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.
As we all know, data transformation involves changing the original form of data into a new form. There are several options for transforming data that we can choose from.
It is essential to select the appropriate form of data transformation based on the characteristics of our data. This is done to ensure that the analysis results align with statistical principles and that the conclusions drawn from the research can be justified.
Why Do We Perform Data Transformation?
Research data needs to be transformed into logarithmic form in some analyses, such as linear regression in the Cobb-Douglas production function.
In the Cobb-Douglas production function, which is originally exponential, it needs to be linearized through transformation into logarithmic form to be analyzed using linear regression. It is one example of the practical application of data transformation.
Additionally, researchers conducting linear regression analysis using the least squares method sometimes encounter unmet assumptions. For instance, the residuals might not follow a normal distribution in normality tests.
To address non-normally distributed residuals, researchers can transform the data in other ways besides checking for outliers. Another example is when testing for stationarity; researchers can transform data from the level to the first and second differences until stationary data is obtained.
Based on the examples I’ve provided, it becomes evident that mastering data transformation is crucial for researchers conducting studies.
Can Data Transformation Be Done More Than Once?
I often receive the question, can data transformation be done more than once? Many students, when processing data, encounter assumptions that are not met. The test results might not align with their expectations even after transforming the data.
Therefore, many students ask, “Can data transformation be performed more than once?”
Based on several books I’ve read and references from various data processing outcomes in research publications in national and international journals, if we perform data transformation, it is generally done only once.
For example, researchers transform data using natural logarithms in research publications, as explained in the methodology section. According to these statements, researchers typically perform the transformation only once for the equation or model under study.
Several articles and books also indicate that we are allowed to choose the appropriate form of transformation based on the characteristics of our data. Until now, I have not found any references allowing us to transform data from previously transformed data. Data transformation is typically done only once, from the original to the chosen transformed form.
It is also crucial to select the appropriate transformation method. For instance, if our data includes many negative or zero values, it’s advisable not to use natural logarithm transformation. This is because using natural logarithm transformation for negative and zero values would yield undefined results.
Therefore, this emphasizes the importance of choosing the right transformation method based on the characteristics of our data.
Can Data Transformation Be Applied to Only One Variable in Regression Analysis?
This question is often asked quite frequently. Many students or researchers opting for linear regression analysis wonder if it’s permissible to transform only one or two independent variables while leaving other independent and dependent variables unchanged.
According to the theory, data transformation should be applied to all variables within a single model or equation. Therefore, it is not allowed to transform only one variable within that equation.
Even if the results show that transforming just one variable produces classical assumption test outputs as expected, it’s essential to note that when the transformation is applied, all variables—both independent and dependent—within a single equation need to undergo the same treatment.
So, that concludes the explanation for this article. Hopefully, the information provided here answers the questions that you might have about data transformation on variables. Stay tuned for more article updates from Kanda Data next week. Thank you.
