ResearchTopics Publications In progress publications Communications
PackagesAnaQol Project PRO-online R Packages Online R-package
Life of the unitProjects Collaborations PhD thesis Traineeships Traineeships propositions Seminars
03 novembre 2018
17 septembre 2018
15 août 2018
27 juillet 2018
27 juillet 2018
Updated16 octobre 2018
The Stata module "Raschtest"
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). 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. raschtestv7 is a version of raschtest for Stata 7.
Type "findit raschtest" or "ssc install raschtest" directly from your Stata browser.
Syntax (version 8.7)
raschtest varlist [if expr] [in range] [, method(cml/mml/gee) test(R/Q/WP) meandiff detail group(numlist) autogroup dirsave(directory) filessaves pause replace icc graph information splittest fitgraph genlt(newvarname) genscore(newvarname) genfit(newvarlist) comp(varname) trace dif(varlist) nold id(varname) time difficulties(vector) iterate(#) covartiates(varlist[, ss1 ss3]) genres(string ) ]
This program requires an access to the following program(s):
Raschtestv7 is a version of raschtest for Stata 7.
You can see here an exemple of outputs produced by raschtest.
- method(cml/mml/gee): specifies the used method to estimate the difficulty parameter among CML (cml - by default), MML (mml) or GEE (gee).
- test(R/Q/WP): specifies the tests to use among R (by default, for the R1c or the R1m test), WP (for the Wright-Panchapakesan test) and Q (for the Q1 test).
- meandiff: centers the difficulty parameters (only with method(cml): by default for the CML estimations, the difficulty parameter to the last item is fixed to 0. With meandiff, only the diagonal elements of the covariance matrix of these parameters are estimated.
- detail: 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
- group(numlist): specifies groups of scores, by defining the superior limits of each group (note that the nul score (0) and this one corresponding to the number of items (number of items) are always isolated)
- autogroup: automatically creates groups of scores (with at least 30 individuals per group)
- dirsave(directory): specifies the directory where the graphs will be saved (by default, the directory defined in c(pwd))
- filessaves: saves all the graphs in .gph files (by default, the graphs are not saved)
- pause: makes a pause between the displaying of each graph
- replace: specifies that the existing graphical files will be replaced
- icc: displays, for each item, the observed and expected (under the Rasch model) ICC in a graph
- graph: (not available with raschtestv7) represents in the same graph the distributions of the difficulty parameters, this one of the scores, and [with MML or GEE] the expected distribution of the latent trait, as a function of the latent trait
- information: represents the information function for the set of the items in function of the latent trait
- 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
- 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
- genlt(newvarname): creates a new variable containing, for each individual, the estimated value of the latent trait
- genscore(newvarname): creates a new variable containing, for each individual, the value of the score
- genfit(newvarlist): creates several new variables. newvarlist must contain 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 fitgraph option).
- comp(varname): tests the equality of the means of the latent trait for two groups of individuals defined by a binary variable (only with method(mml) or method(gee)).
- trace: displays more outputs during the running of the module
- dif(varlist): 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 dif option must have 10 or less modalities, coded from 0 or 1 to an integer k<=10. This option is available only if method(cml).
- nold: Avoids listwise deletion (LD) of the individuals with missing data: all the observed responses are kept for the estimation process (except for meth(gee)). Nevertheless, the individuals with missing data are omitted for computing fit tests.
- id(varname): (required) defined an identifiant variable of the individuals
- time: displays the number of seconds to run the module
- difficulties(vector): 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 method(mml)
- iterate(#): allows defining the maximal number of iterations of the maximisation algorithm. By default, this number is fixed to 200.
- covartiates(varlist[, ss1 ss3]): allows introducing covariates on the model. The ss1 and 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 method(mml).
- genres(string ): 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.
raschtest itemA* itemB2, id(id) method(gee) information icc dirsave(c:\\graphs) filesnames(graphs)
raschtest itemA1 itemA2 itemA3-itemA7 itemB* ,id(id) group(2 3 4 5) test(WP) split graph