spss shift: Using SPSS for t-Tests

Using SPSS for t-Tests

The independent t-test compares the means between two unrelated groups on the same continuous, dependent variable. The SPSS t-test procedure allows the testing of equality of variances (Levene's test) and the t-value for both equal- and unequal-variance. It also provides the relevant descriptive statistics. A statistical guide on the independent t-test is provided here.
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Assumptions
Independent variable consists of two independent groups.
Dependent variable is either interval or ratio (see our guide on Types of Variable).
Dependent variable is approximately normally distributed (see Testing for Normality article)
Similiar variances between the two groups (homogeneity of variances) (tested for in this t-test procedure).
Background to Example
The concentration of cholesterol (a type of fat) in the blood is associated with the risk of developing heart disease, such that higher concentrations of cholesterol indicate a higher level of risk and lower concentrations indicate a lower level of risk. If you lower the concentration of cholesterol in the blood then your risk for developing heart disease can be reduced. Being overweight and/or physically inactive increases the concentration of cholesterol in your blood. Both exercise and weightloss can reduce cholesterol concentration. However, it is not known whether exercise or weightloss is best for lowering blood cholesterol concentration.
Example
A random sample of inactive male individuals that were classified as overweight were recruited to a study to investigate whether an exercise or weight loss intervention is more effective in lowering cholesterol levels. To this end, they randomly split the group into two sub-groups; one group underwent an exercise training programme and the other group undertook a calorie-controlled diet. In order to determine which treatment programme was more effective, the mean cholesterol concentrations were compared between the two groups at the end of the treatment programmes.
Setup in SPSS
In SPSS we separated the groups for analysis by creating a grouping variable called "Group" and gave the exercise group a value of "1" and the diet group a value of "2". Cholesterol concentrations were entered under the variable name "Cholesterol". How to correctly enter data in SPSS to run an independent t-test is explained in our guide here.
Descriptives
Unless you have other reasons to do so, it would be considered normal to present information on the mean and standard deviation for this data. You might also state the number of participants you had in each group that were to be analysed. This is in case you have, for example, missing values and the number of recruited participants is larger than that which can be analysed.
You might also wish to present a diagram so that you can show your results visually in order that a reader might understand them better. You could present a bar chart with error bars (for example, SD or 95% CI) (see our guide here).
Testing assumptions
To determine whether your samples are normally distributed read our Testing for Normality article. What if your samples are not normally distributed? Well, if your data set is large then small deviations are generally tolerable. However, if your samples are small or your data set is largely non-normal then you need to consider a non-parametric test instead, such as the Mann-Whitney U Test.
The assumption of equal variances is tested in SPSS by Levene's Test for Equality of Variances. The result of this test is presented in the output when running an independent t-test and is discussed later in this guide.
Test Procedure in SPSS