🙏 Support the Channel
📧 PayPal:
[email protected]
💳 M-PESA Till Number (Buy Goods and Services): 3676188
💳 M-PESA: 0702333716
Your support helps me continue creating valuable content. Thank you! 💙
📖 Check Out My Book:
The Self-Made Shift: A 21st Century Journey to Freedom and Fulfilment
Available now on Amazon 👉 https://www.amazon.com/dp/B0F1YTHTR7
----------------------------------------------------------------------------------------------------------------------------------------------------
In this tutorial, we'll explore a common technique for handling missing values in Stata: mean imputation. When we encounter missing data in our datasets, we need to decide how to deal with those missing values before we can analyze the data. Mean imputation is a simple and widely used approach that involves replacing missing values with the mean value of the non-missing observations in the same variable.
We'll walk through the steps of identifying missing values in our dataset, calculating the mean for each variable with missing values, and then using the "egen" command in Stata to replace the missing values with the mean. We'll also discuss some of the limitations and potential biases of this technique, as well as some alternatives to consider.
By the end of this tutorial, you'll have a better understanding of how to handle missing data in Stata using mean imputation, and the implications of doing so for your analysis. Whether you're a student, researcher, or data analyst, this tutorial will provide you with a useful tool for dealing with missing values in your Stata projects.
Files
1.Dataset used : https://drive.google.com/file/d/1keQs31GRkXYp2H29FBw3lObtrd2gB33s/view?usp=share_link
Original raw data : https://www.kaggle.com/datasets/sandhyakrishnan02/paneldata
2.Do-file
https://drive.google.com/file/d/1DFQS0EqgwARBbyWW1Zgek8LnagnrEQlW/view?usp=share_link