pairwise.item.fit {pairwise}

Item Fit Indices

Description

function for calculating item fit indices. The procedures for calculating the fit indices are based on the formulas given in Wright & Masters, (1982, P. 100), with further clarification given in http://www.rasch.org/rmt/rmt34e.htm.

Usage

pairwise.item.fit(pers_obj, na_treat = NA)

Arguments

pers_obj

an object of class "pers" as a result from function pers

na_treat

value to be assigned to residual cells which have missing data in the original response matrix. default is set to na_treat=NA to ignore these cells in further calculations. An option is to set these residuals to 0 using na_treat=0, which implys that they are imputed as 'fitting data', i.e., zero residuals. This can attenuate contrasts (see. http://www.rasch.org/rmt/rmt142m.htm).

Details

contrary to many IRT software using Ml based item parameter estimation, pairwise will not exclude persons, showing perfect response vectors (e.g. c(0,0,0) for dataset with three variables), prior to the scaling. Therefor the fit statistics computed with pairwise may deviate somewhat from the fit statistics produced by IRT software using Ml based item parameter estimation (e.g. R-package eRm), depending on the amount of persons with perfect response vectors in the data.

Value

an object of class c("pifit", "data.frame") containing item fit indices.

References

Wright, B. D., & Masters, G. N. (1982). Rating Scale Analysis. Chicago: MESA Press.

Wright, B. D., & Masters, G. N. (1990). Computation of OUTFIT and INFIT Statistics. Rasch Measurement Transactions, 3(4), 84–85.

Examples

########
data(sim200x3)
result <- pers(pair(sim200x3))
pairwise.item.fit(pers_obj=result) # item fit statistic