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Survey Details

Methodology

The survey sample of 771 was selected by Red 7 Media and Readex Research and represented all Folio: subscribers classified as sales management at the time of sample selection.

Data was collected via mail survey from February 25 to April 9, 2008.  The survey was closed for tabulation with 315 responses (a 41% response rate).  To ensure representation of the audience of interest, results have been filtered to include only those 249 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 249 respondents is ±4.8% 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 with 40 years of experience or less and total compensation in the range of $30,000 to $350,000.

Statistically speaking, this model is moderately powerful: it explains 34% of the variation in salary (adjusted R-square = .34), and is significant by the F-test at p<.0000.

Its predictive ability is limited by sampling error.  95% of the time, its prediction around the mean is ±$8,062 (standard error = $4,031, mean = $113,270).

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 34% of the dependent variable’s variation may be described as "moderately powerful," it still 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.