1. Data Analysis Using
SPSS
t-test

2. t-test

Used to test whether there is
significant difference between the
means of two groups, e.g.:
• Male v female
• Full-time v part-time

3. t-test

Typical hypotheses for t-test:
a) There is no difference in affective
commitment (affcomm) between male
and female employees
b) There is no difference in continuance
commitment (concomm) between male
and female employees
c) There is no difference in normative
commitment (norcomm) between male
and female employees

4. Performing T-test
Analyze →
Compare Means →
Independent-Samples T-test

7. Performing T-test

Select the variables to test (Test
Variables), in this case:
• affcomm
• concomm
• norcomm

And bring the variables to the “Test
Variables” box

10. Performing T-test


Select the grouping variable, i.e.
gender; bring it to the “grouping
variable” box
Click “Define Groups”

12. Performing T-test


Choose “Use specified values”
Key in the codes for the variable
“gender” as used in the “Value
Labels”. In this case:
1 - Male
2 - Female

Click “Continue”, then “OK”

14. T-Test: SPSS Output
Group Statistics
affcomm
concomm
norcomm
GENDER OF
RESPONDENT
MALE
FEMALE
MALE
FEMALE
MALE
FEMALE
N
357
315
357
315
357
315
Mean
3.49720
3.38016
3.18838
3.15159
3.24090
3.27540
Std. Deviation
.731988
.696273
.756794
.666338
.665938
.647409
Std. Error
Mean
.038741
.039231
.040054
.037544
.035245
.036477

15. T-test: SPSS Output
Independent Samples Test
Levene's Test for
Equality of Variances
F
affcomm
concomm
norcomm
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
not assumed
Sig.
1.048
t
Sig. (2-tailed)
df
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower
Upper
.656
.418
670
.035
.117040
.055308
.008442
.225638
666.213
.034
.117040
.055135
.008780
.225300
.665
670
.506
.036788
.055335
-.071863
.145440
.670
.021
2.116
2.123
5.353
.306
t-test for Equality of Means
669.997
.503
.036788
.054899
-.071006
.144582
-.679
670
.497
-.034500
.050813
-.134272
.065271
-.680
663.726
.497
-.034500
.050723
-.134097
.065096

16. 
From the SPSS output, we are
able to see that the means of the
respective variables for the two
groups are:
• Affective commitment (affcomm)

Male 3.49720 Female 3.38016
• Continuance commitment (concomm)

Male 3.18838 Female 3.15159
• Normative commitment (norcomm)

Male 3.24090 Female 3.27540

17. T-test: Interpretation

For the variable “affcomm”
• Levene’s Test for Equality of Variances
shows that F (1.048) is not significant
(0.306)* therefore the “Equal variances
assumed” row will be used for the ttest.
* This score (sig.) has to be 0.05 or less to be
considered significant.

18. T-test: Interpretation


Under the “t-test for Equality of
Means” look at “Sig. (2-tailed)”
for “Equal variances assumed”.
The score is 0.035 (which is less
than 0.05), therefore there is a
significant difference between
the means of the two groups.

19. T-test: Interpretation
Independent Samples Test
Levene's Test for
Equality of Variances
F
affcomm
concomm
norcomm
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
not assumed
Sig.
1.048
t
Sig. (2-tailed)
df
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower
Upper
.656
.418
670
.035
.117040
.055308
.008442
.225638
666.213
.034
.117040
.055135
.008780
.225300
.665
670
.506
.036788
.055335
-.071863
.145440
.670
.021
2.116
2.123
5.353
.306
t-test for Equality of Means
669.997
.503
.036788
.054899
-.071006
.144582
-.679
670
.497
-.034500
.050813
-.134272
.065271
-.680
663.726
.497
-.034500
.050723
-.134097
.065096

20. T-test: Interpretation

For the variable “concomm”
• Levene’s Test for Equality of Variances
shows that F (5.353) is significant
(0.021)* therefore the “Equal variances
not assumed” row will be used for the ttest.
* This score (sig.) is less than 0.05, so there
is significant different in the variances of the
two groups.

21. T-test: Interpretation


Under the “t-test for Equality of
Means” look at “Sig. (2-tailed)” for
“Equal variances not assumed”.
The score is 0.503 (which is more
than 0.05), therefore there is no
significant difference between the
means of the two groups.

22. T-test: Interpretation
Independent Samples Test
Levene's Test for
Equality of Variances
F
affcomm
concomm
norcomm
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
not assumed
Equal variances
assumed
Equal variances
not assumed
Sig.
1.048
t
Sig. (2-tailed)
df
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower
Upper
.656
.418
670
.035
.117040
.055308
.008442
.225638
666.213
.034
.117040
.055135
.008780
.225300
.665
670
.506
.036788
.055335
-.071863
.145440
.670
.021
2.116
2.123
5.353
.306
t-test for Equality of Means
669.997
.503
.036788
.054899
-.071006
.144582
-.679
670
.497
-.034500
.050813
-.134272
.065271
-.680
663.726
.497
-.034500
.050723
-.134097
.065096

23. T-test: Interpretation

For the variable “norcomm”
• Levene’s Test for Equality of Variances
shows that F (0.656) is not significant
(0.418)* therefore the “Equal variances
are assumed” row will be used for the ttest.
* This score (sig.) is more than 0.05, so there
is no significant different in the variances of
the two groups.

24. T-test: Interpretation


Under the “t-test for Equality of
Means” look at “Sig. (2-tailed)” for
“Equal variances assumed”.
The score is 0.497 (which is more
than 0.05), therefore there is no
significant difference between the
means of the two groups.