cjerzak/USSocialIssues_2016ElectionSurvey

Survey on Social Issues in the United States (2016 Election Study)

Overview

This data product contains individual-level responses to an online survey experiment conducted in the run-up to—and immediately after—the 2016 U.S. presidential election by Connor Jerzak, Rebecca Goldstein, and Yanilda María González.

Survey began on 12 September 2016 and continued through mid-November 2016, giving researchers a before/after snapshot of attitudes shaped by a highly salient national campaign.

Key design features

• Crime-framing experiment. Respondents read a mock police-blotter story with experimentally varied details (suspect race, number of break-ins, presence/absence of racial information) before answering questions about policy, crime perceptions, and social spending.
• Demographics & ideology. Over 50 items capture party identification, vote choice, income, employment security, family status, education, racial identity, and core value trade-offs.
• Panel structure. A subset of respondents was re-contacted after Election Day, enabling within-person analyses of opinion change.

File Manifest

File	Description
survey_results.csv	Clean, respondent-level dataset (wide format). Each column corresponds to a survey variable prefixed by its original Qualtrics question ID.
Oct21_survey.pdf	Archived survey instrument, including consent form and full questionnaire.

Quick Start (R)

library(tidyverse)
df <- read_csv("survey_results.csv")

# Recode experimental treatment
#   Q42 == "No" -> Control
#   Q42 == "Yes" & Q43 gives race
df <- df %>% 
  mutate(treat = case_when(
    Q42 == "No" ~ "Control",
    Q43 == "Black" ~ "Black",
    Q43 == "White" ~ "White"
  ))

# Estimate effect of racial cue on support for longer sentences
lm(long_sentences ~ treat + party_id + age, data = df)

Variable Highlights

• Safety perceptions: Q2–Q4, Q37, Q39
• Crime policy preferences: Q11, Q12
• Redistribution & welfare attitudes: Q8, Q9, Q46–Q51
• 2016 vote intention & choice: Q41, Q44, Q45
• Economic security: Q29–Q32
• Child-rearing values: Q33–Q36

See Oct21_survey.pdf for exact wording and response options.

Possible Use Cases

1. Election-season opinion dynamics – analyze the before/after panel to examine how campaign events (debates, the Comey letter, Election Day) shifted perceptions of crime, policing, or redistribution.
2. Stereotype activation & policy support – estimate causal effects of suspect-race cues on punitive crime policies or welfare attitudes.
3. Replication exercises – reproduce classic findings from ANES or GSS items using a contemporary MTurk sample; ideal for teaching regression, causal inference, or text analysis (e.g., coding open-ended crime causes in Q10).
4. Value trade-off scaling – model latent moral or parenting value dimensions with the paired choice items (Q33–Q36).
5. Small-N machine-learning demos – demonstrate text classification, topic modeling, or mixed-effects models on a manageable survey.

Sampling & Fieldwork

Respondents were recruited via Amazon Mechanical Turk. Each wave paid $0.25 and took ~5 minutes. The instrument included an informed-consent screen and was approved by the Harvard CUHS IRB. IP geo-coordinates (rounded to 3 decimals) were recorded for coarse location checks; no personally identifying information is included.

Wave	Dates	N (unique)	Notes
Pre-Election	12 Sep – 04 Nov 2016	449	Prior to Election Day
Post-Election	09 Nov – 15 Nov 2016	546	Post Election Dad

Data Quality Notes

• Non-probability sample. MTurk respondents skew younger, more educated, and more politically engaged than the general U.S. adult population.
• Attention checks. Various items (e.g., number of break-ins retention check) facilitate quality screening.
• Missing values. Skipped or invalid responses are coded NA.