Abstract

In the sphere of psychological research the problem of missing data involves many different studies. For example, in longitudinal studies some subjects may leave prematurely; in clinical researches the data regarding the subjects before starting the treatment could be not available; and in constructing psychological tests, researchers have to face the problem of small samples. In these cases, missing data analysis and their subsequent replacement may increase the validity of the research, if made before the standard analysis procedures. This study aims to present a brief review of the various missing data analysis methods in order to underline the importance of these techniques in the field of the research.