The comparison of two paired samples becomes interesting to discuss this time. This two-sample comparison test is often analyzed using a paired t-test. Many researchers or students who are conducting research choose to use this test.
In addition to the influence and relationship tests, you can choose a comparison test between two paired samples. This test was conducted to determine the difference in the average value of the two samples analyzed. The comparison test consists of two paired samples and a comparison test of two independent samples. This article will focus more on the pairwise comparison test of two samples.
The use of this test can be analogous to a case study. For example, a researcher is conducting a study to determine how effective a training program increases farmer technology adoption. As a form of evaluation, this researcher conducted measurements in a pre-test before the program and a post-test after completing the one-year extension program. The training program is carried out for one year. Furthermore, the measure of the program’s success is measured by the level of knowledge and adoption of the farmer.
The program is declared successful if there are differences in the level of knowledge and adoption of farmer technology after the program is implemented. In this case, the researcher can perform statistical tests to prove the difference before and after the program. In this study, the number of samples and tested respondents was the same before and after the program. Thus, the researcher can choose to use a paired t-test.
In using a paired two-sample t-test, some assumptions must be met, where the data are normally distributed. You can do a normality test to test whether the data is normally distributed or not. Data normality tests can be done in several ways. You can test the normality of the data according to statistical rules.
Furthermore, the analysis stage and interpretation are not too difficult. Analysis can use statistical software, excel and can even be calculated manually. For those of you who are still confused about the analysis stages and how to interpret the output, you can watch the audiovisual that has been prepared by “kanda data” as follows (video in Indonesian, please use an English translation):
“Now, do you understand better?” If there are still unclear and need additional explanation, you can chat in the comments column of this article or comment below the video you have watched.
Alright, time for a recap. In essence, the paired t-test can compare two paired samples. The number of samples and the unit of analysis is the same before and after the treatment you do. This treatment can vary, such as providing training, learning programs for students, introducing new technologies, and others. Through these programs, you can find out if there is a difference, and of course, it will be statistically analyzed. See you in the next article!
