Using my data from legal departments of 42 technology companies, I calculated their standardized scores. Standardized scores convert a metric into standard deviations above or below the average (See my post of Aug. 4, 2009: compare differences in terms of standard deviations; July 31, 2009 #4: also known as a z-score analysis; and Jan. 4, 2010: z-scores to create indices.).

The formula takes each department’s figure (such as the number of lawyers it reported) and subtracts from it the average for the data set (26.6 for these tech companies). Then it divides the result by the standard deviation of the set (52.7). That standard deviation tells us that 66 percent of the departments have been 1 lawyer and 79 lawyers.

For one particular department I chose, its standardized score is 0.120, which expresses in terms of a the standard deviation that the department is slightly bigger than the group. On a bell curve, that department would be slightly to the right of the top of the curve. When you calculate standard scores, you can compare many departments on the same basis. Outliers become more visible, since 95 percent of the figures will fall within two standard deviations if the figures are normally distributed.

Using my data from legal departments of 42 technology companies, I calculated their standardized scores. Standardized scores convert a metric into standard deviations above or below the average (See my post of Aug. 4, 2009: compare differences in terms of standard deviations; July 31, 2009 #4: also known as a z-score analysis; and Jan. 4, 2010: z-scores to create indices.).

The formula takes each department’s figure (such as the number of lawyers it reported) and subtracts from it the average for the data set (26.6 for these tech companies). Then it divides the result by the standard deviation of the set (52.7). That standard deviation tells us that 66 percent of the departments have been 1 lawyer and 79 lawyers.

For one particular department I chose, its standardized score is 0.120, which expresses in terms of a the standard deviation that the department is slightly bigger than the group. On a bell curve, that department would be slightly to the right of the top of the curve. When you calculate standard scores, you can compare many departments on the same basis. Outliers become more visible, since 95 percent of the figures will fall within two standard deviations if the figures are normally distributed.