1. Cross-tabulations
T-test
Anova

3. Analyze-Descriptive-Crosstabs

4. Select Variables
Layer: Gender, Display: Bar chart

6. Correlation of Data

7. Bar Chart

8. T-Test
• The t test for independent means is used to
compare means derived from unrelated
(uncorrelated) samples.
• For example: We have 2 independent groups
here, women and men. To test a hypothesis
regarding the differences between the 2
groups, we can use a t-test for independent
means.

9. http://roseproject.no/network/countries/iran/2012_Sarjou_et_al_ROSE_Iranian_students.p

10. Explanation to Table 4 (see
previous slide)
http://roseproject.no/network/countries/iran/2012_Sarjou_et_al_ROSE_Iranian_students.pdf

11. Another example case for T-test
grouping
• Example. Patients with high blood pressure are
randomly assigned to a placebo group and a
treatment group. The placebo subjects receive
an inactive pill, and the treatment subjects
receive a new drug that is expected to lower
blood pressure. After the subjects are treated for
two months, the two-sample t test is used to
compare the average blood pressures for the
placebo group and the treatment group. Each
patient is measured once and belongs to one
group.

12. One-way ANOVA
• The One-Way ANOVA procedure produces a one-way
analysis of variance for a quantitative dependent variable
by a single factor (independent) variable. Analysis of
variance is used to test the hypothesis that several
means are equal. This technique is an extension of the
two-sample t test.
• In addition to determining that differences exist among
the means, you may want to know which means differ.
There are two types of tests for comparing means: a
priori contrasts and post hoc tests. Contrasts are tests
set up before running the experiment, and post hoc tests
are run after the experiment has been conducted. You
can also test for trends across categories.

13. Example Case
• Doughnuts absorb fat in various amounts when
they are cooked. An experiment is set up
involving three types of fat: peanut oil, corn oil,
and lard. Peanut oil and corn oil are unsaturated
fats, and lard is a saturated fat. Along with
determining whether the amount of fat absorbed
depends on the type of fat used, you could set
up an a priori contrast to determine whether the
amount of fat absorption differs for saturated
and unsaturated fats.

14. One-way ANOVA
• If you don’t have pre-test and post-test
data, don’t use this statistical
measurement.