Write a short note on steps in computation of independent t-test using SPSS.

Computation of an independent t-test using SPSS involves several steps to compare the means of two independent groups. Here's a brief overview of the process:

1. Data Entry:
Enter the data into SPSS, ensuring that each case represents a single observation and each variable represents the outcome of interest for each group.

2. Select Analysis:
Go to the "Analyze" menu at the top of the SPSS window and choose "Compare Means" > "Independent-Samples T Test."

3. Variable Selection:
In the dialog box that appears, select the variable representing the outcome (dependent variable) and move it to the "Test Variable(s)" box. Then, select the variable representing the group membership (independent variable) and move it to the "Grouping Variable" box.

4. Options:
Click on the "Options" button to specify additional options, such as confidence intervals and effect size measures (e.g., Cohen's d).

5. Review Settings:
Double-check the settings to ensure that the analysis is configured correctly, including the variables selected, options chosen, and output preferences.

6. Run Analysis:
Click on the "OK" button to run the independent t-test analysis. SPSS will compute the t-statistic, degrees of freedom, p-value, and other relevant statistics to assess the difference in means between the two groups.

7. Interpret Results:
Review the output generated by SPSS, which typically includes a table of means and standard deviations for each group, the t-statistic, degrees of freedom, p-value, and confidence intervals for the mean difference. Interpret the results to determine whether there is a statistically significant difference between the means of the two groups.

By following these steps, users can easily compute an independent t-test using SPSS to compare the means of two independent groups and assess whether the observed difference is statistically significant.