data analysis using spss t-test. t-test used to test whether there is significant difference between...
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Data Analysis Using SPSS
t-test
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
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
Performing T-test
Analyze → Compare Means →
Independent-Samples T-test
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
Performing T-test
Select the grouping variable, i.e. gender; bring it to the “grouping variable” box
Click “Define Groups”
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 - Male2 - Female
Click “Continue”, then “OK”
T-Test: SPSS Output
Group Statistics
357 3.49720 .731988 .038741
315 3.38016 .696273 .039231
357 3.18838 .756794 .040054
315 3.15159 .666338 .037544
357 3.24090 .665938 .035245
315 3.27540 .647409 .036477
GENDER OFRESPONDENTMALE
FEMALE
MALE
FEMALE
MALE
FEMALE
affcomm
concomm
norcomm
N Mean Std. DeviationStd. Error
Mean
Independent Samples Test
1.048 .306 2.116 670 .035 .117040 .055308 .008442 .225638
2.123 666.213 .034 .117040 .055135 .008780 .225300
5.353 .021 .665 670 .506 .036788 .055335 -.071863 .145440
.670 669.997 .503 .036788 .054899 -.071006 .144582
.656 .418 -.679 670 .497 -.034500 .050813 -.134272 .065271
-.680 663.726 .497 -.034500 .050723 -.134097 .065096
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
affcomm
concomm
norcomm
F Sig.
Levene's Test forEquality of Variances
t df Sig. (2-tailed)Mean
DifferenceStd. ErrorDifference Lower Upper
95% ConfidenceInterval of the
Difference
t-test for Equality of Means
T-test: SPSS Output
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
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 t-test.
* This score (sig.) has to be 0.05 or less to be considered significant.
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.
Independent Samples Test
1.048 .306 2.116 670 .035 .117040 .055308 .008442 .225638
2.123 666.213 .034 .117040 .055135 .008780 .225300
5.353 .021 .665 670 .506 .036788 .055335 -.071863 .145440
.670 669.997 .503 .036788 .054899 -.071006 .144582
.656 .418 -.679 670 .497 -.034500 .050813 -.134272 .065271
-.680 663.726 .497 -.034500 .050723 -.134097 .065096
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
affcomm
concomm
norcomm
F Sig.
Levene's Test forEquality of Variances
t df Sig. (2-tailed)Mean
DifferenceStd. ErrorDifference Lower Upper
95% ConfidenceInterval of the
Difference
t-test for Equality of Means
T-test: Interpretation
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 t-test.
* This score (sig.) is less than 0.05, so there is significant different in the variances of the two groups.
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.
Independent Samples Test
1.048 .306 2.116 670 .035 .117040 .055308 .008442 .225638
2.123 666.213 .034 .117040 .055135 .008780 .225300
5.353 .021 .665 670 .506 .036788 .055335 -.071863 .145440
.670 669.997 .503 .036788 .054899 -.071006 .144582
.656 .418 -.679 670 .497 -.034500 .050813 -.134272 .065271
-.680 663.726 .497 -.034500 .050723 -.134097 .065096
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
Equal variancesassumed
Equal variancesnot assumed
affcomm
concomm
norcomm
F Sig.
Levene's Test forEquality of Variances
t df Sig. (2-tailed)Mean
DifferenceStd. ErrorDifference Lower Upper
95% ConfidenceInterval of the
Difference
t-test for Equality of Means
T-test: Interpretation
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 t-test.
* This score (sig.) is more than 0.05, so there is no significant different in the variances of the two groups.
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.