“That is so random!” But is it Representative?

What makes a good sample in research?  One thing.  And it isn’t random selection.  (Surprised?)

Portrait of a diversity Mixed Age and Multi-generation Family embracing and standing together. Isolated on white background. [url=http://www.istockphoto.com/search/lightbox/9786738][img]http://dl.dropbox.com/u/40117171/group.jpg[/img][/url]It is representativeness.  No matter how the sample was picked, it must be representative of all those in the larger population, if the researcher wants to say anything about anyone who wasn’t in the study.  Now, of course, it is true that random selection is more likely to give you a representative sample, but it is no guarantee.  Only likely.

What is random sampling?  It is when every member of the larger population has an equal chance of being selected for the study sample.  Example? Drawing names out of a hat.  It is well-accepted practice to generalize research results from a random sample to others like those being studied (assuming that all other aspects of the study are strong).

In contrast a convenience (or nonprobability) sample is when some people are more likely to be chosen to be in the study than others.  You shouldn’t generalize the results of these studies because the samples may Not represent others.

Example of when random sampling doesn’t work: Let’s say you have a mixture of red, green, & yellow apples, and you select a sample that has only yellow apples.  (The red & green ones are going to be offended!–They’re left out.)  You now have a sample that is biased in favor of yellow apples!   Your sample does Not represent the larger population of apples…even if you used random methods to get it.  If you want to apply the study to red & green & yellow apples…well….you must get some of them in your sample, too. The yellow apples might not be at all like the other types and studying just yellow might mislead you into thinking something about the red & green ones that isn’t true!   Of course you could study all the millions of apples in the world and exclude none, but that would be pretty cumbersome and expensive.   So, it’s better to go for a representative sample!

When else doesn’t random sampling create a representative sample?   If I am doing historical research, say on the Nursing Department at California State University/Northridge, then I want to hand pick the specific RNs by name who were in charge of the Department from the beginning.  Randomly selecting nurses from those who worked at the University won’t represent those leaders.

QUESTIONCritical Thinking:  Take a quick look at the linked abstracts. How were the samples selected?  How representative are the samples of a larger population of interest?  Could you generalize the results to other people, and if so to whom?

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