Ordinal survey data from 110 respondents measuring job or life satisfaction on a 4-point scale, along with gender and income level. Used in regression teaching to illustrate ordinal logistic regression (proportional odds model), the cumulative link formulation, and the proportional odds assumption with a simple, interpretable dataset.
Format
A tibble with 110 rows and 3 columns:
- gender
Respondent gender. Type: character. Values: "F" = female, "M" = male.
- income
Income level (ordinal). Type: numeric. Values: 1 = low, 2 = medium-low, 3 = medium-high, 4 = high.
- satisfaction
Satisfaction rating (ordinal response variable). Type: numeric. Values: 1 = not satisfied, 2 = slightly satisfied, 3 = satisfied, 4 = very satisfied.
Details
This dataset is used in Chapter 13 (Models for Ordinal Responses) to illustrate the ordinal logistic regression (proportional odds) model. The 4-level satisfaction outcome is modeled using the cumulative logit formulation. Key analyses include: fitting the proportional odds model with gender and income as predictors, interpreting cumulative odds ratios, testing the proportional odds assumption, comparing ordinal logit and ordinal probit models, and examining threshold (cut-point) estimates.
Examples
data(satisfaction)
head(satisfaction)
#> # A tibble: 6 × 3
#> gender income satisfaction
#> <chr> <int> <int>
#> 1 F 1 1
#> 2 F 2 1
#> 3 F 3 1
#> 4 F 4 1
#> 5 M 1 1
#> 6 M 2 1
table(satisfaction$satisfaction, satisfaction$gender)
#>
#> F M
#> 1 5 4
#> 2 9 6
#> 3 40 23
#> 4 12 11
# Ordinal logistic regression (requires MASS package)
# MASS::polr(factor(satisfaction) ~ income + gender, data = satisfaction)