library(psych)
Depression <- read.csv("/Users/Lorraine/Desktop/depression(1).csv")
View(Depression)summary(Depression$v01)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 3.716 4.000 4.000 7summary(Depression$v02)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 4.000 3.504 4.000 4.000 12summary(Depression$v03)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 4.000 3.386 4.000 4.000 9summary(Depression$v04)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 1.809 2.000 4.000 7summary(Depression$v05)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 3.763 4.000 4.000 10summary(Depression$v06)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 3.836 4.000 4.000 7summary(Depression$v07)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 1.000 1.531 2.000 4.000 9summary(Depression$v08)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 4.000 3.706 4.000 4.000 7summary(Depression$v09)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 3.885 4.000 4.000 10summary(Depression$v10)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 3.00 4.00 3.53 4.00 4.00 8summary(Depression$v11)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 4.000 4.000 3.779 4.000 4.000 11summary(Depression$v12)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.000 2.235 3.000 4.000 12summary(Depression$v13)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 3.000 3.253 4.000 4.000 7summary(Depression$v14)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.085 3.000 4.000 9There is no outliers.
col <- c("v04","v07","v12","v14")
Depression[ ,col] <- 5 - Depression[ ,col]sum(is.na(Depression)) #Total cells## [1] 125sum(!complete.cases(Depression)) # Total rows## [1] 37Depression$DepressionScore <- rowMeans (Depression[ ,1:14], na.rm = TRUE)describe(Depression, na.rm = TRUE)## vars n mean sd median trimmed mad min max range
## v01 1 1573 3.72 0.57 4.00 3.83 0.00 1.00 4 3.00
## v02 2 1568 3.50 0.81 4.00 3.68 0.00 1.00 4 3.00
## v03 3 1571 3.39 0.82 4.00 3.54 0.00 1.00 4 3.00
## v04 4 1573 3.19 0.77 3.00 3.27 1.48 1.00 4 3.00
## v05 5 1570 3.76 0.53 4.00 3.88 0.00 1.00 4 3.00
## v06 6 1573 3.84 0.45 4.00 3.95 0.00 1.00 4 3.00
## v07 7 1571 3.47 0.71 4.00 3.60 0.00 1.00 4 3.00
## v08 8 1573 3.71 0.54 4.00 3.80 0.00 1.00 4 3.00
## v09 9 1570 3.89 0.39 4.00 4.00 0.00 1.00 4 3.00
## v10 10 1572 3.53 0.69 4.00 3.65 0.00 1.00 4 3.00
## v11 11 1569 3.78 0.58 4.00 3.93 0.00 1.00 4 3.00
## v12 12 1568 2.76 0.93 3.00 2.83 1.48 1.00 4 3.00
## v13 13 1573 3.25 0.71 3.00 3.35 0.00 1.00 4 3.00
## v14 14 1571 2.92 0.94 3.00 3.01 1.48 1.00 4 3.00
## sex 15 1580 1.27 0.44 1.00 1.21 0.00 1.00 2 1.00
## age 16 1580 61.27 5.54 61.00 61.04 4.45 44.00 85 41.00
## DepressionScore 17 1574 3.48 0.43 3.57 3.53 0.32 1.21 4 2.79
## skew kurtosis se
## v01 -2.38 6.55 0.01
## v02 -1.65 1.92 0.02
## v03 -1.33 1.21 0.02
## v04 -0.69 0.04 0.02
## v05 -2.58 7.71 0.01
## v06 -3.27 12.64 0.01
## v07 -1.25 1.15 0.02
## v08 -2.02 4.89 0.01
## v09 -4.21 21.92 0.01
## v10 -1.61 2.66 0.02
## v11 -3.10 10.05 0.01
## v12 -0.28 -0.80 0.02
## v13 -0.91 1.11 0.02
## v14 -0.51 -0.67 0.02
## sex 1.04 -0.91 0.01
## age 0.48 0.68 0.14
## DepressionScore -1.39 2.64 0.01alpha(Depression[ ,1:14])##
## Reliability analysis
## Call: alpha(x = Depression[, 1:14])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.88 0.89 0.34 7.1 0.0045 3.5 0.43 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## v01 0.86 0.86 0.87 0.32 6.2 0.0050 0.0098 0.33
## v02 0.86 0.87 0.88 0.34 6.6 0.0048 0.0113 0.34
## v03 0.86 0.87 0.88 0.34 6.7 0.0048 0.0111 0.34
## v04 0.86 0.87 0.87 0.33 6.4 0.0050 0.0101 0.33
## v05 0.86 0.87 0.88 0.33 6.5 0.0048 0.0108 0.34
## v06 0.87 0.87 0.88 0.35 6.9 0.0046 0.0102 0.35
## v07 0.86 0.87 0.88 0.33 6.5 0.0049 0.0105 0.34
## v08 0.86 0.86 0.87 0.33 6.4 0.0049 0.0102 0.33
## v09 0.87 0.88 0.88 0.35 7.1 0.0046 0.0088 0.35
## v10 0.86 0.86 0.87 0.33 6.4 0.0050 0.0110 0.33
## v11 0.87 0.87 0.88 0.35 6.8 0.0047 0.0109 0.35
## v12 0.86 0.87 0.87 0.33 6.4 0.0051 0.0099 0.33
## v13 0.86 0.87 0.87 0.33 6.4 0.0050 0.0103 0.33
## v14 0.87 0.87 0.88 0.34 6.7 0.0048 0.0099 0.34
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## v01 1573 0.70 0.72 0.71 0.64 3.7 0.57
## v02 1568 0.63 0.62 0.58 0.54 3.5 0.81
## v03 1571 0.62 0.60 0.55 0.53 3.4 0.82
## v04 1573 0.68 0.66 0.64 0.60 3.2 0.77
## v05 1570 0.59 0.64 0.60 0.53 3.8 0.53
## v06 1573 0.46 0.52 0.47 0.40 3.8 0.45
## v07 1571 0.65 0.64 0.61 0.57 3.5 0.71
## v08 1573 0.64 0.68 0.66 0.58 3.7 0.54
## v09 1570 0.40 0.47 0.41 0.34 3.9 0.39
## v10 1572 0.69 0.69 0.66 0.62 3.5 0.69
## v11 1569 0.52 0.54 0.48 0.44 3.8 0.58
## v12 1568 0.71 0.67 0.64 0.62 2.8 0.93
## v13 1573 0.68 0.66 0.63 0.61 3.3 0.71
## v14 1571 0.63 0.58 0.53 0.52 2.9 0.94
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## v01 0.02 0.02 0.20 0.76 0.00
## v02 0.04 0.07 0.21 0.67 0.01
## v03 0.05 0.08 0.32 0.56 0.01
## v04 0.03 0.14 0.45 0.38 0.00
## v05 0.01 0.02 0.17 0.80 0.01
## v06 0.01 0.01 0.12 0.86 0.00
## v07 0.02 0.08 0.33 0.58 0.01
## v08 0.01 0.02 0.24 0.74 0.00
## v09 0.01 0.00 0.09 0.90 0.01
## v10 0.03 0.04 0.31 0.62 0.01
## v11 0.02 0.02 0.12 0.84 0.01
## v12 0.10 0.27 0.39 0.24 0.01
## v13 0.03 0.07 0.52 0.38 0.00
## v14 0.09 0.21 0.39 0.31 0.01α = 0.87
splitHalf(Depression[ ,1:14]) ## Split half reliabilities
## Call: splitHalf(r = Depression[, 1:14])
##
## Maximum split half reliability (lambda 4) = 0.92
## Guttman lambda 6 = 0.89
## Average split half reliability = 0.88
## Guttman lambda 3 (alpha) = 0.88
## Minimum split half reliability (beta) = 0.77
## Average interitem r = 0.34 with median = 0.34Average split half reliability = 0.88
Coefficient alpha is more appropriate in this case becuase it is the average of all possible split half reliabilities. Although the split half reliability is slightly larger than the coefficient alpha by 0.01, I would choose coefficient alpha for the reliability report.
Depression$sex <- factor(Depression$sex, levels = c("1","2"), labels = c("Male","Female"))
Male <- subset(Depression, `sex` == "Male")
Female <- subset(Depression, `sex` == "Female")
alpha(Male[ ,1:14]) #α = 0.87##
## Reliability analysis
## Call: alpha(x = Male[, 1:14])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.88 0.88 0.33 7 0.0054 3.5 0.42 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## v01 0.86 0.86 0.87 0.32 6.2 0.0059 0.0100 0.33
## v02 0.86 0.87 0.88 0.34 6.6 0.0057 0.0112 0.34
## v03 0.86 0.87 0.88 0.34 6.6 0.0058 0.0110 0.34
## v04 0.86 0.86 0.87 0.33 6.3 0.0059 0.0097 0.33
## v05 0.86 0.87 0.87 0.33 6.4 0.0057 0.0108 0.34
## v06 0.87 0.87 0.88 0.34 6.8 0.0055 0.0100 0.34
## v07 0.86 0.87 0.87 0.33 6.4 0.0058 0.0104 0.33
## v08 0.86 0.86 0.87 0.33 6.3 0.0058 0.0102 0.33
## v09 0.87 0.88 0.88 0.35 7.0 0.0055 0.0086 0.34
## v10 0.86 0.86 0.87 0.33 6.3 0.0059 0.0110 0.33
## v11 0.86 0.87 0.88 0.34 6.8 0.0056 0.0106 0.34
## v12 0.86 0.86 0.87 0.33 6.4 0.0060 0.0098 0.33
## v13 0.86 0.86 0.87 0.33 6.4 0.0060 0.0103 0.33
## v14 0.86 0.87 0.88 0.34 6.7 0.0056 0.0098 0.34
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## v01 1150 0.68 0.71 0.69 0.63 3.8 0.54
## v02 1147 0.61 0.60 0.56 0.51 3.5 0.82
## v03 1150 0.63 0.60 0.55 0.53 3.4 0.81
## v04 1150 0.69 0.67 0.65 0.61 3.2 0.78
## v05 1148 0.60 0.64 0.61 0.54 3.8 0.49
## v06 1151 0.46 0.52 0.46 0.39 3.8 0.44
## v07 1148 0.65 0.64 0.61 0.57 3.5 0.70
## v08 1150 0.64 0.69 0.67 0.59 3.8 0.51
## v09 1148 0.40 0.47 0.41 0.34 3.9 0.39
## v10 1149 0.68 0.68 0.65 0.61 3.6 0.70
## v11 1147 0.52 0.53 0.47 0.43 3.8 0.60
## v12 1145 0.71 0.67 0.64 0.62 2.8 0.93
## v13 1150 0.69 0.66 0.64 0.62 3.3 0.72
## v14 1148 0.63 0.57 0.53 0.51 2.9 0.95
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## v01 0.01 0.02 0.18 0.79 0.01
## v02 0.05 0.07 0.21 0.67 0.01
## v03 0.04 0.07 0.33 0.56 0.01
## v04 0.03 0.14 0.44 0.40 0.01
## v05 0.01 0.01 0.15 0.83 0.01
## v06 0.01 0.01 0.13 0.85 0.00
## v07 0.02 0.07 0.31 0.60 0.01
## v08 0.01 0.01 0.19 0.79 0.01
## v09 0.01 0.01 0.09 0.90 0.01
## v10 0.03 0.04 0.29 0.64 0.01
## v11 0.02 0.02 0.11 0.84 0.01
## v12 0.10 0.26 0.39 0.25 0.01
## v13 0.03 0.07 0.51 0.39 0.01
## v14 0.09 0.21 0.38 0.31 0.01alpha(Female[ ,1:14]) #α = 0.88##
## Reliability analysis
## Call: alpha(x = Female[, 1:14])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.9 0.35 7.4 0.0084 3.4 0.44 0.35
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## v01 0.86 0.87 0.88 0.33 6.4 0.0094 0.012 0.33
## v02 0.86 0.87 0.89 0.34 6.7 0.0093 0.013 0.34
## v03 0.87 0.88 0.89 0.35 7.0 0.0088 0.013 0.35
## v04 0.87 0.87 0.89 0.34 6.8 0.0091 0.013 0.35
## v05 0.87 0.87 0.89 0.35 6.8 0.0088 0.013 0.35
## v06 0.87 0.88 0.89 0.36 7.2 0.0087 0.012 0.35
## v07 0.87 0.87 0.89 0.34 6.8 0.0091 0.013 0.35
## v08 0.87 0.87 0.89 0.34 6.7 0.0090 0.012 0.35
## v09 0.88 0.88 0.89 0.36 7.4 0.0086 0.011 0.36
## v10 0.86 0.87 0.89 0.34 6.6 0.0093 0.013 0.33
## v11 0.87 0.88 0.89 0.35 7.0 0.0088 0.013 0.35
## v12 0.87 0.87 0.89 0.34 6.7 0.0093 0.012 0.35
## v13 0.87 0.87 0.89 0.34 6.8 0.0092 0.012 0.35
## v14 0.87 0.87 0.89 0.35 7.0 0.0089 0.012 0.35
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## v01 423 0.74 0.76 0.75 0.68 3.6 0.65
## v02 421 0.69 0.68 0.65 0.61 3.5 0.80
## v03 421 0.61 0.58 0.54 0.51 3.4 0.86
## v04 423 0.65 0.65 0.61 0.57 3.1 0.74
## v05 422 0.59 0.63 0.59 0.52 3.7 0.61
## v06 422 0.48 0.54 0.49 0.42 3.9 0.47
## v07 423 0.64 0.63 0.60 0.56 3.4 0.73
## v08 423 0.64 0.66 0.64 0.57 3.6 0.59
## v09 422 0.41 0.47 0.42 0.36 3.9 0.36
## v10 423 0.71 0.70 0.68 0.64 3.5 0.68
## v11 422 0.55 0.58 0.53 0.48 3.8 0.52
## v12 423 0.71 0.66 0.64 0.61 2.7 0.93
## v13 423 0.66 0.64 0.61 0.59 3.2 0.71
## v14 423 0.64 0.59 0.56 0.54 2.9 0.94
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## v01 0.02 0.02 0.27 0.68 0.00
## v02 0.04 0.07 0.22 0.67 0.01
## v03 0.06 0.08 0.31 0.55 0.01
## v04 0.02 0.14 0.50 0.34 0.00
## v05 0.02 0.03 0.22 0.73 0.00
## v06 0.01 0.02 0.08 0.89 0.00
## v07 0.02 0.09 0.37 0.52 0.00
## v08 0.01 0.02 0.36 0.61 0.00
## v09 0.00 0.00 0.09 0.90 0.00
## v10 0.02 0.04 0.38 0.56 0.00
## v11 0.01 0.01 0.13 0.84 0.00
## v12 0.12 0.29 0.39 0.20 0.00
## v13 0.03 0.08 0.53 0.36 0.00
## v14 0.10 0.20 0.39 0.31 0.00The coefficient alpha for male is 0.87 while the coefficient alpha for female is 0.88. There is not much difference between them, one possibility for the realibility difference is that there are more missing data from male subjects than female subjects.