Used quantile of the normal distribution
for assessing statistical significance
Details
Items are classified into A (negligible DIF), B (moderate DIF) and
C (large DIF) levels according to the
ETS classification system (Longford, Holland & Thayer, 1993, p. 175).
See also Monahan et al. (2007) for further DIF effect size
classifications.
Value
A data frame with following variables:
itemnr
Numeric index of the item
sortDIFindex
Rank of item with respect to the uniform DIF
(from negative to positive values)
item
Item name
N
Sample size per item
R
Value of group variable for reference group
F
Value of group variable for focal group
nR
Sample size per item in reference group
nF
Sample size per item in focal group
p
Item p value
pR
Item p value in reference group
pF
Item p value in focal group
pdiff
Item p value differences
pdiff.adj
Adjusted p value difference
uniformDIF
Uniform DIF estimate
se.uniformDIF
Standard error of uniform DIF
t.uniformDIF
The t value for uniform DIF
sig.uniformDIF
Significance label for uniform DIF
DIF.ETS
DIF classification according to the ETS classification
system (see Details)
uniform.EBDIF
Empirical Bayes estimate of uniform DIF (Longford,
Holland & Thayer, 1993) which takes degree of DIF standard error
into account
DIF.SD
Value of the DIF standard deviation
nonuniformDIF
Nonuniform DIF estimate
se.nonuniformDIF
Standard error of nonuniform DIF
t.nonuniformDIF
The t value for nonuniform DIF
sig.nonuniformDIF
Significance label for nonuniform DIF
Author(s)
Alexander Robitzsch
References
Longford, N. T., Holland, P. W., & Thayer, D. T. (1993).
Stability of the MH D-DIF statistics across populations.
In P. W. Holland & H. Wainer (Eds.). Differential
Item Functioning (pp. 171-196). Hillsdale, NJ: Erlbaum.
Monahan, P. O., McHorney, C. A., Stump, T. E., & Perkins, A. J. (2007).
Odds ratio, delta, ETS classification, and standardization measures of
DIF magnitude for binary logistic regression.
Journal of Educational and Behavioral Statistics, 32, 92-109.
Zumbo, B. D. (1999). A handbook on the theory and methods of differential
item functioning (DIF): Logistic regression modeling as a unitary framework
for binary and Likert-type (ordinal) item scores.
Ottawa ON: Directorate of Human Resources Research and Evaluation,
Department of National Defense.
See Also
For assessing DIF variance see dif.variance and
dif.strata.variance
See also rasch.evm.pcm for assessing differential item
functioning in the partial credit model.
See the difR package for a large collection of DIF detection
methods.