Multiple Correspondence Analysis Calculator
Analyze patterns in categorical data with multiple variables using MCA.
Summary
Observations: 5
Variables: 3
Categories: 6
Total Inertia: 1.0000
Eigenvalues & Inertia
| Dimension | Eigenvalue | % of Inertia | Cumulative % |
|---|---|---|---|
| 1 | 2.0000 | 200.00% | 200.00% |
| 2 | 1.6667 | 166.67% | 366.67% |
| 3 | 1.0749 | 107.49% | 474.16% |
Category Coordinates
| Category | Mass | Dim 1 | Dim 2 | Dim 3 |
|---|---|---|---|---|
| Var1:A | 0.200 | 1.414 | 1.290 | -0.317 |
| Var1:B | 0.133 | 1.414 | -1.936 | 0.475 |
| Var2:X | 0.200 | 1.414 | -0.003 | -1.397 |
| Var2:Y | 0.133 | 1.414 | 0.004 | 2.096 |
| Var3:P | 0.133 | 1.414 | 1.937 | 0.467 |
| Var3:Q | 0.200 | 1.414 | -1.292 | -0.312 |
Discrimination Measures
| Variable | Dim 1 | Dim 2 | Dim 3 |
|---|---|---|---|
| Var1 | 0.667 | 0.833 | 0.050 |
| Var2 | 0.667 | 0.000 | 0.976 |
| Var3 | 0.667 | 0.834 | 0.049 |
Category Map (Dim 1 vs Dim 2)
Interpretation
High discrimination values indicate variables that differentiate well between observations on that dimension. Categories close together in the map are associated with similar response patterns.
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