ddfMLR: Performs DDF detection using Multinomial Log-linear...

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Performs DDF detection procedure based on Multinomial Log-linear Regression model and likelihood ratio test of submodel.

Usage

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ddfMLR(Data, group, focal.name, key, type = "both", match = "zscore", anchor = NULL,
purify = FALSE, nrIter = 10, alpha = 0.05, p.adjust.method = "none")

## S3 method for class 'ddfMLR'
print(x, ...)

## S3 method for class 'ddfMLR'
plot(x, item = "all", title, ...)

## S3 method for class 'ddfMLR'
coef(object, ...)

## S3 method for class 'ddfMLR'
logLik(object, item = "all", ...)

## S3 method for class 'ddfMLR'
AIC(object, item = "all", ...)

## S3 method for class 'ddfMLR'
BIC(object, item = "all", ...)

Arguments

Data

character: either the unscored data matrix only, or the unscored data matrix plus the vector of group. See Details.

group

numeric or character: either the binary vector of group membership or the column indicator of group membership. See Details.

focal.name

numeric or character: indicates the level of group which corresponds to focal group

key

character: the answer key. See Details.

type

character: type of DDF to be tested (either "both" (default), "udif", or "nudif"). See Details.

match

specifies matching criterion. Can be either "zscore" (default, standardized total score), "score" (total test score), or vector of the same length as number of observations in "Data". See Details.

anchor

Either NULL (default) or a vector of item names or item identifiers specifying which items are currently considered as anchor (DIF free) items. Argument is ignored if match is not "zscore" or "score".

purify

logical: should the item purification be applied? (default is FALSE). See Details.

nrIter

numeric: the maximal number of iterations in the item purification (default is 10).

alpha

numeric: significance level (default is 0.05).

p.adjust.method

character: method for multiple comparison correction. See Details.

x

an object of 'ddfMLR' class

...

other generic parameters for print or plot functions.

item

either character ("all"), or numeric vector, or single number corresponding to column indicators. See Details.

title

string: title of plot.

object

an object of 'ddfMLR' class

Details

DDF detection procedure based on Multinomial Log-linear model.

The Data is a matrix whose rows represents examinee unscored answers and columns correspond to the items. The group must be either a vector of the same length as nrow(data) or column indicator of Data. The key must be a vector of correct answers corresponding to columns of Data.

The type corresponds to type of DDF to be tested. Possible values are "both" to detect any DDF (uniform and/or non-uniform), "udif" to detect only uniform DDF or "nudif" to detect only non-uniform DDF.

Argument match represents the matching criterion. It can be either the standardized test score (default, "zscore"), total test score ("score"), or any other continuous or discrete variable of the same length as number of observations in Data. Matching criterion is used in MLR() function as a covariate in multinomial model.

A set of anchor items (DIF free) can be specified through the anchor argument. It need to be a vector of either item names (as specified in column names of Data) or item identifiers (integers specifying the column number). In case anchor items are provided, only these items are used to compute matching criterion match. If the match argument is not either "zscore" or "score", anchor argument is ignored. When anchor items are provided, purification is not applied.

The p.adjust.method is a character for p.adjust function from the stats package. Possible values are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

The output of the ddfMLR is displayed by the print.ddfMLR function.

The characteristic curve for item specified in item option can be plotted. For default option "all" of item, characteristic curves of all converged items are plotted. The drawn curves represent best model.

Missing values are allowed but discarded for item estimation. They must be coded as NA for both, data and group parameters.

Value

A list of class 'ddfMLR' with the following arguments:

Sval

the values of likelihood ratio test statistics.

mlrPAR

the estimates of final model.

mlrSE

standard errors of the estimates of final model.

parM0

the estimates of null model.

parM1

the estimates of alternative model.

alpha

numeric: significance level.

DDFitems

either the column indicators of the items which were detected as DDF, or "No DDF item detected".

type

character: type of DIF that was tested.

purification

purify value.

nrPur

number of iterations in item purification process. Returned only if purify is TRUE.

difPur

a binary matrix with one row per iteration of item purification and one column per item. "1" in i-th row and j-th column means that j-th item was identified as DIF in i-1-th iteration. Returned only if purify is TRUE.

conv.puri

logical indicating whether item purification process converged before the maximal number nrIter of iterations. Returned only if purify is TRUE.

p.adjust.method

character: method for multiple comparison correction which was applied.

pval

the p-values by likelihood ratio test.

adj.pval

the adjusted p-values by likelihood ratio test using p.adjust.method.

df

the degress of freedom of likelihood ratio test.

group

the vector of group membership.

Data

the data matrix.

match

matching criterion.

llM0

log-likelihood of null model.

llM1

log-likelihood of alternative model.

AICM0

AIC of null model.

AICM1

AIC of alternative model.

BICM0

BIC of null model.

BICM1

BIC of alternative model.

Author(s)

Adela Drabinova
Institute of Computer Science, The Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
[email protected]

Patricia Martinkova
Institute of Computer Science, The Czech Academy of Sciences

See Also

p.adjust

Examples

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## Not run: 
# loading data based on GMAT
data(GMATtest, GMATkey)

Data  <- GMATtest[, 1:20]
group <- GMATtest[, "group"]
key <- GMATkey

# Testing both DDF effects
(x <- ddfMLR(Data, group, focal.name = 1, key))

# Testing both DDF effects with Benjamini-Hochberg adjustment method
ddfMLR(Data, group, focal.name = 1, key, p.adjust.method = "BH")

# Testing both DDF effects with item purification
ddfMLR(Data, group, focal.name = 1, key, purify = T)

# Testing uniform DDF effects
ddfMLR(Data, group, focal.name = 1, key, type = "udif")
# Testing non-uniform DDF effects
ddfMLR(Data, group, focal.name = 1, key, type = "nudif")

# Testing both DDF effects with total score as matching criterion
ddfMLR(Data, group, focal.name = 1, key, match = "score")

# Graphical devices
plot(x, item = 1)
plot(x, item = x$DDFitems)
plot(x, item = "all")

# AIC, BIC, logLik
AIC(x)
BIC(x)
logLik(x)

## End(Not run)

drabinova/difNLR documentation built on May 15, 2019, 1:51 p.m.