Adjusting for Age
Survey data for elderly people were extensively researched. The various datasets were found to be generally of small samples, and hard to combine or apply because of the variety of age groups and nationalities. Much of the data is now quite old. We concluded that we could not meet the needs of designers with published data, but could use it to validate an estimated dataset.
Accordingly the age ranges above 64 were calculated as extensions of the dataset for UK 18-64 year-olds. This is a large well-founded dataset from some 85 sources. Net sample sizes are very large and the data has benefited from extensive validation. The elderly data were calculated as proportional variations from these values, using the following logic:
1.Dimensions that grow throughout life were excluded unless raw data were available.
2.Dimensions that depend on muscle bulk and/or fatty tissue were excluded unless raw data were available.
3.Dimensions that depend on bony joint size were excluded unless raw data were available.
4.For each remaining dimension, the correlation with weight and with stature was used to calculate a variable called 'wt.coef', a coefficient that describes the extent to which the dimension varies with weight rather than stature.
(The following two further procedures were applied to means only, not to standard deviations.)
5.A factor termed 'Spinefactor' was calculated from the weighted NHANES 3 dataset, being the ratio between sitting height and stature for each age group (65+, 65-74, 75+, and 85+). This shrinkage was found to be more marked among women than men, reaching a peak of 3.9% among 85+ females. It was 2.7% among 75+ females, and less than 2% for all other groups.
6.Each dimension was allocated a coefficient describing its relationship with spinal length, called 'Spine.coef'. This variable describes how the dimension is affected by the shrinkage in spinal length that occurs with old age.
7.Each dimension was adjusted for each age and sex group by the term (1-spine.coef*spinefactor).
8.A fatty distribution table was created as an adjunct to the normal Z table. It was derived from the real percentile values of weight observed in the UK Department of Health surveys 1994-5. This table has been called the W table.
Thus the many sources of raw data were adjusted for stature, weight and spinal length to give an extensive up-to-date set of data for the key elderly age groups.
The resulting dataset was validated for consistency with raw survey data, for size order at 5th and 95th percentile, and for consistency between sex and age groups. This was in addition to the validations that had been performed during the initial comparisons and agglomeration of the source data into the core PeopleSize dataset.
Younger Adults
Examination of the younger age groups (18-25, 18-39, 25-50 and 40-64) showed that generalising from the 18-64 dataset, using these techniques, produced data that was more consistent both among dimensions within each age group and from one age group to another, compared with using separate datasets. This is because of the larger sample sizes associated with the 18-64 age group: more surveys are available for consolidation and validation, and the sample within each survey is bigger.
See also: