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Data Analysis Using Spss T Test

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Data Analysis Using Spss T Test

  1. 1. Data Analysis Using SPSS t-test
  2. 2. t-test <ul><li>Used to test whether there is significant difference between the means of two groups, e.g.: </li></ul><ul><ul><li>Male v female </li></ul></ul><ul><ul><li>Full-time v part-time </li></ul></ul>
  3. 3. t-test <ul><li>Typical hypotheses for t-test: </li></ul><ul><ul><li>There is no difference in affective commitment (affcomm) between male and female employees </li></ul></ul><ul><ul><li>There is no difference in continuance commitment (concomm) between male and female employees </li></ul></ul><ul><ul><li>There is no difference in normative commitment (norcomm) between male and female employees </li></ul></ul>
  4. 4. Performing T-test <ul><ul><li>Analyze -> </li></ul></ul><ul><ul><ul><li>Compare Means -> </li></ul></ul></ul><ul><ul><ul><ul><li>Independent-Samples T-test </li></ul></ul></ul></ul>
  5. 7. Performing T-test <ul><li>Select the variables to test (Test Variables), in this case: </li></ul><ul><ul><li>affcomm </li></ul></ul><ul><ul><li>concomm </li></ul></ul><ul><ul><li>norcomm </li></ul></ul><ul><li>And bring the variables to the “Test Variables” box </li></ul>
  6. 10. Performing T-test <ul><li>Select the grouping variable, i.e. gender; bring it to the “grouping variable” box </li></ul><ul><li>Click “Define Groups” </li></ul>
  7. 12. Performing T-test <ul><li>Choose “Use specified values” </li></ul><ul><li>Key in the codes for the variable “gender” as used in the “Value Labels”. In this case: </li></ul><ul><ul><li>1 - Male </li></ul></ul><ul><ul><li>2 - Female </li></ul></ul><ul><li>Click “Continue”, then “OK” </li></ul>
  8. 14. T-Test: SPSS Output
  9. 15. T-test: SPSS Output
  10. 16. <ul><li>From the SPSS output, we are able to see that the means of the respective variables for the two groups are: </li></ul><ul><ul><li>Affective commitment (affcomm) </li></ul></ul><ul><ul><ul><li>Male 3.49720 Female 3.38016 </li></ul></ul></ul><ul><ul><li>Continuance commitment (concomm) </li></ul></ul><ul><ul><ul><li>Male 3.18838 Female 3.15159 </li></ul></ul></ul><ul><ul><li>Normative commitment (norcomm) </li></ul></ul><ul><ul><ul><li>Male 3.24090 Female 3.27540 </li></ul></ul></ul>
  11. 17. T-test: Interpretation <ul><li>For the variable “affcomm” </li></ul><ul><ul><li>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. </li></ul></ul><ul><ul><ul><li>* This score (sig.) has to be 0.05 or less to be considered significant. </li></ul></ul></ul>
  12. 18. T-test: Interpretation <ul><li>Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. </li></ul><ul><li>The score is 0.035 (which is less than 0.05), therefore there is a significant difference between the means of the two groups. </li></ul>
  13. 19. T-test: Interpretation
  14. 20. T-test: Interpretation <ul><li>For the variable “concomm” </li></ul><ul><ul><li>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. </li></ul></ul><ul><ul><ul><li>* This score (sig.) is less than 0.05, so there is significant different in the variances of the two groups. </li></ul></ul></ul>
  15. 21. T-test: Interpretation <ul><li>Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances not assumed”. </li></ul><ul><li>The score is 0.503 (which is more than 0.05), therefore there is no significant difference between the means of the two groups. </li></ul>
  16. 22. T-test: Interpretation
  17. 23. T-test: Interpretation <ul><li>For the variable “norcomm” </li></ul><ul><ul><li>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. </li></ul></ul><ul><ul><ul><li>* This score (sig.) is more than 0.05, so there is no significant different in the variances of the two groups. </li></ul></ul></ul>
  18. 24. T-test: Interpretation <ul><li>Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. </li></ul><ul><li>The score is 0.497 (which is more than 0.05), therefore there is no significant difference between the means of the two groups. </li></ul>

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