#nhập liệu
likert=file.choose()
datalikert=read.csv(likert,header=TRUE)
save(datalikert,file="datalikert.rda")
attach(datalikert)
is.data.frame(datalikert)
## [1] TRUE
#Cau 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
#nhan xet
#he so Cronbach's Alpha dat 0,93
#he so tuong quan bien tong deu dat tu 0,60 tro len (dat yeu cau)
#=> khong xem xet loai bo bien nào
#cau11
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
#nhan xet
#he so KMO dat 0.67>0.5 (dat yeu cau )
#he so Bartlett's Test dat 0%<5%
#cau12
fitabcde1<-principal(abcde,cor=TRUE,nfactors=19,rorate="none")
fitabcde1
## Principal Components Analysis
## Call: principal(r = abcde, nfactors = 19, cor = TRUE, rorate = "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 0.09 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 0.11 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 RC16 RC18 RC19 h2 u2 com
## A1 0.02 0.00 0.00 0.00 0.00 0.00 0.00 1 2.1e-06 1.7
## A2 -0.17 -0.11 -0.03 0.01 0.11 0.00 0.00 1 4.4e-04 1.2
## A3 -0.17 -0.11 0.01 0.00 -0.12 -0.01 0.00 1 1.1e-03 1.2
## A4 0.45 0.01 0.00 0.00 0.00 0.00 0.00 1 3.3e-06 1.6
## A5 0.12 0.23 0.02 0.00 0.00 0.01 0.00 1 1.4e-04 1.2
## B1 0.02 0.01 0.22 -0.01 -0.01 -0.02 0.00 1 1.6e-06 1.1
## B2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 1.3e-06 1.6
## B3 -0.02 0.06 -0.09 0.04 0.00 0.14 0.00 1 6.1e-04 1.3
## B4 0.00 -0.06 -0.13 -0.02 0.00 -0.10 0.00 1 4.5e-04 1.1
## C1 -0.06 -0.01 -0.03 0.14 0.01 0.03 0.01 1 4.8e-05 1.3
## C2 0.05 0.01 0.02 -0.12 0.00 -0.03 -0.01 1 5.6e-05 1.1
## C3 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 1 2.1e-06 2.0
## C4 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 1 2.9e-07 2.5
## D1 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 1 2.8e-06 1.4
## D2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 9.2e-07 1.6
## D3 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 1 4.9e-07 1.7
## E1 0.01 0.00 0.01 -0.02 -0.07 0.05 -0.05 1 2.1e-03 1.1
## E2 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 1 3.3e-07 1.8
## E3 -0.09 -0.02 0.02 0.03 0.00 -0.01 0.10 1 1.0e-06 1.1
## E4 0.06 0.02 -0.02 -0.01 0.07 -0.05 -0.05 1 2.1e-03 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 0.94 0.91 0.88 0.78 0.78 0.75
## 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 0.83 0.86 0.90 0.94
## RC13 RC12 RC15 RC14 RC17 RC16 RC18 RC19
## SS loadings 0.60 0.29 0.09 0.07 0.04 0.04 0.04 0.02
## Proportion Var 0.03 0.01 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
## Proportion Explained 0.03 0.01 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
##
## Mean item complexity = 1.4
## Test of the hypothesis that 19 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,rorate="none")
fitabcde2
## Principal Components Analysis
## Call: principal(r = abcde, nfactors = 5, cor = TRUE, rorate = "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
fitabcde3<-principal(abcde,cor=TRUE,nfactors=5, rorate="varimax")
fitabcde3
## Principal Components Analysis
## Call: principal(r = abcde, nfactors = 5, cor = TRUE, rorate = "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
#nhan xet
#so nhan to duoc trich: 5 nhan to
#gia tri Eigenvalue=1.83>1(dat yeu cau)
#gia tri tong phuong sai trich bang 0.80=80%>50% (dat yeu cau)
#cu truc nhan to co phu hop voi du lieu ban dau vi bien/thang do sap xep theo dung nhom nhan to
#cau13
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
#nhan xet
#bien Ftb co moi tuong quan duong voi Atb,Btb,Ctb,Dtb,Etb vi he so tuong quan Pearson cua cac moi quan he nay >0
#moi tuong quan giua Ftb va cac bien Atb,Btb,Ctb,Dtb,Etb co y nghia thong ke vi gia tri Sig.(P-Value)cua cac moi quan he nay nho hon 50%