1-What are the three major steps in data analysis? What specific things would a researcher be looking for in each one of these steps?
2-What is correlation? What are the different types of correlation? Why is it important to determine correlation? If anxiety and depression are correlated, what three possible directions of causality might explain this correlation?
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Let us look at the 2 steps in data analysis. According to http://www.socialresearchmethods.net/kb/analysis.php they are:
- Cleaning and organizing the data for analysis (Data Preparation)
- Describing the data (Descriptive Statistics)
- Testing Hypotheses and Models (Inferential Statistics)
When our participants complete a questionnaire, the data is entered into a database. This is called raw data, and in order to be able to transform this into meaningful, useful info, we need to clean it up and organize it.
A primary issue is if the person filled out a pen-and-paper survey is to make sure that the data was entered properly into the database, and that there were no errors in transcription. Sometimes, we can check every nth entry to for accuracy. Or we can enter the data twice, and have a computer program tell us if there are any errors. We can also set up security mechanisms. For example, in the column “what is your age” if the data entry person enters 340 instead of 34, a pop up box will come up indicating that there is an error.
Now that the data is entered, we can look at the database. We need to establish a comprehensive database of the data. Each column of data should have a title, which describe what the data is. This helps the researcher not make any errors in analysis. Now, we can look at the numbers themselves.
What do we look for when we say ‘clean it up’? Well, we want to remove any suspicious data.
One type is called an outlier. This is when a few numbers are much higher then the others. For example, if we are asking someone how many times a day they have depressed thoughts, and someone answers 300 when the other answers are much lower, we need to remove this outlier. Otherwise, it would skew the data.
Another thing we do is check for the consistency of the data. If we ask someone “On a scale of 1 to 10, how self-confidence do you have?”. They might answer a 7. Then later on, a subsequent question could say “On a scale of 1 to 10, how do you perceive yourself?” Hopefully their answer is close to a 7. If they answered a 1 or 2, then their answers are not …
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