difNLR: DIF detection using non-linear regression method.

In adelahladka/difNLR: DIF and DDF Detection by Non-Linear Regression Models

DIF detection using non-linear regression method.

Description

Performs DIF detection procedure in dichotomous data based on non-linear regression model (generalized logistic regression) and either likelihood-ratio test, F-test, or Wald's test of a submodel.

Usage

difNLR(Data, group, focal.name, model, constraints, type = "all",
       method = "nls", match = "zscore", anchor = NULL, purify = FALSE,
       nrIter = 10, test = "LR", alpha = 0.05, p.adjust.method = "none", start,
       initboot = TRUE, nrBo = 20, sandwich = FALSE)

Arguments

Data	data.frame or matrix: dataset in which rows represent scored examinee answers ("1" correct, "0" incorrect) and columns correspond to the items. In addition, Data can hold the vector of group membership.
group	numeric or character: a binary vector of the same length as nrow(Data) or a column identifier in the Data.
focal.name	numeric or character: indicates the level of the group corresponding to the focal group.
model	character: generalized logistic regression model to be fitted. See Details.
constraints	character: which parameters should be the same for both groups. Possible values are any combinations of parameters "a", "b", "c", and "d". See Details.
type	character: type of DIF to be tested. Possible values are "all" for detecting differences in any parameters (default), "udif" for uniform DIF only (i.e., difference in difficulty parameter "b"), "nudif" for non-uniform DIF only (i.e., difference in discrimination parameter "a"), "both" for uniform and non-uniform DIF (i.e., difference in parameters "a" and "b"), or a combination of parameters "a", "b", "c", and "d". Can be specified as a single value (for all items) or as an item-specific vector.
method	character: an estimation method to be applied. The options are "nls" for non-linear least squares (default), "mle" for the maximum likelihood method using the "L-BFGS-B" algorithm with constraints, "em" for the maximum likelihood estimation with the EM algorithm, "plf" for the maximum likelihood estimation with the algorithm based on parametric link function, and "irls" for the maximum likelihood estimation with the iteratively reweighted least squares algorithm (available for the "2PL" model only). See Details.
match	character or numeric: matching criterion to be used as an estimate of the trait. It can be either "zscore" (default, standardized total score), "score" (total test score), or a numeric vector of the same length as a number of observations in the Data.
anchor	character or numeric: specification of DIF free items. Either NULL (default), or a vector of item identifiers (integers specifying the column number) specifying which items are currently considered as anchor (DIF free) items. Argument is ignored if the match is not "zscore" or "score".
purify	logical: should the item purification be applied? (the default is FALSE).
nrIter	numeric: the maximal number of iterations in the item purification (the default is 10).
test	character: a statistical test to be performed for DIF detection. Can be either "LR" for the likelihood ratio test of a submodel (default), "W" for the Wald's test, or "F" for the F-test of a submodel.
alpha	numeric: a significance level (the default is 0.05).
p.adjust.method	character: a method for a multiple comparison correction. Possible values are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", and "none" (default). For more details see p.adjust.
start	numeric: initial values for the estimation of item parameters. If not specified, starting values are calculated with the startNLR function. Otherwise, a list with as many elements as a number of items. Each element is a named numeric vector representing initial values for estimation of item parameters. Specifically, parameters "a", "b", "c", and "d" are initial values for discrimination, difficulty, guessing, and inattention for the reference group. Parameters "aDif", "bDif", "cDif", and "dDif" are then differences in these parameters between the reference and focal groups. For the method = "irls", default initial values from the glm function are used.
initboot	logical: in the case of convergence issues, should starting values be re-calculated based on bootstrapped samples? (the default is TRUE; newly calculated initial values are applied only to items/models with convergence issues).
nrBo	numeric: the maximal number of iterations for the calculation of starting values using bootstrapped samples (the default is 20).
sandwich	logical: should the sandwich estimator be applied for computation of the covariance matrix of item parameters when using method = "nls"? (the default is FALSE).

Details

DIF detection procedure based on non-linear regression is the extension of the logistic regression procedure (Swaminathan & Rogers, 1990) accounting for possible guessing and/or inattention when responding (Drabinova & Martinkova, 2017; Hladka & Martinkova, 2020).

The unconstrained form of the 4PL generalized logistic regression model for probability of correct answer (i.e., Y_{pi} = 1) using IRT parameterization is

P(Y_{pi} = 1|X_p, G_p) = (c_{iR} \cdot G_p + c_{iF} \cdot (1 - G_p)) + (d_{iR} \cdot G_p + d_{iF} \cdot (1 - G_p) - c_{iR} \cdot G_p - c_{iF} \cdot (1 - G_p)) / (1 + \exp(-(a_i + a_{i\text{DIF}} \cdot G_p) \cdot (X_p - b_p - b_{i\text{DIF}} \cdot G_p))),

where X_p is the matching criterion (e.g., standardized total score) and G_p is a group membership variable for respondent p. Parameters a_i, b_i, c_{iR}, and d_{iR} are discrimination, difficulty, guessing, and inattention for the reference group for item i. Terms a_{i\text{DIF}} and b_{i\text{DIF}} then represent differences between the focal and reference groups in discrimination and difficulty for item i. Terms c_{iF}, and d_{iF} are guessing and inattention parameters for the focal group for item i. In the case that there is no assumed difference between the reference and focal group in the guessing or inattention parameters, the terms c_i and d_i are used.

Alternatively, intercept-slope parameterization may be applied:

P(Y_{pi} = 1|X_p, G_p) = (c_{iR} \cdot G_p + c_{iF} \cdot (1 - G_p)) + (d_{iR} \cdot G_p + d_{iF} \cdot (1 - G_p) - c_{iR} \cdot G_p - c_{iF} \cdot (1 - G_p)) / (1 + \exp(-(\beta_{i0} + \beta_{i1} \cdot X_p + \beta_{i2} \cdot G_p + \beta_{i3} \cdot X_p \cdot G_p))),

where parameters \beta_{i0}, \beta_{i1}, \beta_{i2}, \beta_{i3} are intercept, effect of the matching criterion, effect of the group membership, and their mutual interaction, respectively.

The model and constraints arguments can further constrain the 4PL model. The arguments model and constraints can also be combined. Both arguments can be specified as a single value (for all items) or as an item-specific vector (where each element corresponds to one item).

The model argument offers several predefined models. The options are as follows: Rasch for 1PL model with discrimination parameter fixed on value 1 for both groups, 1PL for 1PL model with discrimination parameter set the same for both groups, 2PL for logistic regression model, 3PLcg for 3PL model with fixed guessing for both groups, 3PLdg for 3PL model with fixed inattention for both groups, 3PLc (alternatively also 3PL) for 3PL regression model with guessing parameter, 3PLd for 3PL model with inattention parameter, 4PLcgdg for 4PL model with fixed guessing and inattention parameter for both groups, 4PLcgd (alternatively also 4PLd) for 4PL model with fixed guessing for both groups, 4PLcdg (alternatively also 4PLc) for 4PL model with fixed inattention for both groups, or 4PL for 4PL model.

The underlying generalized logistic regression model can be further specified in more detail with the constraints argument which specifies what parameters should be fixed for both groups. For example, a choice "ad" means that discrimination (parameter "a") and inattention (parameter "d") are fixed (and estimated for) both groups and other parameters ("b" and "c") are not. The NA value for constraints means no constraints.

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

The function uses intercept-slope parameterization for the estimation via the estimNLR function. Item parameters are then re-calculated into the IRT parameterization using the delta method.

The function offers either the non-linear least squares estimation via the nls function (Drabinova & Martinkova, 2017; Hladka & Martinkova, 2020), the maximum likelihood method with the "L-BFGS-B" algorithm with constraints via the optim function (Hladka & Martinkova, 2020), the maximum likelihood method with the EM algorithm (Hladka, Martinkova, & Brabec, 2024), the maximum likelihood method with the algorithm based on parametric link function (PLF, the default option; Hladka, Martinkova, & Brabec, 2024), or the maximum likelihood method with the iteratively reweighted least squares algorithm via the glm function.

Value

The difNLR() function returns an object of class "difNLR". The output, including values of the test statistics, p-values, and items detected as function differently, is displayed by the print() method.

Object of class "difNLR" is a list with the following components:

Sval

the values of the test statistics.

nlrPAR

the item parameter estimates of the final model.

nlrSE

the standard errors of the item parameter estimates of the final model.

parM0

the item parameter estimates of the null (smaller) model.

seM0

the standard errors of item parameter estimates of the null (smaller) model.

covM0

the covariance matrices of the item parameter estimates of the null (smaller) model.

llM0

the log-likelihood values of the null (smaller) model.

parM1

the item parameter estimates of the alternative (larger) model.

seM1

the standard errors of the item parameter estimates of the alternative (larger) model.

covM1

the covariance matrices of the item parameter estimates of alternative (larger) model.

llM1

the log-likelihood values of the alternative (larger) model.

DIFitems

either the column identifiers of the items which were detected as DIF, or "No DIF item detected" in the case no item was detected as function differently.

model

fitted model.

constraints

constraints for the model.

type

character: type of DIF that was tested. If a combination of the item parameters was specified, the value is "other".

types

character: the parameters (specified by user, type has value "other") which were tested for difference.

p.adjust.method

character: a method for the multiple comparison correction which was applied.

pval

the p-values by the test.

adjusted.pval

adjusted p-values by the p.adjust.method.

df

the degrees of freedom of the test.

test

used test.

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-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.

method

used estimation method.

conv.fail

numeric: number of convergence issues.

conv.fail.which

the identifiers of the items which did not converge.

alpha

numeric: significance level.

Data

the data matrix.

group

the vector of group membership.

group.names

names of groups.

match

matching criterion.

Several methods are available for an object of the "difNLR" class (e.g., methods(class = "difNLR")).

Author(s)

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
[e-mail]

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
[e-mail]

Karel Zvara
Faculty of Mathematics and Physics, Charles University

References

Drabinova, A. & Martinkova, P. (2017). Detection of differential item functioning with nonlinear regression: A non-IRT approach accounting for guessing. Journal of Educational Measurement, 54(4), 498–517, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/jedm.12158")}.

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2020-014")}.

Hladka, A., Martinkova, P., & Brabec, M. (2024). New iterative algorithms for estimation of item functioning. Journal of Educational and Behavioral Statistics. Accepted.

Swaminathan, H. & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1745-3984.1990.tb00754.x")}

See Also

plot.difNLR for a graphical representation of item characteristic curves and DIF statistics.
coef.difNLR for an extraction of item parameters with their standard errors in various parameterizations.
predict.difNLR for prediction.
fitted.difNLR and residuals.difNLR for an extraction of fitted values and residuals.
logLik.difNLR, AIC.difNLR, BIC.difNLR for an extraction of log-likelihood values and information criteria.

p.adjust for multiple comparison corrections.
nls for a nonlinear least squares estimation.
startNLR for a calculation of initial values of fitting algorithms in difNLR().

Examples

# loading data
data(GMAT)
Data <- GMAT[, 1:20] # items
group <- GMAT[, "group"] # group membership variable

# testing both DIF effects using likelihood-ratio test and
# 3PL model with fixed guessing for groups
(x <- difNLR(Data, group, focal.name = 1, model = "3PLcg"))
## Not run: 
# graphical devices
plot(x, item = x$DIFitems)
plot(x, item = "Item1")
plot(x, item = 1, group.names = c("Group 1", "Group 2"))
plot(x, plot.type = "stat")

# coefficients
coef(x)
coef(x, SE = TRUE)
coef(x, SE = TRUE, simplify = TRUE)
coef(x, item = 1, CI = 0)

# fitted values
fitted(x)
fitted(x, item = 1)

# residuals
residuals(x)
residuals(x, item = 1)

# predicted values
predict(x)
predict(x, item = 1)

# predicted values for new subjects
predict(x, item = 1, match = 0, group = c(0, 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)

# testing both DIF effects using Wald test and
# 3PL model with fixed guessing for groups
difNLR(Data, group, focal.name = 1, model = "3PLcg", test = "W")

# testing both DIF effects using F test and
# 3PL model with fixed guessing for groups
difNLR(Data, group, focal.name = 1, model = "3PLcg", test = "F")

# testing both DIF effects using
# 3PL model with fixed guessing for groups and sandwich estimator
# of the covariance matrices
difNLR(Data, group, focal.name = 1, model = "3PLcg", sandwich = TRUE)

# testing both DIF effects using LR test,
# 3PL model with fixed guessing for groups
# and Benjamini-Hochberg correction
difNLR(Data, group, focal.name = 1, model = "3PLcg", p.adjust.method = "BH")

# testing both DIF effects using LR test,
# 3PL model with fixed guessing for groups
# and item purification
difNLR(Data, group, focal.name = 1, model = "3PLcg", purify = TRUE)

# testing both DIF effects using 3PL model with fixed guessing for groups
# and total score as matching criterion
difNLR(Data, group, focal.name = 1, model = "3PLcg", match = "score")

# testing uniform DIF effects using 4PL model with the same
# guessing and inattention
difNLR(Data, group, focal.name = 1, model = "4PLcgdg", type = "udif")

# testing non-uniform DIF effects using 2PL model
difNLR(Data, group, focal.name = 1, model = "2PL", type = "nudif")

# testing difference in parameter b using 4PL model with fixed
# a and c parameters
difNLR(Data, group, focal.name = 1, model = "4PL", constraints = "ac", type = "b")

# testing both DIF effects using LR test,
# 3PL model with fixed guessing for groups
# using maximum likelihood estimation with
# the L-BFGS-B algorithm, the EM algorithm, and the PLF algorithm
difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "mle")
difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "em")
difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "plf")

# testing both DIF effects using LR test and 2PL model
# using maximum likelihood estimation with iteratively reweighted least squares algorithm
difNLR(Data, group, focal.name = 1, model = "2PL", method = "irls")

## End(Not run)

adelahladka/difNLR documentation built on Dec. 23, 2024, 2:20 a.m.