In a repeated-measures study, the degrees of freedom ($df$) for the t-test are calculated based on the number of participants. The formula for the degrees of freedom in this case is:

$df = n - 1$

Where $n$ is the number of participants.

Given:

• $n = 20$

Thus, the degrees of freedom ($df$) would be:

$df = 20 - 1 = 19$

So, the $df$ would be 19.

Would you like further explanation or have any other questions?

Here are some related questions you might consider:

1. How do degrees of freedom affect the critical value in a t-test?
2. What is a repeated-measures study, and why is it used?
3. How does a repeated-measures design differ from an independent-measures design?
4. What assumptions are necessary for a repeated-measures t-test?
5. How do you interpret the t-statistic in a repeated-measures study?
6. What are the advantages and disadvantages of using a repeated-measures design?
7. How would you calculate the effect size for a repeated-measures study?
8. How does sample size impact the power of a repeated-measures t-test?

Tip: In a repeated-measures study, since the same participants are measured more than once, individual differences are controlled for, often leading to a more powerful test.