Bipolar Rating Scales: A Survey and Novel Correlation Measures Based on Nonlinear Bipolar Scoring Functions

Batyrshin, Ildar; Monroy-Tenorio, Fernando; Gelbukh, Alexander; Villa-Vargas, Luis Alfonso; Solovyev, Valery D.; Kubysheva, Nailya

doi:10.12700/aph.14.3.2017.3.3

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Because discrepancy scores are a bipolar, rather than unipolar, scale, this may raise some issues with the use of correlations (Batyrshin et al, 2017). Moreover, correlations do not indicate how under- and overestimation of abilities relate to autistic traits and mental health outcomes.…”

Section: Resultsmentioning

confidence: 99%

Autistic traits predict underestimation of emotional abilities.

Huggins¹,

Cameron²,

Williams³

2021

Journal of Experimental Psychology: General

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People vary in their self-awareness of their own emotions, and this may predict psychological well-being. Evidence suggests that emotional self-awareness is diminished in autism, but these findings may be biased by self-report or confounded by verbal intelligence. To address this issue, we developed the emotional consistency (EC) task, measuring emotional self-awareness through consistency in emotional decision-making. In the EC-Task, we showed participants pairs of emotional images, asking them to judge which evoked the more intense emotional experience. The logical consistency of decision making, based on transitive relationships between stimuli, reflects precision of judgment of experience of emotional intensity, which in turn reflects emotional self-awareness. Emotional consistency significantly correlated with lower self-reported alexithymia but not autistic traits. Instead, autistic traits predicted greater discrepancy between EC-Task performance and self-reported difficulties identifying feelings. Participants with higher autistic traits were more likely to underestimate their emotional self-awareness, possibly because of greater metacognitive difficulties and negative self-beliefs. Our study suggests emotional self-awareness is not diminished in autism and provides a novel method to investigate this issue.

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Section: Resultsmentioning

confidence: 99%

Autistic traits predict underestimation of emotional abilities.

Huggins¹,

Cameron²,

Williams³

2021

Journal of Experimental Psychology: General

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“…The categorical labels are randomly assigned ordered integer values 1-N in consistent distribution. On the other hand, most of the relative bipolar label sets take values of a five-point scale (ranging from 1 to 5) to reflect the degree of strength, whereas the others are designed to take only a three-point scale (ranging from 1 to 3) to avoid uncertainty and overestimate such traits [33]. Table 2 shows the proposed list of soft ear biometrics with their semantic labels derived from a human ear, as shown in Fig.…”

Section: Soft Ear Biometric Traits 321 Fine-grained Soft Ear Attributesmentioning

confidence: 99%

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

Talal Bostaji,

Sami Jaha

2023

Computer Systems Science and Engineering

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Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues. Therefore, biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics. Among different viable biometric modalities, the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems. In recent years, most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results. Nevertheless, such traditional models can be sensitive to several unconstrained environmental factors. As such, some traits may be difficult to extract automatically but can still be semantically perceived as soft biometrics. This research proposes a new group of semantic features to be used as soft ear biometrics, mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each other. Hence, the research study is focused on the fusion of the soft ear biometric traits with traditional (hard) ear biometric features to investigate their validity and efficacy in augmenting human identification performance. The proposed framework has two subsystems: first, a computer vision-based subsystem, extracting traditional (hard) ear biometric traits using principal component analysis (PCA) and local binary patterns (LBP), and second, a crowdsourcing-based subsystem, deriving semantic (soft) ear biometric traits. Several feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm's performance. The obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics, reaching up to 12% for LBP and 5% for PCA descriptors; when fusing all three capacities PCA, LBP, and soft traits using k-nearest neighbors (KNN) classifier.

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“…Krebs (2012) found higher Cronbach's Alpha in bipolar rating scales, while Menold and Raykov (2016) found higher reliability in a unipolar application dimension than in bipolar DA ratings. Researchers also addressed bimodality in bipolar rating scales (Batyrshin et al 2017), which can additionally affect measurement quality. In the present research, besides reliability, we expect bipolar rating scales to negatively affect validity and equidistance:…”

Section: Introductionmentioning

confidence: 99%

Verbalization of Rating Scales Taking Account of Their Polarity

Menold

2023

Field Methods

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While numerical bipolar rating scales may evoke positivity bias, little is known about the corresponding bias in verbal bipolar rating scales. The choice of verbalization of the middle category may lead to response bias, particularly if it is not in line with the scale polarity. Unipolar and bipolar seven-category rating scales in which the verbalizations of the middle categories matched or did not match the implemented polarity were investigated in randomized experiments using a non-probabilistic online access panel in Germany. Bipolar rating scales exhibited higher positivity bias and acquiescence than unipolar rating scales. Reliability, validity, and equidistance tended to be violated if the verbalizations of the middle category did not match scale polarity. The results provide a rationale for rating scale verbalization.

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Bipolar Rating Scales: A Survey and Novel Correlation Measures Based on Nonlinear Bipolar Scoring Functions

Cited by 4 publications

References 27 publications

Autistic traits predict underestimation of emotional abilities.

Autistic traits predict underestimation of emotional abilities.

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

Verbalization of Rating Scales Taking Account of Their Polarity

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