HA8_2C Validitätsanalyse
Psychologische Diagnostik
1 Vorbereitungen
##Pakete laden
# Packages laden und fehlende Packages installieren
#install.packages("psych")
#install.packages("utils")
#install.packages("lm.beta")
#install.packages("compute.es")
library(psych)
library(utils)
library(lm.beta)
library(compute.es)
library(car)## Warning: package 'car' was built under R version 4.4.1## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitlibrary(readr)
setwd("/Users/moemx/Desktop/HU Psy/3_WS 24:25/08 Diagnostik/08 UE Diagnostik/_R Directory")
getwd()## [1] "/Users/moemx/Desktop/HU Psy/3_WS 24:25/08 Diagnostik/08 UE Diagnostik/_R Directory"1.1 Daten einlesen
#Apro data
data_apro_final <- openxlsx::read.xlsx("data_apro_final.xlsx")
head(data_apro_final)## session apro_1a_1
## 1 _7T93yljCxEUgR2c3Ze94uZ-hJFMlrYdQ399djxpdSRA5wf2e9PcTsdtbF0zv5cc 2
## 2 _eVCYZq9o2pW-XOrpZTl66XqSr7pZZ-Q_GIN8R0no0xTTp6dVihj_u6TIMqjuLAJ 2
## 3 _m3V9byl1JdXy2xNZHqV6oAF1SUSDm2w_M-gj4yzYGGQkmn7yAopPnZVwGeRjpFx 6
## 4 _Mw9yFxqnfEQGSDiDFuBw1chLniYrveoZLYAnnEx3IbSFQCTwpQPZ7oJL4XIlIpD 4
## 5 _ZMrefH3xwwr05xfQu5wcfC-30ejFAUy-lWkZ1zadoj-6Xc-qNP57H9YpBCxGrNm 3
## 6 -sFL_W3fdbG59WLmbBA9ctaF-wEOkEVhyafTinnf1AU-iDPBB1B2QDnadB8Pm_zL 2
## apro_1a_2 apro_1a_3 apro_1b_1 apro_1b_2 apro_1b_3 apro_1c_1 apro_1c_2
## 1 3 4 3 5 4 4 5
## 2 1 2 4 5 3 4 4
## 3 6 4 4 4 4 4 4
## 4 2 4 3 3 2 5 4
## 5 5 1 1 2 1 2 4
## 6 5 2 2 2 3 4 4
## apro_1c_3 apro_1d_1 apro_1d_2 apro_1d_3 apro_1e_1 apro_1e_2 apro_1e_3
## 1 3 4 4 2 4 3 2
## 2 4 4 5 5 3 4 4
## 3 4 4 2 4 5 4 4
## 4 4 5 4 2 2 4 4
## 5 2 1 3 1 2 1 2
## 6 4 3 4 5 4 2 2
## apro_2a_1 apro_2a_2 apro_2a_3 apro_2b_1 apro_2b_2 apro_2b_3 apro_2c_1
## 1 3 4 4 2 1 4 2
## 2 4 3 2 4 2 3 2
## 3 6 4 5 2 4 5 6
## 4 5 4 3 2 2 1 4
## 5 1 2 2 1 1 2 5
## 6 4 2 4 2 3 3 2
## apro_2c_2 apro_2c_3 apro_2d_1 apro_2d_2 apro_2d_3 apro_2e_1 apro_2e_2
## 1 4 3 4 1 4 3 4
## 2 5 5 4 3 6 3 2
## 3 1 1 2 5 6 6 6
## 4 3 4 3 3 5 4 4
## 5 2 1 2 1 1 2 1
## 6 5 4 3 3 5 5 4
## apro_2e_3 apro_3a_1 apro_3a_2 apro_3a_3 apro_3a_4 apro_3a_5 apro_3b_1
## 1 2 3 2 1 3 3 1
## 2 4 4 4 5 4 4 4
## 3 4 4 6 4 6 2 6
## 4 4 3 2 4 4 4 1
## 5 2 2 1 1 1 1 5
## 6 2 2 4 1 3 3 1
## apro_3b_2 apro_3b_3 apro_3b_4 apro_3b_5 apro_3c_1 apro_3c_2 apro_3c_3
## 1 4 3 4 1 6 6 3
## 2 3 3 3 2 6 2 3
## 3 4 6 6 4 6 5 4
## 4 2 5 2 1 5 6 5
## 5 3 1 2 2 6 2 2
## 6 3 3 5 2 6 4 4
## apro_3d_1 apro_3d_2 apro_3d_3 apro_3d_4 apro_3d_5 apro_3e_1 apro_3e_2
## 1 4 3 2 1 4 2 3
## 2 5 3 2 1 5 1 5
## 3 5 6 6 5 5 6 4
## 4 4 4 5 2 3 2 3
## 5 2 1 4 1 2 1 2
## 6 3 4 5 1 5 4 3
## apro_3e_3 apro_4a_1 apro_4a_2 apro_4a_3 apro_4b_1 apro_4b_2 apro_4b_3
## 1 5 2 2 4 3 4 1
## 2 4 3 4 4 2 2 5
## 3 6 5 6 4 NA 1 2
## 4 6 4 3 3 5 4 1
## 5 2 1 3 1 2 3 1
## 6 3 3 3 2 3 4 1
## apro_4c_1 apro_4c_2 apro_4c_3 apro_4d_1 apro_4d_2 apro_4d_3 apro_4e_1
## 1 5 6 5 1 3 2 4
## 2 5 3 4 4 5 4 3
## 3 5 5 5 5 5 5 5
## 4 4 2 4 3 5 2 4
## 5 3 1 2 2 2 4 2
## 6 4 2 4 3 2 4 2
## apro_4e_2 apro_4e_3 apro_5a_1 apro_5a_2 apro_5a_3 apro_5b_1 apro_5b_2
## 1 6 1 2 4 4 2 2
## 2 3 3 3 4 4 3 3
## 3 1 6 4 4 4 6 6
## 4 5 2 4 2 5 1 5
## 5 2 6 1 2 3 1 2
## 6 3 4 4 3 2 5 2
## apro_5b_3 apro_5c_1 apro_5c_2 apro_5c_3 apro_5d_1 apro_5d_2 apro_5d_3
## 1 1 1 4 1 5 2 2
## 2 3 3 3 4 4 4 4
## 3 6 6 5 6 2 5 5
## 4 3 2 4 2 3 3 2
## 5 6 1 1 1 2 1 2
## 6 4 3 4 4 4 3 4
## apro_5e_1 apro_5e_2 apro_5e_3 Geschlecht Alter Semester ECTS Note Exams
## 1 2 1 2 2 26 3 60 1.3 NA
## 2 4 3 3 2 21 3 60 3.1 NA
## 3 4 4 5 1 40 8 NA 2.1 5
## 4 2 4 3 2 21 NA NA NA NA
## 5 4 1 1 1 21 5 NA NA 1
## 6 2 2 3 1 22 3 57 1.5 NA
## Methoden Gewiss Neuro AllProk Delay Subdis Control
## 1 NA 4.083333 1.083333 2.555556 3.333333 3.000000 2.692308
## 2 NA 2.833333 1.083333 3.111111 3.333333 2.000000 3.384615
## 3 2.3 4.000000 2.666667 3.888889 4.000000 5.000000 3.307692
## 4 NA 3.250000 2.666667 3.000000 3.000000 2.333333 2.692308
## 5 NA 4.500000 2.916667 1.666667 2.333333 5.000000 1.923077
## 6 1.7 3.750000 2.666667 2.444444 3.666667 4.000000 2.538462dat_apro <- data_apro_final2 Validität
2.1 Vorbereitungen
# Umkodieren
# benötigen wir nicht2.1.1 Berechnung der Mittelwerte
Items Faktoren zuordnen
# PA1 = BehavioralAufschiebeverhalten
items_pa1 <- c("apro_4a_1","apro_4c_2","apro_5e_3","apro_3a_4","apro_3a_5","apro_1c_1","apro_2b_3","apro_5a_1","apro_4e_2","apro_5c_2","apro_1e_2","apro_5d_2","apro_5e_1","apro_4c_3","apro_1a_3","apro_3a_3","apro_3c_3","apro_5e_2","apro_4e_1","apro_3e_3","apro_2a_3","apro_2d_3","apro_1c_3","apro_2e_3","apro_2b_2")
# PA 2 = Emotionalität
items_pa2<- c("apro_4e_3", "apro_1a_2", "apro_5b_3", "apro_4d_3", "apro_2c_1", "apro_3e_1", "apro_3a_2", "apro_5c_1", "apro_3d_4", "apro_1e_1", "apro_3b_4", "apro_1a_1", "apro_5b_1", "apro_1d_3", "apro_4a_2")
#PA3 = Kognition
items_pa3<- c("apro_2b_1", "apro_2a_2", "apro_1d_2", "apro_3e_2", "apro_2d_1", "apro_1b_2", "apro_3b_2", "apro_1c_2", "apro_5d_1", "apro_2a_1", "apro_5c_3", "apro_4a_3", "apro_5a_2", "apro_4c_1", "apro_2e_1", "apro_5a_3")
#Items des gesamten Konstrukts akademische Prokrastination
items_akP <- c("apro_4a_1","apro_4c_2","apro_5e_3","apro_3a_4","apro_3a_5","apro_1c_1","apro_2b_3","apro_5a_1","apro_4e_2","apro_5c_2","apro_1e_2","apro_5d_2","apro_5e_1","apro_4c_3","apro_1a_3","apro_3a_3","apro_3c_3","apro_5e_2","apro_4e_1","apro_3e_3","apro_2a_3","apro_2d_3","apro_1c_3","apro_2e_3","apro_2b_2", "apro_4e_3", "apro_1a_2", "apro_5b_3", "apro_4d_3", "apro_2c_1", "apro_3e_1", "apro_3a_2", "apro_5c_1", "apro_3d_4", "apro_1e_1", "apro_3b_4", "apro_1a_1", "apro_5b_1", "apro_1d_3", "apro_4a_2", "apro_2b_1", "apro_2a_2", "apro_1d_2", "apro_3e_2", "apro_2d_1", "apro_1b_2", "apro_3b_2", "apro_1c_2", "apro_5d_1", "apro_2a_1", "apro_5c_3", "apro_4a_3", "apro_5a_2", "apro_4c_1", "apro_2e_1", "apro_5a_3")
#Tabelle mit Items und Faktoren
Faktoren <- data.frame(
Faktor = c("Faktor 1 BehavioralAufschiebeverhalten", "Faktor 2 Emotionalität", "Faktor 3 Kognition"),
Items = c(paste(items_pa1, collapse = ", "),
paste(items_pa2, collapse = ", "),
paste(items_pa3, collapse = ", "))
)
knitr::kable(Faktoren)| Faktor | Items |
|---|---|
| Faktor 1 BehavioralAufschiebeverhalten | apro_4a_1, apro_4c_2, apro_5e_3, apro_3a_4, apro_3a_5, apro_1c_1, apro_2b_3, apro_5a_1, apro_4e_2, apro_5c_2, apro_1e_2, apro_5d_2, apro_5e_1, apro_4c_3, apro_1a_3, apro_3a_3, apro_3c_3, apro_5e_2, apro_4e_1, apro_3e_3, apro_2a_3, apro_2d_3, apro_1c_3, apro_2e_3, apro_2b_2 |
| Faktor 2 Emotionalität | apro_4e_3, apro_1a_2, apro_5b_3, apro_4d_3, apro_2c_1, apro_3e_1, apro_3a_2, apro_5c_1, apro_3d_4, apro_1e_1, apro_3b_4, apro_1a_1, apro_5b_1, apro_1d_3, apro_4a_2 |
| Faktor 3 Kognition | apro_2b_1, apro_2a_2, apro_1d_2, apro_3e_2, apro_2d_1, apro_1b_2, apro_3b_2, apro_1c_2, apro_5d_1, apro_2a_1, apro_5c_3, apro_4a_3, apro_5a_2, apro_4c_1, apro_2e_1, apro_5a_3 |
2.1.2 Mittelwert Faktor 1
#Kontrolle der Polung
psych::alpha(dat_apro[items_pa1], check.keys = TRUE) #es gab keine Warnmeldung##
## Reliability analysis
## Call: psych::alpha(x = dat_apro[items_pa1], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.97 0.97 0.98 0.59 36 0.0026 3.5 1.2 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.97 0.97 0.98
## Duhachek 0.97 0.97 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## apro_4a_1 0.97 0.97 0.98 0.58 34 0.0028 0.016 0.61
## apro_4c_2 0.97 0.97 0.98 0.60 35 0.0026 0.017 0.62
## apro_5e_3 0.97 0.97 0.98 0.59 34 0.0027 0.017 0.62
## apro_3a_4 0.97 0.97 0.98 0.58 34 0.0027 0.016 0.61
## apro_3a_5 0.97 0.97 0.98 0.59 34 0.0027 0.016 0.61
## apro_1c_1 0.97 0.97 0.98 0.58 34 0.0027 0.016 0.61
## apro_2b_3 0.97 0.97 0.98 0.59 34 0.0027 0.016 0.61
## apro_5a_1 0.97 0.97 0.98 0.59 34 0.0027 0.016 0.61
## apro_4e_2 0.97 0.97 0.98 0.60 35 0.0026 0.016 0.63
## apro_5c_2 0.97 0.97 0.98 0.59 35 0.0026 0.017 0.62
## apro_1e_2 0.97 0.97 0.98 0.59 34 0.0027 0.016 0.61
## apro_5d_2 0.97 0.97 0.98 0.58 34 0.0028 0.016 0.61
## apro_5e_1 0.97 0.97 0.98 0.60 36 0.0026 0.016 0.63
## apro_4c_3 0.97 0.97 0.98 0.59 34 0.0027 0.016 0.61
## apro_1a_3 0.97 0.97 0.98 0.59 35 0.0026 0.017 0.62
## apro_3a_3 0.97 0.97 0.98 0.59 35 0.0026 0.017 0.62
## apro_3c_3 0.97 0.97 0.98 0.59 35 0.0027 0.017 0.61
## apro_5e_2 0.97 0.97 0.98 0.60 36 0.0026 0.016 0.63
## apro_4e_1 0.97 0.97 0.98 0.59 35 0.0027 0.016 0.62
## apro_3e_3 0.97 0.97 0.98 0.60 37 0.0026 0.015 0.63
## apro_2a_3 0.97 0.97 0.98 0.59 34 0.0027 0.016 0.61
## apro_2d_3 0.97 0.97 0.98 0.62 38 0.0024 0.010 0.63
## apro_1c_3 0.97 0.97 0.98 0.59 35 0.0027 0.016 0.62
## apro_2e_3 0.97 0.97 0.98 0.59 35 0.0027 0.016 0.62
## apro_2b_2 0.97 0.97 0.98 0.59 35 0.0027 0.016 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## apro_4a_1 213 0.89 0.89 0.89 0.88 3.1 1.6
## apro_4c_2 212 0.73 0.73 0.72 0.70 3.8 1.7
## apro_5e_3 214 0.82 0.82 0.81 0.80 3.4 1.7
## apro_3a_4 213 0.88 0.88 0.88 0.87 3.5 1.7
## apro_3a_5 215 0.84 0.84 0.83 0.82 3.2 1.6
## apro_1c_1 219 0.88 0.88 0.88 0.87 3.6 1.7
## apro_2b_3 219 0.85 0.85 0.85 0.84 3.5 1.6
## apro_5a_1 213 0.87 0.87 0.87 0.86 3.4 1.6
## apro_4e_2 214 0.71 0.71 0.69 0.68 3.6 1.5
## apro_5c_2 214 0.74 0.74 0.73 0.72 3.7 1.5
## apro_1e_2 219 0.85 0.86 0.85 0.84 3.4 1.5
## apro_5d_2 212 0.90 0.90 0.90 0.89 3.3 1.6
## apro_5e_1 213 0.67 0.66 0.65 0.64 2.8 1.6
## apro_4c_3 212 0.86 0.86 0.86 0.84 3.6 1.5
## apro_1a_3 218 0.77 0.77 0.76 0.75 3.8 1.6
## apro_3a_3 213 0.75 0.75 0.74 0.73 2.5 1.5
## apro_3c_3 213 0.79 0.79 0.79 0.77 3.7 1.5
## apro_5e_2 214 0.67 0.67 0.65 0.64 2.7 1.7
## apro_4e_1 214 0.78 0.78 0.77 0.76 3.4 1.5
## apro_3e_3 215 0.60 0.61 0.58 0.57 4.3 1.4
## apro_2a_3 219 0.84 0.84 0.84 0.82 3.5 1.5
## apro_2d_3 218 0.45 0.45 0.41 0.40 4.7 1.6
## apro_1c_3 219 0.79 0.79 0.79 0.78 3.6 1.5
## apro_2e_3 219 0.79 0.79 0.78 0.77 3.3 1.5
## apro_2b_2 217 0.78 0.78 0.77 0.76 3.0 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## apro_4a_1 0.23 0.19 0.18 0.16 0.15 0.09 0.03
## apro_4c_2 0.15 0.10 0.13 0.22 0.19 0.20 0.03
## apro_5e_3 0.19 0.19 0.14 0.18 0.14 0.16 0.02
## apro_3a_4 0.13 0.24 0.14 0.17 0.13 0.19 0.03
## apro_3a_5 0.20 0.22 0.17 0.18 0.13 0.11 0.02
## apro_1c_1 0.12 0.21 0.15 0.17 0.18 0.16 0.00
## apro_2b_3 0.11 0.18 0.23 0.16 0.20 0.12 0.00
## apro_5a_1 0.15 0.15 0.20 0.21 0.18 0.10 0.03
## apro_4e_2 0.10 0.16 0.15 0.28 0.18 0.13 0.02
## apro_5c_2 0.10 0.14 0.17 0.29 0.20 0.11 0.02
## apro_1e_2 0.08 0.26 0.18 0.22 0.16 0.10 0.00
## apro_5d_2 0.14 0.23 0.17 0.21 0.13 0.13 0.03
## apro_5e_1 0.29 0.21 0.16 0.18 0.07 0.09 0.03
## apro_4c_3 0.11 0.15 0.18 0.26 0.17 0.14 0.03
## apro_1a_3 0.08 0.18 0.15 0.25 0.17 0.17 0.00
## apro_3a_3 0.38 0.23 0.12 0.13 0.10 0.04 0.03
## apro_3c_3 0.07 0.19 0.17 0.23 0.23 0.11 0.03
## apro_5e_2 0.34 0.18 0.14 0.15 0.10 0.09 0.02
## apro_4e_1 0.11 0.20 0.23 0.20 0.15 0.11 0.02
## apro_3e_3 0.03 0.07 0.21 0.14 0.30 0.23 0.02
## apro_2a_3 0.12 0.19 0.21 0.20 0.17 0.11 0.00
## apro_2d_3 0.06 0.06 0.07 0.15 0.18 0.47 0.00
## apro_1c_3 0.06 0.21 0.19 0.23 0.16 0.14 0.00
## apro_2e_3 0.11 0.26 0.20 0.20 0.13 0.10 0.00
## apro_2b_2 0.24 0.26 0.12 0.15 0.12 0.12 0.01#Berechnung der Mittelwerte und Hinzufügen zum Datensatz
dat_apro$BehavioralAufschiebeverhalten <- rowMeans(dat_apro[items_pa1], na.rm=TRUE)2.1.3 Mittelwert Faktor 2
#Kontrolle der Polung
psych::alpha(dat_apro[items_pa2], check.keys = TRUE) #es gab keine Warnmeldung##
## Reliability analysis
## Call: psych::alpha(x = dat_apro[items_pa2], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.95 0.5 15 0.0062 3.6 1.1 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.94 0.95
## Duhachek 0.92 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## apro_4e_3 0.93 0.93 0.94 0.49 14 0.0068 0.015 0.46
## apro_1a_2 0.93 0.93 0.94 0.51 14 0.0065 0.014 0.48
## apro_5b_3 0.93 0.93 0.94 0.50 14 0.0066 0.015 0.47
## apro_4d_3 0.93 0.93 0.94 0.49 14 0.0068 0.015 0.46
## apro_2c_1 0.94 0.94 0.95 0.51 15 0.0063 0.013 0.49
## apro_3e_1 0.93 0.93 0.94 0.49 13 0.0069 0.014 0.46
## apro_3a_2 0.93 0.93 0.94 0.49 13 0.0070 0.013 0.46
## apro_5c_1 0.93 0.93 0.94 0.49 13 0.0070 0.013 0.46
## apro_3d_4 0.93 0.93 0.94 0.49 14 0.0068 0.014 0.46
## apro_1e_1 0.93 0.93 0.94 0.49 13 0.0069 0.013 0.47
## apro_3b_4 0.93 0.93 0.94 0.49 13 0.0070 0.013 0.47
## apro_1a_1 0.94 0.94 0.95 0.51 15 0.0064 0.015 0.51
## apro_5b_1 0.93 0.93 0.94 0.50 14 0.0067 0.014 0.47
## apro_1d_3 0.94 0.94 0.95 0.51 14 0.0064 0.014 0.48
## apro_4a_2 0.94 0.94 0.95 0.51 15 0.0063 0.015 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## apro_4e_3 212 0.76 0.76 0.75 0.72 3.6 1.6
## apro_1a_2 219 0.65 0.65 0.62 0.59 4.3 1.5
## apro_5b_3 214 0.69 0.69 0.67 0.64 4.3 1.5
## apro_4d_3 213 0.77 0.77 0.76 0.73 3.6 1.5
## apro_2c_1 218 0.61 0.61 0.57 0.54 4.0 1.6
## apro_3e_1 215 0.81 0.81 0.80 0.78 3.6 1.5
## apro_3a_2 215 0.82 0.82 0.81 0.78 3.3 1.6
## apro_5c_1 214 0.81 0.81 0.81 0.77 3.3 1.6
## apro_3d_4 215 0.78 0.78 0.76 0.74 2.8 1.6
## apro_1e_1 218 0.81 0.80 0.80 0.77 3.6 1.6
## apro_3b_4 214 0.82 0.82 0.81 0.78 3.3 1.5
## apro_1a_1 218 0.61 0.62 0.58 0.56 4.2 1.4
## apro_5b_1 214 0.74 0.74 0.72 0.69 3.2 1.6
## apro_1d_3 218 0.64 0.64 0.61 0.58 3.1 1.6
## apro_4a_2 214 0.62 0.61 0.57 0.55 3.6 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## apro_4e_3 0.13 0.16 0.17 0.17 0.25 0.12 0.03
## apro_1a_2 0.06 0.10 0.11 0.22 0.30 0.22 0.00
## apro_5b_3 0.04 0.11 0.14 0.22 0.22 0.27 0.02
## apro_4d_3 0.11 0.15 0.19 0.23 0.20 0.11 0.03
## apro_2c_1 0.10 0.15 0.10 0.21 0.21 0.23 0.00
## apro_3e_1 0.10 0.19 0.18 0.22 0.17 0.14 0.02
## apro_3a_2 0.15 0.19 0.20 0.21 0.12 0.13 0.02
## apro_5c_1 0.15 0.22 0.18 0.22 0.08 0.14 0.02
## apro_3d_4 0.27 0.22 0.15 0.19 0.07 0.08 0.02
## apro_1e_1 0.10 0.17 0.19 0.20 0.20 0.14 0.00
## apro_3b_4 0.12 0.25 0.15 0.23 0.14 0.10 0.02
## apro_1a_1 0.03 0.12 0.12 0.24 0.27 0.22 0.00
## apro_5b_1 0.19 0.21 0.20 0.17 0.12 0.11 0.02
## apro_1d_3 0.18 0.29 0.11 0.19 0.13 0.10 0.00
## apro_4a_2 0.12 0.19 0.15 0.18 0.20 0.16 0.02#Berechnung der Mittelwerte und Hinzufügen zum Datensatz
dat_apro$Emotionalität <- rowMeans(dat_apro[items_pa2], na.rm=TRUE)2.1.4 Mittelwert Faktor 3
#Kontrolle der Polung
psych::alpha(dat_apro[items_pa3], check.keys = TRUE) #es gab keine Warnmeldung##
## Reliability analysis
## Call: psych::alpha(x = dat_apro[items_pa3], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.95 0.48 15 0.0062 3.6 1 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.94 0.95
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## apro_2b_1 0.93 0.93 0.94 0.48 14 0.0065 0.016 0.47
## apro_2a_2 0.93 0.93 0.94 0.49 14 0.0064 0.015 0.47
## apro_1d_2 0.93 0.93 0.94 0.48 14 0.0067 0.015 0.46
## apro_3e_2 0.93 0.93 0.94 0.47 13 0.0069 0.015 0.46
## apro_2d_1 0.93 0.93 0.94 0.47 13 0.0069 0.013 0.46
## apro_1b_2 0.93 0.93 0.94 0.48 14 0.0067 0.015 0.47
## apro_3b_2 0.93 0.93 0.94 0.48 14 0.0067 0.016 0.47
## apro_1c_2 0.94 0.94 0.94 0.49 14 0.0064 0.015 0.48
## apro_5d_1 0.93 0.93 0.94 0.47 13 0.0068 0.014 0.46
## apro_2a_1 0.93 0.93 0.94 0.49 14 0.0064 0.016 0.48
## apro_5c_3 0.93 0.93 0.94 0.49 14 0.0064 0.015 0.48
## apro_4a_3 0.93 0.93 0.94 0.47 14 0.0068 0.015 0.46
## apro_5a_2 0.93 0.93 0.94 0.48 14 0.0067 0.015 0.46
## apro_4c_1 0.93 0.93 0.94 0.49 14 0.0064 0.016 0.48
## apro_2e_1 0.93 0.93 0.95 0.49 14 0.0064 0.016 0.47
## apro_5a_3 0.94 0.94 0.95 0.51 16 0.0060 0.010 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## apro_2b_1 218 0.71 0.71 0.69 0.66 3.0 1.4
## apro_2a_2 219 0.68 0.68 0.65 0.62 3.5 1.5
## apro_1d_2 216 0.76 0.76 0.75 0.72 3.9 1.4
## apro_3e_2 215 0.85 0.85 0.84 0.82 3.5 1.4
## apro_2d_1 219 0.84 0.84 0.84 0.81 3.7 1.4
## apro_1b_2 219 0.76 0.76 0.75 0.73 3.9 1.3
## apro_3b_2 215 0.76 0.76 0.74 0.72 3.5 1.5
## apro_1c_2 218 0.65 0.65 0.62 0.59 4.2 1.3
## apro_5d_1 214 0.80 0.80 0.79 0.76 3.6 1.4
## apro_2a_1 219 0.67 0.67 0.65 0.62 3.4 1.5
## apro_5c_3 214 0.66 0.65 0.63 0.60 3.0 1.5
## apro_4a_3 214 0.80 0.79 0.78 0.76 3.3 1.6
## apro_5a_2 211 0.76 0.77 0.75 0.73 3.7 1.4
## apro_4c_1 211 0.65 0.66 0.63 0.61 3.9 1.4
## apro_2e_1 219 0.68 0.68 0.65 0.62 3.8 1.5
## apro_5a_3 214 0.46 0.46 0.40 0.39 3.2 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## apro_2b_1 0.15 0.28 0.21 0.19 0.11 0.06 0.00
## apro_2a_2 0.09 0.20 0.19 0.26 0.16 0.11 0.00
## apro_1d_2 0.03 0.16 0.19 0.26 0.18 0.18 0.01
## apro_3e_2 0.07 0.19 0.21 0.27 0.17 0.08 0.02
## apro_2d_1 0.03 0.22 0.16 0.26 0.23 0.10 0.00
## apro_1b_2 0.04 0.16 0.17 0.31 0.21 0.12 0.00
## apro_3b_2 0.08 0.27 0.13 0.22 0.20 0.09 0.02
## apro_1c_2 0.02 0.12 0.13 0.26 0.29 0.17 0.00
## apro_5d_1 0.07 0.19 0.19 0.25 0.18 0.12 0.02
## apro_2a_1 0.13 0.20 0.16 0.26 0.14 0.10 0.00
## apro_5c_3 0.19 0.26 0.19 0.20 0.10 0.06 0.02
## apro_4a_3 0.13 0.22 0.18 0.21 0.14 0.11 0.02
## apro_5a_2 0.07 0.18 0.18 0.30 0.15 0.12 0.04
## apro_4c_1 0.03 0.15 0.19 0.26 0.21 0.16 0.04
## apro_2e_1 0.08 0.15 0.14 0.24 0.25 0.14 0.00
## apro_5a_3 0.12 0.22 0.19 0.28 0.13 0.05 0.02#Berechnung der Mittelwerte und Hinzufügen zum Datensatz
dat_apro$Kognition <- rowMeans(dat_apro[items_pa3], na.rm=TRUE)###2.2.5 Mittelwert Konstrukt akademische Prokrastination
#Berechnung der Mittelwerte und Hinzufügen zum Datensatz
dat_apro$akadProkrast <- rowMeans(dat_apro[items_akP], na.rm=TRUE)2.1.5 alle Skalen in einem Datensatz
Wir haben - unser Konstrukt (Akadmische Prokrastination), die 3 Faktoren: BehavioralAufschiebeverhalten, Emotionalität, Kognition - diskriminante Maße (Gewissenhaftigkeit und Neurotizismus) - und ein konvergentes Maß (allgemeine Prokrastination)
Außerdem gucken wir uns noch die Note an als Kriterium.
Skalen <- dat_apro[c("BehavioralAufschiebeverhalten", "Emotionalität", "Kognition",
"akadProkrast", "Gewiss","Neuro", "AllProk", "Note")]2.1.6 Deskriptive Statistik
desc <- describe(Skalen)
knitr::kable(desc, digits = 2)| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BehavioralAufschiebeverhalten | 1 | 219 | 3.46 | 1.22 | 3.48 | 3.45 | 1.42 | 1.04 | 5.88 | 4.84 | 0.03 | -0.89 | 0.08 |
| Emotionalität | 2 | 219 | 3.59 | 1.13 | 3.67 | 3.58 | 1.19 | 1.00 | 6.00 | 5.00 | -0.01 | -0.59 | 0.08 |
| Kognition | 3 | 219 | 3.58 | 1.03 | 3.62 | 3.58 | 1.11 | 1.00 | 6.00 | 5.00 | -0.09 | -0.44 | 0.07 |
| akadProkrast | 4 | 219 | 3.53 | 1.03 | 3.50 | 3.52 | 1.14 | 1.20 | 5.91 | 4.71 | 0.06 | -0.60 | 0.07 |
| Gewiss | 5 | 204 | 3.54 | 0.67 | 3.50 | 3.56 | 0.80 | 1.83 | 4.92 | 3.08 | -0.19 | -0.56 | 0.05 |
| Neuro | 6 | 203 | 2.82 | 0.75 | 2.92 | 2.82 | 0.74 | 1.08 | 4.83 | 3.75 | 0.06 | -0.23 | 0.05 |
| AllProk | 7 | 201 | 2.60 | 0.74 | 2.67 | 2.62 | 0.82 | 1.00 | 4.00 | 3.00 | -0.18 | -0.72 | 0.05 |
| Note | 8 | 158 | 1.84 | 0.54 | 1.80 | 1.81 | 0.59 | 1.00 | 3.10 | 2.10 | 0.39 | -0.46 | 0.04 |
multi.hist(Skalen, bcol="blue", freq = TRUE)
2.2 Konstruktvalidität
für Behavioral/Aufschiebeverhalten: diskriminante (Gewissenheit .73 und Neurotizismus .19) niedriger als konvergente (allgemeine Prokrastination .88 + akademische Prokrastination .95) -> gut!
für Emotionalität: diskriminante niedriger als konvergente Maße -> gut!
für Kognition: diskriminante niedriger als konvergente Maße -> gut!
diskriminante: Gewissenhaftigkeit und Neurotizismus konverg: allgemeine Prokr und akademische Prokr.
pairs.panels(Skalen[-8], lm=TRUE)
2.3 Kriteriumsvalidität
Note und allgemeine Prokrastination korrelieren kleiner als Note und akademische Prokrastination (.18 vs. .21) Höchster Zusammenhang zwischen Note und Kognition (.29) und Note und Gewissenhaftigkeit (.23)
pairs.panels(Skalen, lm=TRUE)
## inkrementelle Validität - Kriterium auf diskriminante Maße R2 ist .06
-> Note wird inkrementell zu 6% durch Neuro+Gewissenheit
aufgeklärt
#Regression
##Inkrementell zu Neurotizismus und Gewissenhaftigkeit
lm1 <- lm(Note ~ Gewiss + Neuro, data=Skalen)
summary(lm1)##
## Call:
## lm(formula = Note ~ Gewiss + Neuro, data = Skalen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.06943 -0.41922 0.03104 0.29981 1.34562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.71241 0.30087 9.015 7.87e-16 ***
## Gewiss -0.20106 0.06541 -3.074 0.00251 **
## Neuro -0.05409 0.05572 -0.971 0.33323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5335 on 152 degrees of freedom
## (64 observations deleted due to missingness)
## Multiple R-squared: 0.06055, Adjusted R-squared: 0.04819
## F-statistic: 4.898 on 2 and 152 DF, p-value: 0.008679lm.beta(lm1)##
## Call:
## lm(formula = Note ~ Gewiss + Neuro, data = Skalen)
##
## Standardized Coefficients::
## (Intercept) Gewiss Neuro
## NA -0.24355409 -0.07691975lm2 <- lm(Note ~ Gewiss + Neuro + BehavioralAufschiebeverhalten + Emotionalität + Kognition, data=Skalen)
summary(lm2)##
## Call:
## lm(formula = Note ~ Gewiss + Neuro + BehavioralAufschiebeverhalten +
## Emotionalität + Kognition, data = Skalen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98536 -0.41352 -0.00427 0.35722 1.31849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.09981 0.50088 4.192 4.72e-05 ***
## Gewiss -0.12342 0.09253 -1.334 0.18428
## Neuro -0.11733 0.06289 -1.866 0.06406 .
## BehavioralAufschiebeverhalten -0.15795 0.07074 -2.233 0.02705 *
## Emotionalität 0.04235 0.05377 0.788 0.43219
## Kognition 0.25389 0.07820 3.247 0.00144 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5185 on 149 degrees of freedom
## (64 observations deleted due to missingness)
## Multiple R-squared: 0.1303, Adjusted R-squared: 0.1011
## F-statistic: 4.465 on 5 and 149 DF, p-value: 0.0008069lm.beta(lm2)##
## Call:
## lm(formula = Note ~ Gewiss + Neuro + BehavioralAufschiebeverhalten +
## Emotionalität + Kognition, data = Skalen)
##
## Standardized Coefficients::
## (Intercept) Gewiss
## NA -0.14950487
## Neuro BehavioralAufschiebeverhalten
## -0.16683910 -0.34059984
## Emotionalität Kognition
## 0.08396188 0.46673347deltar2 <- round((summary(lm2)$r.square-summary(lm1)$r.square),2)
# Differenz zwischen R^2 von lm1 und lm2 = 0.07 -> unsere Faktoren klären 7% mehr auf