SPSS - Independent-Sample T test & One-Way Anova

1. NEAR EAST UNIVERSITY
SCHOOL OF TOURISM AND HOTEL
MANAGENENT
THM 400 – SPSS
Student: Elchin MAMMADOV / 20090717
Instructor: Asst.Prof.Dr. Hüseyin Bicen
Project:
Independent-Sample T test & One-Way Anova

2. Performing Two-Sample T-Test
When do we use Two-Sample T-Test?
Two-Sample T-Test is also known as independent
T-Test or between-subjects T-test. We perform this
test when we want to compare the mean of two
different samples.
Example
Comparing the mean scores in a statistics test
between psychology students and law students.
In this example, our null hypothesis is that there is no
difference between the mean score of psychology
students and law students. Our alternative
hypothesis is that there is difference between the
mean score of psychology students and law
students.

5. Here, you see there are two results from two
different t-tests, one assumed equal variance
and the other unequal variance. Which result
to use depends on the result from Levene's
test. Since from A, the p-value of Levene's
test is 0.591, we can assume that the
variance of two groups are the same. (If the
p-value of Levene's test is less than 0.05, we
have to use the "Unequal variance" result)
From B, since the p-value is 0.001, we reject
the null hypothesis and conclude that there is
difference between the mean score of
psychology students and law students at 5%
significance level.

6. One-Way ANOVA
Comparing group differences for one or
more independent and dependent
variables in SPSS. For this section, if
you have one categorical independent
variable and an interval dependent
variable theOne-Way ANOVA procedure
is appropriate.
Example: Does the population mean for
current salary differ by employment
category?

7. Test of Homogeneityof Variances
Current Salary
59.733 2 471 .000
Levene
Statistic df1 df2 Sig.
ANOVA
Current Salary
8.9E+010 2 4.472E+010 434.481 .000
4.8E+010 471 102925714.5
1.4E+011 473
Between Groups
Within Groups
Total
Sum of
Squares df Mean Square F Sig.

8. In the above example in which the hypothesis
is that three categories of employment do not
differ in their salaries, the F statistic has a
value of 434.481 with the associated
significance level of .000 (Technically, the p-
value is less than 0.001). The significance
level tells us that the hypothesis of no
difference among three groups is rejected
under the .05 significance level. Accordingly,
we conclude that the three groups of
employment (Clerical, Custodial, and
Manager) differ in their salaries.