengers in a cockpit feel comfortable. In this study, seven factors that affect passenger comfort in intelligent cockpits are defined. Under these factors, a total of 33 evaluation indicators were developed. The core of the method was to determine the dissatisfaction indicators and degree of dissatisfaction in the intelligent cockpit by analyzing the relationship between people's perceived performance and their expectations. This method was used to evaluate the Tesla Model 3, and it was found in the results that the higher the degree of dissatisfaction with the indicator, the more subjective feedback it had, which in turn proved the effectiveness of the model. According to the degree of dissatisfaction, the indicators affecting comfort were also divided into three levels. This hierarchical division helps clarify which indicators should be prioritized for improvement. Generally, this method has a certain feasibility, which is helpful for the development and redesign of an intelligent car cockpit, and provides some reference strategies for other transportation fields.comfort, evaluation method, expectation, intelligent cockpit, the degree of dissatisfactionWith the rapid development of technology, the number of intelligent cars has also increased in recent years (Arena et al., 2020). Related research describes the intelligent car of the future as not only improving traffic safety like a robot but also further meeting people's demands by connecting to the Internet (Huang et al., 2016;James, 2012). Intelligent vehicles have become a trend now. Companies such as Tesla, Jaguar, and Google are now launching more intelligent vehicles from innovative materials and innovative technologies (Sun et al., 2021).However, for the vast majority of consumers, it is not the technology itself that cares about them, but how they feel in the cockpit (Park et al., 2019). The benefits of intelligent cars include increased safety, reduced need for infrastructure investment, improved fuel economy, and reduced congestion. But most importantly, they make passengers more relaxed and comfortable (Jorlöv et al., 2017). The real benefits of intelligent cars can only be realized when the human driver is comfortable in the intelligent cockpit and the interaction between the driver and the automated system is at a reasonable level (Helldin et al., 2013). Beggiato et al. (2019 believe that comfort plays an important role in the wide acceptance of intelligent vehicles. For some products such as smart cars, consumer satisfaction is a factor for business success (Park et al., 2019;Salomo et al., 2003). Industry and research are working to develop systems that can automatically adapt to maximize well-being (Olugbade et al., 2021). Therefore, making people feel comfortable is

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“…Vehicle seat comfort has also become a research focus of automobile manufacturers. There are two types of research on seat comfort as follows: (i) Subjective evaluation methods, such as semantic difference method, 1 scale method, 2 and questionnaire survey 3 and (ii) objective evaluation methods, such as body pressure distribution measurement, 4 acceleration measurement, 5 myoelectric signals measurement, 6 and posture analysis. 7 The pressure distribution between the human body and the seat has been observed as one of the most crucial indicators for seat comfort evaluation in many studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of subjective discomfort of the seated human body exposed to vertical vibration using dynamic body pressure

Gao

Zhang

Luo

et al. 2022

Journal of Low Frequency Noise, Vibration and Active Control

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Pressure distribution has been regarded as a crucial parameter for static comfort evaluation of the seated human body. However, the association between the pressure distribution and subjective rating, exposed to vertical vibration, is still unclear. This study devotes to characterizing the vibration discomfort of the seated human body on a foam seat using dynamic body pressure distribution and investigating the associations between them. First, the dynamic body pressure of twelve subjects seated on a foam seat exposed to vertical random excitation was measured at frequencies from 0.5 to 20 Hz with six magnitudes (0.2, 0.4, 0.6, 0.8, 1.0, and 1.2 m/s2 r.m.s.). Meanwhile, the subjective discomfort rating of the foam seat was evaluated with the relative magnitude estimation method. Then, the influence of vibration magnitude on subjective discomfort ratings and dynamic body pressure indicators was investigated. The results show that with the increase of vibration magnitude, the mean average dynamic pressure, the mean peak dynamic pressure, the mean dynamic contact area, and the normal dynamic force had no significant change. On the contrary, the subjective discomfort ratings, normal dynamic force change rate root-mean-square, and the indicators related to pressure change rate root-mean-square increased significantly. Finally, the relation between the subjective discomfort ratings and dynamic body pressure indicators was analyzed using Stevens’ power law. The linear regression fitting accuracy of the average dynamic pressure change rate root-mean-square, peak dynamic pressure change rate root-mean-square, area pressure change rate root-mean-square, and normal dynamic force change rate root-mean-square with discomfort ratings were above 95%. The four dynamic body pressure indicators are highly correlated with the subjective discomfort ratings, which can be used as objective indicators for vibration discomfort evaluation.

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An aircraft seat discomfort scale using item response theory

Cited by 21 publications

References 36 publications

Application of Item Response Theory (IRT)-Graded Response Model (GRM) to Entrepreneurial Ecosystem Scale

Application of Item Response Theory (IRT)-Graded Response Model (GRM) to Entrepreneurial Ecosystem Scale

A comfort evaluation method based on an intelligent car cockpit

Evaluation of subjective discomfort of the seated human body exposed to vertical vibration using dynamic body pressure

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