Regression analysis has become a staple tool among researchers. Indeed, regression analysis serves as a familiar associative test, aiming to discern the impact of one variable on another.
Researchers can choose correlation analysis to examine the relationship between variables. The selection of correlation analysis techniques depends on the scale of measurement used for the data. In statistics, the data measurement scale of a variable consists of nominal, ordinal, interval, and ratio scales.
Correlation analysis aims to determine the relationship between variables. Partial correlation analysis is the analysis most often used by researchers.
Based on its methods, statistics can be divided into descriptive statistics and inferential statistics. Researchers can choose to use either of these methods or even combine both methods of data analysis.
Differences test is one of the most commonly used associative tests by researchers. Differences test can be conducted on both parametric and non-parametric variables. For parametric variables, the differences test can utilize the t-test assuming normally distributed data.
