A Step by Step Guide for SPSS and Exercise Studies

Table 17
Statistical tests 65
Assumptions
There are four main assumptions for this test (Vincent, 1999):
1. The data must be parametric, that is, they should be measured on an interval
or ratio scale (see Chapter 1). If this is not the case, use a non-paramatetric
equivalent test (see Non parametric tests-2 independent samples in the
Analyze menu).
2. The samples should be randomly selected from the population, so that the
results of the t test can be generalised from the sample to the population.
3. The two samples should come from populations which have approximately
the same variance (i.e., homogeneity of variance assumption). Use the
Levene test (see below) to test this assumption.
4. The scores of the dependent variable should come from a population which
is normally distributed (i.e., normality assumption). This assumption could
be tested using the Q-Q plot and the normality tests in the Descriptive
Statistics/Explore option of the Analyze menu. In the same option, you can
also ask for a Boxplot to identify possible outliers. You can also request a
Histogram with normal curve in the Descriptive Statistics/Frequencies
option of the same menu. Lastly, in the Frequencies option you can obtain
the skewness and kurtosis values. If the ratio of skewness or kurtosis to their
respective standard errors is above 1.96, the data are probably not normally
distributed.
Bear in mind that the t test is fairly robust to moderate violations of the
homogeneity of variance and normality assumptions. If there is a strong
violation of the assumptions, consider using the non-parametric equivalent test.