The dominance rule applies to tables that present magnitude or continuous variables such as income or turnover. This does not apply to categorical variables or counts. The rule is designed to prevent the re-identification of units that contribute a large percentage of a cell's total value, which could in turn reveal information about individuals, households or businesses. The cell dominance rule defines the number of units that are allowed to contribute a defined percentage of the total.
DataLab has a (1,50) and (2,67) rule. This means that the top contributor cannot contribute more than 50% of the total value to a cell and the top 2 contributors cannot contribute more than 67% of the total value to a cell.
Dominance is required if any mean, total, ratio, proportion or measure of concentration statistic can be calculated for continuous or magnitude variables.
While ratios/proportions can be continuous, if the numerator and denominator of the ratios/proportions are counts, we do not need dominance statistics.
It is also required when there is a regression with a continuous dependent variable and categorical independent variables. In this case, every combination of categorical variables (crosstab) will need to be tested for dominance against the dependent variable.
The below table shows an example of the additional information that analysts need to provide for output clearance when requesting a mean, total, ratio, proportion or measure of concentration
There are multiple instances where the (1,50) (2,67) rule is violated.
The top contributor in LGA 3 contributes 2.51/3.22 = 78% of the total.
This violates the (1,50) rule.
The top 2 contributors in LGA 3 contributes 3.03/3.22 = 94% of the total.
This violates the (2,67) rule.
You may also need to apply consequential suppression to your table so suppressed values cannot be derived.
|LGA||Total Profit ($M)||Top 1 Contributor ($M)||Top 2 Contributors ($M)||Top 1 Contribution to Total Profit (%)||Top 2 Contribution to Total Profit (%)|