INDEPENDENT SAMPLE T TEST
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Using Independent samples t-test in Research
This easy tutorial will show you how to run the Independent samples t-test in SPSS, and how to interpret the result.
The Independent Samples t-Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. First of all, The Independent Samples t-Test is a parametric test.
This test is also known as:
- Student t-Test
- Two-Sample t-Test
- Uncorrelated Scores t-Test
- Unpaired t-Test
- Unrelated t-Test
The variables used in this test are known as:
- Dependent variable, or test variable
- Independent variable, or grouping variable (Source)
An Example in SPSS: Independent samples t-test
Independent samples t-test uses to compare the mean value of a variable (for example, wage) measured in two different groups of people (for example, men and women) or different circumstances (for example, a union member or not a union member).
The independent-samples t-test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly different from each other.
Independent samples t-test examines whether there is a difference in the mean value between the two groups. Therefore, we need two variables to conduct independent samples t-test:
- one categorical variable with two groups (for example, male and female); independent variable
- one continuous variable (for example, annual wage), dependent variable.
H0: µ1 – µ2 = 0 (“the difference between the two population means is equal to 0”)
H1: µ1 – µ2 ≠ 0 (“the difference between the two population means is not 0”)
To show how to run the independent samples t-test in SPSS. We will use one categorical variable (gender) with two groups (male and female), and one continuous variable (wage – annual wage in dollars).
This easy tutorial will show you how to run Independent samples t-test, and how to interpret the result.
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How to Run Independent Samples t Test in SPSS: Explanation Step by Step
From the SPSS menu, choose Analyze – Compare Means – Independent samples t-test.
A new window with variables in the left box will open.
Using an arrow or double click on the variable, transfer variable wage to the Test Variable(s) box. In the same way transfer variable gender to the Grouping Variable box.
Right-click on the variable gender in the Grouping Variable box to see how variable is coded. Click on Variable information and the new box will open to show how the variable is coded.
Click on Define groups option below Grouping variable box. Enter the variable codes (0 and 1). Click Continue and then OK.
The results of independent samples t-test will appear in the output window.
How to report a Chi-square test for independence results: Explanation Step by Step
How to Report Group Statistic Table in SPSS Output?
The first table Group Statistics shows mean, standard deviation, and standard error mean for each group
How to Report Independent samples t-test Table in SPSS Output?
The second table Independent samples test shows the results of independent samples t-test.
If the p-value (column sig. in column Levene’s test for equality of variances) is higher than 0.05, for the reason that we conclude that equal variances assume and use the first row of the table t-test for equality of means to conclude.
Since the p-value in our example is .605, we assume that variances are equal and use the first row of the table t-test for equality of means to make a conclusion.
An independent-samples t-test was conducted to determine whether there is a difference in annual wages between males and females. The results indicate a not significant difference between male (M=28249.31, SD=12697.09) and female (M=32875.05, SD=13583.47), [t(46) = -1.218, p = .229 > .05]. The 95% confidence interval of the difference between means ranged from [-12269.716 to 3018.241] and did not indicate a difference between the means of the sample. Consequently, we fail to reject the null hypothesis that there is no difference between the sample means.
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