R code for deriving Hedge's g (or Cox Index)

#What works clearinghouse version of standardized program difference
#Hedges's g and Cox Index
#See page 15 and 16
#https://ies.ed.gov/ncee/wwc/Docs/referenceresources/WWC_Procedures_Handbook_V4_1_Draft.pdf
#Kaz has the web-based calculator -- to use for QC'ing my results here
#https://www.estat.us/file/calc_t_test1.php

CCC<-filter(match.data1,treat==0)
TTT<-filter(match.data1,treat==1)

#These are for raw database
#raw data
#CCC<-filter(studydata3,treatment==0)
#TTT<-filter(studydata3,treatment==1)
#CCC<-filter(psmdata,treat==0)
#TTT<-filter(psmdata,treat==1)

###############################################################################
#What works clearinghouse version of standardized program difference
#Hedges's g and Cox Index
#See page 15 and 16
#https://ies.ed.gov/ncee/wwc/Docs/referenceresources/WWC_Procedures_Handbook_V4_1_Draft.pdf
#Kaz has the web-based calculator -- to use for QC'ing my results here
#https://www.estat.us/file/calc_t_test1.php

#Hedges' g for continuous variables
#Kaz added the correct, sample size adjusted version of this while meeting with Samara for transition
kaz_macro_lin<-function(kaz1){
col_name <- deparse(substitute(kaz1))
C_mean<-mean(CCC[[col_name]])
T_mean<-mean(TTT[[col_name]])
C_sd<-sd(CCC[[col_name]])
T_sd<-sd(TTT[[col_name]])
C_n<-length(CCC[[col_name]])
T_n<-length(TTT[[col_name]])
total_n<-C_n+T_n
#linear
simple_gap=T_mean-C_mean
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= simple_gap/g3

#I didn't adjust for sample size
omega<-(1-3/( 4*total_n -9))
print(T_n)
print(C_n)
print(total_n)
print(T_mean)
print(C_mean)
print(T_sd)
print(C_sd)
print ("Hedges'g without sample size adjustment")
print(wwc_effect)
print ("Hedges'g with sample size adjustment (Use this)")
print(omega)
}

#Cox Index
#Kaz added the correct, sample size adjusted version of this while meeting with Samara for transition
kaz_macro_bin<-function(kaz1){
col_name <- deparse(substitute(kaz1))
C_mean<-mean(CCC[[col_name]])
T_mean<-mean(TTT[[col_name]])
C_sd<-sd(CCC[[col_name]])
T_sd<-sd(TTT[[col_name]])
C_n<-length(CCC[[col_name]])
T_n<-length(TTT[[col_name]])

#binary
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=(round(LN_DIF/1.65,0.001))
WWC_effect_binary<-(LN_DIF/1.65)

#I didn't use this for writing the report draft
total_n<-C_n+T_n
omega<-(1-3/( 4*total_n -9))
print(T_n)
print(C_n)
print(total_n)
print(T_mean)
print(C_mean)
print("Cox Index without sample size adjustment")
print(WWC_effect_binary)
print("Cox Index with sample size adjustment -- Use this")
print(omega)
}

###############################################################################
table(psmdata\$treat)

kaz_macro_bin(male)
kaz_macro_bin(minority)