simul_confusion/Modules/ado/personal/r/raschtestv7.hlp

{smcl}
{* 5june2019}{...}
{hline}
help for {hi:raschtest} and {hi:raschtestv7}{right:Jean-Benoit Hardouin}
{hline}

{title:Estimation of the parameters of a Rasch model, tests and specific graphs}

{p 8 14 2}{cmd:raschtestv7} {it:varlist} [{cmd:if} {it:exp}] [{cmd:in} {it:range}]
, {cmdab:id}({it:varname})  [{cmdab:meth:od}({it:keyword}) {cmdab:nold}  {cmdab:iterate}({it:#})
{cmdab:t:est}({it:keyword}) {cmdab:diff:iculties}({it:vector})
{cmdab:mean:diff} {cmdab:d:etails} {cmd:group}({it:numlist}) {cmdab:autog:roup}
{cmdab:cov:ariates}({it:varlist}[,{cmd: ss1 ss3}])
{cmdab:dir:save}({it:directory}) {cmdab:files:save} {cmd:png} {cmdab:pause}
{cmdab:rep:lace} {cmdab:nodraw} {cmdab:icc}
{cmdab:inf:ormation} {cmdab:split:test} {cmdab:fit:graph}
{cmdab:genlt}({it:newvarname}[,{cmdab:rep:lace}]) {cmdab:gensco:re}({it:newvarname})
{cmd:genfit}({it:newvarlist}) {cmd:genres}({it:string})
{cmdab:com:p}({it:varname})
{cmdab:dif}({it:varlist})
{cmdab:tr:ace} {cmdab:time}]

{p 8 14 2}{cmd:raschtest} {it:varlist} [{cmd:if} {it:exp}] [{cmd:in} {it:range}]
[, {it:options_of_raschtestv7} {cmdab:gra:ph}]

{p 8 14 2}{it:varlist} is a list of two existing binary variables or more.

{title:Description}

{p 4 8 2}{cmd:raschtest} estimates the parameters of a Rasch model. The estimation
method can be chosen between conditional maximum likelihood (CML), marginal
maximum likelihood (MML) and generalized estimating equations (GEE). {cmd:raschtest}
offer a set of tests, to valuate the fit of the data to the Rasch model, or detect
non homogeneous items (Andersen Z test, First order test (Q1, R1c, R1m, or Wright
Panchapakesan), U test, Split test) and indexes (OUTFIT and INFIT per items or per
individuals). Several graphical representations can be easily obtained: comparison
of the observed and theorical Item Characteristic Curves (ICC), Map difficulty
parameters/Scores, results of the split tests, and information function (for the scale and by item).

{title:Options}

{p 4 8 2}{cmd:method} specifies the used method to estimate the difficulty
parameter among CML ({cmd:method}({it:cml}) - by default), MML ({cmd:method}({it:mml}))
or GEE ({cmd:method}({it:gee})).

{p 4 8 2}{cmd:nold} avoids the Listwise Deletion of the individuals with missing data.
By default, all the individuals with one or more missing data are omited.

{p 4 8 2}{cmd:iterate} allows defining the maximal number of iterations of the maximisation algorithm.
By default, this number is fixed to 200.

{p 4 8 2}{cmd:test} specifies the tests to use among {cmd:test}({it:R}) (by
default, for the R1c or the R1m test), {cmd:test}({it:WP}) (for the Wright-
Panchapakesan test) and {cmd:test}({it:Q}) (for the Q1 test).

{p 4 8 2}{cmd:difficulties} allows fixing the values of the difficulties parameters of the items.
The vector must be a row vector and must contain as many values as items.
This option is available only with {cmd:method}({it:mml}).

{p 4 8 2}{cmd:meandiff} centers the difficulty parameters (only with
{cmd:method}({it:cml})): by default for the CML estimations, the difficulty
parameter to the last item is fixed to 0. With {cmd:meandiff}, only the
diagonal elements of the covariance matrix of these parameters are estimated.

{p 4 8 2}{cmd:details} displays for each group of scores a table containing the
observed and expected number of positive responses and the contribution of this
group to the global first-order statistic.

{p 4 8 2}{cmd:group} specifies groups of scores, by defining the superior
limits of each group (note that the score "0" and this one corresponding to the
number of items are always isolated).

{p 4 8 2}{cmd:autogroup} automatically creates groups of scores (with at least
30 individuals per group).

{p 4 8 2}{cmd:covariates} allows introducing covariates on the model. The {cmd:ss1} and
{cmd:ss3} options allows computing the type 1 and type 3 sums of squares to explain the
variance of the latent trait by these covariates. This option is available only with {cmd:method}({it:mml}).

{p 4 8 2}{cmd:dirsave} specifies the directory where the graphs will be saved
(by default, the directory defined in c(pwd)).

{p 4 8 2}{cmd:filessave} saves all the graphs in .gph files (by default, the
graphs are not saved).

{p 4 8 2}{cmd:png} saves all the graphs in .png files.

{p 4 8 2}{cmd:pause} allows to made a pause between the displaying of each graph.

{p 4 8 2}{cmd:replace} specifies that the existing graphical files will be
replaced.

{p 4 8 2}{cmd:nodraw} avoids displaying of the graphs.

{p 4 8 2}{cmd:icc} displays, for each item, the observed and expected (under the Rasch
model) ICC in a graph.

{p 4 8 2}{cmd:graph} represents in the same graph the distributions of the
difficulty parameters, this one of the scores, and [with {cmd:method}({it:mml}) or
{cmd:method}({it:gee})] the expected distribution of the latent trait, in
function of the latent trait.

{p 4 8 2}{cmd:information} represents the information function for the set of
the items in function of the latent trait.

{p 4 8 2}{cmd:splittest} represents, for each item, the CML estimations of the
difficulty parameters for the others items in the two sub-samples defined by
the individuals who have positively respond to the splitting item for the first
group, and by the individuals who have negatively respond to the splitting item
for the second one.

{p 4 8 2}{cmd:fitgraph} represents four graphs. The first one concerns the
OUTFIT indexes for each item, the second one, the INFIT indexes for each item,
the third one the OUTFIT indexes for each individual, and the last one the
INFIT indexes for each individual.

{p 4 8 2}{cmd:genlt} creates a new variable containing, for each individual,
the estimated value of the latent trait. The {cmd:replace} option allows replacing
an existing variable.

{p 4 8 2}{cmd:genscore} creates a new variable containing, for each individual,
the value of the score.

{p 4 8 2}{cmd:genres} creates new variables containing, for each individual,
the value of the residuals. This option defines the prefix to these new variables
which will be followed by the name of each item.

{p 4 8 2}{cmd:genfit} creates several new variables. {it:newvarlist}
contains two words. The first one represents "outfit" and the second one "infit".
This option generates two variables with this names for the OUTFIT and INFIT
indexes for each individual, and the variables "outfitXX" (by replacing "outfit"
by the first word) for the contribution of the item XX to the OUTFIT index (Note
that the new variables contain unstandardized OUTFIT and INFIT indices, even
the program displays standardized statistics in the results table and with the
{cmd:fitgraph} option).

{p 4 8 2}{cmd:comp} tests the equality of the means of the latent trait for two
groups of individuals defined by a binary variable (only with {cmd:method}({it:mml})
or {cmd:method}({it:gee})).

{p 4 8 2}{cmd:dif} tests the Differential Item Functioning (DIF) on a list of
variables by likelihood ration tests. For each variable defined in the list,
the items parameters are estimated in each groups defined by this variable,
and the test considers the null assumption: the estimations are the same in each group.
The statistic of the test follows a chi-square distribution under the null assumption.
The variable defined in the {cmd:dif} option must have 10 or less modalities, coded
from 0 or 1 to an integer k<=10. This option is available only with {cmd:method}({it:cml}).

{p 4 8 2}{cmd:trace} displays more outputs during the running of the module.

{p 4 8 2}{cmd:time} displays the number of seconds to run the module.

{title:Outputs}

{p 4 8 2}{cmd:e(N)}: Number of observations

{p 4 8 2}{cmd:e(ll)}: (Marginal) Log-likelihood

{p 4 8 2}{cmd:e(cll)}: Conditional log-likelihood

{p 4 8 2}{cmd:e(AIC)}: Akaike Information Criterion

{p 4 8 2}{cmd:e(PSI)} and {cmd:e(PSIadj)}: Personal Separation Indexes (only for {cmd:meth}({it:mml})

{p 4 8 2}{cmd:e(sigma)}: Estimated standard deviation of the latent trait

{p 4 8 2}{cmd:e(sesigma)}: Standard error of the estimated standard deviation of the latent trait

{p 4 8 2}{cmd:e(beta)}: Estimated difficulty parameters

{p 4 8 2}{cmd:e(Varbeta)}: Covariance matrix of the estimated difficulty parameters

{p 4 8 2}{cmd:e(theta)}: Estimated values for the latent trait for each value of the score

{p 4 8 2}{cmd:e(Varbeta)}: Covariance matrix for the estimated values for the latent trait for each value of the score

{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

{p 4 8 2}{cmd:e(globalFit)}: Global first order test (statistic, degrees of freedom, p-value)

{p 4 8 2}{cmd:e(AndersenZ)}: Andersen LR Z test (first order statistic, degree of freedom, p-value) (if {cmd:method}({it:cml}))

{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}))

{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.

{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)

{title:Examples}

{p 4 8 2}{cmd: . raschtest item1-item9, id(id)} /*estimates the parameters by CML approach*/

{p 4 8 2}{cmd: . raschtest item*, id(id) method(gee) information icc dirsave(c:\graphs) filesnames(graphs)}
/*estimates the parameters by GEE, draw the information graph and the ICCs and
save the graphical representations under gph files*/

{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}
/*creates groups of score (1 and 2, 3, 4, 5 and more) to compute the Wright
Panchapakesan tests, computes the split test, and represent the map difficulty
parameters/scores*/

{p 4 8 2}{cmd: . matrix diff=(-1,-.5,0,.5,1)}{p_end}
{p 4 8 2}{cmd: . raschtest item1-item5 , id(id) diff(diff) covariable(group sex age,ss1 ss3) nold}
/*difficulties parameters are fixed, 3 covariables are introduced, no listwise deletion*/

{title:Author}

{p 4 8 2}Jean-Benoit Hardouin, PhD-HDR, associate professor{p_end}
{p 4 8 2}INSERM UMR 1246-SPHERE "Methods in Patients Centered Outcomes and Health Research"{p_end}
{p 4 8 2}Nantes University - Faculty of Pharmaceutical Sciences{p_end}
{p 4 8 2}Intitute of Research in Health 2{p_end}
{p 4 8 2}22 boulevard Bénoni-Goullin{p_end}
{p 4 8 2}44200 Nantes - FRANCE{p_end}
{p 4 8 2}Email:
{browse "mailto:jean-benoit.hardouin@univ-nantes.fr":jean-benoit.hardouin@univ-nantes.fr}{p_end}
{p 4 8 2}Websites {browse "http://www.anaqol.org":AnaQol}


{title:Also see}

{p 4 13 2}Online: help for {help xtlogit}, {help clogit} and {help geekel2d} and {help gllamm} if installed.{p_end}