Comparative Studies Definition and Statistical Analysis

Comparative Studies Definition and Statistical Analysis

Comparative analysis aims to see the difference in the average of the dependent variable between two or more groups. The comparison test (dependent variable) for the two sample groups is the T test. The test for more than two groups of samples is the F test (ANOVA). Furthermore, comparative analysis requires normal distribution. Besides the variance must be homogeneous.

T test consists of one sample sample T test and two sample t test. One sample a test to examine a group of sample against a benchmark/standard value. for example, the researcher wants to test whether one group of students has a TOEFL test score above 500 or below 500. While in the two sample T tests, the researcher compares the population mean in the two sample groups. for example, researchers will compare the average TOEFL score in class A compared with class B. Does class A have an average TOEFL score that is the same as class B?

Furthermore, 2 sample t tests consist of paired samples and independent samples. We use paired samples when we compare two groups of samples which are basically one group. For instance, we compare the TOEFL score of class A before and after training.

It’s clear that the research subjects are the same but only at different times. In this paired sample t test, the number of samples must be the same. The subject will be tested before and after.

In independent samples, the subjects is different. For example we compare class A and class B at one time. The number of samples also allows different. Since the population of class A could be different with the population of class B.

When comparing more than 2 groups, we need ANOVA test. For instance, we examine whether group A, group B and group C have the same TOEFL score. In this test, it is possible to compare the same or different subjects. For example we compare the score of TOEFL in group A group B and group C. This test is only extension of the independent sample test. Likewise comparing group A before training 1, after training 1 and after training 2. The subject is only group A.

ANOVA test is technically different in calculations with T. ANOVA test, the basis of ANOVA is not average but variance. While the basic T test is average. Essentially the purpose of both calculations is the same.

Ayat HIdayat Huang

Lecturer of Statistics and Research Methodology in Jakarta, Indonesia
Ayat HIdayat Huang

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