ABSTRACT
Data analysis aims to find patterns within the content data and to allow researchers to derive meaning if patterns are found. This chapter is a very brief survey of some statistics often used with content analysis. Which statistics are appropriate to a particular study depends on the hypotheses or research questions, the level of measurement of variables, and the nature of the sample. Statistics are used to describe data sets and to infer from a probability sample to the population from which it was taken. Description and inference can be applied to one variable (univariate), two variables (bivariate), or three or more variables (multivariate). The ultimate goal is to accumulate evidence about causal relationships between content and antecedents or content and effects that have important theoretical implications. Statistical methods discussed in this chapter include t-tests, F-tests, z-tests, ANOVA, sampling error, differences of proportion, regression, multiple regression, and causal modeling.
