# install load packages 
library(knitr)
library(tidyr)
library(stats)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)

Typical Sight Word Sample Response

# Load Data 
T_Sight_Child <- read.csv("T_SightWord.csv") 
T_Sight_Seq<- read.csv("T_Sight_WordSeq.csv")
#Data from Typical children sight word test
kable(head(T_Sight_Child[, 2:50]), "simple")
Total_Correct_Sight see up me no the is I like go red home teacher want big make help who should red.1 little brown think play and purple said can eat house read big.1 you around will morning me.1 him why come is.1 spell many up.1 birthday thank because her always
34 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0
29 1 1 1 0 1 1 1 0 1 1 1 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 0 1 0 1 0
16 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0
15 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0
54 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
24 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0

Correlation matrix for point biserial correlation

Typical Sample Sight Word

TS_Cor<- cor(T_Sight_Child )
## Warning in cor(T_Sight_Child): the standard deviation is zero
kable(TS_Cor[2, 2:50], "pipe")
x
Total_Correct_Sight 1.0000000
see 0.1980398
up NA
me 0.0653629
no 0.4899260
the NA
is NA
I NA
like 0.0321937
go 0.5023948
red 0.5275887
home 0.7025234
teacher 0.8302064
want 0.6109571
big 0.5569095
make 0.6994867
help 0.5587616
who 0.7346680
should 0.6024676
red.1 0.4963511
little 0.7350949
brown 0.6504912
think 0.6950564
play 0.4897979
and -0.0120226
purple 0.6408512
said 0.6183899
can 0.3286187
eat 0.7063980
house 0.6109571
read 0.7371061
big.1 0.5084040
you 0.3141321
around 0.8302064
will 0.5787666
morning 0.7230895
me.1 0.3286187
him 0.5123687
why 0.6024676
come 0.7090703
is.1 -0.0574415
spell 0.6565218
many 0.8302064
up.1 -0.0574415
birthday 0.7090730
thank 0.7230895
because 0.4675680
her 0.6265231
always 0.8302064

Sight word ordered by mean correct number (Typical Sample)

Typical Child Sight Words Seq.

Clinical Sight Word Sample Response

# Load Data 
C_Sight_Child <- read.csv("C_SightWord.csv") 
C_Sight_Seq<- read.csv("C_Sight_WordSeq.csv")
#Data from Typical children sight word test
kable(head(C_Sight_Child[, 2:50]), "simple")
Total_Correct_Sight see up me no the is I like go red home teacher want big make help who should red.1 little brown think play and purple said can eat house read big.1 you around will morning me.1 him why come is.1 spell many up.1 birthday thank because her always
17 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
16 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0
5 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Correlation matrix for point biserial correlation

Clinical Sample Sight-Word

CS_Cor<- cor(C_Sight_Child )
## Warning in cor(C_Sight_Child): the standard deviation is zero
kable(CS_Cor[2, 2:50], "pipe")
x
Total_Correct_Sight 1.0000000
see NA
up 0.9971765
me 0.9971765
no NA
the NA
is 0.9971765
I NA
like 0.9971765
go 0.5636215
red NA
home NA
teacher NA
want 0.5636215
big NA
make NA
help 0.5636215
who 0.5636215
should NA
red.1 0.5636215
little 0.5636215
brown NA
think NA
play 0.5636215
and 0.5636215
purple NA
said NA
can 0.5636215
eat NA
house 0.4335550
read NA
big.1 NA
you -0.5636215
around NA
will 0.4335550
morning NA
me.1 0.4335550
him NA
why NA
come NA
is.1 0.4335550
spell NA
many NA
up.1 0.4335550
birthday NA
thank NA
because NA
her NA
always NA

Sight word ordered by mean correct number (Clinical Sample)

Clinical Child Sight Words Seq.

Typical Non-Word Sample Response

# Load Data 
T_NonW_Child <- read.csv("T_NonWord.csv") 
T_NonW_Seq<- read.csv("T_Non_word_seq.csv")
#Data from Typical children Non word test
kable(head(T_NonW_Child[, 2:50]), "simple")
Total_Correct_NonWord ap bi mo ip ket mip bo tib mot dag kath jart mi pid bloss mup flep bert bik mot.1 teck skime marp mi.1 fep bo.1 nesk thup voast pid.1 dag.1 shap fipe nuck tib.1 hool piv hust zet trull dreat ip.1 nuck.1 thup.1 zube prent blim bik.1
18 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1
26 0 1 1 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0
21 1 0 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1
15 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0
54 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
16 1 0 1 1 0 0 0 1 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0

Correlation matrix for point biserial correlation

Typical Sample Non-Word

TN_Cor<- cor(T_NonW_Child )
kable(TN_Cor[2, 2:50], "pipe")
x
Total_Correct_NonWord 1.0000000
ap -0.0812088
bi 0.5661369
mo 0.3097435
ip 0.1631364
ket 0.5694770
mip 0.4046588
bo 0.6655946
tib 0.5219920
mot 0.2853090
dag 0.4857654
kath 0.8386790
jart 0.8386790
mi 0.5624016
pid 0.5721476
bloss 0.7304836
mup 0.4196988
flep 0.6817182
bert 0.5576820
bik 0.6208776
mot.1 0.3190363
teck 0.6655946
skime 0.8205711
marp 0.7287236
mi.1 0.1698649
fep 0.6070695
bo.1 0.4414366
nesk 0.4721612
thup 0.4120677
voast 0.8205711
pid.1 0.3944371
dag.1 0.2524882
shap 0.7810576
fipe 0.8205711
nuck 0.7208921
tib.1 0.6339650
hool 0.5987253
piv 0.3331069
hust 0.6316649
zet 0.6925018
trull 0.5970671
dreat 0.8205711
ip.1 0.2346918
nuck.1 0.5945273
thup.1 0.8386790
zube 0.8205711
prent 0.8205711
blim 0.4744068
bik.1 0.5884073

Non-word ordered by mean correct number (Typical Sample)

Typical Child NoN- Word Seq.

Clinical Non-Word Sample Response

# Load Data 
C_NonW_Child_1<- read.csv("C_NonWord.csv") 
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## incomplete final line found by readTableHeader on 'C_NonWord.csv'
C_NonW_Child<- head(C_NonW_Child_1, -1)
C_NonW_Seq<- read.csv("C_Non_word_Seq.csv")
#Data from Typical children sight word test
kable(head(C_NonW_Child[, 2:50]), "simple")
Total.Correct.for.Nonsense ap bi mo ip ket mip bo tib mot dag kath jart mi pid bloss mup flep bert bik mot.1 teck skime marp mi.1 fep bo.1 nesk thup voast pid.1 dag.1 shap fipe nuck tib.1 hool piv hust zet trull dreat ip.1 nuck.1 thup.1 zube prent blim bik.1
3 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Correlation matrix for point biserial correlation

Clinical Sample Non Word

CN_Cor<- cor(C_NonW_Child )
## Warning in cor(C_NonW_Child): the standard deviation is zero
kable(CN_Cor[2, 2:50], "pipe")
x
Total.Correct.for.Nonsense 1
ap 1
bi NA
mo 1
ip 1
ket NA
mip NA
bo NA
tib NA
mot NA
dag NA
kath NA
jart NA
mi NA
pid NA
bloss NA
mup NA
flep NA
bert NA
bik NA
mot.1 NA
teck NA
skime NA
marp NA
mi.1 NA
fep NA
bo.1 NA
nesk NA
thup NA
voast NA
pid.1 NA
dag.1 NA
shap NA
fipe NA
nuck NA
tib.1 NA
hool NA
piv NA
hust NA
zet NA
trull NA
dreat NA
ip.1 NA
nuck.1 NA
thup.1 NA
zube NA
prent NA
blim NA
bik.1 NA

Non-word ordered by mean correct number (Clinical Sample)

Clinical Child NoN- Word Seq.

Differenrence in Mean of Sight Word Response (Typical Sample Vs. Clinical Sample)

Sight Word Differenrence in Mean (Typical Vs.Clinical)

Differenrence in Mean of Non-Word Response (Typical Sample Vs. Clinical Sample)

Non-Word Differenrence in Mean (Typical Vs.Clinical)