Hi, I want to investigate sex differences and education level on test anxiety among students. My variables are as follows :

Independent variable 1 – Sex difference (male or female)

Independent variable 2- education level (grade 2 or grade 3 students)

Dependent variable – Test anxiety reported by the students.

Is this suitable for a 2 way ANOVA? If yes, when putting in the data, should I input the score of each student on the Test Anxiety Inventory (TAI)? Or the sum of the students who reported test anxiety? I got confused on how I can key in each datum of upto 261 students that participated. Thanks.

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Hello Charles,

Thank you very much for your reply!

1) For experiment 1, both data sets that failed the normality test (p= and p=) are not symmetric, according to the box plot. Therefore, a nonparametric test should be used for the analysis, right?

2) For experiment 2, there are two experimental groups. I only have three values for each group. The data for group A are: , , (normality test P<). The data for group B are: , , (normality test P=). The results from t-test (p=) and Mann-Whitney Rank sum test (p=) are very different.

Thank you!

For example, assume an investor wishes to test whether the average daily return of a stock is greater than 1%. A simple random sample of 50 returns is calculated and has an average of 2%. Assume the standard deviation of the returns is %. Therefore, the null hypothesis is when the average, or mean, is equal to 3%. Conversely, the alternative hypothesis is whether the mean return is greater than 3%. Assume an alpha of % is selected with a two-tailed test. Consequently, there is % of the samples in each tail, and the alpha has a critical value of or -. If the value of z is greater than or less than -, the null hypothesis is rejected.

For example, assume an investor wishes to test whether the average daily return of a stock is greater than 1%. A simple random sample of 50 returns is calculated and has an average of 2%. Assume the standard deviation of the returns is %. Therefore, the null hypothesis is when the average, or mean, is equal to 3%. Conversely, the alternative hypothesis is whether the mean return is greater than 3%. Assume an alpha of % is selected with a two-tailed test. Consequently, there is % of the samples in each tail, and the alpha has a critical value of or -. If the value of z is greater than or less than -, the null hypothesis is rejected.