I think one of the reasons “research” has become so lax in design and execution is this idea of doing research to drive a press release and news coverage. Reliable, actionable research is expensive, and if all you really want to do is gin out a bunch of press, why be scientific about it? Why pay for rigor? After all, your company is not going to use the research to take action, it’s research for press release.
So here’s a few less scientific but more specific ideas to keep in mind when looking at a press release / news story about the latest “research”, ranked in order of saving your time. In other words, if you run into a problem with the research at a certain level, don’t bother to look down to the next level – you’re done with your assessment.
Press about Research is Not Research – it’s really a mistake to make any kind of important decision on research without seeing the original source documentation. For lots of reasons, the press accounts of research output can be selectively blind to the facts of the study.
If there is no way to access the source research document, I would simply ignore the press account of the research. Trust me, if the subject / company really had the goods on the topic, they would make the research document available – why wouldn’t they? Then if / when you get to the research source document, run the numbers a bit for your self to see if they square with the press reports. If not, you still may learn something – just not what the press report on the research was telling you!
Source of Sample – make sure you understand where the sample came from, and assess the reliability of that source. Avoid trusting any source where survey participants are “paid to play”. This PTP “research” is often called a Focus Group and though you can learn something in terms of language and feelings and so forth from a Focus Group, I would never make a strategic decision based on a non-scientific exercise like a Focus Group.
Go ahead and howl about this last statement Marketers, I’m not going to argue the fine points of it here, but those wish to post on this topic either way, go ahead. Please be Less Scientific or More Specific than usual, depending on whether you are a Scientist or a Marketer.
For a very topical and probably to some folks quite important example of this “source” problem, see Poor Study Results Drive Ad Research Foundation Initiative. If you want a focus group, do a focus group. But don’t refer to it as “research” in a scientific way.
Size of Sample – there certainly is a lot of discussion about sample sizes and statistical significance and so forth in web analytics now that those folks have started to enter the more advanced worlds of test design. Does it surprise you the same holds true for research? Shouldn’t, it’s just math (I can feel the stat folks shudder. Take it easy, relax).
Without going all math on this, let’s say someone does a survey of their customers. The survey was “e-mailed to 8,000 customers” and they get 100 responses to the survey. I don’t need to calculate anything to understand the sample is probably not representative of the whole, especially given the methodology of “e-mailed our customers”. Not that a sample of 100 on 8000 is bad, but the way it was sourced is questionable.
What you want to see is something more like “we took a random sample of our customers and 100 interviews were conducted”. It’s the math thing again. Responders, by definition, are a biased sample, probably more of a focus group. This statement is not always true, but is true often enough that you want to verify the responders are representative. Again, check the research documentation.
OK Jim, so how can political surveys be accurate when they only use 300 or so folks to represent millions of households? The answer is simple. They don’t email a bunch of customers or pop-up surveys on a web site. They design and execute their research according to established scientific principles. Stated another way, they know exactly and specifically who they are talking to. That’s because they want the research to be precise and predictive.
How do you know when a survey has been designed and executed properly? Typically, a confidence interval is stated, as in “results have margin of error +- 5%”. This generally means you can trust the design and execution of the survey because you can’t get this information without a truly scientific design (Note to self, watch for “fake confidence level info” to be included with future “research for press release” reporting).