0510
fce
2022-05-10
#輸入data，並將其命名為dta
data(bfi)
#將bfi中的負向題分數轉為正向題計分
bfi[,c(1,9, 10, 11, 12, 22)] = 7 - bfi[,c(1,9, 10, 11, 12, 22)]
dta <- bfi#檢視資料結構
str(dta)## '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 : int 3 3 2 5 3 1 1 3 1 2 ...
## $ 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 ...28個變項，總共2800筆資料點。
#將原始資料的1到8行資料取出，重新定義其為dta
dta <- dta[, 1:25]my_summary <- function(x) {
require(moments)
funs <- c(mean, sd, skewness, kurtosis)
sapply(funs, function(f) f(x, na.rm = T))
}#將my_summary函數應用於數據集中的每一列並將其分配給dta_desc
dta_desc <- apply(dta, 2, my_summary)#查看每筆資料的mean, sd, skewness, kurtosis
head(dta_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 2.4895683
## [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 2.7630094#命名每個列，並將其轉換為data.frame呈現
rownames(dta_desc) <- c("mean", "sd", "skewness", "kurtosis")
rslt1 <- as.data.frame(t(dta_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 | 2.489568 | 1.327959 | 0.7384818 | 2.763009 |
#轉換成 longdata
dtal_desc <- melt(dta_desc)
names(dtal_desc)[1:2] <- c("moments", "items")#查看前6筆資料
head(dtal_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(dtal_desc, moments == "mean"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 1) +
geom_hline(yintercept = mean(t(dta_desc["mean",])) +
c(-1.5, 0, 1.5) * sd(t(dta_desc["mean", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "mean") +
theme_bw()
在mean當中，O5、N1、N5的值極端小於mean的平均；沒有任何值極端大於平均。
ggplot(data = subset(dtal_desc, moments == "sd"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 1) +
geom_hline(yintercept = mean(t(dta_desc["sd",])) +
c(-1.5, 0, 1.5) * sd(t(dta_desc["sd", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "sd") +
theme_bw()
在sd當中，O1極端小於sd的平均；沒有任何值極端大於平均。
ggplot(data = subset(dtal_desc, moments == "skewness"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 1) +
geom_hline(yintercept = mean(t(dta_desc["skewness",])) +
c(-1.5, 0, 1.5) * sd(t(dta_desc["skewness", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "skewness") +
theme_bw()
在skewness當中，沒有任何值極端小於skewness的平均；O5、N5、N1極端大於skewness的平均。
ggplot(data = subset(dtal_desc, moments == "kurtosis"),
aes(x = reorder(items, value, max), y = value, group = moments)) +
geom_point(size = 1) +
geom_hline(yintercept = mean(t(dta_desc["kurtosis",])) +
c(-1.5, 0, 1.5) * sd(t(dta_desc["kurtosis", ])), linetype = "dashed") +
coord_flip() +
labs(x = "items", y = "kurtosis") +
theme_bw()
在kurtosis當中，沒有任何值極端小於kurtosis的平均；O4、A2極端大於kurtosis的平均。
#刪除所有含有缺失資料NA的row
dta1 = na.omit(dta)
dta1$tot <- apply(dta1, 1, sum)#在 dta 中設一個欄位叫 grp，將所有數值設為 NA
dta$grp <- NA#只要分數小於2436*.27，就在 grp 中顯示L代表低；大於2436*.73 則顯示H代表高；其餘仍以NA顯示
dta1$grp[rank(dta1$tot) < 2436*.27] <- "L"
dta1$grp[rank(dta1$tot) > 2436*.73] <- "H"
dta1$grp <- factor(dta1$grp)
head(dta1)## 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 3 81 L
## 61618 3 104 <NA>
## 61620 2 99 <NA>
## 61621 5 89 L
## 61622 3 99 <NA>
## 61623 1 114 H#算高低分組平均數
dtam <- aggregate(dta1[, 1:25], by=list(dta1$grp), mean)
print(dtam)## Group.1 A1 A2 A3 A4 A5 C1 C2
## 1 H 5.007407 5.438519 5.364444 5.325926 5.237037 5.136296 5.102222
## 2 L 4.202080 4.017831 3.693908 3.918276 3.796434 3.897474 3.632987
## C3 C4 C5 E1 E2 E3 E4 E5
## 1 4.885926 5.040000 4.358519 4.746667 4.617778 4.856296 5.145185 5.229630
## 2 3.742942 3.832095 3.005944 3.077266 2.921248 3.062407 3.401189 3.423477
## N1 N2 N3 N4 N5 O1 O2 O3
## 1 3.364444 3.955556 3.680000 3.269630 3.330370 5.299259 4.691852 5.091852
## 2 2.585438 3.200594 2.783061 3.194651 2.649331 4.326895 4.031204 3.790490
## O4 O5
## 1 5.272593 2.334815
## 2 4.679049 2.563150#將第一欄刪除
dtam <- t(dtam[, -1])t-test
#使用 t-test 檢查兩個高低兩組平均值之間的差異
item_t <- sapply(dta1[, 1:25], function(x) t.test(x ~ dta1$grp)$statistic)
print(item_t)## A1.t A2.t A3.t A4.t A5.t C1.t C2.t
## 10.8435846 24.1029485 26.7156154 18.7839003 23.0699742 19.1247670 22.3832065
## C3.t C4.t C5.t E1.t E2.t E3.t E4.t
## 17.1453481 16.9457542 16.0464821 20.4585020 21.2538284 28.5111542 23.8114957
## E5.t N1.t N2.t N3.t N4.t N5.t O1.t
## 28.1321438 9.2082776 9.2587359 10.4863674 0.8665417 7.6373698 16.2839271
## O2.t O3.t O4.t O5.t
## 7.7166981 20.8345645 9.2886876 -3.1290125#將 t-test 結果儲存進 rslt2 並創建另外三個列
rslt2 <- data.frame(Item = rownames(dtam), low.mean.score = dtam[, 2],
high.mean.score = dtam[, 1], mean.dif = dtam[, 1]-dtam[, 2],
t.value = item_t)
print(rslt2)## Item low.mean.score high.mean.score mean.dif t.value
## A1 A1 4.202080 5.007407 0.80532717 10.8435846
## A2 A2 4.017831 5.438519 1.42068791 24.1029485
## A3 A3 3.693908 5.364444 1.67053657 26.7156154
## A4 A4 3.918276 5.325926 1.40764955 18.7839003
## A5 A5 3.796434 5.237037 1.44060316 23.0699742
## C1 C1 3.897474 5.136296 1.23882230 19.1247670
## C2 C2 3.632987 5.102222 1.46923560 22.3832065
## C3 C3 3.742942 4.885926 1.14298388 17.1453481
## C4 C4 3.832095 5.040000 1.20790490 16.9457542
## C5 C5 3.005944 4.358519 1.35257498 16.0464821
## E1 E1 3.077266 4.746667 1.66940069 20.4585020
## E2 E2 2.921248 4.617778 1.69652964 21.2538284
## E3 E3 3.062407 4.856296 1.79388916 28.5111542
## E4 E4 3.401189 5.145185 1.74399648 23.8114957
## E5 E5 3.423477 5.229630 1.80615266 28.1321438
## N1 N1 2.585438 3.364444 0.77900611 9.2082776
## N2 N2 3.200594 3.955556 0.75496120 9.2587359
## N3 N3 2.783061 3.680000 0.89693908 10.4863674
## N4 N4 3.194651 3.269630 0.07497881 0.8665417
## N5 N5 2.649331 3.330370 0.68103902 7.6373698
## O1 O1 4.326895 5.299259 0.97236476 16.2839271
## O2 O2 4.031204 4.691852 0.66064829 7.7166981
## O3 O3 3.790490 5.091852 1.30136151 20.8345645
## O4 O4 4.679049 5.272593 0.59354356 9.2886876
## O5 O5 2.563150 2.334815 -0.22833526 -3.1290125ggplot(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()
#計算全量表的cronbach’s α，在刪除項目本身後顯示相關性評分
itotr <- psych::alpha(dta1[, 1:25])$item.stats[, "r.drop"]## Warning in psych::alpha(dta1[, 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 O5 ) 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#創造一個含有列表的子量表
ldta <- list(A = dta[, 1:5], C = dta[, 6:10], E = dta[ ,11:15],
N = dta[, 16:20], O = dta[, 21:25])#計算所有子量表的Cronbach's α
isubalpha <- lapply(ldta, psych::alpha)## Warning in FUN(X[[i]], ...): 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 ( O5 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionisubr <- c(isubalpha$A$item.stats[, "r.drop"],
isubalpha$C$item.stats[, "r.drop"],
isubalpha$E$item.stats[, "r.drop"],
isubalpha$N$item.stats[, "r.drop"],
isubalpha$O$item.stats[, "r.drop"])rslt3 <- as.data.frame(t(rbind(itotr, isubr)))
names(rslt3) <- c("Item-total correlation without item", "Item-subscale correlation without item")
print(rslt3)## Item-total correlation without item Item-subscale correlation without item
## 1 0.12129886 0.30841774
## 2 0.44014085 0.56361516
## 3 0.46294030 0.58700459
## 4 0.31532083 0.39444409
## 5 0.40108276 0.48856514
## 6 0.31617880 0.45024169
## 7 0.36605572 0.50456471
## 8 0.26908306 0.46420485
## 9 0.24405601 0.55254665
## 10 0.21036732 0.47746920
## 11 0.29545305 0.51627292
## 12 0.31820168 0.60536884
## 13 0.46367816 0.50457255
## 14 0.40479750 0.57799397
## 15 0.46538882 0.45424460
## 16 0.07803633 0.66720571
## 17 0.07567906 0.65261625
## 18 0.11094446 0.67481671
## 19 -0.10220864 0.54280013
## 20 0.04465205 0.48648241
## 21 0.27231436 0.29054250
## 22 0.03491339 0.08778888
## 23 0.36270021 0.28189375
## 24 0.08713837 0.11880816
## 25 -0.18664142 -0.41621051題目信度
itotalpha <- psych::alpha(dta[, 1:25])$alpha.drop[, "raw_alpha"]## Warning in psych::alpha(dta[, 1:25]): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( N1 N2 N3 N4 N5 O4 O5 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionialphad <- c(isubalpha$A$alpha.drop[, "raw_alpha"],
isubalpha$C$alpha.drop[, "raw_alpha"],
isubalpha$E$alpha.drop[, "raw_alpha"],
isubalpha$N$alpha.drop[, "raw_alpha"],
isubalpha$O$alpha.drop[, "raw_alpha"])rslt4 <- as.data.frame(t(rbind(itotalpha, ialphad)))
names(rslt4) <- c("Main Reliability(item drop)", "Sub Reliability (item drop)")
print(rslt4)## Main Reliability(item drop) Sub Reliability (item drop)
## 1 0.6632149 0.71851736
## 2 0.6367700 0.61718004
## 3 0.6323646 0.60025958
## 4 0.6453668 0.68580565
## 5 0.6380217 0.64295296
## 6 0.6473781 0.69400042
## 7 0.6407685 0.67357146
## 8 0.6490963 0.68873410
## 9 0.6522542 0.65382557
## 10 0.6546084 0.68972493
## 11 0.6457509 0.72565472
## 12 0.6438399 0.69018038
## 13 0.6311851 0.72791419
## 14 0.6352193 0.70188850
## 15 0.6313077 0.74363273
## 16 0.6677606 0.75813791
## 17 0.6673696 0.76323265
## 18 0.6647101 0.75534281
## 19 0.6855098 0.79534991
## 20 0.6713194 0.81260218
## 21 0.6496561 -0.23847961
## 22 0.6732647 -0.03098808
## 23 0.6422412 -0.25873151
## 24 0.6635357 -0.04428384
## 25 0.6877593 0.51165759根據信度結果來看，會排除拿掉該題目後，全量表信度和子量表信度皆高者。若在全量表中的信度排序，和子量表中的信度排序有差異，則以子量表信度為優先，因為該題目作為測量該項目的其中一題，會比整體來說更重要。
以子量表親和性(A)來說，會排除掉1和4，因為少了這題，其信度仍優於拿掉其他題目。
以此類推，親和性(A)保留2、3、5；盡責性(C)保留2、3、4；外向性(E)保留1、2、4；神經質(N)保留1、2、3；經驗開放性(O)保留1、3、5。
#factor loading
#因素負載量-因素對構念(construct)的影響
#因素負載量越高越好，越能反映子構念
print.psych(fa(dta[, 1:25],
nfactor = 5,
fm = "pa",
rotate = "Promax"),
cut = .3)## Factor Analysis using method = pa
## Call: fa(r = dta[, 1:25], nfactors = 5, rotate = "Promax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA2 PA1 PA3 PA5 PA4 h2 u2 com
## A1 0.42 0.19 0.81 1.9
## A2 0.62 0.45 0.55 1.1
## A3 0.63 0.52 0.48 1.2
## A4 0.42 0.28 0.72 1.9
## A5 0.31 0.51 0.46 0.54 1.8
## C1 0.56 0.33 0.67 1.2
## C2 0.69 0.45 0.55 1.2
## C3 0.59 0.32 0.68 1.1
## C4 0.62 0.45 0.55 1.2
## C5 0.55 0.43 0.57 1.4
## E1 0.62 0.35 0.65 1.2
## E2 0.72 0.54 0.46 1.0
## E3 0.50 0.44 0.56 2.0
## E4 0.65 0.53 0.47 1.3
## E5 0.47 0.40 0.60 2.2
## N1 0.84 0.65 0.35 1.2
## N2 0.80 0.60 0.40 1.1
## N3 0.72 0.55 0.45 1.0
## N4 0.47 -0.35 0.49 0.51 2.1
## N5 0.49 0.35 0.65 1.8
## O1 0.50 0.31 0.69 1.2
## O2 0.46 0.26 0.74 1.7
## O3 0.60 0.46 0.54 1.3
## O4 0.38 0.25 0.75 2.6
## O5 -0.55 0.30 0.70 1.2
##
## PA2 PA1 PA3 PA5 PA4
## SS loadings 2.62 2.45 1.97 1.81 1.53
## Proportion Var 0.10 0.10 0.08 0.07 0.06
## Cumulative Var 0.10 0.20 0.28 0.35 0.41
## Proportion Explained 0.25 0.24 0.19 0.17 0.15
## Cumulative Proportion 0.25 0.49 0.68 0.85 1.00
##
## With factor correlations of
## PA2 PA1 PA3 PA5 PA4
## PA2 1.00 -0.32 -0.24 0.02 -0.02
## PA1 -0.32 1.00 0.37 0.29 0.17
## PA3 -0.24 0.37 1.00 0.22 0.23
## PA5 0.02 0.29 0.22 1.00 0.19
## PA4 -0.02 0.17 0.23 0.19 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 300 and the objective function was 7.23 with Chi Square of 20163.79
## The degrees of freedom for the model are 185 and the objective function was 0.65
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 2762 with the empirical chi square 1392.16 with prob < 5.6e-184
## The total number of observations was 2800 with Likelihood Chi Square = 1809.1 with prob < 4e-264
##
## Tucker Lewis Index of factoring reliability = 0.867
## RMSEA index = 0.056 and the 90 % confidence intervals are 0.054 0.058
## BIC = 340.68
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## PA2 PA1 PA3 PA5 PA4
## Correlation of (regression) scores with factors 0.93 0.90 0.88 0.87 0.84
## Multiple R square of scores with factors 0.86 0.82 0.78 0.75 0.70
## Minimum correlation of possible factor scores 0.71 0.64 0.56 0.50 0.41#平行分析
fa.parallel(dta[, 1:25], fa = "pc", show.legend = FALSE)
## Parallel analysis suggests that the number of factors = NA and the number of components = 6高於虛線的實際特徵值數目就是建議的因素數目，根據平行分析，建議為5個。