| Sumario: | Sample size is a crucial feature of all research projects, especially affecting generalizability and manipulation or control in quantitative studies and transferability and discovery in qualitative studies. Selecting a minimum number of observations helps ensure the adequacy of a sampling strategy. In general, sample sizes for quantitative studies tend to be relatively large, whereas those for qualitative studies tend to be relatively small, highlighting a trade-off between breadth and depth. An inadequate sample size (i.e., too small or too large) can undermine the soundness of research findings and waste resources for either research tradition. Quantitative studies employ probability sample designs whose most important features derive from randomness, whereas qualitative studies employ nonprobability sample designs whose most important features derive from some predetermined purpose. In both cases, time and resources are important factors in determining sample size, as is representativeness either in terms of a mirror image subset of a parent population (quantitative) or diversity and scope of a sample (qualitative). The respective goals of sample size determination are to control sampling error effects (i.e., Type I and Type II hypothesis testing errors) and to minimize chances of discovery failure. Various scholars already have addressed impacts of spatial autocorrelation on sample size determination, especially in frequentist-based quantitative studies. But to date, little is known about the impacts of spatial autocorrelation on qualitative geographic research in general and on sample size determination for qualitative studies in particular. This article addresses this problem.
|
|---|