First code for accounting DIF scenarios (stata)
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*=================================================================================================================================================
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* Date : 2024-01-23
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* Stata version : Stata 18 SE
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*
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* This program analyses simulated data accounting for DIF through a partial credit model
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*
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* ado-files needed : - pcm (version 5.5 October 25, 2023, available on gitea)
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*
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* outputs : for N=100
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*
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*
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*================================================================================================================================================
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*=================================================================================================================================================
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* Date : 2024-01-23
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* Stata version : Stata 18 SE
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*
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* This program analyses simulated data accounting for DIF through a partial credit model
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*
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* ado-files needed : - pcm (version 5.5 October 25, 2023, available on gitea)
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*
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* outputs : for N=100
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*
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*
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*================================================================================================================================================
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* Load pcm.ado
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adopath+"/home/corentin/Documents/These/Recherche/Simulations/Modules/"
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* Set output folder path
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local path_data = "/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N100"
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local path_res = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100"
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local Nn = 100
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*==========================
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* Scenarios with : J=4
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*==========================
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** Scenario 1: J = 4 items / M = 2 modalities
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* Scenario 1A : H_0 is TRUE
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** Scenario 5: J = 4 items / M = 2 modalities / DIF size 0.3
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local N = "100 200 300"
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foreach Nnn in `N' {
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local Nn = `Nnn'
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local path_data = "/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N`Nn'"
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local path_res = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/DIF/N`Nn'"
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local scenarios = "A B C D E"
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foreach scen in `scenarios' {
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clear
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import delim "`path_data'/scenario_5A_100.csv", encoding(ISO-8859-2) case(preserve) clear
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import delim "`path_data'/scenario_5`scen'_`Nn'.csv", encoding(ISO-8859-2) case(preserve) clear
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rename TT tt
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keep if replication==1
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di `k'
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* log using gsem.txt, text replace
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* Créer une matrice 1000xnbitems+1 (pour beta) à l'avance et populer chaque coefficient à la main à chaque itération
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* Créer une deuxième matrice et faire de même pour les std error
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gsem (1.item1<-THETA@1)///
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(1.item2<-THETA@1 tt)///
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(1.item3<-THETA@1 )///
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(1.item4<-THETA@1)///
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(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent
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* Matrice de taille 1000 * 4 items + 1 DIF + beta + std beta + 1 dif
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local nbitems = 4
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local nbdif = 1
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local taillemat = `nbitems'+`nbdif'+3
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mat outmat = J(1000,`taillemat',.)
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mat colnames outmat = "item1" "item2" "item3" "item4" "dif1" "beta" "se_beta" "dif_item_1"
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di "Scenario 5`scen' / N=`Nnn'"
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forvalues k=1/1000 {
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if (mod(`k',100)==0) {
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di "`k'/1000"
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}
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preserve
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qui keep if replication==`k'
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local difitems1=dif1
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local mod "gsem "
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forvalues i=1/`nbitems' {
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if (`i'==`difitems1') {
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local mod = "`mod'"+"(1.item`i'<-THETA@1 tt)"
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}
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else {
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local mod = "`mod'"+"(1.item`i'<-THETA@1)"
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}
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}
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local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent"
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qui `mod'
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mat V=r(table)
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mat W=V[1..2,1...]
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putexcel set "W.xls", sheet("W") replace
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putexcel A1=matrix(W)
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* log close
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pcm item1 item2 item3 item4, categorical(tt)
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forvalues j=1/`nbitems' {
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if (`j'<`difitems1') {
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mat outmat[`k',`j'] = W[1,3*`j'] // items avant le premier dif
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}
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else {
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mat outmat[`k',`j'] = W[1,1+3*`j'] // items après le premier dif
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}
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}
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mat outmat[`k',`nbitems'+1] = W[1,2*`difitems1'+1] // coef de dif
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mat outmat[`k',`nbitems'+2] = W[1,3*`nbitems'+2] // beta
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mat outmat[`k',`nbitems'+3] = W[2,3*`nbitems'+2] // se beta
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mat outmat[`k',`nbitems'+4] = `difitems1' // numéro item de dif
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restore
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}
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putexcel set "`path_res'/out/5`scen'_`Nn'.xls", sheet("outmat") replace
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putexcel A1=matrix(outmat), colnames
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}
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}
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