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For every keyed item and response option: the count and proportion choosing it among respondents with a non-extreme rest measure, their mean location, and the point-biserial correlation between choosing the option and the person measure. These summaries use the rest measure (the person estimate from the other items), so the analysed item cannot credit its own takers. The keyed option should attract able persons and usually carry a positive point-biserial; a distractor whose takers are abler than the pooled takers of the full-credit option or options (with at least min_n takers) is flagged as a possible miskey. The analysis requires one response row per person; repeated rows do not supply independent taker counts or rest measures.

Usage

distractor_analysis(fit, items = NULL, min_n = 10)

Arguments

fit

A fitted object from rasch run with a key.

items

Optional subset of item names; defaults to every keyed item.

min_n

Minimum takers for an option to be eligible for the miskey flag.

Value

A data frame with one row per item-option: item, option, its assigned score, keyed (full credit), n, prop, mean_location, point_biserial, and flag.

Examples

set.seed(1); Np <- 400
th <- rnorm(Np)
raw <- sapply(seq(-1, 1, length.out = 6), function(d) {
  ok <- rbinom(Np, 1, plogis(th - d))
  ifelse(ok == 1, "A", sample(c("B", "C", "D"), Np, replace = TRUE))
})
colnames(raw) <- paste0("M", 1:6)
fit <- rasch(raw, key = setNames(rep("A", 6), colnames(raw)))
head(distractor_analysis(fit))
#>   item option score keyed   n       prop mean_location point_biserial  flag
#> 1   M1      A     1  TRUE 243 0.71260997     0.2559400      0.2368696 FALSE
#> 2   M1      B     0 FALSE  33 0.09677419    -0.1202694     -0.1070635 FALSE
#> 3   M1      C     0 FALSE  28 0.08211144    -0.2374100     -0.1422987 FALSE
#> 4   M1      D     0 FALSE  37 0.10850440    -0.1274202     -0.1172765 FALSE
#> 5   M2      A     1  TRUE 218 0.62643678     0.2520763      0.2455266 FALSE
#> 6   M2      B     0 FALSE  49 0.14080460    -0.1177247     -0.1061147 FALSE