Commit 5b6f260b authored by Maike Kleemeyer's avatar Maike Kleemeyer
Browse files

Add R-markdown file, Add reliability analyses

parent 736133b5
......@@ -70,10 +70,11 @@ scale_A <- responses_coded %>%
inter_item_A <- scale_A %>%
correlate() %>%
select(-term) %>%
colMeans(na.rm = TRUE)
select(-term)
corr_table_A <- bind_rows(inter_item_A, colMeans(inter_item_A, na.rm = TRUE))
rownames(corr_table_A) <- c(items, "mean")
mean(inter_item_A)
scale_A <- scale_A %>%
mutate(mean_A = rowMeans(., na.rm = TRUE))
......@@ -87,7 +88,12 @@ item_total_A
mean(item_total_A$mean_A)
scale_A$mean_A <- NULL # delete the score column we made earlier
rely_A <- psych::alpha(scale_A, check.keys=FALSE)
rely_A <- psych::alpha(scale_A, use = "complete.obs", check.keys=TRUE)
output_A <- bind_cols(rely_A$item.stats$n, rely_A$item.stats$mean, rely_A$item.stats$sd, rely_A$item.stats$r.drop, rely_A$alpha.drop$raw_alpha)
colnames(output_A) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
psych::alpha(scale_A, check.keys=TRUE)$total$std.alpha
......@@ -96,7 +102,7 @@ scale_B1 <- responses_coded %>%
select(B02:B07)
scale_B2 <- responses_coded %>%
select(B09:B14)
select(B09:B13)
scale_C <- responses_coded %>%
select(C02:C04)
......@@ -129,7 +135,7 @@ scale_H_parentalleave_no <- responses_coded %>%
## OLD R CODE
setwd("~/Seafile/FragebogenMpg/Reanalysen_Feb2020")
file1 = read.csv("20200106_MPG Work Culture_Basic Data_likert.csv",header=TRUE,sep=";")
file1 = read.csv("~/Seafile/FragebogenMpg/Reanalysen_Feb2020/20200106_MPG Work Culture_Basic Data_likert.csv",header=TRUE,sep=";")
library(psych)
library(umx)
......
......@@ -9,381 +9,356 @@ output:
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(ggplot2)
library(dplyr)
library(corrr)
source("get_data_exported.R")
```
# Scale consistency
# Flow of participants
* SCALE A: Group atmosphere
We received BETULA data from 6 waves.
--
* Wave 1 `r nrow(filter(dat, T1_test_wave == 1))` participants (`r nrow(filter(dat, T1_test_wave == 1 & sex == 1))` male).
* Wave 2 `r nrow(filter(dat, T2_test_wave == 2))` participants (`r nrow(filter(dat, T2_test_wave == 2 & sex == 1))` male).
* Wave 3 `r nrow(filter(dat, T3_test_wave == 3))` participants (`r nrow(filter(dat, T3_test_wave == 3 & sex == 1))` male).
* Wave 4 `r nrow(filter(dat, T4_test_wave == 4))` participants (`r nrow(filter(dat, T4_test_wave == 4 & sex == 1))` male).
* Wave 5 `r nrow(filter(dat, T5_test_wave == 5))` participants (`r nrow(filter(dat, T5_test_wave == 5 & sex == 1))` male).
* Wave 6 `r nrow(filter(dat, T6_test_wave == 6))` participants (`r nrow(filter(dat, T6_test_wave == 6 & sex == 1))` male).
```{r echo = FALSE, message = FALSE}
interitem_A <- responses_coded %>%
select(A03:A05) %>%
correlate()
kable(interitem_A, caption = "Interitem correlation scale A")
scale_A <- responses_coded %>%
select(A03:A05)
items <- colnames(scale_A)
rely_A <- psych::alpha(scale_A, use = "complete.obs", check.keys=TRUE)
output_A <- as.data.frame(bind_cols(rely_A$item.stats$n, rely_A$item.stats$mean, rely_A$item.stats$sd, rely_A$item.stats$r.drop, rely_A$alpha.drop$raw_alpha))
colnames(output_A) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_A) <- c(items)
kable(output_A, caption = "Item statistics scale A")
```
Cronbach's alpha for Scale A is `r rely_A$total$std.alpha`.
* SCALE B1: Leadership leadstyle
```{r echo = FALSE, message = FALSE}
interitem_B1 <- responses_coded %>%
select(B02:B07) %>%
correlate()
kable(interitem_B1, caption = "Interitem correlation scale B1")
scale_B1 <- responses_coded %>%
select(B02:B07)
items <- colnames(scale_B1)
rely_B1 <- psych::alpha(scale_B1, use = "complete.obs", check.keys=TRUE)
output_B1 <- as.data.frame(bind_cols(rely_B1$item.stats$n, rely_B1$item.stats$mean, rely_B1$item.stats$sd, rely_B1$item.stats$r.drop, rely_B1$alpha.drop$raw_alpha))
colnames(output_B1) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_B1) <- c(items)
kable(output_B1, caption = "Item statistics scale B1")
```{r echo = FALSE}
data_waves <- data.frame(
waves = c("Total", "TP6", "TP5", "TP4", "TP3", "TP2", "TP1"),
wave1 = c( 1000, 221, 135, 141, 157, 193, 153),
wave2 = c( 2804, 0, 225, 155, 199, 788, 592),
wave2from1 = c( 845, 221, 135, 141, 156, 192, 0),
wave3 = c( 1998, 0, 0, 0, 0, 0, 1),
wave3from2 = c( 1353, 0, 225, 154, 196, 778, 0),
wave3from1 = c( 644, 221, 135, 138, 149, 1, 0),
wave4 = c( 1068, 0, 0, 0, 0, 0, 0),
wave4from2 = c( 572, 0, 225, 154, 185, 8, 0),
wave4from1 = c( 496, 221, 132, 137, 6, 0, 0),
wave5 = c( 1115, 0, 0, 0, 0, 64, 293),
wave5from2 = c( 395, 0, 225, 151, 17, 2, 0),
wave5from1 = c( 363, 221, 133, 6, 3, 0, 0),
wave6 = c( 522, 0, 0, 0, 0, 0, 0),
wave6from5 = c( 64, 0, 0, 0, 0, 64, 0),
wave6from2 = c( 231, 0, 225, 6, 0, 0, 0),
wave6from1 = c( 227, 5, 1, 0, 0, 0, 0),
)
df = data.frame(waves, wave1, wave2, wave2from1, wave3, wave3from2, wave3from1, wave4, wave4from2, wave4from1, wave5, wave5from2, wave5from1, wave6, wave6from5, wave6from2, wave6from1)
```
# Age Distributions
Cronbach's alpha for Scale B1 is `r rely_B1$total$std.alpha`.
* SCALE B2: Leadership development
BETULA has 6 waves. Here is a quick look at the age distribution collapsed across all 6 waves:
```{r echo = FALSE, message = FALSE}
interitem_B2 <- responses_coded %>%
select(B09:B13) %>%
correlate()
```{r cars, echo=FALSE, warning=FALSE}
age <- c(dat$T1_age_T,dat$T2_age_T,dat$T3_age_T,dat$T4_age_T,dat$T5_age_T,dat$T6_age_T)
agedat <- data.frame(age)
pl1 <- ggplot(agedat,aes(x=1,y=age))+geom_jitter(alpha=.2)+geom_boxplot()+theme_minimal()
kable(interitem_B2, caption = "Interitem correlation scale B2")
pl2 <- ggplot(agedat,aes(x=age))+geom_density()+theme_minimal()
scale_B2 <- responses_coded %>%
select(B09:B13)
items <- colnames(scale_B2)
rely_B2 <- psych::alpha(scale_B2, use = "complete.obs", check.keys=TRUE)
output_B2 <- as.data.frame(bind_cols(rely_B2$item.stats$n, rely_B2$item.stats$mean, rely_B2$item.stats$sd, rely_B2$item.stats$r.drop, rely_B2$alpha.drop$raw_alpha))
colnames(output_B2) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_B2) <- c(items)
kable(output_B2, caption = "Item statistics scale B2")
gridExtra::grid.arrange(pl1,pl2, nrow=1)
```
# Sex Distribution
Cronbach's alpha for Scale B2 is `r rely_B2$total$std.alpha`.
* SCALE C: Commitment
```{r echo = FALSE, message = FALSE}
interitem_C <- responses_coded %>%
select(C02:C04) %>%
correlate()
kable(interitem_C, caption = "Interitem correlation scale C")
scale_C <- responses_coded %>%
select(C02:C04)
items <- colnames(scale_C)
rely_C <- psych::alpha(scale_C, use = "complete.obs", check.keys=TRUE)
output_C <- as.data.frame(bind_cols(rely_C$item.stats$n, rely_C$item.stats$mean, rely_C$item.stats$sd, rely_C$item.stats$r.drop, rely_C$alpha.drop$raw_alpha))
colnames(output_C) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_C) <- c(items)
kable(output_C, caption = "Item statistics scale C")
```{r}
ggplot(dat, aes(x=sex))+geom_bar()+theme_minimal()
```
# Dementia Status Distribution
Cronbach's alpha for Scale C is `r rely_C$total$std.alpha`.
* SCALE D: Bullying
```{r echo = FALSE, message = FALSE}
interitem_D <- responses_coded %>%
select(D02:D06) %>%
correlate()
kable(interitem_D, caption = "Interitem correlation scale D")
scale_D <- responses_coded %>%
select(D02:D06)
items <- colnames(scale_D)
rely_D <- psych::alpha(scale_D, use = "complete.obs", check.keys=TRUE)
output_D <- as.data.frame(bind_cols(rely_D$item.stats$n, rely_D$item.stats$mean, rely_D$item.stats$sd, rely_D$item.stats$r.drop, rely_D$alpha.drop$raw_alpha))
colnames(output_D) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_D) <- c(items)
kable(output_D, caption = "Item statistics scale D")
```{r}
ggplot(dat, aes(x=sex))+geom_bar()+theme_minimal()
```
# Available variables
Crystalized intelligence
--
* SRB (v502) total number correct (out of 30) throughout all waves
* BAOTA (baotasum) sum correct answers (out of 26) exists from wave 4
Fluid intelligence
--
* block design (v311) total score throughout all waves
Episodic memory (recall)
--
* sptb (sum score verb and noun; max 16) throughout all waves
Perceptual speed
--
* letter digit (lettd_C; number of correct answers; max 125) exists from wave 3
# Distribution of variables seperated by waves
SRB (Crystalized intelligence)
--
```{r fig.width = 3.5, echo=FALSE, warning=FALSE}
hist(dat$T1_v502 [dat$T1_test_wave == 1],
xlim = c(0,30),
breaks = 10,
col = "red",
main = "wave 1",
xlab = "number correct")
hist(dat$T2_v502 [dat$T2_test_wave == 2],
xlim = c(0,30),
breaks = 10,
col = "orange",
main = "wave 2",
xlab = "number correct")
hist(dat$T3_v502 [dat$T3_test_wave == 3],
xlim = c(0,30),
breaks = 10,
col = "brown",
main = "wave 3",
xlab = "number correct")
hist(dat$T4_v502 [dat$T4_test_wave == 4],
xlim = c(0,30),
breaks = 10,
col = "blue",
main = "wave 4",
xlab = "number correct")
hist(dat$T5_v502 [dat$T5_test_wave == 5],
xlim = c(0,30),
breaks = 10,
col = "purple",
main = "wave 5",
xlab = "number correct")
hist(dat$T6_v502 [dat$T6_test_wave == 6],
xlim = c(0,30),
breaks = 10,
col = "green",
main = "wave 6",
xlab = "number correct")
Cronbach's alpha for Scale D is `r rely_D$total$std.alpha`.
* SCALE E: Gender discrimination
```{r echo = FALSE, message = FALSE}
interitem_E <- responses_coded %>%
select(E03:E05) %>%
correlate()
kable(interitem_E, caption = "Interitem correlation scale E")
scale_E <- responses_coded %>%
select(E03:E05)
items <- colnames(scale_E)
rely_E <- psych::alpha(scale_E, use = "complete.obs", check.keys=TRUE)
output_E <- as.data.frame(bind_cols(rely_E$item.stats$n, rely_E$item.stats$mean, rely_E$item.stats$sd, rely_E$item.stats$r.drop, rely_E$alpha.drop$raw_alpha))
colnames(output_E) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_E) <- c(items)
kable(output_E, caption = "Item statistics scale E")
```
Baota (Crystalized intelligence)
--
Cronbach's alpha for Scale E is `r rely_E$total$std.alpha`.
* SCALE F: Sexual harassment
```{r echo = FALSE, message = FALSE}
interitem_F <- responses_coded %>%
select(F02:F08) %>%
correlate()
kable(interitem_F, caption = "Interitem correlation scale F")
```{r fig.width = 3.5, echo=FALSE, warning=FALSE}
scale_F <- responses_coded %>%
select(F02:F08)
hist(dat$T4_baotasum [dat$T4_test_wave == 4],
xlim = c(0,25),
breaks = 10,
col = "blue",
main = "wave 4",
xlab = "number correct")
items <- colnames(scale_F)
hist(dat$T5_baotasum [dat$T5_test_wave == 5],
xlim = c(0,25),
breaks = 10,
col = "purple",
main = "wave 5",
xlab = "number correct")
rely_F <- psych::alpha(scale_F, use = "complete.obs", check.keys=TRUE)
hist(dat$T6_baotasum [dat$T6_test_wave == 6],
xlim = c(0,25),
breaks = 10,
col = "green",
main = "wave 6",
xlab = "number correct")
output_F <- as.data.frame(bind_cols(rely_F$item.stats$n, rely_F$item.stats$mean, rely_F$item.stats$sd, rely_F$item.stats$r.drop, rely_F$alpha.drop$raw_alpha))
colnames(output_F) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_F) <- c(items)
kable(output_F, caption = "Item statistics scale F")
```
Block design (Fluid intelligence)
--
```{r fig.width = 3.5, echo=FALSE, warning=FALSE}
hist(dat$T1_v311 [dat$T1_test_wave == 1],
xlim = c(0,51),
breaks = 10,
col = "red",
main = "wave 1",
xlab = "total score")
hist(dat$T2_v311 [dat$T2_test_wave == 2],
xlim = c(0,51),
breaks = 10,
col = "orange",
main = "wave 2",
xlab = "total score")
hist(dat$T3_v311 [dat$T3_test_wave == 3],
xlim = c(0,51),
breaks = 10,
col = "brown",
main = "wave 3",
xlab = "total score")
hist(dat$T4_v311 [dat$T4_test_wave == 4],
xlim = c(0,51),
breaks = 10,
col = "blue",
main = "wave 4",
xlab = "total score")
hist(dat$T5_v311 [dat$T5_test_wave == 5],
xlim = c(0,51),
breaks = 10,
col = "purple",
main = "wave 5",
xlab = "total score")
hist(dat$T6_v311 [dat$T6_test_wave == 6],
xlim = c(0,51),
breaks = 10,
col = "green",
main = "wave 6",
xlab = "total score")
Cronbach's alpha for Scale F is `r rely_F$total$std.alpha`.
* SCALE H1: Work-life Balance
```{r echo = FALSE, message = FALSE}
interitem_H1 <- responses_coded %>%
select(H02:H06) %>%
correlate()
kable(interitem_H1, caption = "Interitem correlation scale H1")
scale_H1 <- responses_coded %>%
select(H02:H06)
items <- colnames(scale_H1)
rely_H1 <- psych::alpha(scale_H1, use = "complete.obs", check.keys=TRUE)
output_H1 <- as.data.frame(bind_cols(rely_H1$item.stats$n, rely_H1$item.stats$mean, rely_H1$item.stats$sd, rely_H1$item.stats$r.drop, rely_H1$alpha.drop$raw_alpha))
colnames(output_H1) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_H1) <- c(items)
kable(output_H1, caption = "Item statistics scale H1")
```
Sptb (Episodic memory)
--
```{r fig.width = 3.5, echo=FALSE, warning=FALSE}
hist(dat$T1_sptb [dat$T1_test_wave == 1],
xlim = c(0,16),
breaks = 10,
col = "red",
main = "wave 1",
xlab = "sum score")
hist(dat$T2_sptb [dat$T2_test_wave == 2],
xlim = c(0,16),
breaks = 10,
col = "orange",
main = "wave 2",
xlab = "sum score")
hist(dat$T3_sptb [dat$T3_test_wave == 3],
xlim = c(0,16),
breaks = 10,
col = "brown",
main = "wave 1",
xlab = "sum score")
hist(dat$T4_sptb [dat$T4_test_wave == 4],
xlim = c(0,16),
breaks = 10,
col = "blue",
main = "wave 4",
xlab = "sum score")
hist(dat$T5_sptb [dat$T5_test_wave == 5],
xlim = c(0,16),
breaks = 10,
col = "purple",
main = "wave 5",
xlab = "sum score")
hist(dat$T6_sptb [dat$T6_test_wave == 6],
xlim = c(0,16),
breaks = 10,
col = "green",
main = "wave 6",
xlab = "sum score")
Cronbach's alpha for Scale H1 is `r rely_H1$total$std.alpha`.
* SCALE H2: Work-family Balance
```{r echo = FALSE, message = FALSE}
interitem_H2 <- responses_coded %>%
select(H09:H13) %>%
correlate()
kable(interitem_H2, caption = "Interitem correlation scale H2")
scale_H2 <- responses_coded %>%
select(H09:H13)
items <- colnames(scale_H2)
rely_H2 <- psych::alpha(scale_H2, use = "complete.obs", check.keys=TRUE)
output_H2 <- as.data.frame(bind_cols(rely_H2$item.stats$n, rely_H2$item.stats$mean, rely_H2$item.stats$sd, rely_H2$item.stats$r.drop, rely_H2$alpha.drop$raw_alpha))
colnames(output_H2) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_H2) <- c(items)
kable(output_H2, caption = "Item statistics scale H2")
```
Letter digit (Perceptual speed)
--
```{r fig.width = 4, echo=FALSE, warning=FALSE}
hist(dat$T3_lettd_C [dat$T3_test_wave == 3],
xlim = c(0,55),
breaks = 10,
col = "brown",
main = "wave 3",
xlab = "number correct")
hist(dat$T4_lettd_C [dat$T4_test_wave == 4],
xlim = c(0,55),
breaks = 10,
col = "blue",
main = "wave 4",
xlab = "number correct")
hist(dat$T5_lettd_C [dat$T5_test_wave == 5],
xlim = c(0,55),
breaks = 10,
col = "purple",
main = "wave 5",
xlab = "number correct")
hist(dat$T6_lettd_C [dat$T6_test_wave == 6],
xlim = c(0,55),
breaks = 10,
col = "green",
main = "wave 6",
xlab = "number correct")
Cronbach's alpha for Scale H2 is `r rely_H2$total$std.alpha`.
* SCALE H3: Parental leave - yes
```{r echo = FALSE, message = FALSE}
interitem_H3 <- responses_coded %>%
select(H17:H21) %>%
correlate()
kable(interitem_H3, caption = "Interitem correlation scale H3")
scale_H3 <- responses_coded %>%
select(H17:H21)
items <- colnames(scale_H3)
rely_H3 <- psych::alpha(scale_H3, use = "complete.obs", check.keys=TRUE)
output_H3 <- as.data.frame(bind_cols(rely_H3$item.stats$n, rely_H3$item.stats$mean, rely_H3$item.stats$sd, rely_H3$item.stats$r.drop, rely_H3$alpha.drop$raw_alpha))
colnames(output_H3) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_H3) <- c(items)
kable(output_H3, caption = "Item statistics scale H3")
```
Cronbach's alpha for Scale H3 is `r rely_H3$total$std.alpha`.
# Longitudinal change
* SCALE H4: Parental leave - no
```{r echo = FALSE, message = FALSE}
interitem_H4 <- responses_coded %>%
select(H23:H25) %>%
correlate()
```{r pressure, echo=FALSE, warning=FALSE}
kable(interitem_H4, caption = "Interitem correlation scale H4")
age <- c(dat$T1_age_T,dat$T2_age_T,dat$T3_age_T,dat$T4_age_T,dat$T5_age_T,dat$T6_age_T)
scale_H4 <- responses_coded %>%
select(H23:H25)
# gather longitrudinal 'crystallized' SRB scores
srb <- c(dat$T1_v502,dat$T2_v502,dat$T3_v502,dat$T4_v502,dat$T5_v502,dat$T6_v502)
tp <- rep(1:6,each=nrow(dat))
id <- rep(1:nrow(dat),6)
items <- colnames(scale_H4)
# and plot them
longdat <- data.frame(srb,tp,id,age)
mns <- summarise(group_by(longdat, age),disp=mean(srb,na.rm=TRUE))
rely_H4 <- psych::alpha(scale_H4, use = "complete.obs", check.keys=TRUE)
pl1 <- ggplot(data=longdat,aes(x=age,y=srb,group=id))+geom_line()+theme_minimal()+ggplot2::ggtitle("SRB (Crystallized)")+
geom_smooth(data=mns,aes(x=age,y=disp,group=1),method="lm")+
xlab("Age")+ylab("Score")
output_H4 <- as.data.frame(bind_cols(rely_H4$item.stats$n, rely_H4$item.stats$mean, rely_H4$item.stats$sd, rely_H4$item.stats$r.drop, rely_H4$alpha.drop$raw_alpha))
colnames(output_H4) <- c("total", "mean", "SD", "Corrected Item total r", "Alpha if item deleted")
rownames(output_H4) <- c(items)
# those are not meaningful
#mean(dat$T1_v502,na.rm=TRUE)
#mean(dat$T2_v502,na.rm=TRUE)
#mean(dat$T3_v502,na.rm=TRUE)
#mean(dat$T4_v502,na.rm=TRUE)
#mean(dat$T5_v502,na.rm=TRUE)
#mean(dat$T6_v502,na.rm=TRUE)
kable(output_H4, caption = "Item statistics scale H4")
```
# gather longitudinal 'crystallized' BAOTA scores
age <- c(dat$T4_age_T,dat$T5_age_T,dat$T6_age_T)
baota <- c(dat$T4_baotasum, dat$T5_baotasum, dat$T6_baotasum)
tp <- rep(4:6,each=nrow(dat))
id <- rep(1:nrow(dat),3)
Cronbach's alpha for Scale H4 is `r rely_H4$total$std.alpha`.
longdat <- data.frame(baota,tp,id,age)
mns <- summarise(group_by(longdat, age),disp=mean(baota,na.rm=TRUE))
# Analyses
pl2 <- ggplot(data=longdat,aes(x=age,y=baota,group=id))+geom_line()+theme_minimal()+ggtitle("BAOTA (Crystallized)")+
geom_smooth(data=mns,aes(x=age,y=disp,group=1),method="lm")+
xlab("Age")+ylab("Score")
In total, `r nrow(responses)` participants (partly) completed the questionnaire.
# fluid
# Binary Questions
age <- c(dat$T1_age_T,dat$T2_age_T,dat$T3_age_T,dat$T4_age_T,dat$T5_age_T,dat$T6_age_T)
fld <- c(dat$T1_v311,dat$T2_v311, dat$T3_v311, dat$T4_v311, dat$T5_v311, dat$T6_v311)
tp <- rep(1:6,each=nrow(dat))
id <- rep(1:nrow(dat),6)
longdat <- data.frame(fld,tp,id,age)
mns <- summarise(group_by(longdat, age),disp=mean(fld,na.rm=TRUE))
* Do you work in a group?
```{r echo = FALSE}
table(responses$A01, useNA = "ifany")
```
pl3 <- ggplot(data=longdat,aes(x=age,y=fld,group=id))+geom_line()+theme_minimal()+ ggtitle("Block Design (Fluid)")+
geom_smooth(data=mns,aes(x=age,y=disp,group=1),method="lm")+
xlab("Age")+ylab("Score")