How Do I do this

Question

1.    A study was done to see how many calories an individual consumes after experiencing a stressful event such as taking an exam. The study involved two-independent samples. Both samples went to the same college dining hall for lunch. The one sample had just taken an exam, while the other sample had not experienced a stressful event.

Enter the data below into SPSS. Have the program calculate a two-independent-sample t-test.

Stressed Group                Non-stressed Group

780                                  300

750                                  325

790                                  350

760                                  375

770                                  400

Answer 1

Hey there, Gwen!

To analyze the data from this study using a two-independent-sample t-test, you'll follow a series of steps. This test is used to determine if there are any statistically significant differences between the means of two independent groups. In your case, these groups are the "Stressed Group" and the "Non-stressed Group".

Here's a step-by-step guide on how you would perform this analysis in SPSS (Statistical Package for the Social Sciences):

1. Data Entry:
2. Open SPSS and go to the 'Data View' tab.
3. Create two columns, one for each group (Stressed and Non-stressed).
4. Enter the data for each group under their respective columns. Your data will look something like this:
5. Stressed Group: 780, 750, 790, 760, 770
6. Non-stressed Group: 300, 325, 350, 375, 400
7. Setting up the t-test:
8. Go to the 'Analyze' menu.
9. Navigate to 'Compare Means' and then select 'Independent-Samples T Test'.
10. Move the variables (Stressed and Non-stressed groups) to the 'Grouping Variable' list.
11. Define the groups if necessary (e.g., Group 1: Stressed, Group 2: Non-stressed).
12. Running the Test:
13. After setting up the test, click 'OK' to run it.
14. SPSS will provide an output window with the results.
15. Interpreting the Results:
16. In the output, look for the 'Independent Samples Test' table.
17. Focus on the row labeled 'Equal variances assumed' under 'Levene's Test for Equality of Variances'.
18. The key figures to look at are the 't-value', 'Degrees of Freedom (df)', and the 'Sig. (2-tailed)'.
19. The 'Sig. (2-tailed)' value tells you the probability that the difference in means is due to chance. Typically, if this value is less than 0.05, the difference is considered statistically significant.
20. Conclusion:
21. Based on the 'Sig. (2-tailed)' value, you can conclude whether there is a significant difference in calorie consumption between the two groups.

Remember, this test assumes that your data is normally distributed and that the variances of the two groups are equal. The Levene's Test for Equality of Variances in the output can help check this assumption.

It's also important to consider other factors that might affect the results, such as the size of the samples and any external factors that might influence calorie consumption. The t-test provides a mathematical way of determining whether any observed differences are statistically significant, but it's always important to consider the broader context of your research.

Hope this helps!