Preparation

data<-read.csv("/Users/samanthabouwmeester/Documents/CWTL/merged_data_all.csv")

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,7)
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 NA

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

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

# no missings anymore


### total scores
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","Read_1_end",
                 "Read_2_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","Read_1_end","Read_2_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"
                 ,"Read_1_end","Read_2_end")]
any(is.na(rowSums(end.lit)))

## [1] FALSE

# no missings anymore

### Add total literacy score

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$total_wrt_end/10,
                            data$Read_1_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[,c(6:17,375:386)]

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(2:5,616)],wellbeing[,1:12])
wb.end<-cbind(wb.bas[,1:5],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[,6:17])

Assesments

Long datafile

### Long datafile ###

baseline<-cbind(data[,c(2:5,615,616)],bas.num,bas.lit)
colnames(baseline)

##  [1] "participantID"   "GenderChild"     "School"          "ppt_group"      
##  [5] "Attendance"      "Attendance_dich" "total_nid"       "total_qds"      
##  [9] "total_mis"       "total_add"       "total_sub"       "total_prb"      
## [13] "tot.num"         "total_lid"       "total_pho"       "total_muw"      
## [17] "total_non"       "total_com"       "total_lis"       "total_wrt"      
## [21] "read_1"          "read_2"          "tot.lit"

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))


longdata[c(1:6),]

##      participantID GenderChild                    School ppt_group Attendance
## 1209      16000650        Male Kyamusooni Primary School         B         82
## 2716      16000650        Male Kyamusooni Primary School         B         82
## 1163      16000674        Male Kyamusooni Primary School         B         88
## 2670      16000674        Male Kyamusooni Primary School         B         88
## 1115      16000681        Male Kyamusooni Primary School         B         83
## 2622      16000681        Male Kyamusooni Primary School         B         83
##      Attendance_dich total_nid total_qds total_mis total_add total_sub
## 1209               0        11         6         4         3         4
## 2716               0        14         9         5         4         4
## 1163               1        13        10         6         7         3
## 2670               1        15        10         7         7         6
## 1115               0        15        10        10         7         4
## 2622               0        15        10         8        11         7
##      total_prb   tot.num total_lid total_pho total_muw total_non total_com
## 1209         2 0.3911111        21         1         3         1         0
## 2716         4 0.5533333        22         3         5         0         0
## 1163         6 0.6444444        23         0        13         3         0
## 2670         6 0.7033333        26         6        17         8         2
## 1115         6 0.7400000        24         4        19         9         2
## 2622         5 0.7255556        24         7        16         2         3
##      total_lis total_wrt read_1 read_2   tot.lit WB_total     time
## 1209         1         0      3      3 0.1738624       21 baseline
## 2716         1         0      7     19 0.2083576       27  endline
## 1163         0         1     30     48 0.2643806       43 baseline
## 2670         1         7     29     69 0.5678662       27  endline
## 1115         1         3     17     48 0.5048259       25 baseline
## 2622         4         7     30     67 0.6188884       31  endline

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     14.11      12.17     13.75
##  2:   sd total_nid       2.90      1.65       2.95      2.09
##  3: mean total_qds       8.29      9.43       8.25      9.05
##  4:   sd total_qds       2.05      1.10       2.14      1.52
##  5: mean total_mis       5.82      7.68       5.27      6.99
##  6:   sd total_mis       2.81      2.16       2.94      2.67
##  7: mean total_add       6.97      9.85       5.83      8.29
##  8:   sd total_add       4.10      4.22       3.70      4.26
##  9: mean total_sub       4.63      7.21       3.97      5.45
## 10:   sd total_sub       3.66      4.02       2.91      3.39
## 11: mean total_prb       3.51      4.71       3.44      4.40
## 12:   sd total_prb       1.92      1.41       1.89      1.61
## 13:   mean tot.num       0.55      0.69       0.52      0.63
## 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     23.74      20.41     23.17
##  2:   sd total_lid       8.14      4.33       7.19      4.87
##  3: mean total_pho       3.71      6.76       2.16      2.89
##  4:   sd total_pho       3.51      3.06       2.83      3.17
##  5: mean total_muw       8.37     13.82       7.46     12.15
##  6:   sd total_muw       6.91      6.25       6.54      6.97
##  7: mean total_non       3.73      7.93       2.81      5.66
##  8:   sd total_non       4.72      5.31       4.15      5.23
##  9: mean total_com       0.80      1.75       0.60      1.56
## 10:   sd total_com       1.27      1.50       1.09      1.46
## 11: mean total_lis       0.86      1.47       0.64      1.21
## 12:   sd total_lis       1.26      1.41       1.04      1.33
## 13: mean total_wrt       3.06      4.78       2.51      4.56
## 14:   sd total_wrt       2.92      3.28       2.73      3.32
## 15:    mean read_1      13.72     29.63      10.32     23.86
## 16:      sd read_1      15.90     22.99      13.31     20.70
## 17:    mean read_2      28.22     60.41      23.63     52.60
## 18:      sd read_2      31.68     28.50      30.39     29.32
## 19:   mean tot.lit       0.34      0.52       0.29      0.42
## 20:     sd tot.lit       0.21      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)	11.52	11.10 – 11.95	<0.001	11.55	11.09 – 12.01	<0.001
GenderChild [Male]	0.28	0.03 – 0.52	0.029	0.27	0.03 – 0.52	0.030
time [endline]	1.79	1.38 – 2.20	<0.001	1.96	1.52 – 2.39	<0.001
Attendance	0.01	0.00 – 0.01	0.003	0.01	0.00 – 0.01	0.003
GenderChild [Male] * time [endline]	-0.24	-0.50 – 0.01	0.063	-0.23	-0.49 – 0.03	0.077
time [endline] * Attendance	0.00	-0.00 – 0.01	0.787	0.00	-0.01 – 0.01	0.930
ppt group [B]				-0.06	-0.39 – 0.28	0.727
time [endline] * ppt group [B]				-0.27	-0.53 – -0.02	0.036
Random Effects
σ²	3.19			3.19
τ₀₀	2.73 _{participantID:School}			2.74 _{participantID:School}
	0.10 _School			0.10 _School
ICC	0.47			0.47
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.115 / 0.532			0.118 / 0.533

plot(colnames(dat)[9])

# 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)	8.00	7.70 – 8.31	<0.001	8.00	7.67 – 8.34	<0.001
GenderChild [Male]	-0.00	-0.18 – 0.17	0.963	-0.01	-0.18 – 0.17	0.930
time [endline]	0.77	0.44 – 1.09	<0.001	0.96	0.61 – 1.31	<0.001
Attendance	0.00	0.00 – 0.01	0.044	0.00	0.00 – 0.01	0.038
GenderChild [Male] * time [endline]	-0.12	-0.32 – 0.09	0.258	-0.10	-0.31 – 0.10	0.321
time [endline] * Attendance	0.00	-0.00 – 0.01	0.077	0.00	-0.00 – 0.01	0.134
ppt group [B]				-0.01	-0.26 – 0.23	0.908
time [endline] * ppt group [B]				-0.33	-0.53 – -0.12	0.002
Random Effects
σ²	2.03			2.02
τ₀₀	0.99 _{participantID:School}			1.00 _{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	0.077 / 0.392			0.082 / 0.396

plot(colnames(dat)[10])

# 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)	4.72	4.23 – 5.20	<0.001	5.00	4.48 – 5.52	<0.001
GenderChild [Male]	0.25	-0.01 – 0.52	0.063	0.26	-0.01 – 0.52	0.057
time [endline]	1.57	1.14 – 2.01	<0.001	1.63	1.17 – 2.10	<0.001
Attendance	0.01	0.00 – 0.02	0.001	0.01	0.00 – 0.02	0.001
GenderChild [Male] * time [endline]	-0.33	-0.60 – -0.06	0.016	-0.33	-0.60 – -0.06	0.018
time [endline] * Attendance	0.01	-0.00 – 0.01	0.053	0.01	-0.00 – 0.01	0.062
ppt group [B]				-0.52	-0.94 – -0.10	0.014
time [endline] * ppt group [B]				-0.10	-0.37 – 0.17	0.465
Random Effects
σ²	3.59			3.59
τ₀₀	3.23 _{participantID:School}			3.23 _{participantID:School}
	0.28 _School			0.21 _School
ICC	0.49			0.49
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.113 / 0.551			0.124 / 0.552

plot(colnames(dat)[11])

# 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)	5.16	4.43 – 5.88	<0.001	5.79	5.05 – 6.53	<0.001
GenderChild [Male]	0.22	-0.18 – 0.63	0.282	0.23	-0.18 – 0.64	0.274
time [endline]	2.16	1.40 – 2.92	<0.001	2.37	1.56 – 3.18	<0.001
Attendance	0.02	0.01 – 0.03	<0.001	0.02	0.01 – 0.02	0.001
GenderChild [Male] * time [endline]	-0.56	-1.03 – -0.08	0.021	-0.54	-1.01 – -0.07	0.025
time [endline] * Attendance	0.01	0.00 – 0.02	0.022	0.01	0.00 – 0.02	0.031
ppt group [B]				-1.09	-1.58 – -0.60	<0.001
time [endline] * ppt group [B]				-0.34	-0.82 – 0.13	0.160
Random Effects
σ²	10.96			10.95
τ₀₀	5.28 _{participantID:School}			5.28 _{participantID:School}
	0.54 _School			0.14 _School
ICC	0.35			0.33
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.112 / 0.420			0.134 / 0.421

plot(colnames(dat)[12])

# 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)	3.54	2.90 – 4.18	<0.001	3.93	3.30 – 4.57	<0.001
GenderChild [Male]	0.26	-0.10 – 0.62	0.153	0.24	-0.11 – 0.59	0.185
time [endline]	1.38	0.71 – 2.05	<0.001	2.03	1.31 – 2.74	<0.001
Attendance	0.01	0.00 – 0.02	0.021	0.01	0.00 – 0.02	0.033
GenderChild [Male] * time [endline]	0.10	-0.32 – 0.51	0.655	0.14	-0.27 – 0.56	0.498
time [endline] * Attendance	0.01	0.00 – 0.02	0.050	0.01	-0.00 – 0.02	0.126
ppt group [B]				-0.64	-1.07 – -0.22	0.003
time [endline] * ppt group [B]				-1.07	-1.49 – -0.66	<0.001
Random Effects
σ²	8.59			8.45
τ₀₀	3.69 _{participantID:School}			3.76 _{participantID:School}
	0.45 _School			0.10 _School
ICC	0.33			0.31
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.084 / 0.382			0.114 / 0.392

plot(colnames(dat)[13])

# 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)	3.12	2.80 – 3.43	<0.001	3.14	2.78 – 3.50	<0.001
GenderChild [Male]	0.19	0.02 – 0.36	0.028	0.19	0.02 – 0.36	0.031
time [endline]	1.10	0.78 – 1.43	<0.001	1.23	0.88 – 1.58	<0.001
Attendance	0.00	0.00 – 0.01	0.047	0.00	0.00 – 0.01	0.041
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	-0.00 – 0.01	0.476	0.00	-0.00 – 0.01	0.596
ppt group [B]				-0.07	-0.39 – 0.26	0.691
time [endline] * ppt group [B]				-0.22	-0.42 – -0.01	0.037
Random Effects
σ²	2.02			2.02
τ₀₀	0.80 _{participantID:School}			0.80 _{participantID:School}
	0.15 _School			0.15 _School
ICC	0.32			0.32
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.095 / 0.384			0.098 / 0.386

plot(colnames(dat)[14])

# 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)	0.48	0.46 – 0.51	<0.001	0.50	0.47 – 0.52	<0.001
GenderChild [Male]	0.02	0.00 – 0.03	0.031	0.02	0.00 – 0.03	0.031
time [endline]	0.11	0.09 – 0.13	<0.001	0.13	0.11 – 0.15	<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	0.002	-0.02	-0.03 – -0.01	0.004
time [endline] * Attendance	0.00	0.00 – 0.00	0.013	0.00	0.00 – 0.00	0.031
ppt group [B]				-0.02	-0.05 – -0.00	0.041
time [endline] * ppt group [B]				-0.03	-0.04 – -0.01	<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			0.60
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.173 / 0.676			0.188 / 0.679

plot(colnames(dat)[15])

# 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)	18.74	17.52 – 19.95	<0.001	18.10	16.67 – 19.53	<0.001
GenderChild [Male]	-0.15	-0.76 – 0.47	0.646	-0.18	-0.80 – 0.44	0.561
time [endline]	3.75	2.60 – 4.89	<0.001	4.70	3.49 – 5.92	<0.001
Attendance	0.02	0.00 – 0.03	0.010	0.02	0.01 – 0.03	0.005
GenderChild [Male] * time [endline]	-0.35	-1.06 – 0.36	0.335	-0.28	-0.99 – 0.43	0.441
time [endline] * Attendance	-0.00	-0.02 – 0.02	0.987	-0.00	-0.02 – 0.01	0.692
ppt group [B]				1.11	-0.32 – 2.53	0.128
time [endline] * ppt group [B]				-1.59	-2.30 – -0.88	<0.001
Random Effects
σ²	24.91			24.62
τ₀₀	12.17 _{participantID:School}			12.32 _{participantID:School}
	3.12 _School			3.23 _School
ICC	0.38			0.39
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.078 / 0.429			0.081 / 0.437

plot(colnames(dat)[16])

# 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)	3.29	2.48 – 4.11	<0.001	3.96	3.15 – 4.77	<0.001
GenderChild [Male]	-0.29	-0.59 – 0.01	0.056	-0.34	-0.63 – -0.04	0.025
time [endline]	0.20	-0.47 – 0.86	0.564	1.54	0.86 – 2.22	<0.001
Attendance	-0.00	-0.01 – 0.00	0.361	-0.00	-0.01 – 0.01	0.710
GenderChild [Male] * time [endline]	-0.00	-0.41 – 0.41	0.994	0.10	-0.30 – 0.50	0.626
time [endline] * Attendance	0.03	0.02 – 0.03	<0.001	0.02	0.01 – 0.03	<0.001
ppt group [B]				-1.54	-2.46 – -0.62	0.001
time [endline] * ppt group [B]				-2.24	-2.63 – -1.84	<0.001
Random Effects
σ²	8.35			7.74
τ₀₀	0.40 _{participantID:School}			0.71 _{participantID:School}
	3.25 _School			1.48 _School
ICC	0.30			0.22
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.081 / 0.360			0.243 / 0.410

plot(colnames(dat)[17])

# 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)	5.85	4.59 – 7.11	<0.001	6.20	4.75 – 7.65	<0.001
GenderChild [Male]	-0.75	-1.40 – -0.11	0.022	-0.76	-1.41 – -0.11	0.021
time [endline]	4.55	3.78 – 5.32	<0.001	4.98	4.16 – 5.80	<0.001
Attendance	0.04	0.02 – 0.05	<0.001	0.04	0.02 – 0.05	<0.001
GenderChild [Male] * time [endline]	-0.09	-0.57 – 0.39	0.712	-0.06	-0.54 – 0.42	0.812
time [endline] * Attendance	0.01	-0.00 – 0.02	0.106	0.01	-0.00 – 0.02	0.174
ppt group [B]				-0.74	-2.13 – 0.65	0.298
time [endline] * ppt group [B]				-0.72	-1.20 – -0.24	0.003
Random Effects
σ²	11.20			11.14
τ₀₀	29.18 _{participantID:School}			29.21 _{participantID:School}
	3.13 _School			2.96 _School
ICC	0.74			0.74
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.147 / 0.781			0.155 / 0.783

plot(colnames(dat)[18])

# 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)	2.43	1.52 – 3.33	<0.001	2.84	1.89 – 3.79	<0.001
GenderChild [Male]	0.01	-0.48 – 0.50	0.979	-0.01	-0.49 – 0.48	0.981
time [endline]	2.67	1.90 – 3.44	<0.001	3.44	2.63 – 4.26	<0.001
Attendance	0.01	0.00 – 0.02	0.026	0.01	0.00 – 0.02	0.020
GenderChild [Male] * time [endline]	-0.32	-0.80 – 0.16	0.193	-0.26	-0.74 – 0.22	0.283
time [endline] * Attendance	0.02	0.00 – 0.03	0.004	0.01	0.00 – 0.02	0.014
ppt group [B]				-0.87	-1.61 – -0.14	0.020
time [endline] * ppt group [B]				-1.28	-1.76 – -0.81	<0.001
Random Effects
σ²	11.22			11.03
τ₀₀	11.89 _{participantID:School}			11.99 _{participantID:School}
	1.15 _School			0.59 _School
ICC	0.54			0.53
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.122 / 0.594			0.148 / 0.602

plot(colnames(dat)[19])

# 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)	0.39	0.14 – 0.63	0.002	0.48	0.21 – 0.76	0.001
GenderChild [Male]	-0.03	-0.16 – 0.10	0.682	-0.03	-0.16 – 0.11	0.705
time [endline]	0.77	0.54 – 1.01	<0.001	0.75	0.50 – 1.01	<0.001
Attendance	0.00	0.00 – 0.01	0.001	0.00	0.00 – 0.01	0.001
GenderChild [Male] * time [endline]	-0.11	-0.25 – 0.04	0.154	-0.11	-0.25 – 0.04	0.150
time [endline] * Attendance	0.00	0.00 – 0.01	0.027	0.00	0.00 – 0.01	0.026
ppt group [B]				-0.18	-0.43 – 0.07	0.157
time [endline] * ppt group [B]				0.03	-0.12 – 0.17	0.727
Random Effects
σ²	1.04			1.04
τ₀₀	0.64 _{participantID:School}			0.64 _{participantID:School}
	0.09 _School			0.09 _School
ICC	0.41			0.41
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.126 / 0.485			0.130 / 0.486

plot(colnames(dat)[20])

# 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)	0.56	0.32 – 0.80	<0.001	0.66	0.38 – 0.94	<0.001
GenderChild [Male]	-0.15	-0.28 – -0.03	0.016	-0.15	-0.27 – -0.03	0.017
time [endline]	0.34	0.12 – 0.56	0.002	0.36	0.13 – 0.60	0.003
Attendance	0.00	0.00 – 0.01	0.005	0.00	0.00 – 0.01	0.005
GenderChild [Male] * time [endline]	0.06	-0.07 – 0.20	0.361	0.07	-0.07 – 0.20	0.351
time [endline] * Attendance	0.00	0.00 – 0.01	0.036	0.00	0.00 – 0.01	0.040
ppt group [B]				-0.20	-0.47 – 0.08	0.160
time [endline] * ppt group [B]				-0.03	-0.17 – 0.10	0.633
Random Effects
σ²	0.93			0.93
τ₀₀	0.55 _{participantID:School}			0.55 _{participantID:School}
	0.12 _School			0.12 _School
ICC	0.42			0.42
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.064 / 0.459			0.071 / 0.461

plot(colnames(dat)[21])

# 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)	1.95	1.38 – 2.52	<0.001	2.22	1.55 – 2.88	<0.001
GenderChild [Male]	-0.48	-0.78 – -0.18	0.002	-0.47	-0.77 – -0.17	0.002
time [endline]	1.60	1.22 – 1.98	<0.001	1.38	0.97 – 1.79	<0.001
Attendance	0.02	0.01 – 0.02	<0.001	0.02	0.01 – 0.02	<0.001
GenderChild [Male] * time [endline]	-0.12	-0.36 – 0.12	0.323	-0.14	-0.38 – 0.10	0.261
time [endline] * Attendance	0.01	0.00 – 0.01	0.047	0.01	0.00 – 0.01	0.025
ppt group [B]				-0.48	-1.11 – 0.15	0.137
time [endline] * ppt group [B]				0.37	0.13 – 0.61	0.003
Random Effects
σ²	2.80			2.79
τ₀₀	5.73 _{participantID:School}			5.74 _{participantID:School}
	0.60 _School			0.61 _School
ICC	0.69			0.69
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.113 / 0.728			0.116 / 0.730

plot(colnames(dat)[22])

# 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)	8.42	4.88 – 11.96	<0.001	9.91	5.90 – 13.93	<0.001
GenderChild [Male]	-1.39	-3.20 – 0.42	0.133	-1.40	-3.21 – 0.41	0.128
time [endline]	10.67	8.19 – 13.15	<0.001	11.85	9.20 – 14.49	<0.001
Attendance	0.06	0.02 – 0.10	0.002	0.06	0.02 – 0.11	0.002
GenderChild [Male] * time [endline]	-2.49	-4.04 – -0.95	0.002	-2.41	-3.95 – -0.86	0.002
time [endline] * Attendance	0.08	0.05 – 0.11	<0.001	0.08	0.04 – 0.11	<0.001
ppt group [B]				-3.09	-6.91 – 0.73	0.113
time [endline] * ppt group [B]				-1.96	-3.50 – -0.41	0.013
Random Effects
σ²	116.46			116.06
τ₀₀	199.14 _{participantID:School}			199.34 _{participantID:School}
	25.39 _School			22.09 _School
ICC	0.66			0.66
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.156 / 0.712			0.169 / 0.714

plot(colnames(dat)[23])

# 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)	17.38	11.85 – 22.91	<0.001	19.26	13.07 – 25.46	<0.001
GenderChild [Male]	-2.34	-5.29 – 0.61	0.120	-2.35	-5.31 – 0.60	0.118
time [endline]	28.53	24.54 – 32.52	<0.001	30.36	26.10 – 34.62	<0.001
Attendance	0.14	0.08 – 0.21	<0.001	0.15	0.08 – 0.21	<0.001
GenderChild [Male] * time [endline]	-0.92	-3.41 – 1.56	0.467	-0.78	-3.27 – 1.70	0.536
time [endline] * Attendance	0.04	-0.02 – 0.09	0.164	0.03	-0.02 – 0.08	0.238
ppt group [B]				-3.92	-9.40 – 1.56	0.161
time [endline] * ppt group [B]				-3.05	-5.54 – -0.55	0.017
Random Effects
σ²	302.08			301.13
τ₀₀	538.12 _{participantID:School}			538.56 _{participantID:School}
	47.37 _School			41.74 _School
ICC	0.66			0.66
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.220 / 0.735			0.228 / 0.736

plot(colnames(dat)[24])

# 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)	0.26	0.22 – 0.30	<0.001	0.28	0.24 – 0.33	<0.001
GenderChild [Male]	-0.02	-0.04 – -0.00	0.020	-0.02	-0.04 – -0.00	0.017
time [endline]	0.12	0.09 – 0.14	<0.001	0.15	0.12 – 0.17	<0.001
Attendance	0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	<0.001
GenderChild [Male] * time [endline]	-0.01	-0.02 – 0.01	0.337	-0.01	-0.02 – 0.01	0.485
time [endline] * Attendance	0.00	0.00 – 0.00	<0.001	0.00	0.00 – 0.00	<0.001
ppt group [B]				-0.05	-0.09 – -0.01	0.028
time [endline] * ppt group [B]				-0.05	-0.06 – -0.03	<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	0.75			0.75
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.153 / 0.787			0.182 / 0.793

plot(colnames(dat)[25])

# 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     60.41      23.63     52.60
## 2:   sd total wellbeing      31.68     28.50      30.39     29.32

fit_WB <- lmer(WB_total ~ GenderChild*time+Attendance*time+ (1|participantID:School) + (1 | School), data =longdata )
fit_WB1 <- lmer(WB_total ~ GenderChild+GenderChild*time+Attendance+Attendance*time+time*ppt_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)	34.23	32.99 – 35.47	<0.001	35.99	34.82 – 37.16	<0.001
GenderChild [Male]	0.38	-0.27 – 1.03	0.251	0.39	-0.26 – 1.04	0.234
time [endline]	-0.91	-2.26 – 0.45	0.190	-1.03	-2.47 – 0.42	0.165
Attendance	-0.01	-0.03 – 0.00	0.052	-0.02	-0.03 – -0.00	0.020
GenderChild [Male] * time [endline]	-0.21	-1.05 – 0.63	0.629	-0.22	-1.06 – 0.63	0.614
time [endline] * Attendance	0.02	0.00 – 0.04	0.028	0.02	0.00 – 0.04	0.026
ppt group [B]				-3.21	-3.98 – -2.44	<0.001
time [endline] * ppt group [B]				0.20	-0.65 – 1.05	0.644
Random Effects
σ²	34.76			34.78
τ₀₀	6.33 _{participantID:School}			6.32 _{participantID:School}
	2.80 _School			0.34 _School
ICC	0.21			0.16
N	1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School
Observations	3014			3014
Marginal R² / Conditional R²	0.003 / 0.210			0.057 / 0.208

plot(colnames(dat)[26])

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

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)

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 3.567414e-02 5.687609e-02
##  2:       qds 1.793841e-03 5.090388e-03
##  3:       mis 4.645757e-01 6.152198e-01
##  4:       add 1.599700e-01 2.269737e-01
##  5:       sub 5.010765e-07 2.488336e-06
##  6:       prb 3.722272e-02 5.687609e-02
##  7:        nu 1.110593e-04 3.676789e-04
##  8:       lid 1.278138e-05 5.077766e-05
##  9:       pho 5.040117e-27 1.001165e-25
## 10:       muw 3.395392e-03 7.493977e-03
## 11:       non 1.514319e-07 1.002677e-06
## 12:       com 7.270702e-01 8.495569e-01
## 13:       lis 6.327993e-01 7.856173e-01
## 14:       wrt 2.799579e-03 6.951329e-03
## 15:     read1 1.325972e-02 2.633901e-02
## 16:     read2 1.662530e-02 3.002215e-02
## 17:       lit 1.818246e-09 1.805875e-08

pi0 <- qobj$pi0
lfdr <- qobj$lfdr

#summary(qobj)
#hist(qobj)
#plot(qobj)

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$ppt_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,df.cohensd[,c(4)])

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

##     (Sub)Task    Cohen's d
##  1:       nid  0.113372899
##  2:       qds  0.170951376
##  3:       mis  0.051745703
##  4:       add  0.087589972
##  5:       sub  0.266555531
##  6:       prb  0.115598395
##  7:        nu  0.216328777
##  8:       lid  0.226798885
##  9:       pho  0.584830794
## 10:       muw  0.158585614
## 11:       non  0.286205697
## 12:       com -0.004630069
## 13:       lis  0.031747430
## 14:       wrt -0.142030654
## 15:     read1  0.154478215
## 16:     read2  0.130911013
## 17:       lit  0.331026282

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	12.95	12.36 – 13.55	<0.001
GenderChild [Male]	-0.40	-0.95 – 0.14	0.149	-0.39	-0.94 – 0.16	0.164
time [endline]	1.75	1.37 – 2.14	<0.001	1.78	1.29 – 2.26	<0.001
GenderChild [Male] * time [endline]	-0.17	-0.71 – 0.37	0.533	-0.17	-0.71 – 0.37	0.539
ppt group [B]				-0.44	-1.16 – 0.29	0.239
time [endline] * ppt group [B]				-0.05	-0.59 – 0.50	0.870
Random Effects
σ²	3.08			3.09
τ₀₀	3.17 _{participantID:School}			3.16 _{participantID:School}
	0.25 _School			0.22 _School
ICC	0.53			0.52
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.104 / 0.576			0.111 / 0.576

plot(colnames(dat)[9])

# 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	8.74	8.36 – 9.12	<0.001
GenderChild [Male]	-0.47	-0.82 – -0.12	0.008	-0.47	-0.82 – -0.12	0.009
time [endline]	0.98	0.69 – 1.26	<0.001	1.07	0.71 – 1.44	<0.001
GenderChild [Male] * time [endline]	0.10	-0.31 – 0.50	0.638	0.11	-0.30 – 0.51	0.608
ppt group [B]				-0.15	-0.60 – 0.31	0.521
time [endline] * ppt group [B]				-0.18	-0.59 – 0.24	0.402
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	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.104 / 0.411			0.109 / 0.413

plot(colnames(dat)[10])

# 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	6.62	6.01 – 7.22	<0.001
GenderChild [Male]	-0.25	-0.80 – 0.31	0.382	-0.21	-0.77 – 0.34	0.447
time [endline]	2.16	1.76 – 2.56	<0.001	2.08	1.57 – 2.59	<0.001
GenderChild [Male] * time [endline]	-0.45	-1.01 – 0.12	0.123	-0.45	-1.02 – 0.12	0.118
ppt group [B]				-1.22	-1.95 – -0.49	0.001
time [endline] * ppt group [B]				0.15	-0.42 – 0.72	0.609
Random Effects
σ²	3.40			3.41
τ₀₀	2.96 _{participantID:School}			2.95 _{participantID:School}
	0.52 _School			0.23 _School
ICC	0.51			0.48
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.127 / 0.569			0.168 / 0.570

plot(colnames(dat)[11])

# 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 – 8.09	<0.001	7.94	6.91 – 8.97	<0.001
GenderChild [Male]	-0.49	-1.39 – 0.41	0.289	-0.47	-1.37 – 0.43	0.306
time [endline]	3.10	2.35 – 3.84	<0.001	3.22	2.27 – 4.16	<0.001
GenderChild [Male] * time [endline]	-0.17	-1.21 – 0.88	0.757	-0.15	-1.20 – 0.90	0.773
ppt group [B]				-1.50	-2.77 – -0.22	0.021
time [endline] * ppt group [B]				-0.22	-1.28 – 0.85	0.690
Random Effects
σ²	11.63			11.66
τ₀₀	5.21 _{participantID:School}			5.25 _{participantID:School}
	1.56 _School			0.82 _School
ICC	0.37			0.34
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.114 / 0.440			0.145 / 0.438

plot(colnames(dat)[12])

# 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)	4.84	4.04 – 5.65	<0.001	5.10	4.26 – 5.94	<0.001
GenderChild [Male]	-0.04	-0.84 – 0.76	0.923	-0.07	-0.86 – 0.72	0.861
time [endline]	2.07	1.37 – 2.77	<0.001	3.20	2.33 – 4.06	<0.001
GenderChild [Male] * time [endline]	0.09	-0.89 – 1.07	0.858	0.19	-0.77 – 1.15	0.692
ppt group [B]				-0.77	-1.79 – 0.25	0.141
time [endline] * ppt group [B]				-2.05	-3.02 – -1.08	<0.001
Random Effects
σ²	10.23			9.74
τ₀₀	3.05 _{participantID:School}			3.32 _{participantID:School}
	1.37 _School			0.41 _School
ICC	0.30			0.28
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.071 / 0.351			0.138 / 0.377

plot(colnames(dat)[13])

# 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)	3.79	3.46 – 4.13	<0.001	3.95	3.54 – 4.37	<0.001
GenderChild [Male]	0.23	-0.12 – 0.58	0.189	0.24	-0.11 – 0.59	0.178
time [endline]	0.94	0.64 – 1.24	<0.001	1.11	0.73 – 1.49	<0.001
GenderChild [Male] * time [endline]	-0.28	-0.71 – 0.14	0.187	-0.27	-0.69 – 0.15	0.211
ppt group [B]				-0.34	-0.86 – 0.18	0.195
time [endline] * ppt group [B]				-0.29	-0.72 – 0.13	0.176
Random Effects
σ²	1.89			1.88
τ₀₀	0.63 _{participantID:School}			0.63 _{participantID:School}
	0.20 _School			0.15 _School
ICC	0.31			0.29
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.058 / 0.347			0.080 / 0.351

plot(colnames(dat)[14])

# 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	0.60	0.56 – 0.63	<0.001
GenderChild [Male]	-0.01	-0.04 – 0.02	0.374	-0.01	-0.04 – 0.02	0.411
time [endline]	0.13	0.11 – 0.15	<0.001	0.15	0.12 – 0.17	<0.001
GenderChild [Male] * time [endline]	-0.02	-0.04 – 0.01	0.246	-0.01	-0.04 – 0.01	0.283
ppt group [B]				-0.05	-0.10 – -0.01	0.019
time [endline] * ppt group [B]				-0.02	-0.05 – 0.00	0.083
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	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.160 / 0.688			0.202 / 0.690

plot(colnames(dat)[15])

# 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 – 23.14	<0.001	22.52	20.89 – 24.15	<0.001
GenderChild [Male]	-1.58	-2.73 – -0.43	0.007	-1.56	-2.71 – -0.40	0.008
time [endline]	2.72	1.72 – 3.73	<0.001	2.60	1.32 – 3.88	<0.001
GenderChild [Male] * time [endline]	0.70	-0.71 – 2.12	0.331	0.69	-0.73 – 2.11	0.340
ppt group [B]				-1.22	-3.36 – 0.92	0.263
time [endline] * ppt group [B]				0.22	-1.21 – 1.66	0.761
Random Effects
σ²	21.27			21.33
τ₀₀	6.11 _{participantID:School}			6.10 _{participantID:School}
	3.75 _School			3.63 _School
ICC	0.32			0.31
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.082 / 0.373			0.091 / 0.376

plot(colnames(dat)[16])

# 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	4.31	3.21 – 5.42	<0.001
GenderChild [Male]	-0.26	-0.87 – 0.36	0.414	-0.29	-0.89 – 0.31	0.342
time [endline]	2.59	2.02 – 3.17	<0.001	3.87	3.18 – 4.56	<0.001
GenderChild [Male] * time [endline]	-0.39	-1.19 – 0.42	0.347	-0.27	-1.04 – 0.50	0.493
ppt group [B]				-2.07	-3.59 – -0.54	0.008
time [endline] * ppt group [B]				-2.32	-3.10 – -1.54	<0.001
Random Effects
σ²	6.90			6.26
τ₀₀	0.86 _{participantID:School}			1.18 _{participantID:School}
	4.93 _School			2.33 _School
ICC	0.46			0.36
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.106 / 0.514			0.311 / 0.559

plot(colnames(dat)[17])

# 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 – 13.08	<0.001	12.52	10.60 – 14.45	<0.001
GenderChild [Male]	-2.29	-3.56 – -1.03	<0.001	-2.26	-3.52 – -0.99	<0.001
time [endline]	5.33	4.66 – 6.00	<0.001	5.23	4.37 – 6.08	<0.001
GenderChild [Male] * time [endline]	-0.22	-1.17 – 0.72	0.641	-0.23	-1.18 – 0.71	0.628
ppt group [B]				-1.82	-4.38 – 0.74	0.162
time [endline] * ppt group [B]				0.19	-0.76 – 1.15	0.690
Random Effects
σ²	9.46			9.49
τ₀₀	23.24 _{participantID:School}			23.22 _{participantID:School}
	5.85 _School			5.50 _School
ICC	0.75			0.75
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.177 / 0.798			0.192 / 0.799

plot(colnames(dat)[18])

# 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	5.09	3.64 – 6.53	<0.001
GenderChild [Male]	-0.59	-1.63 – 0.45	0.264	-0.59	-1.62 – 0.45	0.266
time [endline]	4.93	4.20 – 5.67	<0.001	5.88	4.96 – 6.80	<0.001
GenderChild [Male] * time [endline]	-1.31	-2.35 – -0.28	0.013	-1.23	-2.25 – -0.21	0.019
ppt group [B]				-1.43	-3.32 – 0.47	0.139
time [endline] * ppt group [B]				-1.72	-2.75 – -0.69	0.001
Random Effects
σ²	11.35			11.02
τ₀₀	10.84 _{participantID:School}			11.02 _{participantID:School}
	3.98 _School			2.76 _School
ICC	0.57			0.56
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.162 / 0.637			0.209 / 0.649

plot(colnames(dat)[19])

# 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	1.13	0.70 – 1.55	<0.001
GenderChild [Male]	-0.11	-0.41 – 0.19	0.464	-0.11	-0.40 – 0.19	0.484
time [endline]	1.31	1.10 – 1.53	<0.001	1.28	1.00 – 1.56	<0.001
GenderChild [Male] * time [endline]	-0.07	-0.38 – 0.24	0.644	-0.08	-0.38 – 0.23	0.631
ppt group [B]				-0.23	-0.78 – 0.33	0.421
time [endline] * ppt group [B]				0.06	-0.25 – 0.37	0.694
Random Effects
σ²	1.00			1.01
τ₀₀	0.79 _{participantID:School}			0.79 _{participantID:School}
	0.24 _School			0.24 _School
ICC	0.50			0.51
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.170 / 0.589			0.173 / 0.591

plot(colnames(dat)[20])

# 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	1.18	0.77 – 1.59	<0.001
GenderChild [Male]	-0.20	-0.49 – 0.10	0.192	-0.20	-0.49 – 0.10	0.190
time [endline]	0.69	0.45 – 0.92	<0.001	0.78	0.49 – 1.08	<0.001
GenderChild [Male] * time [endline]	0.01	-0.32 – 0.33	0.972	0.01	-0.31 – 0.34	0.928
ppt group [B]				-0.12	-0.65 – 0.42	0.669
time [endline] * ppt group [B]				-0.18	-0.51 – 0.15	0.281
Random Effects
σ²	1.11			1.11
τ₀₀	0.70 _{participantID:School}			0.70 _{participantID:School}
	0.21 _School			0.21 _School
ICC	0.45			0.45
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.060 / 0.482			0.065 / 0.486

plot(colnames(dat)[21])

# 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	3.67 – 5.13	<0.001	4.91	3.94 – 5.87	<0.001
GenderChild [Male]	-0.95	-1.57 – -0.33	0.003	-0.93	-1.55 – -0.31	0.003
time [endline]	2.47	2.12 – 2.82	<0.001	2.25	1.81 – 2.70	<0.001
GenderChild [Male] * time [endline]	-0.30	-0.79 – 0.19	0.233	-0.32	-0.81 – 0.17	0.203
ppt group [B]				-1.01	-2.31 – 0.28	0.126
time [endline] * ppt group [B]				0.39	-0.10 – 0.89	0.120
Random Effects
σ²	2.57			2.56
τ₀₀	5.26 _{participantID:School}			5.26 _{participantID:School}
	1.52 _School			1.45 _School
ICC	0.72			0.72
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.150 / 0.766			0.166 / 0.770

plot(colnames(dat)[22])

# 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	15.39 – 25.86	<0.001	23.62	16.77 – 30.47	<0.001
GenderChild [Male]	-4.33	-8.34 – -0.32	0.034	-4.27	-8.28 – -0.25	0.037
time [endline]	19.96	17.72 – 22.21	<0.001	20.54	17.68 – 23.40	<0.001
GenderChild [Male] * time [endline]	-4.74	-7.90 – -1.57	0.003	-4.69	-7.86 – -1.51	0.004
ppt group [B]				-6.25	-15.56 – 3.05	0.187
time [endline] * ppt group [B]				-1.05	-4.26 – 2.15	0.519
Random Effects
σ²	106.12			106.31
τ₀₀	221.51 _{participantID:School}			221.44 _{participantID:School}
	87.42 _School			80.83 _School
ICC	0.74			0.74
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.178 / 0.790			0.200 / 0.792

plot(colnames(dat)[23])

# 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)	41.24	34.21 – 48.27	<0.001	45.03	35.86 – 54.20	<0.001
GenderChild [Male]	-8.32	-14.56 – -2.09	0.009	-8.20	-14.44 – -1.96	0.010
time [endline]	32.86	29.23 – 36.50	<0.001	33.78	29.16 – 38.41	<0.001
GenderChild [Male] * time [endline]	-3.04	-8.16 – 2.08	0.244	-2.96	-8.08 – 2.17	0.258
ppt group [B]				-7.81	-19.93 – 4.32	0.207
time [endline] * ppt group [B]				-1.68	-6.86 – 3.51	0.526
Random Effects
σ²	277.57			278.08
τ₀₀	518.53 _{participantID:School}			518.32 _{participantID:School}
	129.87 _School			119.49 _School
ICC	0.70			0.70
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.226 / 0.768			0.241 / 0.769

plot(colnames(dat)[24])

# 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)	0.42	0.36 – 0.47	<0.001	0.45	0.39 – 0.52	<0.001
GenderChild [Male]	-0.06	-0.10 – -0.02	0.004	-0.06	-0.10 – -0.02	0.004
time [endline]	0.20	0.18 – 0.22	<0.001	0.22	0.19 – 0.25	<0.001
GenderChild [Male] * time [endline]	-0.02	-0.05 – 0.01	0.209	-0.02	-0.05 – 0.01	0.257
ppt group [B]				-0.08	-0.17 – 0.01	0.065
time [endline] * ppt group [B]				-0.04	-0.07 – -0.01	0.010
Random Effects
σ²	0.01			0.01
τ₀₀	0.03 _{participantID:School}			0.03 _{participantID:School}
	0.01 _School			0.01 _School
ICC	0.80			0.79
N	327 _{participantID}			327 _{participantID}
	20 _School			20 _School
Observations	654			654
Marginal R² / Conditional R²	0.187 / 0.837			0.237 / 0.841

plot(colnames(dat)[25])

# 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 B have lower attendance scores (-7.89) than children in group A. The second model shows that there is a significant relationship between total literacy score. 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 B have lower total literacy scores (averaged over baseline and endline) and children in group B improve less than children in group A. Note that this model explains a bit more variance than model 2 (.791 vs. .796) suggesting that there is a mediation effect of attendance but group also explains some unique variance. In the fourth model this is confirmed as both 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 timen x attendance and time x group. So this would lead to the conclusion that attendance is both a (partial) 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)	69.64	66.57 – 72.70	<0.001	0.25	0.21 – 0.29	<0.001	0.34	0.31 – 0.37	<0.001	0.27	0.23 – 0.31	<0.001	0.25	0.20 – 0.31	<0.001
ppt group [B]	-7.89	-12.23 – -3.54	<0.001				-0.05	-0.10 – -0.01	0.023	-0.05	-0.09 – -0.01	0.024	-0.02	-0.10 – 0.05	0.547
time [endline]				0.11	0.09 – 0.14	<0.001	0.19	0.18 – 0.20	<0.001	0.14	0.12 – 0.17	<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	<0.001
time [endline] * ppt group [B]							-0.05	-0.06 – -0.03	<0.001	-0.05	-0.06 – -0.03	<0.001	0.02	-0.03 – 0.06	0.454
Attendance * ppt group [B]													-0.00	-0.00 – 0.00	0.431
(time [endline] Attendance) ppt group [B]													-0.00	-0.00 – -0.00	0.004
Random Effects
σ²	0.00			0.01			0.01			0.01			0.01
τ₀₀	231.44 _{participantID:School}			0.03 _{participantID:School}			0.03 _{participantID:School}			0.03 _{participantID:School}			0.03 _{participantID:School}
	34.17 _School			0.00 _School			0.00 _School			0.00 _School			0.00 _School
ICC	1.00			0.75			0.75			0.75			0.75
N	1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}			1507 _{participantID}
	30 _School			30 _School			30 _School			30 _School			30 _School
Observations	3014			3014			3014			3014			3014
Marginal R² / Conditional R²	0.055 / 1.000			0.149 / 0.787			0.158 / 0.792			0.179 / 0.793			0.180 / 0.794

CWTL CRCT Analysis

Samantha Bouwmeester

2023-02-15

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)