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I am trying to plan out how long it will take me to clean my survey data. I have about 200 responses. The survey takes about 15 minutes, about 40-60 questions (depending on the logic). I have very few open-ended questions (maybe three total). Someone told me it should only take a few days to clean the data while others say 2 weeks. I am not really sure how to plan for this stage, so any guidance about how long it might take to clean a survey of this scale would be very helpful. I have never cleaned survey data before, and while I have some experience with Stata, I have never used it to clean my own data. Also, if you have any recommendations for resources that detail the cleaning process, I would be very grateful.

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closed as unclear what you're asking by Tim Feb 6 at 19:51

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    $\begingroup$ There is no single answer to this question, it is opinion based. The answer depends on your data, skills, dedication, conscientiousness etc. $\endgroup$ – Tim Feb 6 at 19:53
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    $\begingroup$ Some Stata-specific advice here and here and here. For R and Python, this is great. All these book-length resources cover more than just cleaning, and I don't always follow their advice to the letter, but certainly in spirit. $\endgroup$ – Dimitriy V. Masterov Feb 6 at 22:02
  • $\begingroup$ Thank you so much for these resources - this is very helpful. I came across Mitchell's and Long's book, but have not purchased yet. Do you recommend one over the other? $\endgroup$ – user3424836 Feb 6 at 22:24
  • $\begingroup$ How long is a piece of string? $\endgroup$ – Ben Feb 7 at 1:41
  • $\begingroup$ @user3424836 I would go with Long if I had to get just one. $\endgroup$ – Dimitriy V. Masterov Feb 7 at 9:11
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It will depend on your skill, the "cleanliness" required, and the messiness of the data when you receive it.

It sounds like you are inexperienced. So that will make it take longer. I'd recommend you stick to Excel and not mess with STATA (but either way make sure you save versions, check things against the original, etc.)

Cleanliness: sometimes this is just fixing things like non-numeric values in a numeric field. Other times as part of the analysis process it means re-coding values (if age is a question, and you get one number, you might decide later to group it 10-20, 21-30, etc). The better you can define this up front the less time it will take you (and the more research you can do in advance).

Perhaps someone who has done it before (preferably, someone who says it only takes a few days) can help you-- either sitting with you as you start; or showing you examples of what they've done before.

To summarize: there is no one-size-fits all approach to data cleaning. You will find many examples and tips and packages to help under names like data cleaning/cleansing/tidying/wrangling, but those generally will only help you do something that you already conceptually understand that you need to do what it takes to do it (they'll just help do it better/faster/more accurately).

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  • $\begingroup$ Thank you very much - I appreciate your advice. I have read some articles/chapters about cleaning data but not entirely sure yet what steps apply for my dataset. Thank you for putting this into perspective. $\endgroup$ – user3424836 Feb 6 at 21:19
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    $\begingroup$ I would avoid cleaning the data in Excel. This is not a replicable or audit-able process, and every project I have ever participated in requires iterating on the data wrangling. Using Excel is also arguably more error prone. $\endgroup$ – Dimitriy V. Masterov Feb 6 at 21:30
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    $\begingroup$ I do not disagree with @Dimitriy V. Masterov. That being said, this may not be the time to get better at STATA. If you don't use excel, learn to do this with R not STATA. In grad school for econ many of my profs used STATA but they did their data cleanup in Excel. There are high profile examples of the pitfalls of using Excel for peer-reviewed publishing; i'm assuming your stakes are not that high. $\endgroup$ – Chris Umphlett Feb 6 at 21:41
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    $\begingroup$ There are many examples of prominent economists screwing things up with Excel data cleaning, of which this is the most prominent one. Using R and Stata is the way to go for anything that matters doing well. Publishing something wrong can wreak havoc on your career, but so can making a stupid business decision. $\endgroup$ – Dimitriy V. Masterov Feb 6 at 21:52
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    $\begingroup$ The spelling is Stata. $\endgroup$ – Nick Cox Feb 7 at 1:01

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