TDCM (version 0.1.0)

item.influence: Estimating item influence measures.

Description

Function to estimate estimate item influence measures. Code adapted from (Jurich & Madison, 2023). This function is not available for longitudinal DCMs.

Usage

item.influence(model, data, fullcorrelation = FALSE, progress = TRUE)

Value

A list containing several item influence measures.

Arguments

model

a previously calibrated model; an object of class gdina.

data

a required \(N \times I\) matrix. Binary item responses are in the columns.

fullcorrelation

optional logical argument indicating a full or reduced response-classification correlation matrix.

progress

An optional logical indicating whether the function should print the progress of estimation.

Details

For DCMs, item influence quantifies how much an item impacts classifications. Given an estimated DCM and item response data, this function estimates five item influence measures, including item pull, item override, proportion of attribute information, response-classification correlation (corr1), and response-posterior correlation (corr2).

References

Jurich, D. & Madison, M. J. (2023). Measuring item influence for diagnostic classification models. Educational Assessment.

Examples

# \donttest{
## Item influence illustration
#load data (simulated based on Jurich and Bradshaw (2014))
qmatrix <- CDM::data.sda6$q.matrix
responses <- CDM::data.sda6$data

#Estimate the full LCDM
model1 <- CDM::gdina(responses, qmatrix, linkfct = "logit", method = "ML")

#Estimate item influence measures
influence <- TDCM::item.influence(model1, responses)

#Summarize influence statistics
influence$Pull #item pull
influence$Override #item override
influence$Information #proportion of attribute information
influence$Correlation1 #correlation of responses and classifications
influence$Correlation2 #correlation of responses and posterior probabilities

# }