![how to run a chi square test in spss how to run a chi square test in spss](https://statistics.laerd.com/spss-tutorials/img/cstfa/chi-square-independence-7.png)
In this example, the value of the chi square statistic is 6.718. The chi square statistic appears in the Value column immediately to the right of “Pearson Chi-Square”. We’re interested in the Pearson Chi-Square measure. Chi-Square TestsĪs you can see below, SPSS calculates a number of different measures of association. This is where the chi square statistic comes into play. The question is whether these differences are big enough to allow us to conclude that the Eating variable and Religion variable are associated with each other. And similarly, there are more atheist vegetarians than would be expected, and fewer atheist meat eaters.
![how to run a chi square test in spss how to run a chi square test in spss](https://statistics.laerd.com/spss-tutorials/img/gof/chi-square-gof-3.png)
If you look at the crosstabs table above, you’ll see that there are more Christian meat eaters than would be expected were the null hypothesis (that the variables are independent) true and fewer Christian vegetarians. Put simply, the more these values diverge from each other, the higher the chi square score, the more likely it is to be significant, and the more likely it is we’ll reject the null hypothesis and conclude the variables are associated with each other. If you want to understand the result of a chi square test, you’ve got to pay close attention to the observed and expected counts. Importance of Observed and Expected Counts In our case, the null hypothesis is that there is no association between the Eating variable and the Religion variable, which means the expected count is the predicted frequency for a cell on the assumption that eating and religion are not dependent on each other. The expected count is the predicted frequency for a cell under the assumption that the null hypothesis is true. For example, our table shows that 5 meat eaters (out of a total of 16) have no religion and 3 Christians (out of a total of 14) are vegetarian. The observed count is the observed frequency in a particular cell of the crosstabs table. Our crosstabs table includes information about observed counts (what SPSS calls “Count”) and expected counts. This is the crosstabs table, and it provides a lot of information that is useful for interpreting a chi square test result.
![how to run a chi square test in spss how to run a chi square test in spss](http://s3.amazonaws.com/libapps/accounts/2515/images/spss_one-sample-t_example_dialog-window.png)
In our example, as you can see above, we had 30 valid cases, and no missing cases. Case Processing SummaryĪs its name suggests, the Case Processing Summary is just a summary of the cases that were processed when the crosstabs analysis ran. The output of a crosstabs analysis contains a number of elements. The chi square test allows us to test this hypothesis. The null hypothesis of our hypothetical study is that these variables are not associated with each other – they are independent variables. Each variable has two possible values: No Religion and Christian for the Religion variable Meat Eater and Vegetarian for the Eating variable. The crosstabs analysis above is for two categorical variables, Religion and Eating. You should be looking at a result that looks something like this in the SPSS output viewer.
#How to run a chi square test in spss how to#
( We have a different tutorial explaining how to do a chi square test in SPSS). The tutorial starts from the assumption that you have already calculated the chi square statistic for your data set, and you want to know how to interpret the result that SPSS has generated. This quick tutorial will show you how to interpret the result of a chi square calculation you have performed in SPSS.