Carrying
out such analyses before assessing measurement quality gives preliminary
indications of how well the coding and entering of data have been done, how
good the scales are, and whether there is a suspicion of poor content validity or
systematic bias. Before testing hypotheses, it is useful to check the
assumptions underlying the tests, and to get a feeling for the data in order to
interpret the results of the tests better