Question
Lastly, Bryant and colleagues (2005) predicted that vividness of recall of positive memories would be associated with higher levels of happiness. Although their analysis was
Lastly, Bryant and colleagues (2005) predicted that vividness of recall of positive memories would be associated with higher levels of happiness.
Although their analysis was a bit more complicated than we are prepared for, they basically used multiple regression to examine the associations between condition (beta = 0.30, p <.05, one-tailed) and vividness of recall (beta = 0.63, p <.0001, one-tailed) as predictors of happiness.
Question 31 of 392 Points Which variable (condition or vividness of recall) was the stronger predictor of happiness?
Condition
Vividness of recall Reset Selection | |
Question 32 of 392 Points For the previous question: How do you know?
Thepvalue for conditionwas smaller
The betavalue for controlwas larger
The betavalue for vividness of recall was larger
Thepvalue for vividness of recall was smaller Reset Selection | |
Question 33 of 393 Points For a one unit increase in vividness of imagery, how much does happiness go up?
No way to tell (we need more information)
0.30
0.63
0.001 Reset Selection | |
Question 34 of 392 Points What is one major limitation of the analysis described above?
The researchers ran a multiple regression with a categorical outcome (happiness), thereby violating the assumptions of the test
The researchers used a one-tailed test when two-tailed tests are preferred (might be missing an opposite effect)
None. This is the most perfect analysis I have ever seen
We do not know if either predictor is significant, leaving us wondering whether either variable significantly predicts happiness Reset Selection | |
Question 35 of 392 Points Why is multiple regression preferable to single regression?
It allows you to control for the effects of other predictors
It allows you to examine the interaction between multiple predictors and an outcome
It allows you to examine effects on multiple dependent variables
It allows you to ignore the effect of other predictors Reset Selection |
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