Correlation is a statistical method used to assess the strength and direction of the relationship between two variables you are examining. Correlation does not imply causation, meaning the relationship may not indicate a direct cause-and-effect link. You should try to avoid causal language when observing a correlation.

Step 1:

Select, Analyze, Correlate, then Bivariate

Step 2:

Add a couple of factors

A) Click on the item you would like to add

B) Click the arrow button

The selected item will populate in the Variables field.

Step 3:

When you are done, ensure that the following are selected:

A) Pearson

B) Flag significant correlations

Step 4:

Click the OK button

Results:

The following Output will pop up in a new window.

Leave the window open, as the next activities will be added to the Output.

The Chi-square (X²) test is a statistical method used to evaluate whethere there is a significant difference between the expected and observed frequencies in the variables. It is often used in contingency tables (cross tabulations) to assess the relationship between two or more variables. 

Step 1:

Select Analyze, Descriptive Statistics, then Crosstabs...

Step 2:

A Crosstabs window will pop-up

Click on the Statistics button

Step 3:

Make sure the Chi-square box is selected

Then click the Continue button

Step 4: 

Click the Cells button

Step 5:

Make sure the Observed, Expected and Row check boxes are selected

Step 6:

Click the Continue button

Step 7:

Next you will select your variables

A) Select the Your sex: [SEX] variable 

B) Click on the Row(s) arrow to add it to the Row(s) field

Step 8:

A) Select the Act: Participated in intramural sports [ACT46] variable 

B) Click on the Column(s) arrow to add it to the Column(s) field

Step 9:

Click on the OK button

The process will run and populate the output under your previous (Correlations) output

An Analysis of Variance (One-Way ANOVA) is a statistical technique that is used to compare two or more independent groups to determine whether there is statistical evidence that the associated population means are significantly different. It is most often used when you want to compare average results across different variables or groups.

Step 1:

Select Analyze, Compare Means and Proportions, then One-Way ANOVA...

Step 2:

Select the Race/Ethnicity Group [RACEGROUP] variable

Then click the arrow to add it to the Factor field

Step 3:

Select the If you borrowed money to help pay for college expenses, estimate how much you will owe as of June 30, 2019 [LOANAMT] variable

Then click the arrow to add it to the Dependent List 

Step 4:

Click on the Post Hoc button 

Step 5:

Make sure the Tukey option is selected

Step 6:

Click the Continue button

Step 7:

Click the OK button on the One-Way ANOVA window to run the process

The One-Way ANOVA will populate in your Output window below your previous process

Conduct the following tests and save your output results

• Conduct a Correlation test. Example: Correlation
• Conduct a Cross tabulation. Example: Cross Tabulation
• Conduct a One-way ANOVA. Example: Comparison of Means - One Way ANOVA

For each test, note any statistically significant observations and interesting observations. Additionally provide an interpretation of your findings.