Preparation

data<-read.csv("/Users/samanthabouwmeester/Documents/Out of the Box Plot/CWTL/Copy of RCT_data_full.csv")

### Changes in Copy of RCT_data_full.csv that are processed in datacleaning file. ####
data$read_2[data$index==262]=data$read_1[data$index==262] 
data$read_2[data$index==4]=data$read_1[data$index==4] 
data$read_2[data$index==5]=data$read_1[data$index==5] 
data$read_2[data$index==7]=data$read_1[data$index==7] 
data$read_2[data$index==8]=data$read_1[data$index==8] 

data$Attendance<-rowSums(data[,which(substring(colnames(data),1,4)=="week")],na.rm = TRUE)
data$Attendance_dich=1
data$Attendance_dich[data$Attendance<21*5*.8]=0


# Missings values 

#baseline num
bas.num<-data[,c("total_nid","total_qds","total_mis","total_add","total_sub","total_prb")]

#Are there any missings values?
any(is.na(rowSums(bas.num)))

## [1] FALSE

# FALSE, so no missing values

#baseline lit
bas.lit<-data[,c("total_lid","total_pho","total_muw","total_non","total_com","total_lis","total_wrt","read_1","read_2")]
bas.lit$read_2[is.na(bas.lit$read_2)]=bas.lit$read_1[is.na(bas.lit$read_2)]
bas.lit$read_1[is.na(bas.lit$read_1)]=0 #One Na in read1 has to be 0.

# Are there any missings values?
any(is.na(rowSums(bas.lit)))

## [1] FALSE

# FALSE, so no missing values



#endline num
end.num<-data[,c("total_nid_end","total_qds_end","total_mis_end","total_add_end","total_sub_end","total_prb_end")]

# Are there any missings values?
any(is.na(rowSums(end.num)))

## [1] TRUE

# TRUE, so missing values: multiple imputation

mis.number=rep(NA,6)
for(i in 1:ncol(end.num))
mis.number[i]=length(which(is.na(end.num[,i])))

mis.number

## [1] 69 69 69 69 69 69

# 69 participants miss the endline data for numericy all tasks. This is 69/1507=.046, 4.6%
# These endline scores were imputed.

library(mice)
scores.mice<-mice(end.num,print=FALSE)
end.num.MI<-complete(scores.mice)

data[,c("total_nid_end","total_qds_end","total_mis_end","total_add_end","total_sub_end","total_prb_end")]=end.num.MI


#check
end.num<-data[,c("total_nid_end","total_qds_end","total_mis_end","total_add_end","total_sub_end","total_prb_end")]
any(is.na(rowSums(end.num)))

## [1] FALSE

# FALSE: no missings anymore


### total scores in percentages
totals.num_perc_bas<-data.frame(data$total_nid/15,
                            data$total_qds/10,
                            data$total_mis/10,
                            data$total_add/25,
                            data$total_sub/25,
                            data$total_prb/6)


totals.num_perc_end<-data.frame(data$total_nid_end/15,
                            data$total_qds_end/10,
                            data$total_mis_end/10,
                            data$total_add_end/25,
                            data$total_sub_end/25,
                            data$total_prb_end/6)


end.num$tot.num_end<-rowSums(totals.num_perc_end)/6.  #divided by 6 to get scores between 0 and 1

bas.num$tot.num<-rowSums(totals.num_perc_bas)/6. #divided by 6 to get scores between 0 and 1


#endline lit
end.lit<-data[,c("total_lid_end","total_pho_end","total_muw_end","total_non_end","total_com_end","total_lis_end","total_wrt_end","RCP1_end",
                 "RCP2_end")]


# Are there any missings values?
any(is.na(rowSums(end.lit)))

## [1] TRUE

# TRUE, so missing values: multiple imputation

mis.lit=rep(NA,9)
for(i in 1:ncol(end.lit))
  mis.lit[i]=length(which(is.na(end.lit[,i])))

mis.lit

## [1]  69  69  69  69 416  69  69  69 416

# 69 participants miss the endline data for literacy all tasks. This is 69/1507=.046, 4.6%
# Task total_com and Read_2 has 416 missings values, this is 416/1507=.276, 27.6%
# These endline scores were imputed.

scores.mice<-mice(end.lit,print=FALSE)
end.lit.MI<-complete(scores.mice)

data[,c("total_lid_end","total_pho_end","total_muw_end","total_non_end","total_com_end","total_lis_end","total_wrt_end","RCP1_end","RCP2_end")]=end.lit.MI


#check
end.lit<-data[,c("total_lid_end","total_pho_end","total_muw_end","total_non_end","total_com_end","total_lis_end","total_wrt_end"
                 ,"RCP1_end","RCP2_end")]
any(is.na(rowSums(end.lit)))

## [1] FALSE

# FALSE: no missings anymore

### Add total literacy score as a percentage note that READ_2 is not included

totals.lit_perc_bas<-data.frame(data$total_lid/26,
                            data$total_pho/10,
                            data$total_muw/20,
                            data$total_non/16,
                            data$total_com/5,
                            data$total_lis/5,
                            data$total_wrt/10,
                            data$read_1/99)


totals.lit_perc_end<-data.frame(data$total_lid_end/26,
                            data$total_pho_end/10,
                            data$total_muw_end/20,
                            data$total_non_end/16,
                            data$total_com_end/5,
                            data$total_lis_end/5,
                            data$total_wrt_end/10,
                            data$RCP1_end/99)


end.lit$tot.lit_end<-rowSums(totals.lit_perc_end)/8

bas.lit$tot.lit<-rowSums(totals.lit_perc_bas)/8

### Add total wellbeing score
wellbeing<-data[,which(substring(colnames(data),1,4)=="SCWS")]
                     
for(i in 1:ncol(wellbeing)){
  wellbeing[,i][wellbeing[,i]=="Never"]=0
  wellbeing[,i][wellbeing[,i]=="Not much of the time"]=1
  wellbeing[,i][wellbeing[,i]=="Some of the time"]=2
  wellbeing[,i][wellbeing[,i]=="Quite a lot of the time"]=3
   wellbeing[,i][wellbeing[,i]=="All of the time"]=4
   wellbeing[,i][wellbeing[,i]=="Refused to answer"]=NA
}

wellbeing<-apply(wellbeing,2,as.numeric)
scores.mice<-mice(wellbeing,print=FALSE)
wellbeing<-complete(scores.mice)
wb.bas<-cbind(data[,c(2:4,620)],wellbeing[,1:12])
wb.end<-cbind(wb.bas[,1:4],wellbeing[,13:24])
colnames(wb.end)<-colnames(wb.bas)

WB.long<-rbind(wb.bas,wb.end)
#head(WB.long)
WB.long$totaal<-rowSums(WB.long[,5:16])

Assesments

Long datafile

### Long datafile ###.  

baseline<-cbind(data$participantID,data$GenderChild,data$School,data$Group,data$Attendance,data$Attendance_dich,bas.num,bas.lit)
colnames(baseline)<-c("participantID","GenderChild","School" ,"Group"  ,         "Attendance" ,     "Attendance_dich",colnames(baseline[7:23]))
endline<-cbind(baseline[,1:6],end.num,end.lit)
colnames(endline)=colnames(baseline)

longdata<-rbind(baseline,endline)
longdata<-longdata[order(longdata$participantID),]
longdata$WB_total<-WB.long$totaal
longdata$time<-rep(c("baseline","endline"),nrow(baseline))



write.csv(longdata,"/Users/samanthabouwmeester/Documents/Out of the Box Plot/CWTL/longdata.csv")

Descriptives

dm<-t(descriptives_Num)
colnames(dm)<-c("Baseline A","Endline A","Baseline B","Endline B")
data.table(dm[-c(1,2),],keep.rownames=TRUE)

##                 rn Baseline A Endline A Baseline B Endline B
##  1: mean total_nid      12.25     14.12      12.17     13.75
##  2:   sd total_nid       2.90      1.63       2.95      2.11
##  3: mean total_qds       8.29      9.44       8.25      9.06
##  4:   sd total_qds       2.05      1.10       2.14      1.52
##  5: mean total_mis       5.82      7.69       5.27      7.02
##  6:   sd total_mis       2.81      2.18       2.94      2.68
##  7: mean total_add       6.97      9.86       5.83      8.35
##  8:   sd total_add       4.10      4.25       3.70      4.27
##  9: mean total_sub       4.63      7.24       3.97      5.43
## 10:   sd total_sub       3.66      3.96       2.91      3.38
## 11: mean total_prb       3.51      4.74       3.44      4.43
## 12:   sd total_prb       1.92      1.38       1.89      1.60
## 13:   mean tot.num       0.55      0.69       0.52      0.64
## 14:     sd tot.num       0.16      0.12       0.16      0.14

dl<-t(descriptives_Lit)
colnames(dl)<-c("Baseline A","Endline A","Baseline B","Endline B")
data.table(dl[-c(1,2),],keep.rownames=TRUE)

##                 rn Baseline A Endline A Baseline B Endline B
##  1: mean total_lid      19.39     23.69      20.41     23.13
##  2:   sd total_lid       8.14      4.45       7.19      4.91
##  3: mean total_pho       3.71      6.82       2.16      2.91
##  4:   sd total_pho       3.51      3.03       2.83      3.15
##  5: mean total_muw       8.37     13.87       7.46     12.11
##  6:   sd total_muw       6.91      6.27       6.54      6.95
##  7: mean total_non       3.73      7.99       2.81      5.71
##  8:   sd total_non       4.72      5.31       4.15      5.25
##  9: mean total_com       0.80      1.75       0.60      1.53
## 10:   sd total_com       1.27      1.50       1.09      1.45
## 11: mean total_lis       0.86      1.48       0.64      1.20
## 12:   sd total_lis       1.26      1.41       1.04      1.32
## 13: mean total_wrt       3.06      4.78       2.51      4.52
## 14:   sd total_wrt       2.92      3.26       2.73      3.29
## 15:    mean read_1      13.72     29.90      10.32     23.62
## 16:      sd read_1      15.90     23.04      13.31     20.44
## 17:    mean read_2      28.22     60.97      23.63     52.62
## 18:      sd read_2      31.68     28.20      30.39     28.93
## 19:   mean tot.lit       0.32      0.53       0.27      0.42
## 20:     sd tot.lit       0.20      0.22       0.18      0.22

Analysis

nid

tab_model(fit_nid,fit_nid1)

	total_nid			total_nid
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	11.12	10.61 – 11.63	<0.001	11.09	10.56 – 11.62	<0.001
GenderChild [Male]	0.27	0.03 – 0.52	0.030	0.27	0.02 – 0.52	0.032
time [endline]	1.86	1.37 – 2.35	<0.001	1.75	1.26 – 2.25	<0.001
Attendance	0.01	0.01 – 0.02	<0.001	0.01	0.01 – 0.02	<0.001
GenderChild [Male] × time [endline]	-0.20	-0.46 – 0.05	0.121	-0.19	-0.44 – 0.07	0.147
time [endline] × Attendance	-0.00	-0.01 – 0.01	0.873	-0.00	-0.01 – 0.01	0.735
Group [1]				0.05	-0.30 – 0.40	0.784
time [endline] × Group [1]				0.29	0.03 – 0.55	0.026
Random Effects
σ²	3.20			3.19
τ₀₀	2.70 _{participantID:School}			2.70 _{participantID:School}
	0.13 _School			0.12 _School
ICC	0.47			0.47
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.120 / 0.533			0.122 / 0.534

plot(colnames(dat)[10])

# The thin lines are the means of the schools, thick lines are the overall means.

qds

tab_model(fit_qds,fit_qds1)

	total_qds			total_qds
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	7.86	7.50 – 8.22	<0.001	7.84	7.47 – 8.22	<0.001
GenderChild [Male]	-0.01	-0.18 – 0.17	0.946	-0.01	-0.19 – 0.17	0.911
time [endline]	0.79	0.39 – 1.18	<0.001	0.66	0.26 – 1.05	0.001
Attendance	0.01	0.00 – 0.01	0.013	0.01	0.00 – 0.01	0.011
GenderChild [Male] × time [endline]	-0.12	-0.33 – 0.08	0.230	-0.11	-0.31 – 0.09	0.291
time [endline] × Attendance	0.00	-0.00 – 0.01	0.160	0.00	-0.00 – 0.01	0.252
Group [1]				0.01	-0.23 – 0.25	0.926
time [endline] × Group [1]				0.34	0.13 – 0.54	0.001
Random Effects
σ²	2.04			2.02
τ₀₀	0.98 _{participantID:School}			0.99 _{participantID:School}
	0.06 _School			0.05 _School
ICC	0.34			0.34
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.080 / 0.392			0.085 / 0.396

plot(colnames(dat)[11])

# The thin lines are the means of the schools, thick lines are the overall means.

mis

tab_model(fit_mis,fit_mis1)

	total_mis			total_mis
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.26	3.69 – 4.83	<0.001	4.03	3.44 – 4.62	<0.001
GenderChild [Male]	0.25	-0.02 – 0.51	0.070	0.25	-0.01 – 0.52	0.063
time [endline]	1.75	1.23 – 2.27	<0.001	1.72	1.18 – 2.25	<0.001
Attendance	0.02	0.01 – 0.02	<0.001	0.02	0.01 – 0.02	<0.001
GenderChild [Male] × time [endline]	-0.32	-0.59 – -0.05	0.021	-0.32	-0.59 – -0.04	0.023
time [endline] × Attendance	0.00	-0.00 – 0.01	0.364	0.00	-0.00 – 0.01	0.394
Group [1]				0.51	0.09 – 0.94	0.018
time [endline] × Group [1]				0.09	-0.18 – 0.36	0.521
Random Effects
σ²	3.64			3.65
τ₀₀	3.19 _{participantID:School}			3.19 _{participantID:School}
	0.28 _School			0.21 _School
ICC	0.49			0.48
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.119 / 0.549			0.129 / 0.549

plot(colnames(dat)[12])

# The thin lines are the means of the schools, thick lines are the overall means.

add

tab_model(fit_add,fit_add1)

	total_add			total_add
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.66	3.80 – 5.53	<0.001	4.28	3.44 – 5.12	<0.001
GenderChild [Male]	0.22	-0.19 – 0.63	0.300	0.22	-0.19 – 0.63	0.287
time [endline]	1.86	0.95 – 2.76	<0.001	1.75	0.83 – 2.67	<0.001
Attendance	0.02	0.01 – 0.03	<0.001	0.02	0.01 – 0.03	<0.001
GenderChild [Male] × time [endline]	-0.53	-1.01 – -0.06	0.027	-0.52	-0.99 – -0.05	0.031
time [endline] × Attendance	0.02	0.00 – 0.03	0.009	0.02	0.00 – 0.03	0.012
Group [1]				1.09	0.59 – 1.59	<0.001
time [endline] × Group [1]				0.29	-0.18 – 0.77	0.228
Random Effects
σ²	10.94			10.94
τ₀₀	5.32 _{participantID:School}			5.33 _{participantID:School}
	0.55 _School			0.16 _School
ICC	0.35			0.33
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.118 / 0.426			0.138 / 0.426

plot(colnames(dat)[13])

# The thin lines are the means of the schools, thick lines are the overall means.

sub

tab_model(fit_sub,fit_sub1)

	total_sub			total_sub
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.20	2.45 – 3.95	<0.001	2.99	2.26 – 3.71	<0.001
GenderChild [Male]	0.26	-0.10 – 0.61	0.154	0.24	-0.11 – 0.59	0.186
time [endline]	1.20	0.41 – 1.99	0.003	0.77	-0.03 – 1.57	0.058
Attendance	0.01	0.00 – 0.02	0.004	0.01	0.00 – 0.02	0.008
GenderChild [Male] × time [endline]	0.13	-0.28 – 0.55	0.528	0.18	-0.23 – 0.59	0.379
time [endline] × Attendance	0.01	0.00 – 0.02	0.040	0.01	-0.00 – 0.02	0.101
Group [1]				0.64	0.20 – 1.07	0.004
time [endline] × Group [1]				1.12	0.70 – 1.53	<0.001
Random Effects
σ²	8.37			8.22
τ₀₀	3.73 _{participantID:School}			3.81 _{participantID:School}
	0.49 _School			0.13 _School
ICC	0.34			0.32
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.088 / 0.394			0.119 / 0.404

plot(colnames(dat)[14])

# The thin lines are the means of the schools, thick lines are the overall means.

prb

tab_model(fit_prb,fit_prb1)

	total_prb			total_prb
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	2.99	2.62 – 3.36	<0.001	2.95	2.55 – 3.34	<0.001
GenderChild [Male]	0.19	0.02 – 0.36	0.030	0.19	0.02 – 0.36	0.033
time [endline]	1.40	1.02 – 1.79	<0.001	1.31	0.92 – 1.71	<0.001
Attendance	0.01	0.00 – 0.01	0.017	0.01	0.00 – 0.01	0.015
GenderChild [Male] × time [endline]	-0.25	-0.45 – -0.04	0.017	-0.24	-0.44 – -0.03	0.022
time [endline] × Attendance	-0.00	-0.01 – 0.00	0.367	-0.00	-0.01 – 0.00	0.278
Group [1]				0.06	-0.26 – 0.38	0.698
time [endline] × Group [1]				0.24	0.03 – 0.44	0.022
Random Effects
σ²	1.99			1.98
τ₀₀	0.81 _{participantID:School}			0.81 _{participantID:School}
	0.15 _School			0.14 _School
ICC	0.32			0.33
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.099 / 0.391			0.102 / 0.394

plot(colnames(dat)[15])

# The thin lines are the means of the schools, thick lines are the overall means.

nu

tab_model(fit_nu,fit_nu1)

	tot.num			tot.num
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.46	0.43 – 0.49	<0.001	0.45	0.42 – 0.48	<0.001
GenderChild [Male]	0.02	0.00 – 0.03	0.035	0.02	0.00 – 0.03	0.035
time [endline]	0.12	0.10 – 0.15	<0.001	0.11	0.09 – 0.14	<0.001
Attendance	0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	<0.001
GenderChild [Male] × time [endline]	-0.02	-0.03 – -0.01	0.004	-0.02	-0.03 – -0.01	0.006
time [endline] × Attendance	0.00	-0.00 – 0.00	0.180	0.00	-0.00 – 0.00	0.307
Group [1]				0.02	0.00 – 0.04	0.048
time [endline] × Group [1]				0.03	0.01 – 0.04	<0.001
Random Effects
σ²	0.01			0.01
τ₀₀	0.01 _{participantID:School}			0.01 _{participantID:School}
	0.00 _School			0.00 _School
ICC	0.61			0.61
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.179 / 0.680			0.194 / 0.683

plot(colnames(dat)[16])

# The thin lines are the means of the schools, thick lines are the overall means.

lid

tab_model(fit_lid,fit_lid1)

	total_lid			total_lid
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	17.83	16.43 – 19.24	<0.001	18.30	16.75 – 19.85	<0.001
GenderChild [Male]	-0.16	-0.78 – 0.46	0.618	-0.20	-0.82 – 0.42	0.534
time [endline]	3.39	2.01 – 4.78	<0.001	2.80	1.40 – 4.20	<0.001
Attendance	0.03	0.01 – 0.05	<0.001	0.03	0.02 – 0.05	<0.001
GenderChild [Male] × time [endline]	-0.37	-1.09 – 0.35	0.313	-0.30	-1.02 – 0.42	0.413
time [endline] × Attendance	0.00	-0.01 – 0.02	0.634	0.00	-0.02 – 0.02	0.892
Group [1]				-1.13	-2.52 – 0.26	0.112
time [endline] × Group [1]				1.56	0.84 – 2.28	<0.001
Random Effects
σ²	25.56			25.28
τ₀₀	11.82 _{participantID:School}			11.97 _{participantID:School}
	2.94 _School			3.03 _School
ICC	0.37			0.37
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.081 / 0.417			0.084 / 0.425

plot(colnames(dat)[17])

# The thin lines are the means of the schools, thick lines are the overall means.

pho

tab_model(fit_pho,fit_pho1)

	total_pho			total_pho
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.17	2.29 – 4.05	<0.001	2.28	1.43 – 3.14	<0.001
GenderChild [Male]	-0.29	-0.59 – 0.01	0.055	-0.34	-0.64 – -0.05	0.023
time [endline]	-0.06	-0.85 – 0.73	0.880	-0.93	-1.70 – -0.16	0.018
Attendance	-0.00	-0.01 – 0.01	0.761	0.00	-0.01 – 0.01	0.837
GenderChild [Male] × time [endline]	-0.07	-0.48 – 0.34	0.730	0.03	-0.36 – 0.43	0.874
time [endline] × Attendance	0.03	0.02 – 0.04	<0.001	0.02	0.01 – 0.03	<0.001
Group [1]				1.53	0.62 – 2.44	0.001
time [endline] × Group [1]				2.29	1.89 – 2.69	<0.001
Random Effects
σ²	8.31			7.66
τ₀₀	0.40 _{participantID:School}			0.72 _{participantID:School}
	3.24 _School			1.44 _School
ICC	0.30			0.22
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.085 / 0.363			0.251 / 0.416

plot(colnames(dat)[18])

# The thin lines are the means of the schools, thick lines are the overall means.

muw

tab_model(fit_muw,fit_muw1)

	total_muw			total_muw
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.18	2.73 – 5.63	<0.001	3.81	2.24 – 5.37	<0.001
GenderChild [Male]	-0.77	-1.41 – -0.13	0.019	-0.78	-1.42 – -0.14	0.018
time [endline]	4.25	3.32 – 5.19	<0.001	3.95	3.00 – 4.90	<0.001
Attendance	0.06	0.04 – 0.08	<0.001	0.06	0.04 – 0.08	<0.001
GenderChild [Male] × time [endline]	-0.19	-0.68 – 0.30	0.447	-0.15	-0.64 – 0.33	0.539
time [endline] × Attendance	0.01	0.00 – 0.03	0.038	0.01	-0.00 – 0.02	0.069
Group [1]				0.70	-0.63 – 2.02	0.303
time [endline] × Group [1]				0.80	0.32 – 1.29	0.001
Random Effects
σ²	11.60			11.53
τ₀₀	28.34 _{participantID:School}			28.38 _{participantID:School}
	2.81 _School			2.61 _School
ICC	0.73			0.73
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.163 / 0.773			0.171 / 0.775

plot(colnames(dat)[19])

# The thin lines are the means of the schools, thick lines are the overall means.

non

tab_model(fit_non,fit_non1)

	total_non			total_non
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.77	0.71 – 2.83	0.001	1.35	0.29 – 2.41	0.013
GenderChild [Male]	-0.00	-0.49 – 0.49	0.997	-0.01	-0.50 – 0.47	0.958
time [endline]	2.79	1.86 – 3.73	<0.001	2.30	1.36 – 3.24	<0.001
Attendance	0.02	0.01 – 0.04	0.002	0.02	0.01 – 0.03	0.001
GenderChild [Male] × time [endline]	-0.46	-0.94 – 0.03	0.065	-0.40	-0.88 – 0.09	0.106
time [endline] × Attendance	0.01	0.00 – 0.03	0.021	0.01	-0.00 – 0.02	0.058
Group [1]				0.86	0.13 – 1.58	0.020
time [endline] × Group [1]				1.30	0.82 – 1.79	<0.001
Random Effects
σ²	11.62			11.41
τ₀₀	11.51 _{participantID:School}			11.61 _{participantID:School}
	1.11 _School			0.56 _School
ICC	0.52			0.52
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.128 / 0.582			0.154 / 0.590

plot(colnames(dat)[20])

# The thin lines are the means of the schools, thick lines are the overall means.

com

tab_model(fit_com,fit_com1)

	total_com			total_com
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.20	-0.09 – 0.48	0.180	0.11	-0.19 – 0.42	0.471
GenderChild [Male]	-0.03	-0.16 – 0.10	0.640	-0.03	-0.16 – 0.10	0.659
time [endline]	0.62	0.35 – 0.90	<0.001	0.62	0.34 – 0.90	<0.001
Attendance	0.01	0.00 – 0.01	<0.001	0.01	0.00 – 0.01	<0.001
GenderChild [Male] × time [endline]	-0.12	-0.26 – 0.03	0.115	-0.12	-0.26 – 0.03	0.116
time [endline] × Attendance	0.01	0.00 – 0.01	0.004	0.01	0.00 – 0.01	0.004
Group [1]				0.18	-0.06 – 0.42	0.150
time [endline] × Group [1]				0.00	-0.14 – 0.15	0.983
Random Effects
σ²	1.02			1.02
τ₀₀	0.65 _{participantID:School}			0.65 _{participantID:School}
	0.08 _School			0.08 _School
ICC	0.42			0.42
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.131 / 0.496			0.136 / 0.497

plot(colnames(dat)[21])

# The thin lines are the means of the schools, thick lines are the overall means.

lis

tab_model(fit_lis,fit_lis1)

	total_lis			total_lis
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.43	0.15 – 0.71	0.002	0.34	0.03 – 0.64	0.030
GenderChild [Male]	-0.15	-0.27 – -0.03	0.016	-0.15	-0.27 – -0.03	0.017
time [endline]	0.36	0.09 – 0.62	0.008	0.34	0.07 – 0.61	0.013
Attendance	0.01	0.00 – 0.01	0.001	0.01	0.00 – 0.01	0.001
GenderChild [Male] × time [endline]	0.03	-0.11 – 0.17	0.646	0.03	-0.10 – 0.17	0.623
time [endline] × Attendance	0.00	-0.00 – 0.01	0.080	0.00	-0.00 – 0.01	0.092
Group [1]				0.19	-0.07 – 0.46	0.152
time [endline] × Group [1]				0.05	-0.09 – 0.19	0.477
Random Effects
σ²	0.92			0.92
τ₀₀	0.55 _{participantID:School}			0.55 _{participantID:School}
	0.12 _School			0.11 _School
ICC	0.42			0.42
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.066 / 0.459			0.075 / 0.461

plot(colnames(dat)[22])

# The thin lines are the means of the schools, thick lines are the overall means.

wrt

tab_model(fit_wrt,fit_wrt1)

	total_wrt			total_wrt
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.34	0.68 – 1.99	<0.001	1.14	0.43 – 1.85	0.002
GenderChild [Male]	-0.49	-0.78 – -0.19	0.001	-0.48	-0.77 – -0.18	0.001
time [endline]	1.15	0.70 – 1.60	<0.001	1.29	0.83 – 1.74	<0.001
Attendance	0.02	0.02 – 0.03	<0.001	0.02	0.02 – 0.03	<0.001
GenderChild [Male] × time [endline]	-0.17	-0.40 – 0.07	0.162	-0.18	-0.42 – 0.05	0.125
time [endline] × Attendance	0.01	0.01 – 0.02	<0.001	0.01	0.01 – 0.02	<0.001
Group [1]				0.47	-0.13 – 1.06	0.126
time [endline] × Group [1]				-0.35	-0.58 – -0.11	0.004
Random Effects
σ²	2.70			2.68
τ₀₀	5.65 _{participantID:School}			5.66 _{participantID:School}
	0.53 _School			0.53 _School
ICC	0.70			0.70
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.129 / 0.735			0.133 / 0.737

plot(colnames(dat)[23])

# The thin lines are the means of the schools, thick lines are the overall means.

read 1

tab_model(fit_read1,fit_read11)

	read_1			read_1
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.12	1.03 – 9.20	0.014	3.55	-0.83 – 7.93	0.112
GenderChild [Male]	-1.40	-3.20 – 0.39	0.126	-1.43	-3.22 – 0.37	0.119
time [endline]	9.21	6.29 – 12.13	<0.001	8.28	5.31 – 11.25	<0.001
Attendance	0.11	0.06 – 0.16	<0.001	0.11	0.06 – 0.16	<0.001
GenderChild [Male] × time [endline]	-2.94	-4.47 – -1.42	<0.001	-2.83	-4.36 – -1.31	<0.001
time [endline] × Attendance	0.10	0.06 – 0.14	<0.001	0.10	0.06 – 0.14	<0.001
Group [1]				3.00	-0.73 – 6.73	0.115
time [endline] × Group [1]				2.45	0.93 – 3.98	0.002
Random Effects
σ²	114.05			113.38
τ₀₀	196.80 _{participantID:School}			197.13 _{participantID:School}
	24.64 _School			20.96 _School
ICC	0.66			0.66
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.168 / 0.717			0.182 / 0.720

plot(colnames(dat)[24])

# The thin lines are the means of the schools, thick lines are the overall means.

read 2

tab_model(fit_read2,fit_read21)

	read_2			read_2
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	11.27	4.87 – 17.67	0.001	9.28	2.53 – 16.04	0.007
GenderChild [Male]	-2.38	-5.30 – 0.54	0.111	-2.40	-5.33 – 0.52	0.107
time [endline]	28.41	23.64 – 33.17	<0.001	27.06	22.21 – 31.91	<0.001
Attendance	0.23	0.15 – 0.31	<0.001	0.23	0.15 – 0.31	<0.001
GenderChild [Male] × time [endline]	-1.82	-4.31 – 0.66	0.151	-1.66	-4.15 – 0.82	0.190
time [endline] × Attendance	0.05	-0.01 – 0.11	0.132	0.04	-0.02 – 0.11	0.201
Group [1]				3.78	-1.46 – 9.03	0.157
time [endline] × Group [1]				3.55	1.05 – 6.04	0.005
Random Effects
σ²	303.41			302.05
τ₀₀	521.53 _{participantID:School}			522.18 _{participantID:School}
	43.19 _School			37.05 _School
ICC	0.65			0.65
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.235 / 0.732			0.243 / 0.735

plot(colnames(dat)[25])

# The thin lines are the means of the schools, thick lines are the overall means.

total literacy

tab_model(fit_tot.lit,fit_tot.lit1)

	tot.lit			tot.lit
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.20	0.16 – 0.25	<0.001	0.18	0.13 – 0.23	<0.001
GenderChild [Male]	-0.02	-0.04 – -0.00	0.032	-0.02	-0.04 – -0.00	0.027
time [endline]	0.11	0.08 – 0.15	<0.001	0.09	0.06 – 0.13	<0.001
Attendance	0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	<0.001
GenderChild [Male] × time [endline]	-0.02	-0.03 – 0.00	0.063	-0.01	-0.03 – 0.00	0.111
time [endline] × Attendance	0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	<0.001
Group [1]				0.04	0.00 – 0.08	0.029
time [endline] × Group [1]				0.05	0.04 – 0.07	<0.001
Random Effects
σ²	0.01			0.01
τ₀₀	0.03 _{participantID:School}			0.03 _{participantID:School}
	0.00 _School			0.00 _School
ICC	0.70			0.69
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3013			3013
Marginal R² / Conditional R²	0.194 / 0.755			0.223 / 0.763

plot(colnames(dat)[26])

# The thin lines are the means of the schools, thick lines are the overall means.

Wellbeing

dw<-t(descriptives_WB)
colnames(dw)<-c("Baseline A","Endline A","Baseline B","Endline B")
data.table(dw[-c(1,2),],keep.rownames=TRUE)

##                      rn Baseline A Endline A Baseline B Endline B
## 1: mean total wellbeing      28.22     60.97      23.63     52.62
## 2:   sd total wellbeing      31.68     28.20      30.39     28.93

fit_WB <- lmer(WB_total ~ GenderChild*time+Attendance*time+ (1|participantID:School) + (1 | School), data =longdata )
fit_WB1 <- lmer(WB_total ~ GenderChild*time+Attendance+Attendance*time+time*Group+ (1|participantID:School) + (1 | School), data =longdata )



tab_model(fit_WB,fit_WB1)

	WB_total			WB_total
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	33.56	32.11 – 35.01	<0.001	32.40	31.02 – 33.77	<0.001
GenderChild [Male]	-0.26	-0.92 – 0.39	0.429	-0.25	-0.91 – 0.40	0.445
time [endline]	-0.98	-2.72 – 0.77	0.272	-1.26	-3.03 – 0.52	0.166
Attendance	0.01	-0.01 – 0.02	0.482	0.00	-0.01 – 0.02	0.662
GenderChild [Male] × time [endline]	0.31	-0.60 – 1.22	0.504	0.34	-0.57 – 1.26	0.459
time [endline] × Attendance	0.00	-0.02 – 0.03	0.726	0.00	-0.02 – 0.03	0.823
Group [1]				2.66	1.77 – 3.55	<0.001
time [endline] × Group [1]				0.73	-0.18 – 1.65	0.116
Random Effects
σ²	40.63			40.59
τ₀₀	0.64 _{participantID:School}			0.66 _{participantID:School}
	3.04 _School			0.72 _School
ICC	0.08			0.03
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.002 / 0.085			0.055 / 0.086

plot(colnames(dat)[26])

# wellbeing including group

fit_Wbg <- lmer(WB_total ~ GenderChild*time*Group+Attendance+Attendance*time+time*Group+ (1|participantID:School) + (1 | School), data =longdata )

wellbeing_g<-tab_model(fit_Wbg)

Q-values of the interaction effect time x group

#install.packages("devtools")
#library("devtools")
#install_github("jdstorey/qvalue")
#browseVignettes(package = "qvalue")



pvalues<-na.omit(pvalue)


qvalue_truncp <- function(p, fdr.level = NULL, pfdr = FALSE, lfdr.out = TRUE, pi0 = NULL,...){
  # Argument checks
  p_in <- qvals_out <- lfdr_out <- p
  rm_na <- !is.na(p)
  p <- p[rm_na]
  if (min(p) < 0 || max(p) > 1) {
    stop("p-values not in valid range [0, 1].")
  } else if (!is.null(fdr.level) && (fdr.level <= 0 || fdr.level > 1)) {
    stop("'fdr.level' must be in (0, 1].")
  }
  p <- p / max(p)
  # Calculate pi0 estimate
  if (is.null(pi0)) {
    pi0s <- pi0est(p, ...)
  } else {
    if (pi0 > 0 && pi0 <= 1)  {
      pi0s = list()
      pi0s$pi0 = pi0
    } else {
      stop("pi0 is not (0,1]")
    }
  }
 # Calculate q-value estimates
  m <- length(p)
  i <- m:1L
  o <- order(p, decreasing = TRUE)
  ro <- order(o)
  if (pfdr) {
    qvals <- pi0s$pi0 * pmin(1, cummin(p[o] * m / (i * (1 - (1 - p[o]) ^ m))))[ro]
  } else {
    qvals <- pi0s$pi0 * pmin(1, cummin(p[o] * m /i ))[ro]
  }
  qvals_out[rm_na] <- qvals
  # Calculate local FDR estimates
  if (lfdr.out) {
    lfdr <- lfdr(p = p, pi0 = pi0s$pi0, ...)
    lfdr_out[rm_na] <- lfdr
  } else {
    lfdr_out <- NULL
  }
  
  # Return results
  if (!is.null(fdr.level)) {
    retval <- list(call = match.call(), pi0 = pi0s$pi0, qvalues = qvals_out,
                   pvalues = p_in, lfdr = lfdr_out, fdr.level = fdr.level,
                   significant = (qvals <= fdr.level),
                   pi0.lambda = pi0s$pi0.lambda, lambda = pi0s$lambda,
                   pi0.smooth = pi0s$pi0.smooth)
  } else {
    retval <- list(call = match.call(), pi0 = pi0s$pi0, qvalues = qvals_out,
                   pvalues = p_in, lfdr = lfdr_out, pi0.lambda = pi0s$pi0.lambda,
                   lambda = pi0s$lambda, pi0.smooth = pi0s$pi0.smooth)
  }
  class(retval) <- "qvalue"
  return(retval)
}

qobj <- qvalue_truncp(p = pvalues)


test<-c("nid","qds","mis","add","sub","prb","nu","lid","pho","muw","non",
"com","lis","wrt","read1","read2","lit")

qvalues <- qobj$qvalues

q<-data.frame(test,pvalues,qvalues)

# unique effect interaction time * group

colnames(q)<-c("(Sub)Task","P-value","Q-value")
data.table(q,keep.rownames=FALSE)

##     (Sub)Task      P-value      Q-value
##  1:       nid 2.636084e-02 2.409921e-02
##  2:       qds 1.226402e-03 1.851864e-03
##  3:       mis 5.207866e-01 3.868359e-01
##  4:       add 2.279202e-01 1.934825e-01
##  5:       sub 1.211668e-07 4.683122e-07
##  6:       prb 2.216959e-02 2.195651e-02
##  7:        nu 6.786598e-05 1.344274e-04
##  8:       lid 2.306199e-05 5.481682e-05
##  9:       pho 1.874023e-28 2.227213e-27
## 10:       muw 1.246557e-03 1.851864e-03
## 11:       non 1.576190e-07 4.683122e-07
## 12:       com 9.829968e-01 6.872111e-01
## 13:       lis 4.774235e-01 3.782680e-01
## 14:       wrt 3.591746e-03 4.268670e-03
## 15:     read1 1.676024e-03 2.213220e-03
## 16:     read2 5.368335e-03 5.800079e-03
## 17:       lit 3.907119e-10 2.321740e-09

Effect size Cohen’s d raw data

Cohen’s d was calculated without a correction for variance by school or covariates.

library(rstatix)

## 
## Attaching package: 'rstatix'

## The following object is masked from 'package:stats':
## 
##     filter

cd=list()
for(i in 1:17){
df.effs<-data.frame(diff=endline[,i+6]-baseline[,i+6],group=baseline$Group)
cd[[i]]<-df.effs %>% cohens_d(diff ~ group, var.equal = TRUE)}


test<-c("nid","qds","mis","add","sub","prb","nu","lid","pho","muw","non",
"com","lis","wrt","read1","read2","lit")

df.cohensd<-cd[[1]]
for(i in 2:17)
df.cohensd<-rbind(df.cohensd,cd[[i]])

df<-data.frame(test,round(df.cohensd[,c(4)],2))

colnames(df)<-c("(Sub)Task","Cohen's d")
data.table(df,keep.rownames=FALSE)

##     (Sub)Task Cohen's d
##  1:       nid      0.12
##  2:       qds      0.17
##  3:       mis      0.04
##  4:       add      0.08
##  5:       sub      0.28
##  6:       prb      0.12
##  7:        nu      0.22
##  8:       lid      0.22
##  9:       pho      0.60
## 10:       muw      0.18
## 11:       non      0.28
## 12:       com      0.02
## 13:       lis      0.04
## 14:       wrt     -0.13
## 15:     read1      0.19
## 16:     read2      0.15
## 17:       lit      0.35

Completer analyses on >80% attendance only

nid

tab_model(fit_nid,fit_nid1)

	total_nid			total_nid
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	12.73	12.27 – 13.19	<0.001	12.51	11.94 – 13.09	<0.001
GenderChild [Male]	-0.40	-0.95 – 0.14	0.148	-0.39	-0.94 – 0.16	0.163
time [endline]	1.75	1.37 – 2.14	<0.001	1.74	1.28 – 2.19	<0.001
GenderChild [Male] × time [endline]	-0.16	-0.70 – 0.37	0.552	-0.16	-0.70 – 0.38	0.558
Group [1]				0.44	-0.29 – 1.16	0.237
time [endline] × Group [1]				0.04	-0.51 – 0.58	0.890
Random Effects
σ²	3.07			3.08
τ₀₀	3.16 _{participantID:School}			3.15 _{participantID:School}
	0.25 _School			0.22 _School
ICC	0.53			0.52
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.105 / 0.575			0.112 / 0.576

plot(colnames(dat)[10])

# The thin lines are the means of the schools, thick lines are the overall means.

qds

tab_model(fit_qds,fit_qds1)

	total_qds			total_qds
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	8.67	8.38 – 8.96	<0.001	8.59	8.23 – 8.95	<0.001
GenderChild [Male]	-0.47	-0.82 – -0.12	0.009	-0.47	-0.82 – -0.12	0.009
time [endline]	0.98	0.69 – 1.26	<0.001	0.89	0.55 – 1.23	<0.001
GenderChild [Male] × time [endline]	0.08	-0.32 – 0.49	0.681	0.09	-0.31 – 0.50	0.647
Group [1]				0.14	-0.31 – 0.60	0.535
time [endline] × Group [1]				0.19	-0.23 – 0.60	0.375
Random Effects
σ²	1.75			1.75
τ₀₀	0.82 _{participantID:School}			0.82 _{participantID:School}
	0.09 _School			0.08 _School
ICC	0.34			0.34
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.103 / 0.409			0.108 / 0.411

plot(colnames(dat)[11])

# The thin lines are the means of the schools, thick lines are the overall means.

mis

tab_model(fit_mis,fit_mis1)

	total_mis			total_mis
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.03	5.50 – 6.56	<0.001	5.40	4.82 – 5.98	<0.001
GenderChild [Male]	-0.24	-0.79 – 0.31	0.391	-0.21	-0.76 – 0.34	0.458
time [endline]	2.16	1.76 – 2.56	<0.001	2.23	1.75 – 2.70	<0.001
GenderChild [Male] × time [endline]	-0.45	-1.01 – 0.12	0.119	-0.46	-1.02 – 0.11	0.113
Group [1]				1.21	0.48 – 1.95	0.001
time [endline] × Group [1]				-0.15	-0.72 – 0.43	0.618
Random Effects
σ²	3.39			3.40
τ₀₀	2.95 _{participantID:School}			2.94 _{participantID:School}
	0.52 _School			0.23 _School
ICC	0.51			0.48
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.127 / 0.569			0.168 / 0.570

plot(colnames(dat)[12])

# The thin lines are the means of the schools, thick lines are the overall means.

add

tab_model(fit_add,fit_add1)

	total_add			total_add
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	7.20	6.32 – 8.08	<0.001	6.44	5.45 – 7.43	<0.001
GenderChild [Male]	-0.49	-1.39 – 0.41	0.288	-0.47	-1.37 – 0.43	0.306
time [endline]	3.10	2.36 – 3.84	<0.001	3.01	2.13 – 3.90	<0.001
GenderChild [Male] × time [endline]	-0.14	-1.19 – 0.90	0.791	-0.13	-1.18 – 0.92	0.807
Group [1]				1.50	0.23 – 2.77	0.021
time [endline] × Group [1]				0.19	-0.87 – 1.26	0.718
Random Effects
σ²	11.62			11.65
τ₀₀	5.20 _{participantID:School}			5.23 _{participantID:School}
	1.55 _School			0.82 _School
ICC	0.37			0.34
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.114 / 0.440			0.146 / 0.438

plot(colnames(dat)[13])

# The thin lines are the means of the schools, thick lines are the overall means.

sub

tab_model(fit_sub,fit_sub1)

	total_sub			total_sub
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.85	4.05 – 5.65	<0.001	4.33	3.52 – 5.14	<0.001
GenderChild [Male]	-0.06	-0.86 – 0.74	0.877	-0.10	-0.89 – 0.69	0.813
time [endline]	2.07	1.37 – 2.76	<0.001	1.15	0.34 – 1.95	0.005
GenderChild [Male] × time [endline]	0.09	-0.88 – 1.07	0.849	0.20	-0.75 – 1.16	0.677
Group [1]				0.78	-0.24 – 1.80	0.132
time [endline] × Group [1]				2.04	1.08 – 3.01	<0.001
Random Effects
σ²	10.19			9.71
τ₀₀	3.07 _{participantID:School}			3.35 _{participantID:School}
	1.39 _School			0.40 _School
ICC	0.30			0.28
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.071 / 0.354			0.139 / 0.379

plot(colnames(dat)[14])

# The thin lines are the means of the schools, thick lines are the overall means.

prb

tab_model(fit_prb,fit_prb1)

	total_prb			total_prb
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.80	3.46 – 4.13	<0.001	3.61	3.21 – 4.01	<0.001
GenderChild [Male]	0.23	-0.12 – 0.57	0.201	0.23	-0.11 – 0.58	0.189
time [endline]	0.94	0.65 – 1.24	<0.001	0.81	0.46 – 1.17	<0.001
GenderChild [Male] × time [endline]	-0.28	-0.70 – 0.14	0.189	-0.27	-0.69 – 0.15	0.215
Group [1]				0.35	-0.17 – 0.86	0.189
time [endline] × Group [1]				0.29	-0.13 – 0.72	0.179
Random Effects
σ²	1.88			1.88
τ₀₀	0.63 _{participantID:School}			0.63 _{participantID:School}
	0.20 _School			0.15 _School
ICC	0.31			0.29
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.058 / 0.347			0.080 / 0.351

plot(colnames(dat)[15])

# The thin lines are the means of the schools, thick lines are the overall means.

nu

tab_model(fit_nu,fit_nu1)

	tot.num			tot.num
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.57	0.54 – 0.60	<0.001	0.54	0.51 – 0.58	<0.001
GenderChild [Male]	-0.01	-0.04 – 0.02	0.367	-0.01	-0.04 – 0.02	0.405
time [endline]	0.13	0.11 – 0.15	<0.001	0.12	0.10 – 0.14	<0.001
GenderChild [Male] × time [endline]	-0.02	-0.04 – 0.01	0.246	-0.01	-0.04 – 0.01	0.285
Group [1]				0.05	0.01 – 0.10	0.019
time [endline] × Group [1]				0.02	-0.00 – 0.05	0.084
Random Effects
σ²	0.01			0.01
τ₀₀	0.01 _{participantID:School}			0.01 _{participantID:School}
	0.00 _School			0.00 _School
ICC	0.63			0.61
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.160 / 0.688			0.202 / 0.690

plot(colnames(dat)[16])

# The thin lines are the means of the schools, thick lines are the overall means.

lid

tab_model(fit_lid,fit_lid1)

	total_lid			total_lid
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	21.91	20.68 – 23.15	<0.001	21.32	19.69 – 22.94	<0.001
GenderChild [Male]	-1.56	-2.71 – -0.40	0.008	-1.53	-2.69 – -0.38	0.009
time [endline]	2.72	1.71 – 3.73	<0.001	2.79	1.59 – 4.00	<0.001
GenderChild [Male] × time [endline]	0.62	-0.80 – 2.04	0.391	0.61	-0.81 – 2.04	0.399
Group [1]				1.19	-0.94 – 3.33	0.273
time [endline] × Group [1]				-0.16	-1.60 – 1.29	0.833
Random Effects
σ²	21.48			21.54
τ₀₀	5.98 _{participantID:School}			5.96 _{participantID:School}
	3.74 _School			3.61 _School
ICC	0.31			0.31
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.080 / 0.367			0.089 / 0.370

plot(colnames(dat)[17])

# The thin lines are the means of the schools, thick lines are the overall means.

pho

tab_model(fit_pho,fit_pho1)

	total_pho			total_pho
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.47	2.37 – 4.56	<0.001	2.25	1.11 – 3.39	<0.001
GenderChild [Male]	-0.24	-0.86 – 0.37	0.435	-0.28	-0.88 – 0.32	0.356
time [endline]	2.59	2.02 – 3.17	<0.001	1.54	0.89 – 2.19	<0.001
GenderChild [Male] × time [endline]	-0.41	-1.21 – 0.40	0.323	-0.28	-1.05 – 0.49	0.471
Group [1]				2.06	0.54 – 3.58	0.008
time [endline] × Group [1]				2.33	1.56 – 3.11	<0.001
Random Effects
σ²	6.89			6.24
τ₀₀	0.85 _{participantID:School}			1.17 _{participantID:School}
	4.93 _School			2.33 _School
ICC	0.46			0.36
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.105 / 0.513			0.311 / 0.559

plot(colnames(dat)[18])

# The thin lines are the means of the schools, thick lines are the overall means.

muw

tab_model(fit_muw,fit_muw1)

	total_muw			total_muw
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	11.62	10.16 – 13.09	<0.001	10.73	8.80 – 12.65	<0.001
GenderChild [Male]	-2.23	-3.50 – -0.96	0.001	-2.20	-3.47 – -0.93	0.001
time [endline]	5.33	4.64 – 6.03	<0.001	5.37	4.55 – 6.20	<0.001
GenderChild [Male] × time [endline]	-0.35	-1.33 – 0.62	0.480	-0.36	-1.33 – 0.62	0.475
Group [1]				1.77	-0.79 – 4.33	0.175
time [endline] × Group [1]				-0.09	-1.08 – 0.90	0.859
Random Effects
σ²	10.11			10.15
τ₀₀	22.82 _{participantID:School}			22.80 _{participantID:School}
	5.85 _School			5.51 _School
ICC	0.74			0.74
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.173 / 0.784			0.188 / 0.786

plot(colnames(dat)[19])

# The thin lines are the means of the schools, thick lines are the overall means.

non

tab_model(fit_non,fit_non1)

	total_non			total_non
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.46	3.26 – 5.66	<0.001	3.68	2.24 – 5.11	<0.001
GenderChild [Male]	-0.51	-1.56 – 0.53	0.335	-0.51	-1.55 – 0.53	0.334
time [endline]	4.93	4.18 – 5.68	<0.001	4.12	3.24 – 4.99	<0.001
GenderChild [Male] × time [endline]	-1.43	-2.48 – -0.37	0.008	-1.33	-2.37 – -0.29	0.012
Group [1]				1.37	-0.52 – 3.27	0.155
time [endline] × Group [1]				1.81	0.76 – 2.86	0.001
Random Effects
σ²	11.84			11.47
τ₀₀	10.59 _{participantID:School}			10.79 _{participantID:School}
	3.95 _School			2.74 _School
ICC	0.55			0.54
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.158 / 0.622			0.205 / 0.635

plot(colnames(dat)[20])

# The thin lines are the means of the schools, thick lines are the overall means.

com

tab_model(fit_com,fit_com1)

	total_com			total_com
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.01	0.70 – 1.33	<0.001	0.91	0.48 – 1.33	<0.001
GenderChild [Male]	-0.10	-0.39 – 0.20	0.513	-0.09	-0.39 – 0.20	0.532
time [endline]	1.32	1.10 – 1.54	<0.001	1.34	1.08 – 1.61	<0.001
GenderChild [Male] × time [endline]	-0.10	-0.41 – 0.21	0.511	-0.11	-0.42 – 0.21	0.502
Group [1]				0.21	-0.35 – 0.77	0.452
time [endline] × Group [1]				-0.05	-0.36 – 0.27	0.770
Random Effects
σ²	1.03			1.03
τ₀₀	0.78 _{participantID:School}			0.78 _{participantID:School}
	0.24 _School			0.25 _School
ICC	0.50			0.50
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.167 / 0.582			0.169 / 0.584

plot(colnames(dat)[21])

# The thin lines are the means of the schools, thick lines are the overall means.

lis

tab_model(fit_lis,fit_lis1)

	total_lis			total_lis
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.13	0.82 – 1.43	<0.001	1.06	0.66 – 1.47	<0.001
GenderChild [Male]	-0.20	-0.50 – 0.09	0.182	-0.20	-0.50 – 0.09	0.180
time [endline]	0.69	0.45 – 0.92	<0.001	0.61	0.33 – 0.88	<0.001
GenderChild [Male] × time [endline]	0.01	-0.31 – 0.34	0.934	0.02	-0.30 – 0.35	0.890
Group [1]				0.12	-0.41 – 0.65	0.660
time [endline] × Group [1]				0.17	-0.15 – 0.50	0.299
Random Effects
σ²	1.11			1.11
τ₀₀	0.69 _{participantID:School}			0.70 _{participantID:School}
	0.21 _School			0.21 _School
ICC	0.45			0.45
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.060 / 0.482			0.066 / 0.486

plot(colnames(dat)[22])

# The thin lines are the means of the schools, thick lines are the overall means.

wrt

tab_model(fit_wrt,fit_wrt1)

	total_wrt			total_wrt
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.40	3.66 – 5.13	<0.001	3.91	2.93 – 4.88	<0.001
GenderChild [Male]	-0.92	-1.54 – -0.30	0.004	-0.90	-1.52 – -0.28	0.005
time [endline]	2.47	2.11 – 2.83	<0.001	2.62	2.20 – 3.05	<0.001
GenderChild [Male] × time [endline]	-0.36	-0.87 – 0.15	0.162	-0.38	-0.89 – 0.13	0.142
Group [1]				0.99	-0.31 – 2.28	0.136
time [endline] × Group [1]				-0.34	-0.86 – 0.17	0.189
Random Effects
σ²	2.72			2.72
τ₀₀	5.16 _{participantID:School}			5.16 _{participantID:School}
	1.52 _School			1.46 _School
ICC	0.71			0.71
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.147 / 0.753			0.162 / 0.756

plot(colnames(dat)[23])

# The thin lines are the means of the schools, thick lines are the overall means.

read 1

tab_model(fit_read1,fit_read11)

	read_1			read_1
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	20.62	15.38 – 25.86	<0.001	17.44	10.47 – 24.41	<0.001
GenderChild [Male]	-4.12	-8.14 – -0.10	0.044	-4.07	-8.09 – -0.05	0.047
time [endline]	19.96	17.65 – 22.27	<0.001	19.34	16.59 – 22.09	<0.001
GenderChild [Male] × time [endline]	-5.13	-8.38 – -1.88	0.002	-5.06	-8.31 – -1.80	0.002
Group [1]				6.09	-3.23 – 15.40	0.200
time [endline] × Group [1]				1.38	-1.92 – 4.67	0.413
Random Effects
σ²	112.28			112.39
τ₀₀	217.55 _{participantID:School}			217.52 _{participantID:School}
	87.48 _School			80.93 _School
ICC	0.73			0.73
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.175 / 0.778			0.197 / 0.780

plot(colnames(dat)[24])

# The thin lines are the means of the schools, thick lines are the overall means.

read 2

tab_model(fit_read2,fit_read21)

	read_2			read_2
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	41.32	34.30 – 48.33	<0.001	37.36	28.19 – 46.53	<0.001
GenderChild [Male]	-8.35	-14.54 – -2.16	0.008	-8.23	-14.42 – -2.04	0.009
time [endline]	33.19	29.54 – 36.84	<0.001	32.52	28.18 – 36.85	<0.001
GenderChild [Male] × time [endline]	-3.69	-8.81 – 1.44	0.159	-3.61	-8.75 – 1.53	0.169
Group [1]				7.70	-4.45 – 19.85	0.214
time [endline] × Group [1]				1.50	-3.70 – 6.70	0.572
Random Effects
σ²	279.42			280.00
τ₀₀	506.72 _{participantID:School}			506.50 _{participantID:School}
	130.58 _School			121.03 _School
ICC	0.70			0.69
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.229 / 0.765			0.243 / 0.766

plot(colnames(dat)[25])

# The thin lines are the means of the schools, thick lines are the overall means.

total literacy

tab_model(fit_tot.lit,fit_tot.lit1)

	tot.lit			tot.lit
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.39	0.34 – 0.44	<0.001	0.35	0.28 – 0.41	<0.001
GenderChild [Male]	-0.05	-0.09 – -0.01	0.011	-0.05	-0.09 – -0.01	0.012
time [endline]	0.22	0.20 – 0.25	<0.001	0.20	0.18 – 0.23	<0.001
GenderChild [Male] × time [endline]	-0.03	-0.06 – 0.00	0.076	-0.03	-0.06 – 0.01	0.100
Group [1]				0.08	-0.01 – 0.17	0.072
time [endline] × Group [1]				0.04	0.01 – 0.07	0.009
Random Effects
σ²	0.01			0.01
τ₀₀	0.02 _{participantID:School}			0.02 _{participantID:School}
	0.01 _School			0.01 _School
ICC	0.75			0.74
N	328 _{participantID}			328 _{participantID}
	20 _School			20 _School
Observations	656			656
Marginal R² / Conditional R²	0.218 / 0.808			0.266 / 0.813

plot(colnames(dat)[26])

# The thin lines are the means of the schools, thick lines are the overall means.

Mediation analyses group->attendance->score (for total literacy score only in this example)

The first model shows that there is a significant relationship between group and attendance. Children in group A (=Group 1) have higher attendance scores (3.15) than children in group B (=Group 0). The second model shows that there is a significant relationship between total literacy score and attendance. Children with higher attendance have higher scores on literacy score (averaged over baseline and endline) and they improve more from baseline to endline with higher attendance scores. In the third model shows that children in group A have higher total literacy scores (averaged over baseline and endline) and children in group A improve more than children in group B. In the fourth model it is shown that higher attendance and being in group A results in significant improvement in literacy scores at endline. The final model shows a significant three way interaction between time group and attendance. Higher attendance in group A results in more improvement from baseline to endline than higher attendance in group B. Note that this final model does not have significant interaction effects anymore for time x attendance and time x group. So this would lead to the conclusion that attendance is both a mediator and a moderator.

	Attendance			tot.lit			tot.lit			tot.lit			tot.lit
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	67.83	66.85 – 68.82	<0.001	0.19	0.15 – 0.24	<0.001	0.27	0.24 – 0.30	<0.001	0.17	0.12 – 0.21	<0.001	0.19	0.13 – 0.24	<0.001
Group [1]	3.15	1.76 – 4.54	<0.001				0.05	0.01 – 0.09	0.026	0.04	0.01 – 0.08	0.025	-0.00	-0.09 – 0.09	0.991
time [endline]				0.11	0.08 – 0.14	<0.001	0.15	0.14 – 0.16	<0.001	0.09	0.06 – 0.12	<0.001	0.10	0.07 – 0.14	<0.001
Attendance				0.00	0.00 – 0.00	<0.001				0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	0.001
time [endline] × Attendance				0.00	0.00 – 0.00	<0.001				0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	0.006
time [endline] × Group [1]							0.05	0.04 – 0.07	<0.001	0.05	0.04 – 0.07	<0.001	0.01	-0.05 – 0.07	0.699
Attendance × Group [1]													0.00	-0.00 – 0.00	0.253
(time [endline] × Attendance) × Group [1]													0.00	-0.00 – 0.00	0.178
Random Effects
σ²				0.01			0.01			0.01			0.01
τ₀₀				0.03 _{participantID:School}			0.03 _{participantID:School}			0.03 _{participantID:School}			0.03 _{participantID:School}
				0.00 _School			0.00 _School			0.00 _School			0.00 _School
ICC				0.70			0.70			0.70			0.69
N				1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}
				30 _School			30 _School			30 _School			30 _School
Observations	3014			3013			3013			3013			3013
R² / R² adjusted	0.006 / 0.006			0.189 / 0.755			0.188 / 0.760			0.219 / 0.762			0.220 / 0.762

Effect of gender

Math

There is no significant interaction effect for gender x time x group on Math scales

numericy_g

	total_nid			total_qds			total_mis			total_add			total_sub			total_prb			tot.num
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	11.15	10.60 – 11.69	<0.001	7.89	7.51 – 8.28	<0.001	4.01	3.40 – 4.62	<0.001	4.29	3.42 – 5.15	<0.001	2.96	2.21 – 3.71	<0.001	2.97	2.56 – 3.38	<0.001	0.45	0.42 – 0.48	<0.001
GenderChild [Male]	0.16	-0.19 – 0.51	0.368	-0.11	-0.36 – 0.14	0.403	0.29	-0.08 – 0.67	0.128	0.22	-0.36 – 0.80	0.458	0.28	-0.22 – 0.78	0.266	0.14	-0.10 – 0.38	0.244	0.01	-0.01 – 0.03	0.235
time [endline]	1.69	1.18 – 2.21	<0.001	0.63	0.22 – 1.04	0.003	1.72	1.16 – 2.27	<0.001	1.71	0.75 – 2.67	<0.001	0.79	-0.04 – 1.62	0.062	1.30	0.89 – 1.70	<0.001	0.11	0.08 – 0.14	<0.001
Group [1]	-0.06	-0.50 – 0.37	0.776	-0.09	-0.39 – 0.22	0.573	0.55	0.05 – 1.06	0.032	1.08	0.43 – 1.74	0.001	0.69	0.12 – 1.25	0.018	0.02	-0.34 – 0.38	0.914	0.02	-0.01 – 0.05	0.158
Attendance	0.01	0.01 – 0.02	<0.001	0.01	0.00 – 0.01	0.010	0.02	0.01 – 0.02	<0.001	0.02	0.01 – 0.03	<0.001	0.01	0.00 – 0.02	0.008	0.01	0.00 – 0.01	0.014	0.00	0.00 – 0.00	<0.001
GenderChild [Male] × time [endline]	-0.08	-0.44 – 0.28	0.675	-0.06	-0.35 – 0.23	0.681	-0.32	-0.71 – 0.07	0.106	-0.45	-1.12 – 0.22	0.187	0.15	-0.43 – 0.73	0.607	-0.20	-0.49 – 0.08	0.161	-0.01	-0.03 – 0.00	0.110
GenderChild [Male] × Group [1]	0.22	-0.27 – 0.71	0.385	0.19	-0.16 – 0.54	0.285	-0.08	-0.61 – 0.45	0.768	0.01	-0.81 – 0.82	0.990	-0.09	-0.79 – 0.61	0.801	0.08	-0.26 – 0.42	0.627	0.01	-0.02 – 0.04	0.668
time [endline] × Group [1]	0.40	0.04 – 0.77	0.030	0.39	0.10 – 0.68	0.009	0.09	-0.30 – 0.48	0.660	0.36	-0.31 – 1.04	0.293	1.08	0.49 – 1.67	<0.001	0.27	-0.02 – 0.56	0.069	0.03	0.01 – 0.05	0.002
time [endline] × Attendance	-0.00	-0.01 – 0.01	0.734	0.00	-0.00 – 0.01	0.253	0.00	-0.00 – 0.01	0.394	0.02	0.00 – 0.03	0.012	0.01	-0.00 – 0.02	0.101	-0.00	-0.01 – 0.00	0.278	0.00	-0.00 – 0.00	0.308
(GenderChild [Male] × time [endline]) × Group [1]	-0.22	-0.73 – 0.29	0.393	-0.10	-0.51 – 0.31	0.637	0.00	-0.54 – 0.55	0.991	-0.14	-1.09 – 0.81	0.770	0.06	-0.76 – 0.88	0.880	-0.06	-0.47 – 0.34	0.758	-0.01	-0.03 – 0.02	0.629
Random Effects
σ²	3.19			2.02			3.65			10.94			8.22			1.98			0.01
τ₀₀	2.70 _{participantID:School}			0.99 _{participantID:School}			3.19 _{participantID:School}			5.33 _{participantID:School}			3.81 _{participantID:School}			0.81 _{participantID:School}			0.01 _{participantID:School}
	0.12 _School			0.06 _School			0.21 _School			0.16 _School			0.13 _School			0.14 _School			0.00 _School
ICC	0.47			0.34			0.48			0.33			0.32			0.33			0.61
N	1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School			30 _School			30 _School			30 _School			30 _School			30 _School
Observations	3014			3014			3014			3014			3014			3014			3014
Marginal R² / Conditional R²	0.123 / 0.535			0.085 / 0.397			0.129 / 0.549			0.138 / 0.426			0.119 / 0.404			0.102 / 0.394			0.194 / 0.683

Literacy

There is a one significant effect for the interaction gender x time x group. Boys in group B improved more (4.23) on read 1 than Girls in group B.

literacy_g

	total_lid			total_pho			total_muw			total_non			total_com			total_lis			total_wrt			read_1			read_2			tot.lit
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	18.39	16.81 – 19.97	<0.001	2.27	1.40 – 3.14	<0.001	3.84	2.24 – 5.44	<0.001	1.38	0.29 – 2.47	0.013	0.14	-0.17 – 0.45	0.369	0.35	0.04 – 0.66	0.027	1.07	0.34 – 1.80	0.004	3.86	-0.61 – 8.33	0.090	9.87	2.95 – 16.79	0.005	0.18	0.13 – 0.23	<0.001
GenderChild [Male]	-0.37	-1.25 – 0.51	0.413	-0.31	-0.73 – 0.11	0.145	-0.84	-1.75 – 0.07	0.072	-0.08	-0.77 – 0.61	0.813	-0.09	-0.27 – 0.10	0.360	-0.17	-0.35 – 0.00	0.051	-0.34	-0.76 – 0.07	0.106	-2.00	-4.54 – 0.54	0.123	-3.51	-7.65 – 0.63	0.096	-0.02	-0.05 – 0.00	0.082
time [endline]	2.58	1.12 – 4.03	0.001	-1.02	-1.82 – -0.22	0.013	3.82	2.84 – 4.81	<0.001	2.16	1.18 – 3.13	<0.001	0.55	0.26 – 0.84	<0.001	0.28	0.00 – 0.56	0.047	1.30	0.83 – 1.77	<0.001	7.23	4.15 – 10.30	<0.001	26.03	21.00 – 31.05	<0.001	0.09	0.05 – 0.12	<0.001
Group [1]	-1.30	-2.84 – 0.23	0.095	1.56	0.61 – 2.52	0.001	0.64	-0.84 – 2.11	0.399	0.78	-0.10 – 1.66	0.081	0.12	-0.16 – 0.39	0.404	0.17	-0.12 – 0.47	0.256	0.60	-0.07 – 1.27	0.077	2.42	-1.74 – 6.58	0.255	2.65	-3.39 – 8.69	0.390	0.04	-0.00 – 0.09	0.069
Attendance	0.03	0.02 – 0.05	<0.001	0.00	-0.01 – 0.01	0.849	0.06	0.04 – 0.08	<0.001	0.02	0.01 – 0.03	0.001	0.01	0.00 – 0.01	<0.001	0.01	0.00 – 0.01	0.001	0.02	0.02 – 0.03	<0.001	0.11	0.06 – 0.16	<0.001	0.23	0.15 – 0.31	<0.001	0.00	0.00 – 0.00	<0.001
GenderChild [Male] × time [endline]	0.12	-0.90 – 1.14	0.818	0.20	-0.37 – 0.76	0.495	0.08	-0.60 – 0.77	0.813	-0.13	-0.81 – 0.56	0.713	0.02	-0.18 – 0.23	0.841	0.14	-0.05 – 0.33	0.157	-0.21	-0.54 – 0.12	0.210	-0.85	-3.00 – 1.30	0.437	0.27	-3.24 – 3.79	0.878	0.00	-0.02 – 0.03	0.791
GenderChild [Male] × Group [1]	0.34	-0.90 – 1.58	0.591	-0.06	-0.65 – 0.53	0.837	0.12	-1.17 – 1.40	0.858	0.14	-0.84 – 1.12	0.778	0.11	-0.15 – 0.38	0.393	0.05	-0.20 – 0.29	0.716	-0.27	-0.85 – 0.32	0.375	1.14	-2.45 – 4.73	0.535	2.21	-3.64 – 8.06	0.459	0.01	-0.03 – 0.04	0.803
time [endline] × Group [1]	1.99	0.96 – 3.02	<0.001	2.46	1.89 – 3.02	<0.001	1.04	0.35 – 1.74	0.003	1.58	0.89 – 2.27	<0.001	0.14	-0.07 – 0.35	0.180	0.16	-0.04 – 0.36	0.115	-0.38	-0.72 – -0.04	0.027	4.48	2.30 – 6.66	<0.001	5.53	1.96 – 9.10	0.002	0.07	0.04 – 0.09	<0.001
time [endline] × Attendance	0.00	-0.02 – 0.02	0.894	0.02	0.01 – 0.03	<0.001	0.01	-0.00 – 0.02	0.069	0.01	-0.00 – 0.02	0.059	0.01	0.00 – 0.01	0.004	0.00	-0.00 – 0.01	0.093	0.01	0.01 – 0.02	<0.001	0.10	0.06 – 0.14	<0.001	0.04	-0.02 – 0.11	0.201	0.00	0.00 – 0.00	<0.001
(GenderChild [Male] × time [endline]) × Group [1]	-0.84	-2.28 – 0.60	0.253	-0.33	-1.12 – 0.47	0.420	-0.47	-1.44 – 0.50	0.343	-0.54	-1.51 – 0.43	0.275	-0.27	-0.56 – 0.02	0.064	-0.21	-0.49 – 0.06	0.132	0.06	-0.41 – 0.53	0.810	-3.95	-6.99 – -0.91	0.011	-3.87	-8.84 – 1.10	0.127	-0.03	-0.06 – -0.00	0.050
Random Effects
σ²	25.27			7.66			11.53			11.41			1.02			0.92			2.69			112.97			301.78			0.01
τ₀₀	11.99 _{participantID:School}			0.72 _{participantID:School}			28.40 _{participantID:School}			11.62 _{participantID:School}			0.65 _{participantID:School}			0.55 _{participantID:School}			5.66 _{participantID:School}			197.47 _{participantID:School}			522.76 _{participantID:School}			0.03 _{participantID:School}
	3.03 _School			1.44 _School			2.61 _School			0.55 _School			0.08 _School			0.11 _School			0.53 _School			20.90 _School			37.08 _School			0.00 _School
ICC	0.37			0.22			0.73			0.52			0.42			0.42			0.70			0.66			0.65			0.69
N	1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School			30 _School			30 _School			30 _School			30 _School			30 _School			30 _School			30 _School			30 _School
Observations	3014			3014			3014			3014			3014			3014			3014			3014			3014			3013
Marginal R² / Conditional R²	0.085 / 0.426			0.251 / 0.416			0.171 / 0.775			0.154 / 0.591			0.136 / 0.498			0.075 / 0.462			0.133 / 0.737			0.183 / 0.721			0.243 / 0.735			0.224 / 0.763

Wellbeing

There is no significant interaction effect for gender x time x group on wellbeing

wellbeing_g

	WB_total
Predictors	Estimates	CI	p
(Intercept)	32.19	30.77 – 33.61	<0.001
GenderChild [Male]	0.14	-0.78 – 1.07	0.760
time [endline]	-1.11	-2.96 – 0.73	0.237
Group [1]	3.06	1.95 – 4.18	<0.001
Attendance	0.00	-0.01 – 0.02	0.670
GenderChild [Male] × time [endline]	0.07	-1.22 – 1.36	0.910
GenderChild [Male] × Group [1]	-0.79	-2.10 – 0.51	0.233
time [endline] × Group [1]	0.46	-0.85 – 1.76	0.494
time [endline] × Attendance	0.00	-0.02 – 0.03	0.822
(GenderChild [Male] × time [endline]) × Group [1]	0.54	-1.29 – 2.36	0.563
Random Effects
σ²	40.61
τ₀₀ _{participantID:School}	0.65
τ₀₀ _School	0.72
ICC	0.03
N _{participantID}	1507
N _School	30
Observations	3014
Marginal R² / Conditional R²	0.055 / 0.086

Effect of gender in intervention group

Math

There is a main effect for total_nid for gender. Boys scored .37 higher than girls, p=.031. There is one significant interaction effect gender x time. On the total math score, girls improved 3% more than boys (p=.007).

numericy_g

	total_nid			total_qds			total_mis			total_add			total_sub			total_prb			tot.num
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	12.07	11.80 – 12.34	<0.001	8.25	8.04 – 8.45	<0.001	5.72	5.37 – 6.08	<0.001	6.86	6.41 – 7.32	<0.001	4.55	4.15 – 4.95	<0.001	3.40	3.12 – 3.68	<0.001	0.54	0.52 – 0.55	<0.001
GenderChild [Male]	0.37	0.04 – 0.71	0.030	0.08	-0.15 – 0.32	0.481	0.21	-0.15 – 0.56	0.254	0.22	-0.38 – 0.82	0.472	0.18	-0.37 – 0.72	0.522	0.22	-0.01 – 0.45	0.060	0.02	-0.00 – 0.04	0.066
time [endline]	2.02	1.77 – 2.27	<0.001	1.23	1.03 – 1.44	<0.001	2.02	1.76 – 2.28	<0.001	3.17	2.68 – 3.67	<0.001	2.50	2.05 – 2.95	<0.001	1.36	1.16 – 1.56	<0.001	0.15	0.14 – 0.17	<0.001
GenderChild [Male] × time [endline]	-0.30	-0.66 – 0.06	0.102	-0.16	-0.45 – 0.13	0.289	-0.32	-0.68 – 0.05	0.090	-0.59	-1.30 – 0.11	0.101	0.22	-0.43 – 0.86	0.510	-0.27	-0.55 – 0.02	0.064	-0.02	-0.04 – -0.00	0.023
Random Effects
σ²	3.16			2.10			3.27			12.21			10.04			1.95			0.01
τ₀₀	2.31 _{participantID:School}			0.55 _{participantID:School}			2.83 _{participantID:School}			5.10 _{participantID:School}			4.45 _{participantID:School}			0.65 _{participantID:School}			0.01 _{participantID:School}
	0.07 _School			0.07 _School			0.25 _School			0.14 _School			0.06 _School			0.20 _School			0.00 _School
ICC	0.43			0.23			0.48			0.30			0.31			0.30			0.57
N	753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}
	15 _School			15 _School			15 _School			15 _School			15 _School			15 _School			15 _School
Observations	1506			1506			1506			1506			1506			1506			1506
Marginal R² / Conditional R²	0.139 / 0.509			0.110 / 0.312			0.122 / 0.548			0.108 / 0.376			0.106 / 0.383			0.121 / 0.387			0.211 / 0.662

Literacy

There is one significant effect for the main effect of gender on total_wrt. Girls scores .62 higher than boys. There are three significant interaction effects gender x time. On total_com girls improved .3 more than boys, p=.004. On read_1 girls improved 4.4 more than boys, p<.001. On tot.lit girls improved 2 percent more than boys, p<.037.

literacy_g

	total_lid			total_pho			total_muw			total_non			total_com			total_lis			total_wrt			read_1			read_2			tot.lit
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	19.41	18.23 – 20.60	<0.001	3.89	3.25 – 4.54	<0.001	8.74	7.49 – 9.98	<0.001	3.71	3.03 – 4.39	<0.001	0.79	0.56 – 1.02	<0.001	0.92	0.70 – 1.15	<0.001	3.37	2.79 – 3.96	<0.001	14.17	10.56 – 17.78	<0.001	28.90	23.85 – 33.94	<0.001	0.33	0.29 – 0.37	<0.001
GenderChild [Male]	-0.05	-0.95 – 0.86	0.918	-0.38	-0.82 – 0.07	0.096	-0.74	-1.64 – 0.17	0.110	0.06	-0.66 – 0.77	0.879	0.02	-0.17 – 0.22	0.802	-0.13	-0.32 – 0.06	0.168	-0.62	-1.04 – -0.20	0.004	-0.89	-3.60 – 1.81	0.518	-1.34	-5.50 – 2.82	0.527	-0.02	-0.05 – 0.01	0.167
time [endline]	4.66	3.89 – 5.42	<0.001	3.17	2.76 – 3.59	<0.001	5.69	5.22 – 6.16	<0.001	4.58	4.10 – 5.07	<0.001	1.07	0.92 – 1.22	<0.001	0.65	0.51 – 0.80	<0.001	1.79	1.57 – 2.01	<0.001	18.54	16.95 – 20.13	<0.001	34.51	31.99 – 37.02	<0.001	0.22	0.21 – 0.24	<0.001
GenderChild [Male] × time [endline]	-0.72	-1.81 – 0.37	0.196	-0.13	-0.73 – 0.47	0.668	-0.39	-1.06 – 0.28	0.259	-0.67	-1.36 – 0.03	0.060	-0.25	-0.46 – -0.04	0.018	-0.07	-0.28 – 0.14	0.502	-0.15	-0.47 – 0.16	0.342	-4.81	-7.08 – -2.53	<0.001	-3.59	-7.19 – -0.00	0.050	-0.03	-0.05 – -0.01	0.013
Random Effects
σ²	29.03			8.76			11.02			11.80			1.07			1.05			2.50			126.28			315.77			0.01
τ₀₀	10.41 _{participantID:School}			0.79 _{participantID:School}			28.05 _{participantID:School}			12.64 _{participantID:School}			0.74 _{participantID:School}			0.62 _{participantID:School}			6.04 _{participantID:School}			226.92 _{participantID:School}			519.06 _{participantID:School}			0.03 _{participantID:School}
	3.93 _School			1.25 _School			4.51 _School			0.84 _School			0.14 _School			0.13 _School			1.00 _School			36.88 _School			66.41 _School			0.00 _School
ICC	0.33			0.19			0.75			0.53			0.45			0.41			0.74			0.68			0.65			0.72
N	753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}			753 _{participantID}
	15 _School			15 _School			15 _School			15 _School			15 _School			15 _School			15 _School			15 _School			15 _School			15 _School
Observations	1506			1506			1506			1506			1506			1506			1506			1506			1506			1506
Marginal R² / Conditional R²	0.098 / 0.396			0.186 / 0.340			0.152 / 0.785			0.153 / 0.605			0.106 / 0.508			0.054 / 0.446			0.083 / 0.760			0.152 / 0.725			0.232 / 0.731			0.200 / 0.776

CWTL CRCT Analysis

Samantha Bouwmeester

November 2023

Preparation

Assesments

Long datafile

Descriptives

Analysis

nid

qds

mis

add

sub

prb

nu

lid

pho

muw

non

com

lis

wrt

read 1

read 2

total literacy

Wellbeing

Q-values of the interaction effect time x group

Effect size Cohen’s d raw data

Completer analyses on >80% attendance only

nid

qds

mis

add

sub

prb

nu

lid

pho

muw

non

com

lis

wrt

read 1

read 2

total literacy

Mediation analyses group->attendance->score (for total literacy score only in this example)

Effect of gender

Math

Literacy

Wellbeing

Effect of gender in intervention group

Math

Literacy