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Books on the topic 'Anthropometry Statistical methods'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Schuckers, Michael E. Computational methods in biometric authentication: Statistical methods for performance evaluation. London: Springer, 2010.

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2

D, Fryar Cheryl, Ogden Cynthia L, National Center for Health Statistics (U.S.), and National Health and Nutrition Examination Survey (U.S.), eds. Anthropometric reference data for children and adults: United States, 1988-1994. Hyattsville, Md: U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2009.

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3

), National Center for Health Statistics (U S. Anthropometric reference data for children and adults: United States, 2007-2010 : data from the nathional health and nutrition survey. Hyattsville, Md: U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2012.

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4

de, Bruin R., ed. Paediatric morphometrics: A reference manual. Utrecht: Wetenschappelijke uitgeverij Bunge, 1996.

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5

Fitness, performance, and health norms. Champaign, IL: Human Kinetics, 2006.

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6

WHO child growth standards: Growth velocity based on weight, length and head circumference : methods and development. Geneva, Swtizerland: World Health Organization, Department of Nutrition for Health and Development, 2009.

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7

Schuckers, Michael E. Computational Methods in Biometric Authentication: Statistical Methods for Performance Evaluation. Springer London, Limited, 2012.

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8

Yŏnʼguwŏn, Hanʼguk Pʻyojun Kwahak, ed. Sanŏp chepʻum ŭi pʻyojunchʻi sŏlchŏng ŭl wihan kungmin pʻyojun chʻewi chosa pogosŏ =: National anthropometric survey of Korea 1992. [Seoul]: Kongŏp Chinhŭngchʻŏng, 1992.

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9

Claessens, Albrecht L., Gaston Beunen, and Robert M. Malina. Anthropometry, physique, body composition, and maturity. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0003.

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The appropriate normalization of exercise performance data for differences in body size underpins the clarification of growth and maturational influences on physiological function. Therefore, scaling is an issue of fundamental importance for all paediatric exercise scientists. The selection and application of a scaling method appropriate for the data and research question being addressed is at least as important as ensuring that the methodology used to collect the data is valid, reliable, and appropriate for use with young people. Several scaling methods are available and some methods can be applied in different ways. Unfortunately, taken as a whole, the extant literature presents a confusing picture as to which of these techniques is preferable, how they should be applied, and the meaning of the results obtained. The aim of this chapter is to clarify these issues through a description of the techniques available for analysing both cross-sectional and longitudinal data sets, highlighting their statistical and theoretical derivations.
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10

Development of an anthropometric regression equation to predict body density in African American women. 1994.

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11

Development of regression equations to predict body density in obese non diabetics and obese Type II diabetic adults. 1994.

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12

The prediction of body density in middle-age male marathoners. 1986.

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13

Talbot, P. Amaury, and H. Mulhall. The Physical Anthropology of Southern Nigeria: A Biometric Study in Statistical Method. Cambridge University Press, 2013.

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14

J, Kuczmarski Robert, National Center for Health Statistics (U.S.), and National Health and Nutrition Examination Survey (U.S.), eds. 2000 CDC growth charts for the United States: Methods and development. Hyattsville, Md: Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2002.

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15

Human Factors and Ergonomics Society. and HFES 300 Committee., eds. Guidelines for using anthropometric data in product design. Santa Monica, CA: Human Factors and Ergonomics Society, 2004.

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16

Creadick, Anna. Disability’s Other. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190458997.003.0002.

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The notion of “disability” relies on the concept of “normal.” Like disability, normality has a traceable history as an epistemological category. The mobilization of soldiers during World War II and, to a lesser degree, World War I, meant thousands of minds and bodies could be, and were, measured. A curious obsession with defining “normal” took hold, as doctors, scientists, and anthropologists gathered and applied statistical data to try measure “normal” bodies and describe “normal” character. Enlistees were subjected to psychological testing; sexologists used anthropometric methods to map the “normal” American body; and an interdisciplinary team at Harvard launched a longitudinal study of “normal men.” Taken together, such pursuits of “normality” were inextricable from midcentury anxieties about mental health, embodiment, masculinity, and the nation. By illuminating and gendering the “normal,” such forces functioned both to evoke and then exclude “disabled” bodies from the social body.
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