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More on Sampling
As we mentioned on the previous page, two important characteristics we seek in our samples are randomness and representativeness.  Randomness ensures that there are no biases in our sampling procedure and, if we know the important distinguishing features of the population we are interested in, such as their position in the company, geographic location, gender, or the like, then we can test to see how representative the sample we obtain is of the group as a whole.  When there are imbalances, we can correct for these statistically.  We can weight  the sample to bring it more into line with the proportions of members of the sample in various subgroups in comparison to members of the population in those subgroups.

Distinctions Among Sampling Groups
When you are designing your research program, take time to think about the important distinctions that you are interested in among the group that you are seeking to learn more about.  Are all respondents equal in your eyes?  If so, then a simple random sample is likely to meet your needs well.  There will be many cases, however, that will prompt you to be interested in subdividing your sample when you are interpreting and acting on the data.  You may be interested in where your employees or customers are located, what divisions they are in or what job titles they hold, or how large a customer the company they represents is for your business.  In these cases, you may want to select more specific samples using certain rules or procedures.

Often, this means defining and drawing a stratified, random sample in which you define the subgroups and then select from within them to help to ensure both that you are likely to have enough people among your respondents to make accurate statements about the subgroups themselves and that when grouped together they provide a representative picture of the population as a whole.  Although a random sample is still the goal, stratifying the sample in one or more ways can help to ensure that a complex population that interests you is well-represented within the subgroups of the sample that you obtain data from.  For multinational companies, this may mean sampling to ensure sufficient numbers and representation to look at the data by country or world region.  For other purposes, you may be interested in people within various positions or divisions, in various age groups, or in members of other predefined subgroups within your employee or customer base.  Sophisticated sampling procedures can help to ensure that you get the information you need to manage your business most effectively.

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