*! version 2.4 june2020
*! Myriam Blanchin - Priscilla Brisson
************************************************************************************************************
* ROSALI: RespOnse-Shift ALgorithm at Item-level
* Response-shift detection based on Rasch models family
*
* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, optimization */
* Version 2.3 : December, 2019 (Priscilla Brisson) /* option detail, + petites corrections */
* Version 2.4 : June, 2020 (Myriam Blanchin) /* debug option detail + step C, modifs sorties et help */
*
* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
* myriam.blanchin@univ-nantes.fr
*
* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
* priscilla.brisson@univ-nantes.fr
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
************************************************************************************************************/
program define rosali, rclass
timer clear 1
timer on 1
syntax varlist(min=2 numeric) [if] [,GROUP(varlist) NODIF PRO DETail]
preserve
version 15
tempfile saverspcm
capture qui save `saverspcm',replace
local save1=_rc
if "`if'"!="" {
qui keep `if'
}
if "`pro'" != "" {
di "START"
}
/**************************************************************************/
set more off
set matsize 5000
constraint drop _all
local gp "`group'"
tokenize `varlist'
local nbitems:word count `varlist'
/* Vérif nb d'items pair */
local mod=mod(`nbitems',2)
if `mod'!=0 {
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"
error 198
exit
}
local nbitems=`nbitems'/2
if "`group'"=="" & "`nodif'"!="" {
di as error "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
error 198
exit
}
local nbc: word count `group'
if `nbc' >= 2 {
di as error "Only one variable can be used for group option ({hi:group} option). Please correct this option."
error 198
exit
}
/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
if "`group'"!="" {
qui tab `group'
local nbgrp = r(r)
if `nbgrp' != 2 {
di as error "Only 2 groups are possible for the group option ({hi:group} option). Please correct this option."
error 420
exit
}
}
/* recoder la variable de groupe en 0, 1*/
if "`group'"!="" {
qui tab `gp', matrow(rep)
qui matrix list rep
if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
forvalues i=1/`=rowsof(rep)'{
qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
}
}
forvalues g = 0/1 {
qui tab `gp' if `gp' == `g'
local nbp_gp`g' = r(N)
}
}
/*item rename*/
/*
Items au temps 1 : 1 à nbitems ``j''
Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
*/
local com_z = 0 // Indicatrice de recodage
/*verif modalités répondues*/
if "`gp'" == "" { // Si pas d'option groupe
forvalues j = 1 / `nbitems' {
local recoda_`j' = 0
qui tab ``j'', matrow(rect1_`j') // Récupération des infos moda du temps 1
local minm`j'_t1 = rect1_`j'[1,1]
local maxm`j'_t1 = rect1_`j'[r(r),1]
qui tab ``=`j'+`nbitems''', matrow(rect2_`j') // Récupération des infos moda du temps 2
local minm`j'_t2 = rect2_`j'[1,1]
local maxm`j'_t2 = rect2_`j'[r(r),1]
local minm_`j' = min(`minm`j'_t1',`minm`j'_t2') // Info moda pour l'item j
local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
local nbm_`j' = `=`maxm_`j''-`minm_`j'''
if `minm_`j'' != 0 & `com_z' == 0 {
local com_z = 1
}
//Recodage des réponses en 0, 1, 2, etc...
forvalues r = 0/`=`maxm_`j''-1' {
qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
}
// Vérif. Que toutes les modas sont utilisées & concordance entre temps
forvalues m = 0/`nbm_`j'' {
qui count if ``j'' == `m'
local nb_rn1 = r(N)
qui count if ``=`j'+`nbitems''' == `m'
local nb_rn2 = r(N)
local nb_rn = min(`nb_rn1',`nb_rn2')
if `nb_rn' == 0 { // Une moda n'est pas utilisée
local recoda_`j' = 1
if `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
local stop = 1
forvalues k = 1/`=`nbm_`j''-`m'' {
qui count if ``j'' == `=`m' + `k''
local v`k'1 = r(N)
qui count if ``=`j'+`nbitems''' == `=`m' + `k''
local v`k'2 = r(N)
if (`v`k'1' != 0 | `v`k'2' != 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 `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' { // La (ou les) moda max ne sont pas utilisée(s)
local stop = 1
forvalues k = 1/`m' {
qui count if ``j'' == `=`m' - `k''
local v`k'1 = r(N)
qui count if ``=`j'+`nbitems''' == `=`m' - `k''
local v`k'2 = r(N)
if (`v`k'1' != 0 | `v`k'2' != 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 runiform()>0.5{ // Tirage au sort pour regrouper
local stop = 1
forvalues k = 1/`m' {
qui count if ``j'' == `=`m' - `k''
local v`k'1 = r(N)
qui count if ``=`j'+`nbitems''' == `=`m' - `k''
local v`k'2 = r(N)
if (`v`k'1' != 0 | `v`k'2' != 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''
local v`k'1 = r(N)
qui count if ``=`j'+`nbitems''' == `=`m' + `k''
local v`k'2 = r(N)
if (`v`k'1' != 0 | `v`k'2' != 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
}
}
}
}
}
}
}
}
}
else { // Cas où l'option groupe est utilisée
forvalues j = 1 / `nbitems' {
local recoda_`j' = 0
qui tab ``j'' if `gp' == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j') // Récupération des infos moda du temps 1pour chaque groupe
local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
qui tab ``j'' if `gp' == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
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
local minm`j'_t2_g0 = rect2_g0_`j'[1,1]
local maxm`j'_t2_g0 = rect2_g0_`j'[r(r),1]
qui tab ``=`j'+`nbitems''' if `gp' == 1 , matrow(rect2_g1_`j') matcell(nbrt2_g1_`j')
local minm`j'_t2_g1 = rect2_g0_`j'[1,1]
local maxm`j'_t2_g1 = rect2_g0_`j'[r(r),1]
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
local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
if `minm_`j'' != 0 & `com_z' == 0 {
local com_z = 1
}
//Recodage des réponses en 0, 1, 2, etc...
forvalues r = 0/`=`maxm_`j''-1' {
qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
}
// Vérif. Que toutes les modas sont utilisées & concordance entre temps
forvalues m = 0/`=`nbm_`j''-1' {
qui count if ``j'' == `m' & `gp' == 0
local nb_rn1_g0 = r(N)
qui count if ``j'' == `m' & `gp' == 1
local nb_rn1_g1 = r(N)
qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
local nb_rn2_g0 = r(N)
qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
local nb_rn2_g1 = r(N)
local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
if `nb_rn' == 0 { // Une moda n'est pas utilisée
local recoda_`j' = 1
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
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 `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'"])
*************************************************************
***********************AFFICHAGE*****************************
*************************************************************
//Affichage modèle A
di
di as input "PROCESSING STEP A"
di
if "`detail'" != "" {
/* Affichage des estimations des difficultés modèle A */
di _col(5) as text "{ul:MODEL A:} Overall measurement non-invariance between groups"
di
di %~85s as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 65}"
di _col(31) as text abbrev("`gp'",20) "=0" _col(57) abbrev("`gp'",20) "=1"
di _col(10) "{hline 65}"
forvalues j=1/`nbitems' {
di as text _col(10) abbrev("``j''", 18)
forvalues p=1/`nbdif_`j'' {
di as text _col(10) "`p'" as result _col(30) %6.2f `delta`j'_`p'g0mA' %6.2f " (" %3.2f `delta`j'_`p'g0mA_se' ")" _col(56) %6.2f `delta`j'_`p'g1mA' " (" %3.2f `delta`j'_`p'g1mA_se' ")"
}
}
di as text _col(10) "{hline 65}"
/* Affichage des estimations sur le trait latent du modèle A */
di
di %~85s as text "Latent trait distribution"
di _col(10) "{hline 65}"
di _col(31) "Estimate" _col(57) "Standard error"
di _col(10) "{hline 65}"
di _col(10) "Variance" as result _col(31) %6.2f `=var_mA[1,1]' _col(55) %6.2f `=var_mA[2,1]'
di _col(10) as text "Group effect" as result _col(31) "0 (constrained)"
di _col(10) as text "{hline 65}"
di
di _col(10) as text "No group effect: equality of the latent trait means between groups"
di _col(10) as text "All item difficulties are freely estimated in both groups"
di
}
//*Affichage modèle B
di
di as input "PROCESSING STEP B"
di
/* Affichage des estimations des difficultés modèle B */
if "`detail'" != "" {
di _col(5) as text "{ul:MODEL B:} Overall measurement invariance between groups"
di
di %~85s as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 65}"
di _col(31) abbrev("`gp'",20) "=0" _col(57) abbrev("`gp'",20) "=1"
di _col(10) "{hline 65}"
forvalues j=1/`nbitems' {
di _col(10) as text "``j''"
forvalues p=1/`nbdif_`j'' {
di as text _col(10) "`p'" as result _col(30) %6.2f `delta`j'_`p'g0mB' " (" %3.2f `delta`j'_`p'g0mB_se' ")" _col(56) %6.2f `delta`j'_`p'g1mB' " (" %3.2f `delta`j'_`p'g1mB_se' ")"
}
}
di _col(10) as text "{hline 65}"
/* Affichage des estimations sur le trait latent du modèle B */
di
di %~85s as text "Latent trait distribution"
di _col(10) "{hline 65}"
di _col(28) "Estimate" _col(42) "Standard error" _col(62) "P-value"
di _col(10) "{hline 65}"
di _col(10) "Variance" as result _col(28) %6.2f `=var_mB[1,1]' _col(40) %6.2f `=var_mB[2,1]'
di _col(10) as text "Group effect" as result _col(28) %6.2f `geffmB' _col(40) %6.2f `segeffmB' _col(62) %6.4f `gcmBp'
di _col(10) as text "{hline 65}"
di
di _col(10) as text "Group effect estimated: mean of the latent trait of group 1 freely estimated"
di _col(10) "Equality of the item difficulties between groups"
di
}
*****************************************************
* Modèle A vs Modèle B *
*****************************************************
qui lrtest modeldifA modeldifB
local diftestp=r(p)
local diftestchi=r(chi2)
local diftestdf=r(df)
if "`detail'" != "" {
//affichage lrtest
di as input "LIKELIHOOD-RATIO TEST"
di
di %~60s "Model A vs Model B"
di _col(10) "{hline 40}"
di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
di _col(10) as result %6.2f `diftestchi' _col(28) %2.0f `diftestdf' _col(40) %6.4f `diftestp'
di _col(10) as text "{hline 40}"
di
}
if `diftestp'<0.05{
di as result "DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS LIKELY"
}
else{
di as result "NO DIFFERENCE BETWEEN GROUPS DETECTED"
}
*********************************
*************MODEL C*************
*********************************
// Etape itérative si lrtest significatif
local nb_stepC = 0
if `diftestp'<0.05{ /*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)
}
}
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
//////// PARTIE 2 : RECALIBRATION ? ////////
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
di
di as text _dup(49) "_ "
di
if "`group'" != "" {
di as input "PART 2 : DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIME (RECALIBRATION)"
}
else {
di as input "DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIME (RECALIBRATION)"
}
*********************************
** MODEL 2 **
*********************************
local listconst "" // liste des contraintes si option groupe
local listconst_g "" //LIste des contraintes sans option groupe (Notation peu logique !!)
forvalues j=1/`nbitems'{
if "`group'" == "" { // Contraintes pas de RC pour tous les items
forvalues p=1/`nbdif_`j''{
local listconst_g "`listconst_g' `=400+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
}
}
else {
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 {/*pas de DIF à T1 sur item k: contraintes 1*/
forvalues p=1/`nbdif_`j''{
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 { /*diff T1 U: contraintes 200*/
forvalues p=2/`nbdif_`j''{
local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
}
}
}
forvalues p=1/`nbdif_`j''{ /* egalites entre temps : groupe 0 (401-600)*/
local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
}
forvalues p=1/`nbdif_`j''{ /* egalites entre temps : groupe 1 (601-800)*/
local listconst "`listconst' `=600+`maxdif'*(`j'-1)+`p''"
qui constraint list `=600+`maxdif'*(`j'-1)+`p''
}
}
}
local model ""
forvalues j=1/`nbitems'{
forvalues p=1/`nbdif_`j''{
local model "`model' (`p'.``j''<-THETA1@`p')(`p'.``=`j'+`nbitems'''<-THETA2@`p')"
}
}
if "`group'" != "" {
*di "gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') latent(THETA1 THETA2) nocapslatent"
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') latent(THETA1 THETA2) nocapslatent
}
else {
qui gsem `model', mlogit tol(0.01) iterate(100) means(THETA1@0 THETA2@m20) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) constraint(`listconst_g') latent(THETA1 THETA2) nocapslatent
}
/*Stockage des données du modèle 2 */
estimates store model2
matrix val_m2 = r(table)
matrix esti_2 = e(b)
if "`group'" != "" {
matrix var_m2 = (val_m2[1,"/var(THETA1)#0bn.`gp'"],val_m2[1,"/var(THETA2)#0bn.`gp'"]\val_m2[2,"/var(THETA1)#0bn.`gp'"],val_m2[2,"/var(THETA2)#0bn.`gp'"])
matrix covar_m2 = (val_m2[1,"/cov(THETA1,THETA2)#0.`gp'"],val_m2[1,"/cov(THETA1,THETA2)#1.`gp'"]\val_m2[2,"/cov(THETA1,THETA2)#0.`gp'"],val_m2[2,"/cov(THETA1,THETA2)#1.`gp'"]\val_m2[4,"/cov(THETA1,THETA2)#0.`gp'"],val_m2[4,"/cov(THETA1,THETA2)#1.`gp'"])
}
else {
matrix var_m2 = (val_m2[1,"/var(THETA1)"],val_m2[1,"/var(THETA2)"]\val_m2[2,"/var(THETA1)"],val_m2[2,"/var(THETA2)"])
matrix covar_m2 = (val_m2[1,"/cov(THETA1,THETA2)"]\val_m2[2,"/cov(THETA1,THETA2)"]\val_m2[4,"/cov(THETA1,THETA2)"])
}
/*group effect*/
if "`group'" != "" {
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
local geffm2=r(estimate)
local segeffm2=r(se)
local ubgeffm2 = r(ub)
local lbgeffm2 = r(lb)
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
local gpm2p=r(p)
local gpm2chi=r(chi2)
local gpm2df=r(df)
}
/*time effect*/
if "`group'" != "" {
qui lincom [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'
local teffm2=r(estimate)
local seteffm2=r(se)
local ubteffm2 = r(ub)
local lbteffm2 = r(lb)
qui test [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
local tm2p=r(p)
local tm2chi=r(chi2)
local tm2df=r(df)
}
else {
qui lincom [/]:mean(THETA2) /* [/]:mean(THETA1)*/
local teffm2=r(estimate)
local seteffm2=r(se)
local ubteffm2 = r(ub)
local lbteffm2 = r(lb)
qui test [/]:mean(THETA2) = 0 /* [/]:mean(THETA1)*/
local tm2p=r(p)
local tm2chi=r(chi2)
local tm2df=r(df)
}
*INTERACTION
if "`group'" != "" {
qui lincom [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp'
local interm2=r(estimate)
local seinterm2=r(se)
local ubinterm2 = r(ub)
local lbinterm2 = r(lb)
qui test [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp' = 0
local interm2p=r(p)
local interm2chi=r(chi2)
local interm2df=r(df)
}
if "`group'" != "" {
matrix mod2 = J(7,`=`nbmoda_sum'*4+6',.)
local name_partTwoC ""
forvalues j = 1/`nbitems' {
forvalues p=1/`nbdif_`j'' {
forvalues t=1/2 {
forvalues g = 0/1 {
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_gp`g'_t`t'"
}
}
}
}
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) GROUP_Effect TIME_Effect INTER_TxG "
matrix colnames mod2 = `name_partTwoC'
matrix rownames mod2 = Estimate se Upper_b Lower_b Chi_square DF pvalue
}
else {
matrix mod2 = J(7,`=`nbmoda_sum'*2+4',.)
local name_partTwoC ""
forvalues j = 1/`nbitems' {
forvalues p=1/`nbdif_`j'' {
forvalues t=1/2 {
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_t`t'"
}
}
}
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) TIME_Effect "
matrix colnames mod2 = `name_partTwoC'
matrix rownames mod2 = Estimate se Upper_b Lower_b Chi_square DF pvalue
}
*Difficultés
forvalues j=1/`nbitems'{
forvalues p=1/`nbdif_`j''{
forvalues t=1/2{
if "`group'" != "" { // groupe binaire
forvalues g=0/1 {
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
local delta`t'_`j'_`p'g`g'm2= r(estimate)
local delta`t'_`j'_`p'g`g'm2_se= r(se)
local delta`t'_`j'_`p'g`g'm2_ub=r(ub)
local delta`t'_`j'_`p'g`g'm2_lb=r(lb)
local delta`t'_`j'_`p'g`g'm2_p=r(p)
if `p'>1 {
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp' - [`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
local delta`t'_`j'_`p'g`g'm2=r(estimate)
local delta`t'_`j'_`p'g`g'm2_se=r(se)
local delta`t'_`j'_`p'g`g'm2_ub=r(ub)
local delta`t'_`j'_`p'g`g'm2_lb=r(lb)
local delta`t'_`j'_`p'g`g'm2_p=r(p)
}
local place = 0
local compt = 1
while `compt' < `j' {
local place = `place' + `nbdif_`compt''
local ++compt
}
if `t' == 1 {
matrix mod2[1,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2'
matrix mod2[2,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_se'
matrix mod2[3,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_ub'
matrix mod2[4,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_lb'
matrix mod2[7,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm2_p'
}
if `t' == 2 {
matrix mod2[1,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2'
matrix mod2[2,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_se'
matrix mod2[3,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_ub'
matrix mod2[4,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_lb'
matrix mod2[7,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm2_p'
}
}
}
else { // groupe unique (=gp0)
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']_cons
local delta`t'_`j'_`p'g0m2= r(estimate)
local delta`t'_`j'_`p'g0m2_se= r(se)
local delta`t'_`j'_`p'g0m2_ub=r(ub)
local delta`t'_`j'_`p'g0m2_lb=r(lb)
local delta`t'_`j'_`p'g0m2_p=r(p)
if `p'>1{
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']_cons - [`p'.``=(`t'-1)*`nbitems'+`j''']_cons
local delta`t'_`j'_`p'g0m2=r(estimate)
local delta`t'_`j'_`p'g0m2_se=r(se)
local delta`t'_`j'_`p'g0m2_ub=r(ub)
local delta`t'_`j'_`p'g0m2_lb=r(lb)
local delta`t'_`j'_`p'g0m2_p=r(p)
}
local place = 0
local compt = 1
while `compt' < `j' {
local place = `place' + `nbdif_`compt''
local ++compt
}
if `t' == 1 {
matrix mod2[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2'
matrix mod2[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_se'
matrix mod2[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_ub'
matrix mod2[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_lb'
matrix mod2[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_p'
}
if `t' == 2 {
matrix mod2[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2'
matrix mod2[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_se'
matrix mod2[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_ub'
matrix mod2[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_lb'
matrix mod2[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m2_p'
}
}
}
}
}
if "`group'" != "" {
matrix mod2[1,`=4*`nbmoda_sum'+1'] = (val_m2[1,"/var(THETA1)#0bn.`gp'"], val_m2[1,"/var(THETA2)#0bn.`gp'"])
matrix mod2[2,`=4*`nbmoda_sum'+1'] = (val_m2[2,"/var(THETA1)#0bn.`gp'"],val_m2[2,"/var(THETA2)#0bn.`gp'"])
matrix mod2[3,`=4*`nbmoda_sum'+1'] = (val_m2[6,"/var(THETA1)#0bn.`gp'"],val_m2[6,"/var(THETA2)#0bn.`gp'"])
matrix mod2[4,`=4*`nbmoda_sum'+1'] = (val_m2[5,"/var(THETA1)#0bn.`gp'"],val_m2[5,"/var(THETA2)#0bn.`gp'"])
matrix mod2[1,`=4*`nbmoda_sum'+2+1'] = (val_m2[1,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod2[2,`=4*`nbmoda_sum'+2+1'] = (val_m2[2,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod2[3,`=4*`nbmoda_sum'+2+1'] = (val_m2[6,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod2[4,`=4*`nbmoda_sum'+2+1'] = (val_m2[5,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod2[1,`=4*`nbmoda_sum'+2+1+1'] = `geffm2'
matrix mod2[2,`=4*`nbmoda_sum'+2+1+1'] = `segeffm2'
matrix mod2[3,`=4*`nbmoda_sum'+2+1+1'] = `ubgeffm2'
matrix mod2[4,`=4*`nbmoda_sum'+2+1+1'] = `lbgeffm2'
matrix mod2[5,`=4*`nbmoda_sum'+2+1+1'] = `gpm2chi'
matrix mod2[6,`=4*`nbmoda_sum'+2+1+1'] = `gpm2df'
matrix mod2[7,`=4*`nbmoda_sum'+2+1+1'] = `gpm2p'
matrix mod2[1,`=4*`nbmoda_sum'+2+1+1+1'] = `teffm2'
matrix mod2[2,`=4*`nbmoda_sum'+2+1+1+1'] = `seteffm2'
matrix mod2[3,`=4*`nbmoda_sum'+2+1+1+1'] = `ubteffm2'
matrix mod2[4,`=4*`nbmoda_sum'+2+1+1+1'] = `lbteffm2'
matrix mod2[5,`=4*`nbmoda_sum'+2+1+1+1'] = `tm2chi'
matrix mod2[6,`=4*`nbmoda_sum'+2+1+1+1'] = `tm2df'
matrix mod2[7,`=4*`nbmoda_sum'+2+1+1+1'] = `tm2p'
matrix mod2[1,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2'
matrix mod2[2,`=4*`nbmoda_sum'+2+1+1+1+1'] = `seinterm2'
matrix mod2[3,`=4*`nbmoda_sum'+2+1+1+1+1'] = `ubinterm2'
matrix mod2[4,`=4*`nbmoda_sum'+2+1+1+1+1'] = `lbinterm2'
matrix mod2[5,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2chi'
matrix mod2[6,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2df'
matrix mod2[7,`=4*`nbmoda_sum'+2+1+1+1+1'] = `interm2p'
}
else {
matrix mod2[1,`=2*`nbmoda_sum'+1'] = (val_m2[1,"/var(THETA1)"],val_m2[1,"/var(THETA2)"])
matrix mod2[2,`=2*`nbmoda_sum'+1'] = (val_m2[2,"/var(THETA1)"],val_m2[2,"/var(THETA2)"])
matrix mod2[3,`=2*`nbmoda_sum'+1'] = (val_m2[6,"/var(THETA1)"],val_m2[6,"/var(THETA2)"])
matrix mod2[4,`=2*`nbmoda_sum'+1'] = (val_m2[5,"/var(THETA1)"],val_m2[5,"/var(THETA2)"])
matrix mod2[1,`=2*`nbmoda_sum'+2+1'] = (val_m2[1,"/cov(THETA1,THETA2)"])
matrix mod2[2,`=2*`nbmoda_sum'+2+1'] = (val_m2[2,"/cov(THETA1,THETA2)"])
matrix mod2[3,`=2*`nbmoda_sum'+2+1'] = (val_m2[6,"/cov(THETA1,THETA2)"])
matrix mod2[4,`=2*`nbmoda_sum'+2+1'] = (val_m2[5,"/cov(THETA1,THETA2)"])
matrix mod2[1,`=2*`nbmoda_sum'+2+1+1'] = `teffm2'
matrix mod2[2,`=2*`nbmoda_sum'+2+1+1'] = `seteffm2'
matrix mod2[3,`=2*`nbmoda_sum'+2+1+1'] = `ubteffm2'
matrix mod2[4,`=2*`nbmoda_sum'+2+1+1'] = `lbteffm2'
matrix mod2[5,`=2*`nbmoda_sum'+2+1+1'] = `tm2chi'
matrix mod2[6,`=2*`nbmoda_sum'+2+1+1'] = `tm2df'
matrix mod2[7,`=2*`nbmoda_sum'+2+1+1'] = `tm2p'
}
*********************************
** MODEL 1 **
*********************************
/*PCM longitudinal, no true change, group effect, interaction*/
local listconst ""
forvalues j=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
if dif_rc[`j',1]==. | dif_rc[`j',1]==0 {/*pas de DIF sur item k: contraintes 1*/
forvalues p=1/`nbdif_`j''{
local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
}
}
else{
if `nbdif_`j'' > 1 {
if dif_rc[`j',2]!=. & dif_rc[`j',2] != 0 { /*diff T1 U: contraintes 201*/
forvalues p=2/`nbdif_`j''{
local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
}
}
}
}
}
local model ""
forvalues j=1/`nbitems'{
forvalues p=1/`nbdif_`j''{
local model "`model' (`p'.``j''<-THETA1@`p')(`p'.``=`j'+`nbitems'''<-THETA2@`p')"
}
}
if "`group'"!="" {
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@0) means(1: THETA1@m1 THETA2@m1) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
}
else {
qui gsem `model', mlogit tol(0.01) iterate(100) means(THETA1@0 THETA2@0) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
}
/* Stockage des estimations du modèle 1 */
estimates store model1
matrix val_m1 = r(table)
/* Calcul des difficultés d'item (delta_j) */
matrix delta_m1 = J(`nbitems',`=`nbdif_max'*4',.)
local name_partTwoC ""
forvalues p=1/`nbdif_max' {
forvalues t=1/2 {
forvalues g = 0/1 {
local name_partTwoC "`name_partTwoC' delta_t`t'_`p'_gp`g'"
}
}
}
local name_partTwoL ""
forvalues j=1/`=`nbitems'*2' {
if `j' <= `nbitems' {
local name_partTwoL "`name_partTwoL' ``j''"
}
else {
local name_partTwoL "`name_partTwoL' ``=`nbitems'+`j'''"
}
}
matrix colnames delta_m1 = `name_partTwoC'
matrix rownames delta_m1 = `name_partTwoL'
matrix delta_m1_se = J(`nbitems',`=`nbdif_max'*4',.)
local name_partTwoC_se ""
forvalues p=1/`nbdif_max' {
forvalues t=1/2 {
forvalues g = 0/1 {
local name_partTwoC_se "`name_partTwoC_se' delta_t`t'_`p'_gp`g'_se"
}
}
}
matrix colnames delta_m1_se = `name_partTwoC_se'
matrix rownames delta_m1_se = `name_partTwoL'
if "`group'"!="" {
forvalues t=1/2{
forvalues j=1/`nbitems'{
forvalues p=1/`nbdif_`j''{
forvalues g=0/1{
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
local delta`t'_`j'_`p'g`g'm1= r(estimate)
local delta`t'_`j'_`p'g`g'm1_se= r(se)
if `p'>1{
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp' - [`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
local delta`t'_`j'_`p'g`g'm1=r(estimate)
local delta`t'_`j'_`p'g`g'm1_se=r(se)
}
if `t' == 1 {
matrix delta_m1[`j',`=4*(`p'-1)+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1'
matrix delta_m1_se[`j',`=4*(`p'-1)+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1_se'
}
if `t' == 2 {
matrix delta_m1[`j',`=4*(`p'-1)+1+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1'
matrix delta_m1_se[`j',`=4*(`p'-1)+1+`g'+`t'']=`delta`t'_`j'_`p'g`g'm1_se'
}
}
}
}
}
}
else {
forvalues t=1/2 {
forvalues j=1/`nbitems' {
forvalues p = 1/`nbdif_`j'' {
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:_cons
local delta`t'_`j'_`p'g0m1= r(estimate)
local delta`t'_`j'_`p'g0m1_se= r(se)
if `p'>1{
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:_cons - [`p'.``=(`t'-1)*`nbitems'+`j''']:_cons
local delta`t'_`j'_`p'g0m1=r(estimate)
local delta`t'_`j'_`p'g0m1_se=r(se)
}
if `t' == 1 {
matrix delta_m1[`j',`=4*(`p'-1)+`t'']=`delta`t'_`j'_`p'g0m1'
matrix delta_m1_se[`j',`=4*(`p'-1)+`t'']=`delta`t'_`j'_`p'g0m1_se'
}
if `t' == 2 {
matrix delta_m1[`j',`=4*(`p'-1)+1+`t'']=`delta`t'_`j'_`p'g0m1'
matrix delta_m1_se[`j',`=4*(`p'-1)+1+`t'']=`delta`t'_`j'_`p'g0m1_se'
}
}
}
}
}
if "`group'" != "" {
matrix var_m1 = (val_m1[1,"/var(THETA1)#0bn.`gp'"],val_m1[1,"/var(THETA2)#0bn.`gp'"]\val_m1[2,"/var(THETA1)#0bn.`gp'"],val_m1[2,"/var(THETA2)#0bn.`gp'"])
matrix covar_m1 = (val_m1[1,"/cov(THETA1,THETA2)#0.`gp'"],val_m1[1,"/cov(THETA1,THETA2)#1.`gp'"]\val_m1[2,"/cov(THETA1,THETA2)#0.`gp'"],val_m1[2,"/cov(THETA1,THETA2)#1.`gp'"]\val_m1[4,"/cov(THETA1,THETA2)#0.`gp'"],val_m1[4,"/cov(THETA1,THETA2)#1.`gp'"])
}
else {
matrix var_m1 = (val_m1[1,"/var(THETA1)"],val_m1[1,"/var(THETA2)"]\val_m1[2,"/var(THETA1)"],val_m1[2,"/var(THETA2)"])
matrix covar_m1 = (val_m1[1,"/cov(THETA1,THETA2)"]\val_m1[2,"/cov(THETA1,THETA2)"]\val_m1[4,"/cov(THETA1,THETA2)"])
}
/*group effect*/
if "`group'"!="" {
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
local geffm1=r(estimate)
local segeffm1=r(se)
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp' = 0
local gpm1p=r(p)
local gpm1chi=r(chi2)
local gpm1df=r(df)
}
*************************************************************
***********************AFFICHAGE*****************************
*************************************************************
di
di as input "PROCESSING STEP 1"
di
if "`detail'" != "" {
// Affichage du modèle 1
/* Affichage des estimations des difficultés */
if "`group'" != "" {
di _col(5) as text "{ul:MODEL 1:} Overall longitudinal measurement non-invariance across time (RS on all items)"
di
di %~105s as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 85}"
di _col(38) "Time 1" _col(76) "Time 2"
di as text _col(26) abbrev("`gp'",15) "=0" _col(44) abbrev("`gp'",15) "=1" _col(64) abbrev("`gp'",15) "=0" _col(82) abbrev("`gp'",15) "=1"
di _col(10) "{hline 85}"
}
else {
di _col(5) as text "{ul:MODEL 1:} Overall longitudinal measurement non-invariance across time (RS on all items)"
di
di %~70s as text as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 50}"
di _col(25) "Time 1" _col(42) "Time 2"
di _col(10) "{hline 50}"
}
forvalues j=1/`nbitems' {
di as text _col(10) "``j''"
forvalues p=1/`nbdif_`j'' {
if "`group'" != "" {
di as text _col(10) "`p'" as result _col(25) %6.2f `delta1_`j'_`p'g0m1' " (" %4.2f `delta1_`j'_`p'g0m1_se' ")" _col(43) %6.2f `delta1_`j'_`p'g1m1' " (" %4.2f `delta1_`j'_`p'g1m1_se' ")" ///
_col(63) %6.2f `delta2_`j'_`p'g0m1' " (" %4.2f `delta2_`j'_`p'g0m1_se' ")" _col(81) %6.2f `delta2_`j'_`p'g1m1' " (" %4.2f `delta2_`j'_`p'g1m1_se' ")"
}
else {
di as text _col(10) "`p'" as result _col(25) %6.2f `delta1_`j'_`p'g0m1' " (" %4.2f `delta1_`j'_`p'g0m1_se' ")" _col(42) %6.2f `delta2_`j'_`p'g0m1' " (" %4.2f `delta2_`j'_`p'g0m1_se' ")"
}
}
}
if "`group'" != "" {
di _col(10) as text "{hline 85}"
}
else {
di _col(10) as text "{hline 50}"
}
/* Affichage des estimations du trait latent du modèle 1 */
di
if "`group'" != "" {
di %~85s as text "Latent trait distribution"
di _col(10) "{hline 65}"
di _col(28) "Estimate" _col(46) "Standard error" _col(64) "P-value"
di _col(10) "{hline 65}"
}
else {
di %~70s as text "Latent trait distribution"
di _col(10) "{hline 50}"
di _col(28) as text "Estimate" _col(44) "Standard error" "
di _col(10) "{hline 50}"
}
di _col(10) as text "Variance Time 1" as result _col(28) %6.2f `=var_m1[1,1]' _col(44) %6.2f `=var_m1[2,1]'
di _col(10) as text "Variance Time 2" as result _col(28) %6.2f `=var_m1[1,2]' _col(44) %6.2f `=var_m1[2,2]'
di _col(10) as text "Covariance" as result _col(28) %6.2f `=covar_m1[1,1]' _col(44) %6.2f `=covar_m1[2,1]'
if "`group'" != "" {
di _col(10) as text "Group effect" as result _col(28) %6.2f `geffm1' _col(44) %6.2f `segeffm1' _col(64) %6.4f `gpm1p'
}
di _col(10) as text "Time effect" as result _col(28) "0 (constrained)"
if "`group'" != "" {
di _col(10) as text "TimexGroup inter" as result _col(28) "0 (constrained)"
}
if "`group'" != "" {
di _col(10) as text "{hline 65}"
}
else {
di _col(10) as text "{hline 50}"
}
di
if "`group'" != "" {
di _col(10) as text "Group effect estimated: mean of the latent trait of group 1 at time 1 freely estimated"
di _col(10) as text "No time effect: equality of means of the latent trait of group 0 across time"
di _col(10) as text "All item difficulties freely estimated across time"
}
else{
di _col(10) as text "No time effect: equality of means of the latent trait across time"
di _col(10) as text "All item difficulties freely estimated across time"
}
}
//Affichage du modèle 2
di
di as input "PROCESSING STEP 2"
di
if "`detail'" != "" {
/* Affichage des estimations des difficultés */
di _col(5) as text "{ul:MODEL 2:} Overall longitudinal measurement invariance across time (no RS)"
di
if "`group'" != "" {
di %~105s as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 85}"
di _col(38) "Time 1" _col(76) "Time 2"
di as text _col(26) abbrev("`gp'",15) "=0" _col(44) abbrev("`gp'",15) "=1" _col(64) abbrev("`gp'",15) "=0" _col(82) abbrev("`gp'",15) "=1"
di _col(10) "{hline 85}"
}
else {
di %~70s as text as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 50}"
di _col(25) "Time 1" _col(42) "Time 2"
di _col(10) "{hline 50}"
}
forvalues j=1/`nbitems' {
di as text _col(10) "``j''"
forvalues p=1/`nbdif_`j'' {
if "`group'" != "" {
di as text _col(10) "`p'" as result _col(25) %6.2f `delta1_`j'_`p'g0m2' " (" %4.2f `delta1_`j'_`p'g0m2_se' ")" _col(43) %6.2f `delta1_`j'_`p'g1m2' " (" %4.2f `delta1_`j'_`p'g1m2_se' ")" ///
_col(63) %6.2f `delta2_`j'_`p'g0m2' " (" %4.2f `delta2_`j'_`p'g0m2_se' ")" _col(81) %6.2f `delta2_`j'_`p'g1m2' " (" %4.2f `delta2_`j'_`p'g1m2_se' ")"
}
else {
di as text _col(10) "`p'" as result _col(25) %6.2f `delta1_`j'_`p'g0m2' " (" %4.2f `delta1_`j'_`p'g0m2_se' ")" _col(42) %6.2f `delta2_`j'_`p'g0m2' " (" %4.2f `delta2_`j'_`p'g0m2_se' ")"
}
}
}
if "`group'" != "" {
di as text _col(10) "{hline 85}"
}
else {
di as text _col(10) "{hline 50}"
}
/* Affichage des estimations du trait latent du modèle 2 */
di
di %~85s as text "Latent trait distribution"
di _col(10) "{hline 65}"
di _col(28) as text "Estimate" _col(46) "Standard error" _col(64) "P-value"
di _col(10) "{hline 65}"
if "`group'" == "" {
local fact_k = 2
}
else {
local fact_k = 4
}
di _col(10) as text "Variance Time 1" as result _col(28) %6.2f `=mod2[1,`=`fact_k'*`nbmoda_sum'+1']' _col(44) %6.2f =mod2[2,`=`fact_k'*`nbmoda_sum'+1']
di _col(10) as text "Variance Time 2" as result _col(28) %6.2f `=mod2[1,`=`fact_k'*`nbmoda_sum'+2']' _col(44) %6.2f `=mod2[2,`=`fact_k'*`nbmoda_sum'+2']'
di _col(10) as text "Covariance" as result _col(28) %6.2f `=mod2[1,`=`fact_k'*`nbmoda_sum'+3']' _col(44) %6.2f `=mod2[2,`=`fact_k'*`nbmoda_sum'+3']'
if "`group'" != "" {
di _col(10) as text "Group effect" as result _col(28) %6.2f `geffm2' _col(44) %6.2f `segeffm2' _col(64) %6.4f `gpm2p'
}
di _col(10) as text "Time effect" as result _col(28) %6.2f `teffm2' _col(44) %6.2f `seteffm2' _col(64) %6.4f `tm2p'
if "`group'" != "" {
di _col(10) as text "TimexGroup inter" as result _col(28) %6.2f `interm2' _col(44) %6.2f `seinterm2' _col(64) %6.4f `interm2p'
}
di as text _col(10) "{hline 65}"
di
if "`group'" != "" {
di _col(10) as text "Group effect estimated: mean of the latent trait of group 1 at time 1 freely estimated"
di _col(10) as text "Time effect estimated: mean of the latent trait of group 0 at time 2 freely estimated"
di _col(10) as text "Equality of all item difficulties across time"
}
else {
di _col(10) as text "Time effect estimated: mean of the latent trait at time 2 freely estimated"
di _col(10) as text "Equality of all item difficulties across time"
}
di
}
*****************************************************
* Modèle 1 vs Modèle 2 *
*****************************************************
qui lrtest model2 model1
local rstestp=r(p)
local rstestchi=r(chi2)
local rstestdf=r(df)
if "`detail'" != "" {
di as input "LIKELIHOOD-RATIO TEST
di
di %~60s "Model 1 vs Model 2"
di _col(10) "{hline 40}"
di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
di _col(10) as result %6.2f `rstestchi' _col(28) %2.0f `rstestdf' _col(40) %6.4f `rstestp'
di _col(10) as text "{hline 40}"
di
}
if `rstestp'<0.05{
di as result "DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIME LIKELY"
}
else{
di as result "NO DIFFERENCE IN ITEM DIFFICULTIES ACROSS TIME DETECTED, NO RECALIBRATION DETECTED"
}
*********************************
*************MODEL 3*************
*********************************
// Etape itérative si lrtest significatif
local nb_step3=0
if `rstestp' < 0.05 { /* If pvalue(LRtest)<0.05 then step 3 */
di
di as input "PROCESSING STEP 3"
di
/*test RC pour chaque item*/
local boucle = 1
local stop = 0
//matrix list dif_rc
while `boucle' <= `=`nbitp'-1' & `stop' == 0 { /*on s'arrête quand on a libéré du RC sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
local nb_step3 = `boucle'
local pajust=0.05/`=`nbitp'+1-`boucle'' // local pajust=0.05/`=`nbitems'+1-`boucle'
if "`group'" != "" {
local pajust2 = 0.05/`nbgrp'
}
/*réinitialisation de la matrice de test*/
matrix test_rc_`boucle'=J(`nbitems',9,.)
matrix test_rcCOMM_`boucle'=J(`nbitems',3,.)
matrix test_rcU_`boucle'=J(`nbitems',6,.)
matrix colnames test_rc_`boucle'= chi_RC df_RC pvalue_RC chi_RCg0 df_RCg0 pvalue_RCg0 chi_RCg1 df_RCg1 pvalue_RCg1
matrix colnames test_rcCOMM_`boucle'= chi_RCCOMM df_RCCOMM pvalue_RCCOMM
matrix colnames test_rcU_`boucle'= chi_RCUg0 df_RCUg0 pvalue_RCUg0 chi_RCUg1 df_RCUg1 pvalue_RCUg1
local nbsig=0
local minpval=1
local itemrc=0
if "`detail'" != "" {
di as text "Loop `boucle'"
di _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'' >= 1 {
local model ""
local listconst ""
local listconst_g ""
if dif_rc[`j',3]==. { /*si pas de RC déjà détecté sur l'item j -> test item j*/
/*on libère la RC de l'item j: pas de contraintes*/
forvalues k=1/`nbitems'{
if "`group'" == "" {
if `k'!=`j'{
if dif_rc[`k',3]==. | dif_rc[`k',3]==0 {/*pas de RC sur item k: contraintes 401-600*/
forvalues p=1/`nbdif_`k''{
local listconst_g "`listconst_g' `=400+`maxdif'*(`k'-1)+`p'' "
qui constraint list `=400+`maxdif'*(`k'-1)+`p''
}
}
else {
if dif_rc[`k',6]!=. & dif_rc[`k',6]!=0 { // RC commune unif.
if `nbmoda_`k'' > 2 {
forvalues p=2/`nbdif_`k''{
local listconst_g "`listconst_g' `=1000+`maxdif'*(`k'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`k'-1)+`p''
}
}
}
}
}
}
else {
/* Contraintes de DIF */
if dif_rc[`k',1]==.|dif_rc[`k',1]==0 { // contraintes si pas de DIF (1-200)
forvalues p=1/`nbdif_`k''{
local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
qui constraint list `=0+`maxdif'*(`k'-1)+`p''
}
}
else { // Présence de DIF
if dif_rc[`k',2]!=. & dif_rc[`k',2]!=0 { // contraintes de DIF U (201-400)
if `nbmoda_`k'' > 2 {
forvalues p=2/`nbdif_`k''{
local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
qui constraint list `=200+`maxdif'*(`k'-1)+`p''
}
}
}
}
if `k'!=`j'{ /*contraintes pour les autres items */
if dif_rc[`k',3]==. | dif_rc[`k',3]==0 {/*pas de RC sur item k: contraintes 401-600 601-800*/
forvalues p=1/`nbdif_`k''{
local listconst "`listconst' `=400+`maxdif'*(`k'-1)+`p'' `=600+`maxdif'*(`k'-1)+`p''"
qui constraint list `=400+`maxdif'*(`k'-1)+`p'' `=600+`maxdif'*(`k'-1)+`p''
}
}
else { //RC détectée sur l'item k
if dif_rc[`k',4]==0{ /*RC commune: contraintes 801-1000*/
forvalues p=1/`nbdif_`k''{ /***************************** j=1 ou 2 ?****/
local listconst "`listconst' `=800+`maxdif'*(`k'-1)+`p''"
qui constraint list `=800+`maxdif'*(`k'-1)+`p''
}
if dif_rc[`k',6]!=. & dif_rc[`k',6]!=0 { // RC commune unif.
if `nbmoda_`k'' > 2 {
forvalues p=2/`nbdif_`k''{
local listconst "`listconst' `=1000+`maxdif'*(`k'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`k'-1)+`p''
}
}
}
}
else { // RC diff
if dif_rc[`k',5]==. | dif_rc[`k',5]==0 { // RC gp0 (400)
forvalues p=1/`nbdif_`k''{
local listconst "`listconst' `=400+`maxdif'*(`k'-1)+`p''"
qui constraint list `=400+`maxdif'*(`k'-1)+`p''
}
}
if dif_rc[`k',6]!=. & dif_rc[`k',6]!=0 { // RCU gp0 (1001-1200)
if `nbmoda_`k'' > 2 {
forvalues p=2/`nbdif_`k''{
local listconst "`listconst' `=1000+`maxdif'*(`k'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`k'-1)+`p''
}
}
}
if dif_rc[`k',7]==. | dif_rc[`k',7]==0 { // RC gp1 (600)
forvalues p=1/`nbdif_`k''{
local listconst "`listconst' `=600+`maxdif'*(`k'-1)+`p''"
qui constraint list `=600+`maxdif'*(`k'-1)+`p''
}
}
if dif_rc[`k',8]!=. & dif_rc[`k',8]!=0 { // RCU gp1 (1201-1400)
if `nbmoda_`k'' > 2 {
forvalues p=2/`nbdif_`k''{
local listconst "`listconst' `=1200+`maxdif'*(`k'-1)+`p''"
qui constraint list `=1200+`maxdif'*(`k'-1)+`p''
}
}
}
}
}
}
}
}
local model ""
forvalues jj=1/`nbitems'{
forvalues p=1/`nbdif_`jj''{
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
}
}
if "`group'" == "" { // Sans l'option group
qui gsem `model', mlogit tol(0.01) iterate(100) means(THETA1@0 THETA2@m20) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) constraint(`listconst_g') from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
/*****************/
/*tests RC item j*/
/*****************/
/* RC ? */
qui test [1.``j'']_cons =[1.``=`j'+`nbitems''']_cons
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui test [`p'.``j'']_cons =[`p'.``=`j'+`nbitems''']_cons, acc
}
}
matrix test_rc_`boucle'[`j',1]=(r(chi2),r(df),r(p))
/* RCU ? */
if `nbmoda_`j'' > 2 {
qui test 2*([1.``=`j'+`nbitems''']_cons -[1.``j'']_cons)=[2.``=`j'+`nbitems''']_cons -[2.``j'']_cons
forvalues p=3/`nbdif_`j''{
qui test `p'*([1.``=`j'+`nbitems''']_cons -[1.``j'']_cons)=[`p'.``=`j'+`nbitems''']_cons -[`p'.``j'']_cons , acc
}
matrix test_rcU_`boucle'[`j',1]=(r(chi2), r(df),r(p))
}
}
else { // Avec l'option group
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
/*****************/
/*tests RC item i*/
/*****************/
/* RC ? */
qui test [1.``j'']0bn.`gp'=[1.``=`j'+`nbitems''']0bn.`gp'
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui test [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp', acc
}
}
qui test [1.``j'']1.`gp'=[1.``=`j'+`nbitems''']1.`gp', acc
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui test [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp', acc
}
}
matrix test_rc_`boucle'[`j',1]=(r(chi2),r(df),r(p))
/* RC COMMUNE ? */
qui test [1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp'=[1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp'
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui test [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp', acc
}
}
matrix test_rcCOMM_`boucle'[`j',1]=(r(chi2),r(df),r(p))
/* RC groupe 0 ? */
qui test [1.``j'']0bn.`gp'=[1.``=`j'+`nbitems''']0bn.`gp'
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui test [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp', acc
}
}
matrix test_rc_`boucle'[`j',4]=(r(chi2),r(df),r(p))
/* RCU grp 0 ? */
if `nbmoda_`j'' > 2 {
qui test 2*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[2.``=`j'+`nbitems''']0bn.`gp'-[2.``j'']0bn.`gp'
forvalues p=3/`nbdif_`j''{
qui test `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp', acc
}
matrix test_rcU_`boucle'[`j',1]=(r(chi2),r(df),r(p))
}
/* RC groupe 1 ? */
qui test [1.``j'']1.`gp'=[1.``=`j'+`nbitems''']1.`gp'
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui test [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp', acc
}
}
matrix test_rc_`boucle'[`j',7]=(r(chi2),r(df),r(p))
/* RCU grp 1 ? */
if `nbmoda_`j'' > 2 {
qui test 2*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[2.``=`j'+`nbitems''']1.`gp'-[2.``j'']1.`gp'
forvalues p=3/`nbdif_`j''{
qui test `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp', acc
}
matrix test_rcU_`boucle'[`j',4]=(r(chi2),r(df),r(p))
}
}
/******* Matrice test complète *********/
if "`detail'" != "" {
di as text _col(10) abbrev("``j''",22) as result _col(31) %6.3f test_rc_`boucle'[`j',1] _col(48) test_rc_`boucle'[`j',2] _col(57) %6.4f test_rc_`boucle'[`j',3]
}
}
}
}
//matrix list test_rc_`boucle'
forvalues j=1/`nbitems'{
if test_rc_`boucle'[`j',3]<`pajust'{/*si RC sur item i*/
if test_rc_`boucle'[`j',3]<`minpval'{
local minpval=test_rc_`boucle'[`j',3]
local itemrc=`j'
}
}
}
if `itemrc' != 0 { // itemrc = numéro de l'item avec le test le + sig.
if "`group'" == "" { // Recalibration si pas d'option groupe
local ++nbsig
matrix dif_rc[`itemrc',3]=`boucle'
matrix dif_rc[`itemrc',5]=`boucle'
if `nbmoda_`itemrc'' > 2 {
if "`detail'" != "" {
di as text _col(10) "{hline 65}"
di
di as result "Recalibration on ``itemrc''"
di
di as text _col(10) "{hline 65}"
di as text _col(10) "Test" _col(31) "Chi-Square" _col(48) "DF" _col(57) "P-Value"
di as text _col(10) "{hline 65}"
di as text _col(10) "Uniform RC? " as result _col(31) %4.2f `=test_rcU_`boucle'[`itemrc',1]' _col(48) `=test_rcU_`boucle'[`itemrc',2]' _col(57) %6.4f `=test_rcU_`boucle'[`itemrc',3]'
di as text _col(10) "{hline 65}"
}
if test_rcU_`boucle'[`itemrc',3] >= 0.05 { //RC Uniforme sur itemRC
matrix dif_rc[`itemrc',6]=`boucle'
di
di as result "``itemrc'' : Uniform RC"
di
}
else {
matrix dif_rc[`itemrc',6]=0
di
di as result "``itemrc'' : Non-uniform RC"
di
}
}
else {
di
di as result "``itemrc'' : Recalibration "
di
}
}
else { // Option groupe
if "`detail'" != "" {
di as text _col(10) "{hline 65}"
di
di as result "Recalibration on ``itemrc''"
di
di as text _col(10) "{hline 65}"
di as text _col(10) "Test" _col(31) "Chi-Square" _col(48) "DF" _col(57) "P-Value"
di as text _col(10) "{hline 65}"
di _col(10) as text "Common RC? " as result _col(31) %4.2f `=test_rcCOMM_`boucle'[`itemrc',1]' _col(48) `=test_rcCOMM_`boucle'[`itemrc',2]' _col(57) %6.4f `=test_rcCOMM_`boucle'[`itemrc',3]'
}
if test_rcCOMM_`boucle'[`itemrc',3] < 0.05 { //RC différentielle
if "`detail'" != "" {
di _col(10) as text "RC group 0? " as result _col(31) %4.2f `=test_rc_`boucle'[`itemrc',4]' _col(48) `=test_rc_`boucle'[`itemrc',5]' _col(57) %6.4f `=test_rc_`boucle'[`itemrc',6]' "{it: - with adjusted alpha = `pajust2' }"
}
if test_rc_`boucle'[`itemrc',6] < `pajust2' { //RC gp 0
local ++nbsig
matrix dif_rc[`itemrc',3]=`boucle'
matrix dif_rc[`itemrc',4]=`boucle'
matrix dif_rc[`itemrc',5]=`boucle'
if `nbmoda_`itemrc'' > 2 {
di _col(10) as text "Uniform RC group 0? " as result _col(31) %4.2f `=test_rcU_`boucle'[`itemrc',1]' _col(48) `=test_rcU_`boucle'[`itemrc',2]' _col(57) %6.4f `=test_rcU_`boucle'[`itemrc',3]'
if test_rcU_`boucle'[`itemrc',3] >= 0.05 { // RCU gp 0
matrix dif_rc[`itemrc',6]=`boucle'
local phrase_diff = "``itemrc'' : Uniform differential RC in group 0."
}
else {
matrix dif_rc[`itemrc',6]=0
local phrase_diff = "``itemrc'' : Non-uniform differential RC in group 0."
}
}
else {
local phrase_diff = "``itemrc'' : Differential RC in group 0."
}
}
if "`detail'" != "" {
di _col(10) as text "RC group 1?" as result _col(31) %4.2f `=test_rc_`boucle'[`itemrc',7]' _col(48) `=test_rc_`boucle'[`itemrc',8]' _col(57) %6.4f `=test_rc_`boucle'[`itemrc',9]' "{it: - with adjusted alpha = `pajust2' }"
}
if test_rc_`boucle'[`itemrc',9] < `pajust2' { //RC gp 1
local ++nbsig
matrix dif_rc[`itemrc',3]=`boucle'
matrix dif_rc[`itemrc',4]=`boucle'
matrix dif_rc[`itemrc',7]=`boucle'
if `nbmoda_`itemrc'' > 2 {
if "`detail'" != "" {
di _col(10) as text "Uniform RC group 1? " as result _col(31) %4.2f `=test_rcU_`boucle'[`itemrc',4]' _col(48) `=test_rcU_`boucle'[`itemrc',5]' _col(57) %6.4f `=test_rcU_`boucle'[`itemrc',6]'
}
if test_rcU_`boucle'[`itemrc',6] >= 0.05 { // RCU gp 1
matrix dif_rc[`itemrc',8]=`boucle'
if dif_rc[`itemrc',5] != `boucle' { //RC slmt sur g1
local phrase_diff = "``itemrc'' : Differential RC, uniform RC in group 1."
}
else {
if dif_rc[`itemrc',6] == 0 { // + RCNU g0
local phrase_diff = "``itemrc'' : Differential RC, non-uniform RC in group 0 and uniform RC in group 1."
}
else { // + RCU G0
local phrase_diff = "``itemrc'' : Differential RC, uniform RC in group 0 and uniform RC in group 1."
}
}
}
else { //RCNU gp 1
matrix dif_rc[`itemrc',8]=0
if "`detail'" != "" {
di
}
if dif_rc[`itemrc',5] != `boucle' {
local phrase_diff = "``itemrc'' : Differential RC, non-uniform RC in group 1."
}
else {
if dif_rc[`itemrc',6] == 0 { // + RCNU g0
local phrase_diff = "``itemrc'' : Differential RC, non-uniform RC in group 0 and non-uniform RC in group 1."
}
else { // + RCU G0
local phrase_diff = "``itemrc'' : Differential RC, uniform RC in group 0 and non-uniform RC in group 1."
}
}
}
}
else {
if dif_rc[`itemrc',5] != `boucle' {
local phrase_diff = "``itemrc'' : Differential RC in group 1."
}
else {
local phrase_diff = "``itemrc'' : Differential RC in group 0 and differential RC in group 1."
}
}
}
if "`detail'" != "" {
di as text _col(10) "{hline 65}"
}
di
di as result "`phrase_diff'"
di
}
else { // RC commune -> MAJ modèle 3
/*******************************************************************************************************************/
if `nbmoda_`itemrc'' == 2 {
if "`detail'" != "" {
di
di as result "{ul:``itemrc''}: recalibration"
di _col(20) in ye "Common " in gr "{it:(Chi-s: " %4.2f `=test_rcCOMM_`boucle'[`itemrc',1]' ", DF: `=test_rcCOMM_`boucle'[`itemrc',2]' p-val. : " %4.2f `=test_rcCOMM_`boucle'[`itemrc',3]' ")}"
}
matrix dif_rc[`itemrc',3]=`boucle'
matrix dif_rc[`itemrc',4]=0
matrix dif_rc[`itemrc',5]=`boucle'
matrix dif_rc[`itemrc',7]=`boucle'
local ++nbsig
}
else {
matrix dif_rc[`itemrc',3]=`boucle'
matrix dif_rc[`itemrc',4]=0
matrix dif_rc[`itemrc',5]=`boucle'
matrix dif_rc[`itemrc',7]=`boucle'
//matrix list dif_rc
local model ""
local listconst ""
forvalues j=1/`nbitems'{
/* Contraintes de DIF */
if dif_rc[`j',1]==.|dif_rc[`j',1]==0 { // contraintes si pas de DIF (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 { // Présence de DIF
if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { // contraintes de DIF U (201-400)
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
}
}
}
}
if `j' != `itemrc'{ /*contraintes pour les autres items */
if dif_rc[`j',3]==. | dif_rc[`j',3]==0 {/*pas de RC sur item p: contraintes 401-600 601-800*/
forvalues p=1/`nbdif_`j''{
qui local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''
}
}
else { //RC détectée sur l'item p
if dif_rc[`j',4]==0{ /*RC commune: contraintes 801-1000*/
forvalues p=1/`nbdif_`j''{
qui local listconst "`listconst' `=800+`maxdif'*(`j'-1)+`p''"
qui constraint list `=800+`maxdif'*(`j'-1)+`p''
}
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RC commune unif.
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
}
}
}
}
else if dif_rc[`j',4] != 0 & dif_rc[`j',4]!=0. { // RC diff
if dif_rc[`j',5]==. | dif_rc[`j',5]==0 { // RC gp0 (400)
forvalues p=1/`nbdif_`j''{
qui local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
}
}
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RCU gp0 (1001-1200)
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
}
}
}
if dif_rc[`j',7]==. | dif_rc[`j',7]==0 { // RC gp1 (600)
forvalues p=1/`nbdif_`j''{
qui local listconst "`listconst' `=600+`maxdif'*(`j'-1)+`p''"
qui constraint list `=600+`maxdif'*(`j'-1)+`p''
}
}
if dif_rc[`j',8]!=. & dif_rc[`j',8]!=0 { // RCU gp1 (1201-1400)
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
qui local listconst "`listconst' `=1200+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1200+`maxdif'*(`j'-1)+`p''
}
}
}
}
}
}
else { // Contrainte de RC commune pour l'itemrc
forvalues p=1/`nbdif_`j''{
qui local listconst "`listconst' `=800+`maxdif'*(`itemrc'-1)+`p''"
qui constraint list `=800+`maxdif'*(`itemrc'-1)+`p''
}
}
}
qui di "`listconst'"
local model ""
forvalues jj=1/`nbitems'{
forvalues p=1/`nbdif_`jj''{
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
}
}
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
/************************/
/*tests RC item `itemrc'*/
/************************/
matrix commU_`boucle'=J(`nbitems',3,.) //Matrice des tests de RCU slmt si RC commune
matrix colnames commU_`boucle'= chi_RCU df_RCU p_RCU
/* RCU grp 0 ? */
if `nbmoda_`itemrc'' > 2 {
qui test 2*([1.``=`itemrc'+`nbitems''']0bn.`gp'-[1.``itemrc'']0bn.`gp')=[2.``=`itemrc'+`nbitems''']0bn.`gp'-[2.``itemrc'']0bn.`gp'
forvalues j=3/`nbdif_`itemrc''{
qui test `j'*([1.``=`itemrc'+`nbitems''']0bn.`gp'-[1.``itemrc'']0bn.`gp')=[`j'.``=`itemrc'+`nbitems''']0bn.`gp'-[`j'.``itemrc'']0bn.`gp', acc
}
matrix commU_`boucle'[`itemrc',1]=(r(chi2),r(df),r(p))
if "`detail'" != "" {
di _col(10) as text "Uniform RC?" as result _col(31) %4.2f `=commU_`boucle'[`itemrc',1]' _col(48) `=commU_`boucle'[`itemrc',2]' _col(57) %6.4f `=commU_`boucle'[`itemrc',3]'
di as text _col(10) "{hline 65}"
}
if commU_`boucle'[`itemrc',3] >= 0.05 { // RCU
local ++nbsig
matrix dif_rc[`itemrc',6]=`boucle'
matrix dif_rc[`itemrc',8]=`boucle'
di
//di as result "{ul:``itemrc''}: recalibration"
//di _col(20) "Common " in gr "{it:(Chi-s: " %4.2f `=test_rcCOMM_`boucle'[`itemrc',1]' ", DF: `=test_rcCOMM_`boucle'[`itemrc',2]' p-val. : " %4.2f `=test_rcCOMM_`boucle'[`itemrc',3]' ")}"
di as result "``itemrc'' : Uniform common RC"
di
}
else {
local ++nbsig
matrix dif_rc[`itemrc',6]=0
matrix dif_rc[`itemrc',8]=0
di
di as result "``itemrc'' : Non-uniform common RC"
di
}
}
}
} // fin de RC commune
}
}
else {
local stop = 1
}
/*******************************************************************************************************************/
// Fin de RC sur item i
if `nbsig'==0{
local stop=1
if `boucle' == 1 {
di as text _col(10) "{hline 65}"
di
di as result "No significant tests, no recalibration detected"
di
}
else {
if "`detail'" != "" {
di as text _col(10) "{hline 65}"
di
di as result "No other significant tests"
di
}
}
}
local ++boucle
}
}
*********************************
*** 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
}
*********************************
** MODEL 4 **
*********************************
if "`detail'" != "" {
di
di as input "PROCESSING STEP 4"
di
}
//matrix list dif_rc, title ("Constraints")
local model ""
local listconst ""
local listconst_g ""
forvalues j=1/`nbitems'{
if "`group'" != "" {
if dif_rc[`j',1]==.|dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200 */
forvalues p=1/`nbdif_`j''{
local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
qui constraint list `=0+`maxdif'*(`j'-1)+`p''
}
}
else { // Présence de DIF
if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { // contraintes de DIF U (201-400)
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
qui constraint list `=200+`maxdif'*(`j'-1)+`p''
}
}
}
}
}
if dif_rc[`j',3]==. | dif_rc[`j',3]==0 { /*pas de RC : contraintes 401-600 601-800*/
forvalues p=1/`nbdif_`j''{
if "`group'" == "" {
local listconst_g "`listconst_g' `=400+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
}
else {
local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p'' `=600+`maxdif'*(`j'-1)+`p''
}
}
}
else { //RC détectée sur l'item j
if "`group'" == "" {
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RC unif.
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
local listconst_g "`listconst_g' `=1000+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
}
}
}
}
else {
if dif_rc[`j',4]==0{ /*RC commune: contraintes 801-1000*/
forvalues p=1/`nbdif_`j''{
local listconst "`listconst' `=800+`maxdif'*(`j'-1)+`p''"
qui constraint list `=800+`maxdif'*(`j'-1)+`p''
}
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RC commune unif.
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
}
}
}
}
else { // RC diff
if dif_rc[`j',5]==. | dif_rc[`j',5]==0 { // RC gp0 (400)
forvalues p=1/`nbdif_`j''{
local listconst "`listconst' `=400+`maxdif'*(`j'-1)+`p''"
qui constraint list `=400+`maxdif'*(`j'-1)+`p''
}
}
if dif_rc[`j',6]!=. & dif_rc[`j',6]!=0 { // RCU gp0 (1001-1200)
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
local listconst "`listconst' `=1000+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1000+`maxdif'*(`j'-1)+`p''
}
}
}
if dif_rc[`j',7]==. | dif_rc[`j',7]==0 { // RC gp1 (600)
forvalues p=1/`nbdif_`j''{
local listconst "`listconst' `=600+`maxdif'*(`j'-1)+`p''"
qui constraint list `=600+`maxdif'*(`j'-1)+`p''
}
}
if dif_rc[`j',8]!=. & dif_rc[`j',8]!=0 { // RCU gp1 (1201-1400)
if `nbmoda_`j'' > 2 {
forvalues p=2/`nbdif_`j''{
local listconst "`listconst' `=1200+`maxdif'*(`j'-1)+`p''"
qui constraint list `=1200+`maxdif'*(`j'-1)+`p''
}
}
}
}
}
}
}
local model ""
forvalues jj=1/`nbitems'{
forvalues p=1/`nbdif_`jj''{
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
}
}
if "`group'" != "" {
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
}
else {
qui gsem `model', mlogit tol(0.01) iterate(100) means( THETA1@0 THETA2@m2) var(THETA1@v1 THETA2@v2) cov(THETA1*THETA2@cov12) constraint(`listconst_g') from(esti_2, skip) latent(THETA1 THETA2) nocapslatent
}
/* Stockage des estimations du modèle */
matrix val_m4 = r(table)
matrix esti_4 = e(b)
if "`group'" != "" {
matrix var_m4 = (val_m4[1,"/var(THETA1)#0bn.`gp'"],val_m4[1,"/var(THETA2)#0bn.`gp'"]\val_m4[2,"/var(THETA1)#0bn.`gp'"],val_m4[2,"/var(THETA2)#0bn.`gp'"])
matrix covar_m4 = (val_m4[1,"/cov(THETA1,THETA2)#0.`gp'"],val_m4[1,"/cov(THETA1,THETA2)#1.`gp'"]\val_m4[2,"/cov(THETA1,THETA2)#0.`gp'"],val_m4[2,"/cov(THETA1,THETA2)#1.`gp'"]\val_m4[4,"/cov(THETA1,THETA2)#0.`gp'"],val_m4[4,"/cov(THETA1,THETA2)#1.`gp'"])
}
else {
matrix var_m4 = (val_m4[1,"/var(THETA1)"],val_m4[1,"/var(THETA2)"]\val_m4[2,"/var(THETA1)"],val_m4[2,"/var(THETA2)"])
matrix covar_m4 = (val_m4[1,"/cov(THETA1,THETA2)"]\val_m4[2,"/cov(THETA1,THETA2)"]\val_m4[4,"/cov(THETA1,THETA2)"])
}
/* Matrice des tests effet grp, tps et inter */
matrix effet = J(5,3,.)
matrix colnames effet= Groupe Temps Interaction
matrix rownames effet = Esti Std_Err Pvalue Chi DF
/*group effect*/
if "`group'" != "" {
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
matrix effet[1,1] =r(estimate)
matrix effet[2,1]=r(se)
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp' = 0
matrix effet[3,1]=r(p)
matrix effet[4,1]=r(chi2)
matrix effet[5,1]=r(df)
}
/*time effect*/
if "`group'" != "" {
qui lincom [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'
matrix effet[1,2]=r(estimate)
matrix effet[2,2]=r(se)
qui test [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp' = 0
matrix effet[3,2]=r(p)
matrix effet[4,2]=r(chi2)
matrix effet[5,2]=r(df)
}
else {
qui lincom [/]:mean(THETA2) /* -[/]:mean(THETA1)*/
local teffm4=r(estimate)
local seteffm4=r(se)
local ubteffm4 = r(ub)
local lbteffm4 = r(lb)
qui test [/]:mean(THETA2) /* -[/]:mean(THETA1) */ = 0
local tm4p=r(p)
local tm4chi=r(chi2)
local tm4df=r(df)
}
*INTERACTION
if "`group'" != "" {
qui lincom [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp'
matrix effet[1,3]=r(estimate)
matrix effet[2,3]=r(se)
local ubinterm4=r(ub)
local lbinterm4=r(lb)
qui test [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#1.`gp'+[/]:mean(THETA1)#0bn.`gp' = 0
matrix effet[3,3]=r(p)
matrix effet[4,3]=r(chi2)
matrix effet[5,3]=r(df)
}
if "`group'" != "" {
local effet_tps = 0
local effet_grp = 0
if effet[3,3] >= 0.05 { // Si option group, on s'interesse à l'interaction temps x group, et MAJ modèle >>> modèle final = modèle 4 + contrainte 1999 (Interaction = 0)
/* Affichage des estimations sur le trait latent du modèle 4 */
if "`detail'" != "" {
di
di %~85s as text "Latent trait distribution"
di _col(10) "{hline 65}"
di _col(28) as text "Estimate" _col(46) "Standard error" _col(62) "P-value"
di _col(10) "{hline 65}"
di _col(10) as text "Variance Time 1" as result _col(28) %6.2f `=var_m4[1,1]' _col(44) %6.2f `=var_m4[2,1]'
di _col(10) as text "Variance Time 2" as result _col(28) %6.2f `=var_m4[1,2]' _col(44) %6.2f `=var_m4[2,2]'
di _col(10) as text "Covariance" as result _col(28) %6.2f `=covar_m4[1,1]' _col(44) %6.2f `=covar_m4[2,1]'
if "`group'" != "" {
di _col(10) as text "Group effect" as result _col(28) %6.2f effet[1,1] _col(44) %6.2f effet[2,1] _col(62) %6.4f effet[3,1]
}
di _col(10) as text "Time effect" as result _col(28) %6.2f effet[1,2] _col(44) %6.2f effet[2,2] _col(62) %6.4f effet[3,2]
if "`group'" != "" {
di _col(10) as text "TimexGroup inter" as result _col(28) %6.2f effet[1,3] _col(44) %6.2f effet[2,3] _col(62) %6.4f effet[3,3]
}
di as text _col(10) "{hline 65}"
di
di as result "Time x group interaction : test not significant
di "Reestimation of model 4 with time x group interaction constrained at 0 "
di
}
local yn_inter = 0
local listconst "`listconst' 1999"
qui di "`listconst'"
local model ""
forvalues jj=1/`nbitems'{
forvalues p=1/`nbdif_`jj''{
local model "`model' (`p'.``jj''<-THETA1@`p')(`p'.``=`jj'+`nbitems'''<-THETA2@`p')"
}
}
qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) means(0: THETA1@0 THETA2@m20) means(1: THETA1@m11 THETA2@m21) var(0: THETA1@v1 THETA2@v2) var(1:THETA1@v1 THETA2@v2) cov(0: THETA1*THETA2@cov12) cov(1: THETA1*THETA2@cov12) constraint(`listconst') from(esti_4, skip) latent(THETA1 THETA2) nocapslatent
matrix val_m4 = r(table)
}
else {
local yn_inter = 1
}
/*group effect*/
qui lincom [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
local geffm4=r(estimate)
local segeffm4=r(se)
local ubgeffm4=r(ub)
local lbgeffm4=r(lb)
qui test [/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
local gpm4p=r(p)
local gpm4chi=r(chi2)
local gpm4df=r(df)
/*time effect*/
qui lincom [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'
local teffm4=r(estimate)
local seteffm4=r(se)
local lbteffm4=r(lb)
local ubteffm4=r(ub)
qui test [/]:mean(THETA2)#0bn.`gp'-[/]:mean(THETA1)#0bn.`gp'=0
local tm4p=r(p)
local tm4chi=r(chi2)
local tm4df=r(df)
}
/* Calcul des difficultés (delta_j) */
if "`group'" != "" {
matrix mod4 = J(7,`=`nbmoda_sum'*4+6',.)
local name_partTwoC ""
forvalues j = 1/`nbitems' {
forvalues p=1/`nbdif_`j'' {
forvalues t=1/2 {
forvalues g = 0/1 {
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_gp`g'_t`t'"
}
}
}
}
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) GROUP_Effect TIME_Effect INTER_TxG "
matrix colnames mod4 = `name_partTwoC'
matrix rownames mod4 = Estimate se Upper_b Lower_b Chi_square DF pvalue
}
else {
matrix mod4 = J(7,`=`nbmoda_sum'*2+4',.)
local name_partTwoC ""
forvalues j = 1/`nbitems' {
forvalues p=1/`nbdif_`j'' {
forvalues t=1/2 {
local name_partTwoC "`name_partTwoC' d_j`j'_p`p'_t`t'"
}
}
}
local name_partTwoC "`name_partTwoC' VAR(THETA1) VAR(THETA2) COV(TH1,TH2) TIME_Effect "
matrix colnames mod4 = `name_partTwoC'
matrix rownames mod4 = Estimate se Upper_b Lower_b Chi_square DF pvalue
}
*Difficultés
forvalues j=1/`nbitems'{
forvalues p=1/`nbdif_`j''{
forvalues t=1/2{
if "`group'" != "" { // groupe binaire
forvalues g=0/1 {
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
local delta`t'_`j'_`p'g`g'm4= r(estimate)
local delta`t'_`j'_`p'g`g'm4_se= r(se)
local delta`t'_`j'_`p'g`g'm4_ub=r(ub)
local delta`t'_`j'_`p'g`g'm4_lb=r(lb)
local delta`t'_`j'_`p'g`g'm4_p=r(p)
if `p'>1 {
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp' - [`p'.``=(`t'-1)*`nbitems'+`j''']:`g'.`gp'
local delta`t'_`j'_`p'g`g'm4=r(estimate)
local delta`t'_`j'_`p'g`g'm4_se=r(se)
local delta`t'_`j'_`p'g`g'm4_ub=r(ub)
local delta`t'_`j'_`p'g`g'm4_lb=r(lb)
local delta`t'_`j'_`p'g`g'm4_p=r(p)
}
local place = 0
local compt = 1
while `compt' < `j' {
local place = `place' + `nbdif_`compt''
local ++compt
}
if `t' == 1 {
matrix mod4[1,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4'
matrix mod4[2,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_se'
matrix mod4[3,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_ub'
matrix mod4[4,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_lb'
matrix mod4[7,`=4*(`p'-1)+`g'+`t'+4*`place'']=`delta`t'_`j'_`p'g`g'm4_p'
}
if `t' == 2 {
matrix mod4[1,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4'
matrix mod4[2,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_se'
matrix mod4[3,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_ub'
matrix mod4[4,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_lb'
matrix mod4[7,`=4*(`p'-1)+`g'+`t'+1+4*`place'']=`delta`t'_`j'_`p'g`g'm4_p'
}
}
}
else { // groupe unique (=gp0)
qui lincom -[`p'.``=(`t'-1)*`nbitems'+`j''']_cons
local delta`t'_`j'_`p'g0m4= r(estimate)
local delta`t'_`j'_`p'g0m4_se= r(se)
local delta`t'_`j'_`p'g0m4_ub=r(ub)
local delta`t'_`j'_`p'g0m4_lb=r(lb)
local delta`t'_`j'_`p'g0m4_p=r(p)
if `p'>1{
qui lincom [`=`p'-1'.``=(`t'-1)*`nbitems'+`j''']_cons - [`p'.``=(`t'-1)*`nbitems'+`j''']_cons
local delta`t'_`j'_`p'g0m4=r(estimate)
local delta`t'_`j'_`p'g0m4_se=r(se)
local delta`t'_`j'_`p'g0m4_ub=r(ub)
local delta`t'_`j'_`p'g0m4_lb=r(lb)
local delta`t'_`j'_`p'g0m4_p=r(p)
}
local place = 0
local compt = 1
while `compt' < `j' {
local place = `place' + `nbdif_`compt''
local ++compt
}
if `t' == 1 {
matrix mod4[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4'
matrix mod4[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_se'
matrix mod4[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_ub'
matrix mod4[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_lb'
matrix mod4[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_p'
}
if `t' == 2 {
matrix mod4[1,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4'
matrix mod4[2,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_se'
matrix mod4[3,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_ub'
matrix mod4[4,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_lb'
matrix mod4[7,`=2*(`p'-1)+`t'+2*`place'']=`delta`t'_`j'_`p'g0m4_p'
}
}
}
}
}
if "`group'" != "" {
matrix mod4[1,`=4*`nbmoda_sum'+1'] = (val_m4[1,"/var(THETA1)#0bn.`gp'"], val_m4[1,"/var(THETA2)#0bn.`gp'"])
matrix mod4[2,`=4*`nbmoda_sum'+1'] = (val_m4[2,"/var(THETA1)#0bn.`gp'"],val_m4[2,"/var(THETA2)#0bn.`gp'"])
matrix mod4[3,`=4*`nbmoda_sum'+1'] = (val_m4[6,"/var(THETA1)#0bn.`gp'"],val_m4[6,"/var(THETA2)#0bn.`gp'"])
matrix mod4[4,`=4*`nbmoda_sum'+1'] = (val_m4[5,"/var(THETA1)#0bn.`gp'"],val_m4[5,"/var(THETA2)#0bn.`gp'"])
matrix mod4[1,`=4*`nbmoda_sum'+2+1'] = (val_m4[1,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod4[2,`=4*`nbmoda_sum'+2+1'] = (val_m4[2,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod4[3,`=4*`nbmoda_sum'+2+1'] = (val_m4[6,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod4[4,`=4*`nbmoda_sum'+2+1'] = (val_m4[5,"/cov(THETA1,THETA2)#0.`gp'"])
matrix mod4[1,`=4*`nbmoda_sum'+2+1+1'] = `geffm4'
matrix mod4[2,`=4*`nbmoda_sum'+2+1+1'] = `segeffm4'
matrix mod4[3,`=4*`nbmoda_sum'+2+1+1'] = `ubgeffm4'
matrix mod4[4,`=4*`nbmoda_sum'+2+1+1'] = `lbgeffm4'
matrix mod4[5,`=4*`nbmoda_sum'+2+1+1'] = `gpm4chi'
matrix mod4[6,`=4*`nbmoda_sum'+2+1+1'] = `gpm4df'
matrix mod4[7,`=4*`nbmoda_sum'+2+1+1'] = `gpm4p'
matrix mod4[1,`=4*`nbmoda_sum'+2+1+1+1'] = `teffm4'
matrix mod4[2,`=4*`nbmoda_sum'+2+1+1+1'] = `seteffm4'
matrix mod4[3,`=4*`nbmoda_sum'+2+1+1+1'] = `ubteffm4'
matrix mod4[4,`=4*`nbmoda_sum'+2+1+1+1'] = `lbteffm4'
matrix mod4[5,`=4*`nbmoda_sum'+2+1+1+1'] = `tm4chi'
matrix mod4[6,`=4*`nbmoda_sum'+2+1+1+1'] = `tm4df'
matrix mod4[7,`=4*`nbmoda_sum'+2+1+1+1'] = `tm4p'
if `yn_inter' == 1 { //Slmt si model avec interaction
matrix mod4[1,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[1,3]
matrix mod4[2,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[2,3]
matrix mod4[3,`=4*`nbmoda_sum'+2+1+1+1+1'] = `ubinterm4'
matrix mod4[4,`=4*`nbmoda_sum'+2+1+1+1+1'] = `lbinterm4'
matrix mod4[5,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[4,3]
matrix mod4[6,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[5,3]
matrix mod4[7,`=4*`nbmoda_sum'+2+1+1+1+1'] = effet[3,3]
}
}
else {
matrix mod4[1,`=2*`nbmoda_sum'+1'] = (val_m4[1,"/var(THETA1)"],val_m4[1,"/var(THETA2)"])
matrix mod4[2,`=2*`nbmoda_sum'+1'] = (val_m4[2,"/var(THETA1)"],val_m4[2,"/var(THETA2)"])
matrix mod4[3,`=2*`nbmoda_sum'+1'] = (val_m4[6,"/var(THETA1)"],val_m4[6,"/var(THETA2)"])
matrix mod4[4,`=2*`nbmoda_sum'+1'] = (val_m4[5,"/var(THETA1)"],val_m4[5,"/var(THETA2)"])
matrix mod4[1,`=2*`nbmoda_sum'+2+1'] = (val_m4[1,"/cov(THETA1,THETA2)"])
matrix mod4[2,`=2*`nbmoda_sum'+2+1'] = (val_m4[2,"/cov(THETA1,THETA2)"])
matrix mod4[3,`=2*`nbmoda_sum'+2+1'] = (val_m4[6,"/cov(THETA1,THETA2)"])
matrix mod4[4,`=2*`nbmoda_sum'+2+1'] = (val_m4[5,"/cov(THETA1,THETA2)"])
matrix mod4[1,`=2*`nbmoda_sum'+2+1+1'] = `teffm4'
matrix mod4[2,`=2*`nbmoda_sum'+2+1+1'] = `seteffm4'
matrix mod4[3,`=2*`nbmoda_sum'+2+1+1'] = `ubteffm4'
matrix mod4[4,`=2*`nbmoda_sum'+2+1+1'] = `lbteffm4'
matrix mod4[5,`=2*`nbmoda_sum'+2+1+1'] = `tm4chi'
matrix mod4[6,`=2*`nbmoda_sum'+2+1+1'] = `tm4df'
matrix mod4[7,`=2*`nbmoda_sum'+2+1+1'] = `tm4p'
}
/* Affichage des estimations des difficultés */
di _col(5) as text "{ul:MODEL 4} = Final model"
di
if "`group'" != "" {
di %~105s as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 85}"
di _col(38) "Time 1" _col(76) "Time 2"
di as text _col(25) abbrev("`gp'",15) "=0" _col(43) abbrev("`gp'",15) "=1" _col(64) abbrev("`gp'",15) "=0" _col(82) abbrev("`gp'",15) "=1"
di _col(10) "{hline 85}"
}
else {
di %~70s as text as text "Item difficulties: estimates (s.e.)"
di _col(10) "{hline 50}"
di _col(25) "Time 1" _col(43) "Time 2"
di _col(10) "{hline 50}"
}
forvalues j=1/`nbitems' {
di as text _col(10) "``j''"
forvalues p=1/`nbdif_`j'' {
if "`group'" != "" {
di as text _col(10) "`p'" as result _col(25) %6.2f `delta1_`j'_`p'g0m4' " (" %4.2f `delta1_`j'_`p'g0m4_se' ")" _col(43) %6.2f `delta1_`j'_`p'g1m4' " (" %4.2f `delta1_`j'_`p'g1m4_se' ")" ///
_col(63) %6.2f `delta2_`j'_`p'g0m4' " (" %4.2f `delta2_`j'_`p'g0m4_se' ")" _col(81) %6.2f `delta2_`j'_`p'g1m4' " (" %4.2f `delta2_`j'_`p'g1m4_se' ")"
}
else {
di as text _col(10) "`p'" as result _col(25) %6.2f `delta1_`j'_`p'g0m4' " (" %4.2f `delta1_`j'_`p'g0m4_se' ")" _col(43) %6.2f `delta2_`j'_`p'g0m4' " (" %4.2f `delta2_`j'_`p'g0m4_se' ")"
}
}
}
if "`group'" != "" {
di as text _col(10) "{hline 85}"
}
else {
di as text _col(10) "{hline 50}"
}
/* Affichage des estimations sur le trait latent du modèle final */
di
di %~85s as text "Latent trait distribution"
di _col(10) "{hline 65}"
di _col(28) as text "Estimate" _col(44) "Standard error" _col(62) "P-value"
di _col(10) "{hline 65}"
if "`group'" == "" {
local fact_k = 2
}
else {
local fact_k = 4
}
di _col(10) as text "Variance Time 1" as result _col(28) %6.2f `=mod4[1,`=`fact_k'*`nbmoda_sum'+1']' _col(44) %6.2f =mod4[2,`=`fact_k'*`nbmoda_sum'+1']
di _col(10) as text "Variance Time 2" as result _col(28) %6.2f `=mod4[1,`=`fact_k'*`nbmoda_sum'+2']' _col(44) %6.2f `=mod4[2,`=`fact_k'*`nbmoda_sum'+2']'
di _col(10) as text "Covariance" as result _col(28) %6.2f `=mod4[1,`=`fact_k'*`nbmoda_sum'+3']' _col(44) %6.2f `=mod4[2,`=`fact_k'*`nbmoda_sum'+3']'
if "`group'" != "" {
di _col(10) as text "Group effect" as result _col(28) %6.2f `geffm4' _col(44) %6.2f `segeffm4' _col(62) %6.4f `gpm4p'
}
di _col(10) as text "Time effect" as result _col(28) %6.2f `teffm4' _col(44) %6.2f `seteffm4' _col(62) %6.4f `tm4p'
if "`group'" != "" {
if effet[3,3] < 0.05 {
di _col(10) as text "TimexGroup inter" as result _col(28) %6.2f effet[1,3] _col(44) %6.2f effet[2,3] _col(62) %6.4f effet[3,3]
}
else {
di _col(10) as text "TimexGroup inter" as result _col(28) "0 (constrained)"
}
}
di as text _col(10) "{hline 65}"
/***************************************/
/* Calcul des valeurs de DIF et de RC */
/*************************************/
forvalues j=1/`nbitems' {
if `nbmoda_`j'' >= 2 {
matrix valeur_difrc_`j' = J(`nbdif_`j'',8,.)
matrix colnames valeur_difrc_`j' = DIFT1 DIFT1_SE RC_GP0 RC_GP0_SE RC_GP1 RC_GP1_SE
}
}
forvalues j=1/`nbitems'{
if `nbmoda_`j'' >= 2 {
if "`group'" != "" {
*DIF
if "`nodif'"=="" {
if (dif_rc[`j',1] != . ) {
forvalues p=1/`nbdif_`j'' {
if `p' == 1 {
qui lincom -[1.``j'']:1.`gp'+[1.``j'']:0.`gp'
matrix valeur_difrc_`j'[`p',1] = r(estimate)
matrix valeur_difrc_`j'[`p',2] = round(r(se),0.01)
}
if `p' > 1 {
qui lincom [`=`p'-1'.``j'']:1.`gp' - [`p'.``j'']:1.`gp' -[`=`p'-1'.``j'']:0.`gp' + [`p'.``j'']:0.`gp'
matrix valeur_difrc_`j'[`p',1] = r(estimate)
matrix valeur_difrc_`j'[`p',2] = round(r(se),0.01)
}
}
}
}
*RC GROUP 0
if (dif_rc[`j',3] != . & dif_rc[`j',5] != . ) {
forvalues p=1/`nbdif_`j'' {
qui lincom -[1.``=`j'+`nbitems''']:0.`gp' + [1.``j'']:0.`gp'
matrix valeur_difrc_`j'[`p',3] = r(estimate)
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
if `p' > 1 {
qui lincom [`=`p'-1'.``=`j'+`nbitems''']:0.`gp' - [`p'.``=`j'+`nbitems''']:0.`gp' -[`=`p'-1'.``j'']:0.`gp' + [`p'.``j'']:0.`gp'
matrix valeur_difrc_`j'[`p',3] = r(estimate)
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
}
}
}
*RC GROUP 1
if (dif_rc[`j',3] != . & dif_rc[`j',7] != . ) {
forvalues p=1/`nbdif_`j'' {
qui lincom -[1.``=`j'+`nbitems''']:1.`gp' + [1.``j'']:1.`gp'
matrix valeur_difrc_`j'[`p',5] = r(estimate)
matrix valeur_difrc_`j'[`p',6] = round(r(se),0.01)
if `p' > 1 {
qui lincom [`=`p'-1'.``=`j'+`nbitems''']:1.`gp' - [`p'.``=`j'+`nbitems''']:1.`gp' -[`=`p'-1'.``j'']:1.`gp' + [`p'.``j'']:1.`gp'
matrix valeur_difrc_`j'[`p',5] = r(estimate)
matrix valeur_difrc_`j'[`p',6] = round(r(se),0.01)
}
}
}
}
else {
forvalues p=1/`nbdif_`j'' {
qui lincom -[1.``=`j'+`nbitems''']_cons + [1.``j'']_cons
matrix valeur_difrc_`j'[`p',3] = r(estimate)
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
if `p' > 1 {
qui lincom [`=`p'-1'.``=`j'+`nbitems''']_cons - [`p'.``=`j'+`nbitems''']_cons -[`=`p'-1'.``j'']_cons + [`p'.``j'']_cons
matrix valeur_difrc_`j'[`p',3] = r(estimate)
matrix valeur_difrc_`j'[`p',4] = round(r(se),0.01)
}
}
}
}
}
forvalues j = 1/`nbitems' {
if `nbmoda_`j'' >= 2 {
forvalues p = 1/`nbdif_`j'' {
forvalues k = 1/8 {
if valeur_difrc_`j'[`p',`k'] == . {
matrix valeur_difrc_`j'[`p',`k'] = 0
}
}
}
}
}
/* Affichage des estimations des valeurs de DIF et de RC */
if "`group'" != "" {
di
di %~85s as text "Estimates of differences between groups and recalibration"
}
else {
di
di %~50s as text "Estimates of recalibration"
}
if "`group'" != "" & "`nodif'"==""{
di _col(10) "{hline 65}"
di _col(27) "Difference of" _col(52) "RECALIBRATION"
di _col(27) "groups at T1" _col(47) abbrev("`gp'",15) "=0" _col(62) abbrev("`gp'",15) "=1"
di _col(10) "{hline 65}"
}
else if "`group'" != "" & "`nodif'"!="" {
di _col(10) "{hline 50}"
di _col(32) "RECALIBRATION"
di in ye _col(27) "`gp'=`=rep[1,1]'" _col(47) "`gp'=`=rep[2,1]'"
di _col(10) "{hline 50}"
}
else {
di _col(10) "{hline 30}"
di _col(25) "RECALIBRATION"
di _col(10) "{hline 30}"
}
forvalues j=1/`nbitems' {
if `nbmoda_`j'' >= 2 {
if "`group'" != "" & "`nodif'" == "" {
di as text _col(10) "``j''"
}
else {
di as text _col(10) "``j''"
}
forvalues p=1/`nbdif_`j'' {
if "`group'" != "" & "`nodif'"=="" {
di as text _col(10) "`p'" as result _col(27) %6.2f `=valeur_difrc_`j'[`p',1]' " (" %4.2f `=valeur_difrc_`j'[`p',2]' ")" ///
_col(47) %6.2f `=valeur_difrc_`j'[`p',3]' " (" %4.2f `=valeur_difrc_`j'[`p',4]' ")" _col(62) %6.2f `=valeur_difrc_`j'[`p',5]' " (" %4.2f `=valeur_difrc_`j'[`p',6]' ")"
}
else if "`group'" != "" & "`nodif'"!="" {
di as text _col(10) "`p'" as result _col(25) %6.2f `=valeur_difrc_`j'[`p',3]' " (" %4.2f `=valeur_difrc_`j'[`p',4]' ")" _col(45) %6.2f `=valeur_difrc_`j'[`p',5]' " (" %4.2f `=valeur_difrc_`j'[`p',6]' ")"
}
else {
di as text _col(10) "`p'" as result _col(25) %6.2f `=valeur_difrc_`j'[`p',3]' " (" %4.2f `=valeur_difrc_`j'[`p',4]' ")"
}
}
}
}
if "`group'" != "" & "`nodif'"=="" {
di as text _col(10) "{hline 65}"
}
else if "`group'" != "" & "`nodif'"!=""{
di as text _col(10) "{hline 50}"
}
else {
di as text _col(10) "{hline 30}"
}
di
*******************************************************************************
* New outputs
if "`group'" == "" {
matrix testlrm = J(1,3,.)
matrix colnames testlrm = chi_square df pvalue
matrix rownames testlrm = m1_vs_m2
matrix testlrm[1,1] = (`rstestchi',`rstestdf',`rstestp')
return matrix test_model = testlrm
}
else if "`nodif'" != "" {
matrix testlrm = J(1,3,.)
matrix colnames testlrm = chi_square df pvalue
matrix rownames testlrm = m1_vs_m2
matrix testlrm[1,1] = (`rstestchi',`rstestdf',`rstestp')
return matrix test_model = testlrm
}
else {
matrix testlrm = J(2,3,.)
matrix colnames testlrm = chi_square df pvalue
matrix rownames testlrm = mA_vs_mB m1_vs_m2
matrix testlrm[1,1] = (`diftestchi',`diftestdf',`diftestp')
matrix testlrm[2,1] = (`rstestchi',`rstestdf',`rstestp')
return matrix test_model = testlrm
}
return matrix model_4 = mod4
return matrix model_2 = mod2
capture qui use `saverspcm', clear
end