% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{pew_npors_2025}
\alias{pew_npors_2025}
\title{Pew NPORS 2025: National Public Opinion Reference Survey}
\format{
A data frame with 5,022 rows and 65 variables. The 11 \verb{smuse_*}
variables form a battery asking about social media platform use and share a
\code{"question_preface"} attribute. All other variables are documented
individually below:
\describe{
\item{respid}{Case ID. Unique respondent identifier.}
\item{stratum}{Sampling stratum (10 levels, defined by census block
group demographics).}
\item{basewt}{Base weight — inverse probability of selection, with
adaptive mode adjustment.}
\item{weight}{Final weight — \code{basewt} after raking to Census population
targets. Use for all population-level estimates.}
\item{mode}{Data collection mode: \code{1} = Online, \code{2} = Paper,
\code{3} = Phone.}
\item{language}{Language interview completed in: \code{1} = English,
\code{2} = Spanish.}
\item{languageinitial}{Language interview started in.}
\item{interview_start}{Interview start timestamp.}
\item{interview_end}{Interview end timestamp.}
\item{econ1mod}{Economic conditions in your community today
(Excellent / Good / Fair / Poor).}
\item{econ1bmod}{Economic conditions one year from now
(Better / Worse / Same).}
\item{comtype2}{Community type: Urban / Suburban / Rural.}
\item{unity}{Americans united vs. divided on values.}
\item{crimesafe}{Area safety in terms of crime (Extremely safe – Not at
all safe).}
\item{govprotct}{Government's role in protecting people from themselves.}
\item{moregunimpact}{Impact of more gun ownership on crime.}
\item{fin_sit}{Household financial situation (Comfortable – Can't meet
basics).}
\item{vet1}{Military service in household.}
\item{vol12_cps}{Volunteered for any organization in past 12 months.}
\item{eminuse}{Uses internet or email at least occasionally.}
\item{intmob}{Accesses internet on a mobile device.}
\item{intfreq}{Internet use frequency (6 categories).}
\item{intfreq_collapsed}{Internet use frequency (4 categories, derived).}
\item{home4nw2}{Subscribes to home internet service.}
\item{bbhome}{Home internet type (dial-up, broadband, etc.).}
\item{smuse_fb}{Facebook. Part of social media use battery (see Details).}
\item{smuse_yt}{YouTube. Part of social media use battery (see Details).}
\item{smuse_x}{X (formerly Twitter). Part of social media use battery.}
\item{smuse_ig}{Instagram. Part of social media use battery.}
\item{smuse_sc}{Snapchat. Part of social media use battery.}
\item{smuse_wa}{WhatsApp. Part of social media use battery.}
\item{smuse_tt}{TikTok. Part of social media use battery.}
\item{smuse_rd}{Reddit. Part of social media use battery.}
\item{smuse_bsk}{Bluesky. Part of social media use battery.}
\item{smuse_th}{Threads. Part of social media use battery.}
\item{smuse_ts}{Truth Social. Part of social media use battery.}
\item{radio}{Listens to radio.}
\item{device1a}{Has a cell phone.}
\item{smart2}{Cell phone is a smartphone.}
\item{nhisll}{Has a working landline telephone at home.}
\item{relig}{Current religion (12 categories).}
\item{religcat1}{Religion (4 categories: Protestant, Catholic,
Unaffiliated, Other).}
\item{born}{Born-again or evangelical Christian.}
\item{attendper}{In-person religious service attendance (6 categories).}
\item{attendonline2}{Online/TV religious service participation
(6 categories).}
\item{relimp}{Importance of religion in life (Very – Not at all).}
\item{pray}{Prayer frequency outside of services (7 categories).}
\item{educcat}{Education level (categorical).}
\item{hisp}{Hispanic origin.}
\item{racecmb}{Race (5 categories).}
\item{racethn}{Race-ethnicity (5 categories including Asian
non-Hispanic).}
\item{agegrp}{Age in 13 five-year groups.}
\item{agecat}{Age (4 categories: 18-29, 30-49, 50-64, 65+).}
\item{birthplace}{U.S. born vs. foreign born.}
\item{gender}{Gender (man / woman / other).}
\item{adults}{Number of adults in household.}
\item{inc_sdt1}{Total family income (8 categories from < $30,000 to
$150,000+).}
\item{cregion}{Census region (NE / MW / S / W).}
\item{metro}{Metropolitan area indicator.}
\item{registration}{Registered to vote at current address.}
\item{party}{Party affiliation (Rep / Dem / Ind / Other).}
\item{partyln}{Party lean for Independents (Rep / Dem).}
\item{partysum}{Party summary (Rep+Lean Rep / Dem+Lean Dem / No lean).}
\item{voted2024}{Voted in the 2024 presidential election.}
\item{votegen_post}{2024 presidential vote choice (Trump / Harris /
Other).}
}
}
\source{
Pew Research Center. 2025 National Public Opinion Reference Survey.
\url{https://www.pewresearch.org/datasets/} (free account required to
download raw data; the processed \code{.rda} is included in the package).
Prepared by \verb{data-raw/prepare-pew-npors-2025.R}.
}
\usage{
pew_npors_2025
}
\description{
The 2025 National Public Opinion Reference Survey (NPORS), conducted
February 5 – June 18, 2025, by Pew Research Center (n = 5,022). An
address-based sample (ABS) drawn from the USPS Computerized Delivery
Sequence File, with respondents completing the survey online, by paper,
or by telephone in English or Spanish. All 65 columns from the public
release file are retained.
}
\details{
\strong{Survey design:} Stratified address-based sample with raking
post-stratification — use Taylor series linearization. NPORS has no PSU
(each address is its own unit, effectively a stratified SRS):

\if{html}{\out{<div class="sourceCode r">}}\preformatted{svy <- as_survey(pew_npors_2025,
  strata  = stratum,
  weights = weight
)
}\if{html}{\out{</div>}}

Use \code{basewt} instead of \code{weight} for sensitivity analyses comparing
pre- and post-raking estimates.

\strong{Social media battery:} All 11 \verb{smuse_*} variables share the question
stem \code{"Please indicate whether or not you ever use the following websites or apps."} Values: \code{1} = Selected, \code{2} = Not selected, \code{99} = Refused.
Each variable additionally carries a \code{"question_preface"} attribute with
this shared stem.

\strong{Metadata:}
All columns carry variable labels and value labels as R attributes from the
original SPSS file. The 11 \verb{smuse_*} battery variables additionally carry
a \code{"question_preface"} attribute with the shared question stem. All three
attribute types are automatically extracted into surveycore's metadata
system when you call \code{as_survey()}.
\itemize{
\item \strong{Variable labels} (\code{"label"} attribute): A human-readable description of
each column — for \verb{smuse_*} variables this is just the platform name
(e.g., \code{"Facebook"}). Example: \code{attr(pew_npors_2025$smuse_fb, "label")}
returns \code{"Facebook"}.
\item \strong{Value labels} (\code{"labels"} attribute): A named numeric vector mapping
each code to its meaning. Example:
\code{attr(pew_npors_2025$smuse_fb, "labels")} returns
\code{c(Selected = 1, "Not selected" = 2, Refused = 99)}.
\item \strong{Question preface} (\code{"question_preface"} attribute): The shared question
stem for battery items, set on all \verb{smuse_*} columns. Example:
\code{attr(pew_npors_2025$smuse_fb, "question_preface")} returns
\code{"Please indicate whether or not you ever use the following websites or apps."}.
}
}
\examples{
# Variables in the dataset
names(pew_npors_2025)

# Create survey design (no PSU for ABS design)
svy <- as_survey(
  pew_npors_2025,
  strata = stratum,
  weights = weight
)

# Inspect variable label
attr(pew_npors_2025$smuse_fb, "label")

# Inspect value labels
attr(pew_npors_2025$smuse_fb, "labels")

# Inspect question preface (shared stem for all smuse_* battery items)
attr(pew_npors_2025$smuse_fb, "question_preface")
}
\keyword{datasets}
