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framing

Brader, Valentino and Suhay (2008) Framing Experiment Data


Description

The framing data contains 265 rows and 15 columns of data from a framing experiment conducted by Brader, Valentino and Suhay (2008).

Usage

framing

Format

A data frame containing the following variables:

immigr:

A four-point scale measuring subjects' attitudes toward increased immigration. Larger values indicate more negative attitudes.

english:

A four-point scale indicating whether subjects favor or oppose a law making English the official language of the U.S.

cong_mesg:

Whether subjects requested sending an anti-immigration message to Congress on their behalf.

anti_info:

Whether subjects wanted to receive information from anti-immigration organizations.

tone:

1st treatment; whether the news story is framed positively or negatively.

eth:

2nd treatment; whether the news story features a Latino or European immigrant.

cond:

Four level measure recording joint treatment status of tone and eth.

treat:

Product of the two treatment variables. In the original study the authors only find this cell to be significant.

emo:

Measure of subjects' negative feeling during the experiment. A numeric scale ranging between 3 and 12 where 3 indicates the most negative feeling.

anx:

A four-point scale measuring subjects' anxiety about increased immigration.

p_harm:

Subjects' perceived harm caused by increased immigration. A numeric scale between 2 and 8.

age:

Subjects' age.

educ:

Subjects' highest educational attainments.

gender:

Subjects' gender.

income:

Subjects' income, measured as a 19-point scale.

Source

Brader, T., Valentino, N. and Suhay, E. (2008). What triggers public opposition to immigration? Anxiety, group cues, and immigration threat. American Journal of Political Science 52, 4, 959–978.


mediation

Causal Mediation Analysis

v4.5.0
GPL (>= 2)
Authors
Dustin Tingley <dtingley@gov.harvard.edu>, Teppei Yamamoto <teppei@mit.edu>, Kentaro Hirose <hirose@princeton.edu>, Luke Keele <ljk20@psu.edu>, Kosuke Imai <kimai@princeton.edu>, Minh Trinh <mdtrinh@mit.edu>, Weihuang Wong <wwong@mit.edu>
Initial release
2019-9-13

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