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# T test

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### T test

1. 1. Data Analysis Using SPSS t-test
2. 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. 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. 4. Performing T-test Analyze → Compare Means → Independent-Samples T-test
5. 5. 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
6. 6. Performing T-test   Select the grouping variable, i.e. gender; bring it to the “grouping variable” box Click “Define Groups”
7. 7. 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”
8. 8. 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
9. 9. 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
10. 10.  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
11. 11. 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.
12. 12. 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.
13. 13. 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
14. 14. 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.
15. 15. 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.
16. 16. 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
17. 17. 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.
18. 18. T-test: Interpretation   Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. The score is 0.497 (which is more than 0.05), therefore there is no significant difference between the means of the two groups.