weat_perm.RdThe statistic computed by this function is the mean cosine similarity of each x item to the a attributes minus the mean cosine to the b attributes, summed over x items subtracted for the same quantity computed for the y items. See the paper for details of the statistic, and the effect size.
weat_perm(items, vectors, x_name, y_name, a_name, b_name, b = 1000)
| items | information about the items, typically from
|
|---|---|
| vectors | a matrix of word vectors for all the study items, typically
from |
| x_name | the name of the target item condition, e.g. "Flowers" in WEAT 1 |
| y_name | the name of the target item condition, e.g. "Insects" in WEAT 1 |
| a_name | the name of the first condition, e.g. "Pleasant" in WEAT 1 |
| b_name | the name of the second condition, e.g. "Unpleasant" in WEAT 1 |
| b | number of bootstrap samples. Defaults to 1000. |
a data frame with first column the statistic, the second column the effect size, and the third column permutation test p value.
The p value is constructed by permuting the assignment of words to the x and y conditions. (The a and b attribute items are fixed.) The p value is the proportion of times the statistic computed on the permuted labels is greater than the value of the statistic that is observed.
its <- cbn_get_items("WEAT", 1) its_vecs <- cbn_get_item_vectors("WEAT", 1) res <- weat_perm(its, its_vecs, x_name = "Flowers", y_name = "Insects", a_name = "Pleasant", b_name= "Unpleasant") res#> S_xyab d p_value #> 1 2.238165 1.504315 0