Stata 15 help for irt rsm

[IRT] irt rsm -- Rating scale model


irt rsm varlist [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model listwise drop observations with any missing items

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) notable suppress coefficient table noheader suppress output header display_options control columns and column formats

Integration intmethod(intmethod) integration method intpoints(#) set the number of integration points; default is intpoints(7)

Maximization maximize_options control the maximization process; seldom used

startvalues(svmethod) method for obtaining starting values noestimate do not fit the model; show starting values instead dnumerical use numerical derivative techniques coeflegend display legend instead of statistics -------------------------------------------------------------------------

intmethod Description ------------------------------------------------------------------------- mvaghermite mean-variance adaptive Gauss-Hermite quadrature; the default mcaghermite mode-curvature adaptive Gauss-Hermite quadrature ghermite nonadaptive Gauss-Hermite quadrature -------------------------------------------------------------------------

bootstrap, by, jackknife, statsby, and svy are allowed; see prefix. Weights are not allowed with the bootstrap prefix. vce() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. startvalues(), noestimate, dnumerical, and coeflegend do not appear in the dialog box. See [IRT] irt rsm postestimation for features available after estimation.


Statistics > IRT (item response theory)


irt rsm fits rating scale models to ordinal items. In the rating scale model, items vary in their difficulty but share the same discrimination parameter. The distances between the difficulties of adjacent outcomes are equal across the items.


+-------+ ----+ Model +------------------------------------------------------------

listwise handles missing values through listwise deletion, which means that the entire observation is omitted from the estimation sample if any of the items are missing for that observation. By default, all nonmissing items in an observation are included in the likelihood calculation; only missing items are excluded.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

notable suppresses the estimation table, either at estimation or upon replay.

noheader suppresses the output header, either at estimation or upon replay.

display_options: noci, nopvalues, cformat(fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+-------------+ ----+ Integration +------------------------------------------------------

intmethod(intmethod) specifies the integration method to be used for computing the log likelihood. mvaghermite performs mean and variance adaptive Gauss-Hermite quadrature; mcaghermite performs mode and curvature adaptive Gauss-Hermite quadrature; and ghermite performs nonadaptive Gauss-Hermite quadrature.

The default integration method is mvaghermite.

intpoints(#) sets the number of integration points for quadrature. The default is intpoints(7), which means that seven quadrature points are used to compute the log likelihood.

The more integration points, the more accurate the approximation to the log likelihood. However, computation time increases with the number of integration points.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. Those that require special mention for irt are listed below.

from() accepts a properly labeled vector of initial values or a list of coefficient names with values. A list of values is not allowed.

The following options are available with irt but are not shown in the dialog box:

startvalues() specifies how starting values are to be computed. Starting values specified in from() override the computed starting values.

startvalues(zero) specifies that all starting values be set to 0. This option is typically useful only when specified with the from() option.

startvalues(constantonly) builds on startvalues(zero) by fitting a constant-only model for each response to obtain estimates of intercept and cutpoint parameters.

startvalues(fixedonly) builds on startvalues(constantonly) by fitting a full fixed-effects model for each response variable to obtain estimates of coefficients along with intercept and cutpoint parameters. You can also add suboption iterate(#) to limit the number of iterations irt allows for fitting the fixed-effects model.

startvalues(ivloadings) builds on startvalues(fixedonly) by using instrumental-variable methods with the generalized residuals from the fixed-effects models to compute starting values for latent-variable loadings. This is the default behavior.

noestimate specifies that the model is not to be fit. Instead, starting values are to be shown (as modified by the above options if modifications were made), and they are to be shown using the coeflegend style of output. An important use of this option is before you have modified starting values at all; you can type the following:

. irt ..., ... noestimate

. matrix b = e(b)

. ... (modify elements of b) ...

. irt ..., ... from(b)

dnumerical specifies that during optimization, the gradient vector and Hessian matrix be computed using numerical techniques instead of analytical formulas. By default, irt uses analytical formulas for computing the gradient and Hessian for all integration methods.

coeflegend; see [R] estimation options.


Setup . webuse charity

Fit an RSM . irt rsm ta1-ta5

Use the RSM parameters to plot the category characteristic curves as a function of theta for ta1 . irtgraph icc ta1, xlabel(-4 -.993 1.05 2.18 4, grid)

Use the RSM parameters to plot the category characteristic curves for the first category of all items . irtgraph icc 0.ta*

Video example

Item response theory using Stata: Rating scale models (RSMs)

Stored results

irt rsm stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_eq) number of equations in e(b) e(k_dv) number of dependent variables e(k_rc) number of covariances e(k_rs) number of variances e(irt_k_eq) number of IRT model groups e(k_items1) number of items in first IRT model group e(k_out#) number of categories for the #th item, ordinal e(ll) log likelihood e(N_clust) number of clusters e(n_quad) number of integration points e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if target model converged, 0 otherwise

Macros e(cmd) gsem e(cmd2) irt e(cmdline) command as typed e(model1) rsm e(items1) names of items in first IRT model group e(n_cuts1) numlist of cuts in first IRT model group e(depvar) names of all item variables e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(family#) family for the #th item e(link#) link for the #th item e(intmethod) integration method e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(method) estimation method: ml e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(datasignature) the checksum e(datasignaturevars) variables used in calculation of checksum e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(covariates) list of covariates e(footnote) program used to implement the footnote display

Matrices e(_N) sample size for each item e(b) coefficient vector, slope-intercept parameterization e(b_pclass) parameter class e(out#) categories for the #th item, ordinal e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

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