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223 lines
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223 lines
11 KiB
Plaintext
{smcl}
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{* 5june2019}{...}
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{hline}
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help for {hi:raschtest} and {hi:raschtestv7}{right:Jean-Benoit Hardouin}
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{hline}
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{title:Estimation of the parameters of a Rasch model, tests and specific graphs}
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{p 8 14 2}{cmd:raschtestv7} {it:varlist} [{cmd:if} {it:exp}] [{cmd:in} {it:range}]
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, {cmdab:id}({it:varname}) [{cmdab:meth:od}({it:keyword}) {cmdab:nold} {cmdab:iterate}({it:#})
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{cmdab:t:est}({it:keyword}) {cmdab:diff:iculties}({it:vector})
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{cmdab:mean:diff} {cmdab:d:etails} {cmd:group}({it:numlist}) {cmdab:autog:roup}
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{cmdab:cov:ariates}({it:varlist}[,{cmd: ss1 ss3}])
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{cmdab:dir:save}({it:directory}) {cmdab:files:save} {cmd:png} {cmdab:pause}
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{cmdab:rep:lace} {cmdab:nodraw} {cmdab:icc}
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{cmdab:inf:ormation} {cmdab:split:test} {cmdab:fit:graph}
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{cmdab:genlt}({it:newvarname}[,{cmdab:rep:lace}]) {cmdab:gensco:re}({it:newvarname})
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{cmd:genfit}({it:newvarlist}) {cmd:genres}({it:string})
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{cmdab:com:p}({it:varname})
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{cmdab:dif}({it:varlist})
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{cmdab:tr:ace} {cmdab:time}]
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{p 8 14 2}{cmd:raschtest} {it:varlist} [{cmd:if} {it:exp}] [{cmd:in} {it:range}]
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[, {it:options_of_raschtestv7} {cmdab:gra:ph}]
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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:raschtest} estimates the parameters of a Rasch model. The estimation
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method can be chosen between conditional maximum likelihood (CML), marginal
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maximum likelihood (MML) and generalized estimating equations (GEE). {cmd:raschtest}
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offer a set of tests, to valuate the fit of the data to the Rasch model, or detect
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non homogeneous items (Andersen Z test, First order test (Q1, R1c, R1m, or Wright
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Panchapakesan), U test, Split test) and indexes (OUTFIT and INFIT per items or per
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individuals). Several graphical representations can be easily obtained: comparison
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of the observed and theorical Item Characteristic Curves (ICC), Map difficulty
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parameters/Scores, results of the split tests, and information function (for the scale and by item).
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{title:Options}
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{p 4 8 2}{cmd:method} specifies the used method to estimate the difficulty
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parameter among CML ({cmd:method}({it:cml}) - by default), MML ({cmd:method}({it:mml}))
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or GEE ({cmd:method}({it:gee})).
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{p 4 8 2}{cmd:nold} avoids the Listwise Deletion of the individuals with missing data.
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By default, all the individuals with one or more missing data are omited.
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{p 4 8 2}{cmd:iterate} allows defining the maximal number of iterations of the maximisation algorithm.
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By default, this number is fixed to 200.
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{p 4 8 2}{cmd:test} specifies the tests to use among {cmd:test}({it:R}) (by
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default, for the R1c or the R1m test), {cmd:test}({it:WP}) (for the Wright-
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Panchapakesan test) and {cmd:test}({it:Q}) (for the Q1 test).
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{p 4 8 2}{cmd:difficulties} allows fixing the values of the difficulties parameters of the items.
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The vector must be a row vector and must contain as many values as items.
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This option is available only with {cmd:method}({it:mml}).
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{p 4 8 2}{cmd:meandiff} centers the difficulty parameters (only with
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{cmd:method}({it:cml})): by default for the CML estimations, the difficulty
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parameter to the last item is fixed to 0. With {cmd:meandiff}, only the
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diagonal elements of the covariance matrix of these parameters are estimated.
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{p 4 8 2}{cmd:details} displays for each group of scores a table containing the
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observed and expected number of positive responses and the contribution of this
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group to the global first-order statistic.
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{p 4 8 2}{cmd:group} specifies groups of scores, by defining the superior
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limits of each group (note that the score "0" and this one corresponding to the
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number of items are always isolated).
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{p 4 8 2}{cmd:autogroup} automatically creates groups of scores (with at least
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30 individuals per group).
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{p 4 8 2}{cmd:covariates} allows introducing covariates on the model. The {cmd:ss1} and
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{cmd:ss3} options allows computing the type 1 and type 3 sums of squares to explain the
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variance of the latent trait by these covariates. This option is available only with {cmd:method}({it:mml}).
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{p 4 8 2}{cmd:dirsave} specifies the directory where the graphs will be saved
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(by default, the directory defined in c(pwd)).
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{p 4 8 2}{cmd:filessave} saves all the graphs in .gph files (by default, the
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graphs are not saved).
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{p 4 8 2}{cmd:png} saves all the graphs in .png files.
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{p 4 8 2}{cmd:pause} allows to made a pause between the displaying of each graph.
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{p 4 8 2}{cmd:replace} specifies that the existing graphical files will be
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replaced.
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{p 4 8 2}{cmd:nodraw} avoids displaying of the graphs.
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{p 4 8 2}{cmd:icc} displays, for each item, the observed and expected (under the Rasch
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model) ICC in a graph.
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{p 4 8 2}{cmd:graph} represents in the same graph the distributions of the
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difficulty parameters, this one of the scores, and [with {cmd:method}({it:mml}) or
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{cmd:method}({it:gee})] the expected distribution of the latent trait, in
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function of the latent trait.
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{p 4 8 2}{cmd:information} represents the information function for the set of
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the items in function of the latent trait.
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{p 4 8 2}{cmd:splittest} represents, for each item, the CML estimations of the
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difficulty parameters for the others items in the two sub-samples defined by
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the individuals who have positively respond to the splitting item for the first
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group, and by the individuals who have negatively respond to the splitting item
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for the second one.
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{p 4 8 2}{cmd:fitgraph} represents four graphs. The first one concerns the
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OUTFIT indexes for each item, the second one, the INFIT indexes for each item,
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the third one the OUTFIT indexes for each individual, and the last one the
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INFIT indexes for each individual.
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{p 4 8 2}{cmd:genlt} creates a new variable containing, for each individual,
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the estimated value of the latent trait. The {cmd:replace} option allows replacing
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an existing variable.
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{p 4 8 2}{cmd:genscore} creates a new variable containing, for each individual,
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the value of the score.
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{p 4 8 2}{cmd:genres} creates new variables containing, for each individual,
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the value of the residuals. This option defines the prefix to these new variables
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which will be followed by the name of each item.
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{p 4 8 2}{cmd:genfit} creates several new variables. {it:newvarlist}
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contains two words. The first one represents "outfit" and the second one "infit".
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This option generates two variables with this names for the OUTFIT and INFIT
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indexes for each individual, and the variables "outfitXX" (by replacing "outfit"
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by the first word) for the contribution of the item XX to the OUTFIT index (Note
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that the new variables contain unstandardized OUTFIT and INFIT indices, even
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the program displays standardized statistics in the results table and with the
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{cmd:fitgraph} option).
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{p 4 8 2}{cmd:comp} tests the equality of the means of the latent trait for two
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groups of individuals defined by a binary variable (only with {cmd:method}({it:mml})
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or {cmd:method}({it:gee})).
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{p 4 8 2}{cmd:dif} tests the Differential Item Functioning (DIF) on a list of
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variables by likelihood ration tests. For each variable defined in the list,
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the items parameters are estimated in each groups defined by this variable,
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and the test considers the null assumption: the estimations are the same in each group.
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The statistic of the test follows a chi-square distribution under the null assumption.
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The variable defined in the {cmd:dif} option must have 10 or less modalities, coded
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from 0 or 1 to an integer k<=10. This option is available only with {cmd:method}({it:cml}).
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{p 4 8 2}{cmd:trace} displays more outputs during the running of the module.
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{p 4 8 2}{cmd:time} displays the number of seconds to run the module.
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{title:Outputs}
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{p 4 8 2}{cmd:e(N)}: Number of observations
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{p 4 8 2}{cmd:e(ll)}: (Marginal) Log-likelihood
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{p 4 8 2}{cmd:e(cll)}: Conditional log-likelihood
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{p 4 8 2}{cmd:e(AIC)}: Akaike Information Criterion
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{p 4 8 2}{cmd:e(PSI)} and {cmd:e(PSIadj)}: Personal Separation Indexes (only for {cmd:meth}({it:mml})
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{p 4 8 2}{cmd:e(sigma)}: Estimated standard deviation of the latent trait
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{p 4 8 2}{cmd:e(sesigma)}: Standard error of the estimated standard deviation of the latent trait
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{p 4 8 2}{cmd:e(beta)}: Estimated difficulty parameters
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{p 4 8 2}{cmd:e(Varbeta)}: Covariance matrix of the estimated difficulty parameters
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{p 4 8 2}{cmd:e(theta)}: Estimated values for the latent trait for each value of the score
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{p 4 8 2}{cmd:e(Varbeta)}: Covariance matrix for the estimated values for the latent trait for each value of the score
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{p 4 8 2}{cmd:e(itemFit)}: Statistics of fit for each item (first order statistic, degree of freedom, p-value, OUTFIT index, INFIT index, and (if {cmd:method}({it:cml})) U-test statistic
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{p 4 8 2}{cmd:e(globalFit)}: Global first order test (statistic, degrees of freedom, p-value)
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{p 4 8 2}{cmd:e(AndersenZ)}: Andersen LR Z test (first order statistic, degree of freedom, p-value) (if {cmd:method}({it:cml}))
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{p 4 8 2}{cmd:e(DIF)}: DIF LR Z test (statistic, degree of freedom, p-value for each variable defined in {cmd:dif}) (if {cmd:method}({it:cml}))
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{p 4 8 2}{cmd:e(Zcomp)} and {cmd:e(pZcomp)}: Statistics of test and associated p-value for the test of comparison of the two population defined with the {cmd:comp} option.
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{p 4 8 2}{cmd:e(betacovariates)}, {cmd:e(Vbetacovariates)}, {cmd:e(zcovariates)} and {cmd:e(pcovariates)}: respectivelly the estimated values of the parameters associated to the covariates, the covariance matrix of the estimations, the statistics of the tests to compare the parameters to 0 and the associated p-values (only with the {cmd:covariates} option)
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{title:Examples}
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{p 4 8 2}{cmd: . raschtest item1-item9, id(id)} /*estimates the parameters by CML approach*/
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{p 4 8 2}{cmd: . raschtest item*, id(id) method(gee) information icc dirsave(c:\graphs) filesnames(graphs)}
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/*estimates the parameters by GEE, draw the information graph and the ICCs and
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save the graphical representations under gph files*/
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{p 4 8 2}{cmd: . raschtest item1 item4 item7 item 18 item23 item35-item39 , id(id) group(2 3 4 5) test(WP) split graph}
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/*creates groups of score (1 and 2, 3, 4, 5 and more) to compute the Wright
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Panchapakesan tests, computes the split test, and represent the map difficulty
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parameters/scores*/
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{p 4 8 2}{cmd: . matrix diff=(-1,-.5,0,.5,1)}{p_end}
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{p 4 8 2}{cmd: . raschtest item1-item5 , id(id) diff(diff) covariable(group sex age,ss1 ss3) nold}
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/*difficulties parameters are fixed, 3 covariables are introduced, no listwise deletion*/
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{title:Author}
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{p 4 8 2}Jean-Benoit Hardouin, PhD-HDR, associate professor{p_end}
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{p 4 8 2}INSERM UMR 1246-SPHERE "Methods in Patients Centered Outcomes and Health Research"{p_end}
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{p 4 8 2}Nantes University - Faculty of Pharmaceutical Sciences{p_end}
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{p 4 8 2}Intitute of Research in Health 2{p_end}
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{p 4 8 2}22 boulevard Bénoni-Goullin{p_end}
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{p 4 8 2}44200 Nantes - FRANCE{p_end}
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{p 4 8 2}Email:
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{browse "mailto:jean-benoit.hardouin@univ-nantes.fr":jean-benoit.hardouin@univ-nantes.fr}{p_end}
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{p 4 8 2}Websites {browse "http://www.anaqol.org":AnaQol}
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{title:Also see}
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{p 4 13 2}Online: help for {help xtlogit}, {help clogit} and {help geekel2d} and {help gllamm} if installed.{p_end}
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