Researchers ran a multiple linear regression to determine if belief accuracy in a political claim when given misinformation was related to the following independent variables: One’s level of anger (coded low = 0, high = 1) One’s level of anxiety (coded low = 0, high = 1) Whether the source of misinformation about the claim came from their own political party (in-party, coded from the other party = 0, from their party = 1) Whether or not a correction of misinformation was made (coded correction without evidence = 0, correction with evidence = 1) One’s political knowledge (continuous measurement from 0-4) One of the two issues in question (coded illegal immigration = 0, death penalty = 1) In Model 1, B = 0.015 for political knowledge. How would one interpret this? In Model 1, B = 0.78 for correction. How would one interpret this? Write out the regression equation for Model 1. Belief accuracy score (predicted) = Suppose someone has high anger and anxiety, a political knowledge score of 3, and the source of misinformation about a death penalty issue came from the other party and a correction was made (with evidence). According to Model 1, what would their predicted belief score be? How much did the model improve by adding some interaction terms (going from Model 1 to Model 2)? In Model 2, the interaction term of Anxiety x In-Party has B = 0.63. How would one interpret this? The Adjusted R2 for Model 3 is 0.193. How would one interpret this? In putting together a final model, would you want to remove any variables from Model 3? Why or why not? They did not report standardized regression coefficients (β’s). What would the standardized regression coefficients have told us?

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter12: Probability

Section12.4: Discrete Random Variables; Applications To Decision Making

Problem 28E

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Question

Researchers ran a multiple linear regression to determine if belief accuracy in a political claim when given misinformation was related to the following independent variables:

One’s level of anger (coded low = 0, high = 1)
One’s level of anxiety (coded low = 0, high = 1)
Whether the source of misinformation about the claim came from their own political party (in-party, coded from the other party = 0, from their party = 1)
Whether or not a correction of misinformation was made (coded correction without evidence = 0, correction with evidence = 1)
One’s political knowledge (continuous measurement from 0-4)
One of the two issues in question (coded illegal immigration = 0, death penalty = 1)

In Model 1, B = 0.015 for political knowledge. How would one interpret this?

In Model 1, B = 0.78 for correction. How would one interpret this?

Write out the regression equation for Model 1.

Belief accuracy score (predicted) =

Suppose someone has high anger and anxiety, a political knowledge score of 3, and the source of misinformation about a death penalty issue came from the other party and a correction was made (with evidence). According to Model 1, what would their predicted belief score be?

How much did the model improve by adding some interaction terms (going from Model 1 to Model 2)?

In Model 2, the interaction term of Anxiety x In-Party has B = 0.63. How would one interpret this?

The Adjusted R2 for Model 3 is 0.193. How would one interpret this?

In putting together a final model, would you want to remove any variables from Model 3? Why or why not?

They did not report standardized regression coefficients (β’s). What would the standardized regression coefficients have told us?

Effects of Anxiety and Anger, Partisanship, and Corrections on Belief
Model 1
Model 2
Anxiety
Anger
Correction (coded high)
In-party (coded high)
Anxiety x In-party
Anxiety x Correction
Correction x In-party
Anxiety x Correction * In-party
Anger x In-party
Anger x Correction
Anger x Correction * In-party
Issue (death penalty coded
high)
Political knowledge
Constant
Observations
F (df)
R₂
-.14 (11)
-.09 (.11)
**p<.01.
*** p < .001.
-.13 (.08)
Model 1
.78 (.08) ***
.27 (.08)"
561
.15 (.03)***
2.18 (18)***
.18
20.46 (6,
554)
-.42 (.19)
-.09 (11)
.70 (.15)
-.35 (.15)*
.63 (.24)"
.27 (24)
.23 (.21)
-.71 (.35)
Model
.27 (.08)**
.15 (.03)***
2.25 (19)***
561
13.07
(10, 550)***
.192
Model 3
-.15 (11)
26 (.18)
.98 (.15)***
.15 (.15)
-.36 (.22)*
-.64 (.23)"
-.45 (24)*
.79 (.34)*
Model 3
27 (.08)"
.15 (.03)***
2.02 (20)***
561
13.16
(10, 550)***
.193
Notes: Unstandardized regression coefficients reported. Standard error is listed in
parentheses. p-values are two-tailed.
#p<.10.
*p<.05.

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