01 / 18 · Chi-square

Expected vote distributions differ across party identification

Compare observed and expected counts with standardized residuals. Combining categories changes the question and degrees of freedom.

Three party groups · 944 respondents · ANES96

 Expected vote distributions differ across party identification
This preview shows the existing 16:9 slide. The Pele map below did not generate the full statistical chart.

This companion diagram explains variables, design and tests. It is distinct from the statistical figure in the standalone presentation.

 Analysis relationship map
Counts are observed. E = expected counts. Residual color is descriptive; no cellwise significance claim.
Pele map source
flowchart LR
 P["Party identification"] --- V["Expected vote"]
 P --> C["Observed × expected counts"]
 V --> C
 C --> R["Residuals + Cramer V"]

This actual production branch takes computed estimates and places axes, lines, intervals and labels. Shared helpers and the complete file are available below.

SVG construction branch · JavaScript
if (v.kind === "chi") {
  a.push(
    T(10, 35, "A  Expected vote within each party group", 30, C.ink, 700),
    T(1000, 35, "B  Deviation from independence", 30, C.ink, 700),
    T(1135, 82, "Clinton", 24, C.muted, 600, "middle"),
    T(1425, 82, "Dole", 24, C.muted, 600, "middle"),
  );
  let rh = v.rows.length > 3 ? 65 : 145,
    top = 105;
  v.rows.forEach((r, i) => {
    let yy = top + i * rh,
      cs = v.cells.filter((c) => c.i === i);
    a.push(
      T(
        10,
        yy + 24,
        v.rows.length > 3
          ? [
              "0 Strong Dem.",
              "1 Weak Dem.",
              "2 Lean Dem.",
              "3 Independent",
              "4 Lean Rep.",
              "5 Weak Rep.",
              "6 Strong Rep.",
            ][+r]
          : r,
        26,
        C.ink,
        600,
      ),
      T(10, yy + 56, "n = " + cs.reduce((n, c) => n + c.obs, 0), 23, C.muted),
    );
    let xx = 230;
    for (const c of cs) {
      let width = c.pct * 6.4,
        col = c.j === 0 ? C.blue : C.teal;
      a.push(
        G(
          [
            R(xx, yy, Math.max(1, width), 46, col),
            ...(width > 112
              ? [
                  T(
                    xx + width / 2,
                    yy + 31,
                    fmt(c.pct, 1) + "%",
                    25,
                    "#FFFFFF",
                    600,
                    "middle",
                  ),
                ]
              : []),
          ],
          c.key,
        ),
      );
      xx += width;
      let cx = 1000 + c.j * 290;
      let strength = Math.min(1, Math.abs(c.residual) / 15),
        fill = c.residual > 0 ? "#DCE9F3" : "#EFF2F4";
      a.push(
        G(
          [
            R(cx, yy, 270, 65, fill),
            T(cx + 10, yy + 27, c.obs + " / E " + fmt(c.expected, 1), 25),
            T(
              cx + 260,
              yy + 54,
              "res " + signed(c.residual, 1),
              23,
              C.muted,
              400,
              "end",
            ),
          ],
          c.key,
        ),
      );
    }
  });
  a.push(
    R(12, 587, 18, 18, C.blue),
    T(40, 605, "Clinton", 25),
    R(180, 587, 18, 18, C.teal),
    T(208, 605, "Dole", 25),
    T(
      450,
      605,
      "χ²(" + v.df + ") = " + fmt(v.stat, 1) + " · " + pv(v.p),
      28,
      C.ink,
      650,
    ),
    G(
      [
        T(
          1000,
          605,
          "V " +
            fmt(v.effect, 3) +
            "  [" +
            v.effectCI.map((x) => fmt(x, 3)).join(", ") +
            "]",
          30,
          C.ink,
          700,
        ),
      ],
      v.key,
    ),
  );
  note =
    "Counts are observed. E = expected counts. Residual color is descriptive; no cellwise significance claim.";
  source =
    'flowchart LR\n P["Party identification"] --- V["Expected vote"]\n P --> C["Observed × expected counts"]\n V --> C\n C --> R["Residuals + Cramer V"]';
  map = { P: v.key, V: v.cells[0].key, C: v.cells[1].key, R: v.key };
}

This is preserved reference code, not an executable editor.

Data
Inspect the condition data first. Replace sample, units, coefficients and confidence intervals together. Do not combine different samples or measurement levels on one scale.
Graphic
Fit axis ranges, label widths, type and colors to the target region. Preserve whether intervals include zero and recheck legibility at the final size.
Interpretation
Diagram arrows and model paths are not causal evidence. Distinguish mean-response from prediction intervals, and pointwise from simultaneous intervals.