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73 lines
3.7 KiB
Plaintext
73 lines
3.7 KiB
Plaintext
10 months ago
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{smcl}
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{* 5may2013}{...}
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{hline}
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help for {hi:mmsrm}
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{hline}
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{title:Estimation of the parameters of a Multidimensional Marginally Sufficient Rasch Model (MMSRM)}
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{p 8 14 2}{cmd:mmsrm} {it:varlist} {cmd:id}({it:varname}) [{cmd:,} {cmdab:part:ition}({it:numlist}) {cmdab:nodet:ails} {cmdab:trac:e} {cmdab:it:erate}({it:#}) {cmdab:ad:apt} {cmdab:meth:od}({it:mml/gee})]
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{p 8 14 2}{it:varlist} is a list of two existing binary variables or more.
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{title:Description}
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{p 4 8 2}{cmd:mmsrm} estimates by marginal maximum likelihood (MML) or
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generalized estimating equations (GEE) the parameters of the Multidimensional
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Marginally Sufficient Rasch Model (MMSRM) defined by Hardouin and Mesbah (2004).
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This model is an Item Response Model (IRM) with one or several latent traits.
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This is a particular multidimensionnal extension of the Rasch model. In this
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model, the items are separated in Q groups and each group of items is linked to
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one and only one latent trait. Each group fits a Rasch model relatively to the
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corresponding latent trait, so the score computed in each group of item is a
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sufficient statistics of this latent trait (to a specific value of this score
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is associated only one value for the latent trait). The program allows
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computing the parameter of a MMSRM with less than 4 latent traits.
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To improve the time of computing, the difficulty parameters are estimated in
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each unidimensional Rasch model and used as an offset variable to estimate the
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parameters of the distribution of the multidimensional latent trait. This model
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allows estimating the correlations between different latent traits measured by
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Rasch models.
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{title:Options}
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{p 4 8 2}{cmd:id} defines an identifiant variable of the individuals.
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{p 4 8 2}{cmd:partition} allows defining the number of items relied to each
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latent trait. The sum of the numbers indicated in the {it:numlist} must be
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equal to the total number of items. The number of elements indicates the number of
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latent traits. The items are taken in the same order than this one of {it:varlist}.
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By default, only one latent trait is assumed (Rasch model). The number of elements of
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{cmd:partition} must be inferior or equal to 3.
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{p 4 8 2}{cmd:method} defines the estimation method between the marginal maximum
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likelihood ({it:mml} - by default) and the generalized estimating equations ({it:gee}).
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You cannot estimate the parameters of a MMSRM with 3 dimensions with the GEE method.
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{p 4 8 2}{cmd:nodetails} deletes the details about the procedure.
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{p 4 8 2}{cmd:trace} displays details about the estimation algorithm.
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{p 4 8 2}{cmd:iterate} defines the maximal number of iterations of the estimation algorithm
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(30 by default).
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{p 4 8 2}{cmd:adapt} allows using the adaptive quadrature if you use the MML method of estimation. This option
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is useless if you use GEE.
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{title:Example}
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{p 16 22 2} {inp:. mmsrm item1-item9 , part(4 5) trace method(gee)}
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{p 16 22 2} {inp:. mmsrm item1 item2 item3 item4 /*Rasch model*/}
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{p 16 22 2} {inp:. mmsrm c1-c9 , part(3 3 3) nodetails adapt iterate(10)}
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{title:References}
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{p 4 8 2}Hardouin J.-B. and Mesbah M. Clustering binary variables in subscales using an extended Rasch model and Akaike Information Criterion. {it:Communications in Statistics - Theory and Methods}, {cmd:33}(6), pp. 1277-1294, (2004).
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{title:Author}
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{p 4 8 2}Jean-Benoit Hardouin, Regional Health Observatory (ORS) - 1, rue Porte Madeleine - BP 2439 - 45032 Orleans Cedex 1 - France.
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You can contact the author at {browse "mailto:jean-benoit.hardouin@neuf.fr":jean-benoit.hardouin@neuf.fr} and visit the websites {browse "http://anaqol.free.fr":AnaQol} and {browse "http://freeirt.free.fr":FreeIRT}
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