Appendix

Nießen, D., Groskurth, K., Rammstedt, B, & Lechner, C. M. (2020). An English-language adaptation of the Risk Proneness Short Scale (R-1). Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS). https://doi.org/10.6102/zis286

Terms of use

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

##########

#Analysis

##########

#Clear workspace (run if desired) 

rm(list = ls())

# #List of project directories

# dirs <- list(

#   data = "...",

#   analysis = "...")

#Load required packages

if (!require(psych))  { install.packages("psych") } ; library(psych)

if (!require(car)) { install.packages("car") } ; library(car)

#Load dataset

load(paste0(dirs$data, "R1.rda"))

#########################################################################

######################

#Step 1: Scale values

######################

#Split dataset between countries

R1_D <- subset(R1, subset = (COUN == "1"))

R1_UK <- subset(R1, subset = (COUN == "2"))

##Germany

attach(R1_D)

describe(RISK1)

detach(R1_D)

#########################################################################

##UK

attach(R1_UK)

describe(RISK1)

detach(R1_UK)

#########################################################################

#####################

#Step 2: Reliability

#####################

##Retest reliability for Germany

attach(R1_D)

cor.test(RISK1, RISK1rt, use = "pairwise.complete.obs")

detach(R1_D)

#########################################################################

##Retest reliability for the UK

attach(R1_UK)

cor.test(RISK1, RISK1rt, use = "pairwise.complete.obs")

detach(R1_UK)

#########################################################################

############################

#Step 3: Construct validity

############################

##Germany

attach(R1_D)

#BFI-2-XS

EXTR <- EXTR1R+EXTR2+EXTR3

AGRE <- AGRE1+AGRE2R+AGRE3

CONS <- CONS1R+CONS2R+CONS3

NEGA <- NEGA1+NEGA2+NEGA3R

OPEN <- OPEN1+OPEN2R+OPEN3

cor.test(RISK1, EXTR, use = "pairwise.complete.obs")

cor.test(RISK1, AGRE, use = "pairwise.complete.obs")

cor.test(RISK1, CONS, use = "pairwise.complete.obs")

cor.test(RISK1, NEGA, use = "pairwise.complete.obs")

cor.test(RISK1, OPEN, use = "pairwise.complete.obs")

#RSES

RSES <- RSES1+RSES2R+RSES3+RSES4+RSES5R+RSES6R+RSES7+RSES8R+RSES9R+RSES10

cor.test(RISK1, RSES, use = "pairwise.complete.obs")

#Health

cor.test(RISK1, HEAL, use = "pairwise.complete.obs")

#Employment status

#1) employed

#2) self-employed

#3) out of work and looking for work

#4) out of work but not currently looking for work

#5) doing housework

#6) pupil/student

#7) apprentice/internship

#8) retired

#[9) none of what is mentioned above]

EMPL.unempl <- recode(EMPL, "3:4 = 2; 1:2 = 1; else = NA")

EMPL.selfempl <- recode(EMPL, "2 = 2; 1 = 1; else = NA")

EMPL.retired <- recode(EMPL, "5 = 2; 8 = 2; 1:2 = 1; else = NA")

EMPL.student <- recode(EMPL, "6:7 = 2; 1:2 = 1; else = NA")

cor.test(RISK1, EMPL.unempl, use = "pairwise.complete.obs")

cor.test(RISK1, EMPL.selfempl, use = "pairwise.complete.obs")

cor.test(RISK1, EMPL.retired, use = "pairwise.complete.obs")

cor.test(RISK1, EMPL.student, use = "pairwise.complete.obs")

#Income

cor.test(RISK1, INCO, use = "pairwise.complete.obs")

#Education

cor.test(RISK1, SCHO, use = "pairwise.complete.obs")

#Age

cor.test(RISK1, AGE, use = "pairwise.complete.obs")

#Gender

cor.test(RISK1, SEX, use = "pairwise.complete.obs")

#ASKU

ASKU <- ASKU1+ASKU2+ASKU3

cor.test(RISK1, ASKU, use = "pairwise.complete.obs")

#I-8

URGE <- URGE1+URGE2

PREM <- PREM1+PREM2

PERS <- PERS1+PERS2

SENS <- SENS1+SENS2

cor.test(RISK1, URGE, use = "pairwise.complete.obs")

cor.test(RISK1, PREM, use = "pairwise.complete.obs")

cor.test(RISK1, PERS, use = "pairwise.complete.obs")

cor.test(RISK1, SENS, use = "pairwise.complete.obs")

#IE-4

ILOC <- ILOC1+ILOC2

ELOC <- ELOC1+ELOC2

cor.test(RISK1, ILOC, use = "pairwise.complete.obs")

cor.test(RISK1, ELOC, use = "pairwise.complete.obs")

#L-1

cor.test(RISK1, LISA1, use = "pairwise.complete.obs")

#SOP2

SOP <- PESS1R+OPTI1

cor.test(RISK1, SOP, use = "pairwise.complete.obs")

#KSE-G

SDPQ <- SDPQ1+SDPQ2+SDPQ3

SDNQ <- SDNQ1+SDNQ2+SDNQ3

cor.test(RISK1, SDPQ, use = "pairwise.complete.obs")

cor.test(RISK1, SDNQ, use = "pairwise.complete.obs")

detach(R1_D)

#########################################################################

##UK

attach(R1_UK)

#BFI-2-XS

EXTR <- EXTR1R+EXTR2+EXTR3

AGRE <- AGRE1+AGRE2R+AGRE3

CONS <- CONS1R+CONS2R+CONS3

NEGA <- NEGA1+NEGA2+NEGA3R

OPEN <- OPEN1+OPEN2R+OPEN3

cor.test(RISK1, EXTR, use = "pairwise.complete.obs")

cor.test(RISK1, AGRE, use = "pairwise.complete.obs")

cor.test(RISK1, CONS, use = "pairwise.complete.obs")

cor.test(RISK1, NEGA, use = "pairwise.complete.obs")

cor.test(RISK1, OPEN, use = "pairwise.complete.obs")

#RSES

RSES <- RSES1+RSES2R+RSES3+RSES4+RSES5R+RSES6R+RSES7+RSES8R+RSES9R+RSES10

cor.test(RISK1, RSES, use = "pairwise.complete.obs")

#Health

cor.test(RISK1, HEAL, use = "pairwise.complete.obs")

#Employment status

#1) employed

#2) self-employed

#3) out of work and looking for work

#4) out of work but not currently looking for work

#5) doing housework

#6) pupil/student

#7) apprentice/internship

#8) retired

#[9) none of what is mentioned above]

EMPL.unempl <- recode(EMPL, "3:4 = 2; 1:2 = 1; else = NA")

EMPL.selfempl <- recode(EMPL, "2 = 2; 1 = 1; else = NA")

EMPL.retired <- recode(EMPL, "5 = 2; 8 = 2; 1:2 = 1; else = NA")

EMPL.student <- recode(EMPL, "6:7 = 2; 1:2 = 1; else = NA")

cor.test(RISK1, EMPL.unempl, use = "pairwise.complete.obs")

cor.test(RISK1, EMPL.selfempl, use = "pairwise.complete.obs")

cor.test(RISK1, EMPL.retired, use = "pairwise.complete.obs")

cor.test(RISK1, EMPL.student, use = "pairwise.complete.obs")

#Income

cor.test(RISK1, INCO, use = "pairwise.complete.obs")

#Education

cor.test(RISK1, SCHO, use = "pairwise.complete.obs")

#Age

cor.test(RISK1, AGE, use = "pairwise.complete.obs")

#Gender

cor.test(RISK1, SEX, use = "pairwise.complete.obs")

#ASKU

ASKU <- ASKU1+ASKU2+ASKU3

cor.test(RISK1, ASKU, use = "pairwise.complete.obs")

#I-8

URGE <- URGE1+URGE2

PREM <- PREM1+PREM2

PERS <- PERS1+PERS2

SENS <- SENS1+SENS2

cor.test(RISK1, URGE, use = "pairwise.complete.obs")

cor.test(RISK1, PREM, use = "pairwise.complete.obs")

cor.test(RISK1, PERS, use = "pairwise.complete.obs")

cor.test(RISK1, SENS, use = "pairwise.complete.obs")

#IE-4

ILOC <- ILOC1+ILOC2

ELOC <- ELOC1+ELOC2

cor.test(RISK1, ILOC, use = "pairwise.complete.obs")

cor.test(RISK1, ELOC, use = "pairwise.complete.obs")

#L-1

cor.test(RISK1, LISA1, use = "pairwise.complete.obs")

#SOP2

SOP <- PESS1R+OPTI1

cor.test(RISK1, SOP, use = "pairwise.complete.obs")

#KSE-G

SDPQ <- SDPQ1+SDPQ2+SDPQ3

SDNQ <- SDNQ1+SDNQ2+SDNQ3

cor.test(RISK1, SDPQ, use = "pairwise.complete.obs")

cor.test(RISK1, SDNQ, use = "pairwise.complete.obs")

detach(R1_UK)

#########################################################################

##########################

#Step 4: Reference values

##########################

#Quote 1: male, lower education, 18-29

#Quote 2: male, lower education, 30-49

#Quote 3: male, lower education, 50-69

#Quote 4: male, middle education, 18-29

#Quote 5: male, middle education, 30-49

#Quote 6: male, middle education, 50-69

#Quote 7: male, upper education, 18-29

#Quote 8: male, upper education, 30-49

#Quote 9: male, upper education, 50-69

#Quote 10: female, lower education, 18-29

#Quote 11: female, lower education, 30-49

#Quote 12: female, lower education, 50-69

#Quote 13: female, middle education, 18-29

#Quote 14: female, middle education, 30-49

#Quote 15: female, middle education, 50-69

#Quote 16: female, upper education, 18-29

#Quote 17: female, upper education, 30-49

#Quote 18: female, upper education, 50-69

##Germany

attach(R1_D)

tapply(RISK1, SEX, describe)

AGE1 <- subset(R1_D, AGE == 18 | AGE == 19 | AGE == 20 | AGE == 21 | AGE == 22 | AGE == 23 | AGE == 24 | AGE == 25 | AGE == 26

               | AGE == 27 | AGE == 28 | AGE == 29)

AGE2 <- subset(R1_D, AGE == 30 | AGE == 31 | AGE == 32 | AGE == 33 | AGE == 34 | AGE == 35 | AGE == 36 | AGE == 37 | AGE == 38

               | AGE == 39 | AGE == 40 | AGE == 41 | AGE == 42 | AGE == 43 | AGE == 44 | AGE == 45 | AGE == 46 | AGE == 47

               | AGE == 48 | AGE == 49)

AGE3 <- subset(R1_D, AGE == 50 | AGE == 51 | AGE == 52 | AGE == 53 | AGE == 54 | AGE == 55 | AGE == 56 | AGE == 57 | AGE == 58

               | AGE == 59 | AGE == 60 | AGE == 61 | AGE == 62 | AGE == 63 | AGE == 64 | AGE == 65 | AGE == 66 | AGE == 67

               | AGE == 68 | AGE == 69)

detach(R1_D)

attach(AGE1)

describe(RISK1)

detach(AGE1)

attach(AGE2)

describe(RISK1)

detach(AGE2)

attach(AGE3)

describe(RISK1)

detach(AGE3)

#########################################################################

##UK

attach(R1_UK)

tapply(RISK1, SEX, describe)

AGE1 <- subset(R1_UK, AGE == 18 | AGE == 19 | AGE == 20 | AGE == 21 | AGE == 22 | AGE == 23 | AGE == 24 | AGE == 25 | AGE == 26

               | AGE == 27 | AGE == 28 | AGE == 29)

AGE2 <- subset(R1_UK, AGE == 30 | AGE == 31 | AGE == 32 | AGE == 33 | AGE == 34 | AGE == 35 | AGE == 36 | AGE == 37 | AGE == 38

               | AGE == 39 | AGE == 40 | AGE == 41 | AGE == 42 | AGE == 43 | AGE == 44 | AGE == 45 | AGE == 46 | AGE == 47

               | AGE == 48 | AGE == 49)

AGE3 <- subset(R1_UK, AGE == 50 | AGE == 51 | AGE == 52 | AGE == 53 | AGE == 54 | AGE == 55 | AGE == 56 | AGE == 57 | AGE == 58

               | AGE == 59 | AGE == 60 | AGE == 61 | AGE == 62 | AGE == 63 | AGE == 64 | AGE == 65 | AGE == 66 | AGE == 67

               | AGE == 68 | AGE == 69)

detach(R1_UK)

attach(AGE1)

describe(RISK1)

detach(AGE1)

attach(AGE2)

describe(RISK1)

detach(AGE2)

attach(AGE3)

describe(RISK1)

detach(AGE3)

#########################################################################

################################

#Step 5: Descriptive statistics

################################

##Germany

attach(R1_D)

#Age

summary(AGE)

sd(AGE)

#Proportion women

table(SEX)

#Educational level

table(QUOT)

detach(R1_D)

#########################################################################

##UK

attach(R1_UK)

#Age

summary(AGE)

sd(AGE)

#Proportion women

table(SEX)

#Educational level

table(QUOT)

detach(R1_UK)