Title
irt_coef --
Calculates y* standardized coefficients (discrimination parameters) for
binary and ordinal IRT models
General syntax
irt_coef [varlist] , [options]
Overview
irt_coef calculates y* standardized coefficients (discrimination parameters) for
binary and ordinal IRT models. The raw coefficient, standard error, and p-value are
also reported alongside the y* standardized coefficient.
+---------------------------------+
----+ Required Option for gsem models +----------------------------------------------
latent(name) is required if gsem was used to fit the model of interest. This is the
name of the latent variable that is the indepenent variable (the items
are the dependent variables [i.e. the y's]). The latent( ) option should
not be used if the model was fit with the irt command as irt
automatically names the latent variable Theta.
+---------+
----+ Options +----------------------------------------------------------------------
model(name) specifies the name of saved model estimates to use. See estimates store
for saving model estimates. By default, irt_coef will use the IRT/GSEM
estimates in memory. If the relevant model estimates are not in memory,
you must specify their name.
decimals(#) changes the number of decimal places reported for the statistics. The
default is 3. Any integer between 1 - 8 is allowed.
sort orders the rows of the table based on the values of the y* standardized
coefficients.
title(string)
changes the title of the table of results. A default title is
automatically included.
help prints footnotes below the table describing what the columns in the table
represent.
Examples
webuse charity
irt grm ta1 ta2 ta3 ta4 ta5
irt_coef, help
gsem (Theta -> ta1 ta2 ta3 ta4 ta5, ologit), var(Theta@1)
irt_coef, latent(Theta)
Comments
For details on y* standardized coefficients generally see Long 1997 (pages 69-71;
128-130). In the context of IRT models, see Bartholomew et al. 2008 (pages 224-225;
259-260).
Authorship
irt_coef is written by Trenton D Mize (Department of Sociology & Advanced
Methodologies, Purdue University). Questions can be sent to tmize@purdue.edu
References
Bartholomew, David J., Fiona Steele, Irini Moustaki, and Jane I. Galbrath. 2008.
Analysis of Multivariate Social Science Data. Second Edition. CRC Press.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent
Variables. Sage.