itemstats: Generic item summary statistics

In mirt: Multidimensional Item Response Theory

Generic item summary statistics

Description

Function to compute generic item summary statistics that do not require prior fitting of IRT models. Contains information about coefficient alpha (and alpha if an item is deleted), mean/SD and frequency of total scores, reduced item-total correlations, average/sd of the correlation between items, response frequencies, and conditional mean/sd information given the unweighted sum scores.

Usage

itemstats(
  data,
  group = NULL,
  use_ts = TRUE,
  proportions = TRUE,
  ts.tables = FALSE
)

Arguments

data	An object of class data.frame or matrix with the response patterns
group	optional grouping variable to condition on when computing summary information
use_ts	logical; include information that is conditional on a meaningful total score?
proportions	logical; include response proportion information for each item?
ts.tables	logical; include mean/sd summary information pertaining to the unweighted total score?

Value

Returns a list containing the summary statistics

Author(s)

Phil Chalmers [e-mail]

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v048.i06")}

See Also

empirical_plot

Examples

# dichotomous data example
LSAT7full <- expand.table(LSAT7)
head(LSAT7full)
itemstats(LSAT7full)

# behaviour with missing data
LSAT7full[1:5,1] <- NA
itemstats(LSAT7full)

# data with no meaningful total score
head(SAT12)
itemstats(SAT12, use_ts=FALSE)

# extra total scores tables
dat <- key2binary(SAT12,
                   key = c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,
                           5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5))
itemstats(dat, ts.tables=TRUE)

# grouping information
group <- gl(2, 300, labels=c('G1', 'G2'))
itemstats(dat, group=group)


#####
# polytomous data example
itemstats(Science)

# polytomous data with missing
newScience <- Science
newScience[1:5,1] <- NA
itemstats(newScience)

# unequal categories
newScience[,1] <- ifelse(Science[,1] == 1, NA, Science[,1])
itemstats(newScience)

merged <- data.frame(LSAT7full[1:392,], Science)
itemstats(merged)

mirt documentation built on Oct. 17, 2023, 5:06 p.m.