Clean questionaire in DITA smoothly

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Aug 6th, 2022
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How to Clean questionaire in DITA

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okay so you have your surveymonkey data now we need to jump into cleaning it and what i want you to see are a few things um we have all of this information here that comes from surveymonkey and thats basically who did the survey when did they start when they end its information that we probably wont need but for now lets just leave it here well just sort of park it in that spot we usually dont gather this sort of information email first name last name and so on so i dont usually do anything with that either but for now well just leave it the consent is a big deal we want to make sure that the folks that took the survey did consent and they they did so i assume one is yes two would have been a no all the twos are gone or they maybe they didnt do it so um so this is good information we want to keep this just for our records to make sure that they they did consent to taking the survey um lets see oh actually i see how this works now so sometimes with surveymonkey um or qualtric

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The cleaner you survey data, the more solid insights youll have about what they need, allowing you to make business decisions that will make sense to them. Insights from clean data also make it easier to get buy-in from stakeholders. Clean survey data keeps costs down.
Resolution Sign into Qualtrics. Click Projects. Find selected survey. Click Data Analysis. Click Tools. Click Delete Data. Select what data to be deleted. If all then click All Responses.
How to Clean Your Data Remove duplicate observations. Duplicate data most often occurs during the data collection process. Filter unwanted outliers. Outliers are unusual values in your dataset. Fix structural errors. Fix missing data. Validate your data.
Steps to Clean Data Remove Duplicate and Incomplete Cases: Remove Oversample: Ensure Answers are Formatted Correctly: Remove Nonsense Answers and Unreadable Data: Identify and Review Outliers: Code Open Ended Data. Check Data Consistency: Perform Final Quality Assurance Checks:
Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.
Survey data cleaning involves identifying and removing responses from individuals who either dont match your target audience criteria or didnt answer your questions thoughtfully. If done right, it gives you an improved set of responses that allows you to make better decisions.
If not cleaned, dirty data may lead to incorrect beliefs and assumptions about data-driven insights, poorly informed decisions based on those insights and distrust in the analytics process overall. It can also adversely impact operations reliant on clean data to execute correctly.

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