Multiple-choice items can be rescored polytomously so
that a distractor carrying information about the trait receives partial
credit (Andrich and Styles 2011). This function proposes such a scoring
from the rest-measure distractor analysis: within each keyed item, a
distractor qualifies for credit when it attracts at least min_n
takers, its takers' mean rest location exceeds that of the uncredited
distractors by more than z standard errors of the difference,
and it remains below the keyed option. Qualifying distractors are
ranked by mean location and scored 1, 2, ... below the keyed option's
top score. The result is a proposal for substantive review, not an
automatic decision: inspect plot_distractors and the item
content, edit as needed, then refit with
rasch(raw_data, key = proposal$option_scores).
Arguments
- fit
A fitted object from
raschrun with akey.- items
Optional subset of keyed item names.
- min_n
Minimum takers for a distractor to be considered.
- z
Required separation, in standard errors, between a credited distractor and the uncredited ones.
Value
A list of class "rasch_rescore": option_scores, a
data frame (item, option, score) ready for
rasch(key = ) and covering every observed option of the
examined items, and evidence, the distractor analysis with the
proposed scores and the separation z per option.
References
Andrich, D. and Styles, I. (2011). Distractors with information in multiple choice items: A rationale based on the Rasch model. Journal of Applied Measurement, 12, 67-95.
Examples
set.seed(1); Np <- 600
th <- rnorm(Np)
raw <- sapply(seq(-0.5, 0.5, length.out = 4), function(d) {
x <- vapply(th, function(b) sample(0:2, 1,
prob = item_moments(b, c(d - 0.7, d + 0.7))$P), 0L)
c("D", "B", "A")[x + 1] # B is an informative distractor
})
colnames(raw) <- paste0("M", 1:4)
fit <- rasch(raw, key = setNames(rep("A", 4), colnames(raw)))
pr <- distractor_rescore(fit)
pr$option_scores
#> item option score
#> 1 M1 A 2
#> 2 M1 B 1
#> 3 M1 D 0
#> 4 M2 A 2
#> 5 M2 B 1
#> 6 M2 D 0
#> 7 M3 A 2
#> 8 M3 B 1
#> 9 M3 D 0
#> 10 M4 A 2
#> 11 M4 B 1
#> 12 M4 D 0