The survey mailing list of 624 represented all Folio: subscribers classified as sales management at the time of sample selection.
Data was collected via mail survey from March 22 to May 4, 2012. The survey was closed for tabulation with 193 responses (a 31% response rate). To ensure representation of the audience of interest, results have been filtered to include only those 153 respondents who indicated they work full time and that their job functions are best described as advertising sales director; advertising sales or regional manager; advertising salesperson, account executive, or category manager.
The margin of error for percentages based on all 153 respondents is ±6.9 percentage points at the 95% confidence level.
The Compensation Calculator
The analysis of Folio:'s Advertising Sales Salary Survey data used multiple regression analysis to model the determinants of salary by identifying those variables which, when taken together with appropriate weights, provide the best prediction of any individual's actual compensation.
The final compensation prediction model is somewhat restricted in its applicability-it represents only full-time professionals who indicated their job functions are best described as advertising sales director; advertising sales or regional manager; advertising salesperson, account executive, or category manager under 65 years old and total compensation in the range of $30,000 to $250,000.
Statistically speaking, this model is moderately powerful: it explains 38% of the variation in salary (adjusted R-square = .384), and is significant by the F-test at p<.0000.
Its predictive ability is limited by sampling error.
The confidence interval increases at the tail ends of the distribution, and predictions will tend to be most accurate for salary values in the middle of the range.
While a model explaining 38% of the dependent variable's
variation may be described as "moderately powerful," it still leaves well over half of
the variation unexplained. It is virtually certain that other variables
not captured through this survey also have an effect on salary levels:
individual job performance, for example. To the extent that this model
does not include variables actually important in determining salary, its
conclusions must be interpreted cautiously.