TV16: The Individual Correlates of the Trump Vote in 2016
In stevedata: Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics

TV16	R Documentation

The Individual Correlates of the Trump Vote in 2016

Description

These data come from the 2016 CCES and allow interested students to model the individual correlates of the Trump vote in 2016. Code/analysis heavily indebted to a 2017 analysis I did on my blog (see references).

Usage

TV16

Format

A data frame with 64600 observations on the following 21 variables.

uid: a numeric vector, a unique identifier for the respondent as they first appear in the CCES data.
state: a character vector for the state in which the respondent resides
votetrump: a numeric that equals 1 if the respondent voted says s/he voted for Trump in 2016.
age: a numeric vector for age that is roughly calculated as 2016 - birthyr, as it's coded in the CCES data.
female: a numeric that equals 1 if the respondent is a woman
collegeed: a numeric vector that equals 1 if the respondent says s/he has a college degree
racef: a character vector for the race of the respondent
famincr: a numeric vector for the respondent's household income. Ranges from 1 (Less than $10,000) to 12 ($150,000 or more).
ideo: a numeric vector for the respondent's ideology on a liberal-conservative discrete scale. 1 = very liberal. 5 = very conservative.
pid7na: a numeric vector for the respondent's partisanship on the familiar 1-7 scale. 1 = Strong Democrat. 7 = Strong Republican. Other party supporters (e.g. libertarians) are coded as NA.
bornagain: a numeric vector for whether the respondent self-identifies as a born-again Christian.
religimp: a numeric vector for the importance of religion to the respondent. 1 = not at all important. 4 = very important.
churchatd: a numeric vector for the extent of church attendance for the respondent. 1 = never. 6 = more than once a week.
prayerfreq: a numeric vector for the frequency of prayer for the respondent. 1 = never. 7 = several times a day.
angryracism: a numeric vector for how angry the respondent is that racism exists. 1 = strongly agree (i.e. is angry racism exists). 5 = strongly disagree.
whiteadv: a numeric vector for agreement with statement that white people have advantages over others in the U.S. 1 = strongly agree. 5 = strongly disagree.
fearraces: a numeric vector for agreement with statement that the respondent fears other races. 1 = strongly disagree. 5 = strongly agree.
racerare: a numeric vector for agreement with statement that racism is rare in the U.S. 1 = strongly disagree. 5 = strongly agree.
lrelig: a numeric vector that serves as a latent estimate for religiosity from the bornagain, religimp, churchatd, and prayerfreq variables. Higher values = more religiosity.
lcograc: a numeric vector that serves as a latent estimate for cognitive racism. This is derived from the racerare and whiteadv variables.
lemprac: a numeric vector that serves as a latent estimate for empathetic racism. This is derived from the fearraces and angryracism variables.

Details

The latent estimates for religiosity, cognitive racism, and empathetic racism come from a graded response model estimated in mirt. The concepts of "cognitive racism" and "empathetic racism" come from DeSante and Smith.