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Calculate Item Discrimination for Each Category of Categorical Variables

cat.pbc.Rd

catPbc calculates discrimination statistics for the categories of categorical variables, i.e. the correlations of each category with the total score on the test. This information can be useful in determining which categories of an item are influencing the overall fit and discrimination in item response scaling applications and/or to find mistakes in recoding.

Usage

catPbc(datRaw, datRec, idRaw, idRec, context.vars = NULL, values,
subunits, file.name = NULL, verbose = FALSE)

Arguments

datRaw

A merged unrecoded dataset

datRec

The same dataset as in datRaw, in recoded form

idRaw

Name or column number of the identifier (ID) variable in unrecoded dataset

idRec

Name or column number of the identifier (ID) variable in recoded dataset

context.vars

Names or column numbers of one or more context variables (e.g., sex, school). catPbc will ignore these variables.

values

Data frame with information about values, see inputList for details.

subunits

Data frame with information about subunits, see inputList for details.

file.name

Optional: Full path of csv file for results.

verbose

logical. If TRUE additional diagnostics are printed.

Details

The column names of datRaw and datRec must be consistent with the names provided by the columns subunit and subunitRecoded in data.frame subunits. Otherwise, catPbc will fail.

Value

A data frame with the discrimination values for each category of categorical variables. The data frame contains the following columns:

item

Name of unrecoded item

cat

Name of category

n

Number of responses for this item

freq

Absolute frequency of the category

freq.rel

Relative frequency of the category

catPbc

Discrimination value for the category (correlation with total score)

recodevalue

Recode value for the category

subunitType

Type of subunit, see inputList for details

Author

Nicole Haag

Examples

data(inputDat)
data(inputList)

datRaw <- mergeData(newID = "ID", datList = inputDat, addMbd = TRUE)
#> Start merging of dataset 1.
#> Start merging of dataset 2.
#> Start merging of dataset 3.
#> Start adding mbd according to data pattern.
datRec <- recodeData(datRaw, values = inputList$values,
    subunits=inputList$subunits)
pbcs   <- catPbc(datRaw, datRec, idRaw = "ID", idRec = "ID",
    context.vars = "hisei", values = inputList$values,
    subunits = inputList$subunits)