Means: This procedure computes summary statistics for dependent variables within the levels of one or more independent variables. It has one submenu.

In this on-line workshop, you will find many movie clips. Each movie clip will demonstrate some specific usage of SPSS.

One-Sample t-test : Tests whether the mean of a single variable differs from a specified constant. The assumptions include the population follows normal distribution.

Independent Sample t-test : Helps you to compare the means for two groups. The assumption is each population follows a normal distribution. The variances of two populations may be constant. If this is the case, pooled variance can be used, in order to have a better estimate of the common variance. Otherwise, non-constant variance t-test is more appropriate. The assumption of normality can be checked using Q-Q plot. The assumption of constant variance requires a separate F-test for comparing variances.

Paired Sample t-test : Compares the means of a variable observed at two different situations of a single group. The two situations are often two different times (Before and After a certain treatment). For example, a researcher may be interested in the effect of weight loss of a weight control program. Individuals in the experiment will be measured for various responses such as weight before and after taking the weight control program training. Two weights are measured from the same experimental unit BEFORE and AFTER the treatment. The effect of the weight loss can be examined using Paired t-test.

Each of the t-test procedures has one submenu.