Individual Assignment #2 (due April 20)
Please review the lecture slides for April 4 ~ April 13 for this assignment. Do the following for your assignment #2
• Download your survey data from Qualtrics (.csv file)
• Clean up the raw data file – removing unnecessary rows, deleting incomplete responses,
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• Change the names of your key variables (that are relevant to both your IVs and DVs) – you can rename the variables directly in the raw data file, or use the rename function after importing your data to R. The first option might be easier for you)
• After cleaning up your raw data file, save it to your working directory on your computer.
• Import the data file (that you just saved in your working directory) to R & create an r
object (give whatever name you want to the data object)
• Run the summary function quickly (summary()) and take a quick look at the statistics. If there are any categorical variables that have been treated as continuous variables, factorize those variables.
• Now, summarize your data set (i.e., provide descriptive statistics and graphs)
• For continuous variables: provide means, SD, and histogram
• For categorical variables: provide frequency tables, and bar or pie charts
• Report your main dependent variables by group
• If both of your variables are categorical, provide a frequency table involving two categorical variables
• If one variable is continuous and the other is categorical, provide a box plot
• If both of your variables are continuous, provide a scatter plot
Submit the following materials to Canvas
1. Your raw data file (csv file) that you cleaned up (the one you imported to R for analysis)
2. Your R script file (which includes all your syntax coding)
➢ I should be able to run the syntax and get the same outputs as yours without errors
3. A word document that includes the following information:
• A list of your key variables (i.e., variable name, description (i.e., what the variable measures), and the type (i.e., continuous or categorical)
• Descriptive statistics, tables, and graphs that summarize your data (i.e., outputs of your R analysis)
A Grading Rubric
The more thorough, the better (you can earn more points)
• 23: outstanding
• 20: good
• 18: fair (meet all the requirements)
• 15: incomplete
• 0: no submission
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