Types of two-sample design include independent groups/samples, where two sets of data are provided by different groups of participants, and repeated measures, where both sets of data are provided by the same participants.
An example of an independent samples design is Does aerobic training increase fitness? with two groups matched on the basis of age, weight, height, etc. (however, motivation as a factor cannot be controlled) and assessed by a trainer after 4 weeks.
The assumptions and conditions for independent samples t-test include: the design is a two-group, independent samples design, the data type is interval/ratio, both samples are normally distributed, and there is homogeneity of variances in both samples.
If the data does not meet some of these assumptions, the independent samples t-test cannot be used and a “non-parametric equivalent” to the independent samples t-test, called the Mann-Whitney test, must be used instead.
The independent samples t-test can be performed in SPSS by hypothesis formulation, entering the data into SPSS, generating descriptive statistics, running the test in SPSS, making a decision about the hypothesis, and reporting the results APA style and in Plain English.
The null hypothesis for the independent samples t-test is “there will be no significant difference in BMI scores of the aerobic training group, and the control group”.
A one-tailed hypothesis is also possible, for example: “The aerobic training group will obtain significantly better BMI scores, than the free-training group”.
If Levene’s test is NOT significant (i.e “sig” under Levene’s is > 0.05), the variances between two groups are equal – use TOP row of the t-test results.
To get descriptive data for each group separately in SPSS, use the option “Pop-up window options: Select OPTIONS for a range of other statistics (e.g Variance).
SPSS gives the significance values as two-tailed, if performing the hypothesis/test as a one-tailed test simply divide the SPSS two-tailed sig value by 2, to get the one-tailed sig value.
If Levene’s test is significant (i.e the “sig” value listed under Levene’s equality of Variance <0.05), the variances between two groups are not equal – use BOTTOM row of the t-test results.