Carrega a base de dados
library(readxl)
db <- read_excel("Base de dados ARAE.xlsx", sheet = "Grupo 1")
# db <- db[, c(2, 18:106)]
library(dplyr)
db[,c(2, 18:106)] <- lapply(db[,c(2, 18:106)],function(x) ifelse(x > 5, 5, x)) %>% data.frame()
Corrige os polos
polos_inv <- c(paste0('SENNA_1_', seq(2, 36, 2)))
table(db$SENNA_1_2)
##
## 1 2 3 4 5
## 115 28 33 22 2
for(i in 1:length(polos_inv)){
db[, polos_inv[i]] <- (db[, polos_inv[i]] -6) *-1
}
table(db$SENNA_1_2)
##
## 1 2 3 4 5
## 2 22 33 28 115
Calculando os scores
db$caas_PREO <- rowSums(db[,c(paste0('CAAS_', 1:6))], na.rm = TRUE)
db$caas_CONT <- rowSums(db[,c(paste0('CAAS_', 7:12))], na.rm = TRUE)
db$caas_CURI <- rowSums(db[,c(paste0('CAAS_', 13:18))], na.rm = TRUE)
db$caas_CONF <- rowSums(db[,c(paste0('CAAS_', 19:24))], na.rm = TRUE)
db$caas_COOP <- rowSums(db[,c('CAAS_34','CAAS_28','CAAS_33','CAAS_31','CAAS_29','CAAS_30')], na.rm = TRUE)
db$senna_O <- rowSums(db[,c(paste0('SENNA_1_', 1:8), paste0('SENNA_2_', 1:4))], na.rm = TRUE)
db$senna_C <- rowSums(db[,c(paste0('SENNA_1_', 9:18), paste0('SENNA_2_', 5:9))], na.rm = TRUE)
db$senna_E <- rowSums(db[,c(paste0('SENNA_1_', 19:24), paste0('SENNA_2_', 10:12))], na.rm = TRUE)
db$senna_A <- rowSums(db[,c(paste0('SENNA_1_', 25:30), paste0('SENNA_2_', 13:15))], na.rm = TRUE)
db$senna_N <- rowSums(db[,c(paste0('SENNA_1_', 31:36), paste0('SENNA_2_', 16:18))], na.rm = TRUE)
Summary dos scores
summary(db$caas_PREO)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 20.00 23.00 22.62 26.00 30.00
summary(db$caas_CONT)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 20.00 23.00 23.17 27.00 30.00
summary(db$caas_CURI)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 19.00 22.00 21.98 25.00 30.00
summary(db$caas_CONF)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.00 21.00 24.00 23.92 27.00 30.00
summary(db$caas_COOP)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.0 21.0 24.0 23.8 27.0 30.0
summary(db$senna_O)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.00 43.00 47.00 46.43 50.00 60.00
summary(db$senna_C)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 35.00 55.00 61.00 61.01 66.25 75.00
summary(db$senna_E)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.00 29.00 32.00 32.62 37.00 45.00
summary(db$senna_A)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.00 26.00 32.00 31.27 36.00 45.00
summary(db$senna_N)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.00 30.00 33.00 33.27 38.00 45.00
Correlação
library(Hmisc)
correlacao <- rcorr(as.matrix(db[,91:100]))
correlacao
## SENNA_2_3 SENNA_2_4 SENNA_2_5 SENNA_2_6 SENNA_2_7 SENNA_2_8
## SENNA_2_3 1.00 0.28 0.48 0.36 0.37 0.36
## SENNA_2_4 0.28 1.00 0.20 0.17 0.19 0.18
## SENNA_2_5 0.48 0.20 1.00 0.55 0.52 0.48
## SENNA_2_6 0.36 0.17 0.55 1.00 0.46 0.42
## SENNA_2_7 0.37 0.19 0.52 0.46 1.00 0.37
## SENNA_2_8 0.36 0.18 0.48 0.42 0.37 1.00
## SENNA_2_9 0.32 0.17 0.46 0.38 0.35 0.36
## SENNA_2_10 0.11 0.22 0.10 0.26 0.24 0.11
## SENNA_2_11 0.18 0.05 0.28 0.42 0.33 0.21
## SENNA_2_12 0.03 0.03 0.14 0.19 0.17 0.01
## SENNA_2_9 SENNA_2_10 SENNA_2_11 SENNA_2_12
## SENNA_2_3 0.32 0.11 0.18 0.03
## SENNA_2_4 0.17 0.22 0.05 0.03
## SENNA_2_5 0.46 0.10 0.28 0.14
## SENNA_2_6 0.38 0.26 0.42 0.19
## SENNA_2_7 0.35 0.24 0.33 0.17
## SENNA_2_8 0.36 0.11 0.21 0.01
## SENNA_2_9 1.00 0.16 0.21 0.20
## SENNA_2_10 0.16 1.00 0.25 0.27
## SENNA_2_11 0.21 0.25 1.00 0.29
## SENNA_2_12 0.20 0.27 0.29 1.00
##
## n
## SENNA_2_3 SENNA_2_4 SENNA_2_5 SENNA_2_6 SENNA_2_7 SENNA_2_8
## SENNA_2_3 200 199 200 200 199 200
## SENNA_2_4 199 199 199 199 198 199
## SENNA_2_5 200 199 200 200 199 200
## SENNA_2_6 200 199 200 200 199 200
## SENNA_2_7 199 198 199 199 199 199
## SENNA_2_8 200 199 200 200 199 200
## SENNA_2_9 200 199 200 200 199 200
## SENNA_2_10 200 199 200 200 199 200
## SENNA_2_11 200 199 200 200 199 200
## SENNA_2_12 200 199 200 200 199 200
## SENNA_2_9 SENNA_2_10 SENNA_2_11 SENNA_2_12
## SENNA_2_3 200 200 200 200
## SENNA_2_4 199 199 199 199
## SENNA_2_5 200 200 200 200
## SENNA_2_6 200 200 200 200
## SENNA_2_7 199 199 199 199
## SENNA_2_8 200 200 200 200
## SENNA_2_9 200 200 200 200
## SENNA_2_10 200 200 200 200
## SENNA_2_11 200 200 200 200
## SENNA_2_12 200 200 200 200
##
## P
## SENNA_2_3 SENNA_2_4 SENNA_2_5 SENNA_2_6 SENNA_2_7 SENNA_2_8
## SENNA_2_3 0.0000 0.0000 0.0000 0.0000 0.0000
## SENNA_2_4 0.0000 0.0046 0.0150 0.0083 0.0126
## SENNA_2_5 0.0000 0.0046 0.0000 0.0000 0.0000
## SENNA_2_6 0.0000 0.0150 0.0000 0.0000 0.0000
## SENNA_2_7 0.0000 0.0083 0.0000 0.0000 0.0000
## SENNA_2_8 0.0000 0.0126 0.0000 0.0000 0.0000
## SENNA_2_9 0.0000 0.0157 0.0000 0.0000 0.0000 0.0000
## SENNA_2_10 0.1141 0.0015 0.1669 0.0002 0.0005 0.1112
## SENNA_2_11 0.0111 0.4974 0.0000 0.0000 0.0000 0.0029
## SENNA_2_12 0.6669 0.7157 0.0416 0.0071 0.0137 0.9012
## SENNA_2_9 SENNA_2_10 SENNA_2_11 SENNA_2_12
## SENNA_2_3 0.0000 0.1141 0.0111 0.6669
## SENNA_2_4 0.0157 0.0015 0.4974 0.7157
## SENNA_2_5 0.0000 0.1669 0.0000 0.0416
## SENNA_2_6 0.0000 0.0002 0.0000 0.0071
## SENNA_2_7 0.0000 0.0005 0.0000 0.0137
## SENNA_2_8 0.0000 0.1112 0.0029 0.9012
## SENNA_2_9 0.0220 0.0030 0.0044
## SENNA_2_10 0.0220 0.0003 0.0001
## SENNA_2_11 0.0030 0.0003 0.0000
## SENNA_2_12 0.0044 0.0001 0.0000
Alpha
library(psych)
caas_PREO <- db[,c(paste0('CAAS_', 1:6))]
caas_CONT <- db[,c(paste0('CAAS_', 7:12))]
caas_CURI <- db[,c(paste0('CAAS_', 13:18))]
caas_CONF <- db[,c(paste0('CAAS_', 19:24))]
caas_COOP <- db[,c('CAAS_34','CAAS_28','CAAS_33','CAAS_31','CAAS_29','CAAS_30')]
senna_O <- db[,c(paste0('SENNA_1_', 1:8), paste0('SENNA_2_', 1:4))]
senna_C <- db[,c(paste0('SENNA_1_', 9:18), paste0('SENNA_2_', 5:9))]
senna_E <- db[,c(paste0('SENNA_1_', 19:24), paste0('SENNA_2_', 10:12))]
senna_A <- db[,c(paste0('SENNA_1_', 25:30), paste0('SENNA_2_', 13:15))]
senna_N <- db[,c(paste0('SENNA_1_', 31:36), paste0('SENNA_2_', 16:18))]
psych::alpha(caas_PREO)
##
## Reliability analysis
## Call: psych::alpha(x = caas_PREO)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.81 0.44 4.7 0.019 3.8 0.69 0.43
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.83 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAAS_1 0.79 0.79 0.76 0.43 3.8 0.024 0.0059 0.42
## CAAS_2 0.81 0.81 0.78 0.45 4.1 0.022 0.0072 0.45
## CAAS_3 0.79 0.79 0.76 0.43 3.7 0.024 0.0034 0.42
## CAAS_4 0.80 0.80 0.77 0.44 4.0 0.023 0.0060 0.44
## CAAS_5 0.79 0.79 0.76 0.42 3.7 0.024 0.0053 0.42
## CAAS_6 0.82 0.82 0.79 0.47 4.4 0.021 0.0045 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAAS_1 200 0.76 0.75 0.69 0.63 3.7 1.00
## CAAS_2 200 0.70 0.70 0.61 0.56 4.1 0.95
## CAAS_3 199 0.76 0.76 0.71 0.64 3.6 0.96
## CAAS_4 200 0.72 0.73 0.66 0.59 3.8 0.88
## CAAS_5 200 0.77 0.77 0.72 0.65 3.7 0.95
## CAAS_6 200 0.67 0.67 0.56 0.51 3.8 0.96
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAAS_1 0.01 0.11 0.31 0.34 0.23 0.00
## CAAS_2 0.01 0.04 0.19 0.34 0.42 0.00
## CAAS_3 0.00 0.14 0.30 0.36 0.20 0.01
## CAAS_4 0.00 0.06 0.30 0.41 0.22 0.00
## CAAS_5 0.01 0.10 0.28 0.40 0.22 0.00
## CAAS_6 0.01 0.09 0.27 0.38 0.26 0.00
psych::alpha(caas_CONT)
##
## Reliability analysis
## Call: psych::alpha(x = caas_CONT)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.78 0.76 0.37 3.5 0.025 3.9 0.67 0.4
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.77 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAAS_7 0.75 0.76 0.73 0.38 3.1 0.027 0.0079 0.41
## CAAS_8 0.72 0.72 0.70 0.34 2.6 0.031 0.0103 0.35
## CAAS_9 0.75 0.75 0.72 0.38 3.1 0.027 0.0038 0.40
## CAAS_10 0.73 0.72 0.70 0.34 2.6 0.030 0.0092 0.37
## CAAS_11 0.73 0.73 0.70 0.35 2.8 0.030 0.0087 0.35
## CAAS_12 0.76 0.76 0.74 0.39 3.2 0.027 0.0088 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAAS_7 199 0.68 0.65 0.54 0.48 3.4 1.11
## CAAS_8 200 0.74 0.74 0.67 0.59 3.8 1.02
## CAAS_9 200 0.62 0.65 0.56 0.47 4.3 0.85
## CAAS_10 199 0.72 0.74 0.68 0.58 3.9 0.92
## CAAS_11 199 0.72 0.71 0.64 0.57 3.9 1.01
## CAAS_12 200 0.63 0.63 0.51 0.45 3.9 0.93
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAAS_7 0.03 0.19 0.35 0.22 0.22 0.01
## CAAS_8 0.01 0.12 0.23 0.35 0.29 0.00
## CAAS_9 0.00 0.04 0.14 0.30 0.52 0.00
## CAAS_10 0.01 0.06 0.26 0.36 0.32 0.01
## CAAS_11 0.01 0.11 0.19 0.39 0.30 0.01
## CAAS_12 0.00 0.06 0.26 0.36 0.32 0.00
psych::alpha(caas_CURI)
##
## Reliability analysis
## Call: psych::alpha(x = caas_CURI)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.81 0.44 4.8 0.019 3.7 0.74 0.43
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.83 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAAS_13 0.82 0.82 0.79 0.47 4.5 0.020 0.0047 0.45
## CAAS_14 0.81 0.81 0.78 0.45 4.2 0.022 0.0079 0.43
## CAAS_15 0.80 0.80 0.76 0.44 3.9 0.023 0.0044 0.41
## CAAS_16 0.79 0.79 0.76 0.43 3.7 0.024 0.0043 0.42
## CAAS_17 0.79 0.79 0.76 0.42 3.7 0.024 0.0052 0.40
## CAAS_18 0.79 0.80 0.77 0.44 3.9 0.023 0.0071 0.42
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAAS_13 199 0.66 0.67 0.56 0.50 3.4 1.02
## CAAS_14 200 0.70 0.70 0.61 0.56 3.9 0.95
## CAAS_15 199 0.74 0.74 0.68 0.60 3.7 1.07
## CAAS_16 199 0.77 0.77 0.72 0.65 3.6 0.98
## CAAS_17 200 0.78 0.77 0.72 0.65 3.5 1.05
## CAAS_18 200 0.74 0.74 0.67 0.61 3.9 0.99
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAAS_13 0.02 0.19 0.32 0.31 0.17 0.01
## CAAS_14 0.00 0.08 0.26 0.36 0.30 0.00
## CAAS_15 0.02 0.13 0.24 0.33 0.28 0.01
## CAAS_16 0.03 0.09 0.32 0.37 0.20 0.01
## CAAS_17 0.04 0.16 0.29 0.36 0.16 0.00
## CAAS_18 0.00 0.10 0.19 0.36 0.34 0.00
psych::alpha(caas_CONF)
##
## Reliability analysis
## Call: psych::alpha(x = caas_CONF)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.84 0.83 0.46 5.1 0.018 4 0.65 0.45
##
## lower alpha upper 95% confidence boundaries
## 0.8 0.83 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAAS_19 0.81 0.81 0.80 0.46 4.3 0.021 0.0100 0.45
## CAAS_20 0.81 0.81 0.79 0.46 4.3 0.021 0.0070 0.45
## CAAS_21 0.80 0.80 0.79 0.45 4.0 0.022 0.0102 0.43
## CAAS_22 0.80 0.80 0.79 0.45 4.1 0.022 0.0105 0.43
## CAAS_23 0.81 0.81 0.78 0.46 4.3 0.022 0.0069 0.46
## CAAS_24 0.81 0.82 0.79 0.47 4.4 0.021 0.0050 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAAS_19 200 0.73 0.74 0.66 0.60 4.0 0.87
## CAAS_20 200 0.71 0.73 0.66 0.59 4.2 0.82
## CAAS_21 199 0.76 0.77 0.71 0.64 4.1 0.87
## CAAS_22 199 0.75 0.76 0.69 0.63 4.3 0.83
## CAAS_23 200 0.75 0.74 0.68 0.61 3.8 0.97
## CAAS_24 199 0.73 0.72 0.65 0.58 3.5 0.95
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAAS_19 0.00 0.04 0.22 0.38 0.35 0.00
## CAAS_20 0.00 0.03 0.17 0.36 0.44 0.00
## CAAS_21 0.00 0.04 0.23 0.33 0.41 0.01
## CAAS_22 0.01 0.02 0.16 0.32 0.50 0.01
## CAAS_23 0.01 0.09 0.27 0.37 0.26 0.00
## CAAS_24 0.02 0.12 0.35 0.36 0.16 0.01
psych::alpha(caas_COOP)
##
## Reliability analysis
## Call: psych::alpha(x = caas_COOP)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.78 0.77 0.37 3.5 0.025 4 0.64 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.77 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAAS_34 0.72 0.74 0.73 0.36 2.9 0.031 0.0145 0.37
## CAAS_28 0.72 0.73 0.71 0.35 2.7 0.031 0.0117 0.37
## CAAS_33 0.77 0.78 0.75 0.41 3.5 0.025 0.0047 0.40
## CAAS_31 0.74 0.75 0.73 0.37 2.9 0.029 0.0144 0.38
## CAAS_29 0.74 0.75 0.71 0.37 2.9 0.029 0.0034 0.37
## CAAS_30 0.72 0.73 0.72 0.35 2.7 0.031 0.0124 0.35
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAAS_34 199 0.73 0.71 0.62 0.56 3.7 1.04
## CAAS_28 200 0.72 0.73 0.67 0.57 4.1 0.93
## CAAS_33 199 0.64 0.59 0.47 0.41 3.5 1.13
## CAAS_31 200 0.68 0.69 0.59 0.52 4.1 0.88
## CAAS_29 200 0.64 0.69 0.63 0.51 4.4 0.73
## CAAS_30 199 0.72 0.73 0.66 0.57 4.0 0.89
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAAS_34 0.02 0.11 0.28 0.32 0.28 0.01
## CAAS_28 0.01 0.04 0.22 0.32 0.42 0.00
## CAAS_33 0.03 0.18 0.29 0.26 0.24 0.01
## CAAS_31 0.00 0.04 0.19 0.38 0.38 0.00
## CAAS_29 0.00 0.00 0.13 0.33 0.54 0.00
## CAAS_30 0.01 0.04 0.21 0.40 0.35 0.01
psych::alpha(senna_O)
##
## Reliability analysis
## Call: psych::alpha(x = senna_O)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.67 0.69 0.74 0.16 2.2 0.035 3.9 0.46 0.15
##
## lower alpha upper 95% confidence boundaries
## 0.6 0.67 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SENNA_1_1 0.65 0.67 0.72 0.16 2.0 0.037 0.022 0.15
## SENNA_1_2 0.66 0.67 0.73 0.16 2.1 0.036 0.023 0.15
## SENNA_1_3 0.67 0.69 0.74 0.17 2.2 0.034 0.020 0.16
## SENNA_1_4 0.66 0.69 0.73 0.17 2.2 0.035 0.021 0.16
## SENNA_1_5 0.65 0.67 0.72 0.15 2.0 0.037 0.022 0.15
## SENNA_1_6 0.66 0.68 0.73 0.16 2.1 0.036 0.023 0.15
## SENNA_1_7 0.64 0.66 0.70 0.15 2.0 0.038 0.015 0.15
## SENNA_1_8 0.67 0.69 0.74 0.17 2.2 0.035 0.020 0.15
## SENNA_2_1 0.65 0.67 0.72 0.16 2.0 0.037 0.021 0.15
## SENNA_2_2 0.64 0.67 0.71 0.16 2.0 0.037 0.020 0.15
## SENNA_2_3 0.62 0.64 0.70 0.14 1.8 0.040 0.021 0.11
## SENNA_2_4 0.63 0.66 0.69 0.15 1.9 0.038 0.015 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SENNA_1_1 200 0.46 0.48 0.40 0.32 3.9 0.84
## SENNA_1_2 200 0.45 0.46 0.37 0.27 4.2 1.12
## SENNA_1_3 200 0.43 0.37 0.26 0.21 3.4 1.29
## SENNA_1_4 199 0.42 0.39 0.30 0.24 4.3 1.06
## SENNA_1_5 199 0.47 0.51 0.44 0.35 4.5 0.74
## SENNA_1_6 199 0.37 0.43 0.33 0.28 4.8 0.54
## SENNA_1_7 200 0.53 0.52 0.53 0.38 3.1 0.98
## SENNA_1_8 199 0.37 0.38 0.28 0.20 3.7 1.02
## SENNA_2_1 199 0.44 0.49 0.41 0.33 4.2 0.71
## SENNA_2_2 199 0.54 0.49 0.43 0.35 3.4 1.27
## SENNA_2_3 200 0.63 0.65 0.62 0.52 4.2 0.87
## SENNA_2_4 199 0.55 0.54 0.55 0.40 3.0 1.03
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SENNA_1_1 0.00 0.04 0.28 0.42 0.26 0.00
## SENNA_1_2 0.01 0.11 0.16 0.14 0.58 0.00
## SENNA_1_3 0.12 0.11 0.27 0.26 0.24 0.00
## SENNA_1_4 0.04 0.03 0.17 0.16 0.61 0.01
## SENNA_1_5 0.01 0.02 0.08 0.29 0.61 0.01
## SENNA_1_6 0.01 0.01 0.03 0.10 0.87 0.01
## SENNA_1_7 0.04 0.20 0.48 0.16 0.11 0.00
## SENNA_1_8 0.03 0.06 0.36 0.29 0.26 0.01
## SENNA_2_1 0.00 0.01 0.14 0.49 0.36 0.01
## SENNA_2_2 0.11 0.11 0.25 0.30 0.24 0.01
## SENNA_2_3 0.00 0.06 0.15 0.38 0.42 0.00
## SENNA_2_4 0.05 0.29 0.43 0.12 0.12 0.01
psych::alpha(senna_C)
##
## Reliability analysis
## Call: psych::alpha(x = senna_C)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.87 0.9 0.31 6.6 0.014 4.1 0.52 0.3
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.86 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SENNA_1_9 0.86 0.86 0.89 0.31 6.4 0.015 0.017 0.30
## SENNA_1_10 0.86 0.87 0.90 0.32 6.5 0.014 0.019 0.31
## SENNA_1_11 0.86 0.86 0.89 0.30 6.1 0.015 0.018 0.29
## SENNA_1_12 0.86 0.87 0.89 0.31 6.4 0.014 0.018 0.30
## SENNA_1_13 0.85 0.86 0.88 0.30 6.0 0.015 0.016 0.30
## SENNA_1_14 0.86 0.87 0.89 0.32 6.5 0.014 0.014 0.31
## SENNA_1_15 0.85 0.86 0.89 0.30 6.1 0.015 0.019 0.30
## SENNA_1_16 0.86 0.86 0.89 0.31 6.2 0.015 0.020 0.30
## SENNA_1_17 0.86 0.86 0.88 0.30 6.1 0.015 0.017 0.30
## SENNA_1_18 0.86 0.87 0.89 0.31 6.4 0.014 0.019 0.30
## SENNA_2_5 0.85 0.85 0.88 0.29 5.8 0.016 0.017 0.29
## SENNA_2_6 0.85 0.85 0.88 0.29 5.8 0.016 0.017 0.29
## SENNA_2_7 0.85 0.85 0.88 0.29 5.8 0.016 0.016 0.29
## SENNA_2_8 0.86 0.86 0.89 0.31 6.2 0.015 0.019 0.30
## SENNA_2_9 0.86 0.86 0.89 0.31 6.2 0.015 0.017 0.30
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SENNA_1_9 200 0.50 0.52 0.48 0.42 4.3 0.77
## SENNA_1_10 200 0.48 0.46 0.39 0.37 4.3 0.99
## SENNA_1_11 200 0.60 0.60 0.57 0.53 3.9 0.79
## SENNA_1_12 199 0.54 0.50 0.45 0.42 3.5 1.13
## SENNA_1_13 200 0.66 0.64 0.64 0.58 3.7 1.06
## SENNA_1_14 199 0.50 0.48 0.45 0.40 4.3 0.90
## SENNA_1_15 199 0.61 0.62 0.58 0.53 3.9 0.90
## SENNA_1_16 200 0.57 0.59 0.54 0.51 4.5 0.71
## SENNA_1_17 199 0.59 0.61 0.60 0.52 4.4 0.76
## SENNA_1_18 200 0.49 0.50 0.44 0.40 4.5 0.83
## SENNA_2_5 200 0.73 0.74 0.72 0.67 4.0 0.84
## SENNA_2_6 200 0.73 0.74 0.73 0.68 3.9 0.86
## SENNA_2_7 199 0.73 0.71 0.71 0.67 3.9 0.90
## SENNA_2_8 200 0.59 0.59 0.55 0.50 3.8 0.87
## SENNA_2_9 200 0.57 0.59 0.57 0.49 4.3 0.78
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SENNA_1_9 0.00 0.02 0.10 0.42 0.45 0.00
## SENNA_1_10 0.02 0.04 0.16 0.22 0.56 0.00
## SENNA_1_11 0.00 0.03 0.30 0.45 0.22 0.00
## SENNA_1_12 0.07 0.10 0.30 0.34 0.20 0.01
## SENNA_1_13 0.01 0.10 0.36 0.21 0.32 0.00
## SENNA_1_14 0.01 0.05 0.13 0.31 0.51 0.01
## SENNA_1_15 0.01 0.06 0.29 0.38 0.28 0.01
## SENNA_1_16 0.00 0.00 0.10 0.27 0.63 0.00
## SENNA_1_17 0.00 0.01 0.14 0.31 0.54 0.01
## SENNA_1_18 0.01 0.03 0.06 0.19 0.70 0.00
## SENNA_2_5 0.00 0.02 0.26 0.41 0.31 0.00
## SENNA_2_6 0.00 0.04 0.26 0.42 0.26 0.00
## SENNA_2_7 0.00 0.07 0.27 0.37 0.30 0.01
## SENNA_2_8 0.00 0.06 0.33 0.39 0.22 0.00
## SENNA_2_9 0.00 0.03 0.12 0.36 0.50 0.00
psych::alpha(senna_E)
##
## Reliability analysis
## Call: psych::alpha(x = senna_E)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.77 0.25 3 0.027 3.6 0.63 0.21
##
## lower alpha upper 95% confidence boundaries
## 0.69 0.75 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SENNA_1_19 0.71 0.71 0.72 0.24 2.5 0.031 0.013 0.21
## SENNA_1_20 0.72 0.72 0.74 0.25 2.6 0.030 0.018 0.20
## SENNA_1_21 0.72 0.73 0.74 0.25 2.7 0.030 0.017 0.21
## SENNA_1_22 0.72 0.72 0.74 0.25 2.6 0.030 0.016 0.22
## SENNA_1_23 0.72 0.73 0.74 0.25 2.7 0.030 0.016 0.21
## SENNA_1_24 0.75 0.76 0.77 0.28 3.1 0.027 0.017 0.24
## SENNA_2_10 0.72 0.72 0.74 0.24 2.6 0.030 0.016 0.20
## SENNA_2_11 0.71 0.72 0.73 0.24 2.5 0.031 0.015 0.20
## SENNA_2_12 0.74 0.75 0.77 0.27 3.0 0.028 0.018 0.22
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SENNA_1_19 200 0.64 0.65 0.63 0.51 4.0 1.02
## SENNA_1_20 200 0.61 0.62 0.54 0.47 3.9 1.11
## SENNA_1_21 200 0.58 0.58 0.51 0.43 3.6 1.09
## SENNA_1_22 200 0.61 0.60 0.55 0.46 3.5 1.14
## SENNA_1_23 199 0.56 0.58 0.52 0.43 4.1 0.96
## SENNA_1_24 200 0.45 0.44 0.30 0.26 3.9 1.16
## SENNA_2_10 200 0.61 0.62 0.57 0.47 3.4 1.07
## SENNA_2_11 200 0.64 0.64 0.60 0.51 3.0 1.10
## SENNA_2_12 200 0.50 0.48 0.36 0.31 3.2 1.22
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SENNA_1_19 0.03 0.04 0.26 0.28 0.40 0.00
## SENNA_1_20 0.03 0.07 0.26 0.22 0.42 0.00
## SENNA_1_21 0.02 0.12 0.34 0.22 0.30 0.00
## SENNA_1_22 0.06 0.12 0.32 0.28 0.22 0.00
## SENNA_1_23 0.01 0.06 0.20 0.33 0.41 0.01
## SENNA_1_24 0.04 0.06 0.27 0.20 0.42 0.00
## SENNA_2_10 0.04 0.14 0.34 0.29 0.18 0.00
## SENNA_2_11 0.09 0.26 0.36 0.20 0.10 0.00
## SENNA_2_12 0.09 0.21 0.30 0.21 0.19 0.00
psych::alpha(senna_A)
##
## Reliability analysis
## Call: psych::alpha(x = senna_A)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.85 0.36 5.1 0.017 3.5 0.76 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.8 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SENNA_1_25 0.81 0.81 0.82 0.35 4.3 0.020 0.012 0.33
## SENNA_1_26 0.82 0.82 0.82 0.36 4.5 0.019 0.013 0.34
## SENNA_1_27 0.83 0.83 0.84 0.38 4.9 0.018 0.014 0.36
## SENNA_1_28 0.83 0.83 0.84 0.39 5.0 0.018 0.013 0.38
## SENNA_1_29 0.82 0.82 0.83 0.36 4.6 0.019 0.015 0.34
## SENNA_1_30 0.82 0.82 0.82 0.36 4.5 0.019 0.015 0.34
## SENNA_2_13 0.81 0.81 0.82 0.35 4.3 0.020 0.011 0.34
## SENNA_2_14 0.81 0.81 0.81 0.34 4.2 0.021 0.011 0.32
## SENNA_2_15 0.83 0.83 0.83 0.37 4.8 0.018 0.013 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SENNA_1_25 199 0.73 0.72 0.68 0.62 3.1 1.3
## SENNA_1_26 200 0.69 0.67 0.63 0.57 3.8 1.3
## SENNA_1_27 200 0.59 0.59 0.50 0.46 3.0 1.1
## SENNA_1_28 199 0.54 0.55 0.46 0.41 4.1 1.1
## SENNA_1_29 200 0.65 0.65 0.59 0.53 3.3 1.1
## SENNA_1_30 199 0.65 0.67 0.62 0.55 4.1 1.1
## SENNA_2_13 200 0.72 0.71 0.68 0.62 3.2 1.2
## SENNA_2_14 199 0.76 0.76 0.74 0.68 3.2 1.1
## SENNA_2_15 199 0.60 0.61 0.55 0.48 3.6 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SENNA_1_25 0.13 0.19 0.33 0.18 0.18 0.01
## SENNA_1_26 0.08 0.09 0.19 0.27 0.37 0.00
## SENNA_1_27 0.13 0.18 0.38 0.20 0.10 0.00
## SENNA_1_28 0.04 0.07 0.15 0.27 0.48 0.01
## SENNA_1_29 0.06 0.17 0.36 0.22 0.20 0.00
## SENNA_1_30 0.03 0.04 0.24 0.22 0.48 0.01
## SENNA_2_13 0.10 0.16 0.32 0.28 0.14 0.00
## SENNA_2_14 0.08 0.16 0.40 0.21 0.16 0.01
## SENNA_2_15 0.03 0.11 0.30 0.32 0.24 0.01
psych::alpha(senna_N)
##
## Reliability analysis
## Call: psych::alpha(x = senna_N)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.75 0.79 0.25 2.9 0.028 3.7 0.62 0.21
##
## lower alpha upper 95% confidence boundaries
## 0.68 0.74 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SENNA_1_31 0.72 0.74 0.77 0.26 2.8 0.030 0.026 0.24
## SENNA_1_32 0.74 0.75 0.79 0.27 3.0 0.028 0.025 0.24
## SENNA_1_33 0.72 0.73 0.76 0.25 2.7 0.030 0.023 0.23
## SENNA_1_34 0.71 0.72 0.75 0.24 2.6 0.031 0.027 0.21
## SENNA_1_35 0.71 0.72 0.76 0.24 2.6 0.031 0.027 0.21
## SENNA_1_36 0.72 0.73 0.78 0.26 2.7 0.030 0.032 0.22
## SENNA_2_16 0.69 0.71 0.76 0.23 2.4 0.034 0.030 0.18
## SENNA_2_17 0.69 0.70 0.76 0.23 2.4 0.033 0.027 0.18
## SENNA_2_18 0.70 0.70 0.75 0.23 2.4 0.032 0.024 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SENNA_1_31 200 0.55 0.50 0.43 0.37 2.6 1.21
## SENNA_1_32 200 0.49 0.44 0.33 0.28 3.7 1.30
## SENNA_1_33 200 0.53 0.55 0.50 0.36 3.7 1.09
## SENNA_1_34 200 0.58 0.58 0.54 0.43 4.3 1.01
## SENNA_1_35 200 0.53 0.59 0.53 0.42 4.3 0.78
## SENNA_1_36 200 0.48 0.52 0.41 0.34 4.6 0.93
## SENNA_2_16 200 0.68 0.65 0.60 0.53 2.8 1.25
## SENNA_2_17 200 0.66 0.67 0.62 0.52 3.2 1.16
## SENNA_2_18 200 0.63 0.67 0.64 0.51 4.1 0.95
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## SENNA_1_31 0.23 0.25 0.26 0.20 0.06 0
## SENNA_1_32 0.10 0.06 0.24 0.22 0.38 0
## SENNA_1_33 0.03 0.11 0.28 0.30 0.28 0
## SENNA_1_34 0.03 0.03 0.17 0.20 0.58 0
## SENNA_1_35 0.00 0.02 0.10 0.41 0.47 0
## SENNA_1_36 0.03 0.04 0.04 0.14 0.76 0
## SENNA_2_16 0.16 0.30 0.23 0.18 0.12 0
## SENNA_2_17 0.04 0.25 0.32 0.20 0.19 0
## SENNA_2_18 0.01 0.05 0.20 0.33 0.42 0
Cohen’s D
library(effsize)
db$Grupo[db$Grupo == 1] <- 'Experimental'
db$Grupo[db$Grupo == 2] <- 'Controle'
cohen.d(db$caas_PREO, db$Grupo)
##
## Cohen's d
##
## d estimate: -1.02548 (large)
## 95 percent confidence interval:
## lower upper
## -1.3221293 -0.7288303
cohen.d(db$caas_CONT, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.8225862 (large)
## 95 percent confidence interval:
## lower upper
## -1.113026 -0.532146
cohen.d(db$caas_CURI, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.9360139 (large)
## 95 percent confidence interval:
## lower upper
## -1.2297737 -0.6422541
cohen.d(db$caas_CONF, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.8987067 (large)
## 95 percent confidence interval:
## lower upper
## -1.1913317 -0.6060818
cohen.d(db$caas_COOP, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.7815014 (medium)
## 95 percent confidence interval:
## lower upper
## -1.0708365 -0.4921663
cohen.d(db$senna_O, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.5328435 (medium)
## 95 percent confidence interval:
## lower upper
## -0.8166346 -0.2490524
cohen.d(db$senna_C, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.9696097 (large)
## 95 percent confidence interval:
## lower upper
## -1.2644271 -0.6747924
cohen.d(db$senna_E, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.4278734 (small)
## 95 percent confidence interval:
## lower upper
## -0.7099318 -0.1458150
cohen.d(db$senna_A, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.549847 (medium)
## 95 percent confidence interval:
## lower upper
## -0.8339532 -0.2657407
cohen.d(db$senna_N, db$Grupo)
##
## Cohen's d
##
## d estimate: -0.4462616 (small)
## 95 percent confidence interval:
## lower upper
## -0.7285969 -0.1639263