irt_coef help file

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.