The chi square test for independence is used to determine whether there is a relationship between the two variables that are categorical in the level of measurement. In this case, the variables are: employment level and treatment condition. It tests whether there is a difference between groups. The research question for the study is: Is there a relationship between the independent variable, treatment, and the dependent variable, employment level? In other words, is there a difference in the number of participants who are not employed, employed part-time and employed full-time in the program and the control group (i.e., waitlist group)? The hypotheses are:
H0 (The null hypothesis): There is no difference in the proportions of individuals in the three employment categories between the treatment group and the waitlist group. In other words, the frequency distribution for variable 2 (employment) has the same proportions for both categories of variable 1 (program participation).
** It is the null hypothesis that is actually tested by the statistic. A chi square statistic that is found to be statistically significant, (e.g. p< .05) indicates that we can reject the null hypothesis (understanding that there is less than a 5% chance that the relationship between the variables is due to chance).
H1 (The alternative hypothesis): There is a difference in the proportions of individuals in the three employment categories between the treatment group and the waitlist group.
** The alternative hypothesis states that there is a difference. It would allow us to say that it appears that the treatment (voc rehab program) is effective in increasing the employment status of participants.
Assume that the data has been collected to answer the above research question. Someone has entered the data into SPSS. A chi-square test was conducted, and you were given the following SPSS output data: