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.
However, a frequently asked question is: why should data transformation be done only once? In this article, Kanda Data will discuss the main reasons why data transformation should ideally be performed just once.
Avoiding Data Distortion
Each time data is transformed, the fundamental properties of the original data change. Repeated transformations can lead to data distortion.
Every data transformation can introduce new changes to the data’s structure, complicating the interpretation of results. Therefore, data transformation should ideally be done once to avoid unwanted changes in the data.
Maintaining Ease of Interpretation
The primary goal of data analysis is to draw meaningful conclusions. Repeated data transformations can make the final results difficult to understand.
For example, if data is transformed using the natural logarithm, then square root, and then another method, the final results may be hard to interpret. Performing the transformation only once can help make the analysis results easier to understand.
Consistency in Modeling
Repeated data transformations can lead to inconsistencies in modeling. Statistical models have underlying assumptions, and each transformation affects how the data meets these assumptions.
If transformations are done repeatedly, the resulting model may no longer align with the required assumptions, rendering the analysis invalid. By transforming the data only once, we can ensure that the model remains consistent and valid.
Avoiding Overfitting
Repeated data transformations can contribute to overfitting, as the model becomes overly specific to the repeatedly transformed data. A single transformation helps reduce the risk of overfitting and ensures that the model is more generalizable.
Based on the points discussed by Kanda Data, we can conclude that performing data transformation once is sufficient to achieve the analysis goals, such as meeting model assumptions or enhancing data linearity.
Repeated transformations can not only alter the fundamental properties of the data but also make the analysis results difficult to interpret and potentially cause overfitting. Therefore, data transformation should be done once with a clear purpose and in line with the analysis needs.
This concludes the article by Kanda Data. We hope it is beneficial for our loyal readers. Stay tuned for updates from us in the next article. Thank you.
