T-test (Revised version)
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Transcript of T-test (Revised version)
Data Analysis Using Data Analysis Using SPSSSPSS
t-testt-test
(Revised)(Revised)
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
Compare Means
Analyze
Independent-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
Test variables are selected and carried to the box on the right by pressing the arrow
The test variables: affcomm, concomm, and norcomm
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”
Gender is the grouping variable
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 - Male
2 - Female 2 - Female
Click “Continue”, then “OK”Click “Continue”, then “OK”
Specified values for gender are: 1 (Male) and 2 (Female)
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
Mean scores for “Male” on the three test variables
The mean scores for “Female”
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
(1) Sig. is 0.306 (> 0.05) so there is no significant difference in the variances of the two groups(2) so the row “Equal variances assumed” will be used to read the sig. of t-test(3) Sig. level for t-test is 0.035 (<0.05)
Therefore there is a significant difference in the levels of affective commitment (affcomm) between male and female employees.
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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.
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
1. Sig. is 0.021 (<0.05), there is significant difference between the variances2. The row “Equal variances not assumed” is used for interpreting the t-test3. The relevant significant level for t-test is 0.503 (>0.05)
Therefore, there is no significant difference between the two groups
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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.
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)” 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.
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
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1. The sig. is 0.418 (>0.05) so there is no significant difference between the variances2. “Equal variances assumed” will be used to determine t-test3. The Sig. of t-test is 0.497 (>0.05)
Therefore there is no significant difference between the means of the two groups
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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.
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)” 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.