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#### Regression Analysis SummaryThis table presents the results of a regression analysis examining the effects of anxiety, anger, partisanship, and corrections on beliefs. It includes three models with various independent variables and their interaction effects.#### Model Descriptions:- **Model 1** focuses on the direct effects of anxiety, anger, and correction (coded high) on belief.- **Model 2** includes interactions between anxiety/anger and in-party affiliation, as well as the combined effects of anxiety, correction, and in-party.- **Model 3** adds interactions between anger, correction, and in-party affiliation.#### Key Variables and Interactions:- **Anxiety**: Shows a negative relationship with belief across models, significant in Model 2.- **Anger**: Shows a negative relationship in Model 1 and 2, turning positive in Model 3, with a significant interaction with the in-party in Model 3.- **Correction (Coded High)**: Positive and significant in all models.- **In-party (Coded High)**: Negative in Model 2, turning positive in Model 3.- **Interactions**: - **Anxiety × In-party**: Positive in Model 2. - **Anxiety × Correction × In-party**: Negative and significant in Model 2. - **Anger × Correction × In-party**: Positive and significant in Model 3.#### Additional Factors: - **Issue (Death Penalty Coded High)**: Consistently positive and significant across all models.- **Political Knowledge**: Positively influences beliefs in all models.#### Model Statistics:- The number of observations is consistent across models (561).- **F-statistics** indicate the overall significance of each model.- **R²** values show the proportion of variance in belief explained by each model, with small increases from Model 1 to Model 3.#### Notes:- The numbers in parentheses are standard errors.- p-values indicate statistical significance levels: - # p < .10 - * p < .05 - ** p < .01 - *** p < .001This analysis helps in understanding the complex relationships between emotions, partisanship, and corrective information in shaping beliefs.

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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?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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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

#### Regression Analysis Summary

This table presents the results of a regression analysis examining the effects of anxiety, anger, partisanship, and corrections on beliefs. It includes three models with various independent variables and their interaction effects.

#### Model Descriptions:

- **Model 1** focuses on the direct effects of anxiety, anger, and correction (coded high) on belief.
- **Model 2** includes interactions between anxiety/anger and in-party affiliation, as well as the combined effects of anxiety, correction, and in-party.
- **Model 3** adds interactions between anger, correction, and in-party affiliation.

#### Key Variables and Interactions:

- **Anxiety**: Shows a negative relationship with belief across models, significant in Model 2.
- **Anger**: Shows a negative relationship in Model 1 and 2, turning positive in Model 3, with a significant interaction with the in-party in Model 3.
- **Correction (Coded High)**: Positive and significant in all models.
- **In-party (Coded High)**: Negative in Model 2, turning positive in Model 3.
- **Interactions**:
- **Anxiety × In-party**: Positive in Model 2.
- **Anxiety × Correction × In-party**: Negative and significant in Model 2.
- **Anger × Correction × In-party**: Positive and significant in Model 3.

#### Additional Factors:

- **Issue (Death Penalty Coded High)**: Consistently positive and significant across all models.
- **Political Knowledge**: Positively influences beliefs in all models.

#### Model Statistics:

- The number of observations is consistent across models (561).
- **F-statistics** indicate the overall significance of each model.
- **R²** values show the proportion of variance in belief explained by each model, with small increases from Model 1 to Model 3.

#### Notes:

- The numbers in parentheses are standard errors.
- p-values indicate statistical significance levels:
- # p < .10
- * p < .05
- ** p < .01
- *** p < .001

This analysis helps in understanding the complex relationships between emotions, partisanship, and corrective information in shaping beliefs.

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