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How to Use Stata to Replace Missing Values Based on Surrounding Rows

Learn how to effectively manage missing values in Stata by replacing them based on the adjacent rows, while preserving your dataset's structure and labels. --- This video is based on the question https://stackoverflow.com/q/68122107/ asked by the user 'Mike' ( https://stackoverflow.com/u/7244125/ ) and on the answer https://stackoverflow.com/a/68123611/ provided by the user 'Marcelo Avila' ( https://stackoverflow.com/u/8237186/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Stata: Replace values of one row based on another if data are missing Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Addressing Missing Data in Stata In research and data analysis, dealing with missing values is a common challenge. It becomes particularly tricky when those missing values are surrounded by valid entries you wish to retain. This guide will walk you through a straightforward yet effective way to replace missing values in Stata based on the entries in adjacent rows. We'll look at a practical example to illustrate the solution. The Challenge Imagine you have a dataset where some values are missing, yet you know that the correct values can be found just above or below these blanks. For example, consider the following sample data in Stata: [[See Video to Reveal this Text or Code Snippet]] In the dataset above, we see that the values for var1 and var2 are missing for "misspelled". Our goal is to use the values from "correctly-spelled" to fill in those gaps, resulting in: [[See Video to Reveal this Text or Code Snippet]] Given the complexities of your dataset—such as numerous labels and the presence of both string and numeric data—how can we achieve this without losing critical information? Proposed Solution Using Row-Specific Logic To replace the values, you will need a method that utilizes row-specific logic without resorting to operations like collapse, which can distort your dataset's structure. Here's a step-by-step guide to implement an effective solution: Step 1: Define Variables to Update First, create a list of the variables you wish to check for missing values: [[See Video to Reveal this Text or Code Snippet]] Step 2: Replace Missing Values We’ll loop through the list and replace missing values with the adjacent entries. The use of the _n system variable is key here: [[See Video to Reveal this Text or Code Snippet]] The first line replaces the missing value with the previous row’s value. The second line replaces the missing value with the following row’s value. The mi() function checks for missing values, while !mi() ensures that valid entries are not inadvertently overwritten. Step 3: Filter the Dataset Finally, you want to keep only the correctly spelled IDs. You can generate a flag for that: [[See Video to Reveal this Text or Code Snippet]] Final Review Here's how your dataset will look after applying these steps: [[See Video to Reveal this Text or Code Snippet]] You will then have successfully managed the missing data while keeping the integrity of your rows intact. Conclusion By leveraging Stata's programming capabilities, you can seamlessly replace missing values with adjacent valid data without losing essential information in your dataset. This method is particularly suitable for large datasets where precision is critical. Remember, handling missing data effectively is not just about filling gaps but ensuring that your analysis remains accurate and comprehensive. Happy data managing!