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. Which measured factors are associated with that number — and what does the fitted model imply under stated scenarios?
Pin every respondent to one chosen response level per factor and see how the fitted model implies the headline would differ under that scenario.
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.
Think a different specification explains this better? Challenge this model — opens the Model Builder with these predictors loaded, so you can add, drop, or replace them and refit.
How to Use This Tool
Explore how measured factors are associated with approval in the fitted model
How to Use This Tool
Explore how measured factors are associated with approval in the fitted model
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 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-implied range — the difference in approval between the displayed response levels under the fitted model, with the other settings held 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 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 at every displayed level of that variable, holding your other settings 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 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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Want to shift distributions instead of pinning to one value? Fine-Tuning 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 scenario
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.
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.
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. This shows which individual factor settings produce the largest model-implied changes in the current scenario.