generate_irtree_steps: Generate data for Steps Model.

In hplieninger/mpt2irt: Bringing Multinomial Processing Tree Models To Item Response Theory To Investigate Response Styles

Description

Function generates categorical data 1...5 for N persons and J items with paramters from rnorm().

Usage

generate_irtree_steps(N = NULL, J = NULL, prop.rev = 0.5,
  revItem = NULL, traitItem = NULL)

Arguments

N	number of persons
J	number of items
prop.rev	proportion of reversed items (rounded to next integer). Can be a vector if multiple traits are specified by traitItem. Only used if revItem = NULL.
revItem	vector of length J specifying reversed items (1=reversed, 0=regular). Overrides argument prop.rev.
traitItem	vector of length J specifying the underlying traits (e.g., indexed from 1...5). Standard: only a single trait is measured by all items. If the Big5 are measured, might be something like c(1,1,1,2,2,2,...,5,5,5,5)

Value

The function returns a list containing the generated matrix of responses X, a vector revItem indicating reversed items and true, latent values of the parameters.

References

Tutz, G. (1997). Sequential models for ordered responses. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of Modern Item Response Theory (pp. 139-152). doi:10.1007/978-1-4757-2691-6_8

Verhelst, N. D., Glas, C. A. W., & de Vries, H. H. (1997). A steps model to analyze partial credit. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 123-138). doi:10.1007/978-1-4757-2691-6_7

Examples

N <- 20
J <- 10
dat <- generate_irtree_steps(N = N, J = J)

hplieninger/mpt2irt documentation built on May 17, 2019, 4:54 p.m.