Understanding and Applying Research Design

15

STATISTICAL METHODS OF DIFFERENCE: T TEST¹

There are many types of statistical procedures that attempt to detect statistical differences between and among research conditions. We have already looked at chi square, which examines whether the frequencies of the categories of one variable are distributed equally across the categories of another variable. Statistical procedures that use a higher level of data, namely interval data, also detect differences among variables.

Independent t tests determine whether the two categories of a predictor (independent) variable result in statistically different mean values of an outcome (dependent) variable. Thus, do men and women postal workers (independent variable) indicate different levels of job satisfaction (dependent variable)? This procedure assumes that the predictor categories are “independent,” or unrelated to one another. Other t tests can accommodate for the two predictor categories being related or dependent (e.g., when the same people are tested twice as in a pre-post test), but they use different formulas.

ANOVA (analysis of variance) tests do essentially the same thing, but instead of comparing two categories of a predictor variable (or factor), they compare three or more categories. Thus, which of three different instructional methods leads to greater math achievement scores? The added complexity is that the procedure uses special drill-down methods to determine the differences among each of the pairs of categories if the overall ...