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Guru Search Results: 6 matches were found

Friday, March 19, 1999 #2400
I need to know the calculation to work out margin of error for TV reach and frequency results. E.g. what is the margin of error of 40% @ 2+ depending on the size of the sample, penetration etc.

The Media Guru Answers(Saturday, March 20, 1999 ):
Assuming you are using a model to calculate reach and frequency, your error is no longer an aspect of sample size but of the reliability of the model.

For instance, suppose your schedule consisted of 20 advertisements with an average rating of 10. And, based on sample size, the 10 rating was +/- 2 rating points (or 20% relative error). But your total schedule of 200 GRP is not going to be +/- 40 points. Because error is plus or minus, there is an equal chance that one 10 rating is really PLUS 2 and the next 10 rating is really MINUS 2. So, in a schedule, most of the error cancels out. This is one reason why ratings minima for buying are often short-sighted.

When it comes to reach analysis, someone might have built a model by compiling several actual schedules measured by the original research and finding a formula for the straight line formed by the average frequency of each. Since the actual schedules came from the orignal research, the sampling error of each (minimized by the plus or minus aspect of the schedule elements, as above) could have been calculated. But now the "curve" coming out of the model is only judged by its ability to match back to actual schedules.

Monday, November 23, 1998 #2170
Dear Guru! Since there are several media planning softwares in the market I wanted to ask: are there any guidelines for measuring the gap between the prediction and the actual results. What I mean is: Is there a "normal" gap, for example: 20% gap between the predicted reach\Grps (pre campaign)to the results (post campaign). Thank you!Irene Kol.

The Media Guru Answers(Monday, November 23, 1998 ):
This is a two part question:

1- The "gap" in GRPs will not be due to the software, it is based on your buyers' estimating ability and the accuracy of post analysis as well as the reliability of your audience research.

2- Since reach is derived from models based on averages, there can be variance. Variance will also depend on the medium you are considering and how it is measured.

For example, if your magazine audience research is conducted once a year when you plan a quarter's campaaign of 1 insertion in each of 5 magazines and then buy exactly that, how will you ever know if the reach was different than you planned?

On the other hand, suppose you plan radio based on a specific number of GRP on a specific number of stations, in a specific daypart mix, and you buy exactly that. How would you judge that the reach goal wasn't met, unless the buy did not deliver as planned, whether because of poor estimating, station failing to schedule properly or a new ratings book?

In no case are you dealing with the accuracy of the planning software.

Many agencies and clients agree to a +/- 10% range in delivery of broadcast GRPs. Other standards are often agreed as well.

Wednesday, October 07, 1998 #2081
Are Television Rating Points really effective in measuring viewership? If not, is/arre there any other method/s of measuring the same?

The Media Guru Answers(Wednesday, October 07, 1998 ):
Combining "rating points," "measuring" and "effective" in the question confuses the issue.

Let us define viewership simply as the number of people watching programs. "Rating points" isn't the measurement, it's merely the system of quantities used to describe the results of measurement, as "pounds" describes the result of the measurement made by the butcher's scale.

The various ratings measurement systems; i.e. meters and diaries, have their pluses and minuses in accuracy and reliability, but rating points is a simple and well understood way to describe the audience measured: The number of viewers expressed as a percentage of the possible viewers, or population.

Wednesday, March 25, 1998 #1553
How can be measurement error calculated? I would like to know is there any correlation between sample size and data validity? Thank you

The Media Guru Answers(Monday, April 06, 1998 ):
Sample size and data reliability are in a "rule of squares" relationship: A sample four times as large is twice as reliable. Note that "reliable" is the statistical term referring to the chances that a duplicate study with the same size random sample will get the same results, give or take a specified range of error.

"Validity" refers to correctness. It might have to do with whether a question asked can corectly produce a result that is desired. For example, a question like "What will you have for breakfast a week from Tuesday?" may not be a valid predictor of what people will actually eat on that day. But, with a proper sample, it will be reliable in predicting with a set degree of variability what people will eat.

The formula for calculation of error for a given sample is:

The Square root of (P x Q over N)


p = the percentage result to be tested (e.g. 10% of the people will have bacon)

q = the complement, or difference vs 100% (if p = 10% then q = 90%

n = the sample size

So, if a sample of 500 produces the result that 10% will have bacon, the sampling error for this result is

the square root of (10 x 90)500 or

+/- 1.342

so the answer of 10% should be read as "between 8.658 and 11.342" and really means that 68% of the time the same study repeated would produce a percent of bacon eaters between 8.658 and 11.342.

If the sample is quadrupled, to 2000, then the error is halved, to 0.671.

Tuesday, December 17, 1996 #1092
Dear Media Guru Guy:Where can I find out more info on standard error? First, how is it calculated, or what affects standard error? Second, I'm thinking about magazine readership research, and I'm wondering what standard errors some of the popular studies have (MRI or Simmons, for example). And then, I'm also curious as to what, among media gurus, is considered an acceptable or unacceptable error. Do the same standards apply for other media research studies, for example, Nielsen ratings? Thanks Mr. Guru.

The Media Guru Answers(Friday, December 20, 1996 ):
Standard error reflects the range of "tolerance," due to sample size, around the reported answer where the "truth" lies. Inother words, statistical data like "10% of women 18-49 read MagazineX" in reality means that within the range of error expected, if thesame study were repeated 100 times, with a sample of 225, the resultwould between 8 and 12 percent, 68 times out of the 100.

The formula isthe square root of (P times Q divided by N)

P= the percentage to be tested
( e.g. the 10% in the Guru's example, above)

Q=100 minus P ( 90% in our example)

N= total sample size (225 in our example)


10 X 90 = 900

900 divided by 225 = 4

square root of 4 = 2

One standard error is the amount of variance sampling causes 68% of the time.

So, at one standard error, 10% is between 8% and 12%with 68% "confidence" At 2 standard errors or within+/- 4 points, or6 to 14% we have an answer we are confident our research will repeat95% of the time. This is why the concept is also referred to as"reliability". It is really way to express confidence that the samesampling procedure will produce the same result.

Most statistical texts can give you considerably more on the topic.

Thursday, January 25, 1996 #1774
I have a total universe of 9500 people and I would like to know how big a sample I would need for as good study. This is for a phone interview.

The Media Guru Answers(Friday, February 02, 1996 ):
The population size is a relatively insignificant factor in calculating reliability of a sample; 500 respondents is just about as reliable in surveying a small town as for the United States as a whole.

To plan a "good study," you need to consider the size of the typical answer you will get.

If your typical response will be 50% of respondents said "yes" then a far smaller sample could be suitable than if answers are "10% use brand B."

You also need to decide what level of reliabilty you require, or how much swing, +/-, is acceptable and to what tolerance.

For example:

If your sample size is 500, a 50% answer is actually reliable +/- 4.4 percentage points, 95% of the time and +/- 3.7 points 90% of the time.

If the sample size is 125, a 50% answer is reliable +/- 8.8 points, 95% of the time. This is double the relative error of the 500 sample (rule of thumb, 4x as much sample reduces error by half).

If the answers anticipated were 10%, then for the 125 sample it varies +/- 5.2 points, or over 50% relative error.

A 10% answer from a 500 total sample yields +/- 2.6% points at 95% tolerance or 26% relative error, which is possibly acceptable for your need.

To examine other possibilities, the formula for 95% tolerance is:

1.96 x squareroot of ((PxQ)/N)


P = the answer size expressed as a decimal fraction
Q = the remaing fraction of the sample
N = the total sample size

To examine 90% tolerance, substitute 1.645 for 1.96