Hypothesis Testing - Chi Square Univariate – Goodness of Fit Bivariate Tables –Test for Association The Logic of Chi Square The Computation of Chi Square The Chi Square Test for Independence

Chi Square Test Most frequently used test of hypothesis in the social sciences. Assumes only that variables are measured at the nominal level. Requires no assumption about the shape of the population or sampling distribution.

Chi Square Test Appropriate for situations in which the variables of interest have been organized into table format. Can be used with variables at any level of measurement and that have many categories or scores.

Bivariate Tables Display the scores of cases on two different variables at the same time. Used to ascertain if there is a significant relationship between the variables. Two dimensions: rows and columns.

Null Hypothesis for Chi Square The two variables are independent. -OR- Classification of a case into a category on one variable has no effect on the probability that the case will be classified into any category of the second variable.

Chi Square Test Find the frequencies that would appear in the cells if the variables were independent. Compare those frequencies cell by cell. If null is true, expected and observed frequencies should be close in value. Greater the difference between the observed and expected frequencies, the greater the possibility of rejecting the null.

Limitations of Chi Square Test Difficult to interpret when variables have many categories. As sample size decreases, the test becomes less trustworthy. With very large samples, relatively trivial relationships may be declared statically significant.