GMAT2: Dichotomous dataset based on GMAT.

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

Dichotomous dataset based on GMAT.

Description

The GMAT2 is a generated dataset based on parameters from Graduate Management Admission Test (GMAT, Kingston et al., 1985). First two items were considered to function differently in uniform and non-uniform way respectively. The dataset represents responses of 1,000 subjects to multiple-choice test of 20 items. A correct answer is coded as 1 and incorrect answer as 0. The column group represents group membership, where 0 indicates reference group and 1 indicates focal group. Groups are the same size (i.e. 500 per group).

Usage

data(GMAT2)

Format

A GMAT2 data frame consists of 1,000 observations on the following 21 variables:

Item1-Item20

dichotomously scored items of the test

group

group membership vector, "0" reference group, "1" focal group

Author(s)

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
[e-mail]

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

References

Kingston, N., Leary, L., & Wightman, L. (1985). An exploratory study of the applicability of item response theory methods to the Graduate Management Admission Test. ETS Research Report Series, 1985(2): 1–64.

Martinkova, P., Drabinova, A., Liaw, Y. L., Sanders, E. A., McFarland, J. L., & Price, R. M. (2017). Checking equity: Why differential item functioning analysis should be a routine part of developing conceptual assessments. CBE–Life Sciences Education, 16(2), rm2, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1187/cbe.16-10-0307")}.

See Also

GMAT2test, GMAT2key

drabinova/difNLR documentation built on Feb. 2, 2024, 7:14 p.m.