How to Export and Analyze Data from KoboToolbox?

I’ve been using KoboToolbox to collect survey data for a research project. Now that I have a substantial amount of data, I need to export it for analysis. I’m not sure of the best format to export the data in, or how to ensure that all the relevant information is included. Additionally, I’m looking for tips on how to clean and prepare the data for analysis, as well as recommendations for tools and techniques to analyze the data effectively. Any advice on handling large datasets and ensuring data integrity during the export and analysis process would also be appreciated.

Welcome to the community, @Abd56ullah! You should be able to download your data in different file formats by following this support article Exporting and Downloading Your Data.

Cleaning a dataset is a bit vast topic. There are a lot of methods and techniques to do this. Besides, it also differs from the software you are using to clean the dataset. If your dataset is pretty large, then try using software such as Python or R. Else if its not that heavy then, you could achieve this through software such as SPSS, STATA and so on.

I think, STATA, SPSS (or PSPP) should cover well most surveys and data set sizes. For ex.

  • Stata/IC is standard Stata. **Up to 2,047 variables are allowed … (Stata/MP, Stata/SE, and Stata/IC all allow up to 2,147,583,647 observations, assuming you have enough memory)."
  • SPSS: “SPSS 64 bit has no real limitation except the specifications of your computer. … The largest number [of observations] that can be stored there is 2,147,493,647.”
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Hi @Abd56ullah,
Data cleaning and data management is one of the most important part before data analysis. It is important to clean the dataset after data collection and it is related with data quality. Because if you have a cleaned dataset, your data analysis will be smooth and easy. If your dataset is not properly cleaned, it hampers data analysis and takes much time. As you are collecting the data in KoboToolbox and if you applied the logical conditions then most of your variables are expected in good quality. I am sharing some of the methods that we follow to clean the dataset after completing the survey:

  1. Frequency (to see outlier)
  2. Range check (to see outlier)
  3. Consistency/Logical check (using cross tab with two or more variables dependent with other variables)

You can use syntax because it is very useful to see the process and it reduces the time if you need to do the same task several times.

Hope it will be helpful for your.

Best
Arshad

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Also, boxplots (and other graphes) are helpful to check for outliners, esp. for numeric variables.

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Hello @Abd56ullah,

Regarding data cleaning and analysis, there is a command called kobo2stata that allows you to automatically assign labels to the entire database using the statistical program STATA. For quality checks, it is important:

  • to review duplicates in identification variables to ensure the same person was not interviewed multiple times
  • check for missing values for each variable
  • identify outliers in numerical variables
  • verify logical jumps between questions. For example, if a question asks whether the respondent knows their date of birth, it should be followed by the actual date of birth question.
    For a better understanding of this topic, the Development Impact area of the World Bank offers an open resource where they provide a guide on data cleaning and analysis.
    I hope this is helpful :slight_smile:
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