Data transformation is an essential step in inferential statistical analysis. It can be a solution to ensure that research data meets certain required statistical model assumptions, such as normality, linearity, and homoscedasticity.
The assumption that data must be normally distributed is often a prerequisite for using certain inferential statistical tests. However, sometimes the test results do not meet expectations, indicating that the data is not normally distributed.
The topic I’m writing about today is prompted by questions on how to handle data that is not normally distributed. We know that in quantitative analysis, several statistical tests require that the data be normally distributed. This is an interesting topic that we will delve deeper into in this article.
In statistical analysis, data measurement scales are divided into four main categories namely nominal scale, ordinal scale, interval scale, and ratio scale. A proper understanding of the differences among these scales is crucial for determining the appropriate data analysis method.
Multiple linear regression analysis is a well-known technique frequently used by researchers to analyze the influence of independent variables on dependent variables. The ordinary least squares (OLS) method is one of the most commonly used methods in this analysis.
