Address the following items:
How does data screening differ from data cleaning?
What part of data screening checks for the actual number of responses you have for each variable?
How is data screening related to the assumptions of the statistic you will choose to analyze your data?
Why are descriptive statistics a part of data screening?
What kinds of plots are useful in data screening?
What can you do if your data do not meet the assumptions of your statistic?
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Data screening serves to be enacted to examine data for the purposes of discovering any issues that are present; the intention is to check all data in order to determine if any errors, inconsistencies, or the like are in the data prior to engaging in analysis. Many problems can emerge if data is inaccurate or missing. Data cleaning is the process used to remove any of these issues or to alter the data due to a malfunction. The reasons for the issues could be attributed to incorrect human input, ambiguous information, etc. ...
This post involves the details regarding data screening. It focuses on the process and the different features that are engaged during data screening.