Baseline Check using SAS

proc means data=both4 stackodsoutput;
class treat;
var
male minority age
risk_miss
risk_08
risk_09
risk_10
felony_binary
misdem_Sum
felony_Sum
misdem_binary;
ods output summary=temp1a;
run;

proc means data=outgs2 stackodsoutput;
class treat;
var
male minority age
risk_miss
risk_08
risk_09
risk_10
felony_binary
misdem_Sum
felony_Sum
misdem_binary;
ods output summary=temp1b;
run;

data temp1a;
set temp1a;
datatype="(1)raw data";
run;

data temp1b;
set temp1b;
datatype="(2)sample";
run;

data temp1;
set temp1a temp1b;run;

data T;set temp1;
if treat=1;
suji=_n_;
T_N=N;
T_Mean=Mean;
T_SD=StdDev;
T_Min=Min;
T_Max=Max;
keep suji variable T_N T_mean T_SD T_min T_max datatype;
run;
proc sort;by variable;run;

data C;set temp1;
if treat=0;
suji=_n_;
C_N=N;
C_Mean=Mean;
C_SD=StdDev;
C_Min=Min;
C_Max=Max;
keep variable C_N C_mean C_SD C_min C_max;
run;
proc sort;by variable;run;

data TC;
merge T C;
by variable;

/*create statistics*/
mean_dif=(T_Mean-C_Mean);
/*Standardized effects*/
g1=
((T_N-1)*(T_SD*T_SD))
+((C_N-1)*(C_SD*C_SD));

g2=T_N + C_N -2;
g3=sqrt(g1/g2);
WWC_effect=mean_dif/g3;

outcome_type="interval";
if T_Min=0 and T_Max=1 and C_Min=0 and C_Max=1 then do;
outcome_type="binary";
/*&usethis._Mean_Yes-&usethis._Mean_NO*/
Odds_C=(C_Mean/(1-C_Mean));
Odds_T=(T_Mean/(1-T_Mean));

Odds_ratio=Odds_T/Odds_C;

LN_C=LOG(Odds_C);
LN_T=LOG(Odds_T);
LN_DIF=LN_T-LN_C;

WWC_effect=/*abs*/(round(LN_DIF/1.65,0.001));/*fixed 06 01 2016*/

/*If greater than 02, Small Effect
If greater than 0.5, Medium Effect
If greater 0.8 then Large Effect*/
end;

if WWC_effect ne . then do;
if abs(WWC_effect) > 0.2 then cohen="Small ";
if abs(WWC_effect) > 0.5 then cohen="Medium";
if abs(WWC_effect) > 0.8 then cohen="Large";
end;

drop
LN_DIF
LN_C
LN_T
Odds_ratio
Odds_T
Odds_C
g3
g2
g1
;

run;
proc sort;by suji;run;

 

Proc psmatch

 

proc psmatch data=asdf1 region=cs;
class FLAG2 econ_status rural size ;
psmodel FLAG2(Treated="Y")=
n_10th_graders
prop_minority_10G;
match method=greedy(K=1) exact=(cate) stat=lps caliper=0.25;
output out(obs=match)=outgs2 lps=_Lps matchid=_matchID;
run;

Citation for alpha level (reliability) .70 or .80 as thresholds

Kline, P. (1999). Handbook of Psychological Testing(2nd ed.). London: Routledge. P.13

Despite the dangers of boosting the reliability of a test by making the items highly similar to each other, in which case validity is reduced, reliabilities should ideally be high, around .9, especially for ability tests. Certainly alphas should never drop below .7, a value stressed by both Guilford (1956) and Nunnally(1978). The rationale and proof of these claims are bound up in psychometric theory and are given in Chapter 3.

 

For 0.7; Nunnally,J . (1978). Psychometric Theory. New York, McGraw-Hill.

For 0.8; Nunally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.) New Yort: McGraw-Hill.

 

Reference:

Abell, N., Springer, D. W., & Kamata, A. (2009). Developing and Validating Rapid Assessment Instruments. Oxford University Press.

 

QC Comparison of WWC effect size code using R and SAS

I wrote this entry when I wanted to QC my WWC effect size calculation using R and SAS.

 

WWC standard doc

https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v2_1_standards_handbook.pdf

Page 37.

 

In SAS:

data test;

N_Yes=4300;
Mean_Yes=400;
StdDev_Yes=200;

N_No=4000;
Mean_NO=300;
StdDev_NO=200;

/*create statistics*/
mean_dif=(Mean_Yes-Mean_NO);
/*Standardized effects*/

g1=((N_Yes-1)*(StdDev_Yes*StdDev_Yes)) +((N_No-1)*(StdDev_No*StdDev_No));
g2=N_Yes + N_No -2;
g3=sqrt(g1/g2);
WWC_effect=mean_dif/g3;

run;

 

In R:

FGC_l_N_YES=4300
FGC_l_Mean_YES=400
FGC_l_StdDev_YES=200

REG_l_N_YES=4000
REG_l_Mean_YES=300
REG_l_StdDev_YES=200

simple_gap=FGC_l_Mean_YES-REG_l_Mean_YES

g1<- ((FGC_l_N_YES-1)*(FGC_l_StdDev_YES*FGC_l_StdDev_YES))+((REG_l_N_YES-1)*(REG_l_StdDev_YES*REG_l_StdDev_YES))
g2= FGC_l_N_YES + REG_l_N_YES -2
g3= sqrt(g1/g2)
simple_gap_std= simple_gap/g3
simple_gap_std

g1
g2
g3
simple_gap_std

 

My web calculator

https://www.estat.us/file/calc_t_test1b.php

 

Treatment N:4300
Treatment mean:400
Treatment SD:200

Comparison N:4000
Comparison mean:300
Comparison SD:200
The group mean difference:100

[RESULTS FOR CONTINUOUS OUTCOME]

Probability: Under Development (Still working on this)
T-score is: 22.761
Significant at alpha 0.05 (two tail test;I used a z-test and ignored degree freedom; threshold 1.96)

T-test (the same test as above but with three thresholds)T 1.96, 2.576, 3.291, each for p=0.05, 0.01, 0.001
Sig at p=.001***

Hedges d 0.5