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How to Choose Regression, Correlation, or Difference Test for Variable Association Analysis

Selecting the appropriate analysis method will prevent errors in concluding research results. There are various methods of data analysis that researchers can choose from. The selection of data analysis methods depends on the research objectives and the characteristics of the collected data.

Each research objective is attempted to be proven through testing and analysis of the collected data. Therefore, to address research objectives, research hypotheses are formulated. Research hypotheses can be tested through a series of research activities, including both experimental and survey research.

Subsequently, researchers must formulate the research and analysis methods before collecting data. The collected and tabulated data is then analyzed using the appropriate analysis method.

Variable Association Tests

There are many choices for inferential statistics tests. However, the associative tests are the ones most frequently selected by researchers. These associative tests include influence tests, relationship tests, and difference tests. Influence tests are often associated with regression analysis, while relationship tests are associated with correlation analysis. As for difference tests, they are often represented by mean tests or t-tests.

Given the importance of these three associative tests, it is essential to understand their differences thoroughly. Therefore, in this opportunity, Kanda Data will try to discuss these three associative tests: regression analysis, correlation analysis, and difference tests.

Linear Regression Analysis

Regression analysis is a method used to test the influence of one variable on another. In regression analysis, there are two types: linear regression and non-linear regression.

Researchers commonly use linear regression analysis using the Ordinary Least Square (OLS) method. In linear regression analysis with the OLS method, there are several assumptions that researchers must meet, such as testing the normality of residuals, heteroscedasticity, multicollinearity, and linearity for cross-sectional data. For time series data, an additional test for autocorrelation is required.

Linear regression with the OLS method can be applied when the measurement scale of the data is on the interval and ratio scales. Researchers should consider non-parametric tests if the measurement scale is nominal or ordinal.

Among the alternative tests that can be chosen are logistic regression, ordinal regression, and other tests for non-parametric variables.

Based on the previous paragraph, linear regression is intended to test the influence of independent variables on the dependent variable. Therefore, researchers need to specify which variables belong to the dependent variable and which ones are included in the independent variables.

Furthermore, researchers need to determine the measurement scale of the variables used for independent and dependent variables. It is done to facilitate the interpretation of the estimated coefficients obtained.

Correlation Analysis

Unlike regression analysis, correlation analysis aims to test the relationship between variables. While regression analysis involves a dependent variable and independent variables, correlation analysis does not use these types of variables. In essence, correlation analysis tests the two-way relationship between variables, whereas regression analysis tests a one-way influence.

For example, suppose a researcher tests the correlation between variable X and variable Y. In that case, it can be interpreted that X is related to Y. Similarly, it can also be interpreted that Y is related to X. This is what is meant by a two-way relationship.

In comparison to regression analysis, let’s say X influences Y. However, Y may not necessarily influence X. This is an example of a one-way association test. So now, we understand that correlation analysis examines the two-way relationship.

Next, are there different options for correlation analysis? Yes, just like other types of analysis, correlation analysis also has several test options depending on the research objectives and data characteristics.

Correlation analysis can also be divided into partial correlation and multiple correlation tests. Partial correlation is the most commonly used analysis by researchers. Researchers may consider using Pearson correlation for variables measured on an interval or ratio scale.

If the measurement scale is non-parametric (nominal or ordinal scale), researchers can choose tests suitable for non-parametric variables. For instance, if researchers use an ordinal scale, they can consider using Spearman’s rank correlation or Kendall’s tau. Researchers may consider using the Chi-square test if the measurement scale is nominal.

Therefore, the measurement scale of data, whether nominal, ordinal, interval, or ratio, will determine the appropriate choice of correlation test.

Difference Test

The difference test determines the variance in mean values between the tested variables. The scale of data measurement will also determine the choice of difference test. The difference test can also be conducted between two sample groups or more than two sample groups.

The difference test between two sample groups is known as the t-test. Meanwhile, the difference test between more than two sample groups can use the one-way ANOVA test.

In the t-test and one-way ANOVA, some assumptions need to be met. The t-test tests the mean values of two sample groups assuming the data is normally distributed.

Variables measured on the interval and ratio scales may consider using the t-test. On the other hand, for non-parametric variables (nominal and ordinal scales), the difference test for non-parametric variables can be considered.

In the one-way ANOVA test, besides normal data distribution, it is necessary to fulfill the homogeneity assumption. Thus, the research objectives and characteristics of the data measurement scale will determine the appropriate choice of the difference test.

Conclusion

Regression analysis aims to test the influence of independent variables on the dependent variable. Correlation analysis, on the other hand, is intended to test the relationship between variables, and the difference test is used to examine the differences in mean values of variables.

The research objectives and data measurement scale will determine the appropriate choice of associative test. Researchers can independently detect suitable tests based on their research objectives and the scale of data measurement.

I hope this article can be beneficial and provide insights for everyone. See you in the next educational article on Kanda Data’s website next week.

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