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# Calculation of Chi-square test

In this section, we will learn how to calculate the **chi-square test** in SPSS. To calculate the **chi-square** test, we will open our **Data set** by going to the **File menu**, then go to **Recently used Data** as follows:

Now we will click on the above **Employee Data** option and see our **Employee Data** set as follows:

Look at this data set. In this data set, we have the recode of various **employees** with their **ID, gender** of the employee, **education, job category, current salary, beginning salary, job time, previous experience** and **minority classification**. In **gender, m** stands for **males** and **f** stands for **females**. In **minority classification, 0** means **No**, which shows the person is not from a minority background, and **1** means **Yes**, which shows the person belongs to the minority background. **9** is for the **missing value**.

Using this data set, we want to test if there is a significantly higher number of males as compared to females from **minority communities**. So the **researcher** is currently interested in the **gender variable** and its **relationship** with the **minority classification**.

The question could be whether there is a significant difference between the number of **males** and **females** belonging to the **minority classification**. So, in this case, we will use the **chi-square** test. We will use the **chi-square** test because both variables, **gender** and **minority classification** are nominal variables or **frequency variables**. They are **frequency** variable, so any other **parameter** stats like **t-test** cannot be applied. The question is, should we consider one variable as an **independent variable** and another variable as a **dependent variable** while calculating **chi-square**. The answer is yes. **Yes**, we can select a variable as an **independent** variable. We can see whether it impacts the **dependent** variable significantly by using the **Crosstab option**.

But all in all, while calculating the **chi-square test, IBM SPSS** does not make a distinction between the **independent** and **dependent** variables. It calculates an interaction between the two **frequency variables** that we choose and derive inference based on our **interpretation**. So before moving into that, let us see how we can calculate the **chi-square test**. So we can calculate the **chi-square** test in many ways. **For example**, we can calculate the **chi-square** test using **Descriptive statistics** and the **Crosstabs** option that we are going to use currently.

**Alternatively**, since we know, **chi-square** is a **non-parametric** test. So we can also calculate the chi-square test by going to **Non-parametric tests**, and then we can go to **Legacy Dialogs**, and then we can calculate the **Chi-square test**.

So currently, we are going to demonstrate a **chi-square test** using the **Crosstab** option. To calculate **chi-square** using cross tab options, we will go to **Descriptive statistics** and then go to **crosstabs**. After clicking on the **crosstab** option, we can see all our **variables** populated like this:

Now we want to see whether males and females are significantly different in number in reference to their **minority classification**. So we will take **gender** as an **independent variable** for all practical purposes in this study. So if we want to take a variable as an **independent** variable, we can take it in a **column dialog** box. So we will take **gender** in our **column** dialog box.

Our **dependent variable** is **minority classification**. So take **minority classification** in our **row dialog** box. But practically itâ€™s going to make no difference in the result either we choose gender in a column dialog box or row dialog box or visa versa. Itâ€™s only going to tell us the interaction between frequencies.

#### Note: Chi-square is just a test of association, and it does not matter whether we are taking our independent variables in row or column. But as a convention, if we hypothesize an independent variable, then take it in the column. The dependent variable will be taken in a row.