As many marketing researchers are aware, there are statistical tests built into the programs we use to show survey data. Most of these are set to operate at the 90 or 95 percent confidence level, and automatically test the difference between percentages in specifi columns, as shown in the mock data example in Table 1.
As some marketing researchers are aware, the automatic test built into the survey programs is not the right test to use when there are more than two subgroups. You ne a statistical test that will look at three percentages simultaneously, and that test is the chi-square (not to be confus with its cousin, the chi-square goodness of fit test).
The chi-square test looks at all
The percentages and tests to see if what we have is different than what we would expect to have by chance alone. The logic behind it is actually deeper than this article will go, but, at one level it is cool.
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Take a look at the mock data in Table 2 as an example. We want to know if the three percentages differ significantly statistically at the 95 percent confidence level. If they do, we will hypothesize that awareness decreases with ucation level.
As Table 2a shows, the first step is to eliminate all the things that make the table pretty, and (oddly enough), to eliminate the percentages that we are interest in testing. We also add a new row. Since awareness is a zero-one concept (you are either aware or you are not), we add the number not aware, which we get just by subtracting the number aware from the total. Next, we add the rows to get totals, and put the bases in as the column totals.
The chi-square test actually
Compares all the numbers in the cells to the number that you would expect to be in the cell by chance. You get this number for one cell by multiplying the row total by the column total and dividing by the total. For the first cell of 250, we would expect (421×501)/1003 = 210 to be in it. For the Some College/Tech School + Aware cell, (421×200)/1003 = 84, etc. The idea is neat. Table 2b shows the actual numbers and the expect values in boldface.
That’s it. There is no ne to go through the whole formula for chi-square, since it can be found many, many places, and the rest of the logic of the test is the same as for all statistical tests of difference. (Compare the obtain chi-square to the table value of chi-square that one would expect if the percentages did not differ; if it is bigger, the percentages differ. If it is not, they do not.)
The logic compares the actual cell values to the cell values you would expect if the percentages do not differ, given that the row totals and column totals are what they are. You are comparing all of the percentages at once, but the logic is bas on the number you expect to see in each cell. Better still, you can calculate that number and see for yourself, if you are so inclin.
Is our example
Chi-square significant at the 95 percent confidence level? It certainly is! Of course, it was construct to have large differences, so that is really no surprise.
The point is, it’s time we stopp always reaching for the standard answers. It’s also time to put to rest criticism that certain why my list ofphone number is better than yours methodologies are innately superior. One methodology is not better than another for every project. Online panels will not solve every quantitative ne (nor will mail surveys, IVR or outbound phone surveys). Online focus groups are not going to replace traditional focus groups (nor should they be ignor as a possible approach).
Only when we stop worrying about what is better, and start worrying about what is most appropriate for this particular project, are we truly bringing to our clients all of the strengths that research can offer.
Researchers have many
Steps they can take to improve and enhance respondent cooperation, including rucing survey length, watching for respondent fatigue and offering effective incentives.
Survey researchers depend on a common resource: the respondent. This population is a common reserve us by researchers to meet their objectives. In this light, it is a resource with shar stewardship. On the whole, market researchers have done a commendable job sharing and managing this critical resource.
In a Utopian world
This resource is manag by thoughtful phone number my stewards interest in the long-term health of an irreplaceable input to the research process. Unfortunately, we do not live in a perfect world. The dynamic soiling this idealistic portrait can best be understood through a theory postulat by William Lloyd, a 19th century mathematician, known as the Tragy of the Commons.
The Tragy of the Commons is often illustrat in this way: Imagine an open range of lush grassland shar by a number of ranchers. Each rancher places his cattle out to pasture in the shar field to graze. The ranchers, being smart and ambitious, would like to maximize the value of their herd.
In order to optimize their return they add cattle to the ranch. For every cow add, the ranchers are return the full value of the animal at market. The cost is the additional grazing on the range, shar among all users of the grassland.
The rancher seeking to maximize his personal gain will add one cow, and then another and another and yet another until over-grazing leads to famine.
Philosophers point out
Freom in a commons brings ruin to all.” This concept can be demonstrat in a number of real-world examples, such as the overfishing of some parts of the ocean, tossing trash out of a car window or e-mail spam. Thankfully this conundrum has a simple solution: temperance. Through mandat or voluntary restraint the tragy can be avoid. Our industry has a wonderful record of self-regulation and cooperative behavior. With this heritage in mind, I would like to share my thoughts on how we can engage in enlighten cooperative behavior which will move us further from the Tragy of the Commons.
Responsible prictive dialer use
Prictive dialers are tremendous productivity tools. They remove much of the idle time an interviewer would otherwise spend manually dialing numbers and recording call dispositions, such as no-answer and busy signals.
By definition, prictive dialers dial phone numbers ahead of available interviewers, pricting when an interviewer will become available. Adjusting the pacing manually sets the aggressiveness of this dial-ahead capability.
Obviously, there is strong motivation for call-center managers to increase the pacing and minimize the time an interviewer spends between calls.
