Hello world,
Hope, you are doing well!
I am working on Annual Survey of Industries (ASI) microdata and using STATA for analysis.
(1) In some datasets, I found that values of variable H_Itm1 not unique within a1. Here, H_Itm1 is serial no and a1 is uniqueID.
(2) In some datasets, I found that variable Year not constant within a1. Here, a1 is uniqueID.
How can I solve it?
I tried to use these commands
duplicates report
duplicates list (var.) sepby(a1)
duplicates tag, gen(dup)
duplicates drop a1 J_Itm1, force
xtset a1 year
but nearly 84k observations were removed out of 128k observations.
Please give your opinion or suggestions on it.
You have to understand why this is so. You need to inspect duplicates to see if they are duplicate data or that they represent separate eligible data records. If the latter, you can append some text to the serial number to make them unique. Likewise year – you need to know why your data are the way they are before you start feeding them into the modelling machine.Otherwise you may find findings that reflect your misunderstanding of the data!
If you have duplicates in the data within H_Itm1 in a1, make sure to check the cause of the duplication, as there might be errors in the records or data entry. If the duplicates represent records that need to be merged, use the collapse function in STATA to properly aggregate the data.
For inconsistent year values, ensure that the year is consistent within the same a1. If multiple years exist, you need to select the correct year or manually correct the data.
Do not delete duplicates randomly. Use commands like duplicates report and duplicates list to better understand the duplication, so you don’t lose important information.
After processing, ensure that the corrections haven’t negatively impacted your analysis by using commands like summarize to verify the data quality.
If you have missing data, consider using techniques like missing data analysis or proxy variables to handle it.
In general, avoid bulk deletion of data and focus on carefully handling duplicates and inconsistent values to maintain data quality.
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