# Nhập liệu
likert=file.choose()
datalikert=read.csv(likert,header=TRUE)
save(datalikert,file="datalikert.rda")
attach(datalikert)
is.data.frame(datalikert)
## [1] TRUE
head(datalikert)
## STT GTINH TDO DTUOI TNHAP A1 A2 A3 A4 A5 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 E1
## 1 1 2 4 1 3 5 5 3 5 4 3 2 3 4 5 5 4 4 4 5 4 2
## 2 2 2 2 2 3 3 3 3 5 4 4 5 4 5 2 3 3 2 3 4 3 3
## 3 3 2 4 1 2 3 3 3 3 3 4 4 4 5 4 4 3 4 3 4 4 4
## 4 4 1 4 2 2 5 3 3 4 4 5 5 5 5 1 2 4 3 5 4 1 3
## 5 5 2 3 1 1 5 3 3 5 4 3 3 3 3 3 4 3 3 4 4 3 4
## 6 6 1 4 2 2 5 3 3 5 4 3 4 3 3 3 4 5 3 2 3 3 4
## E2 E3 E4 F1 F2 F3 F4 F5 Atb Btb Ctb Dtb Etb Ftb
## 1 3 3 4 4 5 3 4 5 4.4 3.00 4.50 4.33 3.00 4.2
## 2 4 3 4 3 3 3 4 5 3.6 4.50 2.50 3.33 3.50 3.6
## 3 3 3 4 4 4 4 4 5 3.0 4.25 3.75 3.67 3.50 4.2
## 4 4 3 4 5 5 4 4 5 3.8 5.00 2.50 3.33 3.50 4.6
## 5 3 3 4 2 5 4 4 5 4.0 3.00 3.25 3.67 3.50 4.0
## 6 4 3 4 3 3 5 4 5 4.0 3.25 3.75 2.67 3.75 4.0
dim(datalikert)
## [1] 198 36
# câu 12
library(psych)
ff=data.frame(A1,A2,A3,A4,A5,B1,B2,B3,B4,C1,C2,C3,C4,D1,D2,D3,E1,E2,E3,E4)
KMO(ff)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = ff)
## 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
cortest.bartlett(ff)
## R was not square, finding R from data
## $chisq
## [1] 5048.334
##
## $p.value
## [1] 0
##
## $df
## [1] 190
principal(ff,cor=TRUE,nfactors=20,ratate="none")
## Principal Components Analysis
## Call: principal(r = ff, nfactors = 20, cor = TRUE, ratate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC3 RC4 RC2 RC5 RC10 RC9 RC6 RC11 RC8 RC7 RC13
## A1 0.51 0.10 0.01 0.05 -0.05 0.04 -0.03 0.02 0.01 0.85 -0.04 0.06
## A2 0.96 -0.02 0.04 0.03 0.03 0.07 0.00 -0.05 -0.02 0.09 -0.02 0.03
## A3 0.96 -0.02 0.05 0.02 0.02 0.05 -0.01 -0.05 -0.03 0.09 0.00 0.03
## A4 0.87 -0.08 0.00 0.00 -0.05 0.02 -0.07 -0.01 -0.05 0.13 -0.04 -0.05
## A5 0.95 -0.09 0.03 0.00 0.02 0.02 -0.05 0.11 0.02 0.09 -0.05 -0.01
## B1 0.02 -0.04 0.97 0.02 0.05 0.06 -0.01 0.04 0.03 -0.02 -0.03 -0.01
## B2 0.00 0.02 0.48 0.04 0.05 0.08 0.01 0.86 0.10 0.02 -0.02 0.08
## B3 0.03 -0.09 0.94 0.03 0.08 0.05 0.02 0.25 0.07 0.01 -0.04 0.04
## B4 0.05 -0.06 0.97 0.04 0.01 0.07 0.01 0.08 -0.02 0.02 0.00 0.01
## C1 0.03 -0.02 0.05 0.94 0.13 0.09 0.13 0.07 0.13 -0.03 -0.05 0.12
## C2 0.03 0.00 0.01 0.97 0.07 0.06 0.09 -0.05 0.09 0.05 0.01 0.03
## C3 -0.08 0.06 0.10 0.50 0.09 0.11 0.11 0.12 0.81 0.01 0.03 0.17
## C4 0.03 0.03 0.07 0.58 0.12 0.06 0.16 0.13 0.25 0.09 0.00 0.72
## D1 0.02 0.01 0.11 0.22 0.92 0.25 0.15 0.04 0.07 -0.04 -0.01 0.06
## D2 0.15 0.02 0.17 0.17 0.28 0.89 0.18 0.07 0.08 0.03 -0.01 0.03
## D3 -0.10 0.20 0.02 0.31 0.18 0.19 0.87 0.01 0.09 -0.03 0.11 0.10
## E1 -0.05 0.98 -0.05 0.00 0.00 0.00 0.06 0.02 0.01 0.04 0.09 0.01
## E2 -0.10 0.51 -0.08 -0.05 -0.02 -0.01 0.12 -0.02 0.02 -0.04 0.84 0.00
## E3 -0.01 0.98 -0.06 0.00 0.03 0.02 0.07 -0.01 0.03 0.00 0.12 0.03
## E4 -0.06 0.98 -0.06 0.00 -0.01 0.01 0.04 0.00 0.01 0.05 0.09 -0.01
## RC12 RC15 RC14 RC17 RC18 RC16 RC19 RC20 h2 u2 com
## A1 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 3.3e-16 1.7
## A2 -0.17 -0.11 -0.03 0.01 -0.01 0.12 0.00 -0.01 1 -4.4e-16 1.2
## A3 -0.17 -0.11 0.01 0.00 -0.01 -0.12 0.00 0.01 1 1.6e-15 1.2
## A4 0.45 0.01 0.00 0.00 0.00 0.00 0.00 0.00 1 4.4e-16 1.6
## A5 0.12 0.23 0.02 0.00 0.02 0.00 0.00 0.00 1 1.0e-15 1.2
## B1 0.02 0.01 0.22 -0.01 -0.03 -0.01 0.00 0.00 1 3.3e-16 1.1
## B2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 1.3e-15 1.6
## B3 -0.02 0.06 -0.09 0.04 0.14 0.00 0.00 0.01 1 4.4e-16 1.3
## B4 0.00 -0.06 -0.13 -0.02 -0.10 0.01 0.00 -0.01 1 7.8e-16 1.1
## C1 -0.06 -0.01 -0.03 0.14 0.03 0.01 0.01 0.00 1 3.3e-16 1.3
## C2 0.05 0.01 0.02 -0.12 -0.03 -0.01 -0.01 0.00 1 2.2e-16 1.1
## C3 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 -2.2e-16 2.0
## C4 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 7.8e-16 2.5
## D1 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 3.3e-16 1.4
## D2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 7.8e-16 1.6
## D3 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 0.0e+00 1.7
## E1 0.01 0.00 0.01 -0.01 0.03 -0.05 -0.05 0.08 1 1.3e-15 1.1
## E2 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 5.6e-16 1.8
## E3 -0.09 -0.02 0.02 0.03 0.00 0.00 0.10 0.00 1 1.3e-15 1.1
## E4 0.06 0.01 -0.02 -0.01 -0.03 0.05 -0.05 -0.08 1 7.8e-16 1.1
##
## RC1 RC3 RC4 RC2 RC5 RC10 RC9 RC6 RC11 RC8 RC7
## SS loadings 3.84 3.24 3.07 2.61 1.02 0.95 0.91 0.88 0.78 0.78 0.76
## Proportion Var 0.19 0.16 0.15 0.13 0.05 0.05 0.05 0.04 0.04 0.04 0.04
## Cumulative Var 0.19 0.35 0.51 0.64 0.69 0.74 0.78 0.82 0.86 0.90 0.94
## Proportion Explained 0.19 0.16 0.15 0.13 0.05 0.05 0.05 0.04 0.04 0.04 0.04
## Cumulative Proportion 0.19 0.35 0.51 0.64 0.69 0.74 0.78 0.82 0.86 0.90 0.94
## RC13 RC12 RC15 RC14 RC17 RC18 RC16 RC19 RC20
## SS loadings 0.60 0.29 0.09 0.07 0.04 0.04 0.03 0.02 0.01
## Proportion Var 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## Cumulative Var 0.97 0.99 0.99 0.99 1.00 1.00 1.00 1.00 1.00
## Proportion Explained 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## Cumulative Proportion 0.97 0.99 0.99 0.99 1.00 1.00 1.00 1.00 1.00
##
## Mean item complexity = 1.4
## 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
principal(ff,cor=TRUE,nfactors=5,ratate="none")
## Principal Components Analysis
## Call: principal(r = ff, nfactors = 5, cor = TRUE, ratate = "none")
## 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
principal(ff,nfactors=5,ratate="varimax")
## Principal Components Analysis
## Call: principal(r = ff, nfactors = 5, ratate = "varimax")
## 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 từ 5 nhóm nhân tố
# Giá trị Eigenvalue = 1,83 > 1 => đạt yêu cầu
# Giá trị tổng phương sai trích = 0,80 > 50% => đạt yêu cầu
# Cấu trúc nhân tố phù hợp với dữ liệu ban đầu vì các biến/thang đo sắp xếp theo đúng nhóm nhân tố