Question

Perform a common analysis used to compare means for Exam Anxiety in students: Once you have retrieved your data set, go to Analyze, then Compare Means, then Independent Samples t-test. Conduct a compare means analysis using an independent samples t-test in SPSS. The grouping variable for Exam Anxiety in students will be gender, grouped as (1, 2).

Answer 1

The independent-samples t-test (or independent t-test, for short) compares the means between two unrelated groups on the same continuous, dependent variable. Alternately, you could use an independent t-test to understand whether there is a difference in test anxiety based on educational level (i.e., your dependent variable would be "test anxiety" and your independent variable would be "educational level", which has two groups: "undergraduates" and "postgraduates").

SPSS Statistics:

Assumptions: When you choose to analyze your data using an independent t-test, part of the process involves checking to make sure that the data you want to analyze can actually be analyzed using an independent t-test. You need to do this because it is only appropriate to use an independent t-test if your data "passes" six assumptions that are required for an independent t-test to give you a valid result. In practice, checking for these six assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task

Assumption #1: Your dependent variable should be measured on a continuous scale (i.e., it is measured at the interval or ratio level).

Assumption #2: Your independent variable should consist of two categorical, independent groups

Assumption #3: You should have independence of observations, which means that there is no relationship between the observations in each group or between the groups themselves

Assumption #4: There should be no significant outliers

Assumption #5: Your dependent variable should be approximately normally distributed for each group of the independent variable

Assumption #6: There needs to be homogeneity of variances

It is also worth noting that in addition to reporting the results from your assumptions and independent t-test, you are increasingly expected to report effect sizes. Whilst there are many different ways you can do this, we show you how to calculate effect sizes from your SPSS Statistics results in our enhanced independent t-test guide. Effect sizes are important because whilst the independent t-test tells you whether differences between group means are "real" (i.e., different in the population), it does not tell you the "size" of the difference. Providing an effect size in your results helps to overcome this limitation.