The general approach to determining sample size assumes that a simple random sample is the sampling design. More complex designs, e.g., stratified random samples, must take into account the variances of subpopulations, strata, or clusters before an estimate of the variability in the population as a whole can be made.
The sample size should be appropriate for the analysis that is planned. If descriptive statistics are to be used, e.g., then any reasonable sample size will suffice. On the other hand, a good size sample, e.g., n=150+, is usually needed for multiple regression, analysis of covariance, or log-linear analysis, which might be performed for more rigorous state impact evaluations. In addition, an adjustment in the sample size may be needed to accommodate a comparative analysis of subgroups (e.g., such as an evaluation of program participants with nonparticipants). Also, skewed distributions can result in serious departures from normality even for moderate size samples, causing the need for a larger sample.
Finally, the sample size formulas provide the number of responses that need to be obtained. Many researchers commonly add 25%+ to the planned sample size to compensate for persons that the researcher must remove from the sample for some reason. The number of mailed surveys or planned interviews also can be substantially larger than the number required, based on the assumed response rate.