Tag: Data Transformation
When Should Natural Logarithmic Data Transformation Be Applied?
When researchers, practitioners, or students are conducting data analysis on research results, they are often faced with data that do not meet the assumptions required by the chosen analytical method. After testing, it may turn out that the data distribution is highly skewed, the variance is not constant, or non-linear relationships between variables are observed. These conditions represent common challenges in statistical analysis, especially when using parametric methods such as linear regression analysis.
Why Should Data Transformation Be Done Only Once?
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.
Data Transformation to Address Non-Normally Distributed Data
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 data that cannot be transformed using natural logarithm (Ln)
In quantitative data analysis, to ensure unbiased and consistent estimations, it’s important to meet several assumptions required in the conducted tests. However, sometimes, the test results may not meet the desired expectations.
Data That Cannot Be Transformed Using Natural Logarithm (Ln)
In quantitative data analysis, data transformation is not a new concept. It is a process of converting the original form of data into another form to improve the data and meet the assumptions required for quantitative data analysis.
Can Data Transformation Be Done More Than Once?
For those of us accustomed to conducting research, understanding how to analyze data is a crucial skill to master. In the process, when we are processing data, we are sometimes faced with the choice of data transformation.