extract_base_rate_items.RdThis function processes a typicality matrix to identify base-rate items by comparing typicality scores of descriptions between all unique pairs of groups.
extract_base_rate_items(typicality_matrix)A data frame with the following columns:
The group with the higher typicality score for the description.
The group with the lower typicality score.
The description (e.g., adjective) being compared.
The typicality score for Group1.
The typicality score for Group2.
The log-ratio: log(Score1 / Score2). Always >= 0.
For each pair of groups and each description (e.g., adjective), it identifies which group received the higher typicality score. The output includes the names of both groups, their scores, and the log-ratio between the higher and lower score.
It can be quite slow for large matrices, as the number of items becomes very large.
By construction, the returned Group1 always has a higher or equal typicality score
than Group2 for a given description. This ensures that the resulting StereotypeStrength
(defined as log(Score1 / Score2)) is always positive or zero, and represents the strength
of the stereotypical association in favor of Group1.
mat <- matrix(runif(9, 1, 100), nrow = 3,
dimnames = list(c("GroupA", "GroupB", "GroupC"),
c("smart", "brave", "greedy")))
extract_base_rate_items(mat)
#> Group1 Group2 Description Score1 Score2 StereotypeStrength
#> 1 GroupB GroupA smart 83.59897 8.994264 2.2294442
#> 2 GroupA GroupB brave 16.56364 1.732545 2.2576184
#> 3 GroupA GroupB greedy 50.27996 29.686957 0.5268988
#> 4 GroupC GroupA smart 60.47533 8.994264 1.9056485
#> 5 GroupC GroupA brave 47.17296 16.563636 1.0466111
#> 6 GroupC GroupA greedy 73.55532 50.279961 0.3804311
#> 7 GroupB GroupC smart 83.59897 60.475328 0.3237957
#> 8 GroupC GroupB brave 47.17296 1.732545 3.3042295
#> 9 GroupC GroupB greedy 73.55532 29.686957 0.9073299