item_drift: Item drift analysis

In woshikaqia/MIRTutils: MIRT Utility Package

Item drift analysis

Description

Conduct item difficulty drift analysis and flag items with potential drift Model currently supported are 1-3 PL, GPCM, and Rasch Testlet, and a mix of these models.

Usage

item_drift(
  theta,
  SA_dat = NULL,
  Cluster_dat = NULL,
  SA_parm = NULL,
  Cluster_parm = NULL,
  drift = 0.3,
  Alpha = 0.05,
  Dv = 1,
  n.nodes = 21,
  missing_as_incorrect = FALSE
)

Arguments

theta	a vector of examinee ability. The length of the vector should be equal to the number of examinees
SA_dat	For one examinee, a vector of response to standalone items. For more than one examinee, a matrix or dataframe of response to standalone items. One assertion per column. Column order must match row order in SA_parm. Use NA for missing responses
Cluster_dat	For one examinee, a vector of response to cluster items. For more than one examinee, a matrix or dataframe of response to cluster items. One assertion per column. Column order must match row order in Cluster_parm. Use NA for missing responses.
SA_parm	a matrix or dataframe of item parameters for standalone items, where columns are a (slope), b1, b2, ..., b_k (difficulty or step difficulty), g (guessing), ItemID, and AssertionID. Columns must follow the above order. See example_SA_parm for an example. Use ?example_SA_parm for detailed column descriptions
Cluster_parm	a matrix or dataframe of item parameters for cluster items, where columns are a (slope), b (difficulty), cluster variance, cluster position, ItemID, and AssertionID. Columns must follow the above order. See example_Cluster_parm for an example. Use ?example_Cluster_parm for detailed column descriptions
drift	a numeric scalar for the amount of item difficulty parameter drift tested
Alpha	a numeric vector for one or more nominal type I error rates for flagging aberrant responses.
Dv	scaling factor for IRT model (usually 1 or 1.7)
n.nodes	number of nodes used when integrating out the nuisance dimension
missing_as_incorrect	by default, missings (NAs) are treated as missing; if TRUE, missings are treated as incorrect

Value

a list of

• RR = N (number of examinees) by n (number of assertions) dataframe of assertion residuals
  

• LRR = N by n dataframe of the lower bound assertion residuals
  

• URR = N by n dataframe of the upper bound assertion residuals
  If run for more than one examinees, returns an additional drift_output table in the list
  

Note

If the test does not have SA items or Cluster items, use default (NULL) for the corresponding data and parameter arguments.

Author(s)

Zhongtian Lin [e-mail]

Examples

data(example_SA_parm)
data(example_Cluster_parm)
sigma <- diag(c(1, sqrt(unique(example_Cluster_parm$cluster_var))))
mu <- rep(0, nrow(sigma))
thetas <- MASS::mvrnorm(7,mu,sigma)
thetas[,1] <- seq(-3,3,1) #overall dimension theta values
itmDat <- sim_data(thetas = thetas, SA_parm = example_SA_parm, Cluster_parm = example_Cluster_parm)
SA_dat <- itmDat[,1:20]
Cluster_dat <- itmDat[,-1:-20]
out_scoring <- list()
for (i in 1:nrow(itmDat)) {
  out_scoring[[i]] <- scoring(SA_dat[i,], Cluster_dat[i,], example_SA_parm, example_Cluster_parm, n.nodes = 11, SE=TRUE)
}
est_theta <- sapply(out_scoring, function(x) x$par)
rst <- item_drift(est_theta, SA_dat=SA_dat, Cluster_dat=Cluster_dat, SA_parm=example_SA_parm, Cluster_parm=example_Cluster_parm,
                  drift = 0.3, Alpha=0.05, Dv=1, n.nodes = 21, missing_as_incorrect = FALSE)

woshikaqia/MIRTutils documentation built on Aug. 21, 2024, 4:30 p.m.