JFK Approval Simulator
Published model · Fine-Tuning Scenarios · ← All-or-Nothing Simulator
Change the modeled distribution of how respondents rated JFK on key issues and see what the fitted model implies for overall approval.
Shift the mix of responses gradually — for example, moving some respondents from "Poor" to "Only fair" rather than moving everyone at once.
Curious what's happening inside the model when you apply a scenario? See the companion walkthrough Inside the Models — it traces one respondent's answers through the equation and shows how the headline emerges from all respondents' answers.
How to Use This Tool
Shift response distributions continuously and inspect the fitted model's implied approval
How to Use This Tool
Shift response distributions continuously and inspect the fitted model's implied approval
1. Pick the outcome (optional)
The Harris/Newsweek survey asked Americans about three topics: Presidential Approval, 1964 Vote Intention, and Tax Cut Support. Two of them can be explored more than one way. Approval has the binary approve/disapprove cut and the full ordered 4-point scale (an ordered-logit model on the Excellent-to-Poor ratings that shows how the whole approval distribution shifts, not just the top box). Vote Intention has the binary Kennedy-vs-Goldwater cut and the full three-way leaned vote (a multinomial model across Kennedy / Goldwater / still-undecided that shows how the whole three-way split shifts, with P(Kennedy) as the headline). You're on the published Approval model — the reference specification used for the primary case study. Use the outcome picker at the top to explore the others as exploratory auto-built models — same data, same sliders, different predictors chosen by Auto-Build.
2. Filter to a subgroup (optional)
Leave the filter at All respondents to use the published full-sample model, or filter to a subgroup like Republicans or college-educated voters. When you pick a subgroup, the simulator searches for a custom model built on that subgroup's data only — the variables may differ from the headline set. A blue callout names the discovered variables; a gray notice tells you when the sample was too small for a reliable custom model and the headline variable set is being refit on that subgroup instead.
3. Set the distribution
Use the preset buttons for a quick start, or drag the sliders to change the mix of responses for any variable. Unlike pinning everyone to one answer, sliders let you shift just some respondents — for example, moving 10% of Poor ratings to Only fair. The colored bar under each header shows the variable's model-implied range across its displayed response levels under the fitted model. Each variable's percentages must sum to 100%.
4. Run the scenario
Click Run scenario to send your slider settings through the active model. The result shows the model-implied approval rate under that scenario and a 95% confidence interval. This is a conditional implication of the fitted model, not an estimate of what causing those conditions would do. The How certain is this result? chart shows 10,000 simulated outcomes so you can see how much the baseline and your scenario overlap.
5. Explore each factor
Click Explore Each Factor after a run to see a full per-variable sensitivity breakdown. Each card shows the model-implied approval if that one variable's distribution were set to 100% at each level, holding your other sliders fixed. Combined high/low scenario cards show the model-implied result of setting every variable to the displayed level associated with the highest or lowest fitted outcome.
6. Switch to All-or-Nothing
Fine-Tuning shifts distributions, approximating the way public opinion usually moves. If you'd rather pin every respondent to one specific answer per question — useful for testing extreme scenarios that make the model's assumptions visible — use the All-or-Nothing Simulator link above the scenario panel. Your subgroup filter carries over.
7. Save, share, and reset
Every run is saved to the Saved Scenarios drawer at the bottom of the page — click Load on any card to restore its slider settings, or Remove to drop it. Use Share scenario to copy a link with your settings encoded, or Reset to baseline to return sliders to the starting distributions. The baseline shown is the model's estimate for whichever filter is active.
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Want to set every respondent to one specific value instead? All-or-Nothing Simulator →
Auto-Build Actionable found no movable predictors for this outcome, so this simulator uses the best Standard model instead — its predictors may not be classified as directly movable.
Set the distribution
The number on each card is the variable's incremental score as a share of its standalone score — how much of its one-at-a-time separation on the outcome remains once the other variables are included. Bars and ordering use the incremental score itself; the share is a display convention. Cards are ordered largest incremental first. It is a model-specific diagnostic, not a measure of causal leverage or actionability.
Note: this page doesn't pin everyone to one specific value. It shifts what kind of responses show up in the model's average — emphasizing some, de-emphasizing others — so the answers stay grounded in situations the data actually contains.
Predicted outcome
How did you change the survey responses?
Each pair of bars compares the actual distribution (green) to the distribution under your scenario (red where you changed it). This is what you changed — not the predicted impact, just the input.
How certain is this result?
Every prediction has wiggle room — these histograms show how much. The green bars are the plausible answers for the baseline; the red bars are the plausible answers for your scenario. Where the colors overlap, the two answers are close enough that the model can't cleanly tell them apart.
Calibration: predicted vs. observed
How closely the model's predicted probabilities track the observed outcome rates, binned by predicted decile. Bubble size shows respondents per bin. The dashed diagonal is perfect calibration; the blue scenario line is your run; the gray line (when present) is the unmodified base model for comparison.
Explore each factor
For each variable, see how the predicted outcome would change if you switched that one distribution to 100% at each level — holding all your other selections fixed. Reveals which individual levels have the most leverage given your current scenario.