ddfMLR: DDF detection for nominal data.

Description

Performs DDF detection procedure for nominal data based on multinomial log-linear regression model and likelihood ratio test of a submodel.

Usage

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

Value

The ddfMLR() function returns an object of class

"ddfMLR". The output including values of the test statistics, p-values, and items marked as DDF is displayed by the

print() method.

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.
llM0: log-likelihood of null model.
llM1: log-likelihood of alternative model.
AIC0: AIC of null model.
AIC1: AIC of alternative model.
BIC0: BIC of null model.
BIC1: BIC of alternative model.
DDFitems: either the column identifiers of the items which were detected as DDF, or "No DDF item detected" in case no item was detected as DDF.
type: character: type of DDF that was tested.
purification: purify value.
nrPur: number of iterations in item purification process. Returned only if purify is TRUE.
ddfPur: 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 DDF in i-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.
alpha: numeric: significance level.
Data: the data matrix.
group: the vector of group membership.
group.names: levels of grouping variable.
key: key of correct answers.
match: matching criterion.

For an object of class "ddfMLR" several methods are available (e.g. methods(class = "ddfMLR")).

Arguments

Data: data.frame or matrix: dataset which rows represent unscored examinee answers (nominal) and columns correspond to the items. In addition, Data can hold the vector of group membership.
group: numeric or character: a dichotomous vector of the same length as nrow(Data) or a column identifier of Data.
focal.name: numeric or character: indicates the level of group which corresponds to focal group.
key: character: the answer key. Each element corresponds to the correct answer of one item.
type: character: type of DDF to be tested. Either "both" for uniform and non-uniform DDF (i.e., difference in parameters "a" and "b") (default), or "udif" for uniform DDF only (i.e., difference in difficulty parameter "b"), or "nudif" for non-uniform DDF only (i.e., difference in discrimination parameter "a"). Can be specified as a single value (for all items) or as an item-specific vector.
match: numeric or character: matching criterion to be used as an estimate of trait. Can be either "zscore" (default, standardized total score), "score" (total test score), or vector of the same length as number of observations in Data.
anchor: numeric or character: specification of DDF free items. Either NULL (default), or a vector of item names (column names of Data), or item identifiers (integers specifying the column number) determining which items are currently considered as anchor (DDF free) items. Argument is ignored if match is not "zscore" or "score".
purify: logical: should the item purification be applied? (default is FALSE).
nrIter: numeric: the maximal number of iterations in the item purification (default is 10).
p.adjust.method: character: method for multiple comparison correction. Possible values are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", and "none" (default). For more details see p.adjust.
alpha: numeric: significance level (default is 0.05).
parametrization: deprecated. Use coef.ddfMLR for different parameterizations.

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
hladka@cs.cas.cz

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz

Details

Performs DDF detection procedure for nominal data based on multinomial log-linear regression model and likelihood ratio test of submodel. Probability of selection the $k$-th category (distractor) is $$P(y = k) = exp((a_k + a_kDif * g) * (x - b_k - b_kDif * g))) / (1 + \sum exp((a_l + a_lDif * g) * (x - b_l - b_lDif * g))), $$ where $x$ is by default standardized total score (also called Z-score) and $g$ is a group membership. Parameters $a_k$ and $b_k$ are discrimination and difficulty for the $k$-th category. Terms $a_kDif$ and $b_kDif$ then represent differences between two groups (reference and focal) in relevant parameters. Probability of correct answer (specified in argument key) is $$P(y = k) = 1/(1 + \sum exp((a_l + a_lDif * g)*(x - b_l - b_lDif * g))). $$ Parameters are estimated via neural networks. For more details see multinom.

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

References

Agresti, A. (2010). Analysis of ordinal categorical data. Second edition. John Wiley & Sons.

Hladka, A. (2021). Statistical models for detection of differential item functioning. Dissertation thesis. Faculty of Mathematics and Physics, Charles University.

Hladka, A. & Martinkova, P. (2020). difNLR: Generalized logistic regression models for DIF and DDF detection. The R Journal, 12(1), 300--323, tools:::Rd_expr_doi("10.32614/RJ-2020-014").

Examples

Run this code

if (FALSE) {
# loading data
data(GMATtest, GMATkey)
Data <- GMATtest[, 1:20] # items
group <- GMATtest[, "group"] # group membership variable
key <- GMATkey # correct answers

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

# graphical devices
plot(x, item = "Item1", group.names = c("Group 1", "Group 2"))
plot(x, item = x$DDFitems)
plot(x, item = 1)

# AIC, BIC, log-likelihood
AIC(x)
BIC(x)
logLik(x)

# AIC, BIC, log-likelihood for the first item
AIC(x, item = 1)
BIC(x, item = 1)
logLik(x, item = 1)

# estimated parameters
coef(x)
coef(x, SE = TRUE)
coef(x, SE = TRUE, simplify = TRUE)

# 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 = TRUE)

# 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")
}

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