UCT Psychology Honours Statistics
Tutorial 6
Create an R project, copy the data file into the folder, create the .Rmd Notebook file yourself, set it up, do the analysis, knit to an html report, zip the entire folder, upload it to VULA when finished. THE MAXIMUM LENGTH of the report is 4 pages (you can determine the length of your document by simulating a print to pdf from RStudio). Questions 1 and 2 are each worth 50 marks. If you submit Question 3, too, we will award you the two highest marks among the three. You may use an extra 2 pages if you complete Question 3.
Remember that it is very important to convey your results in a very clear fashion to readers. Well-constructed tables and figures, and an accompanying narrative, are crucial.
Question 1
A study by Caldwell and colleagues (2018) investigated whether Biofeedback paired psychotherapy decreased depression symptoms and increased heart rate variability - a good thing! - over and above psychotherapy alone. Data is contained in the Biofeedback.csv file, with variable descriptions contained in the README_Biofeedback_Treatment word doc.
Run a simple linear model that regresses the change between pre- and post- heart rate variability on treatment type. Start, as always, by looking at suitable descriptive statistics and graphics. Are there any differences? Do the results of the linear model support this interpretation? Are there any issues with respect to residuals, and other diagnostic tests, that we should be concerned about?
Question 2
Feng and colleagues (2018) sought to investigate whether neuroticism mediates the relationship between mobile phone dependence and loneliness. Data is stored in the Personality_mobile_dependence_loneliness.sav file, variable names and descriptions are provided under “Free-form tutorial 2”. Is there a case for mediation? Consider using the mediate command in the psych package. For more details follow the link:
http://personality-project.org/r/psych/HowTo/mediation.pdf
Optional advanced section
Question 3
Simulate a sample of 500 draws of heart rate variability (more specifically the change score from pre- to post- treatment) for each treatment group, based on data provided in the Biofeedback.csv file, and use this simulated data to replicate analyses under Question 1. Consider using either the sample() or rnorm() command.
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