Clean up questionaire in csv

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Aug 6th, 2022
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How to clean up questionaire in csv

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hi everyone I am just doing this recording to show you how to do the data cleaning on a SPSS file that you have downloaded from Qualtrics this is if you are using Qualtrics for an online survey so in Qualtrics your survey will look like this so we have some categorical questions then we have some Lockett questions in the form of a matrix table in this one I asked of using the attitude towards object model about three brands of noodles so what you do to get your SPSS file is you go to data and analysis we have closed the survey so all of the responses are now recorded as responses in this case thereamp;#39;s four hundred and twelve people who have attempted the survey thatamp;#39;s a different number than will have completed the survey and it will be a different number than the number of responses that you end up deciding to keep at the end so we choose export and import export data selecting SPSS for this data file and download and itamp;#39;s going to think for a bit before it let

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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:
Analyzing CSV data involves importing the data into analysis tools, cleaning and organizing it, and then applying statistical methods to extract insights. This process is crucial for handling large data sets efficiently and accurately.
Here are 8 effective data cleaning techniques: Remove duplicates. Remove irrelevant data. Standardize capitalization. Convert data type. Clear formatting. Fix errors. Language translation. Handle missing values.
How to clean data Step 1: Remove duplicate or irrelevant observations. Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Step 2: Fix structural errors. Step 3: Filter unwanted outliers. Step 4: Handle missing data. Step 5: Validate and QA.
1 Check for errors. The first step to clean CSV data is to check for any errors or anomalies that could impede reading or processing the data correctly. 2 Handle missing values. 3 Standardize values. 4 Normalize data. 5 Detect outliers. 6 Validate data. 7 Heres what else to consider.
5 Ways to Display Your Survey Results Bar graphs are the most popular way to display results. Line graphs show how results change over time by tracking the ups and downs of the data. Pie charts show the breakup of a whole into sections. Venn diagrams show the interaction between respondents and their answers.
Click the Data tab, and select From Text. Find your CSV file stored on your computer, and click Open. In the Text Import Wizard window, be sure Delimited is selected as your file type. In the File origin drop down, select 65001 : Unicode (UTF-8) Check the My Data has headers box. Click the Next button.
Cleaning data means getting rid of any anomalous, incorrectly filled or otherwise odd results that could skew your analysis. Some examples include: Straight-lining, where the respondent has selected the first response to every question, regardless of the question.

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