JFK Approval Simulator
Published model · All-or-Nothing Scenarios · Part 2: Fine-Tuning Simulator →
Just before his assassination, 57% of Americans approved of President Kennedy's performance. What drove that number — and what could have changed it?
Pin every respondent to one chosen response level per factor and see how the headline shifts.
Curious what's happening inside the model when you apply a scenario? See the companion walkthrough Inside the Model — it traces one respondent's answers through the equation and shows how the headline emerges from all respondents' answers.
How to Use This Tool
Explore what drove approval in November 1963
How to Use This Tool
Explore what drove approval in November 1963
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 primary case study with hand-picked predictors and a fully calibrated fit. Use the outcome picker at the top to explore the others as exploratory auto-built models — same data, same simulator, 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 scenario
Use the preset buttons for a quick start, or use the dropdowns to set each factor yourself. The colored bar beneath each label shows that variable's model leverage — the approval swing from its worst to best level, holding the others fixed. You can set one factor or several at once; the model accounts for all variables jointly.
4. Run the scenario
Click Run scenario to send your settings through the active model. The result shows the projected approval rate and a 95% confidence interval. 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 predicted approval at every level of that variable, holding your other settings fixed. Combined best/worst cards show the result of taking every variable's best or worst level at once.
6. Switch to Fine-Tuning
All-or-Nothing pins every respondent to one answer per question — useful for testing extreme scenarios that make the model's assumptions visible. If you'd rather shift distributions incrementally (move 10% of Poor to Only fair, for example), use the Fine-Tuning 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 settings, or Remove to drop it. Use Share scenario to copy a link with your settings encoded, or Reset to baseline to clear and start over. The baseline shown is the model's estimate for whichever filter is active.
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Modeling on this page is restricted to binary (yes/no) outcomes.
Want to shift distributions instead of pinning to one value? Fine-Tuning Simulator →
Auto-Build Actionable found no movable levers for this outcome, so this simulator uses the best Standard model instead — its predictors may not be directly actionable.
Set the scenario
The number on each card is the variable's swing on this page — how far the prediction would move if every respondent had this variable's lowest-leverage value versus its highest-leverage value, holding the other variables at their observed values. A bigger swing means a bigger lever.
Predicted outcome
How did you change the survey?
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.
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
For each variable's level, the predicted outcome if every respondent had that response, all else unchanged. This is the analytic view of the simulator.
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 just that one selection — holding all your other selections fixed. Reveals which individual choices have the most leverage given your current scenario.