#nhập liệu
likert=file.choose()
datalikert=read.csv(likert,head=TRUE)
save(datalikert,file="datalikert.rda")
attach(datalikert)
is.data.frame(datalikert)
## [1] TRUE
#Câu 10:
library(psych)
fa=data.frame(A1,A2,A3,A4,A5)
alpha(fa)
##
## Reliability analysis
## Call: alpha(x = fa)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.94 0.95 0.74 14 0.0083 4.1 0.72 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.93 0.95
## Duhachek 0.92 0.93 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## A1 0.96 0.96 0.97 0.86 25.2 0.0049 0.0057 0.87
## A2 0.90 0.91 0.91 0.71 9.7 0.0125 0.0270 0.67
## A3 0.90 0.91 0.91 0.71 9.7 0.0125 0.0270 0.67
## A4 0.91 0.92 0.92 0.73 11.1 0.0111 0.0361 0.72
## A5 0.90 0.91 0.91 0.70 9.5 0.0121 0.0277 0.67
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## A1 198 0.74 0.73 0.60 0.60 4.1 0.89
## A2 198 0.94 0.94 0.95 0.90 4.0 0.83
## A3 198 0.94 0.94 0.95 0.90 4.0 0.83
## A4 198 0.89 0.90 0.89 0.84 4.1 0.74
## A5 198 0.94 0.94 0.95 0.90 4.0 0.73
##
## Non missing response frequency for each item
## 2 3 4 5 miss
## A1 0.00 0.33 0.20 0.47 0
## A2 0.02 0.30 0.35 0.33 0
## A3 0.02 0.30 0.35 0.33 0
## A4 0.01 0.21 0.45 0.33 0
## A5 0.01 0.24 0.48 0.28 0
#Nhận xét:
#Giá trị Cronbach'alpha = 0,93 > 0.6; như vậy nhân tố A đảm bảo độ tin cậy
#Hệ số tương quan biến tổng các biến/thang đo đều >0,3 (Đạt yêu cầu )
#=> nên không loại bất kỳ biến/thang đo nào
#Câu 11:
abcde=data.frame(A1,A2,A3,A4,A5,B1,B2,B3,B4,C1,C2,C3,C4,D1,D2,D3,E1,E2,E3,E4)
library(psych)
cortest.bartlett(abcde)
## R was not square, finding R from data
## $chisq
## [1] 5048.334
##
## $p.value
## [1] 0
##
## $df
## [1] 190
KMO(abcde)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = abcde)
## Overall MSA = 0.67
## MSA for each item =
## A1 A2 A3 A4 A5 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3
## 0.77 0.70 0.61 0.75 0.68 0.78 0.81 0.53 0.56 0.58 0.53 0.86 0.84 0.72 0.74 0.88
## E1 E2 E3 E4
## 0.60 0.82 0.74 0.61
#Nhận xét:
#Giá trị KMO=0.67>0.5 đảm bảo yêu cầu phân tích EFA
#Giá trị Sig. (P-Value) của Barlett's = 0.000<5%
#Do đó kếtquar EFA có thể sử dụng phân tích
#Câu12:
fitabcde1<-principal(abcde,cor=TRUE,nfactors=20,rotate="none")
fitabcde1
## Principal Components Analysis
## Call: principal(r = abcde, nfactors = 20, rotate = "none", cor = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
## A1 0.45 -0.26 0.51 0.05 0.21 -0.20 -0.25 -0.52 0.11 -0.04 0.16 0.03
## A2 0.64 -0.46 0.53 0.01 -0.06 0.09 0.09 0.07 0.02 -0.08 -0.13 -0.16
## A3 0.63 -0.47 0.53 0.03 -0.05 0.10 0.10 0.07 0.03 -0.10 -0.12 -0.16
## A4 0.56 -0.56 0.46 0.00 0.03 0.03 0.04 0.08 -0.07 0.13 0.10 0.27
## A5 0.65 -0.53 0.44 0.02 0.00 -0.06 0.11 0.16 -0.06 0.02 0.02 0.13
## B1 0.54 0.16 -0.43 0.61 0.06 0.24 -0.07 -0.02 0.08 0.00 -0.05 0.07
## B2 0.45 0.27 -0.27 0.44 0.13 -0.52 0.18 0.13 -0.24 0.03 0.18 -0.09
## B3 0.61 0.19 -0.46 0.59 0.07 0.04 0.01 0.00 0.00 -0.05 0.00 0.02
## B4 0.57 0.15 -0.41 0.61 0.08 0.24 -0.03 -0.08 0.04 0.00 -0.02 -0.03
## C1 0.51 0.59 0.03 -0.50 0.21 0.16 -0.09 0.13 -0.11 0.04 0.11 -0.11
## C2 0.44 0.52 0.09 -0.52 0.27 0.28 -0.13 0.06 -0.07 0.14 0.20 -0.01
## C3 0.38 0.64 -0.02 -0.27 0.23 -0.22 0.15 0.07 0.30 0.15 -0.27 0.22
## C4 0.48 0.57 0.08 -0.35 0.26 -0.17 0.09 -0.09 0.07 -0.26 -0.09 -0.13
## D1 0.40 0.44 -0.01 -0.13 -0.61 -0.08 -0.19 0.19 0.18 -0.29 0.20 0.12
## D2 0.53 0.33 0.08 -0.02 -0.59 -0.13 -0.11 -0.12 0.03 0.39 -0.07 -0.17
## D3 0.17 0.67 0.17 -0.15 -0.32 0.14 0.26 -0.30 -0.36 -0.11 -0.12 0.17
## E1 -0.29 0.49 0.66 0.43 0.08 -0.02 -0.15 0.08 -0.06 0.00 -0.04 0.02
## E2 -0.34 0.40 0.45 0.29 -0.05 0.16 0.50 -0.08 0.25 0.07 0.28 -0.06
## E3 -0.27 0.50 0.68 0.40 0.03 0.00 -0.12 0.10 -0.01 -0.04 -0.10 -0.05
## E4 -0.30 0.48 0.66 0.42 0.08 -0.01 -0.17 0.07 -0.05 0.03 -0.02 0.05
## PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 h2 u2 com
## A1 -0.11 0.01 -0.01 0.00 0.01 -0.01 0.00 0.00 1 3.3e-16 5.2
## A2 -0.10 -0.05 0.03 -0.07 0.06 0.06 -0.01 -0.02 1 -4.4e-16 3.4
## A3 -0.10 0.04 0.05 0.09 -0.06 -0.02 0.02 0.03 1 1.6e-15 3.5
## A4 0.20 -0.06 0.07 0.06 0.05 0.00 -0.02 0.00 1 4.4e-16 4.1
## A5 0.04 0.06 -0.14 -0.07 -0.05 -0.04 0.01 -0.01 1 1.0e-15 3.3
## B1 0.04 0.19 0.04 0.00 0.05 0.01 0.01 0.00 1 3.3e-16 3.8
## B2 -0.08 0.02 0.07 -0.01 0.00 0.00 0.00 0.00 1 1.3e-15 5.8
## B3 -0.02 -0.07 -0.14 0.04 -0.01 0.05 -0.03 0.02 1 4.4e-16 3.3
## B4 0.02 -0.13 0.06 -0.03 -0.05 -0.05 0.01 -0.02 1 7.8e-16 3.5
## C1 -0.07 -0.04 -0.07 0.05 0.09 -0.03 0.04 -0.01 1 3.3e-16 4.1
## C2 -0.04 0.04 0.05 -0.04 -0.07 0.03 -0.04 0.01 1 2.2e-16 5.0
## C3 -0.14 -0.02 0.02 0.00 0.00 0.00 0.00 0.00 1 -2.2e-16 4.7
## C4 0.33 0.01 0.00 -0.01 -0.01 0.00 0.00 0.00 1 7.8e-16 5.1
## D1 -0.05 -0.01 0.02 0.00 0.00 0.00 0.00 0.00 1 3.3e-16 4.6
## D2 0.11 0.01 -0.02 0.00 0.00 0.00 0.00 0.00 1 7.8e-16 4.1
## D3 -0.07 0.01 0.01 0.00 0.00 0.00 0.00 0.00 1 0.0e+00 4.1
## E1 0.02 0.01 -0.01 0.07 -0.05 0.04 0.02 -0.05 1 1.3e-15 3.4
## E2 0.01 0.00 -0.02 0.00 0.01 0.00 0.00 0.00 1 5.6e-16 6.2
## E3 -0.03 0.02 -0.01 0.00 0.04 -0.06 -0.07 0.00 1 1.3e-15 3.2
## E4 0.05 -0.05 0.02 -0.07 0.01 0.01 0.04 0.05 1 7.8e-16 3.5
##
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
## SS loadings 4.59 4.23 3.45 2.65 1.17 0.71 0.62 0.54 0.43 0.41 0.39
## Proportion Var 0.23 0.21 0.17 0.13 0.06 0.04 0.03 0.03 0.02 0.02 0.02
## Cumulative Var 0.23 0.44 0.61 0.75 0.80 0.84 0.87 0.90 0.92 0.94 0.96
## Proportion Explained 0.23 0.21 0.17 0.13 0.06 0.04 0.03 0.03 0.02 0.02 0.02
## Cumulative Proportion 0.23 0.44 0.61 0.75 0.80 0.84 0.87 0.90 0.92 0.94 0.96
## PC12 PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20
## SS loadings 0.31 0.24 0.08 0.07 0.04 0.03 0.02 0.01 0.01
## Proportion Var 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## Cumulative Var 0.98 0.99 0.99 0.99 1.00 1.00 1.00 1.00 1.00
## Proportion Explained 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## Cumulative Proportion 0.98 0.99 0.99 0.99 1.00 1.00 1.00 1.00 1.00
##
## Mean item complexity = 4.2
## Test of the hypothesis that 20 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0
## with the empirical chi square 0 with prob < NA
##
## Fit based upon off diagonal values = 1
fitabcde2<-principal(abcde,cor=TRUE,nfactors=5,rotate="varimax")
fitabcde2
## Principal Components Analysis
## Call: principal(r = abcde, nfactors = 5, rotate = "varimax", cor = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC3 RC4 RC2 RC5 h2 u2 com
## A1 0.72 0.12 0.03 0.13 -0.12 0.57 0.430 1.2
## A2 0.94 -0.05 0.02 0.00 0.12 0.90 0.099 1.0
## A3 0.94 -0.04 0.02 -0.02 0.10 0.90 0.103 1.0
## A4 0.90 -0.12 -0.01 -0.06 -0.03 0.83 0.169 1.0
## A5 0.94 -0.14 0.06 -0.02 0.04 0.90 0.095 1.1
## B1 0.02 -0.07 0.93 0.01 0.08 0.88 0.121 1.0
## B2 0.00 0.03 0.73 0.16 0.05 0.56 0.437 1.1
## B3 0.03 -0.11 0.97 0.06 0.10 0.96 0.036 1.1
## B4 0.06 -0.08 0.94 0.02 0.08 0.90 0.099 1.0
## C1 0.03 -0.06 0.04 0.92 0.21 0.89 0.110 1.1
## C2 0.06 -0.04 -0.04 0.90 0.12 0.82 0.176 1.1
## C3 -0.09 0.07 0.16 0.78 0.14 0.68 0.323 1.2
## C4 0.05 0.04 0.11 0.84 0.14 0.75 0.251 1.1
## D1 -0.01 -0.02 0.11 0.24 0.82 0.74 0.256 1.2
## D2 0.18 0.01 0.21 0.19 0.80 0.76 0.245 1.4
## D3 -0.15 0.29 -0.01 0.43 0.58 0.63 0.368 2.6
## E1 -0.01 0.98 -0.02 0.03 0.00 0.95 0.047 1.0
## E2 -0.14 0.73 -0.09 -0.04 0.07 0.56 0.440 1.1
## E3 0.01 0.97 -0.04 0.04 0.05 0.95 0.048 1.0
## E4 -0.02 0.97 -0.04 0.03 -0.01 0.94 0.055 1.0
##
## RC1 RC3 RC4 RC2 RC5
## SS loadings 4.08 3.54 3.34 3.30 1.83
## Proportion Var 0.20 0.18 0.17 0.17 0.09
## Cumulative Var 0.20 0.38 0.55 0.71 0.80
## Proportion Explained 0.25 0.22 0.21 0.21 0.11
## Cumulative Proportion 0.25 0.47 0.68 0.89 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 5 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.03
## with the empirical chi square 71.75 with prob < 0.99
##
## Fit based upon off diagonal values = 0.99
#Nhận xét:
#Số nhân tố được trích: 5 nhóm nhân tố
#Giá trị Eigenvalue=1.83>1 (Đạt yêu cầu)
#Giá trị Tổng phương sai trích = 0.80 = 80% > 50% (Đạt yêu cầu)
#Câu 13:
library(psych)
vars=cbind(Atb,Btb,Ctb,Dtb,Etb,Ftb)
pairs.panels(vars)
library(Hmisc)
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:base':
##
## format.pval, units
result=rcorr(as.matrix(vars),type="pearson")
result$r
## Atb Btb Ctb Dtb Etb Ftb
## Atb 1.00000000 0.06221638 0.03185639 0.04014488 -0.08600054 0.2222299
## Btb 0.06221638 1.00000000 0.16097635 0.22801725 -0.10776531 0.2511952
## Ctb 0.03185639 0.16097635 1.00000000 0.50335736 0.02399959 0.5915879
## Dtb 0.04014488 0.22801725 0.50335736 1.00000000 0.13044247 0.5733869
## Etb -0.08600054 -0.10776531 0.02399959 0.13044247 1.00000000 0.2096923
## Ftb 0.22222985 0.25119517 0.59158791 0.57338686 0.20969230 1.0000000
result$P
## Atb Btb Ctb Dtb Etb Ftb
## Atb NA 0.3838835582 6.559338e-01 5.744306e-01 0.228316785 0.0016512737
## Btb 0.383883558 NA 2.347636e-02 1.234270e-03 0.130738634 0.0003574484
## Ctb 0.655933822 0.0234763641 NA 4.085621e-14 0.737161764 0.0000000000
## Dtb 0.574430580 0.0012342702 4.085621e-14 NA 0.066996016 0.0000000000
## Etb 0.228316785 0.1307386340 7.371618e-01 6.699602e-02 NA 0.0030269715
## Ftb 0.001651274 0.0003574484 0.000000e+00 0.000000e+00 0.003026971 NA
#Nhận xét:
#Biến Ftb có mối tương quan dương với Atb, Btb,Ctb,Dtb, Etb vì hệ số tương quan Pearson của các mối quan hệ này>0
#Mối tương quan giữa Ftb và các biến Atb, Btb, Ctb,Dtb,Etb có ý nghĩa thống kê vì giá trị Sig. (P-Value)có mối quan hệ này nhỏ hơn 5%