Chapter Summary

As scientists we develop theories about how the world works. In order to make those theories as accurate as possible, we posit hypotheses which make predictions about what we should observe if the theories are correct. With an experimental research design we frequently use analysis of variance because it compares an interval level variable across different groups. With ANOVA we describe the pattern by comparing the means for the groups. We identify the statistical significance by using the F-statistic to determine whether there appears to be a relationship. We evaluate the substantive significance by using eta, interpreted as a standard measure of association, and eta-squared, interpreted as a PRE measure.

Learning Objectives

After reading this chapter, you should:

  • Know the elements of a hypothesis
  • Be able to properly state a hypothesis
  • Understand the relationship of the null hypothesis to the hypothesis
  • Be able to calculate an Analysis of Variance for a dichotomous independent variable
  • Understand when ANOVA is an appropriate statistical tool
  • Be able to articulate the three steps of statistical analysis
  • Be able to distinguish between PRE and other measures of association
  • Be able to interpret the results of an Analysis of Variance
  • Know how to conduct an ANOVA in SPSS