0. Dữ liệu chung
obs <-571
sig <-0.3
p <-0.5
a <-rep(c(1:5),c(0.05*obs,0.1*obs,0.15*obs,0.2*obs,0.5*obs))
tt <-rep(c(1:5),round(obs/2))
OIBB <- replace( a,rbinom(obs,1,p)==1,tt)
IST <- replace( a,rbinom(obs,1,p)==1,tt)
SI <- replace (a, rbinom(obs,1,p)==1,tt)
SM <-replace(a, rbinom(obs,1,p)==1,tt)
HM <-replace(a, rbinom(obs,1,p)==1,tt)
dulieu0 <- cbind(OIBB, IST, SI, SM, HM)
library("PerformanceAnalytics")
chart.Correlation(dulieu0, histogram=TRUE, pch=19)
1. Thang đo OIBB
for(i in 1:5){
chuan1 <-replace(OIBB,rbinom(obs,1,sig)==1,tt)
assign(paste("OIBB", i, sep = ""), chuan1)
}
dulieu1 <-cbind(OIBB1, OIBB2, OIBB3, OIBB4, OIBB5)
head(dulieu1)
## OIBB1 OIBB2 OIBB3 OIBB4 OIBB5
## [1,] 1 1 1 1 1
## [2,] 1 1 1 1 1
## [3,] 1 1 1 1 1
## [4,] 2 2 2 2 2
## [5,] 1 1 1 1 1
## [6,] 3 2 3 3 3
library(psych)
alpha(dulieu1)
##
## Reliability analysis
## Call: alpha(x = dulieu1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.78 0.47 4.5 0.012 3.4 1.1 0.48
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.82 0.84
## Duhachek 0.79 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## OIBB1 0.78 0.78 0.72 0.46 3.4 0.015 0.00034 0.47
## OIBB2 0.79 0.79 0.74 0.48 3.8 0.014 0.00024 0.48
## OIBB3 0.78 0.78 0.73 0.47 3.5 0.015 0.00042 0.48
## OIBB4 0.78 0.78 0.73 0.47 3.6 0.015 0.00077 0.48
## OIBB5 0.79 0.79 0.73 0.48 3.7 0.015 0.00042 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## OIBB1 571 0.78 0.78 0.70 0.63 3.3 1.4
## OIBB2 571 0.74 0.74 0.65 0.58 3.4 1.4
## OIBB3 571 0.77 0.77 0.69 0.62 3.4 1.4
## OIBB4 570 0.76 0.76 0.67 0.61 3.4 1.4
## OIBB5 570 0.75 0.75 0.66 0.60 3.3 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## OIBB1 0.16 0.16 0.19 0.19 0.31 0
## OIBB2 0.14 0.16 0.18 0.20 0.32 0
## OIBB3 0.15 0.16 0.18 0.20 0.31 0
## OIBB4 0.14 0.17 0.18 0.19 0.32 0
## OIBB5 0.16 0.16 0.18 0.20 0.30 0
2. Thang đo SM
for(i in 1:5){
chuan2 <-replace(SM,rbinom(obs,1,sig)==1,tt)
assign(paste("SM", i, sep = ""), chuan2)
}
dulieu2 <-cbind(SM1, SM2, SM3, SM4, SM5)
#dulieu2 <- 6-dulieu2
alpha(dulieu2)
##
## Reliability analysis
## Call: alpha(x = dulieu2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.79 0.49 4.8 0.012 3.4 1.1 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.83 0.85
## Duhachek 0.8 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SM1 0.80 0.80 0.75 0.50 4.1 0.014 0.00046 0.51
## SM2 0.80 0.80 0.75 0.49 3.9 0.014 0.00058 0.49
## SM3 0.78 0.78 0.73 0.47 3.6 0.015 0.00061 0.48
## SM4 0.79 0.79 0.74 0.49 3.8 0.014 0.00130 0.49
## SM5 0.79 0.79 0.74 0.48 3.7 0.014 0.00118 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SM1 571 0.74 0.74 0.64 0.58 3.3 1.4
## SM2 570 0.76 0.76 0.67 0.61 3.4 1.4
## SM3 570 0.79 0.79 0.72 0.65 3.4 1.4
## SM4 570 0.77 0.77 0.69 0.63 3.4 1.4
## SM5 570 0.78 0.78 0.70 0.63 3.4 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SM1 0.15 0.18 0.18 0.19 0.29 0
## SM2 0.15 0.16 0.17 0.21 0.32 0
## SM3 0.15 0.17 0.18 0.20 0.31 0
## SM4 0.14 0.16 0.17 0.19 0.34 0
## SM5 0.15 0.17 0.17 0.21 0.30 0
3. Thang đo HM
for(i in 1:5){
chuan3 <-replace(HM,rbinom(obs,1,sig)==1,tt)
assign(paste("HM", i, sep = ""), chuan3)
}
dulieu3 <-cbind(HM1, HM2, HM3, HM4, HM5)
head(dulieu3)
## HM1 HM2 HM3 HM4 HM5
## [1,] 1 1 1 1 1
## [2,] 1 1 1 1 1
## [3,] 2 2 2 2 2
## [4,] 1 1 1 1 2
## [5,] 3 3 1 2 3
## [6,] 1 1 2 1 1
alpha(dulieu3)
##
## Reliability analysis
## Call: alpha(x = dulieu3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.79 0.48 4.6 0.012 3.4 1.1 0.48
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.82 0.84
## Duhachek 0.8 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## HM1 0.78 0.78 0.73 0.47 3.5 0.015 0.00087 0.45
## HM2 0.79 0.79 0.74 0.48 3.7 0.014 0.00102 0.48
## HM3 0.80 0.80 0.75 0.49 3.9 0.014 0.00056 0.50
## HM4 0.78 0.78 0.73 0.47 3.6 0.015 0.00071 0.46
## HM5 0.78 0.78 0.73 0.47 3.6 0.015 0.00053 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## HM1 570 0.78 0.78 0.70 0.63 3.4 1.4
## HM2 570 0.76 0.76 0.67 0.60 3.4 1.4
## HM3 571 0.74 0.74 0.63 0.58 3.4 1.4
## HM4 570 0.78 0.78 0.70 0.63 3.4 1.4
## HM5 570 0.77 0.77 0.69 0.62 3.4 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## HM1 0.15 0.16 0.17 0.21 0.31 0
## HM2 0.14 0.17 0.17 0.20 0.32 0
## HM3 0.14 0.16 0.17 0.22 0.31 0
## HM4 0.15 0.16 0.18 0.19 0.32 0
## HM5 0.13 0.16 0.17 0.21 0.33 0
4. Thang đo IST
for(i in 1:13){
chuan4 <-replace(IST,rbinom(obs,1,sig)==1,tt)
assign(paste("IST", i, sep = ""), chuan4)
}
IST3 <-replace(IST3, rbinom(obs,1,0.6)==1,tt)
IST12 <-replace(IST12, rbinom(obs,1,0.5)==1,tt)
tam4 <-cbind(IST1, IST2, IST3, IST4, IST5, IST6, IST7, IST8, IST9, IST10, IST11, IST12, IST13)
dulieu4 <-cbind(IST1, IST2, IST4, IST5, IST6, IST7, IST8, IST9, IST10, IST11, IST12)
#dulieu4 <-6-dulieu4
alpha(tam4)
##
## Reliability analysis
## Call: alpha(x = tam4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.89 0.4 8.5 0.0064 3.4 0.95 0.45
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.91
## Duhachek 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## IST1 0.89 0.89 0.88 0.39 7.8 0.0069 0.0142 0.45
## IST2 0.89 0.89 0.88 0.39 7.8 0.0069 0.0141 0.45
## IST3 0.90 0.90 0.90 0.43 9.1 0.0061 0.0074 0.46
## IST4 0.89 0.89 0.88 0.39 7.7 0.0070 0.0143 0.45
## IST5 0.89 0.89 0.88 0.39 7.7 0.0070 0.0141 0.45
## IST6 0.88 0.88 0.88 0.39 7.6 0.0071 0.0138 0.44
## IST7 0.88 0.88 0.88 0.39 7.7 0.0070 0.0143 0.45
## IST8 0.89 0.89 0.88 0.39 7.8 0.0069 0.0139 0.45
## IST9 0.88 0.88 0.88 0.39 7.6 0.0071 0.0137 0.44
## IST10 0.88 0.88 0.88 0.38 7.4 0.0072 0.0131 0.44
## IST11 0.88 0.88 0.88 0.39 7.6 0.0071 0.0141 0.44
## IST12 0.90 0.90 0.89 0.42 8.8 0.0062 0.0105 0.46
## IST13 0.89 0.89 0.88 0.39 7.8 0.0070 0.0143 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## IST1 570 0.68 0.68 0.64 0.61 3.4 1.4
## IST2 570 0.68 0.68 0.65 0.61 3.4 1.4
## IST3 571 0.40 0.40 0.31 0.29 3.1 1.4
## IST4 570 0.70 0.70 0.67 0.63 3.3 1.4
## IST5 571 0.70 0.70 0.67 0.63 3.4 1.4
## IST6 570 0.74 0.74 0.71 0.68 3.4 1.5
## IST7 570 0.71 0.71 0.68 0.64 3.4 1.4
## IST8 569 0.68 0.68 0.65 0.61 3.4 1.4
## IST9 569 0.73 0.73 0.71 0.67 3.4 1.4
## IST10 570 0.77 0.77 0.75 0.71 3.4 1.4
## IST11 569 0.72 0.72 0.69 0.66 3.4 1.4
## IST12 569 0.46 0.46 0.38 0.36 3.2 1.4
## IST13 570 0.69 0.69 0.65 0.62 3.4 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## IST1 0.14 0.16 0.19 0.20 0.31 0
## IST2 0.14 0.17 0.17 0.21 0.31 0
## IST3 0.19 0.18 0.19 0.20 0.25 0
## IST4 0.15 0.17 0.18 0.19 0.31 0
## IST5 0.14 0.16 0.19 0.20 0.31 0
## IST6 0.15 0.17 0.18 0.18 0.33 0
## IST7 0.14 0.17 0.18 0.19 0.32 0
## IST8 0.14 0.17 0.17 0.20 0.33 0
## IST9 0.14 0.17 0.17 0.20 0.32 0
## IST10 0.13 0.16 0.17 0.18 0.35 0
## IST11 0.14 0.16 0.17 0.21 0.32 0
## IST12 0.17 0.18 0.17 0.22 0.25 0
## IST13 0.14 0.15 0.18 0.20 0.32 0
alpha(dulieu4)
##
## Reliability analysis
## Call: alpha(x = dulieu4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.89 0.43 8.3 0.0066 3.4 1 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.91
## Duhachek 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## IST1 0.88 0.88 0.88 0.43 7.6 0.0072 0.0089 0.46
## IST2 0.88 0.88 0.88 0.43 7.6 0.0072 0.0084 0.46
## IST4 0.88 0.88 0.88 0.43 7.5 0.0073 0.0088 0.46
## IST5 0.88 0.88 0.88 0.43 7.5 0.0073 0.0084 0.46
## IST6 0.88 0.88 0.87 0.42 7.3 0.0074 0.0083 0.45
## IST7 0.88 0.88 0.88 0.43 7.5 0.0073 0.0083 0.46
## IST8 0.88 0.88 0.88 0.43 7.6 0.0072 0.0088 0.46
## IST9 0.88 0.88 0.87 0.42 7.4 0.0074 0.0080 0.46
## IST10 0.88 0.88 0.87 0.42 7.2 0.0076 0.0079 0.45
## IST11 0.88 0.88 0.87 0.43 7.4 0.0074 0.0087 0.46
## IST12 0.90 0.90 0.89 0.47 8.8 0.0063 0.0012 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## IST1 570 0.68 0.68 0.64 0.60 3.4 1.4
## IST2 570 0.69 0.70 0.65 0.62 3.4 1.4
## IST4 570 0.71 0.71 0.67 0.63 3.3 1.4
## IST5 571 0.71 0.71 0.67 0.63 3.4 1.4
## IST6 570 0.74 0.74 0.71 0.67 3.4 1.5
## IST7 570 0.70 0.70 0.67 0.63 3.4 1.4
## IST8 569 0.69 0.70 0.65 0.62 3.4 1.4
## IST9 569 0.74 0.74 0.71 0.67 3.4 1.4
## IST10 570 0.77 0.77 0.75 0.71 3.4 1.4
## IST11 569 0.73 0.73 0.69 0.66 3.4 1.4
## IST12 569 0.47 0.47 0.38 0.36 3.2 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## IST1 0.14 0.16 0.19 0.20 0.31 0
## IST2 0.14 0.17 0.17 0.21 0.31 0
## IST4 0.15 0.17 0.18 0.19 0.31 0
## IST5 0.14 0.16 0.19 0.20 0.31 0
## IST6 0.15 0.17 0.18 0.18 0.33 0
## IST7 0.14 0.17 0.18 0.19 0.32 0
## IST8 0.14 0.17 0.17 0.20 0.33 0
## IST9 0.14 0.17 0.17 0.20 0.32 0
## IST10 0.13 0.16 0.17 0.18 0.35 0
## IST11 0.14 0.16 0.17 0.21 0.32 0
## IST12 0.17 0.18 0.17 0.22 0.25 0
5. Thang do SI
for(i in 1:17){
chuan5 <-replace(SI,rbinom(obs,1,sig)==1,tt)
assign(paste("SI", i, sep = ""), chuan5)
}
SI7 <-replace(SI7, rbinom(obs,1,0.6)==1,tt)
SI8 <-replace(SI8, rbinom(obs,1,0.6)==1,tt)
SI10 <-replace(SI10, rbinom(obs,1,0.6)==1,tt)
SI11 <-replace(SI11, rbinom(obs,1,0.6)==1,tt)
SI16 <-replace(SI16, rbinom(obs,1,0.6)==1,tt)
SI17 <-replace(SI17, rbinom(obs,1,0.6)==1,tt)
tam5 <-cbind(SI1, SI2, SI3, SI4, SI5, SI6, SI7, SI8, SI9, SI10, SI11, SI12, SI13, SI14, SI15, SI16, SI17)
dulieu5 <-cbind(SI1, SI2, SI3, SI4, SI5, SI6, SI9, SI12, SI13, SI14, SI15)
head(dulieu5)
## SI1 SI2 SI3 SI4 SI5 SI6 SI9 SI12 SI13 SI14 SI15
## [1,] 1 1 1 1 1 1 1 1 1 1 1
## [2,] 1 1 1 1 1 1 2 1 1 1 1
## [3,] 2 2 2 2 2 2 2 2 2 2 2
## [4,] 3 3 3 3 1 3 3 2 3 3 3
## [5,] 1 1 1 1 2 3 1 1 1 1 1
## [6,] 1 1 1 1 1 4 4 1 1 1 1
tam5 <-6-tam5
#dulieu5 <-6-dulieu5
head(dulieu5)
## SI1 SI2 SI3 SI4 SI5 SI6 SI9 SI12 SI13 SI14 SI15
## [1,] 1 1 1 1 1 1 1 1 1 1 1
## [2,] 1 1 1 1 1 1 2 1 1 1 1
## [3,] 2 2 2 2 2 2 2 2 2 2 2
## [4,] 3 3 3 3 1 3 3 2 3 3 3
## [5,] 1 1 1 1 2 3 1 1 1 1 1
## [6,] 1 1 1 1 1 4 4 1 1 1 1
alpha(tam5)
##
## Reliability analysis
## Call: alpha(x = tam5)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.89 0.32 7.9 0.0066 2.7 0.86 0.26
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.89 0.9
## Duhachek 0.87 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SI1 0.88 0.88 0.88 0.31 7.1 0.0073 0.023 0.26
## SI2 0.88 0.88 0.88 0.31 7.1 0.0073 0.024 0.25
## SI3 0.88 0.88 0.88 0.31 7.2 0.0073 0.023 0.25
## SI4 0.88 0.88 0.88 0.31 7.1 0.0073 0.023 0.25
## SI5 0.88 0.88 0.88 0.31 7.1 0.0073 0.023 0.25
## SI6 0.88 0.88 0.88 0.31 7.2 0.0072 0.024 0.26
## SI7 0.89 0.89 0.89 0.33 7.9 0.0066 0.024 0.27
## SI8 0.89 0.89 0.90 0.34 8.3 0.0064 0.022 0.28
## SI9 0.88 0.88 0.88 0.31 7.1 0.0074 0.023 0.25
## SI10 0.89 0.89 0.89 0.33 7.9 0.0066 0.025 0.27
## SI11 0.89 0.89 0.89 0.34 8.1 0.0065 0.024 0.28
## SI12 0.88 0.88 0.88 0.31 7.1 0.0073 0.023 0.26
## SI13 0.88 0.88 0.88 0.31 7.2 0.0073 0.023 0.26
## SI14 0.88 0.88 0.88 0.31 7.2 0.0072 0.024 0.26
## SI15 0.88 0.88 0.88 0.31 7.1 0.0073 0.024 0.25
## SI16 0.89 0.89 0.89 0.34 8.3 0.0064 0.022 0.28
## SI17 0.89 0.89 0.89 0.33 8.0 0.0066 0.024 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SI1 569 0.71 0.71 0.70 0.66 2.7 1.5
## SI2 570 0.72 0.72 0.71 0.67 2.6 1.4
## SI3 570 0.71 0.71 0.69 0.65 2.7 1.4
## SI4 570 0.73 0.73 0.72 0.68 2.7 1.4
## SI5 569 0.73 0.73 0.72 0.68 2.6 1.4
## SI6 571 0.67 0.68 0.66 0.62 2.6 1.4
## SI7 570 0.44 0.44 0.37 0.35 2.9 1.4
## SI8 570 0.33 0.33 0.25 0.24 2.9 1.4
## SI9 569 0.73 0.73 0.72 0.68 2.7 1.5
## SI10 570 0.45 0.44 0.38 0.36 2.8 1.4
## SI11 571 0.39 0.39 0.32 0.30 2.9 1.4
## SI12 569 0.73 0.73 0.72 0.67 2.6 1.4
## SI13 569 0.70 0.70 0.68 0.64 2.6 1.4
## SI14 569 0.68 0.68 0.66 0.62 2.7 1.4
## SI15 569 0.71 0.71 0.70 0.66 2.7 1.4
## SI16 571 0.34 0.33 0.26 0.24 2.8 1.4
## SI17 570 0.41 0.41 0.35 0.33 2.9 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SI1 0.30 0.20 0.18 0.16 0.16 0
## SI2 0.31 0.20 0.19 0.16 0.14 0
## SI3 0.30 0.20 0.18 0.16 0.16 0
## SI4 0.29 0.21 0.18 0.16 0.16 0
## SI5 0.31 0.20 0.19 0.15 0.15 0
## SI6 0.31 0.20 0.19 0.16 0.14 0
## SI7 0.24 0.19 0.19 0.19 0.18 0
## SI8 0.24 0.19 0.20 0.18 0.19 0
## SI9 0.31 0.20 0.17 0.17 0.16 0
## SI10 0.25 0.19 0.21 0.17 0.18 0
## SI11 0.23 0.22 0.18 0.19 0.19 0
## SI12 0.31 0.21 0.17 0.17 0.15 0
## SI13 0.29 0.21 0.19 0.17 0.14 0
## SI14 0.28 0.21 0.20 0.17 0.14 0
## SI15 0.29 0.21 0.18 0.17 0.15 0
## SI16 0.26 0.20 0.19 0.18 0.18 0
## SI17 0.24 0.20 0.19 0.19 0.18 0
alpha(dulieu5)
##
## Reliability analysis
## Call: alpha(x = dulieu5)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.91 0.49 11 0.0053 3.3 1 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SI1 0.91 0.91 0.9 0.49 9.7 0.0059 0.00107 0.49
## SI2 0.91 0.91 0.9 0.49 9.7 0.0058 0.00100 0.49
## SI3 0.91 0.91 0.9 0.49 9.7 0.0058 0.00098 0.49
## SI4 0.91 0.91 0.9 0.49 9.6 0.0059 0.00099 0.49
## SI5 0.91 0.91 0.9 0.49 9.6 0.0059 0.00086 0.49
## SI6 0.91 0.91 0.9 0.50 10.0 0.0057 0.00075 0.50
## SI9 0.91 0.91 0.9 0.49 9.6 0.0059 0.00103 0.49
## SI12 0.91 0.91 0.9 0.49 9.6 0.0059 0.00097 0.49
## SI13 0.91 0.91 0.9 0.49 9.8 0.0058 0.00108 0.50
## SI14 0.91 0.91 0.9 0.50 9.9 0.0057 0.00075 0.50
## SI15 0.91 0.91 0.9 0.49 9.8 0.0058 0.00089 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SI1 569 0.74 0.74 0.71 0.68 3.3 1.5
## SI2 570 0.73 0.73 0.70 0.67 3.4 1.4
## SI3 570 0.73 0.73 0.70 0.67 3.3 1.4
## SI4 570 0.75 0.75 0.72 0.68 3.3 1.4
## SI5 569 0.75 0.75 0.72 0.68 3.4 1.4
## SI6 571 0.70 0.70 0.66 0.63 3.4 1.4
## SI9 569 0.76 0.76 0.73 0.69 3.3 1.5
## SI12 569 0.75 0.75 0.72 0.69 3.4 1.4
## SI13 569 0.73 0.73 0.69 0.66 3.4 1.4
## SI14 569 0.71 0.71 0.67 0.64 3.3 1.4
## SI15 569 0.73 0.73 0.69 0.66 3.3 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SI1 0.16 0.16 0.18 0.20 0.30 0
## SI2 0.14 0.16 0.19 0.20 0.31 0
## SI3 0.16 0.16 0.18 0.20 0.30 0
## SI4 0.16 0.16 0.18 0.21 0.29 0
## SI5 0.15 0.15 0.19 0.20 0.31 0
## SI6 0.14 0.16 0.19 0.20 0.31 0
## SI9 0.16 0.17 0.17 0.20 0.31 0
## SI12 0.15 0.17 0.17 0.21 0.31 0
## SI13 0.14 0.17 0.19 0.21 0.29 0
## SI14 0.14 0.17 0.20 0.21 0.28 0
## SI15 0.15 0.17 0.18 0.21 0.29 0
6. Thang đo moderation
dataMod1 <-cbind(dulieu2,dulieu5)
dataMod1 <-data.frame(dataMod1)
dataMod2 <-cbind(dulieu3,dulieu5)
dataMod2 <-data.frame(dataMod2)
dulieu2 <-data.frame(dulieu2)
dulieu3 <-data.frame(dulieu3)
dulieu5 <-data.frame(dulieu5)
nrow(dataMod2)
## [1] 571
## Cach1 Dùng matrix tạo interaction
as.data.frame(model.matrix(~ (. + .)^2 - 1, dataMod1))->mod1
#MOD1 %>% select(-(1:26)) ->MOD1
as.data.frame(model.matrix(~ (. + .)^2 - 1, dataMod2))->mod2
as.data.frame(model.matrix(~ (. + .)^2 - 1, dulieu2))->dl2
as.data.frame(model.matrix(~ (. + .)^2 - 1, dulieu3))->dl3
as.data.frame(model.matrix(~ (. + .)^2 - 1, dulieu5))->dl5
## Cach 2 Dùng interact.data tạo interaction
#interact.data(dulieu2) ->dl2
#data.frame(interact.data(dulieu3)) ->dl3
#data.frame(interact.data(dulieu5))->dl5
#data.frame(interact.data(dataMod1)) ->mod1
#data.frame(interact.data(dataMod2)) ->mod2
mod1[setdiff(names(mod1), names(dl2))]->mod1
mod1[setdiff(names(mod1), names(dl5))]->MOD1
mod2[setdiff(names(mod2), names(dl3))]->mod2
mod2[setdiff(names(mod2), names(dl5))]->MOD2
MOD1 <-na.omit(MOD1)
nrow(MOD1)
## [1] 569
ncol(MOD1)
## [1] 55
MOD2 <-na.omit(MOD2)
nrow(MOD2)
## [1] 569
ncol(MOD2)
## [1] 55
#summary(alpha(mod1))
#summary(alpha(mod2))
Chạy PLSPM
library(plspm)
dulieuold <-cbind(dulieu1, dulieu2, dulieu3, dulieu4, dulieu5)
dulieu<-dulieuold
dulieu <-na.omit(dulieu)
nrow(dulieu)
## [1] 569
ncol(dulieu)
## [1] 37
dulieu <-cbind(dulieu,MOD1)
dulieu <-cbind(dulieu,MOD2)
nrow(dulieu)
## [1] 569
# Tạo quan hệ
SM = c(0,0,0,0,0,0,0)
HM = c(0,0,0,0,0,0,0)
SI = c(0,0,0,0,0,0,0)
MOD1 = c(0,0,0,0,0,0,0)
MOD2 = c(0,0,0,0,0,0,0)
IST = c(1,1,0,0,0,0,0)
OIBB = c(1,1,1,1,1,1,0)
modedo <- c("A","A","A","A","A","A","A")
mohinh = rbind( SM, HM ,SI, MOD1, MOD2,IST,OIBB)
colnames(mohinh) = rownames(mohinh)
mohinh
## SM HM SI MOD1 MOD2 IST OIBB
## SM 0 0 0 0 0 0 0
## HM 0 0 0 0 0 0 0
## SI 0 0 0 0 0 0 0
## MOD1 0 0 0 0 0 0 0
## MOD2 0 0 0 0 0 0 0
## IST 1 1 0 0 0 0 0
## OIBB 1 1 1 1 1 1 0
innerplot(mohinh)
biencon = list(6:10, 11:15, 16:26,37:92,93:147,27:37,1:5)
sem <- plspm(Data=dulieu, path_matrix=mohinh, blocks= biencon, modes= modedo)
names(sem)
## [1] "outer_model" "inner_model" "path_coefs" "scores"
## [5] "crossloadings" "inner_summary" "effects" "unidim"
## [9] "gof" "boot" "data" "manifests"
## [13] "model"
Xem kết quả
#sem$outer_model
sem$inner_model
## $IST
## Estimate Std. Error t value Pr(>|t|)
## Intercept 2.702842e-16 0.04104439 6.585169e-15 1.000000e+00
## SM 1.106353e-01 0.04166727 2.655208e+00 8.149133e-03
## HM 1.669943e-01 0.04166727 4.007804e+00 6.948424e-05
##
## $OIBB
## Estimate Std. Error t value Pr(>|t|)
## Intercept -4.912087e-16 0.03914019 -1.254998e-14 1.0000000000
## SM 3.684309e-01 0.12729203 2.894375e+00 0.0039466573
## HM 3.752603e-01 0.12780761 2.936134e+00 0.0034593380
## SI 1.497991e-01 0.04125168 3.631346e+00 0.0003077249
## MOD1 -3.489905e-01 0.18223751 -1.915031e+00 0.0559958262
## MOD2 -3.641238e-01 0.18708209 -1.946332e+00 0.0521128570
## IST 5.627762e-01 0.15721567 3.579644e+00 0.0003738204
Đồ thị
innerplot(sem, lcol = "blue")
#outerplot(sem)
Lưu data
setwd("/Users/thuphan/Desktop/R-STUDIO")
library(openxlsx)
write.xlsx(dulieuold, 'dulieuold.xlsx')