November 1963 · Harris/Newsweek Survey

Model Builder

Inspect & challenge the published model

Refit the published model — or build a competing one from scratch — with whichever predictors you choose. Pick one of three outcomes, offered in six forms — Presidential Approval (approve-vs-not, the full ordered scale, or five categories), Vote Intention (a Kennedy-vs-Goldwater head-to-head or the three-way race that keeps the undecided), or Tax Cut Support — then add or remove survey questions or restrict the fit to a subgroup.

Only Presidential Approval has a published model. For Vote Intention and Tax Cut Support, you can build your own models here. The simulators cover all three outcomes — for those two, they use a model Auto-Build selects — and the Nonresponse Test and Survey Explorer work with every outcome too.

Want to see how the factors connect to one another? Open the Bayesian Network Analysis — no need to build a model first. Pick an outcome, and it shows which factors link to it directly and which link only through other factors. For Approval it uses the published model’s factors; for the other outcomes, a set Auto-Build selects. A link means the factors are related in the survey data, not that one causes the other.

How to Use This Tool

Pick the outcome you want to model, then check the predictor variables you want to test and click Run Analysis to fit. Variable cards start empty — for Approval, click Load default model to drop in the published model’s six variables as a starting point, or let Auto-Build search for you.

1. Pick an outcome

Three outcomes are available in six forms: Presidential Approval (the case study's headline, approve vs. not), Approval — ordered scale (the full Excellent→Poor ratings, with top-two-box as the headline), Approval — five categories (the same question fit unordered, so “Not sure” is modeled as its own class rather than dropped), Vote Intention — head-to-head (Kennedy vs. Goldwater), Vote Intention — three-way (Kennedy / Goldwater / Undecided, predicting each category's share), and Tax Cut Support. Switch outcomes anytime with the chooser at the top of the variable section.

2. Pick predictors

Use the search box and category buttons to find variables across Political, Policy, Evaluation, and Demographics. Cards start empty — for Presidential Approval, Load default model loads the six published-model predictors; the other outcomes have no published model, so start from scratch or use Auto-Build. Run Analysis is capped at 20 predictors; Auto-Build searches the full candidate set and retains at most ten predictors; on small subgroups the cap is lower, and the results say so.

3. Fit and read

Click Run Analysis to fit the model with whatever variables you've checked. Or use Auto-Build Standard to let the algorithm search all variables automatically, or Auto-Build Actionable Predictors to restrict the candidate pool to variables classified as more readily interpretable for planning or communication — such as issue evaluations and policy attitudes — which excludes demographic and other fixed characteristics from the pool. The label does not imply that changing a selected variable would causally change the outcome.

Optional: subgroup

By default, the model is fit on every respondent with complete data on the outcome and the predictors you select, so the case count shown with the results depends on your specification (the published six-variable Approval model uses 1,159 of the survey’s 1,283 respondents). Optionally restrict it to one subgroup at a time using the Whose data? panel — e.g. just Republicans, or just respondents under 35 — to see how the model behaves within that group. Subgroup levels with fewer than 100 respondents are hidden.

Where to start. Variable cards start empty, so you are never anchored to one model. Three ways to begin: click Load default model to load a published starting model (the six-variable Approval model; the other outcomes have no published model), use Auto-Build to let the tool search the full predictor set, or just check the variables you want. Switching outcomes clears the grid so each model starts clean.

Why three approval outcomes? Presidential Approval collapses each rating into approve (Excellent or Pretty good) vs. not and fits a standard logit. Approval — ordered scale keeps the full Excellent→Poor ordering and fits an ordered logit, so it uses the gradient between categories rather than a single cut. Approval — five categories fits the same question unordered, so “Not sure” is modeled as its own class rather than dropped. All three report the same top-two-box headline, so their headline numbers stay directly comparable — the ordered and five-category versions additionally show how the fitted model associates each predictor with the full distribution across response levels.

Why two vote-intention outcomes? Vote Intention — head-to-head models the straight Kennedy-vs.-Goldwater choice and fits a standard logit, reporting the chance of choosing Kennedy. Vote Intention — three-way keeps Undecided as its own category alongside Kennedy and Goldwater and fits a multinomial (unordered) logit, predicting the share landing in each of the three. Use the head-to-head when you only care about who wins among decided voters, and the three-way when the undecided bloc is itself part of the story.

Want to see the math? The companion page Inside the Model walks through how one respondent's six answers become a model-implied probability, and how those probabilities aggregate into the approval headline. Recommended for first-time visitors and academic readers.

How are the selected factors conditionally related? The Bayesian Network Analysis is a standalone tool — you don't need to build or Auto-Build a model here first. Pick an outcome and it uses the published model’s predictors for Approval, or an Auto-Build set for outcomes without a published model; narrow to a subgroup and it discovers that subgroup’s own factor set instead. It then fits a constrained Bayesian network and maps conditional dependencies among those measured variables. It shows which variables are connected to the outcome in the fitted network and whether those connections are represented as direct or indirect paths. A what-if view lets you set a factor and inspect the model-implied outcome under that setting. These outputs are exploratory properties of the fitted network, not identified intervention effects.

After you run — what to expect

Review results

Results show how well the model predicts the outcome and how much each predictor contributes. The Other Variables in This Survey section lists every variable not yet in your model — each card shows whether adding it would likely improve fit.

Use the simulator

Click Launch in the Simulator panel to open an interactive tool. Set any combination of survey responses — e.g. economy rating "Poor" and Vietnam handling "Excellent" — and inspect the approval probability implied by the fitted model under that stated scenario.

Iterate and compare

Each run is saved in the Saved Analyses tray. Click Load to restore a run, or Pin two runs to view them side by side. Each card shows the model's fit metrics — Tjur R² (or McFadden R² for the ordered, five-category, and three-way outcomes), AUC, and Brier — where higher R² and AUC, and lower Brier, mean a better-performing model.

If you ran with a synthetic variable, results show three sections:

Full Model

Complete model including all selected predictors and the synthetic variable.

Base Model

Model performance without the synthetic variable.

Synthetic Variable Performance

How well the synthetic met its specifications and its impact on model fit.

What are you trying to predict?

Choose the outcome your model will try to explain.

Required

Loading outcomes…

Survey Questions to Include in Your Model

Download Harris/Newsweek Questionnaire (PDF)

Search variables

Filter by topic

View Group by category

Quick actions

Hide variable cards

Loading variables…

Outcome: …

0 of … predictor variables selected

Synthetic-Variable Sensitivity Module — optional vulnerability check using a hypothetical omitted factor

Enable the check — targets the selected outcome

What this checks. Adds a hypothetical omitted factor with a specified relationship to the outcome and a cap on its overlap with the model's predictors, then shows how much the model's fit diagnostics and predicted margins change. The exercise does not show that the factor exists or identify what it represents; it tests how vulnerable the published explanation would be to a missing factor with those properties.

Target Correlation with Outcome

r =

-0.8 0.8

Max Correlation with Any Predictor

r =

0 0.8

# Response Categories

2 (Binary) 5 (Max)

The model you're about to fit

Predictors (X)

No predictors selected yet. Check variable cards above or click Auto-Build below.

Outcome (Y)

…

A logistic regression model uses the predictors on the left to estimate the probability of the outcome on the right. This summary updates as you change your selection — and shows exactly what gets submitted when you click Run Analysis. Auto-Build ignores this selection and chooses predictors for you.

Run Analysis

Build a model from exactly the predictors you've selected above. Full analyst control.

Returns: model fit, predictor strengths, calibration, and an interactive simulator.

Auto-Build

Ignores your selection above. Searches the full set of available predictors and chooses a subset under the selected rule. Standard prioritizes held-out predictive fit; Actionable Predictors applies an additional movability classification. That classification is a planning aid, not evidence of causal effect.

Prioritizes predictors classified as more movable — a planning filter, not causal evidence.

Returns: a chosen subset of predictors plus full model fit, strengths, and an interactive simulator.

Bayesian Network Analysis

Runs a Bayesian network on the predictors you've selected above — which are directly vs. indirectly connected to the outcome, and how those connections are represented in the fitted network.

Returns: direct vs. indirect connections, link confidence, and a what-if simulator.

Auto-Building Optimal Model… 0s

Initializing…
Screening candidate predictors
Running forward stepwise selection
Fitting final model
Computing diagnostics & margins

Full Model Performance

Complete model including all predictor variables and the synthetic variable

Model fit metrics Tap to view Predicted Probability, Pseudo R², Tjur R², AUC, Brier, AIC, LL, LR test

Predicted Probability

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Fit index — how it is built Scoring Details

The index averages the fit statistics available for this model: AUC, Brier, Tjur R², and pseudo R². The likelihood-ratio statistic and its nominal p-value are shown only as descriptive weighted-pseudolikelihood comparisons and are excluded from the index; under the analysis weight, the p-value is not interpreted as a significance test. The AIC improvement over the null model is also excluded because it measures distance from the null rather than predictive fit. On a binary outcome the four indexed measures describe closely related aspects of the same fit, so read the index as one summary rather than as four independent dimensions.

How the score is built

Each of the four measures is scored against the cut-offs below — the same ones its tile uses, plus an excellent mark above Strong. The score turns the four into a single number out of 100: Moderate on all four gives 60, Strong on all four gives 80, excellent on all four gives 100.

Discrimination (AUC)

Weak below 0.70 · Moderate 0.70 · Strong 0.80 · Excellent 0.90

Prediction error (Brier), lower is better

Weak above 0.18 · Moderate 0.18 · Strong 0.12 · Excellent 0.06

Separation (Tjur R²)

Weak below 0.10 · Moderate 0.10 · Strong 0.20 · Excellent 0.35

Pseudo R²

Weak below 0.20 · Moderate 0.20 · Strong 0.30 · Excellent 0.40

Values between two cut-offs score in between, in proportion; a measure past its excellent mark counts as excellent. Zero means chance or no better than a model with no predictors (AUC 0.50, Brier 0.25, Tjur and pseudo R² 0).

Aggregation Method

Clamp to [0,1], average equally over the metrics present, map to a 0–100 index. The cuts are the ones the metric tiles on this card already use for their Weak, Moderate and Strong chips, plus an excellent mark above Strong: each metric scores 0 at its floor, 0.6 at the Moderate cut, 0.8 at the Strong cut and 1 at the excellent mark. A model sitting on every Strong cut therefore scores 80, one on every Moderate cut scores 60, and the printed bands describe the tiles themselves, so the index cannot disagree with the chips beside it. The cuts and the bands are display conventions calibrated to this demonstration, not standards. The index summarizes fit only: it carries no information about whether the model contains the right predictors.

Display bands (fit only)

≥80

Strong

60–79

Moderate

<60

Weak

—

Model Fit

Pseudo R²

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Higher is better

Benchmark: — (strong ≥ .30)

Separation (Tjur R²)

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Higher is better

Benchmark: — (strong ≥ .20)

Discrimination (AUC)

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0.5 = random, 1 = perfect

Benchmark: — (strong ≥ .80)

Prediction Error (Brier)

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Lower is better

Benchmark: — (good ≤ .12)

Each tile rates one statistic against its own display cut-off used in this prototype. The overall verdict above combines them with held-out fit, so it can read lower.

Information Criteria

AIC Comparison

Null Model

—

Change

Final Model

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Lower is better

Log Likelihood Comparison

Null Model

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Change

Final Model

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Higher is better

LR χ²

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p-value

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Synthetic-variable impact Whether adding the synthetic variable improved fit, and how it shifted predicted margins

Synthetic variable impact analysis

A sensitivity analysis: how the model's diagnostics change when adding a synthetic variable — a hypothetical unmeasured factor built to the strength you specified, with its overlap with your predictors held under your cap — versus the base model.

Δ McFadden R²

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Δ Tjur R²

—

Δ AIC

—

Δ Log Likelihood

—

Δ AUC

—

Δ Brier

—

Summary:

Adding the synthetic variable produces small changes across the displayed fit diagnostics, with the largest change in model fit as measured by AIC.

Model Builder

How to Use This Tool

1. Pick an outcome

2. Pick predictors

3. Fit and read

Optional: subgroup

Review results

Use the simulator

Iterate and compare

If you ran with a synthetic variable, results show three sections:

What are you trying to predict?

Whose data?

Survey Questions to Include in Your Model

Run a null model?

Full Model Performance

Predicted Probability

How the score is built

Aggregation Method

Display bands (fit only)

Model Fit

Information Criteria

Sample Completeness

Detailed Results

Predicted Margins (Table)

Predicted Margins (Chart)

Base Model — Detailed Results

Predicted Margins (Base Model)

Synthetic variable impact analysis

Synthetic-variable change in predicted margins: Full (less) Base