In statistics, correlation tests are frequently used by researchers to examine associations. The purpose of conducting a correlation test is to determine the relationship between two observed variables. However, many people still ask me about one specific correlation test for variables measured on an ordinal scale.
The tolerance value and Variance Inflation Factor (VIF) are important metrics that you can use to detect multicollinearity among independent variables. If we recall the basic theory, multicollinearity testing is an assumption test in the Ordinary Least Squares (OLS) regression method, which aims to ensure that there is no strong correlation between independent variables.
In linear regression analysis, to obtain the best linear unbiased estimator, you need to perform a series of assumption tests. One of the assumption tests required in linear regression is the multicollinearity test.
If you are conducting research, you certainly have a hypothesis for your study. Hypothesis testing is crucial in research, especially if you’re performing inferential statistical analysis. In statistical hypothesis testing, you are often faced with the choice of using an alpha value of 5% or 1% for your study.
When conducting linear regression analysis on your research data, you naturally hope that some independent variables significantly affect the dependent variable. Achieving this indicates that you’ve successfully selected independent variables that are presumed to influence the dependent variable.
