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«University of Nebraska - Lincoln DigitalCommons of Nebraska - Lincoln Dissertations and Theses in Statistics Statistics, Department of 8-2010 ...»

-- [ Page 11 ] --

Data Sim_Scores;

Set Sim_Scores;

Time = Ceil(I/2);

If Mod(I,2) = 1 then Test = 'CRT'; Else Test = 'MAT';

Keep Student Time Test Score;

Run;

****************************************************;

*** Merge Student Scores and Teacher Assignments ***;

****************************************************;

Proc Sort Data = Sim_Scores;

By Student Time;

Proc Sort Data = Tch_Assignments;

By Student Time;

Run;

Data Sim_Scores_n_Tch;

Merge Sim_Scores Tch_Assignments;

By Student Time;

Run;

*******************************;

*** Create Layered Z-matrix ***;

*******************************;

/* step 1: separate MAT & CRT scores to create z-matrix for each type of test*/ Data Sim_Scores_MAT;

Set Sim_Scores_n_Tch;

If Test = 'MAT';

Run;

Data Sim_Scores_CRT;

Set Sim_Scores_n_Tch;

If Test = 'CRT';

Run;

/* step 2: macro creates the layered z-matrix for the general case when the teachers are not 1, 2, 3,...., but instead are names.*/ %let Student=Student;

%let Ranvar=Teacher;

%let zmax=1;

%macro Randvar_Full(dataname1,w11,w21,w22,w31,w32,w33,w41,w42,w43, w44);

Proc Sort data=&data_o; By &ranvar;

Data Z(keep=&ranvar);

Set &data_o;

By &ranvar;

If first.&ranvar;

Run;

Data zz;

Set z;

Rank=_N_;

Run;

Proc sql;

Select max(rank) into: zmax from zz;

Data new_1;

Merge &data_o zz;

By &ranvar;

Proc Sort Data=new_1; By &student Time;

Data New_2 (Drop=Zmat%eval(&zmax+1));

Set new_1;

By &student Time;

/*Create lag variable*/ Array reset(3) lagrank1-lagrank3;

lagrank1=lag(rank);

lagrank2=lag2(rank);

lagrank3=lag3(rank);

If first.&student then count=1;

Do i=count to 3;

Reset(i)=%eval(&zmax+1);

End;

Count+1;

%Do ii=1 %to %Eval(&zmax+1);

Zmat&ii=0;

%End;

Array Zmat(%eval(&zmax+1)) Zmat1-Zmat%eval(&zmax+1);

If time = 1 then Zmat[rank]+&w11;

Else if Time = 2 then do; Zmat[rank]+&w21;

Zmat[lagrank1]+&w22; End;

Else if Time = 3 then do; Zmat[rank]+&w31;

Zmat[lagrank1]+&w32; Zmat[lagrank2]+&w33; End;

Else if Time = 4 then do; Zmat[rank]+&w41;

Zmat[lagrank1]+&w42; Zmat[lagrank2]+&w43;

Zmat[lagrank3]+&w44; End;

Run;

Data &dataname1(drop=count i);

Set New_2;

Run;

%mend;

/* step 3: run macro for both tests */ %Let Data_o=Sim_Scores_CRT;

%Randvar_full(Sim_Scores_CRT1,1,1,1,1,1,1,1,1,1,1);

%Let Data_o=Sim_Scores_MAT;

%Randvar_full(Sim_Scores_MAT1,1,1,1,1,1,1,1,1,1,1);

/* step 4: merge data for both tests */ DATA Sim_Scores;

SET Sim_Scores_CRT1 Sim_Scores_MAT1;

Run;

Proc Sort Data = Sim_Scores;

BY Student Time Test;

Run;

*****************************;

*** Create Final Data Set ***;

*****************************;

/* step 1: Generate random teacher effects and find ranks and percentiles by year */ Data Gen_tch_value;

Seed=&seed3;

Do Teacher=1 to 80;

t_eff=rannor(seed)*(sqrt(22.5));

Output;

End;

Run;

Data Gen_tch_value;

Set Gen_tch_value;

Year = 1;

If Teacher 20 then Year = 2;

If Teacher 40 then Year = 3;

If Teacher 60 then Year = 4;

Run;

Proc Sort Data = Gen_tch_value;

By Year Teacher;

Run;

Proc Rank Data = Gen_tch_value Out=Rank;

By Year;

Ranks True_Rank;

Var T_Eff;

Run;

Data Gen_tch_value_Temp;

Set Rank;

Exp = ≁

True_Effect = t_eff;

True_Percentile=100*(True_Rank/21);

Drop t_eff;

Run;

*Send true teacher effects to one permanent file, updated for each simulation;

%If %eval(&nsim) = 1 %then %do;

Data Results.True_Teacher_Effects;

Set Gen_tch_value_Temp;

Run;

%End;

%Else %do;

Data Results.True_Teacher_Effects;

Set Results.True_Teacher_Effects Gen_tch_value_Temp;

Run;

%End;

/* step 2: merge student data, teacher assignments, and teacher values into single data set */ Proc IML;

Use Sim_Scores;

Read all var{student score time test teacher rank lagrank1 lagrank2 lagrank3 Zmat1 Zmat2 Zmat3 Zmat4 Zmat5 Zmat6 Zmat7 Zmat8 Zmat9 Zmat10 Zmat11 Zmat12 Zmat13 Zmat14 Zmat15 Zmat16 Zmat17 Zmat18 Zmat19 Zmat20 Zmat21 Zmat22 Zmat23 Zmat24 Zmat25 Zmat26 Zmat27 Zmat28 Zmat29 Zmat30 Zmat31 Zmat32 Zmat33 Zmat34 Zmat35 Zmat36 Zmat37 Zmat38 Zmat39 Zmat40 Zmat41 Zmat42 Zmat43 Zmat44 Zmat45 Zmat46 Zmat47 Zmat48 Zmat49 Zmat50 Zmat51 Zmat52 Zmat53 Zmat54 Zmat55 Zmat56 Zmat57 Zmat58 Zmat59 Zmat60 Zmat61 Zmat62 Zmat63 Zmat64 Zmat65 Zmat66 Zmat67 Zmat68 Zmat69 Zmat70 Zmat71 Zmat72 Zmat73 Zmat74 Zmat75 Zmat76 Zmat77 Zmat78 Zmat79 Zmat80};

Use gen_tch_value;

Read all var{t_eff};

Z= Zmat1||Zmat2||Zmat3||Zmat4||Zmat5||Zmat6||Zmat7||Zmat8|| Zmat9||Zmat10||Zmat11||Zmat12||Zmat13||Zmat14||Zmat15|| Zmat16||Zmat17||Zmat18||Zmat19||Zmat20||Zmat21||Zmat22|| Zmat23||Zmat24||Zmat25||Zmat26||Zmat27||Zmat28||Zmat29|| Zmat30||Zmat31||Zmat32||Zmat33||Zmat34||Zmat35||Zmat36|| Zmat37||Zmat38||Zmat39||Zmat40||Zmat41||Zmat42||Zmat43|| Zmat44||Zmat45||Zmat46||Zmat47||Zmat48||Zmat49||Zmat50|| Zmat51||Zmat52||Zmat53||Zmat54||Zmat55||Zmat56||Zmat57|| Zmat58||Zmat59||Zmat60||Zmat61||Zmat62||Zmat63||Zmat64|| Zmat65||Zmat66||Zmat67||Zmat68||Zmat69||Zmat70||Zmat71|| Zmat72||Zmat73||Zmat74||Zmat75||Zmat76||Zmat77||Zmat78|| Zmat79||Zmat80;





Score_Total = Score + Z*t_eff;

Test2 = J((&nstudent)*8,1,0);

Do i = 1 to (&nstudent)*8;

If test[i,1] = 'MAT' then test2[i,1] = 2; Else test2[i,1] = 1;

End;

Scores = Z||student||score||score_total||time||teacher||test2;

Create tch_student_scores from Scores;

Append from Scores;

Print ≁

Run;

Quit;

/* step 3: rename and create variables for PP (Dataname 2) Dataset*/ Data &dataname2;

Set tch_student_scores;

Array Z[80] Z1-Z80;

Array Col[80] Col1-Col80;

Do i = 1 to 80;

Z[i] = Col[i];

End;

Exp = ≁

Student=col81;

No_Tch_Score=col82;

Tch_Score_Final=col83;

If Tch_Score_Final0 then Tch_Score_Final=0;

Time=col84;

Teacher=col85;

If Col86 = 1 then Test = 'CRT'; Else Test = 'MAT';

Drop Col1-Col86;

Run;

/* Step 4: Create Zcrt (Dataname 3) and Zmat (Dataname 4) Datasets */ Data Standard;

Set &dataname2;

sum_z=sum(of Z1-Z%eval(&zmax));

Run;

Data New3;

Set Standard;

%Do ii=1 %to &zmax;

if sum_z ne 0 then Z&ii=(Z&ii/sum_z)**0.5; else Z&ii=0;

%End;

Run;

Proc Sort Data = New3;

By Time Test;

Run;

Proc Means Data = New3 Mean Std noprint;

By Time Test;

Var Tch_Score_Final;

Output Out = Stats Mean = Mean Std = Std;

Run;

Data Stats;

Set Stats;

Drop _Type_ _Freq_;

Run;

Data New;

Merge New3 Stats;

By Time Test;

Run;

Data New2;

Set New;

Zscore = (Tch_Score_Final - Mean)/Std;

Drop No_Tch_Score Tch_Score_Final Mean Std Sum_Z;

Run;

Data &Dataname3;

Set New2;

If Test = 'CRT';

Run;

Data &Dataname4;

Set New2;

If Test = 'MAT';

Run;

Proc Sort Data = &Dataname2;

By Student Time Test;

Proc Sort Data = &Dataname3;

By Student Time;

Proc Sort Data = &Dataname4;

By Student Time;

Run;

Quit; *This makes sure a Proc is not still running;

%mend;

***************************************************;

*** Create Macro to Analyze Simulated Data Sets ***;

***************************************************;

*Delete Old Predicted Teacher Effects File before reanalyzing data sets;

Data Results.Pred_Teacher_Effects;

Delete;

Run;

*Delete Old Variance Components File before re-analyzing data sets;

Data Results.Pred_Variance_pp;

Delete;

Run;

Data Results.Pred_Variance_zc;

Delete;

Run;

Data Results.Pred_Variance_zm;

Delete;

Run;

%macro Analyze(data,model);

*Export simulated SAS data set as a text file;

PROC EXPORT DATA= &data OUTFILE= "C:\Users\Jenny\Desktop\Flash Drive June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\AnalyzeThis.dat" DBMS=DLM REPLACE;

DELIMITER='20'x;

PUTNAMES=YES;

RUN;

%If &model = pp %then %do;

*Create ASReml.as file for Curve of Factors;

Filename tmp "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores.as";

Data _null_;

File tmp;

put 'Curve of Factors for 80 Teacher, 2000 Students';

put ' Z1';

put ' Z2';

put ' Z3';

put ' Z4';

put ' Z5';

put ' Z6';

put ' Z7';

put ' Z8';

put ' Z9';

put ' Z10';

put ' Z11';

put ' Z12';

put ' Z13';

put ' Z14';

put ' Z15';

put ' Z16';

put ' Z17';

put ' Z18';

put ' Z19';

put ' Z20';

put ' Z21';

put ' Z22';

put ' Z23';

put ' Z24';

put ' Z25';

put ' Z26';

put ' Z27';

put ' Z28';

put ' Z29';

put ' Z30';

put ' Z31';

put ' Z32';

put ' Z33';

put ' Z34';

put ' Z35';

put ' Z36';

put ' Z37';

put ' Z38';

put ' Z39';

put ' Z40';

put ' Z41';

put ' Z42';

put ' Z43';

put ' Z44';

put ' Z45';

put ' Z46';

put ' Z47';

put ' Z48';

put ' Z49';

put ' Z50';

put ' Z51';

put ' Z52';

put ' Z53';

put ' Z54';

put ' Z55';

put ' Z56';

put ' Z57';

put ' Z58';

put ' Z59';

put ' Z60';

put ' Z61';

put ' Z62';

put ' Z63';

put ' Z64';

put ' Z65';

put ' Z66';

put ' Z67';

put ' Z68';

put ' Z69';

put ' Z70';

put ' Z71';

put ' Z72';

put ' Z73';

put ' Z74';

put ' Z75';

put ' Z76';

put ' Z77';

put ' Z78';

put ' Z79';

put ' Z80';

put ' i';

put ' exp !I';

put ' student !I 2000';

put ' notchscore';

put ' tchscorefinal';

put ' time !I';

put ' teacher !I';

put ' Test !A';

put 'AnalyzeThis.dat !SKIP=1 !MAXIT=100 !BRIEF';

put 'tchscorefinal ~ time.Test !r ![ Z1 Z2 Z3 Z4 Z5 Z6 Z7 Z8 Z9 Z10 Z11 Z12 Z13 Z14 Z15 Z16 Z17 Z18 Z19 Z20,';

put ' Z21 Z22 Z23 Z24 Z25 Z26 Z27 Z28 Z29 Z30 Z31 Z32 Z33 Z34 Z35 Z36 Z37 Z38 Z39 Z40 Z41 Z42 Z43 Z44 Z45 Z46,';

put ' Z47 Z48 Z49 Z50 Z51 Z52 Z53 Z54 Z55 Z56 Z57 Z58 Z59 Z60 Z61 Z62 Z63 Z64 Z65 Z66 Z67 Z68 Z69 Z70 Z71 Z72,';

put ' Z73 Z74 Z75 Z76 Z77 Z78 Z79 Z80 !] time.student';

put '0 0 2 # Measurement error 0 = Default (I_sigmasquared) 0 = No terms in Direct Product 2 = Two Random Effects on G-side';

put 'Z1 1';

put '80 0 I 1 !GP';

put 'time.student 2';

put 'time 0 US !GP';

put '1';

put '0 1';

put '0 0 1';

put '0 0 0 1';

put 'student 0 I';

Run;

%End;

%Else %do;

*Create ASReml.as file for Z-score Method;

Filename tmp "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores.as";

Data _null_;

File tmp;

put 'Z-score Method for 80 Teachers, 2000 Students';

put ' Z1';

put ' Z2';

put ' Z3';

put ' Z4';

put ' Z5';

put ' Z6';

put ' Z7';

put ' Z8';

put ' Z9';

put ' Z10';

put ' Z11';

put ' Z12';

put ' Z13';

put ' Z14';

put ' Z15';

put ' Z16';

put ' Z17';

put ' Z18';

put ' Z19';

put ' Z20';

put ' Z21';

put ' Z22';

put ' Z23';

put ' Z24';

put ' Z25';

put ' Z26';

put ' Z27';

put ' Z28';

put ' Z29';

put ' Z30';

put ' Z31';

put ' Z32';

put ' Z33';

put ' Z34';

put ' Z35';

put ' Z36';

put ' Z37';

put ' Z38';

put ' Z39';

put ' Z40';

put ' Z41';

put ' Z42';

put ' Z43';

put ' Z44';

put ' Z45';

put ' Z46';

put ' Z47';

put ' Z48';

put ' Z49';

put ' Z50';

put ' Z51';

put ' Z52';

put ' Z53';

put ' Z54';

put ' Z55';

put ' Z56';

put ' Z57';

put ' Z58';

put ' Z59';

put ' Z60';

put ' Z61';

put ' Z62';

put ' Z63';

put ' Z64';

put ' Z65';

put ' Z66';

put ' Z67';

put ' Z68';

put ' Z69';

put ' Z70';

put ' Z71';

put ' Z72';

put ' Z73';

put ' Z74';

put ' Z75';

put ' Z76';

put ' Z77';

put ' Z78';

put ' Z79';

put ' Z80';

put ' i';

put ' exp !I';

put ' student !I 2000';

put ' time !I';

put ' teacher !I';

put ' Test !A';

put ' Zscore';

put 'AnalyzeThis.dat !SKIP=1 !MAXIT=100 !BRIEF';

put 'Zscore ~ mu !r ![ Z1 Z2 Z3 Z4 Z5 Z6 Z7 Z8 Z9 Z10 Z11 Z12 Z13 Z14 Z15 Z16 Z17 Z18 Z19 Z20,';

put ' Z21 Z22 Z23 Z24 Z25 Z26 Z27 Z28 Z29 Z30 Z31 Z32 Z33 Z34 Z35 Z36 Z37 Z38 Z39 Z40 Z41 Z42 Z43 Z44 Z45 Z46,';

put ' Z47 Z48 Z49 Z50 Z51 Z52 Z53 Z54 Z55 Z56 Z57 Z58 Z59 Z60 Z61 Z62 Z63 Z64 Z65 Z66 Z67 Z68 Z69 Z70 Z71 Z72,';

put ' Z73 Z74 Z75 Z76 Z77 Z78 Z79 Z80 !]';

put '1 2 1';

put '2000 0 I';

put '4 0 US !GP !S2==1';

put '1';

put '0 1';

put '0 0 1';

put '0 0 0 1';

put 'Z1 1';put '80 0 I 1 !GP';Run;%End;

*Run.as file in ASReml;

%Let asfile=tch_student_scores.as;

Data _null_;

Call System('cd C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze');

X "'C:\Program Files\ASREML3\BIN\asreml.exe' -NS6 &asfile";

Run;

*Select and send predicted teacher effects, ranks, and percentiles to one permanent file, updated for each simulation;

Filename tmp2 "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores.sln";

Data Teacher_Temp;

%If &model = pp %then %do;

Infile tmp2 firstobs = 9 obs = 88 lrecl=71;;

Input Teacher 4-5 Pred_Effect 46-59 SE_Pred_Error 62-71;

%End;

%Else %do; *Check for Z-score method output;

Infile tmp2 firstobs = 2 obs = 81 lrecl=71;;

Input Teacher 4-5 Pred_Effect 46-59 SE_Pred_Error 62-71;

%End;

Exp = ≁

Model = "&model";

Year = 1;

If Teacher 20 then Year = 2;

If Teacher 40 then Year = 3;

If Teacher 60 then Year = 4;

Run;

Proc Sort Data = Teacher_Temp;

By Year Teacher;

Run;

Proc Rank Data = Teacher_Temp Out=Rank;

By Year;

Ranks Pred_Rank;

Var Pred_Effect;

Run;

Data Teacher_Temp;

Set Rank;

Pred_Percentile=100*(Pred_Rank/21);

Run;

%If %eval(&nsim) = 1 and &model = pp %then %do;

Data Results.Pred_Teacher_Effects;

Set Teacher_Temp;

Run;

%End;

%Else %do;

Data Results.Pred_Teacher_Effects;

Set Results.Pred_Teacher_Effects Teacher_Temp;

Run;

%End;

*Select and send estimated variance components to one permanent file, updated for each simulation;

%If &model = pp %then %do;

*Create ASReml.pin file for PP Variance Components;

Filename tmp "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores.pin";

Data _null_;

File tmp;

put 'F error_var 1 * 1';

put 'F teacher_var 2 * 1';

put 'F D1_Var 3 * 1';

put 'F D2D1_Cov 4 * 1';

put 'F D2_Var 5 * 1';

put 'F D3D1_Cov 6 * 1';

put 'F D3D2_Cov 7 * 1';

put 'F D3_Var 8 * 1';



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