Description Usage Arguments Value References Examples
This function calculates difficulty mismatch (DM ) function as introduced in Wise, Kingsbury & Webb (2015)
1 | DM(estimated.theta, items.administered, bank)
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estimated.theta |
A data matrix that has the provisional ability estimates upon administering a sequence of items. In this input, rows are individuals taking the CAT. The columns are the items administered to individuals. The values in the cells are the provisional ability estimates after each item administration. |
items.administered |
A data matrix that has the set of item items administered to individuals. This input assumes that every row in the data frame corresponds to the set of item names/identifiers administered to an individual. |
bank |
A data matrix that have item parameters in the following order: discrimination, difficulty, guessing and slipping. |
Returns a matrix for DM in which every row is the DM for each test taker
Wise, S. L., Kingsbury, G. G., & Webb, N. L. (2015). Evaluating content alignment in computerized adaptive testing. Educational Measurement: Issues and Practice, 34(4), 41-48.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | library(catR)
N=1000 #number of students
bank=250 #number of items
items=45
theta=rnorm(N,0,1) #level of trait
model="2PL" #IRT model to use
start <- list(theta = -1:1, randomesque = 1)
stop <- list(rule = c( "length"), thr = items)
final <- list(method = "ML")
test=list(method = "ML", itemSelect = "MFI")
bank=genDichoMatrix(items =bank, cbControl = NULL,
model = model)
res <- simulateRespondents(thetas = theta, bank,
start = start, test = test, stop = stop,
final = final, model = NULL)
estimated.theta=res$responses.df[,grepl("estimated.theta",names( res$responses.df ) ) ]
items.administered=res$responses.df[,grepl("items.administrated",names( res$responses.df ) ) ]
DM(estimated.theta, items.administered, bank)
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