data analysis using spss t-test. t-test used to test whether there is significant difference between...

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.