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1150 lines
37 KiB
Plaintext
1150 lines
37 KiB
Plaintext
7 months ago
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*! version 2.4 june2020
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*! Myriam Blanchin - Priscilla Brisson
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************************************************************************************************************
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* ROSALI: RespOnse-Shift ALgorithm at Item-level
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* Response-shift detection based on Rasch models family
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*
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* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
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* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
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* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
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* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
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* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, optimization */
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* Version 2.3 : December, 2019 (Priscilla Brisson) /* option detail, + petites corrections */
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* Version 2.4 : June, 2020 (Myriam Blanchin) /* debug option detail + step C, modifs sorties et help */
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*
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* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
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* myriam.blanchin@univ-nantes.fr
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*
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* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
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* priscilla.brisson@univ-nantes.fr
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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************************************************************************************************************/
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program define rosali_nolrt, rclass
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timer clear 1
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timer on 1
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syntax varlist(min=2 numeric) [if] [,GROUP(varlist) NODIF PRO DETail]
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preserve
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version 15
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tempfile saverspcm
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capture qui save `saverspcm',replace
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local save1=_rc
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if "`if'"!="" {
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qui keep `if'
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}
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if "`pro'" != "" {
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di "START"
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}
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/**************************************************************************/
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set more off
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set matsize 5000
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constraint drop _all
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local gp "`group'"
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tokenize `varlist'
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local nbitems:word count `varlist'
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/* Vérif nb d'items pair */
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local mod=mod(`nbitems',2)
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if `mod'!=0 {
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di as error "You must enter an even number of items : the first half of the items represents the items at time 1 and the second half the items at time 2"
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error 198
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exit
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}
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local nbitems=`nbitems'/2
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if "`group'"=="" & "`nodif'"!="" {
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di as error "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
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error 198
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exit
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}
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local nbc: word count `group'
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if `nbc' >= 2 {
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di as error "Only one variable can be used for group option ({hi:group} option). Please correct this option."
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error 198
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exit
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}
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/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
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if "`group'"!="" {
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qui tab `group'
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local nbgrp = r(r)
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if `nbgrp' != 2 {
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di as error "Only 2 groups are possible for the group option ({hi:group} option). Please correct this option."
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error 420
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exit
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}
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}
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/* recoder la variable de groupe en 0, 1*/
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if "`group'"!="" {
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qui tab `gp', matrow(rep)
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qui matrix list rep
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if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
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forvalues i=1/`=rowsof(rep)'{
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qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
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di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
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}
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}
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forvalues g = 0/1 {
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qui tab `gp' if `gp' == `g'
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local nbp_gp`g' = r(N)
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}
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}
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/*item rename*/
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/*
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Items au temps 1 : 1 à nbitems ``j''
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Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
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Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
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*/
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local com_z = 0 // Indicatrice de recodage
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/*verif modalités répondues*/
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if "`gp'" == "" { // Si pas d'option groupe
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forvalues j = 1 / `nbitems' {
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local recoda_`j' = 0
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qui tab ``j'', matrow(rect1_`j') // Récupération des infos moda du temps 1
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local minm`j'_t1 = rect1_`j'[1,1]
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local maxm`j'_t1 = rect1_`j'[r(r),1]
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qui tab ``=`j'+`nbitems''', matrow(rect2_`j') // Récupération des infos moda du temps 2
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local minm`j'_t2 = rect2_`j'[1,1]
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local maxm`j'_t2 = rect2_`j'[r(r),1]
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local minm_`j' = min(`minm`j'_t1',`minm`j'_t2') // Info moda pour l'item j
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local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
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local nbm_`j' = `=`maxm_`j''-`minm_`j'''
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if `minm_`j'' != 0 & `com_z' == 0 {
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local com_z = 1
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}
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//Recodage des réponses en 0, 1, 2, etc...
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forvalues r = 0/`=`maxm_`j''-1' {
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qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
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qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
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}
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// Vérif. Que toutes les modas sont utilisées & concordance entre temps
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forvalues m = 0/`nbm_`j'' {
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qui count if ``j'' == `m'
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local nb_rn1 = r(N)
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qui count if ``=`j'+`nbitems''' == `m'
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local nb_rn2 = r(N)
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local nb_rn = min(`nb_rn1',`nb_rn2')
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if `nb_rn' == 0 { // Une moda n'est pas utilisée
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local recoda_`j' = 1
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if `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'+`k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged "
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local stop = 0
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}
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}
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}
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else if `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' { // La (ou les) moda max ne sont pas utilisée(s)
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local stop = 1
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forvalues k = 1/`m' {
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qui count if ``j'' == `=`m' - `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''=`=`m' - `k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m' - `k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
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local stop = 0
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}
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}
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}
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else {
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if runiform()>0.5{ // Tirage au sort pour regrouper
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local stop = 1
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forvalues k = 1/`m' {
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qui count if ``j'' == `=`m' - `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' - `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'-`k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems''' =`=`m'-`k'' if ``=`j'+`nbitems''' ==`m'
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di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
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local stop = 0
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}
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}
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}
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else {
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k''
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local v`k'1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k''
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local v`k'2 = r(N)
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if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
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qui replace ``j''=`=`m' + `k'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
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local stop = 0
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}
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else {
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if `stop' != 0 {
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qui replace ``j''= `nbm_`j'' if ``j''==`m'
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qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
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di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
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local stop = 0
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}
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}
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}
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}
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}
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}
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}
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}
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}
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else { // Cas où l'option groupe est utilisée
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forvalues j = 1 / `nbitems' {
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local recoda_`j' = 0
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qui tab ``j'' if `gp' == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j') // Récupération des infos moda du temps 1pour chaque groupe
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local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
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local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
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qui tab ``j'' if `gp' == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
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local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
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local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
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qui tab ``=`j'+`nbitems''' if `gp' == 0, matrow(rect2_g0_`j') matcell(nbrt2_g0_`j') // Récupération des infos moda du temps 2 pour chaque groupe
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local minm`j'_t2_g0 = rect2_g0_`j'[1,1]
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local maxm`j'_t2_g0 = rect2_g0_`j'[r(r),1]
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qui tab ``=`j'+`nbitems''' if `gp' == 1 , matrow(rect2_g1_`j') matcell(nbrt2_g1_`j')
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local minm`j'_t2_g1 = rect2_g0_`j'[1,1]
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local maxm`j'_t2_g1 = rect2_g0_`j'[r(r),1]
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local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t2_g0',`minm`j'_t1_g1',`minm`j'_t2_g1') // Info moda pour l'item j
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local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
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local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
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if `minm_`j'' != 0 & `com_z' == 0 {
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local com_z = 1
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}
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//Recodage des réponses en 0, 1, 2, etc...
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forvalues r = 0/`=`maxm_`j''-1' {
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qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
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qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
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}
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// Vérif. Que toutes les modas sont utilisées & concordance entre temps
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forvalues m = 0/`=`nbm_`j''-1' {
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qui count if ``j'' == `m' & `gp' == 0
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local nb_rn1_g0 = r(N)
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qui count if ``j'' == `m' & `gp' == 1
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local nb_rn1_g1 = r(N)
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qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
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local nb_rn2_g0 = r(N)
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qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
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local nb_rn2_g1 = r(N)
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local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
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if `nb_rn' == 0 { // Une moda n'est pas utilisée
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local recoda_`j' = 1
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if `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t2_g0' | `m' < `minm`j'_t1_g1' | `m' < `minm`j'_t2_g1' { // La moda 0 n'est pas utilisée
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local stop = 1
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forvalues k = 1/`=`nbm_`j''-`m'' {
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qui count if ``j'' == `=`m' + `k'' & `gp' == 0
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local v`k'1_0 = r(N)
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qui count if ``j'' == `=`m' + `k'' & `gp' == 1
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local v`k'1_1 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
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local v`k'2_0 = r(N)
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qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
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local v`k'2_1 = r(N)
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if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
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qui replace ``j''= `=`m'+`k'' if ``j''==`m'
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|
|
qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
|
||
|
|
di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
|
||
|
|
local stop = 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t2_g0' | `m' >= `maxm`j'_t1_g1' | `m' >= `maxm`j'_t2_g1' { // La moda max n'est pas utilisée
|
||
|
|
local stop = 1
|
||
|
|
forvalues k = 1/`=`m'' {
|
||
|
|
qui count if ``j'' == `=`m' - `k'' & `gp' == 0
|
||
|
|
local v`k'1_0 = r(N)
|
||
|
|
qui count if ``j'' == `=`m' - `k'' & `gp' == 1
|
||
|
|
local v`k'1_1 = r(N)
|
||
|
|
qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
|
||
|
|
local v`k'2_0 = r(N)
|
||
|
|
qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
|
||
|
|
local v`k'2_1 = r(N)
|
||
|
|
if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0 ) & `stop' != 0 {
|
||
|
|
qui replace ``j''= `=`m' - `k'' if ``j''==`m'
|
||
|
|
qui replace ``=`j'+`nbitems'''= `=`m' - `k'' if ``=`j'+`nbitems'''==`m'
|
||
|
|
di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
|
||
|
|
local stop = 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else { // Moda central non utilisée
|
||
|
|
if runiform()>0.5{ // Tirage au sort pour regrouper
|
||
|
|
local stop = 1
|
||
|
|
forvalues k = 1/`m' {
|
||
|
|
qui count if ``j'' == `=`m' - `k'' & `gp' == 0
|
||
|
|
local v`k'1_0 = r(N)
|
||
|
|
qui count if ``j'' == `=`m' - `k'' & `gp' == 1
|
||
|
|
local v`k'1_1 = r(N)
|
||
|
|
qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
|
||
|
|
local v`k'2_0 = r(N)
|
||
|
|
qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
|
||
|
|
local v`k'2_1 = r(N)
|
||
|
|
if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
|
||
|
|
qui replace ``j''= `=`m'-`k'' if ``j''==`m'
|
||
|
|
qui replace ``=`j'+`nbitems'''=`=`m'-`k'' if ``=`j'+`nbitems'''==`m'
|
||
|
|
di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
|
||
|
|
local stop = 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local stop = 1
|
||
|
|
forvalues k = 1/`=`nbm_`j''-`m'' {
|
||
|
|
qui count if ``j'' == `=`m' + `k'' & `gp' == 0
|
||
|
|
local v`k'1_0 = r(N)
|
||
|
|
qui count if ``j'' == `=`m' + `k'' & `gp' == 1
|
||
|
|
local v`k'1_1 = r(N)
|
||
|
|
qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
|
||
|
|
local v`k'2_0 = r(N)
|
||
|
|
qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
|
||
|
|
local v`k'2_1 = r(N)
|
||
|
|
if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0{
|
||
|
|
qui replace ``j''=`=`m' + `k'' if ``j''==`m'
|
||
|
|
qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
|
||
|
|
di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
|
||
|
|
local stop = 0
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if `stop' != 0 {
|
||
|
|
qui replace ``j''= `nbm_`j'' if ``j''==`m'
|
||
|
|
qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
|
||
|
|
di "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
|
||
|
|
local stop = 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
if `com_z' == 1 {
|
||
|
|
di
|
||
|
|
di "WARNING : Automatic recoding, the first response category is 0. see {help rosali:help rosali}."
|
||
|
|
di
|
||
|
|
}
|
||
|
|
|
||
|
|
forvalues j =1/`nbitems' {
|
||
|
|
qui tab ``j'', matrow(rec) // Récupération des infos moda du temps 1
|
||
|
|
local nbm`j'_t1 = r(r)
|
||
|
|
|
||
|
|
qui tab ``=`j'+`nbitems''' // Récupération des infos moda du temps 2
|
||
|
|
local nbm`j'_t2 = r(r)
|
||
|
|
|
||
|
|
local nbm_`j' = max(`nbm`j'_t1', `nbm`j'_t2')
|
||
|
|
//Recodage des réponses en 0, 1, 2, etc...
|
||
|
|
forvalues r = 0/`=`nbm_`j''-1' {
|
||
|
|
qui replace ``j'' = `r' if ``j'' == `=rec[`=`r'+1',1]'
|
||
|
|
qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=rec[`=`r'+1',1]'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/* Calcul de nbmoda & nbdif */
|
||
|
|
forvalues j = 1/`nbitems' {
|
||
|
|
qui tab ``j''
|
||
|
|
local nbmoda_`j' = r(r)
|
||
|
|
local nbdif_`j' = r(r) - 1
|
||
|
|
}
|
||
|
|
|
||
|
|
local maxdif = 0
|
||
|
|
local nbmoda_sum = 0
|
||
|
|
forvalues j = 1/`nbitems' {
|
||
|
|
if `maxdif' < `nbdif_`j'' {
|
||
|
|
local maxdif = `nbdif_`j''
|
||
|
|
}
|
||
|
|
local nbmoda_sum = `nbmoda_sum' + `nbdif_`j''
|
||
|
|
}
|
||
|
|
|
||
|
|
/* Au moins 2 moda par item */
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
if `nbmoda_`j'' == 1 {
|
||
|
|
di as error "``j'' have only one response category, the analysis can be performed only if each item has at least 2 response categories"
|
||
|
|
error 198
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
local coln ""
|
||
|
|
forvalues j =1 /`nbitems' {
|
||
|
|
local coln "`coln' ``j''"
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix nbmod = J(2,`nbitems',.)
|
||
|
|
|
||
|
|
matrix colnames nbmod = `coln'
|
||
|
|
matrix rownames nbmod = NbModa Recoding
|
||
|
|
|
||
|
|
forvalues j = 1/`nbitems' {
|
||
|
|
matrix nbmod[1,`j'] = `nbmoda_`j''
|
||
|
|
matrix nbmod[2,`j'] = `recoda_`j''
|
||
|
|
}
|
||
|
|
|
||
|
|
*Erreur si plus de 200 difficultés
|
||
|
|
local nb_test = 0
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local nb_test = `nb_test'+`nbmoda_`j'' -1
|
||
|
|
}
|
||
|
|
|
||
|
|
if `nb_test' >= 200 {
|
||
|
|
di as error "The total number of items difficulties to be estimated must be less than 200 ({hi:moda} option option)."
|
||
|
|
error 198
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
|
||
|
|
local nbitp = 0
|
||
|
|
|
||
|
|
forvalues j = 1/`nbitems' {
|
||
|
|
if `nbmoda_`j'' >= 2 {
|
||
|
|
local nbitp = `nbitp' + 1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
qui count
|
||
|
|
local nbpat = r(N)
|
||
|
|
|
||
|
|
|
||
|
|
/*********************************
|
||
|
|
* AFFICHAGE INITIAL
|
||
|
|
*********************************/
|
||
|
|
di
|
||
|
|
di _col(5) "{hline 78}"
|
||
|
|
di _col(15) "Time 1" _col(42) "Time 2" _col(65) "Nb of Answer Cat."
|
||
|
|
di _col(5) "{hline 78}"
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
di as text _col(15) abbrev("``j''",20) _col(42) abbrev("``=`j'+`nbitems'''",20) _col(65) `nbmoda_`j''
|
||
|
|
}
|
||
|
|
di _col(5) "{hline 78}"
|
||
|
|
if "`group'" != "" {
|
||
|
|
di _col(10) "Nb of patients: " abbrev("`gp'",20) " 0 = `nbp_gp0' ;", abbrev("`gp'",20) " 1 = `nbp_gp1'"
|
||
|
|
di _col(5) "{hline 78}"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
di _col(10) "Nb. of patients: `nbpat'"
|
||
|
|
di _col(5) "{hline 78}"
|
||
|
|
}
|
||
|
|
di
|
||
|
|
if `nbitems' == 1 {
|
||
|
|
di as error "The analysis can only be performed with at least 2 items."
|
||
|
|
error 198
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
forvalues j = 1/`nbitems' {
|
||
|
|
if `nbmoda_`j'' == 2 {
|
||
|
|
di "WARNING: ``j'' has only 2 response categories, no distinction can be made between uniform or non-uniform recalibration."
|
||
|
|
}
|
||
|
|
if `nbmoda_`j'' == 1 {
|
||
|
|
di as error "Only `nbmoda_`j'' response categories of item ``j'' were used by the sample, the analysis cannot be performed."
|
||
|
|
error 198
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
if `nbmoda_`j'' == 0 {
|
||
|
|
di as error "No response categories of item ``j'' were used by the sample, the analysis cannot be performed."
|
||
|
|
error 198
|
||
|
|
exit
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di
|
||
|
|
if "`group'" != "" {
|
||
|
|
di _col(2) as text "For all models : - mean of the latent trait in `gp' 0 at time 1 is constrained at 0"
|
||
|
|
di _col(19) "- equality of variances between groups"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
di _col(2) as text "For all models : mean of the latent trait at time 1 is constrained at 0"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
/*********************************
|
||
|
|
* DEFINITION DES CONTRAINTES
|
||
|
|
*********************************/
|
||
|
|
|
||
|
|
if "`group'"!="" { // Contraintes si option groupe
|
||
|
|
*EGALITE ENTRE GROUPES A T1 (1-200)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
constraint `=0+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
*DIF UNIFORME A T1 (201-400)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=2/`nbdif_`j''{
|
||
|
|
constraint `=200+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp'=`p'*[1.``j'']1.`gp'-`p'*[1.``j'']0bn.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
*EGALITES ENTRE T1 et T2, groupe 0 (401-600)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
*EGALITES ENTRE T1 et T2, groupe 1 (601-800)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
constraint `=600+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
* RC COMMUNE (801-1000)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
constraint `=800+`maxdif'*(`j'-1)+`p'' [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
* RC UNIFORME, groupe 0 (1001-1200)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=2/`nbdif_`j''{
|
||
|
|
constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
* RC UNIFORME, groupe 1 (1201-1400)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=2/`nbdif_`j''{
|
||
|
|
constraint `=1200+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
*Sans interaction temps x groupe
|
||
|
|
constraint 1999 [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'=[/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
|
||
|
|
}
|
||
|
|
else { //Contraintes si pas d'option groupe
|
||
|
|
*EGALITE ENTRE T1 et T2 (401-600)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']:_cons = [`p'.``=`j'+`nbitems''']:_cons
|
||
|
|
}
|
||
|
|
}
|
||
|
|
*RC UNIFORME (1001-1200)
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=2/`nbdif_`j''{
|
||
|
|
constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']:_cons - [1.``j'']:_cons)=[`p'.``=`j'+`nbitems''']:_cons -[`p'.``j'']:_cons
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/*********************************
|
||
|
|
* MATRICE DES RESULTATS
|
||
|
|
*********************************/
|
||
|
|
matrix dif_rc=J(`nbitems',8,.)
|
||
|
|
matrix colnames dif_rc=DIFT1 DIFU RC RC_DIF RCG0 RCUG0 RCG1 RCUG1
|
||
|
|
local rown ""
|
||
|
|
|
||
|
|
forvalues j =1 /`nbitems' {
|
||
|
|
local rown "`rown' ``j''"
|
||
|
|
}
|
||
|
|
matrix rownames dif_rc = `rown'
|
||
|
|
|
||
|
|
*Nb modalité max
|
||
|
|
local nbdif_max = 0
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
if `nbdif_max' < `nbdif_`j'' {
|
||
|
|
local nbdif_max = `nbdif_`j''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
////////////////////////////////////////////////////////////////////////////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////
|
||
|
|
//////// PARTIE 1 : DIF A T1 ? ////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////
|
||
|
|
////////////////////////////////////////////////////////////////////////////////
|
||
|
|
|
||
|
|
if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "nodif"
|
||
|
|
|
||
|
|
di _dup(49) "_ "
|
||
|
|
di
|
||
|
|
di as input "PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT TIME 1"
|
||
|
|
|
||
|
|
*********************************
|
||
|
|
** MODEL B **
|
||
|
|
*********************************
|
||
|
|
|
||
|
|
local model ""
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
local model "`model' (`p'.``j''<-THETA@`p')"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading cons) var(0: THETA@v) var(1:THETA@v) latent(THETA) nocapslatent
|
||
|
|
/* Stockage des estimations du modèle */
|
||
|
|
estimates store modeldifB
|
||
|
|
matrix val_mB = r(table)
|
||
|
|
matrix esti_B = e(b)
|
||
|
|
|
||
|
|
|
||
|
|
/* Calcul des difficultés d'item (delta_j) */
|
||
|
|
matrix delta_mB=J(`nbitems',`=`nbdif_max'*2',.)
|
||
|
|
local name_partOneC ""
|
||
|
|
|
||
|
|
forvalues p=1/`nbdif_max' {
|
||
|
|
forvalues g=0/1 {
|
||
|
|
local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
local name_partOneL ""
|
||
|
|
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local name_partOneL "`name_partOneL' ``j''"
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix colnames delta_mB = `name_partOneC'
|
||
|
|
matrix rownames delta_mB = `name_partOneL'
|
||
|
|
matrix delta_mB_se=J(`nbitems',`=`nbdif_max'*2',.)
|
||
|
|
local name_partOneC_se ""
|
||
|
|
|
||
|
|
forvalues p=1/`nbdif_max' {
|
||
|
|
forvalues g=0/1 {
|
||
|
|
local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix colnames delta_mB_se = `name_partOneC_se'
|
||
|
|
matrix rownames delta_mB_se = `name_partOneL'
|
||
|
|
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
forvalues g=0/1{
|
||
|
|
qui lincom -[`p'.``j'']:`g'.`gp'
|
||
|
|
local delta`j'_`p'g`g'mB=r(estimate)
|
||
|
|
local delta`j'_`p'g`g'mB_se=r(se)
|
||
|
|
if `p'>1{
|
||
|
|
qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
|
||
|
|
local delta`j'_`p'g`g'mB = r(estimate)
|
||
|
|
local delta`j'_`p'g`g'mB_se = r(se)
|
||
|
|
}
|
||
|
|
matrix delta_mB[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB'
|
||
|
|
matrix delta_mB_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB_se'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix var_mB = (val_mB[1,"/var(THETA)#0bn.`gp'"]\val_mB[2,"/var(THETA)#0bn.`gp'"])
|
||
|
|
|
||
|
|
/*group effect*/
|
||
|
|
qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
|
||
|
|
local geffmB=r(estimate)
|
||
|
|
local segeffmB=r(se)
|
||
|
|
qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
|
||
|
|
local gcmBp=r(p)
|
||
|
|
local gcmBchi=r(chi2)
|
||
|
|
local gcmBdf=r(df)
|
||
|
|
|
||
|
|
|
||
|
|
*********************************
|
||
|
|
** MODEL A **
|
||
|
|
*********************************
|
||
|
|
|
||
|
|
local model ""
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
local model "`model' (`p'.``j''<-THETA@`p')"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading means) var(0: THETA@v) var(1:THETA@v) from(esti_B, skip) latent(THETA) nocapslatent
|
||
|
|
|
||
|
|
/* Stockage des estimations du modèle */
|
||
|
|
estimates store modeldifA
|
||
|
|
matrix val_mA = r(table)
|
||
|
|
matrix esti_A = e(b)
|
||
|
|
|
||
|
|
/* Calcul des difficultés d'item (delta_j) */
|
||
|
|
matrix delta_mA=J(`nbitems',`=`nbdif_max'*2',.)
|
||
|
|
local name_partOneC ""
|
||
|
|
|
||
|
|
forvalues p=1/`nbdif_max' {
|
||
|
|
forvalues g=0/1 {
|
||
|
|
local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
local name_partOneL ""
|
||
|
|
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local name_partOneL "`name_partOneL' ``j''"
|
||
|
|
}
|
||
|
|
matrix colnames delta_mA = `name_partOneC'
|
||
|
|
matrix rownames delta_mA = `name_partOneL'
|
||
|
|
matrix delta_mA_se=J(`nbitems',`=`nbdif_max'*2',.)
|
||
|
|
local name_partOneC_se ""
|
||
|
|
|
||
|
|
forvalues p=1/`nbdif_max' {
|
||
|
|
forvalues g=0/1 {
|
||
|
|
local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix colnames delta_mA_se = `name_partOneC_se'
|
||
|
|
matrix rownames delta_mA_se = `name_partOneL'
|
||
|
|
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
forvalues g=0/1{
|
||
|
|
qui lincom -[`p'.``j'']:`g'.`gp'
|
||
|
|
local delta`j'_`p'g`g'mA=r(estimate)
|
||
|
|
local delta`j'_`p'g`g'mA_se=r(se)
|
||
|
|
if `p'>1{
|
||
|
|
qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
|
||
|
|
local delta`j'_`p'g`g'mA = r(estimate)
|
||
|
|
local delta`j'_`p'g`g'mA_se = r(se)
|
||
|
|
}
|
||
|
|
matrix delta_mA[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA'
|
||
|
|
matrix delta_mA_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA_se'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
//Variance et se mA
|
||
|
|
matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
|
||
|
|
|
||
|
|
*********************************
|
||
|
|
*************MODEL C*************
|
||
|
|
*********************************
|
||
|
|
// Etape itérative si lrtest significatif
|
||
|
|
local nb_stepC = 0
|
||
|
|
local diftestp = 1
|
||
|
|
if `diftestp'<2{ /*If pvalue(LRtest)<0.05 then step C*/
|
||
|
|
di
|
||
|
|
di as input "PROCESSING STEP C"
|
||
|
|
di
|
||
|
|
|
||
|
|
/*test DIF pour chaque item*/
|
||
|
|
local boucle = 1
|
||
|
|
local stop = 0
|
||
|
|
while `boucle'<=`=`nbitp'-1' & `stop'==0{ /*on s'arrête quand on a libéré du DIF sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
|
||
|
|
local nb_stepC = `boucle'
|
||
|
|
local pajust=0.05/`=`nbitp'+1-`boucle''
|
||
|
|
/*réinitialisation de la matrice de test*/
|
||
|
|
matrix test_difu_`boucle'=J(`nbitems',3,.)
|
||
|
|
matrix colnames test_difu_`boucle'=chi_DIFU df_DIFU pvalueDIFU
|
||
|
|
matrix test_dif_`boucle'=J(`nbitems',3,.)
|
||
|
|
matrix colnames test_dif_`boucle'=chi_DIF df_DIF pvalueDIF
|
||
|
|
local nbsig=0
|
||
|
|
local minpval=1
|
||
|
|
local itemdif=0
|
||
|
|
if "`detail'" != ""{
|
||
|
|
|
||
|
|
di as text "Loop `boucle'"
|
||
|
|
di as text _col(5) "Adjusted alpha: " %6.4f `pajust'
|
||
|
|
di
|
||
|
|
di as text _col(10) "{hline 65}"
|
||
|
|
di as text _col(10) "Freed item" _col(31) "Chi-Square" _col(48) "DF" _col(57) "P-Value"
|
||
|
|
di as text _col(10) "{hline 65}"
|
||
|
|
}
|
||
|
|
/*boucle de test*/
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
//if `nbdif_`j'' > 2 {
|
||
|
|
local model ""
|
||
|
|
local listconst ""
|
||
|
|
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF déjà détecté sur l'item j*/
|
||
|
|
/*on libère le DIF de l'item i: pas de contraintes*/
|
||
|
|
forvalues k=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
|
||
|
|
if `k'!=`j' & `nbmoda_`j'' >= 2 {
|
||
|
|
if dif_rc[`k',1]==. | dif_rc[`k',1]==0 {/*pas de DIF sur item k: contraintes 1-200*/
|
||
|
|
forvalues p=1/`nbdif_`k''{
|
||
|
|
qui local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
|
||
|
|
qui constraint list `=0+`maxdif'*(`k'-1)+`p''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else{
|
||
|
|
if dif_rc[`k',2]!=. & dif_rc[`k',2]!= 0 & `nbmoda_`k'' > 2 { /*DIF U: contraintes 201-400*/
|
||
|
|
forvalues p=2/`nbdif_`k''{
|
||
|
|
qui local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
|
||
|
|
qui constraint list `=200+`maxdif'*(`k'-1)+`p''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
forvalues jj=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`jj''{
|
||
|
|
local model "`model' (`p'.``jj''<-THETA@`p')"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
|
||
|
|
estimates store modeldif3b`boucle'it`i'
|
||
|
|
|
||
|
|
*************************
|
||
|
|
*****test DIF item i*****
|
||
|
|
*************************
|
||
|
|
qui test [1.``j'']1.`gp'=[1.``j'']0bn.`gp'
|
||
|
|
if `nbmoda_`j'' > 2 {
|
||
|
|
forvalues p=2/`nbdif_`j''{
|
||
|
|
qui test [`p'.``j'']1.`gp'=[`p'.``j'']0bn.`gp', acc
|
||
|
|
}
|
||
|
|
}
|
||
|
|
matrix test_dif_`boucle'[`j',1]=(r(chi2),r(df),r(p))
|
||
|
|
|
||
|
|
/* Test DIF Uniforme */
|
||
|
|
if `nbmoda_`j'' > 2 {
|
||
|
|
qui test 2*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[2.``j'']1.`gp'-[2.``j'']0bn.`gp'
|
||
|
|
forvalues p=3/`nbdif_`j''{
|
||
|
|
qui test `p'*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp', acc
|
||
|
|
}
|
||
|
|
matrix test_difu_`boucle'[`j',1]=(r(chi2), r(df), r(p))
|
||
|
|
}
|
||
|
|
|
||
|
|
if test_dif_`boucle'[`j',3]<`pajust'{/*si DIF sur item i*/
|
||
|
|
local ++nbsig
|
||
|
|
if test_dif_`boucle'[`j',3]<`minpval'{
|
||
|
|
local minpval=test_dif_`boucle'[`j',3]
|
||
|
|
local itemdif=`j'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if "`detail'" != "" {
|
||
|
|
di as text _col(10) abbrev("``j''",15) as result _col(31) %6.3f test_dif_`boucle'[`j',1] _col(48) test_dif_`boucle'[`j',2] _col(57) %6.4f test_dif_`boucle'[`j',3]
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/*si nb de tests significatifs=0, on arrête*/
|
||
|
|
if `nbsig'==0{
|
||
|
|
local stop=1
|
||
|
|
if `boucle' == 1 {
|
||
|
|
if "`detail'" != "" {
|
||
|
|
di as text _col(10) "{hline 65}"
|
||
|
|
di
|
||
|
|
di as result "No significant test: no difference between groups detected, no DIF detected"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if "`detail'" != ""{
|
||
|
|
di as text _col(10) "{hline 65}"
|
||
|
|
di
|
||
|
|
di as result "No other significant tests"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
|
||
|
|
matrix dif_rc[`itemdif',1]=`boucle'
|
||
|
|
if "`detail'" != ""{
|
||
|
|
di as text _col(10) "{hline 65}"
|
||
|
|
di
|
||
|
|
di as result "Difference between groups on ``itemdif'' at time 1"
|
||
|
|
}
|
||
|
|
if `nbmoda_`itemdif'' > 2 {
|
||
|
|
if "`detail'" != "" {
|
||
|
|
|
||
|
|
di
|
||
|
|
di %~60s as text "Test of uniform difference"
|
||
|
|
di _col(10) "{hline 40}"
|
||
|
|
di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
|
||
|
|
di _col(10) as result %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(28) `=test_difu_`boucle'[`itemdif',2]' _col(40) %4.2f `=test_difu_`boucle'[`itemdif',3]'
|
||
|
|
di _col(10) as text "{hline 40}"
|
||
|
|
}
|
||
|
|
if test_difu_`boucle'[`itemdif',3]<0.05{ /*DIF NU détectée*/
|
||
|
|
matrix dif_rc[`itemdif',2]=0
|
||
|
|
di
|
||
|
|
di as result "``itemdif'' : Non-uniform differences of item difficulties between groups at T1"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
else{/*DIF U détectée*/
|
||
|
|
matrix dif_rc[`itemdif',2]=`boucle'
|
||
|
|
di
|
||
|
|
di as result "``itemdif'' : Uniform differences of item difficulties between groups at T1"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
// Différence entre groupes au temps 1 mais slmt 2 moda. donc pas de U ou NU
|
||
|
|
di _col(15) _dup(60) "-"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
local ++boucle
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/* MODELE FINAL DE LA PARTIE 1. Si DIFT1 détecté (=Au moins 2 boucles dans l'étape C)*/
|
||
|
|
if `nb_stepC' > 1 {
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
local model ""
|
||
|
|
local listconst ""
|
||
|
|
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200*/
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
|
||
|
|
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { /*DIF U: contraintes 201-400*/
|
||
|
|
forvalues p=2/`nbdif_`j''{
|
||
|
|
qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
|
||
|
|
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
local model "`model' (`p'.``j''<-THETA@`p')"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
|
||
|
|
/* Stockage des estimations du modèle */
|
||
|
|
estimates store modeldifCFin
|
||
|
|
matrix val_mC = r(table)
|
||
|
|
|
||
|
|
/* Calcul des difficultés d'item (delta_j) */
|
||
|
|
matrix delta_mCFin=J(`nbitems',`=`nbdif_max'*2',.)
|
||
|
|
local name_partOneC ""
|
||
|
|
forvalues p=1/`nbdif_max' {
|
||
|
|
forvalues g=0/1 {
|
||
|
|
local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
local name_partOneL ""
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local name_partOneL "`name_partOneL' ``j''"
|
||
|
|
}
|
||
|
|
matrix colnames delta_mCFin = `name_partOneC'
|
||
|
|
matrix rownames delta_mCFin = `name_partOneL'
|
||
|
|
|
||
|
|
matrix delta_mCFin_se=J(`nbitems',`=`nbdif_max'*2',.)
|
||
|
|
local name_partOneC_se ""
|
||
|
|
|
||
|
|
forvalues p=1/`nbdif_max' {
|
||
|
|
forvalues g=0/1 {
|
||
|
|
local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
matrix colnames delta_mCFin_se = `name_partOneC_se'
|
||
|
|
matrix rownames delta_mCFin_se = `name_partOneL'
|
||
|
|
|
||
|
|
forvalues j=1/`nbitems'{
|
||
|
|
forvalues p=1/`nbdif_`j''{
|
||
|
|
forvalues g=0/1{
|
||
|
|
qui lincom -[`p'.``j'']:`g'.`gp'
|
||
|
|
local delta`j'_`p'g`g'mCFin=r(estimate)
|
||
|
|
local delta`j'_`p'g`g'mCFin_se=r(se)
|
||
|
|
if `p'>1{
|
||
|
|
qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
|
||
|
|
local delta`j'_`p'g`g'mCFin = r(estimate)
|
||
|
|
local delta`j'_`p'g`g'mCFin_se = r(se)
|
||
|
|
}
|
||
|
|
matrix delta_mCFin[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin'
|
||
|
|
matrix delta_mCFin_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin_se'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if "`group'" != "" { //Variance et se mA
|
||
|
|
matrix var_mC = (val_mC[1,"/var(THETA)#0bn.`gp'"]\val_mC[2,"/var(THETA)#0bn.`gp'"])
|
||
|
|
}
|
||
|
|
/*group effect*/
|
||
|
|
qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
|
||
|
|
local geffmCFin=r(estimate)
|
||
|
|
local segeffmCFin=r(se)
|
||
|
|
qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
|
||
|
|
local gcmCFinp=r(p)
|
||
|
|
local gcmCFinchi=r(chi2)
|
||
|
|
local gcmCFindf=r(df)
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
*********************************
|
||
|
|
*** BILAN ***
|
||
|
|
*********************************
|
||
|
|
|
||
|
|
if "`group'" != "" & "`nodif'" == "" {
|
||
|
|
di
|
||
|
|
di %~84s as result "SUMMARY"
|
||
|
|
di as result _col(2) "{hline 80}"
|
||
|
|
di as result _col(18) "Difference in"
|
||
|
|
di as result _col(2) "Item" _col(18) "groups at T1" _col(36) "Recalibration" _col(54) "RC " abbrev("`gp'",10) " 0" _col(72) "RC " abbrev("`gp'",10) " 1"
|
||
|
|
di as result _col(2) "{hline 80}"
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local RC
|
||
|
|
local RCg0
|
||
|
|
local RCg1
|
||
|
|
local difft1
|
||
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
|
||
|
|
local RC "Common"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
|
||
|
|
local RC "Differential"
|
||
|
|
}
|
||
|
|
if `nbmoda_`j'' > 2 {
|
||
|
|
if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
|
||
|
|
local RCg0 "Uniform"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',6] == 0) {
|
||
|
|
local RCg0 "Non-uniform"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
|
||
|
|
local RCg1 "Uniform"
|
||
|
|
}
|
||
|
|
if ( dif_rc[`j',8] == 0) {
|
||
|
|
local RCg1 "Non-uniform"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',1] != . ) {
|
||
|
|
if (dif_rc[`j',2]!=0) {
|
||
|
|
local difft1 "Uniform"
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local difft1 "Non-uniform"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if dif_rc[`j',6] != . {
|
||
|
|
local RCg0 " X "
|
||
|
|
}
|
||
|
|
if dif_rc[`j',8] != . {
|
||
|
|
local RCg1 " X "
|
||
|
|
}
|
||
|
|
if dif_rc[`j',1] != . {
|
||
|
|
local difft1 " X "
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di as result _col(2) abbrev("``j''",15) as text _col(18) "`difft1'" _col(36) "`RC'" _col(54) "`RCg0'" _col(72) "`RCg1'"
|
||
|
|
}
|
||
|
|
di as result _col(2) "{hline 80}"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
else if "`group'" != "" & "`nodif'" != "" {
|
||
|
|
di
|
||
|
|
di %~90s as result "SUMMARY"
|
||
|
|
di as result _col(10) "{hline 70}"
|
||
|
|
di as result _col(10) "Item" _col(26) "Recalibration" _col(46) "RC `gp' 0" _col(62) "RC `gp' 1"
|
||
|
|
di _col(10) "{hline 70}"
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local RC
|
||
|
|
local RCg0
|
||
|
|
local RCg1
|
||
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
|
||
|
|
local RC "Common"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
|
||
|
|
local RC "Differential"
|
||
|
|
}
|
||
|
|
if `nbmoda_`j'' > 2 {
|
||
|
|
if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
|
||
|
|
local RCg0 "Uniform"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',6] == 0) {
|
||
|
|
local RCg0 "Non-uniform"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
|
||
|
|
local RCg1 "Uniform"
|
||
|
|
}
|
||
|
|
if ( dif_rc[`j',8] == 0) {
|
||
|
|
local RCg1 "Non-uniform"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
if dif_rc[`j',6] != . {
|
||
|
|
local RCg0 " X "
|
||
|
|
}
|
||
|
|
if dif_rc[`j',8] != . {
|
||
|
|
local RCg1 " X "
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di as result _col(10) "``j''" as text _col(26) "`RC'" _col(44) "`RCg0'" _col(62) "`RCg1'"
|
||
|
|
}
|
||
|
|
di as result _col(10) "{hline 70}"
|
||
|
|
}
|
||
|
|
else if "`group'" == "" {
|
||
|
|
di
|
||
|
|
di %~60s as result "SUMMARY"
|
||
|
|
di as result _col(10) "{hline 40}"
|
||
|
|
di _col(10) "Item" _col(36) "Recalibration"
|
||
|
|
di _col(10) "{hline 40}"
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
local RC
|
||
|
|
if dif_rc[`j',3] != . {
|
||
|
|
if `nbmoda_`j'' > 2 {
|
||
|
|
if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
|
||
|
|
local RC "Uniform"
|
||
|
|
}
|
||
|
|
if (dif_rc[`j',6] == 0) {
|
||
|
|
local RC "Non-uniform"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
local RC " X "
|
||
|
|
}
|
||
|
|
}
|
||
|
|
di as result _col(10) "``j''" as text _col(38) "`RC'"
|
||
|
|
}
|
||
|
|
di as result _col(10) "{hline 40}"
|
||
|
|
di
|
||
|
|
}
|
||
|
|
|
||
|
|
matrix dif_detect = J(1,`nbitems',.)
|
||
|
|
local numdif=1
|
||
|
|
forvalues j=1/`nbitems' {
|
||
|
|
if dif_rc[`j',1] != . {
|
||
|
|
matrix dif_detect[1,`numdif']=`j'
|
||
|
|
local numdif = `numdif'+1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return matrix difitems = dif_detect
|
||
|
|
|
||
|
|
capture qui use `saverspcm', clear
|
||
|
|
|
||
|
|
end
|