Itemanalyse
# Itemanalyse ----
library(psych)
setwd("~/Documents/R Sachen")
# Daten einlesen
dat <- read.csv("Perfektionismus.csv")
# numerische Umwandlung
dat$age <- as.numeric(dat$age)
# Umkodierung
dat$P105_08_r <- 6 - dat$P105_08
# Item den Facetten/Subskalen zuordnen
dat_EN <- data.frame(dat[c("P105_01", "P105_02", "P105_03", "P105_04", "P105_05", "P105_06", "P105_07", "P105_08_r","P105_09", "P105_10", "P105_11", "P105_12", "P105_13", "P105_14", "P105_15", "P105_16", "P105_17", "P105_18", "P105_19", "P105_20")])
dat_Anspruch <- data.frame(dat[c("P105_01", "P105_02", "P105_03", "P105_04", "P105_05")])
dat_Eltern <- data.frame(dat[c("P105_06", "P105_07", "P105_08_r", "P105_09", "P105_10")])
dat_Sorge <- data.frame(dat[c("P105_11", "P105_12", "P105_13", "P105_14", "P105_15")])
dat_Zweifel <- data.frame(dat[c("P105_16", "P105_17", "P105_18", "P105_19", "P105_20")])
# Itemkennwerte
# inkl. M, SD, Min, Max, Range, Schiefe, Kurtosis
# damit auch Schwierigkeit, Varianz
describe(dat_EN)
# 5.4. Homogenität der Items (bestimmt über Interkorrelationen der Items)
matrix <- cor(dat_Anspruch, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_01 P105_02 P105_03 P105_04 P105_05
## P105_01 1.00 0.61 0.23 0.37 0.34
## P105_02 0.61 1.00 0.35 0.49 0.39
## P105_03 0.23 0.35 1.00 0.05 0.07
## P105_04 0.37 0.49 0.05 1.00 0.46
## P105_05 0.34 0.39 0.07 0.46 1.00
matrix <- cor(dat_Anspruch, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_01 P105_02 P105_03 P105_04 P105_05
## P105_01 1.00 0.61 0.23 0.37 0.34
## P105_02 0.61 1.00 0.35 0.49 0.39
## P105_03 0.23 0.35 1.00 0.05 0.07
## P105_04 0.37 0.49 0.05 1.00 0.46
## P105_05 0.34 0.39 0.07 0.46 1.00
matrix <- cor(dat_Eltern, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_06 P105_07 P105_08_r P105_09 P105_10
## P105_06 1.00 0.67 0.07 0.58 0.40
## P105_07 0.67 1.00 0.07 0.55 0.60
## P105_08_r 0.07 0.07 1.00 0.14 0.22
## P105_09 0.58 0.55 0.14 1.00 0.55
## P105_10 0.40 0.60 0.22 0.55 1.00
matrix <- cor(dat_Eltern, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_06 P105_07 P105_08_r P105_09 P105_10
## P105_06 1.00 0.67 0.07 0.58 0.40
## P105_07 0.67 1.00 0.07 0.55 0.60
## P105_08_r 0.07 0.07 1.00 0.14 0.22
## P105_09 0.58 0.55 0.14 1.00 0.55
## P105_10 0.40 0.60 0.22 0.55 1.00
# P105_08 - "Ich werde den Leistungsansprüchen meiner Erziehungsberechtigten gerecht" rausschmeißen, da nicht homogen
matrix <- cor(dat_Sorge, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_11 P105_12 P105_13 P105_14 P105_15
## P105_11 1.00 0.46 0.57 0.53 0.18
## P105_12 0.46 1.00 0.69 0.36 0.39
## P105_13 0.57 0.69 1.00 0.56 0.46
## P105_14 0.53 0.36 0.56 1.00 0.38
## P105_15 0.18 0.39 0.46 0.38 1.00
matrix <- cor(dat_Sorge, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_11 P105_12 P105_13 P105_14 P105_15
## P105_11 1.00 0.46 0.57 0.53 0.18
## P105_12 0.46 1.00 0.69 0.36 0.39
## P105_13 0.57 0.69 1.00 0.56 0.46
## P105_14 0.53 0.36 0.56 1.00 0.38
## P105_15 0.18 0.39 0.46 0.38 1.00
matrix <- cor(dat_Zweifel, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_16 P105_17 P105_18 P105_19 P105_20
## P105_16 1.00 0.53 0.25 0.27 0.39
## P105_17 0.53 1.00 0.33 0.32 0.58
## P105_18 0.25 0.33 1.00 0.26 0.40
## P105_19 0.27 0.32 0.26 1.00 0.35
## P105_20 0.39 0.58 0.40 0.35 1.00
matrix <- cor(dat_Zweifel, use = "pairwise.complete.obs")
round(matrix, 2)
## P105_16 P105_17 P105_18 P105_19 P105_20
## P105_16 1.00 0.53 0.25 0.27 0.39
## P105_17 0.53 1.00 0.33 0.32 0.58
## P105_18 0.25 0.33 1.00 0.26 0.40
## P105_19 0.27 0.32 0.26 1.00 0.35
## P105_20 0.39 0.58 0.40 0.35 1.00
# Reliabilitätsanalyse der Subskalen inkl. Trennschärfe
psych::alpha(dat_Anspruch) # P105_03 rausschmeißen, da raw_alpha mit 0.72 und ohne 0.76
##
## Reliability analysis
## Call: psych::alpha(x = dat_Anspruch)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.71 0.34 2.5 0.051 3.2 0.82 0.36
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.60 0.72 0.81
## Duhachek 0.62 0.72 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P105_01 0.63 0.63 0.62 0.30 1.7 0.069 0.0382 0.37
## P105_02 0.57 0.57 0.54 0.25 1.3 0.081 0.0278 0.28
## P105_03 0.76 0.76 0.72 0.44 3.2 0.045 0.0097 0.42
## P105_04 0.67 0.66 0.64 0.33 2.0 0.062 0.0320 0.34
## P105_05 0.68 0.68 0.67 0.35 2.2 0.060 0.0387 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P105_01 74 0.75 0.74 0.67 0.56 3.0 1.2
## P105_02 74 0.83 0.83 0.82 0.70 3.6 1.2
## P105_03 74 0.49 0.50 0.29 0.23 2.9 1.2
## P105_04 74 0.70 0.69 0.59 0.48 2.7 1.3
## P105_05 74 0.64 0.66 0.52 0.44 3.9 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P105_01 0.11 0.30 0.14 0.36 0.09 0
## P105_02 0.08 0.14 0.14 0.43 0.22 0
## P105_03 0.15 0.23 0.23 0.34 0.05 0
## P105_04 0.23 0.27 0.14 0.30 0.07 0
## P105_05 0.07 0.05 0.07 0.55 0.26 0
psych::alpha(dat_Eltern)
##
## Reliability analysis
## Call: psych::alpha(x = dat_Eltern)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.76 0.77 0.38 3.1 0.045 2.2 0.93 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.65 0.75 0.83
## Duhachek 0.67 0.75 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P105_06 0.68 0.69 0.68 0.36 2.2 0.061 0.0575 0.38
## P105_07 0.65 0.66 0.64 0.33 1.9 0.066 0.0464 0.31
## P105_08_r 0.83 0.84 0.81 0.56 5.1 0.031 0.0084 0.57
## P105_09 0.66 0.67 0.69 0.34 2.0 0.065 0.0691 0.31
## P105_10 0.68 0.68 0.68 0.35 2.1 0.060 0.0802 0.35
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P105_06 74 0.77 0.76 0.72 0.60 2.0 1.3
## P105_07 74 0.81 0.81 0.79 0.67 2.1 1.3
## P105_08_r 74 0.42 0.42 0.18 0.15 2.7 1.3
## P105_09 74 0.80 0.79 0.73 0.64 2.4 1.4
## P105_10 74 0.76 0.78 0.71 0.62 1.6 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P105_06 0.50 0.24 0.05 0.12 0.08 0
## P105_07 0.45 0.30 0.04 0.15 0.07 0
## P105_08_r 0.19 0.36 0.20 0.08 0.16 0
## P105_09 0.39 0.22 0.11 0.20 0.08 0
## P105_10 0.72 0.08 0.08 0.09 0.03 0
psych::alpha(dat_Sorge) # P105_15 rausschmeißen, da raw-alpha mit 0.8 und ohne 0.82
##
## Reliability analysis
## Call: psych::alpha(x = dat_Sorge)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.81 0.8 0.46 4.2 0.037 2.9 1 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.72 0.8 0.87
## Duhachek 0.73 0.8 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P105_11 0.78 0.78 0.76 0.47 3.6 0.043 0.016 0.42
## P105_12 0.76 0.76 0.74 0.45 3.2 0.047 0.021 0.49
## P105_13 0.71 0.72 0.69 0.39 2.5 0.056 0.014 0.39
## P105_14 0.77 0.77 0.75 0.46 3.4 0.046 0.029 0.46
## P105_15 0.82 0.82 0.79 0.53 4.5 0.035 0.012 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P105_11 74 0.73 0.73 0.64 0.55 2.6 1.4
## P105_12 74 0.77 0.77 0.71 0.62 2.9 1.4
## P105_13 74 0.86 0.87 0.86 0.77 2.9 1.3
## P105_14 74 0.75 0.75 0.67 0.60 3.3 1.4
## P105_15 74 0.65 0.64 0.50 0.44 3.0 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P105_11 0.32 0.20 0.11 0.26 0.11 0
## P105_12 0.20 0.27 0.11 0.31 0.11 0
## P105_13 0.20 0.20 0.19 0.30 0.11 0
## P105_14 0.11 0.24 0.12 0.30 0.23 0
## P105_15 0.23 0.16 0.19 0.18 0.24 0
psych::alpha(dat_Zweifel)
##
## Reliability analysis
## Call: psych::alpha(x = dat_Zweifel)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.74 0.72 0.37 2.9 0.045 2.8 0.94 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.64 0.75 0.83
## Duhachek 0.66 0.75 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P105_16 0.71 0.70 0.66 0.37 2.4 0.055 0.0123 0.34
## P105_17 0.65 0.65 0.59 0.32 1.9 0.066 0.0046 0.31
## P105_18 0.74 0.73 0.69 0.41 2.7 0.048 0.0154 0.37
## P105_19 0.74 0.74 0.70 0.41 2.8 0.049 0.0154 0.39
## P105_20 0.67 0.66 0.61 0.33 1.9 0.062 0.0114 0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P105_16 74 0.71 0.69 0.59 0.50 3.1 1.4
## P105_17 74 0.81 0.79 0.75 0.64 2.7 1.5
## P105_18 74 0.63 0.64 0.48 0.41 3.1 1.3
## P105_19 74 0.60 0.62 0.46 0.40 2.6 1.2
## P105_20 74 0.77 0.77 0.71 0.61 2.7 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P105_16 0.16 0.23 0.09 0.34 0.18 0
## P105_17 0.31 0.20 0.15 0.18 0.16 0
## P105_18 0.12 0.24 0.22 0.24 0.18 0
## P105_19 0.18 0.42 0.12 0.24 0.04 0
## P105_20 0.22 0.28 0.16 0.24 0.09 0
Faktorenanalyse
#### Faktorenanalyse ----
# Working Directory setzen und Daten einlesen
setwd("~/Documents/R Sachen")
dat <- read.csv("Perfektionismus.csv")
# notwendige Packages laden (und ggf. installieren)
library(psych)
library(GPArotation) #install.packages("GPArotation")
#### 1. Auswahl der relevanten Variablen für die Faktorenanalyse ####
# (nach Itemrevision/-selektion muss die Auswahl ggf. angepasst werden!)
dat_pca <- data.frame(dat[c("P105_01", "P105_02", "P105_04", "P105_05", "P105_06", "P105_07",
"P105_09", "P105_10", "P105_11", "P105_12", "P105_13", "P105_14", "P105_16", "P105_17",
"P105_18", "P105_19", "P105_20")])
#ungeeignete Items wurden entfernt
#### 2. Voraussetzungen prüfen ####
# Korrelationen der Variablen in einer Heat Map
cor.plot(cor(dat_pca, use = "pairwise.complete.obs"))
#hier ist es irrelevant ob die Korrelationen pos. oder neg. sind, es geht um ihre Höhe
# Bartlett Test, sollte in jedem Fall signifikant sein (p < 0.05)
cortest.bartlett(dat_pca, n = nrow(dat_pca))
## R was not square, finding R from data
## $chisq
## [1] 640.3596
##
## $p.value
## [1] 2.231687e-66
##
## $df
## [1] 136
# KMO (Kaiser-Meyer-Olkin-Kriterium), sollte min. .50 sein. Je höher, desto besser.
kmo <- KMO(dat_pca)
kmo #overall MSA (Measure of Sampling Adequacy)-Wert entspricht dem KMO
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = dat_pca)
## Overall MSA = 0.82
## MSA for each item =
## P105_01 P105_02 P105_04 P105_05 P105_06 P105_07 P105_09 P105_10 P105_11 P105_12
## 0.72 0.55 0.80 0.87 0.77 0.74 0.92 0.73 0.84 0.86
## P105_13 P105_14 P105_16 P105_17 P105_18 P105_19 P105_20
## 0.89 0.88 0.85 0.82 0.76 0.87 0.86
# ist irgendeiner der MSAi Werte kleiner als .50?
any(kmo$MSAi < 0.50)
## [1] FALSE
#### 3. Faktorenextraktion ####
# Wir starten mit einem Faktorenmodell, dass so viele Hauptkomponenten wie Items umfasst
# Rotation spielt an dieser Stelle noch KEINE Rolle
pca_full <- principal(dat_pca, nfactors = 17, rotate = "none") #16, weil wir 16 Items in die Analyse geben
pca_full # inspiziere Kommunalität (h^2) und Eigenwerteverlauf (SS loadings)
## Principal Components Analysis
## Call: principal(r = dat_pca, nfactors = 17, rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
## P105_01 0.34 0.65 0.26 -0.01 0.28 -0.42 0.07 -0.11 0.04 0.18 -0.12 0.16
## P105_02 0.31 0.79 0.33 -0.05 0.09 0.13 -0.13 0.18 0.00 0.04 -0.06 -0.01
## P105_04 0.55 0.47 0.03 0.25 -0.37 0.00 -0.16 -0.02 0.44 -0.02 0.08 -0.15
## P105_05 0.67 0.29 -0.12 0.34 0.13 -0.02 -0.27 0.07 -0.30 -0.26 -0.01 -0.10
## P105_06 0.51 -0.31 0.58 -0.31 0.00 0.26 -0.05 -0.11 0.12 -0.02 -0.05 0.18
## P105_07 0.60 -0.26 0.58 0.20 0.08 0.20 -0.12 -0.06 0.09 -0.14 0.07 0.03
## P105_09 0.68 -0.39 0.28 -0.18 -0.04 -0.10 0.02 0.06 -0.05 0.14 -0.32 -0.35
## P105_10 0.51 -0.39 0.43 0.30 0.12 -0.38 0.15 0.10 -0.11 0.05 0.24 -0.06
## P105_11 0.72 -0.27 -0.25 -0.16 -0.15 0.04 -0.35 0.23 -0.05 0.06 0.07 0.07
## P105_12 0.74 0.05 0.04 -0.11 -0.32 -0.12 0.36 0.09 -0.06 -0.29 0.07 0.14
## P105_13 0.79 0.08 -0.23 -0.03 -0.30 -0.11 0.06 -0.10 -0.11 -0.14 -0.27 0.11
## P105_14 0.72 -0.06 -0.22 0.02 0.22 0.10 -0.23 -0.46 -0.14 0.05 0.05 0.03
## P105_16 0.52 -0.20 -0.32 0.30 0.47 0.22 0.18 0.30 0.21 -0.03 -0.17 0.11
## P105_17 0.70 -0.22 -0.38 0.12 0.05 -0.09 0.20 -0.26 0.26 0.06 0.03 -0.07
## P105_18 0.51 0.25 -0.17 -0.62 0.36 -0.02 0.09 0.04 0.08 -0.21 0.19 -0.17
## P105_19 0.56 0.36 0.01 0.10 -0.12 0.46 0.40 -0.03 -0.24 0.26 0.10 -0.08
## P105_20 0.78 -0.09 -0.23 -0.15 -0.09 -0.13 -0.15 0.19 -0.01 0.35 0.12 0.13
## PC13 PC14 PC15 PC16 PC17 h2 u2 com
## P105_01 0.08 0.20 0.02 -0.03 -0.06 1 3.3e-16 4.2
## P105_02 -0.08 -0.17 -0.02 -0.08 0.21 1 5.6e-16 2.4
## P105_04 -0.06 0.06 0.00 0.14 -0.05 1 0.0e+00 5.0
## P105_05 0.24 -0.04 -0.10 0.04 -0.06 1 2.2e-16 4.1
## P105_06 0.21 -0.05 -0.04 0.18 0.02 1 -8.9e-16 4.9
## P105_07 -0.08 0.01 0.07 -0.25 -0.12 1 1.1e-16 3.8
## P105_09 -0.05 0.06 -0.09 -0.01 -0.01 1 7.8e-16 3.7
## P105_10 -0.05 -0.07 0.12 0.13 0.08 1 -6.7e-16 6.3
## P105_11 0.07 0.26 0.13 -0.05 0.12 1 2.2e-15 3.3
## P105_12 -0.10 0.10 -0.21 -0.04 0.04 1 1.1e-16 3.0
## P105_13 -0.05 -0.15 0.25 0.02 -0.04 1 1.1e-16 2.5
## P105_14 -0.26 0.04 -0.08 0.07 0.06 1 -1.1e-16 3.1
## P105_16 -0.10 0.03 -0.01 0.08 -0.01 1 3.3e-16 6.4
## P105_17 0.24 -0.09 -0.03 -0.15 0.12 1 5.6e-16 3.5
## P105_18 -0.02 -0.02 0.09 0.01 -0.07 1 5.6e-16 4.2
## P105_19 0.06 0.08 0.07 0.02 -0.04 1 2.2e-16 5.2
## P105_20 -0.03 -0.20 -0.12 -0.03 -0.14 1 1.1e-15 2.5
##
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
## SS loadings 6.48 2.20 1.61 1.00 0.90 0.78 0.73 0.56 0.53 0.49 0.36
## Proportion Var 0.38 0.13 0.09 0.06 0.05 0.05 0.04 0.03 0.03 0.03 0.02
## Cumulative Var 0.38 0.51 0.61 0.66 0.72 0.76 0.81 0.84 0.87 0.90 0.92
## Proportion Explained 0.38 0.13 0.09 0.06 0.05 0.05 0.04 0.03 0.03 0.03 0.02
## Cumulative Proportion 0.38 0.51 0.61 0.66 0.72 0.76 0.81 0.84 0.87 0.90 0.92
## PC12 PC13 PC14 PC15 PC16 PC17
## SS loadings 0.32 0.29 0.24 0.19 0.18 0.14
## Proportion Var 0.02 0.02 0.01 0.01 0.01 0.01
## Cumulative Var 0.94 0.96 0.97 0.98 0.99 1.00
## Proportion Explained 0.02 0.02 0.01 0.01 0.01 0.01
## Cumulative Proportion 0.94 0.96 0.97 0.98 0.99 1.00
##
## Mean item complexity = 4
## Test of the hypothesis that 17 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
#Wie viele Faktoren müssten wir also laut Kaiser-Kriterium extrahieren?
# Inspiziere Scree-Plot
plot(pca_full$values, type = "b")
# Parallelanalyse
fa1 <- fa.parallel(dat_pca, fa = "pc") #"pc" steht für Hauptkomponentenanalyse
## Parallel analysis suggests that the number of factors = NA and the number of components = 2
print(fa1)
## Call: fa.parallel(x = dat_pca, fa = "pc")
## Parallel analysis suggests that the number of factors = NA and the number of components = 2
##
## Eigen Values of
##
## eigen values of factors
## [1] 5.89 1.41 0.93 0.33 0.24 0.13 0.02 -0.01 -0.07 -0.15 -0.19 -0.27
## [13] -0.33 -0.37 -0.47 -0.53 -0.67
##
## eigen values of simulated factors
## [1] NA
##
## eigen values of components
## [1] 6.48 2.20 1.61 1.00 0.90 0.78 0.73 0.56 0.53 0.49 0.36 0.32 0.29 0.24 0.19
## [16] 0.18 0.14
##
## eigen values of simulated components
## [1] 1.94 1.75 1.60 1.42 1.31 1.22 1.10 1.01 0.91 0.85 0.79 0.71 0.63 0.55 0.48
## [16] 0.41 0.34
#Empfehlung aus Bühner, 2021:
fa2 <- fa.parallel(dat_pca, fm = "ml", fa = "pc", n.iter = 2000) #besser wäre n.iter = 10.000
## Parallel analysis suggests that the number of factors = NA and the number of components = 2
print(fa2)
## Call: fa.parallel(x = dat_pca, fm = "ml", fa = "pc", n.iter = 2000)
## Parallel analysis suggests that the number of factors = NA and the number of components = 2
##
## Eigen Values of
##
## eigen values of factors
## [1] 5.90 1.40 0.92 0.33 0.25 0.14 0.03 -0.01 -0.06 -0.15 -0.19 -0.27
## [13] -0.33 -0.37 -0.48 -0.56 -0.69
##
## eigen values of simulated factors
## [1] NA
##
## eigen values of components
## [1] 6.48 2.20 1.61 1.00 0.90 0.78 0.73 0.56 0.53 0.49 0.36 0.32 0.29 0.24 0.19
## [16] 0.18 0.14
##
## eigen values of simulated components
## [1] 1.95 1.73 1.57 1.44 1.32 1.21 1.11 1.02 0.93 0.85 0.77 0.70 0.62 0.55 0.48
## [16] 0.41 0.33
# MAP Test - funktioniert nicht!!!
map <- VSS(dat_pca, n = 17)
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
map
##
## Very Simple Structure
## Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
## n.obs = n.obs, plot = plot, title = title, use = use, cor = cor)
## VSS complexity 1 achieves a maximimum of 0.78 with 1 factors
## VSS complexity 2 achieves a maximimum of 0.87 with 3 factors
##
## The Velicer MAP achieves a minimum of 0.03 with 3 factors
## BIC achieves a minimum of -257.01 with 3 factors
## Sample Size adjusted BIC achieves a minimum of -14.78 with 7 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex
## 1 0.78 0.00 0.041 119 2.8e+02 2.4e-15 12.1 0.78 0.136 -229 145.6 1.0
## 2 0.65 0.86 0.040 103 2.0e+02 3.6e-08 7.5 0.86 0.112 -243 81.1 1.3
## 3 0.60 0.87 0.034 88 1.2e+02 1.0e-02 4.9 0.91 0.071 -257 20.3 1.5
## 4 0.57 0.84 0.040 74 9.2e+01 7.5e-02 4.1 0.92 0.056 -226 6.9 1.7
## 5 0.46 0.76 0.046 61 7.2e+01 1.5e-01 3.4 0.94 0.048 -190 2.0 2.0
## 6 0.44 0.75 0.053 49 5.5e+01 2.5e-01 2.7 0.95 0.039 -156 -1.3 2.0
## 7 0.37 0.65 0.063 38 2.9e+01 8.5e-01 2.3 0.96 0.000 -135 -14.8 2.2
## 8 0.37 0.61 0.074 28 2.0e+01 8.7e-01 1.9 0.97 0.000 -101 -12.5 2.3
## 9 0.34 0.58 0.091 19 9.7e+00 9.6e-01 1.6 0.97 0.000 -72 -12.2 2.5
## 10 0.33 0.57 0.113 11 3.4e+00 9.8e-01 1.4 0.97 0.000 -44 -9.3 2.6
## 11 0.34 0.56 0.136 4 1.1e+00 9.0e-01 1.0 0.98 0.000 -16 -3.6 2.6
## 12 0.35 0.58 0.169 -2 1.5e-03 NA 1.2 0.98 NA NA NA 2.5
## 13 0.33 0.56 0.220 -7 6.8e-08 NA 1.1 0.98 NA NA NA 2.5
## 14 0.33 0.55 0.302 -11 1.5e-07 NA 1.0 0.98 NA NA NA 2.5
## 15 0.33 0.54 0.480 -14 1.2e-08 NA 1.0 0.98 NA NA NA 2.5
## 16 0.33 0.54 1.000 -16 0.0e+00 NA 1.0 0.98 NA NA NA 2.5
## 17 0.33 0.54 NA -17 0.0e+00 NA 1.0 0.98 NA NA NA 2.5
## eChisq SRMR eCRMS eBIC
## 1 3.2e+02 1.3e-01 0.136 -189
## 2 1.5e+02 8.6e-02 0.099 -294
## 3 5.7e+01 5.3e-02 0.066 -322
## 4 3.9e+01 4.4e-02 0.060 -279
## 5 2.6e+01 3.6e-02 0.054 -236
## 6 1.6e+01 2.9e-02 0.048 -194
## 7 8.1e+00 2.0e-02 0.038 -155
## 8 4.4e+00 1.5e-02 0.032 -116
## 9 2.0e+00 9.9e-03 0.026 -80
## 10 5.8e-01 5.4e-03 0.019 -47
## 11 1.3e-01 2.5e-03 0.015 -17
## 12 2.2e-04 1.0e-04 NA NA
## 13 6.4e-09 5.6e-07 NA NA
## 14 1.6e-08 8.8e-07 NA NA
## 15 1.4e-09 2.7e-07 NA NA
## 16 7.9e-17 6.3e-11 NA NA
## 17 7.9e-17 6.3e-11 NA NA
#n bezieht sich hier auf die Anzahl der möglichen Faktoren die man extrahieren kann
#dies entspricht hier also der Anzahl der Items, also 16
#den Plot können Sie ignorieren, wichtig ist der Output in der Console
# Nachdem man sich für eine bestimmte Anzahl an zu extrahierenden Faktoren entschieden hat,
# wird das Modell mit dieser Anzahl an Faktoren berechnet
pca_red <- principal(dat_pca, nfactors = 2, rotate = "none")
pca_red # inspiziere Kommunalität und Varianzaufklärung (proportion und cumulative var)
## Principal Components Analysis
## Call: principal(r = dat_pca, nfactors = 2, rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 h2 u2 com
## P105_01 0.34 0.65 0.53 0.47 1.5
## P105_02 0.31 0.79 0.72 0.28 1.3
## P105_04 0.55 0.47 0.52 0.48 2.0
## P105_05 0.67 0.29 0.53 0.47 1.4
## P105_06 0.51 -0.31 0.36 0.64 1.6
## P105_07 0.60 -0.26 0.43 0.57 1.4
## P105_09 0.68 -0.39 0.62 0.38 1.6
## P105_10 0.51 -0.39 0.42 0.58 1.9
## P105_11 0.72 -0.27 0.59 0.41 1.3
## P105_12 0.74 0.05 0.55 0.45 1.0
## P105_13 0.79 0.08 0.63 0.37 1.0
## P105_14 0.72 -0.06 0.52 0.48 1.0
## P105_16 0.52 -0.20 0.32 0.68 1.3
## P105_17 0.70 -0.22 0.55 0.45 1.2
## P105_18 0.51 0.25 0.32 0.68 1.5
## P105_19 0.56 0.36 0.44 0.56 1.7
## P105_20 0.78 -0.09 0.62 0.38 1.0
##
## PC1 PC2
## SS loadings 6.48 2.20
## Proportion Var 0.38 0.13
## Cumulative Var 0.38 0.51
## Proportion Explained 0.75 0.25
## Cumulative Proportion 0.75 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.1
## with the empirical chi square 190.05 with prob < 3.9e-07
##
## Fit based upon off diagonal values = 0.93
# erinnern Sie sich: Kommunalität einer Variable gibt an, welcher Anteil der Varianz
# dieser Variable durch alle Faktoren insgesamt abgebildet werden kann
#### 4. Rotation ####
# Wir beginnen mit einer orthogonalen Rotation (Varimax)
pca_red_varimax <- principal(dat_pca, nfactors = 2, rotate = "varimax")
print(pca_red_varimax, cut = .2, sort = TRUE)# Output nach Größe sortieren, ansonsten wird es unübersichtlich
## Principal Components Analysis
## Call: principal(r = dat_pca, nfactors = 2, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item RC1 RC2 h2 u2 com
## P105_09 7 0.79 0.62 0.38 1.0
## P105_11 9 0.75 0.59 0.41 1.1
## P105_17 14 0.71 0.55 0.45 1.2
## P105_20 17 0.70 0.35 0.62 0.38 1.5
## P105_07 6 0.65 0.43 0.57 1.1
## P105_10 8 0.65 0.42 0.58 1.0
## P105_14 12 0.63 0.35 0.52 0.48 1.6
## P105_13 11 0.61 0.50 0.63 0.37 1.9
## P105_06 5 0.60 0.36 0.64 1.0
## P105_12 10 0.59 0.45 0.55 0.45 1.9
## P105_16 13 0.55 0.32 0.68 1.1
## P105_02 2 0.83 0.72 0.28 1.1
## P105_01 1 0.73 0.53 0.47 1.0
## P105_04 3 0.70 0.52 0.48 1.2
## P105_19 16 0.27 0.61 0.44 0.56 1.4
## P105_05 4 0.40 0.61 0.53 0.47 1.7
## P105_18 15 0.29 0.49 0.32 0.68 1.6
##
## RC1 RC2
## SS loadings 5.19 3.48
## Proportion Var 0.31 0.20
## Cumulative Var 0.31 0.51
## Proportion Explained 0.60 0.40
## Cumulative Proportion 0.60 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.1
## with the empirical chi square 190.05 with prob < 3.9e-07
##
## Fit based upon off diagonal values = 0.93
# oblique Rotation (Promax u. GeominQ) # Items 13,05,18 rausgeschmissen
pca_red_promax <- principal(dat_pca, nfactors = 2, rotate = "promax")
print(pca_red_promax, cut = .2, sort = TRUE)
## Principal Components Analysis
## Call: principal(r = dat_pca, nfactors = 2, rotate = "promax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item RC1 RC2 h2 u2 com
## P105_09 7 0.85 0.62 0.38 1.1
## P105_11 9 0.79 0.59 0.41 1.0
## P105_17 14 0.75 0.55 0.45 1.0
## P105_10 8 0.72 -0.26 0.42 0.58 1.3
## P105_20 17 0.71 0.62 0.38 1.1
## P105_07 6 0.69 0.43 0.57 1.0
## P105_06 5 0.65 0.36 0.64 1.1
## P105_14 12 0.64 0.52 0.48 1.1
## P105_13 11 0.59 0.34 0.63 0.37 1.6
## P105_16 13 0.58 0.32 0.68 1.0
## P105_12 10 0.57 0.28 0.55 0.45 1.5
## P105_02 2 -0.33 0.94 0.72 0.28 1.2
## P105_01 1 -0.20 0.80 0.53 0.47 1.1
## P105_04 3 0.68 0.52 0.48 1.0
## P105_19 16 0.56 0.44 0.56 1.2
## P105_05 4 0.34 0.51 0.53 0.47 1.7
## P105_18 15 0.23 0.42 0.32 0.68 1.5
##
## RC1 RC2
## SS loadings 5.58 3.09
## Proportion Var 0.33 0.18
## Cumulative Var 0.33 0.51
## Proportion Explained 0.64 0.36
## Cumulative Proportion 0.64 1.00
##
## With component correlations of
## RC1 RC2
## RC1 1.00 0.43
## RC2 0.43 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.1
## with the empirical chi square 190.05 with prob < 3.9e-07
##
## Fit based upon off diagonal values = 0.93
pca_red_geomin <- principal(dat_pca, nfactors = 2, rotate = "geominQ")
print(pca_red_geomin, cut = .2, sort = TRUE)
## Principal Components Analysis
## Call: principal(r = dat_pca, nfactors = 2, rotate = "geominQ")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item TC1 TC2 h2 u2 com
## P105_09 7 0.80 0.62 0.38 1.1
## P105_11 9 0.77 0.59 0.41 1.0
## P105_17 14 0.74 0.55 0.45 1.0
## P105_20 17 0.73 0.62 0.38 1.1
## P105_07 6 0.67 0.43 0.57 1.0
## P105_14 12 0.66 0.52 0.48 1.2
## P105_10 8 0.66 -0.21 0.42 0.58 1.2
## P105_13 11 0.65 0.34 0.63 0.37 1.5
## P105_12 10 0.62 0.29 0.55 0.45 1.4
## P105_06 5 0.61 0.36 0.64 1.1
## P105_16 13 0.57 0.32 0.68 1.0
## P105_02 2 0.87 0.72 0.28 1.1
## P105_01 1 0.74 0.53 0.47 1.0
## P105_04 3 0.23 0.64 0.52 0.48 1.3
## P105_19 16 0.30 0.54 0.44 0.56 1.6
## P105_05 4 0.44 0.50 0.53 0.47 2.0
## P105_18 15 0.31 0.41 0.32 0.68 1.9
##
## TC1 TC2
## SS loadings 5.75 2.92
## Proportion Var 0.34 0.17
## Cumulative Var 0.34 0.51
## Proportion Explained 0.66 0.34
## Cumulative Proportion 0.66 1.00
##
## With component correlations of
## TC1 TC2
## TC1 1.00 0.21
## TC2 0.21 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.1
## with the empirical chi square 190.05 with prob < 3.9e-07
##
## Fit based upon off diagonal values = 0.93
#idealerweise sind beide Lösungen sehr ähnlich
# Würden Sie ein oder mehrere Items entfernen? Welche(s) und warum?
# Items 13,05,18 rausgeschmissen, da nicht eindimensional - uneindeutig. Item 12 behalten da inhaltlich wertvoll
#so sieht eine Faktorenanalyse aus -> im Prinzip gleiches Vorgehen wie bei der PCA
efa_promax <- fa(dat_pca, nfactors = 3, fm = "pa", rotate = "promax")
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
print(efa_promax, cut = .2, sort = TRUE)
## Factor Analysis using method = pa
## Call: fa(r = dat_pca, nfactors = 3, rotate = "promax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item PA1 PA2 PA3 h2 u2 com
## P105_17 14 0.92 -0.22 0.64 0.362 1.1
## P105_11 9 0.78 0.59 0.414 1.1
## P105_20 17 0.78 0.63 0.369 1.0
## P105_13 11 0.77 0.66 0.338 1.1
## P105_14 12 0.68 0.51 0.492 1.0
## P105_16 13 0.59 0.29 0.706 1.1
## P105_12 10 0.48 0.50 0.496 1.7
## P105_05 4 0.48 0.35 0.48 0.519 1.8
## P105_18 15 0.39 0.25 0.27 0.733 1.8
## P105_02 2 -0.43 1.12 0.98 0.018 1.3
## P105_01 1 0.67 0.40 0.602 1.1
## P105_04 3 0.25 0.50 0.42 0.583 1.5
## P105_19 16 0.28 0.41 0.36 0.644 1.8
## P105_07 6 0.89 0.76 0.244 1.1
## P105_06 5 0.78 0.56 0.445 1.0
## P105_10 8 0.63 0.45 0.554 1.1
## P105_09 7 0.32 0.60 0.62 0.377 1.6
##
## PA1 PA2 PA3
## SS loadings 4.43 2.37 2.30
## Proportion Var 0.26 0.14 0.14
## Cumulative Var 0.26 0.40 0.54
## Proportion Explained 0.49 0.26 0.25
## Cumulative Proportion 0.49 0.75 1.00
##
## With factor correlations of
## PA1 PA2 PA3
## PA1 1.00 0.48 0.53
## PA2 0.48 1.00 0.22
## PA3 0.53 0.22 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 136 and the objective function was 9.63 with Chi Square of 640.36
## The degrees of freedom for the model are 88 and the objective function was 1.89
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 74 with the empirical chi square 57.24 with prob < 1
## The total number of observations was 74 with Likelihood Chi Square = 121.76 with prob < 0.01
##
## Tucker Lewis Index of factoring reliability = 0.892
## RMSEA index = 0.071 and the 90 % confidence intervals are 0.037 0.102
## BIC = -257
## Fit based upon off diagonal values = 0.98
Skalenanalyse
#### Skalenanalyse und Validierung ----
# Working Directory setzen und Daten einlesen
setwd("~/Documents/R Sachen")
dat <- read.csv("Perfektionismus.csv")
# notwendige Packages laden (und ggf. installieren)
library(psych)
library(GPArotation) #install.packages("GPArotation")
#### 1. Skalenanalyse ####
# 1.1. Erstellen der Skalenwerte (Subskalenwerte, Globalskalawert)
# Auswahl der relevanten Items pro *SUBSKALA*, die in die Item-/Skalenanalyse einfließen sollen
# (ACHTUNG: nach Itemrevision/-selektion muss die Auswahl angepasst werden!)
Leistung <- data.frame(dat[c("P105_06", "P105_07",
"P105_09", "P105_10", "P105_11", "P105_12", "P105_14", "P105_16", "P105_17",
"P105_20")])
Sorge <- data.frame(dat[c("P105_01", "P105_02", "P105_04",
"P105_19")])
# schauen Sie hier noch mal in Ihre Faktorenanalyse -> Welche Items bilden einen Faktor (also eine Subskala)?
# 1.2. Mittel- oder Summenwerte erstellen
dat$Leistung_x <- rowMeans(Leistung, na.rm = TRUE) #auch bei fehlenden Werten berechnet
dat$Leistung_s <- rowSums(Leistung) #na.rm = TRUE hier nicht verwenden!
dat$Sorge_x <- rowMeans(Sorge, na.rm = TRUE)
dat$Sorge_s <- rowSums(Sorge)
# 1.3. Deskriptive Statistik und Häufigkeitsverteilung der Skalenwerte
describe(dat$Leistung_x)
describe(dat$Leistung_s)
describe(dat$Sorge_x)
describe(dat$Sorge_s)
# usw. für alle Subskalen und ggf. die Gesamtskala (sofern inhaltlich sinnvoll)
# 1.4. Reliabilitätsanalyse der Subskalen (inkl. Itemtrennschärfe)
psych::alpha(Leistung)
##
## Reliability analysis
## Call: psych::alpha(x = Leistung)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.9 0.42 7.2 0.021 2.5 0.93 0.4
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.88 0.92
## Duhachek 0.84 0.88 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P105_06 0.87 0.87 0.89 0.43 6.8 0.022 0.014 0.40
## P105_07 0.87 0.86 0.88 0.42 6.4 0.023 0.017 0.40
## P105_09 0.86 0.86 0.88 0.40 6.0 0.025 0.018 0.38
## P105_10 0.87 0.87 0.89 0.43 6.7 0.023 0.018 0.40
## P105_11 0.86 0.86 0.88 0.41 6.2 0.025 0.017 0.39
## P105_12 0.87 0.87 0.89 0.42 6.5 0.024 0.020 0.40
## P105_14 0.86 0.87 0.89 0.42 6.4 0.024 0.019 0.39
## P105_16 0.88 0.88 0.90 0.44 7.1 0.022 0.016 0.42
## P105_17 0.86 0.86 0.88 0.41 6.3 0.024 0.018 0.39
## P105_20 0.86 0.86 0.88 0.40 6.1 0.025 0.016 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P105_06 74 0.61 0.62 0.58 0.51 2.0 1.3
## P105_07 74 0.69 0.70 0.68 0.60 2.1 1.3
## P105_09 74 0.77 0.78 0.75 0.70 2.4 1.4
## P105_10 74 0.63 0.64 0.60 0.54 1.6 1.1
## P105_11 74 0.75 0.74 0.72 0.67 2.6 1.4
## P105_12 74 0.68 0.68 0.62 0.59 2.9 1.4
## P105_14 74 0.70 0.69 0.65 0.61 3.3 1.4
## P105_16 74 0.58 0.57 0.50 0.46 3.1 1.4
## P105_17 74 0.73 0.71 0.68 0.64 2.7 1.5
## P105_20 74 0.76 0.76 0.75 0.69 2.7 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P105_06 0.50 0.24 0.05 0.12 0.08 0
## P105_07 0.45 0.30 0.04 0.15 0.07 0
## P105_09 0.39 0.22 0.11 0.20 0.08 0
## P105_10 0.72 0.08 0.08 0.09 0.03 0
## P105_11 0.32 0.20 0.11 0.26 0.11 0
## P105_12 0.20 0.27 0.11 0.31 0.11 0
## P105_14 0.11 0.24 0.12 0.30 0.23 0
## P105_16 0.16 0.23 0.09 0.34 0.18 0
## P105_17 0.31 0.20 0.15 0.18 0.16 0
## P105_20 0.22 0.28 0.16 0.24 0.09 0
psych::alpha(Sorge)
##
## Reliability analysis
## Call: psych::alpha(x = Sorge)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.71 0.43 3 0.048 3 0.92 0.41
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.64 0.75 0.83
## Duhachek 0.66 0.75 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P105_01 0.70 0.70 0.61 0.44 2.4 0.060 0.0022 0.43
## P105_02 0.61 0.61 0.52 0.35 1.6 0.077 0.0051 0.37
## P105_04 0.70 0.70 0.64 0.43 2.3 0.061 0.0294 0.43
## P105_19 0.74 0.74 0.68 0.49 2.9 0.053 0.0141 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P105_01 74 0.74 0.74 0.63 0.52 3.0 1.2
## P105_02 74 0.84 0.84 0.79 0.68 3.6 1.2
## P105_04 74 0.76 0.75 0.61 0.53 2.7 1.3
## P105_19 74 0.68 0.69 0.52 0.45 2.6 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P105_01 0.11 0.30 0.14 0.36 0.09 0
## P105_02 0.08 0.14 0.14 0.43 0.22 0
## P105_04 0.23 0.27 0.14 0.30 0.07 0
## P105_19 0.18 0.42 0.12 0.24 0.04 0
# In welchem Fall macht es keinen Sinn Cronbachs Alpha für die Gesamtskala zu berechnen?
#Umkodierung
dat$P104_02_r <- 6 - dat$P104_02
dat$P104_05_r <- 6 - dat$P104_05
dat$P104_06_r <- 6 - dat$P104_06
dat$P104_08_r <- 6 - dat$P104_08
dat$P104_09_r <- 6 - dat$P104_09
dat$P101_02_r <- 6 - dat$P101_02
dat$P101_07_r <- 6 - dat$P101_07
dat$P101_08_r <- 6 - dat$P101_08
dat$P101_16_r <- 6 - dat$P101_16
dat$P101_17_r <- 6 - dat$P101_17
# 2.1. Auswahl der Items einer Validierungsskala (bzw. -subskala)
rses_k1 <- data.frame(dat[c("P102_01", "P102_02", "P102_03", "P102_04", "P102_05",
"P102_06", "P102_07", "P102_08", "P102_09", "P102_10", "P102_11", "P102_12")]) # konvergentes Maß - Neurotizismus
rses_k2 <- data.frame(dat[c("P103_01", "P103_02", "P103_03", "P103_04", "P103_05",
"P103_06", "P103_07", "P103_08", "P103_09", "P103_10")]) # konvergentes Maß - Gewissenhaftigkeit
rses_k3 <- data.frame(dat[c("P101_01", "P101_02_r", "P101_03", "P101_04", "P101_05",
"P101_06", "P101_07_r", "P101_08_r", "P101_09", "P101_10", "P101_11", "P101_12", "P101_13", "P101_14", "P101_15",
"P101_16_r", "P101_17_r", "P101_18", "P101_19", "P101_20", "P101_21", "P101_22", "P101_23", "P101_24", "P101_25",
"P101_26", "P101_27", "P101_28", "P101_29", "P101_30", "P101_31", "P101_32", "P101_33", "P101_34", "P101_35")]) # konvergentes Maß - Perfektionismus
rses_d1 <- data.frame(dat[c("P104_01", "P104_02_r", "P104_03", "P104_04", "P104_05_r",
"P104_06_r", "P104_07", "P104_08_r", "P104_09_r", "P104_10")]) # divvergentes Maß - Selbstwert
# 2.2. Berichte Reliabilität der Validierungsskala
psych::alpha(rses_k1)
##
## Reliability analysis
## Call: psych::alpha(x = rses_k1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.95 0.53 13 0.012 3.1 0.97 0.53
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.90 0.93 0.95
## Duhachek 0.91 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
## P102_01 0.92 0.92 0.94 0.52 12 0.014 0.013 0.51
## P102_02 0.92 0.92 0.94 0.52 12 0.013 0.013 0.51
## P102_03 0.92 0.92 0.94 0.52 12 0.013 0.014 0.53
## P102_04 0.92 0.92 0.94 0.52 12 0.014 0.013 0.51
## P102_05 0.93 0.93 0.94 0.54 13 0.012 0.012 0.56
## P102_06 0.93 0.93 0.94 0.54 13 0.013 0.010 0.55
## P102_07 0.92 0.92 0.94 0.52 12 0.014 0.012 0.51
## P102_08 0.92 0.92 0.93 0.51 12 0.014 0.012 0.51
## P102_09 0.93 0.93 0.94 0.54 13 0.012 0.013 0.56
## P102_10 0.92 0.92 0.93 0.52 12 0.014 0.012 0.51
## P102_11 0.93 0.93 0.94 0.54 13 0.012 0.012 0.55
## P102_12 0.92 0.92 0.94 0.52 12 0.014 0.014 0.53
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P102_01 74 0.80 0.80 0.79 0.75 3.2 1.2
## P102_02 74 0.76 0.77 0.75 0.72 3.7 1.2
## P102_03 74 0.77 0.78 0.76 0.73 2.9 1.2
## P102_04 74 0.79 0.79 0.77 0.75 3.0 1.3
## P102_05 74 0.64 0.64 0.61 0.57 3.0 1.2
## P102_06 74 0.70 0.70 0.68 0.63 2.8 1.3
## P102_07 74 0.80 0.80 0.79 0.75 3.5 1.3
## P102_08 74 0.84 0.84 0.83 0.80 3.2 1.3
## P102_09 74 0.66 0.65 0.61 0.58 3.3 1.4
## P102_10 74 0.82 0.82 0.81 0.78 3.1 1.3
## P102_11 74 0.66 0.66 0.62 0.59 2.7 1.4
## P102_12 74 0.79 0.78 0.76 0.73 2.7 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P102_01 0.11 0.20 0.22 0.34 0.14 0
## P102_02 0.07 0.15 0.04 0.47 0.27 0
## P102_03 0.14 0.30 0.22 0.24 0.11 0
## P102_04 0.15 0.30 0.12 0.31 0.12 0
## P102_05 0.11 0.30 0.23 0.23 0.14 0
## P102_06 0.22 0.23 0.22 0.24 0.09 0
## P102_07 0.09 0.15 0.16 0.38 0.22 0
## P102_08 0.15 0.19 0.14 0.36 0.16 0
## P102_09 0.15 0.15 0.16 0.30 0.24 0
## P102_10 0.14 0.24 0.19 0.27 0.16 0
## P102_11 0.24 0.24 0.16 0.24 0.11 0
## P102_12 0.27 0.23 0.16 0.20 0.14 0
psych::alpha(rses_k2)
##
## Reliability analysis
## Call: psych::alpha(x = rses_k2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.89 0.9 0.44 7.8 0.02 3.1 0.85 0.45
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.88 0.92
## Duhachek 0.84 0.88 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P103_01 0.88 0.88 0.89 0.45 7.4 0.021 0.017 0.45
## P103_02 0.88 0.88 0.89 0.45 7.3 0.022 0.021 0.45
## P103_03 0.89 0.89 0.90 0.47 8.0 0.019 0.015 0.47
## P103_04 0.87 0.87 0.89 0.43 6.7 0.024 0.019 0.43
## P103_05 0.87 0.87 0.88 0.43 6.7 0.023 0.016 0.41
## P103_06 0.87 0.87 0.89 0.44 7.0 0.022 0.018 0.45
## P103_07 0.87 0.88 0.89 0.44 7.0 0.023 0.017 0.45
## P103_08 0.86 0.87 0.88 0.42 6.5 0.024 0.018 0.39
## P103_09 0.87 0.87 0.89 0.42 6.6 0.024 0.017 0.43
## P103_10 0.88 0.88 0.89 0.45 7.3 0.022 0.018 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P103_01 74 0.63 0.64 0.59 0.53 3.0 1.2
## P103_02 74 0.65 0.66 0.61 0.56 2.6 1.1
## P103_03 74 0.55 0.53 0.45 0.42 2.8 1.4
## P103_04 74 0.77 0.76 0.73 0.69 2.8 1.3
## P103_05 74 0.77 0.77 0.77 0.70 3.9 1.2
## P103_06 74 0.71 0.72 0.69 0.63 3.9 1.1
## P103_07 74 0.72 0.71 0.68 0.63 2.6 1.3
## P103_08 74 0.81 0.81 0.80 0.75 3.9 1.1
## P103_09 74 0.78 0.78 0.76 0.71 3.6 1.3
## P103_10 74 0.65 0.65 0.61 0.57 2.5 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P103_01 0.12 0.28 0.16 0.38 0.05 0
## P103_02 0.18 0.31 0.26 0.22 0.04 0
## P103_03 0.26 0.22 0.09 0.32 0.11 0
## P103_04 0.19 0.31 0.18 0.20 0.12 0
## P103_05 0.07 0.08 0.09 0.39 0.36 0
## P103_06 0.05 0.09 0.11 0.43 0.31 0
## P103_07 0.26 0.28 0.15 0.23 0.08 0
## P103_08 0.07 0.05 0.08 0.53 0.27 0
## P103_09 0.09 0.16 0.14 0.31 0.30 0
## P103_10 0.19 0.38 0.23 0.16 0.04 0
psych::alpha(rses_k3)
## Warning in psych::alpha(rses_k3): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( P101_17_r P101_31 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
##
## Reliability analysis
## Call: psych::alpha(x = rses_k3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.96 0.17 7 0.019 2.5 0.54 0.17
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.88 0.91
## Duhachek 0.84 0.88 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P101_01 0.87 0.87 0.96 0.17 6.8 0.020 0.070 0.17
## P101_02_r 0.88 0.88 0.96 0.18 7.3 0.019 0.066 0.18
## P101_03 0.87 0.87 0.96 0.17 6.7 0.020 0.070 0.17
## P101_04 0.87 0.87 0.96 0.16 6.5 0.020 0.069 0.17
## P101_05 0.87 0.87 0.96 0.16 6.7 0.020 0.070 0.17
## P101_06 0.87 0.87 0.96 0.16 6.6 0.020 0.070 0.17
## P101_07_r 0.88 0.88 0.96 0.18 7.3 0.018 0.066 0.18
## P101_08_r 0.88 0.88 0.96 0.18 7.4 0.019 0.066 0.18
## P101_09 0.87 0.87 0.96 0.16 6.5 0.020 0.069 0.17
## P101_10 0.87 0.87 0.96 0.17 6.7 0.020 0.071 0.17
## P101_11 0.87 0.87 0.96 0.16 6.6 0.020 0.070 0.17
## P101_12 0.87 0.87 0.96 0.16 6.7 0.020 0.069 0.17
## P101_13 0.87 0.87 0.96 0.16 6.6 0.020 0.070 0.17
## P101_14 0.87 0.87 0.96 0.16 6.6 0.020 0.070 0.17
## P101_15 0.87 0.87 0.96 0.16 6.6 0.020 0.070 0.17
## P101_16_r 0.88 0.88 0.96 0.17 7.2 0.019 0.069 0.18
## P101_17_r 0.89 0.89 0.97 0.19 8.0 0.017 0.060 0.18
## P101_18 0.87 0.87 0.96 0.16 6.5 0.020 0.069 0.17
## P101_19 0.87 0.87 0.96 0.16 6.6 0.020 0.069 0.17
## P101_20 0.87 0.87 0.96 0.17 6.7 0.020 0.069 0.17
## P101_21 0.87 0.87 0.96 0.16 6.5 0.020 0.069 0.17
## P101_22 0.87 0.87 0.96 0.16 6.5 0.020 0.069 0.17
## P101_23 0.87 0.87 0.96 0.16 6.6 0.020 0.069 0.17
## P101_24 0.88 0.87 0.96 0.17 6.9 0.019 0.070 0.17
## P101_25 0.87 0.86 0.96 0.16 6.4 0.021 0.068 0.16
## P101_26 0.88 0.87 0.96 0.17 6.9 0.019 0.070 0.17
## P101_27 0.88 0.88 0.96 0.18 7.3 0.019 0.066 0.18
## P101_28 0.87 0.87 0.96 0.16 6.6 0.020 0.069 0.17
## P101_29 0.88 0.88 0.96 0.18 7.3 0.018 0.066 0.18
## P101_30 0.87 0.87 0.96 0.17 6.7 0.020 0.070 0.17
## P101_31 0.89 0.88 0.96 0.18 7.5 0.018 0.064 0.18
## P101_32 0.87 0.87 0.96 0.17 6.8 0.020 0.071 0.17
## P101_33 0.88 0.87 0.96 0.17 6.9 0.019 0.070 0.17
## P101_34 0.87 0.87 0.96 0.16 6.7 0.020 0.069 0.17
## P101_35 0.87 0.87 0.96 0.16 6.6 0.020 0.069 0.17
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P101_01 74 0.488 0.490 0.484 0.4345 2.5 1.27
## P101_02_r 74 0.054 0.059 0.046 -0.0029 2.4 1.08
## P101_03 74 0.507 0.505 0.494 0.4520 1.9 1.33
## P101_04 74 0.662 0.652 0.653 0.6178 2.3 1.38
## P101_05 74 0.544 0.540 0.535 0.4914 2.2 1.33
## P101_06 74 0.607 0.610 0.603 0.5653 3.2 1.17
## P101_07_r 74 0.106 0.101 0.093 0.0387 2.7 1.29
## P101_08_r 74 0.021 0.026 0.014 -0.0303 2.2 0.98
## P101_09 74 0.663 0.657 0.656 0.6198 2.3 1.35
## P101_10 74 0.511 0.515 0.500 0.4651 3.3 1.11
## P101_11 74 0.574 0.581 0.576 0.5360 1.8 1.04
## P101_12 74 0.530 0.528 0.524 0.4744 3.0 1.38
## P101_13 74 0.581 0.579 0.573 0.5328 2.4 1.29
## P101_14 74 0.574 0.579 0.572 0.5290 2.2 1.20
## P101_15 74 0.635 0.641 0.638 0.5980 1.8 1.11
## P101_16_r 74 0.168 0.163 0.144 0.1012 3.0 1.28
## P101_17_r 74 -0.434 -0.438 -0.465 -0.4871 2.9 1.29
## P101_18 74 0.664 0.664 0.663 0.6238 2.6 1.28
## P101_19 74 0.591 0.590 0.589 0.5439 3.0 1.30
## P101_20 74 0.509 0.515 0.515 0.4612 2.0 1.16
## P101_21 74 0.655 0.655 0.650 0.6160 2.3 1.20
## P101_22 74 0.692 0.690 0.691 0.6510 2.1 1.37
## P101_23 74 0.621 0.625 0.617 0.5855 1.8 1.05
## P101_24 74 0.412 0.407 0.399 0.3510 3.0 1.32
## P101_25 74 0.764 0.762 0.765 0.7337 2.2 1.28
## P101_26 74 0.403 0.405 0.389 0.3461 2.1 1.24
## P101_27 74 0.073 0.080 0.067 0.0220 4.0 0.96
## P101_28 74 0.588 0.587 0.576 0.5412 2.3 1.29
## P101_29 74 0.071 0.075 0.064 0.0122 3.6 1.11
## P101_30 74 0.507 0.509 0.496 0.4587 2.9 1.18
## P101_31 74 -0.056 -0.057 -0.068 -0.1211 3.3 1.25
## P101_32 74 0.476 0.474 0.461 0.4225 2.5 1.25
## P101_33 74 0.399 0.399 0.378 0.3404 2.9 1.27
## P101_34 74 0.559 0.559 0.551 0.5157 2.3 1.12
## P101_35 74 0.571 0.570 0.568 0.5248 2.0 1.24
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P101_01 0.27 0.32 0.14 0.20 0.07 0
## P101_02_r 0.23 0.35 0.22 0.19 0.01 0
## P101_03 0.57 0.18 0.08 0.09 0.08 0
## P101_04 0.43 0.16 0.16 0.16 0.08 0
## P101_05 0.45 0.22 0.12 0.15 0.07 0
## P101_06 0.08 0.24 0.16 0.41 0.11 0
## P101_07_r 0.20 0.34 0.15 0.22 0.09 0
## P101_08_r 0.26 0.46 0.16 0.11 0.01 0
## P101_09 0.38 0.28 0.11 0.14 0.09 0
## P101_10 0.07 0.19 0.19 0.45 0.11 0
## P101_11 0.54 0.24 0.12 0.08 0.01 0
## P101_12 0.18 0.20 0.22 0.22 0.19 0
## P101_13 0.32 0.28 0.15 0.18 0.07 0
## P101_14 0.36 0.30 0.18 0.11 0.05 0
## P101_15 0.58 0.23 0.07 0.09 0.03 0
## P101_16_r 0.14 0.28 0.20 0.24 0.14 0
## P101_17_r 0.12 0.35 0.12 0.27 0.14 0
## P101_18 0.27 0.20 0.27 0.18 0.08 0
## P101_19 0.18 0.18 0.27 0.24 0.14 0
## P101_20 0.49 0.24 0.12 0.12 0.03 0
## P101_21 0.31 0.35 0.07 0.26 0.01 0
## P101_22 0.51 0.18 0.08 0.16 0.07 0
## P101_23 0.54 0.24 0.09 0.12 0.00 0
## P101_24 0.12 0.31 0.16 0.23 0.18 0
## P101_25 0.41 0.27 0.08 0.20 0.04 0
## P101_26 0.43 0.24 0.14 0.15 0.04 0
## P101_27 0.01 0.08 0.12 0.45 0.34 0
## P101_28 0.35 0.27 0.11 0.23 0.04 0
## P101_29 0.01 0.23 0.12 0.42 0.22 0
## P101_30 0.12 0.31 0.26 0.22 0.09 0
## P101_31 0.07 0.30 0.12 0.34 0.18 0
## P101_32 0.28 0.28 0.18 0.20 0.05 0
## P101_33 0.15 0.27 0.18 0.30 0.11 0
## P101_34 0.27 0.38 0.11 0.24 0.00 0
## P101_35 0.47 0.26 0.08 0.15 0.04 0
psych::alpha(rses_d1)
##
## Reliability analysis
## Call: psych::alpha(x = rses_d1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.93 0.39 6.4 0.024 3.6 0.84 0.4
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.87 0.91
## Duhachek 0.82 0.87 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## P104_01 0.84 0.84 0.91 0.37 5.3 0.028 0.085 0.37
## P104_02_r 0.85 0.85 0.92 0.39 5.8 0.027 0.074 0.41
## P104_03 0.87 0.87 0.92 0.42 6.5 0.024 0.056 0.41
## P104_04 0.86 0.86 0.92 0.40 6.1 0.025 0.076 0.41
## P104_05_r 0.85 0.85 0.92 0.39 5.8 0.028 0.087 0.40
## P104_06_r 0.85 0.86 0.92 0.40 5.9 0.027 0.073 0.41
## P104_07 0.86 0.85 0.92 0.39 5.8 0.026 0.079 0.41
## P104_08_r 0.86 0.86 0.93 0.41 6.3 0.025 0.073 0.41
## P104_09_r 0.84 0.84 0.91 0.37 5.3 0.029 0.084 0.37
## P104_10 0.84 0.84 0.91 0.36 5.1 0.029 0.086 0.34
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## P104_01 74 0.77 0.79 0.78 0.70 3.4 1.24
## P104_02_r 74 0.70 0.66 0.64 0.60 3.2 1.43
## P104_03 74 0.46 0.51 0.50 0.36 4.1 0.98
## P104_04 74 0.57 0.61 0.58 0.47 3.9 1.11
## P104_05_r 74 0.69 0.67 0.63 0.60 3.8 1.19
## P104_06_r 74 0.69 0.65 0.62 0.58 3.2 1.39
## P104_07 74 0.63 0.66 0.64 0.53 4.0 1.20
## P104_08_r 74 0.60 0.55 0.51 0.47 2.9 1.38
## P104_09_r 74 0.80 0.78 0.76 0.73 3.9 1.22
## P104_10 74 0.81 0.82 0.81 0.74 3.5 1.29
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## P104_01 0.14 0.14 0.08 0.54 0.11 0
## P104_02_r 0.14 0.30 0.04 0.30 0.23 0
## P104_03 0.05 0.01 0.05 0.50 0.38 0
## P104_04 0.05 0.07 0.15 0.41 0.32 0
## P104_05_r 0.03 0.18 0.12 0.31 0.36 0
## P104_06_r 0.12 0.27 0.14 0.23 0.24 0
## P104_07 0.07 0.05 0.15 0.28 0.45 0
## P104_08_r 0.16 0.34 0.15 0.16 0.19 0
## P104_09_r 0.05 0.11 0.15 0.27 0.42 0
## P104_10 0.11 0.15 0.15 0.36 0.23 0
# 2.3. Erstelle die Skalenwerte der Validierungsskalen
dat$rses_k1_x <- rowMeans(rses_k1, na.rm = TRUE)
dat$rses_k1_s <- rowSums(rses_k1)
dat$rses_k2_x <- rowMeans(rses_k2, na.rm = TRUE)
dat$rses_k2_s <- rowSums(rses_k2)
dat$rses_k3_x <- rowMeans(rses_k3, na.rm = TRUE)
dat$rses_k3_s <- rowSums(rses_k3)
dat$rses_d1_x <- rowMeans(rses_d1, na.rm = TRUE)
dat$rses_d1_s <- rowSums(rses_d1)
# 2.4. Berechne Korrelationskoeffizienten, zur Untersuchung der Konstruktvalidität (konvergent, diskriminant)
valid <- data.frame(dat[c("Leistung_x", "Sorge_x", "rses_k1_x", "rses_k2_x", "rses_k3_x", "rses_d1_x")])
corr.test(valid)
## Call:corr.test(x = valid)
## Correlation matrix
## Leistung_x Sorge_x rses_k1_x rses_k2_x rses_k3_x rses_d1_x
## Leistung_x 1.00 0.34 0.65 -0.07 0.54 -0.61
## Sorge_x 0.34 1.00 0.54 0.41 0.34 0.04
## rses_k1_x 0.65 0.54 1.00 0.21 0.44 -0.46
## rses_k2_x -0.07 0.41 0.21 1.00 -0.16 0.35
## rses_k3_x 0.54 0.34 0.44 -0.16 1.00 -0.47
## rses_d1_x -0.61 0.04 -0.46 0.35 -0.47 1.00
## Sample Size
## [1] 74
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## Leistung_x Sorge_x rses_k1_x rses_k2_x rses_k3_x rses_d1_x
## Leistung_x 0.00 0.02 0.00 1.00 0.00 0.00
## Sorge_x 0.00 0.00 0.00 0.00 0.02 1.00
## rses_k1_x 0.00 0.00 0.00 0.28 0.00 0.00
## rses_k2_x 0.58 0.00 0.07 0.00 0.51 0.02
## rses_k3_x 0.00 0.00 0.00 0.17 0.00 0.00
## rses_d1_x 0.00 0.74 0.00 0.00 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option