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--- |
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dataset: |
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- name: USSocialIssues_2016ElectionSurvey |
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tags: |
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- survey |
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- public-opinion |
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- crime |
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- welfare |
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- united-states |
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- election-2016 |
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- panel-data |
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license: mit |
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--- |
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# Survey on Social Issues in the United States (2016 Election Study) |
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## Overview |
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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](https://connorjerzak.com/), Rebecca Goldstein, and Yanilda María González. |
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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. |
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**Key design features** |
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* **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. |
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* **Demographics & ideology.** Over 50 items capture party identification, vote choice, income, employment security, family status, education, racial identity, and core value trade-offs. |
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* **Panel structure.** A subset of respondents was re-contacted after Election Day, enabling within-person analyses of opinion change. |
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## File Manifest |
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| File | Description | |
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|------|-------------| |
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| `survey_results.csv` | Clean, respondent-level dataset (wide format). Each column corresponds to a survey variable prefixed by its original Qualtrics question ID. | |
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| `Oct21_survey.pdf` | Archived survey instrument, including consent form and full questionnaire. | |
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## Quick Start (R) |
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```r |
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library(tidyverse) |
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df <- read_csv("survey_results.csv") |
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# Recode experimental treatment |
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# Q42 == "No" -> Control |
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# Q42 == "Yes" & Q43 gives race |
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df <- df %>% |
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mutate(treat = case_when( |
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Q42 == "No" ~ "Control", |
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Q43 == "Black" ~ "Black", |
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Q43 == "White" ~ "White" |
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)) |
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# Estimate effect of racial cue on support for longer sentences |
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lm(long_sentences ~ treat + party_id + age, data = df) |
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``` |
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## Variable Highlights |
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* **Safety perceptions:** `Q2`–`Q4`, `Q37`, `Q39` |
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* **Crime policy preferences:** `Q11`, `Q12` |
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* **Redistribution & welfare attitudes:** `Q8`, `Q9`, `Q46`–`Q51` |
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* **2016 vote intention & choice:** `Q41`, `Q44`, `Q45` |
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* **Economic security:** `Q29`–`Q32` |
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* **Child-rearing values:** `Q33`–`Q36` |
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See `Oct21_survey.pdf` for exact wording and response options. |
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## Possible Use Cases |
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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. |
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2. **Stereotype activation & policy support** – estimate causal effects of suspect-race cues on punitive crime policies or welfare attitudes. |
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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`). |
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4. **Value trade-off scaling** – model latent moral or parenting value dimensions with the paired choice items (`Q33`–`Q36`). |
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5. **Small-N machine-learning demos** – demonstrate text classification, topic modeling, or mixed-effects models on a manageable survey. |
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## Sampling & Fieldwork |
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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. |
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| Wave | Dates | N (unique) | Notes | |
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|------|-------|------------|-------| |
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| Pre-Election | 12 Sep – 04 Nov 2016 | 449 | Prior to Election Day | |
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| Post-Election | 09 Nov – 15 Nov 2016 | 546 | Post Election Dad | |
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## Data Quality Notes |
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* **Non-probability sample.** MTurk respondents skew younger, more educated, and more politically engaged than the general U.S. adult population. |
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* **Attention checks.** Various items (e.g., number of break-ins retention check) facilitate quality screening. |
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* **Missing values.** Skipped or invalid responses are coded `NA`. |
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## Citation |
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``` |
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@misc{SocialIssuesSurvey2016, |
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author = {Jerzak, Connor and Rebecca Goldstein and Yanilda González}, |
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title = {{USSocialIssues\_2016ElectionSurvey}}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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doi = {10.57967/hf/5892}, |
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url = {https://huggingface.co/datasets/cjerzak/USSocialIssues_2016ElectionSurvey}, |
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howpublished = {Hugging Face Dataset} |
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} |
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``` |
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