ABSTRACT
When small samples are analyzed, several issues typically arise. This chapter discusses five problem areas and potential solutions. First, multivariate analysis using Ordinary Least Squares estimation does not assume large samples, and therefore works well with small samples, although these still lead to tests with low power. Second, analysis of small samples is more vulnerable to violations of assumptions, and data characteristics and analysis choices in estimation become more important. As a result, data cleaning and examination of potential violations of assumptions are crucial. Finally, problems with small samples are ameliorated by using research designs that yield more information.
