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Data Analysis Using Spss T Test

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  • 1. Data Analysis Using SPSS t-test
  • 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. t-test
    • Typical hypotheses for t-test:
      • There is no difference in affective commitment (affcomm) between male and female employees
      • There is no difference in continuance commitment (concomm) between male and female employees
      • There is no difference in normative commitment (norcomm) between male and female employees
  • 4. Performing T-test
      • Analyze ->
        • Compare Means ->
          • Independent-Samples T-test
  • 5.  
  • 6.  
  • 7. 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
  • 8.  
  • 9.  
  • 10. Performing T-test
    • Select the grouping variable, i.e. gender; bring it to the “grouping variable” box
    • Click “Define Groups”
  • 11.  
  • 12. 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”
  • 13.  
  • 14. T-Test: SPSS Output
  • 15. T-test: SPSS Output
  • 16.
    • 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
  • 17. 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.
  • 18. T-test: Interpretation
    • Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances assumed”.
    • The score is 0.035 (which is less than 0.05), therefore there is a significant difference between the means of the two groups.
  • 19. T-test: Interpretation
  • 20. 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.
  • 21. 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.
  • 22. T-test: Interpretation
  • 23. 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.
  • 24. 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.