datafile1=read.csv("/Users/theanh/Downloads/Likert.csv",header=TRUE)
save(datafile1,file="datafile1.rda")
attach(datafile1)
is.data.frame(datafile1)
## [1] TRUE
# C10
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's 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 bất kì loại biến / thang đo nào
# C11
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 kết quả EFA có thể sử dụng để phân tích
# C12
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 0.0e+00 5.2
## A2 -0.10 -0.05 0.03 -0.07 0.06 0.06 -0.01 -0.02 1 0.0e+00 3.4
## A3 -0.10 0.04 0.05 0.09 -0.06 -0.02 0.02 0.03 1 -8.9e-16 3.5
## A4 0.20 -0.06 0.07 0.06 0.05 0.00 -0.02 0.00 1 0.0e+00 4.1
## A5 0.04 0.06 -0.14 -0.07 -0.05 -0.04 0.01 -0.01 1 -1.6e-15 3.3
## B1 0.04 0.19 0.04 0.00 0.05 0.01 0.01 0.00 1 2.2e-16 3.8
## B2 -0.08 0.02 0.07 -0.01 0.00 0.00 0.00 0.00 1 5.6e-16 5.8
## B3 -0.02 -0.07 -0.14 0.04 -0.01 0.05 -0.03 0.02 1 1.2e-15 3.3
## B4 0.02 -0.13 0.06 -0.03 -0.05 -0.05 0.01 -0.02 1 8.9e-16 3.5
## C1 -0.07 -0.04 -0.07 0.05 0.09 -0.03 0.04 -0.01 1 1.4e-15 4.1
## C2 -0.04 0.04 0.05 -0.04 -0.07 0.03 -0.04 0.01 1 7.8e-16 5.0
## C3 -0.14 -0.02 0.02 0.00 0.00 0.00 0.00 0.00 1 1.2e-15 4.7
## C4 0.33 0.01 0.00 -0.01 -0.01 0.00 0.00 0.00 1 1.1e-15 5.1
## D1 -0.05 -0.01 0.02 0.00 0.00 0.00 0.00 0.00 1 1.3e-15 4.6
## D2 0.11 0.01 -0.02 0.00 0.00 0.00 0.00 0.00 1 1.6e-15 4.1
## D3 -0.07 0.01 0.01 0.00 0.00 0.00 0.00 0.00 1 1.7e-15 4.1
## E1 0.02 0.01 -0.01 0.07 -0.05 0.04 0.02 -0.05 1 1.1e-16 3.4
## E2 0.01 0.00 -0.02 0.00 0.01 0.00 0.00 0.00 1 6.7e-16 6.2
## E3 -0.03 0.02 -0.01 0.00 0.04 -0.06 -0.07 0.00 1 3.3e-16 3.2
## E4 0.05 -0.05 0.02 -0.07 0.01 0.01 0.04 0.05 1 -2.2e-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="none")
fitabcde2
## Principal Components Analysis
## Call: principal(r = abcde, nfactors = 5, rotate = "none", cor = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 PC4 PC5 h2 u2 com
## A1 0.45 -0.26 0.51 0.05 0.21 0.57 0.430 2.9
## A2 0.64 -0.46 0.53 0.01 -0.06 0.90 0.099 2.8
## A3 0.63 -0.47 0.53 0.03 -0.05 0.90 0.103 2.8
## A4 0.56 -0.56 0.46 0.00 0.03 0.83 0.169 2.9
## A5 0.65 -0.53 0.44 0.02 0.00 0.90 0.095 2.7
## B1 0.54 0.16 -0.43 0.61 0.06 0.88 0.121 3.0
## B2 0.45 0.27 -0.27 0.44 0.13 0.56 0.437 3.5
## B3 0.61 0.19 -0.46 0.59 0.07 0.96 0.036 3.1
## B4 0.57 0.15 -0.41 0.61 0.08 0.90 0.099 2.9
## C1 0.51 0.59 0.03 -0.50 0.21 0.89 0.110 3.2
## C2 0.44 0.52 0.09 -0.52 0.27 0.82 0.176 3.5
## C3 0.38 0.64 -0.02 -0.27 0.23 0.68 0.323 2.4
## C4 0.48 0.57 0.08 -0.35 0.26 0.75 0.251 3.1
## D1 0.40 0.44 -0.01 -0.13 -0.61 0.74 0.256 2.7
## D2 0.53 0.33 0.08 -0.02 -0.59 0.76 0.245 2.6
## D3 0.17 0.67 0.17 -0.15 -0.32 0.63 0.368 1.9
## E1 -0.29 0.49 0.66 0.43 0.08 0.95 0.047 3.1
## E2 -0.34 0.40 0.45 0.29 -0.05 0.56 0.440 3.7
## E3 -0.27 0.50 0.68 0.40 0.03 0.95 0.048 2.9
## E4 -0.30 0.48 0.66 0.42 0.08 0.94 0.055 3.1
##
## PC1 PC2 PC3 PC4 PC5
## SS loadings 4.59 4.23 3.45 2.65 1.17
## Proportion Var 0.23 0.21 0.17 0.13 0.06
## Cumulative Var 0.23 0.44 0.61 0.75 0.80
## Proportion Explained 0.29 0.26 0.21 0.16 0.07
## Cumulative Proportion 0.29 0.55 0.76 0.93 1.00
##
## Mean item complexity = 2.9
## 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 Eigenva = 1.83 > 1 (đạt yêu cầu)
# Giá trị tổng phương sai trích = 0.80 > 50% (đạt yêu cầu)
# Cấu trúc nhân tố có phù hợp với dữ liệu ban đầu vì các biến/thang được sắp xếp theo đúng nhóm nhân tố
# C13
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
## Atb Btb Ctb Dtb Etb Ftb
## Atb 1.00 0.06 0.03 0.04 -0.09 0.22
## Btb 0.06 1.00 0.16 0.23 -0.11 0.25
## Ctb 0.03 0.16 1.00 0.50 0.02 0.59
## Dtb 0.04 0.23 0.50 1.00 0.13 0.57
## Etb -0.09 -0.11 0.02 0.13 1.00 0.21
## Ftb 0.22 0.25 0.59 0.57 0.21 1.00
##
## n= 198
##
##
## P
## Atb Btb Ctb Dtb Etb Ftb
## Atb 0.3839 0.6559 0.5744 0.2283 0.0017
## Btb 0.3839 0.0235 0.0012 0.1307 0.0004
## Ctb 0.6559 0.0235 0.0000 0.7372 0.0000
## Dtb 0.5744 0.0012 0.0000 0.0670 0.0000
## Etb 0.2283 0.1307 0.7372 0.0670 0.0030
## Ftb 0.0017 0.0004 0.0000 0.0000 0.0030