sib: SIBTEST

In jweese1441/DIFSIB: SIBTEST Suite of Procedures

SIBTEST

Description

This function assesses uniform DIF for dichotmous data using the SIBTEST procedure (Shealy & Stout, 1993) with a sophisticated regression correction (Jiang & Stout, 1998). The function outputs the uniform DIF magnitudes as well as the significance testing.

Usage

sib(
  data_ref,
  data_foc,
  minc = 2,
  cusr = 0.2,
  idw = 0,
  suspect_items,
  matching_items,
  listwise = 1
)

Arguments

data_ref	The dataset that contains the reference group participants. Dichotomous data must only contain 0's and 1's.
data_foc	The dataset that contains the focal group participants. Dichotomous data must only contain 0's and 1's.
minc	The minimum number of individuals in a give total test score category. Initialized to 2 per Shealy and Stout (1993) recommendation
cusr	The guess correction factor. This is initialized to 0.2, but can be changed based on the items used. The cusr value is the average of guess correction across the items in the dataset.
idw	The weighting factor. This is initialized to use the reference and focal group weighting recommended by Shealy and Stout (1993)
suspect_items	the item(s) to be assess for DIF. Listing one item number will assess for DIF on that item, multiple items (e.g. c(2,3,8)) will assess DIF for the bundle of items.
matching_items	the list of items to match on.
listwise	initialized to 1, this inidicates that the procedure will not delete any rows with missing data, changing this to 2 will listwise delete.

References

DIF-Pack (2021) Measured Progress. https://psychometrics.onlinehelp.measuredprogress.org/tools/dif/

Jiang, H., & Stout, W. (1998). Improved Type I Error Control and Reduced Estimation Bias for DIF Detection Using SIBTEST. Journal of Educational and Behavioral Statistics, 23(4), 291–322. https://doi.org/10.3102/10769986023004291

Shealy, R. & Stout, W. (1993). A model-based standardization approach that separates true bias/DIF from group ability differences and detect test bias/DTF as well as item bias/DIF. Psychometrika, 58, 159-194.

Weese, J. D., Turner, R. C., Ames, A., Crawford, B., & Liang, X. (2022). Reevaluating the SIBTEST Classification Heuristics for Dichotomous Differential Item Functioning. Educational and Psychological Measurement, 82(2), 307–329. https://doi.org/10.1177/00131644211017267

Weese, J. D. (2020). Development of an Effect Size to Classify the Magnitude of DIF in Dichotomous and Polytomous Items. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3896

Examples

## Not run: 

#perform SIBTEST with one suspect item and the remaining items as a matching subtest

sib(data_ref = data_ref, data_foc = data_foc, minc = 2, 
cusr = .2, suspect_items = c(20), matching_items = c(1:19))

#perform SIBTEST with one suspect item and no guessing (cusr = 0) 

sib(data_ref = data_ref, data_foc = data_foc, minc = 2, 
cusr = 0, suspect_items = c(20), matching_items = c(1:19))

#perform SIBTEST with a bundle of suspect items and the remaining items as a 
matching subtest

sib(data_ref = data_ref, data_foc = data_foc, minc = 2, 
cusr = .2, suspect_items = c(16,17,18,19,20), matching_items = c(1:15))


## End(Not run)

jweese1441/DIFSIB documentation built on April 3, 2022, 8:58 a.m.