Survey sampling is both a science and an art.  The experience of the survey consultant will allow the wedding of common sense to methodological rigor.  The purpose of sampling is the accomplishment of efficiency, representation, and minimal disruption.   We assume that you elected to use a sample survey to permit savings in time and expense, minimize the disruption of the staff, and yet, maintain statistical representation during the project. 

There are some serious drawbacks to sampling.  For example, very complicated stratified random samples might put an increased burden on the client's staff for resources.  This may involve greater use of consultants, client time, and computer usage to test and manage the sampling model.  This added resource cost may offset any savings in a sample survey administration.  Also, the requirement for representation in many sub-groups raises the specter of surveying most of the population, anyway.

Sampling resources:  Resources must be identified within your organization that can supply computed or case-wise data from the personnel resources database(s).  In a two-step process, your survey consultant will: 

• Conduct a discovery process to understand the makeup of your organization's population on a person level (i.e., demographics, title, job level, etc.), and on a unit level (region, function, Field office, etc.);  and
• Specify, in detail, a sampling plan for data collection that will yield representative results with acceptable levels of reliability and precision. 

The efficiency by which a survey sample is developed will depend on:  internal skills in your company for data query and reporting, consolidation versus dispersion of relevant databases, data accuracy, and the extent to which there are multiple files with multiple data structures. 

Your survey consultant may expect to download computed and case-wise data for their own tabulation and analysis, in addition to computations and analyses provided by your organization's resources.  Your survey consultant should understand they will not have free access into your personnel data systems, but that you will provide data that they regard as necessary.  These data may have e-mail addresses, office telephones, and possibly fax numbers.

For example, if you assume that field office number 1 is average in satisfaction (60 percent favorable), and you wouldn't take action unless the new survey number was at least 30 points lower, then you have a trigger point that is very far away.  If you observed survey results at 28 percent favorable, but would decide not to take immediate steps, but only watch the field office for a while, then there is little cost to your decision.  As a result, sample sizes can be quite small.  This is the advantage of a bayesian approach over a classical approach. 

On the other hand, if you expect to take action if the results are only two points lower (a very close trigger point), and your decision will be to fire the regional director, institute retraining for everyone, and reassign all the staff, then the costs of your decisions are quite high.  As a result, sample sizes will be rather high.  Classical and bayesian sampling approaches would tend to yield the same sample sizes in this example.

In our experience with typical Organizational Surveys, we can usually reduce the sample sizes from a classical approach by half.  Depending on the circumstances it can be significantly lower.

Another issue in sampling is proportional representation.  It is often assumed that an overall representation of 10 percent, as an example, should be represented in each of the subgroups.  So if we sample 10 percent of the field population, 200 people, then, it is assumed, we should also sample 10 percent of the Headquarters staff, 800 people.  This assumption is false. 

If we are to compare field vs. headquarters with equal reliability, then we need equal sample sizes.  The Standard Error of the Mean (SEM) is a function of one thing, the sample size, n.  This is evident in the formula, SEM = s /( [square root] n ), where s is the standard deviation, and n is the sample size.  Therefore, we only need 200 headquarters staff to give results as reliable as 200 field people.  This is another technique, often overlooked by survey consultants, that can keep the sample size down to a minimum.

An example will be given to demonstrate the technique. - UNDER CONSTRUCTION

There is a way to reduce this sample size dramatically.  The recommended approach requires a cell size of 1, and a total sample yield of 120 people.  At first blush this may seem nonsensical because error variance is estimated from within-cell variance.  A cell size of 1 has no variance, thus no within-cell variance, and is of no use in estimating error variance.  The solution is to use another source of variance as your estimate of the error variance.  To do this you examine the ratio of the variance of the 3rd and 4th interaction terms.  If the ratio is not significant, then pool the variances and use them as an estimate of the error variance.

There is one major technical flaw with this technique.  You are changing your model, contingent upon your data.  This would be a serious matter in a design requiring great precision and strict adherence to experimental method.  However, in the context of hypothesis generation, pilot study, minimal concern about interactions, focusing only on main effects, and interim observations, it can be very efficient and minimize the demand on the survey consultant's resources and on your client.