pairwise (version 0.1.4)

grmSE.dicho: Graphical Model Check for dichotomous 1PL Model

Description

This function makes the basic calculations for the graphical model check. It is more or less a wraper function, internaly calling the function ipSE.dicho. Several splitting options are available (see arguments).

Usage

grmSE.dicho(daten, teil = "no", splitseed = "no",
    sortdif = FALSE, ...)

Arguments

daten

The response data as a data.frame or a matrix, potentially with missing values, comprising dichotomous responses of respondents (rows) on some items (colums) coded in the 0 1 manner.

teil

Specifies the splitting criterion. Basicly there are three different options available - each with several modes - which are controlled by passing the corresponding character expression to the argument.

1) Using the rawscore for splitting into su

splitseed

numeric, used for set.seed(splitseed) for random splitting - see argument teil

sortdif

logical, In contrast to to the default setting in the function ipSE.dicho the argument sortdif here is set to FALSE, so the items will kept in original order -- see descri

...

additional arguments nsample, size, seed, pot for caling ipSE.dicho are passed through - see description for

Value

• A (list) object of class grmSE containing the item difficulty parameter Sigma and their standard errors for two or more subsamples.

A Note on Standard Errors

Estimation of standard errors is done by repeated calculation of item parameters for subsamples of the given data. This procedure is mainly controlled by the arguments nsample and size (see arguments). With regard to calculation time, the argument nsample is the 'time killer'. On the other hand, things (estimation of standard errors) will not necessarily get better when choosing large values for nsample. For example choosing nsample=400 will only result in minimal change for standard error estimation in comparison to (nsample=30) which is the default setting (see examples).

Details

The data is splitted in two or more subsamples and then item parameter (Sigma) and their standard error (SE) for dichotomous items according the 1PL Model are calculatetd for each subsample. Additional arguments (see description of function ipSE.dicho) for parameter calculation are passed through.

WARNING: When using data based on booklet designs with systematically missing values (by design) you have to ensure that in each of the booklet the maximum raw value to reach is equal while using the raw value as splitting criterion.

References

description of function ipSE.dicho {pairwise}.

Examples

data(cog) # loading example data set

# calculating itemparameters and SE for two random allocated subsamples
grmse<-grmSE.dicho(daten=cog[,4:34], teil = "random")

# some examples for plotting options ########
# plotting item difficulties for two subsamples against each other
# with elipses for a CI = 95\% .
plot(grmse)

# using triangles as plotting pattern
plot(grmse,pch=2)

#plotting without CI ellipses
plot(grmse,ci=0,pch=2)

# plotting with item names
plot(grmse,ci=0, itemNames=TRUE,srt=90)

# Changing the size of the item names (too big to read)
plot(grmse,itemNames=TRUE, cex.names = 1.3)

# plotting with item names and rotate them about 90 degrees
plot(grmse,itemNames=TRUE,srt=90, cex.names = .5)

# Changing the color of the CI ellipses
plot(grmse,itemNames=TRUE, srt=90, cex.names = .5, col.error="green")

###### example from details section 'Some Notes on Standard Errors' ########
# grmse_400<-grmSE.dicho(daten=cog[,4:34], teil = "random",nsample=400)
######
# plot(grmse)
# plot(grmse_400)