Skip to main content
United States
Jump To
Support
Register or Log In
Support
Register or Log In
Instructors
Browse Products
Getting Started
Students
Browse Products
Getting Started
Return to Research Methods Using R 1e Student Resources
Chapter 17 Multiple choice questions
Quiz Content
*
not completed
.
Before you start, make sure you have the
BayesFactor
package installed and activated.
Download the data file for the multiple-choice questions for Chapter 17
, and open it in
R.
The variable
q1data
contains some synthetic data created using the
rnorm
function, for 100 samples with a mean of 1 and a standard deviation of 3. Compute a one-sample t-test Bayes factor using the
ttestBF
function. The resulting Bayes factor:
Suggests some evidence for the alternative hypothesis
correct
incorrect
Suggests some evidence for the null hypothesis
correct
incorrect
Suggests overwhelming evidence for the null hypothesis
correct
incorrect
Cannot distinguish between the null and alternative hypotheses
correct
incorrect
*
not completed
.
The variable
q2data
contains another set of 100 values. Calculate a Bayes factor for an independent samples t-test comparing
q1data
and
q2data
. The resulting Bayes factor:
Suggests strong evidence for the alternative hypothesis
correct
incorrect
Suggests some evidence for the null hypothesis
correct
incorrect
Suggests some evidence for the alternative hypothesis
correct
incorrect
Cannot distinguish between the null and alternative hypotheses
correct
incorrect
*
not completed
.
The variable
q3data
contains two variables (dv1 and dv2) that are correlated with
r
=0.48. Calculate a Bayes factor for a linear regression between the two variables using the
lmBF
function. The Bayes factor:
Suggests some evidence for the alternative hypothesis
correct
incorrect
Suggests some evidence for the null hypothesis
correct
incorrect
Suggests very strong evidence for the alternative hypothesis
correct
incorrect
Cannot distinguish between the null and alternative hypotheses
correct
incorrect
*
not completed
.
The variable
q4data
is similar in structure to
q3data
, and produces a Bayes factor of 34. The two variables:
Show a strong positive relationship
correct
incorrect
Show a strong negative relationship
correct
incorrect
Are unrelated to each other
correct
incorrect
There is no way to determine how the variables are related
correct
incorrect
*
not completed
.
The variable
q5data
contains data formatted for a one-way ANOVA with three groups (DV1-3). Plot these data to examine them. Then compute a standard ANOVA using aov(alldata ~ group, data = q5data), and a Bayes factor using the
anovaBF
function. The results reveal:
A significant effect of group, with a Bayes factor >10
correct
incorrect
A significant effect of group, but a weak Bayes factor
correct
incorrect
No significant effect of group, but a large Bayes factor >10
correct
incorrect
No significant effect of group, and a weak Bayes factor
correct
incorrect
*
not completed
.
We can produce plots of the posterior distributions for the t-test in Q1 by entering: plot(posterior(ttestBF(q1data),iterations=1000)). The peak of the sig2 density plot is around:
1
correct
incorrect
10
correct
incorrect
0.1
correct
incorrect
0.3
correct
incorrect
*
not completed
.
The dataset
puzzles
is built into the
BayesFactor
package, and you can load it into the workspace by entering data(puzzles). It contains data on puzzle completion times (the DV, called
RT
) for different shapes (IV1, called
shape
) and colours (IV2, called
color
) of puzzles (e.g. jigsaws). Run a Bayes factor analysis for a two-way ANOVA with subject ID as a random factor as follows: anovaBF(RT ~ shape*color + ID, whichRandom = "ID", data = puzzles). Which model has the largest Bayes factor?
Shape alone
correct
incorrect
Colour alone
correct
incorrect
Shape and colour with an interaction (shape:color)
correct
incorrect
Shape and colour with no interaction
correct
incorrect
*
not completed
.
Calculate a Bayes factor for the correlation between the two variables stored in
q8data
. The result suggests that:
There is a positive relationship
correct
incorrect
There is a negative relationship
correct
incorrect
There is insufficient evidence to say if there is a relationship
correct
incorrect
There is more evidence to support the null hypothesis
correct
incorrect
*
not completed
.
The within-participant correlation affects the power of repeated measures designs. The data object
q9data
is a 2 x 10 x 12 array. The first dimension is the two conditions from an experiment. The second dimension represents the correlation between repeated measures (10 levels increasing in magnitude). The third dimension represents observations from 12 subjects. Calculate the Bayes factor for a paired t-test for each level of correlation. As the correlation increases:
Bayes factors decrease
correct
incorrect
Bayes factors increase
correct
incorrect
Bayes factors stay approximately constant
correct
incorrect
Bayes factors change randomly or non-monotonically
correct
incorrect
*
not completed
.
Suppose that the probability of a man sporting a moustache is 0.1. However, during November lots more people grow moustaches for charity, and the probability of an individual man having a moustache increases to 0.3. Use Bayes rule to calculate the conditional probability that it is November given that you see a man with a moustache. The probability is:
0.03
correct
incorrect
0.08
correct
incorrect
0.25
correct
incorrect
0.36
correct
incorrect
Exit Quiz
Next Question
Review all Questions
Submit Quiz
Reset
Are you sure?
You have some unanswered questions. Do you really want to submit?
Back to top
Printed from , all rights reserved. © Oxford University Press, 2024
Select your Country