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Thematische Bibliographien / Individual differences scaling

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Zeitschriftenartikel

Auswahl der wissenschaftlichen Literatur zum Thema „Individual differences scaling“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 10. Februar 2022

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Zeitschriftenartikel zum Thema "Individual differences scaling"

1

Malkoc, G., P. Kay, and M. A. Webster. "Individual differences in hue scaling." Journal of Vision 3, no. 12 (2010): 34. http://dx.doi.org/10.1167/3.12.34.

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2

Emery, Kara, David Peterzell, Vicki Volbrecht, and Michael Webster. "Factors underlying individual differences in hue scaling." Journal of Vision 16, no. 12 (2016): 1148. http://dx.doi.org/10.1167/16.12.1148.

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3

Ruette, T., and D. Speelman. "Transparent aggregation of variables with Individual Differences Scaling." Literary and Linguistic Computing 29, no. 1 (2013): 89–106. http://dx.doi.org/10.1093/llc/fqt011.

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4

Kreiman, Jody, Bruce R. Gerratt, Kristin Precoda, and Gerald S. Berke. "Individual Differences in Voice Quality Perception." Journal of Speech, Language, and Hearing Research 35, no. 3 (1992): 512–20. http://dx.doi.org/10.1044/jshr.3503.512.

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Sixteen listeners (10 expert, 6 naive) judged the dissimilarity of pairs of voices drawn from pathological and normal populations. Separate nonmetric multidimensional scaling solutions were calculated for each listener and voice set. The correlations between individual listeners’ dissimilarity ratings were low However, scaling solutions indicated that each subject judged the voices in a reliable, meaningful way. Listeners differed more from one another in their judgments of the pathological voices (which varied widely on a number of acoustic parameters) than they did for the normal voices (which formed a much more homogeneous set acoustically). The acoustic features listeners used to judge dissimilarity were predictable from the characteristics of the stimulus sets’ only parameters that showed substantial variability were perceptually salient across listeners. These results are consistent with prototype models of voice perception They suggest that traditional means of assessing listener reliability n voice perception tasks may not be appropriate, and highlight the importance of using explicit comparisons between stimuli when studying voice quality perception

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5

Adams, L., N. Chronos, R. Lane, and A. Guz. "The measurement of breathlessness induced in normal subjects: individual differences." Clinical Science 70, no. 2 (1986): 131–40. http://dx.doi.org/10.1042/cs0700131.

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1. Normal subjects show wide variability in their sensory scaling of breathlessness for equivalent degrees of ventilatory stimulation and behave ‘characteristically’ irrespective of stimulus type. 2. Observed differences are not explained by physical characteristics, ventilatory sensitivity or pattern of breathing although there is a weak association with the degree of physical fitness. 3. Differences are seen when scaling is performed with reference to both rigidly defined extremes of breathlessness (visual analogue scaling) and a subject's own relative changes in the intensity of this sensation (magnitude estimation). 4. These findings may explain the common observation, in patients with respiratory disease, of dyspnoea out of proportion to the pathophysiological state.

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6

Köhn, Hans-Friedrich. "Combinatorial individual differences scaling within the city-block metric." Computational Statistics & Data Analysis 51, no. 2 (2006): 931–46. http://dx.doi.org/10.1016/j.csda.2005.09.013.

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7

Shrivastav, Rahul. "Multidimensional Scaling of Breathy Voice Quality: Individual Differences in Perception." Journal of Voice 20, no. 2 (2006): 211–22. http://dx.doi.org/10.1016/j.jvoice.2005.04.005.

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8

Winter, Edward M. "Scaling: Partitioning out Differences in Size." Pediatric Exercise Science 4, no. 4 (1992): 296–301. http://dx.doi.org/10.1123/pes.4.4.296.

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In his “Editor’s Notes,” Rowland (15) raised an issue that is fundamentally important but frequently appears to be misunderstood. The issue is scaling, that is, the means of partitioning out differences in size. Because physiological variables are often dependent on size, an adjustment has to be made to “normalize” for body dimensions and assess more precisely a particular characteristic. These adjustments have four main applications: (a) to an individual who is compared against standards for the purposes of assessment; (b) in comparisons between groups; (c) in longitudinal studies, especially with children, in which for instance the effects of training have to be disentangled from the effects of growth and development; and (d) in studies that explore the relationship between physiological variables and performance. The purpose of this review is to assess the most appropriate means of scaling and demonstrate how well-intentioned, but nevertheless incorrect, scaling techniques have probably produced misleading results and retarded progress in our understanding of the physiology of exercise.

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9

Nowicki, Julie R., and Bruce G. Coury. "Individual Differences in Processing Strategy for a Bargraph Display." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 19 (1993): 1315–19. http://dx.doi.org/10.1518/107118193784162317.

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The bargraph has been described in several ways: as a separable display, as an integral display, and as a configural display with emergent features. The versatility of the bargraph may be in part due to the support it provides for different individual processing strategies. This research identifies two general types of strategies - holistic and analytic - which are developed by individuals to solve a classification problem on the bargraph. Multidimensional scaling (MDS), response times, and verbal reports are used to analyze individual strategies. Individuals who developed holistic strategies produced significantly faster reaction times, and reported simple, efficient strategies, with the emergent feature of bargraph shape as an important dimension. The results indicate that the bargraph provides perceptual features which can support several general types of processing strategy.

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10

Kim, Kyung-Sun, Eun-Young Yoo, Nahyun Kwon, and Sei-Ching Joanna Sin. "Individual differences in source selection behavior: Profile analyses via multidimensional scaling." Proceedings of the American Society for Information Science and Technology 46, no. 1 (2009): 1–5. http://dx.doi.org/10.1002/meet.2009.14504603119.

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