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* File      HALCyon cognitive syntax_ELSA.do
* BY        Rebecca Hardy, MRC Unit for Lifelong Health and Ageing at UCL
* FOR       HALCyon cross-cohort collaboration (funded by the New Dynamics of Ageing (RES-353-25-0001))
* DATASET   ELSA
* Date Created    07/04/2010
* PURPOSE         Recodes and derives cognitive variables for use in cross-cohort analyses
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# delimit;
set more off;

*To obtain age and sex variables, please first run HALCyon PC covariate syntax_ELSA.do

/*run demographic recodes*/
do "Q:\aix\halcyon\NDAanalysis\ELSA\ELSA_v1.do" ;

/*Creating break between newly recoded cognitive variables and variables in existing dataset with a blank variable */
gen cogrecodes_below=. ;

/*********************************************************************************/

/*no measure of crystallised cognitive ability*/

/*10-item word list - immediate and delayed recall*/
/*set to missing -9=refusal, -8=don't know, -1=not applicable*/
gen wordlist_w1=cflisenw1 ;
label variable wordlist_w1 "10-item word list immediate recall at wave 1" ;
gen wordlistd_w1=cflisdw1 ;
label variable wordlistd_w1 "10-item word list delayed recall at wave 1" ;
mvdecode wordlist_w1 wordlistd_w1, mv(-1, -9, -8) ;
gen wordlist_w2=CfLisEnw2 ;
label variable wordlist_w2 "10-item word list immediate recall at wave 2" ;
gen wordlistd_w2=CfLisDw2 ;
label variable wordlistd_w2 "10-item word list delayed recall at wave 2"  ; 
mvdecode wordlist_w2 wordlistd_w2, mv(-1, -9, -8) ;
gen wordlist_w3=CfLisEnw3 ;
label variable wordlist_w3 "10-item word list immediate recall at wave 3" ;
gen wordlistd_w3=CfLisDw3  ; 
label variable wordlistd_w3 "10-item word list delayed recall at wave 3" ;  
mvdecode wordlist_w3 wordlistd_w3, mv(-1, -9, -8) ;

/*derive word list total*/
/*range 0-20*/
gen wlt_w1=wordlist_w1+wordlistd_w1 ;
label variable wlt_w1 "10-item word list immediate+delayed recall at wave 1" ;
gen wlt_w2=wordlist_w2+wordlistd_w2 ;
label variable wlt_w2 "10-item word list immediate+delayed recall at wave 2" ;
gen wlt_w3=wordlist_w3+wordlistd_w3 ;
label variable wlt_w3 "10-item word list immediate+delayed recall at wave 3" ;

/*standardise word list to whole sample*/
egen wltz_w1=std(wlt_w1) if sex!=.;
label variable wltz_w1 "standardised word list recall total at wave 1" ;
egen wltz_w2=std(wlt_w2) if sex!=.;
label variable wltz_w2 "standardised word list recall total at wave 2" ;
egen wltz_w3=std(wlt_w3) if sex!=.;
label variable wltz_w3 "standardised word list recall total at wave 3" ;

/*standardise word list for men and women seprately*/
egen wltzm_w1=std(wlt_w1) if sex==1;
egen wltzf_w1=std(wlt_w1) if sex==2;
label variable wltzm_w1 "standardised word list total for men at wave 1" ;
label variable wltzf_w1 "standardised word list total for women at wave 1" ;
egen wltzm_w2=std(wlt_w2) if sex==1;
egen wltzf_w2=std(wlt_w2) if sex==2;
label variable wltzm_w2 "standardised word list total for men at wave 2" ;
label variable wltzf_w2 "standardised word list total for women at wave 2" ;
egen wltzm_w3=std(wlt_w3) if sex==1;
egen wltzf_w3=std(wlt_w3) if sex==2;
label variable wltzm_w3 "standardised word list total for men at wave 3" ;
label variable wltzf_w3 "standardised word list total for women at wave 3" ;
/*combine in one variable*/
gen wltzs_w1=. ;
replace wltzs_w1=wltzm_w1 if sex==1;
replace wltzs_w1=wltzf_w1 if sex==2;
label variable wltzs_w1 "sex-standardised word list total at wave 1" ;
gen wltzs_w2=.;
replace wltzs_w2=wltzm_w2 if sex==1;
replace wltzs_w2=wltzf_w2 if sex==2;
label variable wltzs_w2 "sex-standardised word list total at wave 2" ;
gen wltzs_w3=. ;
replace wltzs_w3=wltzm_w3 if sex==1;
replace wltzs_w3=wltzf_w3 if sex==2;
label variable wltzs_w3 "sex-standardised word list total at wave 3" ;

/*age and sex-standardised word list - standardised residuals from regression on age and sex*/
xi: regress wlt_w1 age_w1 i.sex ;
predict wltzage_w1, rstandard ;
label variable wltzage_w1 "age and sex-standardised word list total at wave 1" ;
xi: regress wlt_w2 age_w2 i.sex ;
predict wltzage_w2, rstandard ;
label variable wltzage_w2 "age and sex-standardised word list total at wave 2" ;
xi: regress wlt_w3 age_w3 i.sex ;
predict wltzage_w3, rstandard ;
label variable wltzage_w3 "age and sex-standardised word list total at wave 3" ;

/*letter cancellation task*/
/*set -1 to missing*/
/*search speed*/
gen searchsp_w1=nrowclmw1 ;
label variable searchsp_w1 "Search speed at wave 1";
mvdecode searchsp_w1, mv(-1);
gen searchsp_w2=nrowclw2;
label variable searchsp_w2 "Search speed at wave 2";
mvdecode searchsp_w2, mv(-1);
gen searchsp_w3=NRowClmw3;
label variable searchsp_w3 "Search speed at wave 3";
mvdecode searchsp_w3, mv(-1, -2);

/*standardise word list to whole sample*/
egen searchspz_w1=std(searchsp_w1) if sex!=.;
label variable searchspz_w1 "Search speed at at wave 1" ;
egen searchspz_w2=std(searchsp_w2) if sex!=. ;
label variable searchspz_w2 "Search speed at at wave 2" ;
egen searchspz_w3=std(searchsp_w3) if sex!=. ;
label variable searchspz_w3 "Search speed at at wave 3" ;

/*standardise seacrh speed for men and women seprately*/
egen searchspzm_w1=std(searchsp_w1) if sex==1;
egen searchspzf_w1=std(searchsp_w1) if sex==2;
label variable searchspzm_w1 "standardised search speed for men at wave 1" ;
label variable searchspzf_w1 "standardised search speed for women at wave 1" ;
egen searchspzm_w2=std(searchsp_w2) if sex==1;
egen searchspzf_w2=std(searchsp_w2) if sex==2;
label variable searchspzm_w2 "standardised search speed for men at wave 2" ;
label variable searchspzf_w2 "standardised search speed for women at wave 2" ;
egen searchspzm_w3=std(searchsp_w3) if sex==1;
egen searchspzf_w3=std(searchsp_w3) if sex==2;
label variable searchspzm_w3 "standardised search speed for men at wave 3" ;
label variable searchspzf_w3 "standardised search speed for women at wave 3" ;
/*combine in one variable*/
gen searchspzs_w1=. ;
replace searchspzs_w1=searchspzm_w1 if sex==1;
replace searchspzs_w1=searchspzf_w1 if sex==2;
label variable searchspzs_w1 "sex-standardised search speed at wave 1" ;
gen searchspzs_w2=.;
replace searchspzs_w2=searchspzm_w2 if sex==1;
replace searchspzs_w2=searchspzf_w2 if sex==2;
label variable searchspzs_w2 "sex-standardised search speed at wave 2" ;
gen searchspzs_w3=. ;
replace searchspzs_w3=searchspzm_w3 if sex==1;
replace searchspzs_w3=searchspzf_w3 if sex==2;
label variable searchspzs_w3 "sex-standardised search speed at wave 3" ;

/*age and sex-standardised search speed - standardised residuals from regression on age and sex*/
xi: regress searchsp_w1 age_w1 i.sex ;
predict searchspzage_w1, rstandard ;
label variable searchspzage_w1 "age and sex-standardised search speed at wave 1" ;
xi: regress searchsp_w2 age_w2 i.sex ;
predict searchspzage_w2, rstandard ;
label variable searchspzage_w2 "age and sex-standardised search speed at wave 2" ;
xi: regress searchsp_w3 age_w3 i.sex ;
predict searchspzage_w3, rstandard ;
label variable searchspzage_w3 "age and sex-standardised search speed at wave 3" ;

/*other variables from letter cancellation task*/
/*search accuracy=number searched-number missed*/
/*set -1 and -2 to missing*/
gen ncorr_w1=ncorrecw1;
label variable ncorr_w1 "letter cancellation task (number correct) - wave 1";
gen nmissed_w1=nmissedw1;
label variable nmissed_w1 "letter cancellation task (number missed) - wave 1";
mvdecode ncorr_w1 nmissed_w1, mv(-1);
gen searchacc_w1=ncorr_w1-nmissed_w1 ;
label variable searchacc_w1 "letter cancellation task (number correct-number missed) - wave 1";
gen ncorr_w2=nncorrew2;
label variable ncorr_w2 "letter cancellation task (number correct) - wave 2";
gen nmissed_w2=nnmissew2;
label variable nmissed_w2 "letter cancellation task (number missed) - wave 2";
mvdecode ncorr_w2 nmissed_w2, mv(-1);
gen searchacc_w2=ncorr_w2-nmissed_w2 ;
label variable searchacc_w2 "letter cancellation task (number correct-number missed) - wave 2";
gen ncorr_w3=NCorrectw3;
label variable ncorr_w3 "letter cancellation task (number correct) - wave 3";
gen nmissed_w3=NMissedw3;
label variable nmissed_w3 "letter cancellation task (number missed) - wave 3";
mvdecode ncorr_w3 nmissed_w3, mv(-2, -1);
gen searchacc_w3=ncorr_w3-nmissed_w3; 
label variable searchacc_w3 "letter cancellation task (number correct-number missed) - wave 3";
histogram searchacc_w3, width(1);

/*category fluency*/
/*set -9, -8 and -1 to missing*/
gen catfl_w1=cfaniw1;
label variable catfl_w1 "category fluency (animal naming) score at wave 1";
mvdecode catfl_w1, mv(-9, -8, -1);
gen catfl_w2=CfAniw2;
label variable catfl_w2 "category fluency (animal naming) score at wave 2";
mvdecode catfl_w2, mv(-9, -8, -1);
gen catfl_w3=CfAniw3;
label variable catfl_w3 "category fluency (animal naming) score at wave 3";
mvdecode catfl_w3, mv(-9, -8, -1);

/*standardise category fluency score*/
egen catflz_w1=std(catfl_w1) if sex!=. ;
label variable catflz_w1 "standardised category fluency (animal naming) at wave 1" ;
egen catflz_w2=std(catfl_w2) if sex!=. ;
label variable catflz_w2 "standardised category fluency (animal naming) at wave 2" ;
egen catflz_w3=std(catfl_w3) if sex!=. ;
label variable catflz_w3 "standardised category fluency (animal naming) at wave 3" ;

/*standardise category fluency (animal naming) for men and women seprately*/
egen catflzm_w1=std(catfl_w1) if sex==1;
egen catflzf_w1=std(catfl_w1) if sex==2;
label variable catflzm_w1 "standardised category fluency (animal naming) for men at wave 1" ;
label variable catflzf_w1 "standardised category fluency (animal naming) for women at wave 1" ;
egen catflzm_w2=std(catfl_w2) if sex==1;
egen catflzf_w2=std(catfl_w2) if sex==2;
label variable catflzm_w2 "standardised category fluency (animal naming) for men at wave 2" ;
label variable catflzf_w2 "standardised category fluency (animal naming) women at wave 2" ;
egen catflzm_w3=std(catfl_w3) if sex==1;
egen catflzf_w3=std(catfl_w3) if sex==2;
label variable catflzm_w3 "standardised category fluency (animal naming) for men at wave 3" ;
label variable catflzf_w3 "standardised category fluency (animal naming) for women at wave 3" ;
/*combine in one variable*/
gen catflzs_w1=. ;
replace catflzs_w1=catflzm_w1 if sex==1;
replace catflzs_w1=catflzf_w1 if sex==2;
label variable catflzs_w1 "sex-standardised category fluency (animal naming) at wave 1" ;
gen catflzs_w2=.;
replace catflzs_w2=catflzm_w2 if sex==1;
replace catflzs_w2=catflzf_w2 if sex==2;
label variable catflzs_w2 "sex-standardised category fluency (animal naming) at wave 2" ;
gen catflzs_w3=. ;
replace catflzs_w3=catflzm_w3 if sex==1;
replace catflzs_w3=catflzf_w3 if sex==2;
label variable catflzs_w3 "sex-standardised category fluency (animal naming) at wave 3" ;

/*age and sex-standardised category fluency (animal naming) - standardised residuals from regression on age and sex*/
xi: regress catfl_w1 age_w1 i.sex ;
predict catflzage_w1, rstandard ;
label variable catflzage_w1 "age and sex-standardised category fluency (animal naming) at wave 1" ;
xi: regress catfl_w2 age_w2 i.sex ;
predict catflzage_w2, rstandard ;
label variable catflzage_w2 "age and sex-standardised category fluency (animal naming) at wave 2" ;
xi: regress catfl_w3 age_w3 i.sex ;
predict catflzage_w3, rstandard ;
label variable catflzage_w3 "age and sex-standardised category fluency (animal naming) at wave 3" ;

/*derive fluid cognitive ability - Gf*/
factor wltz_w1 catflz_w1 searchspz_w1, factors(1) pcf ;
predict Gf_w1 ;
label variable Gf_w1 "Gf at wave 1" ;
factor wltz_w2 catflz_w2 searchspz_w2, factors(1) pcf ;
predict Gf_w2 ;
label variable Gf_w2 "Gf at wave 2" ;
factor wltz_w3 catflz_w3 searchspz_w3, factors(1) pcf ;
predict Gf_w3 ;
label variable Gf_w1 "Gf at wave 3" ;

/*using age and sex-standardised scores*/
factor wltzage_w1 catflzage_w1 searchspzage_w1, factors(1) pcf ;
predict Gfage_w1 ;
label variable Gfage_w1 "Gf (age and sex-adjusted) at wave 1" ;
factor wltzage_w2 catflzage_w2 searchspzage_w2, factors(1) pcf ;
predict Gfage_w2 ;
label variable Gfage_w2 "Gf (age and sex-adjusted) at wave 2" ;
factor wltzage_w3 catflzage_w3 searchspzage_w3, factors(1) pcf ;
predict Gfage_w3 ;
label variable Gfage_w1 "Gf (age and sex-adjusted) at wave 3" ;