Goodman–Kruskal lambda
How much knowing one category reduces errors about another.
When to use it
Use lambda when both variables are categories, ordered or not, and you want the reduction in prediction errors. Lambda is 0 when the extra variable does not help, and 1 when it predicts the other variable perfectly.
Assumptions
Both columns are categorical. Lambda can be zero even when chi-square is significant, because it asks a stricter prediction question.
Running it in Tensr
Options
Prop
Type
Reading the output
How much knowing the teaching method reduces errors in predicting the satisfaction rating.
| method | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| Lecture | 5 | 12 | 12 | 3 |
| Online | 4 | 12 | 7 | 9 |
| Workshop | 4 | 7 | 13 | 8 |
Goodman–Kruskal λ for method and satisfaction, n = 96. The primary result is significant (p = < .001). This is large enough, in this sample, to treat the comparison this page is about as a real association rather than noise. Metrics: λ (symmetric) = 0.301; λ (A|B) = 0.289; λ (B|A) = 0.313; ASE = 0.047; z = 6.433; p-value = < .001.
Reporting (APA 7)
Goodman–Kruskal λ for method and satisfaction, n = 96. This result is significant (p = < .001). The effect is moderate, so report its size with the p-value.
Coming from SPSS
Analyze → Correlate → Lambda.