PSY350 - Computational Homework Assignment

Assignment Task

Anova

In this assignment, you’ll use R to compare group means in the class survey data using an analysis of variance.

Homework assignments are designed to follow from the R Tutorials, so make sure you’ve completed R Tutorials 7 and 8 before you begin this assignment. We’ll also use some basic principles and ideas from previous homework assignments. Remember that the tutorials include only some of the variables from the survey, whereas your full data set includes all of them. It’s best to practice an analysis within the tutorial first, then move over to RStudio and repeat that exact same analysis to ensure you’re getting the same results. Then move on and adapt that analysis to new variables to answer the homework questions.

Computational Section:

As before, use RStudio to answer the following questions, then enter your answers in Canvas. Round all values to 2 decimal places and pay attention to APA rules about when to report leading zeros (zeros before the decimal point).

In this assignment, you will explore whether there is any association between a person’s study preferences and their conscientiousness.

1. Describe your variables and your data.

• Which of these is your grouping variable and which is your outcome variable? The table() and/or describe() functions may help you determine this.
• How many people do you have at each level of your grouping variable?

2. What is the shape of your potential effect?

• Find the mean for your outcome variable at each level of your grouping variable. You found means at each level of a grouping variable in HW #2, following Tutorial 3, but with different variables; use the same procedure here.

3. Use a one-way ANOVA to assess the size and significance of this effect.

• Report the sum of squares, degrees of freedom, and mean square between groups.
• Report the sum of squares, degrees of freedom, and mean square within groups.
• Based on the above, calculate and report your effect size (η²).
• Report your test statistic and its associated p-value.
• Is your effect significant? Can you reject the null hypothesis?
• Explain how you got to your answer in part e. What values were you looking at, and what do they mean?

4. Finally, confirm the shape of the effect by testing which group means, if any, are significantly different from the others. Make sure you load the necessary package before you run your function.

• Which test should you use to answer this question? Be specific. Hint: In class, we discussed two different tests that help you answer this question. One test in particular is appropriate here whereas the other is not.
• Explain why that test is more appropriate than the other.
• What are the results of your test? Which means, if any, are different? How does this compare to the omnibus result you found earlier?

As before, to show your work, submit a screen shot of your full RStudio window, showing as much of your script as possible, and make sure that your last name is included in the name of your data set (e.g., mine is “gibbons.data”)

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