This is for cleaning data from the system it needs to be based on a survey file which has this script icon.
Trying these steps on a data file, will not work.
If you would like to remove any completed surveys due to quality checks, these are the steps for that.
Getting the Data:
On your project, click on the three dots on the right side and select “Analyze”.
1. Select the “Data” icon.
2. Drag any questions you want to check into the area that says “Drop rows here” – these will be the questions you want to check for quality.
3. Select “Options” and tick the option below, this will include the system ID that we will need to use for removals.
4. Click export to get the questions in excel format.
Data Editing:
1. Click on the 3 dots to the right of your project and select "status report".
2. This is an example of the file that will be exported.
3.Create a blank excel and add two column headers (or modify the existing file).
a. Col A (respid) is where you would paste the respid of only those you want to remove.
b. Col B (Status) This is where you would set the status you want them to be, the example below shows all the possible statuses.
4. Here is an example of how the file could look for disqualifying, where the first Id would be flagged with the SCREENED status and the second id would be a QUALITY FAIL.
5. Now we need to upload the file so the removals can happen. Select “Edit Data”
6. Select your file and upload.
The data and quotas will be updated once the process is completed.
7. On a final note, if you need to requalify any completes, you can download the status file and then change the status back to COMPLETED.
Additional tips and tricks:
There are two types of data cleaning, Walr uses both of them to check data before it is loaded for analysis. If you are using the Walr platform to “DIY”, please refer to the recommended processes below for cleaning your data.
1. Data Logic check: Checking the logic of the survey questionnaire versus what was programmed. This is done by, for example, checking the base of each question in the data versus what is expected from the questionnaire.
2. Data Quality check: This includes bad open-end checks, straight liners check and speeders check.
2a. Open-End Check: Identifying any bad open-end responses, which are any respondents that are answering the question in any way other then what was required.
2b. Straight liners: Checking if respondents have answered the same option in, for example, grids or carousels. You can identify an acceptable level and exclude anyone outside those parameters.
2c. Speeders: Minimum acceptable duration spent on certain questions, sections, or the entire survey.