Between-group variance (BGV) is an absolute measure of inequality that considers all population subgroups. Subgroups are weighted according to their population share.
bgv(est, se = NULL, pop, conf.level = 0.95, ...)The estimated BGV value, corresponding estimated standard error,
and confidence interval as a data.frame.
The subgroup estimate. Estimates must be available for at least 85% of subgroups.
The standard error of the subgroup estimate. If this is missing, 95% confidence intervals cannot be calculated.
The number of people within each subgroup.Population size must be available for all subgroups.
Confidence level of the interval. Default is 0.95 (95%).
Further arguments passed to or from other methods.
BGV is calculated as the weighted average of squared differences between the subgroup estimates and the setting average. Squared differences are weighted by each subgroup’s population share. For more information on this inequality measure see Schlotheuber (2022) below.
Interpretation: BGV has only positive values, with larger values indicating higher levels of inequality. BGV is 0 if there is no inequality. BGV is reported as the squared unit of the indicator. BGV is more sensitive to outlier estimates as it gives more weight to the estimates that are further from the setting average.
Type of summary measure: Complex; absolute; weighted
Applicability: Non-ordered dimensions of inequality with more than two subgroups
Warning: The confidence intervals are approximate and might be biased. See Ahn (2018) below for further information on the standard error formula.
Schlotheuber, A, Hosseinpoor, AR. Summary measures of health inequality: A review of existing measures and their application. Int J Environ Res Public Health. 2022;19(6):3697. doi:10.3390/ijerph19063697.
Ahn J, Harper S, Yu M, Feuer EJ, Liu B, Luta G. Variance estimation and confidence intervals for 11 commonly used health disparity measures. JCO Clin Cancer Inform. 2018;2:1-19. doi:10.1200/CCI.18.00031.
# example code
data(NonorderedSample)
head(NonorderedSample)
with(NonorderedSample,
bgv(est = estimate,
pop = population,
se = se))
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