Data Analysis Using SPSS T-Test

Data Analysis Using Data Analysis Using SPSSSPSS

t-testt-test

t-testt-test Used to test whether there is Used to test whether there is

significant difference between the significant difference between the means of two groups, e.g.:means of two groups, e.g.:• Male v female Male v female • Full-time v part-time Full-time v part-time

t-testt-test Typical hypotheses for t-test:Typical hypotheses for t-test:

a)a) There is no difference in affective There is no difference in affective commitment (affcomm) between male commitment (affcomm) between male and female employeesand female employees

b)b) There is no difference in continuance There is no difference in continuance commitment (concomm) between male commitment (concomm) between male and female employeesand female employees

c)c) There is no difference in normative There is no difference in normative commitment (norcomm) between male commitment (norcomm) between male and female employeesand female employees

Performing T-testPerforming T-test

Analyze Analyze → → Compare Means → Compare Means →

Independent-Samples T-testIndependent-Samples T-test

Performing T-testPerforming T-test Select the variables to test (Test Select the variables to test (Test

Variables), in this case:Variables), in this case:• affcommaffcomm• concommconcomm• norcommnorcomm

And bring the variables to the “Test And bring the variables to the “Test Variables” boxVariables” box

Performing T-testPerforming T-test Select the grouping variable, i.e. Select the grouping variable, i.e.

gender; bring it to the “grouping gender; bring it to the “grouping variable” boxvariable” box

Click “Define Groups”Click “Define Groups”

Performing T-testPerforming T-test Choose “Use specified values”Choose “Use specified values” Key in the codes for the variable Key in the codes for the variable

“gender” as used in the “Value “gender” as used in the “Value Labels”. In this case:Labels”. In this case:1 - Male1 - Male2 - Female 2 - Female

Click “Continue”, then “OK”Click “Continue”, then “OK”

T-Test: SPSS Output

Group Statistics

357 3.49720 .731988 .038741315 3.38016 .696273 .039231357 3.18838 .756794 .040054315 3.15159 .666338 .037544357 3.24090 .665938 .035245315 3.27540 .647409 .036477

GENDER OFRESPONDENTMALEFEMALEMALEFEMALEMALEFEMALE

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 variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual 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 From the SPSS output, we are able to see that the means of the to see that the means of the respective variables for the two respective variables for the two groups are:groups are:

• Affective commitment (affcomm) Affective commitment (affcomm) Male 3.49720 Female 3.38016Male 3.49720 Female 3.38016

• Continuance commitment (concomm)Continuance commitment (concomm) Male 3.18838 Female 3.15159Male 3.18838 Female 3.15159

• Normative commitment (norcomm)Normative commitment (norcomm) Male 3.24090 Female 3.27540Male 3.24090 Female 3.27540

T-test: InterpretationT-test: Interpretation For the variable “affcomm”For the variable “affcomm”

• Levene’s Test for Equality of Variances Levene’s Test for Equality of Variances shows that F (1.048) is not significant shows that F (1.048) is not significant (0.306)* therefore the “Equal variances (0.306)* therefore the “Equal variances assumed” row will be used for the t-test.assumed” row will be used for the t-test.

* This score (sig.) has to be 0.05 or less to be * This score (sig.) has to be 0.05 or less to be considered significant.considered significant.

T-test: InterpretationT-test: Interpretation Under the “t-test for Equality of Under the “t-test for Equality of

Means” look at “Sig. (2-tailed)” Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. for “Equal variances assumed”.

The score is 0.035 (which is less The score is 0.035 (which is less than 0.05), therefore there is a than 0.05), therefore there is a significant difference between significant difference between the means of the two groups.the means of the two groups.


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


affcomm

concomm

norcomm

F Sig.





Difference


T-test: Interpretation

T-test: InterpretationT-test: Interpretation For the variable “concomm”For the variable “concomm”

• Levene’s Test for Equality of Variances Levene’s Test for Equality of Variances shows that F (5.353) is significant shows that F (5.353) is significant (0.021)* therefore the “Equal variances (0.021)* therefore the “Equal variances not assumed” row will be used for the t-not assumed” row will be used for the t-test.test.

* This score (sig.) is less than 0.05, so there is * This score (sig.) is less than 0.05, so there is significant different in the variances of the significant different in the variances of the two groups.two groups.


Means” look at “Sig. (2-tailed)” for Means” look at “Sig. (2-tailed)” for “Equal variances not assumed”. “Equal variances not assumed”.

The score is 0.503 (which is more The score is 0.503 (which is more than 0.05), therefore there is no than 0.05), therefore there is no significant difference between the significant difference between the means of the two groups.means of the two groups.


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


affcomm

concomm

norcomm

F Sig.





Difference


T-test: Interpretation

T-test: InterpretationT-test: Interpretation For the variable “norcomm”For the variable “norcomm”

• Levene’s Test for Equality of Variances Levene’s Test for Equality of Variances shows that F (0.656) is not significant shows that F (0.656) is not significant (0.418)* therefore the “Equal variances (0.418)* therefore the “Equal variances are assumed” row will be used for the t-are assumed” row will be used for the t-test.test.

* This score (sig.) is more than 0.05, so there * This score (sig.) is more than 0.05, so there is no significant different in the variances of is no significant different in the variances of the two groups.the two groups.


Means” look at “Sig. (2-tailed)” for Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. “Equal variances assumed”.

The score is 0.497 (which is more The score is 0.497 (which is more than 0.05), therefore there is no than 0.05), therefore there is no significant difference between the significant difference between the means of the two groups.means of the two groups.

Data Analysis Using SPSS T-Test

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