R/rd-gss_spending.R

In stevedata: Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics

#' @importFrom tibble tibble
NULL

#' Attitudes Toward National Spending in the General Social Survey (2018)
#'
#' This is a toy data set that collects attitudes on toward national spending for
#'  various things in the General Social Survey for 2018. I use these data for
#'  in-class illustration about ordinal variables and ordinal models.
#'
#' @format A data frame with 2348 observations on the following 33 variables.
#' \describe{
#'     \item{\code{year}}{a numeric constant for the GSS survey year (2018)}
#'     \item{\code{id}}{a unique identifier for the survey respondent}
#'     \item{\code{age}}{a numeric vector for the age of the respondent (min: 18, max: 89)}
#'     \item{\code{sex}}{a numeric vector for the respondent's sex (1 = female, 0 = male)}
#'     \item{\code{educ}}{a numeric vector for the highest year of school completed (min: 0, max: 20)}
#'     \item{\code{degree}}{a numeric vector for the respondent's highest degree (0 = did not graduate high school, 1 = high school, 2 = junior college, 3 = bachelor degree, 4 = graduate degree)}
#'     \item{\code{race}}{a numeric vector for the respondent's race (1 = white, 2 = black, 3 = other)}
#'     \item{\code{rincom16}}{a numeric vector for the respondent's yearly income (min: 1 (under $1,000), max: 26 ($170,000 or over))}
#'     \item{\code{partyid}}{a numeric vector for the respondent's party identification on the familiar seven-point scale. NOTE: D to R partisanship in this variable goes from 0 to 6. 7 = supporters of other parties. You may want to recode this if you want an interval-level measure of partisanship.}
#'     \item{\code{polviews}}{a numeric vector for the respondent's ideology (min: 1 (extremely liberal), max: 7 (extremely conservative))}
#'     \item{\code{xnorcsiz}}{a numeric vector for the NORC size code. This is a measure of what kind of area in which the respondent took the survey (i.e. lives). 1 = city, greater than 250k residents. 2 = city, between 50k-250k residents. 3 = suburbs of a large city. 4 = suburbs of a medium-sized city. 5 = unincorporated area of a large city. 6 = unincorporated area of a medium city. 7 = city, between 10-50k residents. 8 = town, greater than 2,500 residents. 9 = smaller areas. 10 = open country.}
#'     \item{\code{news}}{a numeric vector for how often the respondent reads the newspapers. 1 = everyday. 2 = a few times a week. 3 = once a week. 4 = less than once a week. 5 = never.}
#'     \item{\code{wrkstat}}{a numeric vector for the respondent's work status. 1 = working full-time. 2 = working part-time. 3 = temporarily not working. 4 = unemployed/laid off. 5 = retired. 6 = in school. 7 = house-keeping work. 8 = other.}
#'     \item{\code{natspac}}{a numeric vector for attitudes toward spending on the space program. See details below for this variable and all other variables beginning with \code{nat}.}
#'     \item{\code{natenvir}}{a numeric vector for attitudes toward spending on improving/protecting the environment.}
#'     \item{\code{natheal}}{a numeric vector for attitudes toward spending on improving/protecting the nation's health.}
#'     \item{\code{natcity}}{a numeric vector for attitudes toward spending on solving the big city's problems.}
#'     \item{\code{natcrime}}{a numeric vector for attitudes toward spending on halting the "rising crime rate." This question is subtly hilarious.}
#'     \item{\code{natdrug}}{a numeric vector for attitudes toward spending on dealing with drug addiction.}
#'     \item{\code{nateduc}}{a numeric vector for attitudes toward spending on improving the nation's education system.}
#'     \item{\code{natrace}}{a numeric vector for attitudes toward spending on improving the condition of black people.}
#'     \item{\code{natarms}}{a numeric vector for attitudes toward spending on the military/armaments/defense.}
#'     \item{\code{nataid}}{a numeric vector for attitudes toward spending on foreign aid.}
#'     \item{\code{natfare}}{a numeric vector for attitudes toward spending on welfare.}
#'     \item{\code{natroad}}{a numeric vector for attitudes toward spending on highways and bridges.}
#'     \item{\code{natsoc}}{a numeric vector for attitudes toward spending on social security.}
#'     \item{\code{natmass}}{a numeric vector for attitudes toward spending on mass transportation.}
#'     \item{\code{natpark}}{a numeric vector for attitudes toward spending on parks and recreation.}
#'     \item{\code{natchld}}{a numeric vector for attitudes toward spending on assistance for child care.}
#'     \item{\code{natsci}}{a numeric vector for attitudes toward spending on scientific research.}
#'     \item{\code{natenrgy}}{a numeric vector for attitudes toward spending on alternative sources of energy.}
#'     \item{\code{sumnat}}{a numeric vector for the sum total of responses to all the aforementioned spending variables (i.e. those that begin with \code{nat}). This creates an interval-ish measure with a nice and mostly normal distribution.}
#'     \item{\code{sumnatsoc}}{a numeric vector for the sum of all responses toward various "social" prompts (i.e. \code{natenvir}, \code{natheal}, \code{natdrug}, \code{nateduc}, \code{natrace}, \code{natfare}, \code{natroad}, \code{natmass}, \code{natpark},  \code{natsoc}, \code{natchld}). This creates an interval-ish measure with a mostly normal (but small left skew) distribution.}
#' }
#'
#' @source General Social Survey, 2018
#'
#' @details For all the variables beginning with \code{nat}, note that I rescaled the original data so that -1 = respondent thinks country is spending too much on this topic, 0 = respondent thinks country is spending "about (the) right" amount, and 1 = respondent thinks country is spending too little on this topic. I do this to facilitate reading each \code{nat} prompt as increasing support for more spending (the extent to which increasing values means the respondent thinks the country spends too little on a given prompt). I think this is more intuitive.
#'
#' Also, the \code{natspac}, \code{natenvir}, \code{natheal}, \code{natcity}, \code{natcrime}, \code{natdrug}, \code{nateduc}, \code{natrace}, \code{natarms}, \code{nataid}, and \code{natfare} have "alternate" prompts in later GSS waves in which a subset of respondents get a slightly different prompt. For example, one set of respondents for \code{natcity} gets a prompt of "Solving the problems of the big cities" (the legacy prompt) whereas another set of respondents gets a prompt of "Assistance to big cities" (typically noted as "version y" in the GSS). I, perhaps problematically if I were interested in publishing analyses on these data, combine both prompts into a single variable. I don't think it's a huge problem for what I want the data to do, but FYI.
#'
#'
#'
"gss_spending"