0503 HW
CJX
2022-05-03
library(moments)library(psych)library(lavaan)library(reshape2)library(tidyverse)data(bfi)轉換反向題
keys <-
list(agree=c("-A1","A2","A3","A4","A5"),conscientious=c("C1","C2","C3","-C4","-C5"),
extraversion=c("-E1","-E2","E3","E4","E5"),neuroticism=c("N1","N2","N3","N4","N5"),
openness = c("O1","-O2","O3","O4","-O5"))head(keys)## $agree
## [1] "-A1" "A2" "A3" "A4" "A5"
##
## $conscientious
## [1] "C1" "C2" "C3" "-C4" "-C5"
##
## $extraversion
## [1] "-E1" "-E2" "E3" "E4" "E5"
##
## $neuroticism
## [1] "N1" "N2" "N3" "N4" "N5"
##
## $openness
## [1] "O1" "-O2" "O3" "O4" "-O5"head(bfi$A1)## [1] 2 2 5 4 2 6bfi[, c(1, 9, 10, 11, 12, 22, 25)] = 7 - bfi[, c(1, 9, 10, 11, 12, 22, 25)]head(bfi$A1)## [1] 5 5 2 3 5 1看看資料結構
str(bfi)## 'data.frame': 2800 obs. of 28 variables:
## $ A1 : num 5 5 2 3 5 1 5 3 3 5 ...
## $ A2 : int 4 4 4 4 3 6 5 3 3 5 ...
## $ A3 : int 3 5 5 6 3 5 5 1 6 6 ...
## $ A4 : int 4 2 4 5 4 6 3 5 3 6 ...
## $ A5 : int 4 5 4 5 5 5 5 1 3 5 ...
## $ C1 : int 2 5 4 4 4 6 5 3 6 6 ...
## $ C2 : int 3 4 5 4 4 6 4 2 6 5 ...
## $ C3 : int 3 4 4 3 5 6 4 4 3 6 ...
## $ C4 : num 3 4 5 2 4 6 5 5 3 5 ...
## $ C5 : num 3 3 2 2 5 4 4 3 2 6 ...
## $ E1 : num 4 6 5 2 5 5 3 4 2 5 ...
## $ E2 : num 4 6 3 4 5 6 4 1 4 5 ...
## $ E3 : int 3 6 4 4 5 6 4 4 NA 4 ...
## $ E4 : int 4 4 4 4 4 5 5 2 4 5 ...
## $ E5 : int 4 3 5 4 5 6 5 1 3 5 ...
## $ N1 : int 3 3 4 2 2 3 1 6 5 5 ...
## $ N2 : int 4 3 5 5 3 5 2 3 5 5 ...
## $ N3 : int 2 3 4 2 4 2 2 2 2 5 ...
## $ N4 : int 2 5 2 4 4 2 1 6 3 2 ...
## $ N5 : int 3 5 3 1 3 3 1 4 3 4 ...
## $ O1 : int 3 4 4 3 3 4 5 3 6 5 ...
## $ O2 : num 1 5 5 4 4 4 5 5 1 6 ...
## $ O3 : int 3 4 5 4 4 5 5 4 6 5 ...
## $ O4 : int 4 3 5 3 3 6 6 5 6 5 ...
## $ O5 : num 4 4 5 2 4 6 6 4 6 5 ...
## $ gender : int 1 2 2 2 1 2 1 1 1 2 ...
## $ education: int NA NA NA NA NA 3 NA 2 1 NA ...
## $ age : int 16 18 17 17 17 21 18 19 19 17 ...head(bfi)## A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4
## 61617 5 4 3 4 4 2 3 3 3 3 4 4 3 4 4 3 4 2 2 3 3 1 3 4
## 61618 5 4 5 2 5 5 4 4 4 3 6 6 6 4 3 3 3 3 5 5 4 5 4 3
## 61620 2 4 5 4 4 4 5 4 5 2 5 3 4 4 5 4 5 4 2 3 4 5 5 5
## 61621 3 4 6 5 5 4 4 3 2 2 2 4 4 4 4 2 5 2 4 1 3 4 4 3
## 61622 5 3 3 4 5 4 4 5 4 5 5 5 5 4 5 2 3 4 4 3 3 4 4 3
## 61623 1 6 5 6 5 6 6 6 6 4 5 6 6 5 6 3 5 2 2 3 4 4 5 6
## O5 gender education age
## 61617 4 1 NA 16
## 61618 4 2 NA 18
## 61620 5 2 NA 17
## 61621 2 2 NA 17
## 61622 4 1 NA 17
## 61623 6 2 3 21summary(bfi)## A1 A2 A3 A4 A5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.0 1st Qu.:4.00
## Median :5.000 Median :5.000 Median :5.000 Median :5.0 Median :5.00
## Mean :4.587 Mean :4.802 Mean :4.604 Mean :4.7 Mean :4.56
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.0 3rd Qu.:5.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.0 Max. :6.00
## NA's :16 NA's :27 NA's :26 NA's :19 NA's :16
## C1 C2 C3 C4 C5
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:2.000
## Median :5.000 Median :5.00 Median :5.000 Median :5.000 Median :4.000
## Mean :4.502 Mean :4.37 Mean :4.304 Mean :4.447 Mean :3.703
## 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :21 NA's :24 NA's :20 NA's :26 NA's :16
## E1 E2 E3 E4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :4.026 Mean :3.858 Mean :4.001 Mean :4.422
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :23 NA's :16 NA's :25 NA's :9
## E5 N1 N2 N3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :5.000 Median :3.000 Median :4.000 Median :3.000
## Mean :4.416 Mean :2.929 Mean :3.508 Mean :3.217
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :21 NA's :22 NA's :21 NA's :11
## N4 N5 O1 O2 O3
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000
## Median :3.000 Median :3.00 Median :5.000 Median :5.000 Median :5.000
## Mean :3.186 Mean :2.97 Mean :4.816 Mean :4.287 Mean :4.438
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :36 NA's :29 NA's :22 NA's :28
## O4 O5 gender education age
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.00 Min. : 3.00
## 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:1.000 1st Qu.:3.00 1st Qu.:20.00
## Median :5.000 Median :5.00 Median :2.000 Median :3.00 Median :26.00
## Mean :4.892 Mean :4.51 Mean :1.672 Mean :3.19 Mean :28.78
## 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:2.000 3rd Qu.:4.00 3rd Qu.:35.00
## Max. :6.000 Max. :6.00 Max. :2.000 Max. :5.00 Max. :86.00
## NA's :14 NA's :20 NA's :223bfi <- bfi[, 1:25]sum(is.na(bfi))## [1] 508看看基本統計量
my_summary <- function(x) {
require(moments)
funs <- c(mean, sd, skewness, kurtosis)
sapply(funs, function(f) f(x, na.rm = T))
}bfi_desc <- apply(bfi, 2, my_summary)head(bfi_desc)## A1 A2 A3 A4 A5 C1 C2
## [1,] 4.5865661 4.802380 4.603821 4.699748 4.560345 4.5023390 4.3699568
## [2,] 1.4077372 1.172020 1.301834 1.479633 1.258512 1.2413465 1.3183465
## [3,] -0.8254883 -1.124894 -0.998997 -1.031499 -0.847690 -0.8551631 -0.7422207
## [4,] 2.6942957 4.057765 3.444524 3.042640 3.161176 3.3068088 2.8656243
## C3 C4 C5 E1 E2 E3
## [1,] 4.3039568 4.4466474 3.70330460 4.0255672 3.8581178 4.0007207
## [2,] 1.2885518 1.3751181 1.62854187 1.6315055 1.6052103 1.3527188
## [3,] -0.6918287 -0.5964955 -0.06620282 -0.3736569 -0.2209396 -0.4706335
## [4,] 2.8697332 2.3802970 1.78461246 1.9090390 1.8526925 2.5367154
## E4 E5 N1 N2 N3 N4 N5
## [1,] 4.4224292 4.416337 2.9290857 3.50773660 3.2165651 3.1856006 2.9696860
## [2,] 1.4575174 1.334768 1.5709175 1.52594359 1.6029021 1.5696851 1.6186474
## [3,] -0.8241831 -0.777486 0.3714298 -0.07698521 0.1506797 0.1969966 0.3744599
## [4,] 2.6977079 2.908401 1.9885722 1.95035250 1.8227046 1.9090309 1.9401121
## O1 O2 O3 O4 O5
## [1,] 4.8160547 4.286786 4.4383117 4.892319 4.5104317
## [2,] 1.1295303 1.565152 1.2209011 1.221250 1.3279590
## [3,] -0.8973669 -0.585679 -0.7730516 -1.218247 -0.7384818
## [4,] 3.4277033 2.188889 3.3043641 4.082686 2.7630094rownames(bfi_desc) <- c("mean", "sd", "skewness", "kurtosis")rslt1 <- as.data.frame(t(bfi_desc))
rslt1 |> knitr::kable()| mean | sd | skewness | kurtosis | |
|---|---|---|---|---|
| A1 | 4.586566 | 1.407737 | -0.8254883 | 2.694296 |
| A2 | 4.802380 | 1.172020 | -1.1248938 | 4.057765 |
| A3 | 4.603821 | 1.301834 | -0.9989970 | 3.444524 |
| A4 | 4.699748 | 1.479633 | -1.0314991 | 3.042640 |
| A5 | 4.560345 | 1.258512 | -0.8476900 | 3.161176 |
| C1 | 4.502339 | 1.241346 | -0.8551631 | 3.306809 |
| C2 | 4.369957 | 1.318347 | -0.7422207 | 2.865624 |
| C3 | 4.303957 | 1.288552 | -0.6918287 | 2.869733 |
| C4 | 4.446647 | 1.375118 | -0.5964955 | 2.380297 |
| C5 | 3.703305 | 1.628542 | -0.0662028 | 1.784612 |
| E1 | 4.025567 | 1.631506 | -0.3736569 | 1.909039 |
| E2 | 3.858118 | 1.605210 | -0.2209396 | 1.852693 |
| E3 | 4.000721 | 1.352719 | -0.4706335 | 2.536715 |
| E4 | 4.422429 | 1.457517 | -0.8241831 | 2.697708 |
| E5 | 4.416337 | 1.334768 | -0.7774860 | 2.908401 |
| N1 | 2.929086 | 1.570917 | 0.3714298 | 1.988572 |
| N2 | 3.507737 | 1.525944 | -0.0769852 | 1.950352 |
| N3 | 3.216565 | 1.602902 | 0.1506797 | 1.822705 |
| N4 | 3.185601 | 1.569685 | 0.1969966 | 1.909031 |
| N5 | 2.969686 | 1.618647 | 0.3744599 | 1.940112 |
| O1 | 4.816055 | 1.129530 | -0.8973669 | 3.427703 |
| O2 | 4.286786 | 1.565152 | -0.5856790 | 2.188889 |
| O3 | 4.438312 | 1.220901 | -0.7730516 | 3.304364 |
| O4 | 4.892319 | 1.221250 | -1.2182471 | 4.082686 |
| O5 | 4.510432 | 1.327959 | -0.7384818 | 2.763009 |
bfil_desc <- melt(bfi_desc)names(bfil_desc)[1:2] <- c("moments", "items")head(bfil_desc)## moments items value
## 1 mean A1 4.5865661
## 2 sd A1 1.4077372
## 3 skewness A1 -0.8254883
## 4 kurtosis A1 2.6942957
## 5 mean A2 4.8023801
## 6 sd A2 1.1720199ggplot(data = subset(bfil_desc, moments == "mean"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 2) +
geom_hline(yintercept = mean(t(bfi_desc["mean",])) +
c(-1.5, 0, 1.5) * sd(t(bfi_desc["mean", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "mean") +
theme_bw()
ggplot(data = subset(bfil_desc, moments == "skewness"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 2) +
geom_hline(yintercept = mean(t(bfi_desc["skewness",])) +
c(-1.5, 0, 1.5) * sd(t(bfi_desc["skewness", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "skewness") +
theme_bw()
ggplot(data = subset(bfil_desc, moments == "kurtosis"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 2) +
geom_hline(yintercept = mean(t(bfi_desc["kurtosis",])) +
c(-1.5, 0, 1.5) * sd(t(bfi_desc["kurtosis", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "kurtosis") +
theme_bw()
去除遺漏值
nabfi <- na.omit(bfi)鑑別度指標
計算區變度，以總分為主，選取低分組與高分組，比較各題在兩組上的差異。
nabfi$tot <- apply(nabfi, 1, sum)nabfi$grp <- NAnabfi$grp[rank(nabfi$tot) < 2800*.27] <- "L"
nabfi$grp[rank(nabfi$tot) > 2800*.73] <- "H"
nabfi$grp <- factor(nabfi$grp)
head(nabfi)## A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4
## 61617 5 4 3 4 4 2 3 3 3 3 4 4 3 4 4 3 4 2 2 3 3 1 3 4
## 61618 5 4 5 2 5 5 4 4 4 3 6 6 6 4 3 3 3 3 5 5 4 5 4 3
## 61620 2 4 5 4 4 4 5 4 5 2 5 3 4 4 5 4 5 4 2 3 4 5 5 5
## 61621 3 4 6 5 5 4 4 3 2 2 2 4 4 4 4 2 5 2 4 1 3 4 4 3
## 61622 5 3 3 4 5 4 4 5 4 5 5 5 5 4 5 2 3 4 4 3 3 4 4 3
## 61623 1 6 5 6 5 6 6 6 6 4 5 6 6 5 6 3 5 2 2 3 4 4 5 6
## O5 tot grp
## 61617 4 82 L
## 61618 4 105 <NA>
## 61620 5 102 <NA>
## 61621 2 86 L
## 61622 4 100 <NA>
## 61623 6 119 H算高低分組平均數
bfim <- aggregate(nabfi[, 1:25], by=list(nabfi$grp), mean)
print(bfim)## Group.1 A1 A2 A3 A4 A5 C1 C2
## 1 H 5.146341 5.569106 5.533875 5.455285 5.430894 5.284553 5.235772
## 2 L 4.208556 4.117647 3.796791 4.018717 3.848930 3.911765 3.676471
## C3 C4 C5 E1 E2 E3 E4 E5
## 1 4.95122 5.268293 4.512195 4.883469 4.859079 5.102981 5.246612 5.371274
## 2 3.76738 3.795455 3.016043 3.106952 2.937166 3.097594 3.518717 3.493316
## N1 N2 N3 N4 N5 O1 O2 O3
## 1 3.368564 3.981030 3.785908 3.311653 3.279133 5.479675 4.989160 5.346883
## 2 2.680481 3.258021 2.891711 3.258021 2.739305 4.287433 3.902406 3.754011
## O4 O5
## 1 5.447154 5.211382
## 2 4.660428 4.100267將第一列刪除
bfim <- t(bfim[, -1])t-test
item_t <- sapply(nabfi[, 1:25], function(x) t.test(x ~ nabfi$grp)$statistic)
print(item_t)## A1.t A2.t A3.t A4.t A5.t C1.t C2.t
## 11.1945059 24.6592025 27.2286394 18.1181944 23.9859251 20.1784506 22.8399194
## C3.t C4.t C5.t E1.t E2.t E3.t E4.t
## 15.1728244 19.2355084 15.1650148 19.2682311 21.7245113 29.8583238 22.0618203
## E5.t N1.t N2.t N3.t N4.t N5.t O1.t
## 27.9870044 6.5989063 7.3359003 8.8548083 0.5245383 5.0110134 19.8655575
## O2.t O3.t O4.t O5.t
## 11.6591368 25.9396177 11.9439768 15.1045027rslt2 <- data.frame(Item = rownames(bfim), low.mean.score = bfim[, 2],
high.mean.score = bfim[, 1], mean.dif = bfim[, 1]-bfim[,2],
t.value = item_t)
rslt2 |> knitr::kable()| Item | low.mean.score | high.mean.score | mean.dif | t.value | |
|---|---|---|---|---|---|
| A1 | A1 | 4.208556 | 5.146342 | 0.9377853 | 11.1945059 |
| A2 | A2 | 4.117647 | 5.569106 | 1.4514586 | 24.6592025 |
| A3 | A3 | 3.796791 | 5.533875 | 1.7370839 | 27.2286394 |
| A4 | A4 | 4.018717 | 5.455285 | 1.4365680 | 18.1181944 |
| A5 | A5 | 3.848930 | 5.430894 | 1.5819638 | 23.9859251 |
| C1 | C1 | 3.911765 | 5.284553 | 1.3727881 | 20.1784506 |
| C2 | C2 | 3.676471 | 5.235772 | 1.5593018 | 22.8399194 |
| C3 | C3 | 3.767380 | 4.951219 | 1.1838398 | 15.1728244 |
| C4 | C4 | 3.795454 | 5.268293 | 1.4728381 | 19.2355084 |
| C5 | C5 | 3.016043 | 4.512195 | 1.4961523 | 15.1650148 |
| E1 | E1 | 3.106952 | 4.883469 | 1.7765170 | 19.2682311 |
| E2 | E2 | 2.937166 | 4.859079 | 1.9219128 | 21.7245113 |
| E3 | E3 | 3.097594 | 5.102981 | 2.0053874 | 29.8583238 |
| E4 | E4 | 3.518717 | 5.246613 | 1.7278959 | 22.0618203 |
| E5 | E5 | 3.493316 | 5.371274 | 1.8779582 | 27.9870044 |
| N1 | N1 | 2.680481 | 3.368564 | 0.6880824 | 6.5989063 |
| N2 | N2 | 3.258021 | 3.981030 | 0.7230084 | 7.3359003 |
| N3 | N3 | 2.891711 | 3.785908 | 0.8941966 | 8.8548083 |
| N4 | N4 | 3.258021 | 3.311653 | 0.0536317 | 0.5245383 |
| N5 | N5 | 2.739305 | 3.279133 | 0.5398280 | 5.0110134 |
| O1 | O1 | 4.287433 | 5.479675 | 1.1922416 | 19.8655575 |
| O2 | O2 | 3.902406 | 4.989160 | 1.0867535 | 11.6591368 |
| O3 | O3 | 3.754011 | 5.346883 | 1.5928728 | 25.9396177 |
| O4 | O4 | 4.660428 | 5.447154 | 0.7867267 | 11.9439768 |
| O5 | O5 | 4.100267 | 5.211382 | 1.1111147 | 15.1045027 |
ggplot(data = rslt2, aes(x = reorder(Item, t.value, max), y = t.value)) +
geom_point() +
geom_hline(yintercept = 2, linetype = "dashed") +
coord_flip() +
labs(x = "Items", y = "t-value") +
theme_bw()
項目相關
itotr <- psych::alpha(bfi[, 1:25])$item.stats[, "r.drop"]## Some items ( N1 N2 N3 N4 N5 O4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionlbfi <- list(x = nabfi[, 1:5], y = nabfi[, 6:10], z = nabfi[ , 11:15], a = nabfi[ , 16:20], b = nabfi[ , 21:25])
isubalpha <- lapply(lbfi, psych::alpha)
isubr <- c(isubalpha$x$item.stats[, "r.drop"],
isubalpha$y$item.stats[, "r.drop"],
isubalpha$z$item.stats[, "r.drop"],
isubalpha$a$item.stats[, "r.drop"],
isubalpha$b$item.stats[, "r.drop"])rslt3 <- as.data.frame(t(rbind(itotr, isubr)))
names(rslt3) <- c("項目總分相關", "分量表項目總分相關")
rslt3 |> knitr::kable()| 項目總分相關 | 分量表項目總分相關 |
|---|---|
| 0.1338772 | 0.3190962 |
| 0.4329093 | 0.5759233 |
| 0.4465656 | 0.6035693 |
| 0.2810327 | 0.4145254 |
| 0.3935261 | 0.5004352 |
| 0.3072427 | 0.4654163 |
| 0.3573829 | 0.5128535 |
| 0.2590389 | 0.4769297 |
| 0.2668326 | 0.5731250 |
| 0.2134501 | 0.4860793 |
| 0.3011816 | 0.5153693 |
| 0.3132431 | 0.6142087 |
| 0.4642659 | 0.5049821 |
| 0.3699658 | 0.5827738 |
| 0.4670182 | 0.4634332 |
| 0.0529007 | 0.6778437 |
| 0.0677008 | 0.6548330 |
| 0.0951782 | 0.6781411 |
| -0.1043410 | 0.5485366 |
| 0.0195477 | 0.4874632 |
| 0.3172428 | 0.3981233 |
| 0.0963448 | 0.3509392 |
| 0.4134673 | 0.4546552 |
| 0.1310377 | 0.2167170 |
| 0.1861993 | 0.4197456 |
題目信度
itotalpha <- psych::alpha(nabfi[, 1:25])$alpha.drop[, "raw_alpha"]## Warning in psych::alpha(nabfi[, 1:25]): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( N1 N2 N3 N4 N5 O4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionialphad <- c(isubalpha$x$alpha.drop[, "raw_alpha"],
isubalpha$y$alpha.drop[, "raw_alpha"],
isubalpha$z$alpha.drop[, "raw_alpha"],
isubalpha$a$item.stats[, "r.drop"],
isubalpha$b$item.stats[, "r.drop"])rslt4 <- as.data.frame(t(rbind(itotalpha, ialphad)))
names(rslt4) <- c("Main Reliability(item drop)", "Sub Reliability (item drop)")
rslt4 |> knitr::kable()| Main Reliability(item drop) | Sub Reliability (item drop) |
|---|---|
| 0.6979605 | 0.7314607 |
| 0.6762664 | 0.6332003 |
| 0.6734577 | 0.6150842 |
| 0.6850740 | 0.6963136 |
| 0.6783576 | 0.6582421 |
| 0.6833182 | 0.7044913 |
| 0.6801730 | 0.6869874 |
| 0.6885173 | 0.7000899 |
| 0.6868631 | 0.6630853 |
| 0.6925096 | 0.7031818 |
| 0.6840630 | 0.7312733 |
| 0.6821853 | 0.6924954 |
| 0.6712580 | 0.7329196 |
| 0.6781666 | 0.7056423 |
| 0.6714425 | 0.7457366 |
| 0.7066986 | 0.6778437 |
| 0.7048568 | 0.6548330 |
| 0.7032029 | 0.6781411 |
| 0.7199257 | 0.5485366 |
| 0.7107374 | 0.4874632 |
| 0.6852134 | 0.3981233 |
| 0.7017610 | 0.3509392 |
| 0.6773863 | 0.4546552 |
| 0.6979447 | 0.2167170 |
| 0.6938900 | 0.4197456 |