source("0_helpers.R")
knitr::opts_chunk$set(warning = FALSE, message = FALSE)

load("data/cleaned.rdata")

knitr::opts_chunk$set(echo = FALSE, error = TRUE, dev = "CairoPNG",
                      cache = FALSE)
library(ggplot2)
theme_set(theme_bw())

library(codebook)

attributes(s3_daily$grooming_broad)$scale_item_names <- NULL
s3_daily <- s3_daily %>% filter(!is.na(session), !is.na(created))
# s3_daily <- s3_daily %>% select(-!!names(lab))
s3_daily <- s3_daily %>% ungroup()
s3_daily$sleep_fell_asleep_time <- as.numeric(s3_daily$sleep_fell_asleep_time)
s3_daily$sleep_awoke_time <- as.numeric(s3_daily$sleep_awoke_time)
s3_daily$DAL <- as.numeric(s3_daily$DAL)
s3_daily$window_length <- as.numeric(s3_daily$window_length)
attributes(s3_daily$menstruation_imputed)$names <- NULL
attributes(s3_daily$menstruation)$names <- NULL
vars <- c("session", "short", "created_date", setdiff(names(s3_daily), names(lab)))
s3_daily <- s3_daily[, vars]

library(future)
if (file.exists("codings/reliabilities_new.rdata")) {
  load("codings/reliabilities_new.rdata")
  } else {
    library(future.batchtools)
  # plan(tweak(multicore, workers = 3))
  login <- tweak(remote, workers = "arslan@arc-lin-cpt10.mpib-berlin.mpg.de") # Your username goes before the @
  plan(list(login, tweak(multicore, workers = 10))) # this tells the future package that we want to use a remote server for running our future operations
  
  # login <- tweak(remote, workers = "arslan@tardis.mpib-berlin.mpg.de")
  # qsub <- tweak(batchtools_torque, template = 'torque-lido.tmpl', 
  #             # workers = "export LSF_ENVDIR=/opt/lsf/conf",
  #                 resources = list(job.name = 'multilevel_reliability',
  #                                 queue = 'default',
  #                                 walltime = "5:0:0",
  #                                 memory = 32, #gb
  #                                 ncpus = 1))
  # ## Specify future topology
  # ## login node -> { cluster node (compile brms model) } -> { run chains on multiple cores }
  # plan(list(
  #   login,
  #   qsub
  # ))
  
  reliabilities_diary = future::value(future({
      reliabilities_diary <- codebook::compute_reliabilities(s3_daily, 'repeated_many')
      save(reliabilities_diary, file = "codings/reliabilities_new.rdata")
      reliabilities_diary
  }))
  save(reliabilities_diary, file = "codings/reliabilities_new.rdata")
}

plan(multicore)

Demographics

Participants filled out these surveys before being invited to the daily diary. After finishing them, they were invited to fill out the diary on the next day.

Metadata

Description

Dataset name: s1_demo

The dataset has N=1660 rows and 114 columns. 0 rows have no missing values on any column.

Metadata for search engines

Date published: 2020-06-11

x
session
created
modified
ended
expired
info_study
lab_code
age
gender
gender_other
education_years
education_level
education_level_special
occupational_status
occupation
net_income
study_major
postal_code
religion
religiosity
sex_orientation
sex_orientation_special
relationship_status
relationship_count
relationship_details
duration_relationship_years
duration_relationship_month
partner_gender
partner_gender_other
age_partner
abode_with_partner
abode_flat_share
abode_flat_share_description
abode_alone
abode_special_case_description
long_distance_relationship
distance_partner
days_same_place_partner_monthly
days_with_partner
nights_with_partner
has_children
nr_children
partner_father
family_description_optional
pregnant
breast_feeding
pregnant_stress
pregnant_trying
wish_for_children
contraception_at_all
contraception_other_reasons_not_to
contraception_method
contraception_method_other
contraception_combi
contraception_method_combination_other
contraception_calendar_abstinence
contraception_hormonal_pill
other_pill_name
contraception_hormonal_other
contraception_hormonal_other_name
contraception_pill_estrogen
contraception_pill_gestagen_type
contraception_pill_gestagen
contraception_other_estrogen
contraception_other_gestagen_type
contraception_other_gestagen
contraception_app
contraception_app_name
hormonal_contraception_last3m
contraception_meeting_partner
self_rated_health
height
weight
hormonal_med
hormonal_med_name
psychoactive_med
psychoactive_med_name
alcohol_weekly
cigarettes_daily
sport_weekly
sport_kinds
meat_eating
menarche
menarche_certainty
number_sexual_partner
number_sexual_partner_certainty
first_time
first_time_certainty
mother_menopause_yes
mother_menopause_age
mother_menopause_certainty
menopause_yes
menopause_age
menopause_certainty
menstruation_regular
menstruation_last
menstruation_last_certainty
menstruation_length
menstruation_length_certainty
menstruation_regularity
menstruation_regularity_certainty
free_not_covered
hetero_relationship
short
ended_date
contraception_other_pill_estrogen
contraception_other_pill_gestagen
contraception_other_pill_gestagen_type
hormonal_contraception
contraception_method_broad
contraception_hormonal_pill_estrogen
contraception_hormonal_pill_gestagen_type
estrogen_progestogen
living_situation

Survey overview

1607 completed rows, 1651 who entered any information, 9 only viewed the first page. There are 9 expired rows (people who did not finish filling out in the requested time frame). In total, there are 1660 rows including unfinished and expired rows.

There were 1660 unique participants, of which 1607 finished filling out at least one survey.

This survey was not repeated.

The first session started on 2016-05-02 17:00:26, the last session on 2017-01-13 13:20:58.

Starting date times

People took on average 1702.97 minutes (median 11.07) to answer the survey.

Duration people took for answering the survey

#Variables

info_study

Wie sind Sie auf unsere Studie aufmerksam geworden?

Distribution

Distribution of values for info_study

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
select_or_add_one	info_study	Wie sind Sie auf unsere Studie aufmerksam geworden?	0			2

Value labels

Response choices
name	value
1	1
2	2
3	3
4	4
5	5
6	6

lab_code

Nehmen Sie auch an unserer Laborstudie Attraktivitätsbeurteilungen in Göttingen teil und haben einen Code für diese Studie erhalten? Wenn ja, geben Sie bitte hier unbedingt Ihren persönlichen Code ein. Wenn nein, lassen Sie dieses Feld einfach aus.

Distribution

Distribution of values for lab_code

1453 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
lab_code	Nehmen Sie auch an unserer Laborstudie Attraktivitätsbeurteilungen in Göttingen teil und haben einen Code für diese Studie erhalten? Wenn ja, geben Sie bitte hier unbedingt Ihren persönlichen Code ein. Wenn nein, lassen Sie dieses Feld einfach aus.	text	character	1			3	1453	0.1247	162	0	2	15	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	lab_code	Nehmen Sie auch an unserer Laborstudie Attraktivitätsbeurteilungen in Göttingen teil und haben einen Code für diese Studie erhalten? Wenn ja, geben Sie bitte hier unbedingt Ihren persönlichen Code ein. Wenn nein, lassen Sie dieses Feld einfach aus.	1			3

Value labels

Response choices
name	value

age

Ihr Alter

Distribution

Distribution of values for age

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	18,120,1	age	Ihr Alter	0			4

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

gender

Ihr Geschlecht

Distribution

Distribution of values for gender

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	gender	Ihr Geschlecht	0			5

Value labels

Response choices
name	value
weiblich	1
anderes	2
Item was never rendered for this user.	NA

gender_other

anderes:

Distribution

Distribution of values for gender_other

1658 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
gender_other	anderes:	text	character	1	gender == 2		6	1658	0.0012	2	0	8	49	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	gender_other	anderes:	1	gender == 2		6

Value labels

Response choices
name	value

education_years

Wie viele Bildungsjahre (Schule, Studium an Universität oder FH, Jahre als Doktorand/in, NICHT Berufsschule etc.) haben Sie hinter sich?

Distribution

Distribution of values for education_years

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,26,1	education_years	Wie viele Bildungsjahre (Schule, Studium an Universität oder FH, Jahre als Doktorand/in, NICHT Berufsschule etc.) haben Sie hinter sich?	0			7

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

education_level

Was ist ihr höchster, bislang erreichter Bildungsgrad

Distribution

Distribution of values for education_level

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc		education_level	Was ist ihr höchster, bislang erreichter Bildungsgrad	0	mc_vertical			8

Value labels

Response choices
name	value
1	0_not_finished
2	1_hauptschule
3	2_realschule
4	3_polytechnic
5	4_professional_school
6	5_fachabitur
7	6_abitur
8	7_vocational_uni_bachelor
9	8_uni_bachelor
10	9_vocational_uni_master_level
11	10_uni_master_level
12	11_uni_doctorate
13	12_uni_habilitation

education_level_special

Haben Sie einen Bildungsgrad, der sich hier nicht gut einsortieren lässt (bspw. Meister)? Wenn die obige Liste nicht ausreicht, können Sie das hier frei ergänzen.

Distribution

Distribution of values for education_level_special

1562 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
education_level_special	Haben Sie einen Bildungsgrad, der sich hier nicht gut einsortieren lässt (bspw. Meister)? Wenn die obige Liste nicht ausreicht, können Sie das hier frei ergänzen.	textarea	character	1			9	1562	0.059	89	0	1	137	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	education_level_special	Haben Sie einen Bildungsgrad, der sich hier nicht gut einsortieren lässt (bspw. Meister)? Wenn die obige Liste nicht ausreicht, können Sie das hier frei ergänzen.	1			9

Value labels

Response choices
name	value

occupational_status

Welchen beruflichen Status haben Sie derzeit?

Distribution

Distribution of values for occupational_status

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_multiple		occupational_status	Welchen beruflichen Status haben Sie derzeit?	0			10

Value labels

Response choices
name	value
1	not_working
2	pupil
3	trainee
4	student
5	homemaker
6	employed
7	intern

occupation

Welchen Beruf üben Sie aus?

Distribution

Distribution of values for occupation

1187 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
occupation	Welchen Beruf üben Sie aus?	textarea	character	0	occupational_status %contains% “employed”		11	1187	0.2849	384	0	1	282	1

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	occupation	Welchen Beruf üben Sie aus?	0	occupational_status %contains% “employed”		11

Value labels

Response choices
name	value

net_income

Wie viel Geld haben Sie monatlich zur Verfügung (netto)?

Distribution

Distribution of values for net_income

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc		net_income	Wie viel Geld haben Sie monatlich zur Verfügung (netto)?	0			12

Value labels

Response choices
name	value
1	euro_lt_500
2	euro_500_1000
3	euro_1000_2000
4	euro_2000_3000
5	euro_gt_3000
6	dont_tell

study_major

Was studieren Sie derzeit oder haben Sie studiert?

Distribution

Distribution of values for study_major

232 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
study_major	Was studieren Sie derzeit oder haben Sie studiert?	textarea	character	0	education_level.indexOf(‘uni’) > -1 \|\| occupational_status.indexOf(‘student’) > -1 //js_only		13	232	0.8602	781	0	3	225	2

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	study_major	Was studieren Sie derzeit oder haben Sie studiert?	0	education_level.indexOf(‘uni’) > -1 \|\| occupational_status.indexOf(‘student’) > -1 //js_only		13

Value labels

Response choices
name	value

postal_code

Wie lauten die ersten drei Ziffern ihrer Postleitzahl?

Distribution

Distribution of values for postal_code

21 missing values.

Summary statistics

name	label	type	type_options	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
postal_code	Wie lauten die ersten drei Ziffern ihrer Postleitzahl?	text	[0-9][0-9][0-9]	character	0			14	21	0.9873	423	0	3	3	0

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
text	[0-9][0-9][0-9]	postal_code	Wie lauten die ersten drei Ziffern ihrer Postleitzahl?	0			14

Value labels

Response choices
name	value

religion

Welcher Religion bzw. welcher Weltanschauung sind Sie zugehörig?

Distribution

Distribution of values for religion

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
select_or_add_one	religion	Welcher Religion bzw. welcher Weltanschauung sind Sie zugehörig?	0			15

Value labels

Response choices
name	value
1	1
2	2
3	3
4	4
5	5
6	6
7	7

religiosity

Für wie religiös halten Sie sich?

Distribution

Distribution of values for religiosity

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	1,6,1	religiosity	Für wie religiös halten Sie sich?	0			16

Value labels

Response choices
name	value
1: nicht religiös	1
2	2
3	3
4	4
5	5
6: religiös	6
Item was never rendered for this user.	NA

sex_orientation

Was beschreibt am besten Ihre sexuelle Orientierung?

Distribution

Distribution of values for sex_orientation

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	class	showif	value	item_order
mc_button	sex_orientation	Was beschreibt am besten Ihre sexuelle Orientierung?	0	mc_vertical			17

Value labels

Response choices
name	value
Ausschließlich heterosexuell	1
Überwiegend heterosexuell, nur gelegentlich homosexuell	2
Überwiegend heterosexuell, aber mehr als gelegentlich homosexuell	3
Gleichermaßen heterosexuell wie homosexuell	4
Überwiegend homosexuell, aber mehr als gelegentlich heterosexuell	5
Überwiegend homosexuell, nur gelegentlich heterosexuell	6
Ausschließlich homosexuell	7
Asexuell oder aromantisch	8
Item was never rendered for this user.	NA

sex_orientation_special

Wenn Ihnen die obigen Kategorien nicht ausreichen, können Sie es hier in Ihre eigenen Worte fassen.

Distribution

Distribution of values for sex_orientation_special

1577 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
sex_orientation_special	Wenn Ihnen die obigen Kategorien nicht ausreichen, können Sie es hier in Ihre eigenen Worte fassen.	textarea	character	1			18	1577	0.05	83	0	1	557	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	sex_orientation_special	Wenn Ihnen die obigen Kategorien nicht ausreichen, können Sie es hier in Ihre eigenen Worte fassen.	1			18

Value labels

Response choices
name	value

relationship_status

Welchen Beziehungsstatus haben Sie?

Distribution

Distribution of values for relationship_status

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	class	showif	value	item_order
mc	relationship_status	Welchen Beziehungsstatus haben Sie?	0	mc_vertical			19

Value labels

Response choices
name	value
Single	1
lose Beziehung	2
feste Partnerschaft	3
verlobt	4
verheiratet	5
andere	6
Item was never rendered for this user.	NA

relationship_count

Sind Sie …

Distribution

Distribution of values for relationship_count

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for relationship_count

Item

Item options
type	name	label	optional	class	showif	value	item_order
mc	relationship_count	Sind Sie …	0	mc_vertical	relationship_status > 1		20

Value labels

Response choices
name	value
monogam (ein Partner)	1
polyamor (mehrere Partner)	2
in einer offenen Beziehung	3
andere	4
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

relationship_details

Sie haben eine Beziehungsform angegeben, die in unserem Fragebogen vielleicht nicht gut erfasst wird. Bitte beschreiben Sie hier Ihre Beziehungsform einfach frei.

Distribution

Distribution of values for relationship_details

1632 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
relationship_details	Sie haben eine Beziehungsform angegeben, die in unserem Fragebogen vielleicht nicht gut erfasst wird. Bitte beschreiben Sie hier Ihre Beziehungsform einfach frei.	textarea	character	1	relationship_status > 5 \| relationship_count > 3		21	1632	0.0169	28	0	16	444	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	relationship_details	Sie haben eine Beziehungsform angegeben, die in unserem Fragebogen vielleicht nicht gut erfasst wird. Bitte beschreiben Sie hier Ihre Beziehungsform einfach frei.	1	relationship_status > 5 \| relationship_count > 3		21

Value labels

Response choices
name	value

duration_relationship_years

Jahre

Distribution

Distribution of values for duration_relationship_years

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for duration_relationship_years

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,100	duration_relationship_years	Jahre	0	relationship_status > 1		24

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

duration_relationship_month

Monate

Distribution

Distribution of values for duration_relationship_month

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for duration_relationship_month

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,12	duration_relationship_month	Monate	0	relationship_status > 1		25

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

partner_gender

Welches Geschlecht hat Ihr Partner?

Distribution

Distribution of values for partner_gender

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_gender

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	partner_gender	Welches Geschlecht hat Ihr Partner?	0	relationship_status > 1		26

Value labels

Response choices
name	value
weiblich	1
männlich	2
anderes	3
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

partner_gender_other

anderes:

Distribution

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed

## No non-missing values to show.

1660 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	whitespace
partner_gender_other	anderes:	text	character	1	partner_gender == 3		27	1660	0	0	0	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	partner_gender_other	anderes:	1	partner_gender == 3		27

Value labels

Response choices
name	value

age_partner

Wie alt ist Ihr Partner?

Distribution

Distribution of values for age_partner

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for age_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	14,120,1	age_partner	Wie alt ist Ihr Partner?	0	relationship_status > 1		28

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

abode_with_partner

Wohnen Sie mit Ihrem Partner zusammen?

Distribution

Distribution of values for abode_with_partner

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for abode_with_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		abode_with_partner	Wohnen Sie mit Ihrem Partner zusammen?	0	relationship_status > 1		29

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

abode_flat_share

Wohnen Sie in einer Wohngemeinschaft (abgesehen von Partner)?

Distribution

Distribution of values for abode_flat_share

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	abode_flat_share	Wohnen Sie in einer Wohngemeinschaft (abgesehen von Partner)?	0			30

Value labels

Response choices
name	value
nein	1
mit Eltern/Großeltern	2
ja	3
Item was never rendered for this user.	NA

abode_flat_share_description

Mit wem wohnen Sie zusammen?

Nutzen Sie bitte dieses offene Feld, um uns Angaben über Ihre Mitbewohner zu machen. Am einfachsten ist es für uns, wenn Sie einfach Geschlecht, Alter und gegebenenfalls Verwandtschaftsstatus angeben, bspw. so:

w,55,Mutter w,26,Schwester m,30,Bruder

Sich selbst und Ihren Partner (ggf.) müssen Sie nicht extra nennen.

Distribution

Distribution of values for abode_flat_share_description

898 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
abode_flat_share_description	Mit wem wohnen Sie zusammen? Nutzen Sie bitte dieses offene Feld, um uns Angaben über Ihre Mitbewohner zu machen. Am einfachsten ist es für uns, wenn Sie einfach Geschlecht, Alter und gegebenenfalls Verwandtschaftsstatus angeben, bspw. so: w,55,Mutter w,26,Schwester m,30,Bruder Sich selbst und Ihren Partner (ggf.) müssen Sie nicht extra nennen.	textarea	character	0	abode_flat_share > 1		31	898	0.459	737	0	4	411	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	abode_flat_share_description	Mit wem wohnen Sie zusammen? Nutzen Sie bitte dieses offene Feld, um uns Angaben über Ihre Mitbewohner zu machen. Am einfachsten ist es für uns, wenn Sie einfach Geschlecht, Alter und gegebenenfalls Verwandtschaftsstatus angeben, bspw. so: `w,55,Mutter w,26,Schwester m,30,Bruder` Sich selbst und Ihren Partner (ggf.) müssen Sie nicht extra nennen.	0	abode_flat_share > 1		31

Value labels

Response choices
name	value

abode_alone

Wohnen Sie also alleine?

Distribution

Distribution of values for abode_alone

1455 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for abode_alone

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		abode_alone	Wohnen Sie also alleine?	0	abode_flat_share == 1 & abode_with_partner == 0		32

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

abode_special_case_description

Bitte beschreiben Sie Ihre aktuelle Wohnsituation etwas ausführlicher.

Distribution

Distribution of values for abode_special_case_description

1643 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
abode_special_case_description	Bitte beschreiben Sie Ihre aktuelle Wohnsituation etwas ausführlicher.	textarea	character	0	abode_alone == 0		33	1643	0.0102	17	0	7	177	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	abode_special_case_description	Bitte beschreiben Sie Ihre aktuelle Wohnsituation etwas ausführlicher.	0	abode_alone == 0		33

Value labels

Response choices
name	value

long_distance_relationship

Haben Sie eine Fernbeziehung?

Distribution

Distribution of values for long_distance_relationship

1057 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for long_distance_relationship

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		long_distance_relationship	Haben Sie eine Fernbeziehung?	0	abode_with_partner == 0		34

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

distance_partner

Wie weit wohnen Sie von Ihrem Partner entfernt?

Distribution

Distribution of values for distance_partner

1397 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for distance_partner

Item

Item options
type	name	label	optional	showif	value	item_order
mc	distance_partner	Wie weit wohnen Sie von Ihrem Partner entfernt?	0	long_distance_relationship == 1		35

Value labels

Response choices
name	value
< 1 Std.	1
1 - 2 Std.	2
2-3 Std.	3
3 - 5 Std.	4
5 - 9 Std.	5
9 - 12 Std.	6
&gt; 12 Std.	7
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

days_same_place_partner_monthly

Wie viele Tage verbringen Sie im Monat an demselben Ort wie Ihr Partner?

Distribution

Distribution of values for days_same_place_partner_monthly

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for days_same_place_partner_monthly

Item

Item options
type	name	label	optional	showif	value	item_order
mc	days_same_place_partner_monthly	Wie viele Tage verbringen Sie im Monat an demselben Ort wie Ihr Partner?	0	relationship_status > 1		36

Value labels

Response choices
name	value
< 3 Tage	1
3 - 4 Tage	2
5 - 6 Tage	3
7 - 14 Tage	4
14-21 Tage	5
21-29 Tage	6
&gt; 29 Tage	7
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

days_with_partner

Wie viele Tage pro Woche verbringen Sie durchschnittlich an demselben Ort wie ihr Partner?

Distribution

Distribution of values for days_with_partner

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for days_with_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	0,7,1	days_with_partner	Wie viele Tage pro Woche verbringen Sie durchschnittlich an demselben Ort wie ihr Partner?	0	relationship_status > 1		37

Value labels

Response choices
name	value
0: Tage	0
1	1
2	2
3	3
4	4
5	5
6	6
7: Tage	7
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

nights_with_partner

Wie viele Nächte pro Woche verbringen Sie durchschnittlich mit Ihrem Partner?

Distribution

Distribution of values for nights_with_partner

549 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for nights_with_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	0,7,1	nights_with_partner	Wie viele Nächte pro Woche verbringen Sie durchschnittlich mit Ihrem Partner?	0	relationship_status > 1		38

Value labels

Response choices
name	value
0: Nächte	0
1	1
2	2
3	3
4	4
5	5
6	6
7: Nächte	7
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

has_children

Haben Sie Kinder?

Distribution

Distribution of values for has_children

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		has_children	Haben Sie Kinder?	0			39

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

nr_children

Wieviele leibliche Kinder haben Sie?

Distribution

Distribution of values for nr_children

1482 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for nr_children

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,20,1	nr_children	Wieviele leibliche Kinder haben Sie?	0	has_children == 1		40

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

partner_father

Ist Ihr aktueller Partner der Vater Ihrer Kinder?

Distribution

Distribution of values for partner_father

1482 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_father

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		partner_father	Ist Ihr aktueller Partner der Vater Ihrer Kinder?	0	has_children == 1		41

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

family_description_optional

Hier können Sie andere Angaben zu Ihren Familienverhältnissen, die in unseren Fragebogen sonst nicht passen, ergänzen (bspw. Adoptivkinder).

Distribution

Distribution of values for family_description_optional

1628 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
family_description_optional	Hier können Sie andere Angaben zu Ihren Familienverhältnissen, die in unseren Fragebogen sonst nicht passen, ergänzen (bspw. Adoptivkinder).	textarea	character	1	has_children == 1		42	1628	0.0193	32	0	13	304	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	family_description_optional	Hier können Sie andere Angaben zu Ihren Familienverhältnissen, die in unseren Fragebogen sonst nicht passen, ergänzen (bspw. Adoptivkinder).	1	has_children == 1		42

Value labels

Response choices
name	value

pregnant

Sind Sie aktuell schwanger oder waren Sie es in den letzten drei Monaten?

Distribution

Distribution of values for pregnant

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		pregnant	Sind Sie aktuell schwanger oder waren Sie es in den letzten drei Monaten?	0			43

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

breast_feeding

Stillen Sie aktuell oder haben Sie in den letzten drei Monaten gestillt?

Distribution

Distribution of values for breast_feeding

21 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		breast_feeding	Stillen Sie aktuell oder haben Sie in den letzten drei Monaten gestillt?	0			44

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

pregnant_stress

Wenn Sie heute erfahren würden, dass Sie schwanger sind, fänden Sie das…

Distribution

Distribution of values for pregnant_stress

59 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for pregnant_stress

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	1,6,1	pregnant_stress	Wenn Sie heute erfahren würden, dass Sie schwanger sind, fänden Sie das…	0	pregnant == 0		47

Value labels

Response choices
name	value
1: sehr belastend	1
2	2
3	3
4	4
5	5
6: sehr erfreulich	6
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

pregnant_trying

Versuchen Sie derzeit schwanger zu werden?

Distribution

Distribution of values for pregnant_trying

59 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for pregnant_trying

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	1,6,1	pregnant_trying	Versuchen Sie derzeit schwanger zu werden?	0	pregnant == 0		48

Value labels

Response choices
name	value
1: versuche es zu vermeiden	1
2	2
3	3
4	4
5	5
6: versuche schwanger zu werden	6
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

wish_for_children

Warum nicht?

Distribution

Distribution of values for wish_for_children

120 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc		wish_for_children	Warum nicht?	0	mc_vertical left100	pregnant_trying < 4		49

Value labels

Response choices
name	value
1	not_actively_trying
2	not_in_current_life_situation
3	not_with_this_partner
4	dont_have_partner
5	partner_doesnt_want
6	rather_adopt
7	cant_imagine_having_kids

contraception_at_all

Verhüten Sie üblicherweise, wenn Sie Sex haben (Pille, Kondom, Kalendermethode, coitus interruptus, etc.) ?

Distribution

Distribution of values for contraception_at_all

59 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for contraception_at_all

Item

Item options
type	name	label	optional	class	showif	value	item_order
mc	contraception_at_all	Verhüten Sie üblicherweise, wenn Sie Sex haben (Pille, Kondom, Kalendermethode, coitus interruptus, etc.) ?	0	mc_vertical	pregnant == 0		51

Value labels

Response choices
name	value
ja	1
meistens	2
ja, aber ich lasse es auch etwas “drauf ankommen”	3
nein, ich lasse es “drauf ankommen”	4
nein, ich habe zur Zeit gar keinen Sex	5
nein, andere Gründe	6
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

contraception_other_reasons_not_to

Hier können Sie andere Gründe, die in unser Fragebogenformat nicht gut passen, angeben

Distribution

Distribution of values for contraception_other_reasons_not_to

1605 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contraception_other_reasons_not_to	Hier können Sie andere Gründe, die in unser Fragebogenformat nicht gut passen, angeben	textarea	character	0	contraception_at_all == 6		52	1605	0.0331	54	0	5	401	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	contraception_other_reasons_not_to	Hier können Sie andere Gründe, die in unser Fragebogenformat nicht gut passen, angeben	0	contraception_at_all == 6		52

Value labels

Response choices
name	value

contraception_method

Wie verhüten Sie zur Zeit üblicherweise?

Sie können mehrere Methoden angeben.

Distribution

Distribution of values for contraception_method

273 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc_multiple		contraception_method	Wie verhüten Sie zur Zeit üblicherweise? Sie können mehrere Methoden angeben.	0	mc_vertical	contraception_at_all < 4		53

Value labels

Response choices
name	value
1	hormonal_pill
2	hormonal_other
3	barrier_condoms
4	barrier_other
5	barrier_intrauterine_pessar
6	barrier_coitus_interruptus
7	barrier_no_penetrative_sex
8	awareness_calendar
9	awareness_temperature_billings
10	awareness_computer
11	barrier_spermicide
12	barrier_chemical
13	hormonal_morning_after_pill
14	breast_feeding
15	infertile
16	partner_infertile
17	sterilised
18	partner_sterilised
19	not_listed

contraception_method_other

Welche andere, nicht in der Liste vorkommende, Methode benutzen Sie?

Distribution

Distribution of values for contraception_method_other

1651 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contraception_method_other	Welche andere, nicht in der Liste vorkommende, Methode benutzen Sie?	text	character	0	contraception_method %contains% ‘not_listed’		54	1651	0.0054	9	0	3	56	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	contraception_method_other	Welche andere, nicht in der Liste vorkommende, Methode benutzen Sie?	0	contraception_method %contains% ‘not_listed’		54

Value labels

Response choices
name	value

contraception_combi

Sie haben angegeben, mehrere Verhütungsmethoden zu kombinieren. Aus welchem Grund, oder welchen Gründen tun Sie das?

Distribution

Distribution of values for contraception_combi

1197 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc_multiple		contraception_combi	Sie haben angegeben, mehrere Verhütungsmethoden zu kombinieren. Aus welchem Grund, oder welchen Gründen tun Sie das?	0	mc_vertical	contraception_method %contains% “,”		55

Value labels

Response choices
name	value
1	multiple_to_decrease_infection_risk
2	multiple_to_decrease_conception_risk
3	fallback_if_forgotten
4	fallback_if_fertile
5	barrier_if_partner_sick
6	different_methods_for_different_partners
7	other

contraception_method_combination_other

anderer Grund:

Distribution

Distribution of values for contraception_method_combination_other

1636 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contraception_method_combination_other	anderer Grund:	textarea	character	0	contraception_combi %contains% “other”		56	1636	0.0145	24	0	13	403	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	contraception_method_combination_other	anderer Grund:	0	contraception_combi %contains% “other”		56

Value labels

Response choices
name	value

contraception_calendar_abstinence

Distribution

Distribution of values for contraception_calendar_abstinence

1520 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
contraception_calendar_abstinence	character	1520	0.0843	10	0	8	38	0	NA

contraception_hormonal_pill

Wie heißt Ihre Anti-Baby-Pille?

Falls Sie ein <abbr title=“Eine wirkstoffgleiche Kopie, Nachahmerpräparat, bspw. Dienovel=Valette” class=“hastooltip”>Generikum</abbr> nehmen und es in dieser Liste fehlt, können Sie gerne den Markennamen angeben.

Distribution

Distribution of values for contraception_hormonal_pill

1086 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	median	max	whitespace	n_value_labels
contraception_hormonal_pill	Wie heißt Ihre Anti-Baby-Pille? Falls Sie ein <abbr title=“Eine wirkstoffgleiche Kopie, Nachahmerpräparat, bspw. Dienovel=Valette” class=“hastooltip”>Generikum</abbr> nehmen und es in dieser Liste fehlt, können Sie gerne den Markennamen angeben.	haven_labelled	1086	0.3458	69	0	3	NA	19	0	97

other_pill_name

Sie haben angegeben, dass Ihre Pille in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihrer Pille.

Distribution

Distribution of values for other_pill_name

1581 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
other_pill_name	Sie haben angegeben, dass Ihre Pille in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihrer Pille.	text	character	0	contraception_hormonal_pill == ‘other’		59	1581	0.0476	49	0	5	31	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	other_pill_name	Sie haben angegeben, dass Ihre Pille in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihrer Pille.	0	contraception_hormonal_pill == ‘other’		59

Value labels

Response choices
name	value

contraception_hormonal_other

Welches hormonelle Verhütungsmittel verwenden Sie?

Distribution

Distribution of values for contraception_hormonal_other

1563 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	median	max	whitespace	n_value_labels
contraception_hormonal_other	Welches hormonelle Verhütungsmittel verwenden Sie?	haven_labelled	1563	0.0584	7	0	4	NA	13	0	8

Value labels

Response choices
name	value
1	nuvaring
2	circlet
3	evra
4	mirena
5	depo_provera
6	depo_clinovir
7	implanon
8	other

contraception_hormonal_other_name

Sie haben angegeben, dass Ihr Verhütungsmittel in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihres Verhütungsmittels.

Distribution

Distribution of values for contraception_hormonal_other_name

1634 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	class	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contraception_hormonal_other_name	Sie haben angegeben, dass Ihr Verhütungsmittel in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihres Verhütungsmittels.	text	character	0	contraception_hormonal_other == ‘other’		61	select2hormonal	1634	0.0157	10	0	6	31	0

Item

Item options
type	name	label	optional	class	showif	value	item_order
text	contraception_hormonal_other_name	Sie haben angegeben, dass Ihr Verhütungsmittel in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihres Verhütungsmittels.	0	select2hormonal	contraception_hormonal_other == ‘other’		61

Value labels

Response choices
name	value

contraception_pill_estrogen

Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, wieviel Mikrogramm (µg) Östrogen Ihre Pille enthält. Die meisten Pillen enthalten zwischen 0 und 50 Mikrogramm Östrogen. Manchmal wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.

Distribution

Distribution of values for contraception_pill_estrogen

1581 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for contraception_pill_estrogen

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,100,1	contraception_pill_estrogen	Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, wieviel Mikrogramm (µg) Östrogen Ihre Pille enthält. Die meisten Pillen enthalten zwischen 0 und 50 Mikrogramm Östrogen. Manchmal wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.	0	contraception_hormonal_pill == ‘other’		62

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

contraception_pill_gestagen_type

Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, welcher Typ Gestagen verwendet wird.

Distribution

Distribution of values for contraception_pill_gestagen_type

1581 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc		contraception_pill_gestagen_type	Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, welcher Typ Gestagen verwendet wird.	0	mc_vertical left500	contraception_hormonal_pill == ‘other’		63

Value labels

Response choices
name	value
1	CMA
2	CPA
3	DNG
4	DSG
5	DSP
6	GSD
7	LYN
8	LNG
9	NEA
10	NES
11	NGT
12	other_gestagen

contraception_pill_gestagen

Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, wieviel Mikrogramm (µg) des jeweiligen Gestagens Ihre Pille enthält. Die meisten Pillen enthalten zwischen 50 und 3000 Mikrogramm Gestagen. Häufig wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.

Distribution

Distribution of values for contraception_pill_gestagen

1581 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for contraception_pill_gestagen

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,4000,1	contraception_pill_gestagen	Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, wieviel Mikrogramm (µg) des jeweiligen Gestagens Ihre Pille enthält. Die meisten Pillen enthalten zwischen 50 und 3000 Mikrogramm Gestagen. Häufig wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.	0	contraception_hormonal_pill == ‘other’		64

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

contraception_other_estrogen

Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, wieviel Mikrogramm (µg) Östrogen Ihr Verhütungsmittel enthält. Die meisten Verhütungsmittel enthalten zwischen 0 und 50 Mikrogramm Östrogen. Manchmal wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.

Distribution

Distribution of values for contraception_other_estrogen

1581 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for contraception_other_estrogen

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,100,1	contraception_other_estrogen	Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, wieviel Mikrogramm (µg) Östrogen Ihr Verhütungsmittel enthält. Die meisten Verhütungsmittel enthalten zwischen 0 und 50 Mikrogramm Östrogen. Manchmal wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.	0	contraception_hormonal_pill == ‘other’		65

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

contraception_other_gestagen_type

Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, welcher Typ Gestagen verwendet wird.

Distribution

Distribution of values for contraception_other_gestagen_type

1581 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc		contraception_other_gestagen_type	Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, welcher Typ Gestagen verwendet wird.	0	mc_vertical left500	contraception_hormonal_pill == ‘other’		66

Value labels

Response choices
name	value
1	CMA
2	CPA
3	DNG
4	DSG
5	DSP
6	GSD
7	LYN
8	LNG
9	NEA
10	NES
11	NGT
12	other_gestagen

contraception_other_gestagen

Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, wieviel Mikrogramm (µg) des jeweiligen Gestagens Ihr Verhütungsmittel enthält. Die meisten Verhütungsmittel enthalten zwischen 50 und 3000 Mikrogramm Gestagen. Häufig wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.

Distribution

Distribution of values for contraception_other_gestagen

1581 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for contraception_other_gestagen

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,4000,1	contraception_other_gestagen	Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, wieviel Mikrogramm (µg) des jeweiligen Gestagens Ihr Verhütungsmittel enthält. Die meisten Verhütungsmittel enthalten zwischen 50 und 3000 Mikrogramm Gestagen. Häufig wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (µg) zu erhalten.	0	contraception_hormonal_pill == ‘other’		67

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

contraception_app

Benutzen Sie eine Zyklus-App?

Distribution

Distribution of values for contraception_app

36 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		contraception_app	Benutzen Sie eine Zyklus-App?	0			68

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

contraception_app_name

Wie heißt diese App?

Distribution

Distribution of values for contraception_app_name

1249 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contraception_app_name	Wie heißt diese App?	text	character	0	contraception_app == 1		69	1249	0.2476	173	0	3	91	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	contraception_app_name	Wie heißt diese App?	0	contraception_app == 1		69

Value labels

Response choices
name	value

hormonal_contraception_last3m

Haben Sie in den letzten drei Monaten hormonell verhütet?

Distribution

Distribution of values for hormonal_contraception_last3m

36 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		hormonal_contraception_last3m	Haben Sie in den letzten drei Monaten hormonell verhütet?	0			70

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

contraception_meeting_partner

Haben Sie hormonell verhütet (z.B. Pille), als Sie Ihren Partner kennengelernt haben?

Distribution

Distribution of values for contraception_meeting_partner

560 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for contraception_meeting_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		contraception_meeting_partner	Haben Sie hormonell verhütet (z.B. Pille), als Sie Ihren Partner kennengelernt haben?	0	relationship_status > 1		71

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

self_rated_health

Bitte beurteilen Sie Ihren allgemeinen Gesundheitszustand zum jetzigen Zeitpunkt.

Distribution

Distribution of values for self_rated_health

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
mc	self_rated_health	Bitte beurteilen Sie Ihren allgemeinen Gesundheitszustand zum jetzigen Zeitpunkt.	0			75

Value labels

Response choices
name	value
sehr gut	1
gut	2
teils gut/teils schlecht	3
schlecht	4
sehr schlecht	5
Item was never rendered for this user.	NA

height

Bitte geben Sie Ihre Körpergröße in cm an (ohne Schuhe).

Distribution

Distribution of values for height

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	80,230	height	Bitte geben Sie Ihre Körpergröße in cm an (ohne Schuhe).	0			76

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

weight

Bitte geben Sie Ihr Gewicht in kg an (ohne Kleidung).

Distribution

Distribution of values for weight

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	40,200	weight	Bitte geben Sie Ihr Gewicht in kg an (ohne Kleidung).	0			77

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

hormonal_med

Haben Sie in den letzten 3 Monaten hormonelle Medikamente (außer Verhütung) genommen?

Distribution

Distribution of values for hormonal_med

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		hormonal_med	Haben Sie in den letzten 3 Monaten hormonelle Medikamente (außer Verhütung) genommen?	0			78

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

hormonal_med_name

Welche?

Distribution

Distribution of values for hormonal_med_name

1548 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
select_or_add_multiple	hormonal_med_name	Welche?	0	hormonal_med == 1		79

Value labels

Response choices
name	value
1	1
2	2
3	3
4	4

psychoactive_med

Nehmen Sie psychoaktive Medikamente (z. B. gegen Depression)?

Distribution

Distribution of values for psychoactive_med

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		psychoactive_med	Nehmen Sie psychoaktive Medikamente (z. B. gegen Depression)?	0			80

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

psychoactive_med_name

Welche?

Distribution

Distribution of values for psychoactive_med_name

1605 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
psychoactive_med_name	Welche?	text	character	0	psychoactive_med ==1		81	1605	0.0331	44	0	5	51	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	psychoactive_med_name	Welche?	0	psychoactive_med ==1		81

Value labels

Response choices
name	value

alcohol_weekly

Wie viel Gläser Alkohol trinken Sie im Schnitt in der Woche?

Distribution

Distribution of values for alcohol_weekly

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,50, 0.5	alcohol_weekly	Wie viel Gläser Alkohol trinken Sie im Schnitt in der Woche?	0			82

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

cigarettes_daily

Wie viele Zigaretten rauchen Sie im Schnitt am Tag?

Distribution

Distribution of values for cigarettes_daily

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,100,1	cigarettes_daily	Wie viele Zigaretten rauchen Sie im Schnitt am Tag?	0			83

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

sport_weekly

Wie viele Stunden pro Woche treiben Sie schweißtreibenden Sport?

Distribution

Distribution of values for sport_weekly

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,60,0.5	sport_weekly	Wie viele Stunden pro Woche treiben Sie schweißtreibenden Sport?	0			84

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

sport_kinds

Wenn Sie Sport treiben – welche Sportarten sind dies typischerweise? Wählen Sie aus der Liste der vorgeschlagenen Sportarten aus und/oder fügen Sie „Ihre“ Sportart(en) hinzu. Sie können mehrere Sportarten angeben oder auch gar keine.

Distribution

Distribution of values for sport_kinds

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
select_or_add_multiple	sport_kinds	Wenn Sie Sport treiben – welche Sportarten sind dies typischerweise? <small><em>Wählen Sie aus der Liste der vorgeschlagenen Sportarten aus und/oder fügen Sie „Ihre“ Sportart(en) hinzu. Sie können mehrere Sportarten angeben oder auch gar keine.</em></small>	0			85

Value labels

Response choices
name	value
1	1
2	2
3	3
4	4
5	5
6	6
7	7

meat_eating

Essen Sie Fleisch und tierische Produkte?

Distribution

Distribution of values for meat_eating

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc		meat_eating	Essen Sie Fleisch und tierische Produkte?	0			86

Value labels

Response choices
name	value
1	1_every_day
2	2_frequently
3	3_rarely
4	4_only_poultry
5	5_only_fish
6	6_vegetarian
7	7_vegan

menarche

Wie alt waren Sie, als Sie Ihre erste Monatsblutung (Periode, Tage, Menstruation) hatten?

Distribution

Distribution of values for menarche

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	8,20	menarche	Wie alt waren Sie, als Sie Ihre erste Monatsblutung (Periode, Tage, Menstruation) hatten?	0			87

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

menarche_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for menarche_certainty

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	menarche_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0			88

Value labels

Response choices
name	value
stimmt genau	1
±0.5 Jahre	2
±1 Jahr	3
±2 Jahre	4
±3 Jahre	5
±4 Jahre	6
unsicherer	7
Item was never rendered for this user.	NA

number_sexual_partner

Mit wie vielen Personen haben Sie insgesamt in Ihrem Leben Geschlechtsverkehr gehabt?

Distribution

Distribution of values for number_sexual_partner

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	0,400	number_sexual_partner	Mit wie vielen Personen haben Sie insgesamt in Ihrem Leben Geschlechtsverkehr gehabt?	0			89

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

number_sexual_partner_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for number_sexual_partner_certainty

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	number_sexual_partner_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0			90

Value labels

Response choices
name	value
stimmt genau	1
±1	2
±2	3
±3	4
±5	5
±10	6
±20	7
unsicherer	8
Item was never rendered for this user.	NA

first_time

Wie alt waren Sie, als Sie das erste Mal Geschlechtsverkehr hatten?

Distribution

Distribution of values for first_time

134 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for first_time

Item

Item options
type	name	label	optional	showif	value	item_order
number	first_time	Wie alt waren Sie, als Sie das erste Mal Geschlechtsverkehr hatten?	0	number_sexual_partner > 0		91

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

first_time_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for first_time_certainty

134 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for first_time_certainty

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	first_time_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0	number_sexual_partner > 0		92

Value labels

Response choices
name	value
stimmt genau	1
±0.5 Jahre	2
±1 Jahr	3
±2 Jahre	4
±3 Jahre	5
±4 Jahre	6
unsicherer	7
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

mother_menopause_yes

Hat Ihre Mutter die Menopause erreicht?

Distribution

Distribution of values for mother_menopause_yes

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	mother_menopause_yes	Hat Ihre Mutter die Menopause erreicht?	0			93

Value labels

Response choices
name	value
Ja	1
Nein, ist zur Zeit in den Wechseljahren	2
Nein, noch nicht	3
Nein, vorher verstorben	4
Item was never rendered for this user.	NA

mother_menopause_age

Wie alt war Ihre Mutter beim Einsetzen ihrer Menopause?

Wenn Sie es den Zeitpunkt nicht genau benennen können, versuchen Sie sich zu erinnern, wann Ihre Mutter über typische Begleiterscheinungen (z.B. Hitzewallungen, etc.) gesprochen hat. Idealerweise fragen Sie Ihre Mutter kurz (telefonisch), aber Sie können auch fortfahren, ohne dies zu tun, wenn es grade nicht geht.

Distribution

Distribution of values for mother_menopause_age

895 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for mother_menopause_age

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	30,60	mother_menopause_age	Wie alt war Ihre Mutter beim Einsetzen ihrer Menopause? Wenn Sie es den Zeitpunkt nicht genau benennen können, versuchen Sie sich zu erinnern, wann Ihre Mutter über typische Begleiterscheinungen (z.B. Hitzewallungen, etc.) gesprochen hat. Idealerweise fragen Sie Ihre Mutter kurz (telefonisch), aber Sie können auch fortfahren, ohne dies zu tun, wenn es grade nicht geht.	0	mother_menopause_yes == 1		94

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

mother_menopause_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for mother_menopause_certainty

895 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for mother_menopause_certainty

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	mother_menopause_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0	mother_menopause_yes == 1		95

Value labels

Response choices
name	value
stimmt genau <small>(ich habe sie gefragt)</small>	1
±0.5 Jahre	2
±1 Jahr	3
±2 Jahre	4
±3 Jahre	5
±4 Jahre	6
unsicherer <small>(ich habe geraten)</small>	7
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menopause_yes

Ist bei Ihnen selbst die Menopause eingetreten?

Distribution

Distribution of values for menopause_yes

1597 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menopause_yes

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	menopause_yes	Ist bei Ihnen selbst die Menopause eingetreten?	0	age > 43		96

Value labels

Response choices
name	value
Ja	1
Nein, bin zur Zeit in den Wechseljahren	2
Nein, noch nicht	3
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menopause_age

Wie alt waren Sie selbst zum Eintritt Ihrer Menopause?

Distribution

Distribution of values for menopause_age

1637 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menopause_age

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	30,60	menopause_age	Wie alt waren Sie selbst zum Eintritt Ihrer Menopause?	0	menopause_yes == 1		97

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menopause_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for menopause_certainty

1637 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menopause_certainty

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	menopause_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0	menopause_yes == 1		98

Value labels

Response choices
name	value
stimmt genau	1
±0.5 Jahre	2
±1 Jahr	3
±2 Jahre	4
±3 Jahre	5
±4 Jahre	6
±7 Jahre	7
unsicherer	8
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menstruation_regular

Haben Sie zur Zeit regelmäßig (ungefähr monatlich) Ihre Periode?

Distribution

Distribution of values for menstruation_regular

53 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc_button		menstruation_regular	Haben Sie zur Zeit regelmäßig (ungefähr monatlich) Ihre Periode?	0			99

Value labels

Response choices
name	value
Nein	0
Ja	1
Item was never rendered for this user.	NA

menstruation_last

Nennen Sie bitte den ersten Tag (Start) Ihrer letzten Periode (Menstruationsblutung). Wenn möglich schauen Sie das Datum bitte in Ihrem Kalender nach.

Distribution

## 246  unique, categorical values, so not shown.

355 missing values.

Summary statistics

name	label	type	type_options	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	min	median	max
menstruation_last	Nennen Sie bitte den ersten Tag (Start) Ihrer letzten Periode (Menstruationsblutung). Wenn möglich schauen Sie das Datum bitte in Ihrem Kalender nach.	date	-2months,today	Date	0	menstruation_regular == 1		100	355	0.7861	246	2016-04-01	2016-08-30	2017-01-08

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
date	-2months,today	menstruation_last	Nennen Sie bitte den ersten Tag (Start) Ihrer letzten Periode (Menstruationsblutung). Wenn möglich schauen Sie das Datum bitte in Ihrem Kalender nach.	0	menstruation_regular == 1		100

Value labels

Response choices
name	value

menstruation_last_certainty

Wie sicher sind Sie bei dieser Angabe?

Distribution

Distribution of values for menstruation_last_certainty

355 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menstruation_last_certainty

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	menstruation_last_certainty	Wie sicher sind Sie bei dieser Angabe?	0	menstruation_regular == 1		101

Value labels

Response choices
name	value
stimmt genau	1
±1 Tag	2
±2 Tage	3
±3 Tage	4
±4 Tage	5
±5 Tage	6
±6 Tage	7
unsicherer	8
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menstruation_length

Wie lange dauert Ihr Menstruationszyklus (Tage vom Beginn einer Monatsblutung bis zum Beginn der nächsten Monatsblutung; meist zwischen 25-35 Tagen)?

Distribution

Distribution of values for menstruation_length

355 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menstruation_length

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	15,50,1	menstruation_length	Wie lange dauert Ihr Menstruationszyklus (Tage vom Beginn einer Monatsblutung bis zum Beginn der nächsten Monatsblutung; meist zwischen 25-35 Tagen)?	0	menstruation_regular == 1		102

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menstruation_length_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for menstruation_length_certainty

355 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menstruation_length_certainty

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	menstruation_length_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0	menstruation_regular == 1		103

Value labels

Response choices
name	value
stimmt genau	1
±1 Tag	2
±2 Tage	3
±3 Tage	4
±4 Tage	5
±5 Tage	6
±6 Tage	7
unsicherer	8
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menstruation_regularity

Wie regelmäßig war in den letzten 3 Monaten Ihr Menstruationszyklus?

Distribution

Distribution of values for menstruation_regularity

355 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menstruation_regularity

Item

Item options
type	name	label	optional	class	showif	value	item_order
mc	menstruation_regularity	Wie regelmäßig war in den letzten 3 Monaten Ihr Menstruationszyklus?	0	mc_vertical	menstruation_regular == 1		104

Value labels

Response choices
name	value
Völlig (gar keine Schwankungen)	1
Sehr (max. 1-2 Tage Schwankungen)	2
Etwas (3-5 Tage Schwankungen)	3
Kaum (über 5 Tage Schwankungen)	4
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

menstruation_regularity_certainty

Wie sicher sind Sie sich bei dieser Angabe?

Distribution

Distribution of values for menstruation_regularity_certainty

355 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for menstruation_regularity_certainty

Item

Item options
type	name	label	optional	showif	value	item_order
mc_button	menstruation_regularity_certainty	Wie sicher sind Sie sich bei dieser Angabe?	0	menstruation_regular == 1		105

Value labels

Response choices
name	value
stimmt genau	1
±1 Tag	2
±2 Tage	3
±3 Tage	4
±4 Tage	5
±5 Tage	6
±6 Tage	7
unsicherer	8
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

free_not_covered

Falls Fragen für Sie nicht oder nur falsch beantwortbar waren, oder Sie ansonsten den Eindruck haben, dass uns hier etwas wichtiges entging, geben Sie es bitte in diesem offenen Feld an. optional

Distribution

Distribution of values for free_not_covered

1471 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
free_not_covered	Falls Fragen für Sie nicht oder nur falsch beantwortbar waren, oder Sie ansonsten den Eindruck haben, dass uns hier etwas wichtiges entging, geben Sie es bitte in diesem offenen Feld an. <small>optional</small>	textarea	character	1			107	1471	0.1139	189	0	1	1002	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	free_not_covered	Falls Fragen für Sie nicht oder nur falsch beantwortbar waren, oder Sie ansonsten den Eindruck haben, dass uns hier etwas wichtiges entging, geben Sie es bitte in diesem offenen Feld an. <small>optional</small>	1			107

Value labels

Response choices
name	value

hetero_relationship

Distribution

Distribution of values for hetero_relationship

36 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
hetero_relationship	numeric	36	0.9783	0	1	1	0.6718	0.4697	▃▁▁▁▇	NA

short

Distribution

Distribution of values for short

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
short	character	0	1	1660	0	7	7	0	NA

ended_date

user finished survey

Distribution

## 215  unique, categorical values, so not shown.

53 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	min	median	max
ended_date	user finished survey	Date	53	0.9681	215	2016-05-02	2016-09-15	2017-01-14

contraception_other_pill_estrogen

Distribution

Distribution of values for contraception_other_pill_estrogen

1588 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
contraception_other_pill_estrogen	numeric	1588	0.0434	20	2700	2700	1480	1337	▆▁▁▁▇	NA

contraception_other_pill_gestagen

Distribution

Distribution of values for contraception_other_pill_gestagen

1557 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
contraception_other_pill_gestagen	numeric	1557	0.062	75	11700	150000	22694	30969	▇▂▁▁▁	NA

contraception_other_pill_gestagen_type

Distribution

Distribution of values for contraception_other_pill_gestagen_type

1557 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
contraception_other_pill_gestagen_type	character	1557	0.062	6	0	9	25	0	NA

hormonal_contraception

Distribution

Distribution of values for hormonal_contraception

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	count	mean	label
hormonal_contraception	logical	0	1	FAL: 982, TRU: 678	0.4084	NA

contraception_method_broad

Distribution

Distribution of values for contraception_method_broad

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
contraception_method_broad	character	0	1	7	273	0	10	0	NA

contraception_hormonal_pill_estrogen

Distribution

Distribution of values for contraception_hormonal_pill_estrogen

1127 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
contraception_hormonal_pill_estrogen	numeric	1127	0.3211	0	30	100	27.23	8.007	▃▇▁▁▁	NA

contraception_hormonal_pill_gestagen_type

Distribution

Distribution of values for contraception_hormonal_pill_gestagen_type

1086 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
contraception_hormonal_pill_gestagen_type	character	1086	0.3458	12	79	0	14	0	NA

estrogen_progestogen

Distribution

Distribution of values for estrogen_progestogen

8 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
estrogen_progestogen	character	8	0.9952	3	0	12	24	0	NA

living_situation

Distribution

Distribution of values for living_situation

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
living_situation	character	0	1	7	0	5	13	0	NA

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "s1_demo",
  "datePublished": "2020-06-11",
  "description": "The dataset has N=1660 rows and 114 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.3).",
  "keywords": ["session", "created", "modified", "ended", "expired", "info_study", "lab_code", "age", "gender", "gender_other", "education_years", "education_level", "education_level_special", "occupational_status", "occupation", "net_income", "study_major", "postal_code", "religion", "religiosity", "sex_orientation", "sex_orientation_special", "relationship_status", "relationship_count", "relationship_details", "duration_relationship_years", "duration_relationship_month", "partner_gender", "partner_gender_other", "age_partner", "abode_with_partner", "abode_flat_share", "abode_flat_share_description", "abode_alone", "abode_special_case_description", "long_distance_relationship", "distance_partner", "days_same_place_partner_monthly", "days_with_partner", "nights_with_partner", "has_children", "nr_children", "partner_father", "family_description_optional", "pregnant", "breast_feeding", "pregnant_stress", "pregnant_trying", "wish_for_children", "contraception_at_all", "contraception_other_reasons_not_to", "contraception_method", "contraception_method_other", "contraception_combi", "contraception_method_combination_other", "contraception_calendar_abstinence", "contraception_hormonal_pill", "other_pill_name", "contraception_hormonal_other", "contraception_hormonal_other_name", "contraception_pill_estrogen", "contraception_pill_gestagen_type", "contraception_pill_gestagen", "contraception_other_estrogen", "contraception_other_gestagen_type", "contraception_other_gestagen", "contraception_app", "contraception_app_name", "hormonal_contraception_last3m", "contraception_meeting_partner", "self_rated_health", "height", "weight", "hormonal_med", "hormonal_med_name", "psychoactive_med", "psychoactive_med_name", "alcohol_weekly", "cigarettes_daily", "sport_weekly", "sport_kinds", "meat_eating", "menarche", "menarche_certainty", "number_sexual_partner", "number_sexual_partner_certainty", "first_time", "first_time_certainty", "mother_menopause_yes", "mother_menopause_age", "mother_menopause_certainty", "menopause_yes", "menopause_age", "menopause_certainty", "menstruation_regular", "menstruation_last", "menstruation_last_certainty", "menstruation_length", "menstruation_length_certainty", "menstruation_regularity", "menstruation_regularity_certainty", "free_not_covered", "hetero_relationship", "short", "ended_date", "contraception_other_pill_estrogen", "contraception_other_pill_gestagen", "contraception_other_pill_gestagen_type", "hormonal_contraception", "contraception_method_broad", "contraception_hormonal_pill_estrogen", "contraception_hormonal_pill_gestagen_type", "estrogen_progestogen", "living_situation"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "session",
      "@type": "propertyValue"
    },
    {
      "name": "created",
      "description": "user first opened survey",
      "@type": "propertyValue"
    },
    {
      "name": "modified",
      "description": "user last edited survey",
      "@type": "propertyValue"
    },
    {
      "name": "ended",
      "description": "user finished survey",
      "@type": "propertyValue"
    },
    {
      "name": "expired",
      "@type": "propertyValue"
    },
    {
      "name": "info_study",
      "description": "Wie sind Sie auf unsere Studie aufmerksam geworden?",
      "value": "1. Einladung durch das Studienteam,\n2. Aushänge Georg-August-Universität Göttingen,\n3. Privater Kontakt,\n4. www.psytests.de,\n5. Medien,\n6. andere",
      "maxValue": "6",
      "minValue": "1",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "lab_code",
      "description": "Nehmen Sie auch an unserer Laborstudie _Attraktivitätsbeurteilungen_ in Göttingen teil und haben einen Code für diese Studie erhalten? \nWenn ja, geben Sie bitte hier unbedingt Ihren persönlichen Code ein.\nWenn nein, lassen Sie dieses Feld einfach aus.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Ihr Alter",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Ihr Geschlecht",
      "value": "1. weiblich,\n2. anderes,\nNA. Item was never rendered for this user.",
      "maxValue": 2,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "gender_other",
      "description": "anderes:",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "education_years",
      "description": "Wie viele Bildungsjahre (Schule, Studium an Universität oder FH, Jahre als Doktorand/in, NICHT Berufsschule etc.)                                                                                                                        haben Sie hinter sich?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "education_level",
      "description": "Was ist ihr höchster, bislang erreichter Bildungsgrad",
      "value": "0_not_finished. ohne Schulabschluss,\n1_hauptschule. Hauptschulabschluss,\n2_realschule. Realschulabschluss,\n3_polytechnic. Abschluss der polytechnischen Oberschule,\n4_professional_school. Berufsschulabschluss,\n5_fachabitur. Fachhochschulreife,\n6_abitur. Hochschulreife,\n7_vocational_uni_bachelor. Fachhochschulabschluss Bachelor,\n8_uni_bachelor. Hochschulabschluss Bachelor,\n9_vocational_uni_master_level. Fachhochschulabschluss Master/Magister/Diplom,\n10_uni_master_level. Hochschulabschluss Master/Magister/Diplom,\n11_uni_doctorate. Promotion,\n12_uni_habilitation. Habilitation",
      "maxValue": "9_vocational_uni_master_level",
      "minValue": "0_not_finished",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "education_level_special",
      "description": "Haben Sie einen Bildungsgrad, der sich hier nicht gut einsortieren lässt (bspw. Meister)?\nWenn die obige Liste nicht ausreicht, können Sie das hier frei ergänzen.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "occupational_status",
      "description": "Welchen beruflichen Status haben Sie derzeit? ",
      "value": "not_working. nicht berufstätig,\npupil. Schülerin,\ntrainee. Auszubildende,\nstudent. Studentin,\nhomemaker. Haus- und Familienarbeit ,\nemployed. Berufstätig,\nintern. Praktikantin",
      "maxValue": "trainee",
      "minValue": "employed",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "occupation",
      "description": "Welchen Beruf üben Sie aus?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "net_income",
      "description": "Wie viel Geld haben Sie monatlich zur Verfügung (netto)?",
      "value": "euro_lt_500.  < 500€,\neuro_500_1000. 500-1000€,\neuro_1000_2000. 1000-2000€,\neuro_2000_3000. 2000-3000€,\neuro_gt_3000. \\> 3000€,\ndont_tell. möchte ich nicht angeben",
      "maxValue": "euro_lt_500",
      "minValue": "dont_tell",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "study_major",
      "description": "Was studieren Sie derzeit oder haben Sie studiert?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "postal_code",
      "description": "Wie lauten die ersten drei Ziffern ihrer Postleitzahl?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "religion",
      "description": "Welcher Religion bzw. welcher Weltanschauung sind Sie zugehörig? ",
      "value": "1. Christentum ,\n2. Islam,\n3. Buddhismus,\n4. Hinduismus,\n5. Judentum ,\n6. andere ,\n7. nicht gläubig",
      "maxValue": "7",
      "minValue": "1",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "religiosity",
      "description": "Für wie religiös halten Sie sich? ",
      "value": "1. 1: nicht religiös,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6: religiös,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "sex_orientation",
      "description": "Was beschreibt am besten Ihre sexuelle Orientierung?",
      "value": "1. Ausschließlich heterosexuell,\n2. Überwiegend heterosexuell, nur gelegentlich homosexuell,\n3. Überwiegend heterosexuell, aber mehr als gelegentlich homosexuell,\n4. Gleichermaßen heterosexuell wie homosexuell,\n5. Überwiegend homosexuell, aber mehr als gelegentlich heterosexuell,\n6. Überwiegend homosexuell, nur gelegentlich heterosexuell,\n7. Ausschließlich homosexuell,\n8. Asexuell oder aromantisch,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "sex_orientation_special",
      "description": "Wenn Ihnen die obigen Kategorien nicht ausreichen, können Sie es hier in Ihre eigenen Worte fassen.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "relationship_status",
      "description": "Welchen Beziehungsstatus haben Sie?",
      "value": "1. Single,\n2. lose Beziehung,\n3. feste Partnerschaft,\n4. verlobt,\n5. verheiratet,\n6. andere,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "relationship_count",
      "description": "Sind Sie ...",
      "value": "1. monogam (ein Partner),\n2. polyamor (mehrere Partner),\n3. in einer offenen Beziehung,\n4. andere,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 4,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "relationship_details",
      "description": "Sie haben eine Beziehungsform angegeben, die in unserem Fragebogen vielleicht nicht gut erfasst wird. Bitte beschreiben Sie hier Ihre Beziehungsform einfach frei.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "duration_relationship_years",
      "description": "Jahre",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "duration_relationship_month",
      "description": "Monate",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "partner_gender",
      "description": "Welches Geschlecht hat Ihr Partner?",
      "value": "1. weiblich,\n2. männlich,\n3. anderes,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 3,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "partner_gender_other",
      "description": "anderes:",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "age_partner",
      "description": "Wie alt ist Ihr Partner?",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "abode_with_partner",
      "description": "Wohnen Sie mit Ihrem Partner zusammen?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "abode_flat_share",
      "description": "Wohnen Sie in einer Wohngemeinschaft (abgesehen von Partner)?",
      "value": "1. nein,\n2. mit Eltern/Großeltern,\n3. ja,\nNA. Item was never rendered for this user.",
      "maxValue": 3,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "abode_flat_share_description",
      "description": "Mit wem wohnen Sie zusammen?\n\nNutzen Sie bitte dieses offene Feld, um uns Angaben über Ihre Mitbewohner zu machen.\nAm einfachsten ist es für uns, wenn Sie einfach Geschlecht, Alter und gegebenenfalls Verwandtschaftsstatus angeben, bspw. so:\n\n     w,55,Mutter\n     w,26,Schwester\n     m,30,Bruder\n\nSich selbst und Ihren Partner (ggf.) müssen Sie nicht extra nennen.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "abode_alone",
      "description": "Wohnen Sie also alleine?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "abode_special_case_description",
      "description": "Bitte beschreiben Sie Ihre aktuelle Wohnsituation etwas ausführlicher.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "long_distance_relationship",
      "description": "Haben Sie eine Fernbeziehung?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "distance_partner",
      "description": "Wie weit wohnen Sie von Ihrem Partner entfernt?",
      "value": "1. < 1 Std.,\n2. 1 - 2 Std.,\n3. 2-3 Std.,\n4. 3 - 5 Std.,\n5. 5 - 9 Std.,\n6. 9 - 12 Std.,\n7. \\> 12 Std.,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "days_same_place_partner_monthly",
      "description": "Wie viele Tage verbringen Sie im Monat  an demselben Ort wie Ihr Partner?",
      "value": "1. < 3 Tage,\n2. 3 - 4 Tage,\n3. 5 - 6 Tage,\n4. 7 - 14 Tage,\n5. 14-21 Tage,\n6. 21-29 Tage,\n7. \\> 29 Tage,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "days_with_partner",
      "description": "Wie viele Tage pro Woche verbringen Sie durchschnittlich an demselben Ort wie ihr Partner?",
      "value": "0. 0: Tage,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7: Tage,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "nights_with_partner",
      "description": "Wie viele Nächte pro Woche verbringen Sie durchschnittlich mit Ihrem Partner?",
      "value": "0. 0: Nächte,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7: Nächte,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "has_children",
      "description": "Haben Sie Kinder?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "nr_children",
      "description": "Wieviele leibliche Kinder haben Sie?",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "partner_father",
      "description": "Ist Ihr aktueller Partner der Vater Ihrer Kinder?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "family_description_optional",
      "description": "Hier können Sie andere Angaben zu Ihren Familienverhältnissen, die in unseren Fragebogen sonst nicht passen, ergänzen (bspw. Adoptivkinder).",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "pregnant",
      "description": "Sind Sie aktuell schwanger oder waren Sie es in den letzten drei Monaten?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "breast_feeding",
      "description": "Stillen Sie aktuell oder haben Sie in den letzten drei Monaten gestillt?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "pregnant_stress",
      "description": "Wenn Sie heute erfahren würden, dass Sie schwanger sind, fänden Sie das...",
      "value": "1. 1: sehr belastend,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6: sehr erfreulich,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "pregnant_trying",
      "description": "Versuchen Sie derzeit schwanger zu werden?",
      "value": "1. 1: versuche es zu vermeiden,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6: versuche schwanger zu werden,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "wish_for_children",
      "description": "Warum nicht?",
      "value": "not_actively_trying. Ich könnte mir vorstellen, demnächst schwanger zu werden, aber versuche es zur Zeit nicht aktiv.,\nnot_in_current_life_situation. `r ifelse(relationship_status == 1, 'Ich könnte mir vorstellen schwanger zu werden, aber eher nicht in meiner derzeitigen Lebenssituation.', 'Ich könnte mir vorstellen mit meinem aktuellen Partner schwanger zu werden, aber eher nicht in meiner derzeitigen Lebenssituation.')`,\nnot_with_this_partner. `r ifelse(relationship_status == 1, '', 'Ich könnte mir vorstellen schwanger zu werden, aber eher nicht mit meinem aktuellen Partner.')`,\ndont_have_partner. `r ifelse(relationship_status == 1, 'Ich könnte mir vorstellen schwanger zu werden, aber habe keinen Partner, der in Frage käme.', '')`,\npartner_doesnt_want. `r ifelse(relationship_status == 1, '', 'Ich könnte mir vorstellen schwanger zu werden, aber mein Partner möchte das nicht.')`,\nrather_adopt. Ich möchte Kinder, aber möchte eher adoptieren / Pflegekinder aufnehmen.,\ncant_imagine_having_kids. Ich kann mir nicht vorstellen, jemals Kinder zu bekommen.",
      "maxValue": "rather_adopt",
      "minValue": "cant_imagine_having_kids",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_at_all",
      "description": "Verhüten Sie üblicherweise, wenn Sie Sex haben (Pille, Kondom, Kalendermethode, coitus interruptus, etc.) ?",
      "value": "1. ja,\n2. meistens,\n3. ja, aber ich lasse es auch etwas \"drauf ankommen\",\n4. nein, ich lasse es \"drauf ankommen\",\n5. nein, ich habe zur Zeit gar keinen Sex,\n6. nein, andere Gründe,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_reasons_not_to",
      "description": "Hier können Sie andere Gründe, die in unser Fragebogenformat nicht gut passen, angeben",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method",
      "description": "Wie verhüten Sie zur Zeit üblicherweise?\n\nSie können mehrere Methoden angeben.",
      "value": "hormonal_pill. Orale Kontrazeptiva (Pille),\nhormonal_other. andere hormonelle Verhütung (Depotspritze, Hormonimplantat, Verhütungsring, Verhütungspflaster, Hormonspirale),\nbarrier_condoms. Kondome,\nbarrier_other. Andere Barrieremethoden (Diaphragma, Frauenkondom, Portiokappe),\nbarrier_intrauterine_pessar. Kupferspirale oder -kette (Intrauterinpessar),\nbarrier_coitus_interruptus. Coitus interruptus,\nbarrier_no_penetrative_sex. Verzicht auf penetrativen Geschlechtsverkehr,\nawareness_calendar. Kalendermethode,\nawareness_temperature_billings. Temperatur- / Billings- / Symptothermale Methode,\nawareness_computer. Verhütungscomputer (Persona, LadyComp, Cyclotest, …),\nbarrier_spermicide. Spermizide,\nbarrier_chemical. Chemische Verhütung,\nhormonal_morning_after_pill. \"Pille danach\",\nbreast_feeding. Stillen,\ninfertile. ich bin unfruchtbar,\npartner_infertile. mein Partner ist unfruchtbar,\nsterilised. ich bin sterilisiert,\npartner_sterilised. mein Partner ist sterilisiert,\nnot_listed. Andere",
      "maxValue": "sterilised",
      "minValue": "awareness_calendar",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method_other",
      "description": "Welche andere, nicht in der Liste vorkommende, Methode benutzen Sie?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_combi",
      "description": "Sie haben angegeben, mehrere Verhütungsmethoden zu kombinieren. Aus welchem Grund, oder welchen Gründen tun Sie das?",
      "value": "multiple_to_decrease_infection_risk. immer mehrere gleichzeitig (Verhütung + sexuelle übertragbare Krankheiten vermeiden),\nmultiple_to_decrease_conception_risk. immer mehrere gleichzeitig (Verhütungssicherheit erhöhen),\nfallback_if_forgotten. falle auf zweite Methode zurück, wenn ich oder mein Partner die erste vergessen habe zu nehmen/ zu kaufen (z.B. Pille + Kondome, oder Kondome + Koitus interruptus),\nfallback_if_fertile. falle auf zweite Methode zurück, wenn ich fruchtbar bin (z.B. Kalendermethode + Kondome oder Koitus interruptus im fruchtbaren Fenster),\nbarrier_if_partner_sick. eine Methode zur Verhütung, andere zur Vermeidung von nicht-sexueller Ansteckung (z.B. Pille + Abstinenz, wenn Geschlechtspartner gerade erkältet),\ndifferent_methods_for_different_partners. eine Methode für bestimmte Partner, andere Methode für andere Partner (z.B. Pille bei Sex mit gut bekannter/vertrauter Person, Kondome bei Sex mit weniger gut bekannter Person),\nother. anderer Grund",
      "maxValue": "other",
      "minValue": "barrier_if_partner_sick",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method_combination_other",
      "description": "anderer Grund: ",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_calendar_abstinence",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_pill",
      "description": "Wie heißt Ihre Anti-Baby-Pille?\n\nFalls Sie ein <abbr title=\"Eine wirkstoffgleiche Kopie, Nachahmerpräparat, bspw. Dienovel=Valette\" class=\"hastooltip\">Generikum<\/abbr> nehmen und es in dieser Liste fehlt, können Sie gerne den Markennamen angeben.",
      "value": "other. Meine Pille kommt in dieser Liste nicht vor.,\n28_mini. 28 mini ,\naida. Aida,\nalessia_hexal. Alessia HEXAL,\nasumate_20. Asumate 20,\nattempta_35. Attempta ratiopharm-35,\nbalanca. Balanca,\nbelara. Belara,\nbellahexal_35. BellaHexal-35,\nbellissima. Bellissima,\nbilmon. Bilmon,\nbiviol. Biviol,\nbonadea. BonaDea,\ncerazette. Cerazette ,\nchariva. Chariva,\nchloee. Chloee,\ncilest. Cilest,\nclevia. Clevia,\nconceplan_m. Conceplan M,\ncyprella. Cyprella ,\ncyproderm. Cyproderm,\ncypronette_al. Cypronette AL,\ndamara. Damara ,\ndaylette. Daylette,\ndesirett. Desirett,\ndesmin_20. Desmin  20,\ndesmin_30. Desmin  30,\ndiamilla. Diamilla,\ndiane_35. Diane-35,\nenriqa. Enriqa,\nergalea. Ergalea,\nestelle. Estelle,\neufem. eufem,\nevaluna_20. Evaluna 20,\nevaluna_30. Evaluna 30,\neve_20. EVE 20,\nfemigoa. Femigoa,\nfemigyne_ratiopharm. Femigyne-ratiopharm,\nfemikadin_20. Femikadin 20,\nfemikadin_30. Femikadin 30,\nfemovan. Femovan,\nfinic. Finic,\ngabrielle. Gabrielle,\ngravistat_125. Gravistat 125,\nillina. Illina,\njennifer. Jennifer,\njubrele. Jubrele ,\njuliane_20. Juliane 20,\njuliane_30. Juliane 30,\njuliette. Juliette,\nlamuna_20. Lamuna20,\nlamuna_30. Lamuna30,\nleios. Leios,\nleonahexal. LeonaHexal,\nlevina. Levina,\nlevomin. Levomin,\nliana. Liana,\nlilia. Lilia,\nlisette. Lisette,\nlovelle. Lovelle,\nmadinette. Madinette,\nmaitalon_20. Maitalon 20,\nmaitalon_30. Maitalon 30,\nmarvelon. Marvelon,\nmaxim. Maxim,\nmayra. mayra,\nmicrogynon. Microgynon,\nmicrolut. Microlut ,\nminette. Minette,\nminisiston. Minisiston,\nminisiston_20_fem. Minisiston 20 fem,\nminulet. Minulet,\nmiranova. Miranova,\nmonahexal. MonaHexal,\nmonostep. MonoStep,\nmorea_sanol. Morea sanol,\nneo_eunomin. Neo-Eunomin,\nnovastep. NovaStep,\nnovial. Novial ,\npetibelle. Petibelle,\npink_luna. Pink Luna,\npramino. Pramino,\nqlaira. Qlaira,\nseculact. Seculact,\nsynphasec. Synphasec,\ntrigoa. Trigoa,\ntrinovum. TriNovum,\ntriquilar. Triquilar,\ntrisiston. Trisiston,\nvalette. Valette,\nvatrice. Vatrice,\nvelafee. Velafee,\nverana. Verana,\nyasmin. Yasmin,\nyasminelle. Yasminelle,\nyaz. Yaz,\nzoely. Zoely",
      "maxValue": "zoely",
      "minValue": "28_mini",
      "@type": "propertyValue"
    },
    {
      "name": "other_pill_name",
      "description": "Sie haben angegeben, dass Ihre Pille in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihrer Pille.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_other",
      "description": "Welches hormonelle Verhütungsmittel verwenden Sie?",
      "value": "nuvaring. NuvaRing,\ncirclet. Circlet,\nevra. Evra,\nmirena. Mirena,\ndepo_provera. Depo Provera,\ndepo_clinovir. Depo Clinovir,\nimplanon. Implanon NXT,\nother. Der Name meines Verhütungsmittels kommt in dieser Liste nicht vor.",
      "maxValue": "other",
      "minValue": "circlet",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_other_name",
      "description": "Sie haben angegeben, dass Ihr Verhütungsmittel in der Liste nicht vorkommt. Bitte nennen Sie hier den Namen Ihres Verhütungsmittels.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_pill_estrogen",
      "description": "Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, wieviel Mikrogramm (*µ*g) Östrogen Ihre Pille enthält. Die meisten Pillen enthalten zwischen 0 und 50 Mikrogramm Östrogen. Manchmal wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (*µ*g)  zu erhalten.",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_pill_gestagen_type",
      "description": "Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, welcher Typ Gestagen verwendet wird.",
      "value": "CMA. Chlormadinonacetat,\nCPA. Cyproteronacetat,\nDNG. Dienogest,\nDSG. Desogestrel,\nDSP. Drospirenon,\nGSD. Gestoden,\nLYN. Lynestrenol,\nLNG. Levonorgestrel,\nNEA. Norethisteronacetat,\nNES. Norethisteron,\nNGT. Norgestimat,\nother_gestagen. Kommt in dieser Liste nicht vor.",
      "maxValue": "other_gestagen",
      "minValue": "CMA",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_pill_gestagen",
      "description": "Bitte lesen Sie in der Packungsbeilage Ihrer Pille nach, wieviel Mikrogramm (*µ*g) des jeweiligen Gestagens Ihre Pille enthält. Die meisten Pillen enthalten zwischen 50 und 3000 Mikrogramm Gestagen. Häufig wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (*µ*g)  zu erhalten.\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_estrogen",
      "description": "Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, wieviel Mikrogramm (*µ*g) Östrogen Ihr Verhütungsmittel enthält. Die meisten Verhütungsmittel enthalten zwischen 0 und 50 Mikrogramm Östrogen. Manchmal wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (*µ*g)  zu erhalten.                                                                                                                                                    ",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_gestagen_type",
      "description": "Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, welcher Typ Gestagen verwendet wird.",
      "value": "CMA. Chlormadinonacetat,\nCPA. Cyproteronacetat,\nDNG. Dienogest,\nDSG. Desogestrel,\nDSP. Drospirenon,\nGSD. Gestoden,\nLYN. Lynestrenol,\nLNG. Levonorgestrel,\nNEA. Norethisteronacetat,\nNES. Norethisteron,\nNGT. Norgestimat,\nother_gestagen. Kommt in dieser Liste nicht vor.",
      "maxValue": "other_gestagen",
      "minValue": "CMA",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_gestagen",
      "description": "Bitte lesen Sie in der Packungsbeilage Ihres Verhütungsmittels nach, wieviel Mikrogramm (*µ*g) des jeweiligen Gestagens Ihr Verhütungsmittel enthält. Die meisten Verhütungsmittel enthalten zwischen 50 und 3000 Mikrogramm Gestagen. Häufig wird die Gewichtsangabe auch in Milligramm (mg) gemacht. Diese Zahl müssen Sie dann mit 1000 multiplizieren, um Mikrogramm (*µ*g)  zu erhalten.",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_app",
      "description": "Benutzen Sie eine Zyklus-App? ",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_app_name",
      "description": "Wie heißt diese App?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_contraception_last3m",
      "description": "Haben Sie in den letzten drei Monaten hormonell verhütet?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_meeting_partner",
      "description": "Haben Sie hormonell verhütet (z.B. Pille), als Sie Ihren Partner kennengelernt haben?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "self_rated_health",
      "description": "Bitte beurteilen Sie Ihren allgemeinen Gesundheitszustand zum jetzigen Zeitpunkt.",
      "value": "1. sehr gut,\n2. gut,\n3. teils gut/teils schlecht,\n4. schlecht,\n5. sehr schlecht,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "height",
      "description": "Bitte geben Sie Ihre Körpergröße in cm an (ohne Schuhe).",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "weight",
      "description": "Bitte geben Sie Ihr Gewicht in kg an (ohne Kleidung).",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_med",
      "description": "Haben Sie in den letzten 3 Monaten hormonelle Medikamente (außer Verhütung) genommen?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_med_name",
      "description": "Welche?",
      "value": "1. Insulin,\n2. Thyroxin,\n3. Escitalopram,\n4. Mönchspfeffer",
      "maxValue": "4",
      "minValue": "1",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "psychoactive_med",
      "description": "Nehmen Sie psychoaktive Medikamente (z. B. gegen Depression)?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "psychoactive_med_name",
      "description": "Welche?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "alcohol_weekly",
      "description": "Wie viel Gläser Alkohol trinken Sie im Schnitt in der Woche?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "cigarettes_daily",
      "description": "Wie viele Zigaretten rauchen Sie im Schnitt am Tag?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "sport_weekly",
      "description": "Wie viele Stunden pro Woche treiben Sie schweißtreibenden Sport?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "sport_kinds",
      "description": "Wenn Sie Sport treiben – welche Sportarten sind dies typischerweise?\n<small><em>Wählen Sie aus der Liste der vorgeschlagenen Sportarten aus und/oder fügen Sie „Ihre“ Sportart(en) hinzu. Sie können mehrere Sportarten angeben oder auch gar keine.<\/em><\/small>",
      "value": "1. Laufen,\n2. Schwimmen,\n3. Fitness-/Krafttraining (Geräte),\n4. Aerobic (z.B. Step Aerobic; Zumba),\n5. Klettern/Bouldern,\n6. Yoga,\n7. Mannschaftssport (z.B. Hand-;Volley-; Fussball)",
      "maxValue": "7",
      "minValue": "1",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "meat_eating",
      "description": "Essen Sie Fleisch und tierische Produkte?",
      "value": "1_every_day. ja, sehr viel,\n2_frequently. ja, oft,\n3_rarely. ja, selten,\n4_only_poultry. nur Geflügel,\n5_only_fish. nur Fisch,\n6_vegetarian. nein, Vegetarierin,\n7_vegan. nein, Veganerin",
      "maxValue": "7_vegan",
      "minValue": "1_every_day",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menarche",
      "description": "Wie alt waren Sie, als Sie Ihre erste Monatsblutung (Periode, Tage, Menstruation) hatten?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menarche_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±0.5 Jahre,\n3. ±1 Jahr,\n4. ±2 Jahre,\n5. ±3 Jahre,\n6. ±4 Jahre,\n7. unsicherer,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "number_sexual_partner",
      "description": "Mit wie vielen Personen haben Sie insgesamt in Ihrem Leben Geschlechtsverkehr gehabt?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "number_sexual_partner_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±1,\n3. ±2,\n4. ±3,\n5. ±5,\n6. ±10,\n7. ±20,\n8. unsicherer,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "first_time",
      "description": "Wie alt waren Sie, als Sie das erste Mal Geschlechtsverkehr hatten?",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "first_time_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±0.5 Jahre,\n3. ±1 Jahr,\n4. ±2 Jahre,\n5. ±3 Jahre,\n6. ±4 Jahre,\n7. unsicherer,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "mother_menopause_yes",
      "description": "Hat Ihre Mutter die Menopause erreicht?",
      "value": "1. Ja,\n2. Nein, ist zur Zeit in den Wechseljahren,\n3. Nein, noch nicht,\n4. Nein, vorher verstorben,\nNA. Item was never rendered for this user.",
      "maxValue": 4,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "mother_menopause_age",
      "description": "Wie alt war Ihre Mutter beim Einsetzen ihrer Menopause?\n\n*Wenn Sie es den Zeitpunkt nicht genau benennen können, versuchen Sie sich zu erinnern, wann Ihre Mutter über typische Begleiterscheinungen (z.B. Hitzewallungen, etc.) gesprochen hat. Idealerweise fragen Sie Ihre Mutter kurz (telefonisch), aber Sie können auch fortfahren, ohne dies zu tun, wenn es grade nicht geht.*",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "mother_menopause_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau <small>(ich habe sie gefragt)<\/small>,\n2. ±0.5 Jahre,\n3. ±1 Jahr,\n4. ±2 Jahre,\n5. ±3 Jahre,\n6. ±4 Jahre,\n7. unsicherer <small>(ich habe geraten)<\/small>,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menopause_yes",
      "description": "Ist bei Ihnen selbst die Menopause eingetreten?",
      "value": "1. Ja,\n2. Nein, bin zur Zeit in den Wechseljahren,\n3. Nein, noch nicht,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 3,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menopause_age",
      "description": "Wie alt waren Sie selbst zum Eintritt Ihrer Menopause?",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menopause_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±0.5 Jahre,\n3. ±1 Jahr,\n4. ±2 Jahre,\n5. ±3 Jahre,\n6. ±4 Jahre,\n7. ±7 Jahre,\n8. unsicherer,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_regular",
      "description": "Haben Sie zur Zeit regelmäßig (ungefähr monatlich) Ihre Periode?",
      "value": "0. Nein,\n1. Ja,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_last",
      "description": "Nennen Sie bitte den ersten Tag (Start) Ihrer letzten Periode (Menstruationsblutung). Wenn möglich schauen Sie das Datum bitte in Ihrem Kalender nach.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_last_certainty",
      "description": "Wie sicher sind Sie bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±1 Tag,\n3. ±2 Tage,\n4. ±3 Tage,\n5. ±4 Tage,\n6. ±5 Tage,\n7. ±6 Tage,\n8. unsicherer,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_length",
      "description": "Wie lange dauert Ihr Menstruationszyklus (Tage vom Beginn einer Monatsblutung bis zum Beginn der nächsten Monatsblutung; meist zwischen 25-35 Tagen)?",
      "value": "NA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_length_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±1 Tag,\n3. ±2 Tage,\n4. ±3 Tage,\n5. ±4 Tage,\n6. ±5 Tage,\n7. ±6 Tage,\n8. unsicherer,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_regularity",
      "description": "Wie regelmäßig war in den letzten 3 Monaten Ihr Menstruationszyklus?",
      "value": "1. Völlig (gar keine Schwankungen),\n2. Sehr (max. 1-2 Tage Schwankungen),\n3. Etwas (3-5 Tage Schwankungen),\n4. Kaum (über 5 Tage Schwankungen),\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 4,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_regularity_certainty",
      "description": "Wie sicher sind Sie sich bei dieser Angabe?",
      "value": "1. stimmt genau,\n2. ±1 Tag,\n3. ±2 Tage,\n4. ±3 Tage,\n5. ±4 Tage,\n6. ±5 Tage,\n7. ±6 Tage,\n8. unsicherer,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "free_not_covered",
      "description": "Falls Fragen für Sie nicht oder nur falsch beantwortbar waren, oder Sie ansonsten den Eindruck haben, dass uns hier etwas wichtiges entging, geben Sie es bitte in diesem offenen Feld an.\n<small>optional<\/small>",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hetero_relationship",
      "@type": "propertyValue"
    },
    {
      "name": "short",
      "@type": "propertyValue"
    },
    {
      "name": "ended_date",
      "description": "user finished survey",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_pill_estrogen",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_pill_gestagen",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_pill_gestagen_type",
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_contraception",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method_broad",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_pill_estrogen",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_pill_gestagen_type",
      "@type": "propertyValue"
    },
    {
      "name": "estrogen_progestogen",
      "@type": "propertyValue"
    },
    {
      "name": "living_situation",
      "@type": "propertyValue"
    }
  ]
}`

Personality

Metadata

Description

Dataset name: s2_initial

The dataset has N=1512 rows and 191 columns. 0 rows have no missing values on any column.

Metadata for search engines

Date published: 2020-06-11

x
session
created
modified
ended
expired
bfi_open_1
narq_5
narq_3
bfi_agree_2
pvd_infectability_1
bfi_consc_2r
soi_r_attitude_6r
asendorpf_shyness_4r
bfi_neuro_2r
bfi_open_2
bfi_extra_1
bfi_extra_3
bfi_agree_3r
soi_r_attitude_4
narq_15
bfi_consc_3
soi_r_attitude_5
narq_2
asendorpf_shyness_2
bfi_agree_1r
bfi_consc_1
narq_11
bfi_neuro_1
bfi_extra_2r
bfi_neuro_3
narq_18
bfi_open_3
asendorpf_shyness_5
bfi_extra_4
narq_16
bfi_agree_4
bfi_consc_9r
bfi_neuro_4
bfi_open_4
bfi_extra_5r
bfi_agree_5
narq_1
bfi_consc_4r
bfi_neuro_5r
bfi_open_5
bfi_extra_6
narq_14
bfi_agree_6r
bfi_consc_5
asendorpf_shyness_3r
pvd_germ_aversion_2
pvd_germ_aversion_3R
bfi_neuro_8
pvd_infectability_3R
pvd_germ_aversion_1
feel_safe_walking_dark
feel_safe_violent_crime
feel_safe_sexual_assault
feel_safe_theft
bfi_open_6
narq_17
bfi_extra_7r
bfi_agree_7
narq_4
asendorpf_shyness_1
bfi_consc_6
bfi_neuro_6r
bfi_open_7r
narq_13
bfi_extra_8
bfi_agree_8r
narq_6
bfi_consc_7
bfi_neuro_7
narq_7
bfi_open_8
narq_12
bfi_open_9r
bfi_agree_9
narq_8
bfi_consc_8r
narq_9
narq_10
bfi_open_10
pvd_infectability_2
pvd_germ_aversion_4R
spms_partner_1
spms_partner_2
spms_partner_3R
satisfaction_sexual_intercourse
satisfaction_single_life
investment_potential_partner
timeperiod_potential_partner
characteristics_potential_partner
quantity_potential_partner
sexual_partner
fling_frequency
fling_frequency_2
relationship_importance
relationship_importance_partner
partner_attractiveness_longterm
partner_attractiveness_shortterm
partner_attractiveness_face
partner_attractiveness_body
attractiveness_warmth
partner_attractiveness_trust
net_income_partner
partner_sexiness
partner_strength
partner_feel_safe
spms_self_1
spms_self_2
spms_self_3R
meet_potential_partner
partner_height
meet_potential_partner_other
partner_weight
soi_r_behavior_1
soi_r_behavior_2
soi_r_behavior_3
soi_r_desire_9
soi_r_desire_7
soi_r_desire_8
relationship_problems
relationship_satisfaction_overall
relationship_conflict
relationship_satisfaction_2
relationship_satisfaction_3
alternatives_1
alternatives_2
alternatives_3
alternatives_4
alternatives_5
alternatives_6
investment_1
investment_2
investment_3
commitment_1
commitment_2
commitment_3
communal_strength_1
communal_strength_2R
communal_strength_3
communal_strength_4
sexual_communal_strength_1
sexual_communal_strength_2
sexual_communal_strength_3
ecr_avo_1R
ecr_anx_1
ecr_avo_2
ecr_anx_2
ecr_anx_3
ecr_avo_3R
ecr_avo_4
ecr_anx_4R
ecr_avo_5R
ecr_anx_5
ecr_avo_6
ecr_anx_6
free_not_covered
narq
bfi_open
bfi_extra
bfi_agree
soi_r_attitude
bfi_consc
asendorpf_shyness
bfi_neuro
pvd_germ_aversion
pvd_infectability
spms_partner
spms_self
relationship_satisfaction
alternatives
investment
commitment
communal_strength
sexual_communal_strength
ecr_avo
ecr_anx
short
soi_r_desire
soi_r_behavior_1_discrete
soi_r_behavior_2_discrete
soi_r_behavior_3_discrete
soi_r_behavior
soi_r
spms_rel
partner_attractiveness_sexual
relationship_conflict_R
relationship_problems_R

Survey overview

1411 completed rows, 1484 who entered any information, 28 only viewed the first page. There are 13 expired rows (people who did not finish filling out in the requested time frame). In total, there are 1512 rows including unfinished and expired rows.

There were 1512 unique participants, of which 1411 finished filling out at least one survey.

This survey was not repeated.

The first session started on 2016-05-02 21:46:49, the last session on 2017-01-14 22:45:37.

Starting date times

People took on average 1217.46 minutes (median 14.4) to answer the survey.

Duration people took for answering the survey

#Variables

feel_safe_walking_dark

Ich fühle mich in meiner Gegend sicher, wenn ich alleine in der Dunkelheit unterwegs bin.

Distribution

Distribution of values for feel_safe_walking_dark

93 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	feel_safe_walking_dark	Ich fühle mich in meiner Gegend sicher, wenn ich alleine in der Dunkelheit unterwegs bin.	0			55

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was never rendered for this user.	NA

feel_safe_violent_crime

Ich fühle mich in meiner Gegend sicher vor Gewaltverbrechen.

Distribution

Distribution of values for feel_safe_violent_crime

93 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	feel_safe_violent_crime	Ich fühle mich in meiner Gegend sicher vor Gewaltverbrechen.	0			55

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was never rendered for this user.	NA

feel_safe_sexual_assault

Ich fühle mich in meiner Gegend sicher vor sexuellen Übergriffen.

Distribution

Distribution of values for feel_safe_sexual_assault

93 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	feel_safe_sexual_assault	Ich fühle mich in meiner Gegend sicher vor sexuellen Übergriffen.	0			55

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was never rendered for this user.	NA

feel_safe_theft

Ich fühle mich in meiner Gegend sicher vor Diebstahl.

Distribution

Distribution of values for feel_safe_theft

93 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	feel_safe_theft	Ich fühle mich in meiner Gegend sicher vor Diebstahl.	0			55

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was never rendered for this user.	NA

satisfaction_sexual_intercourse

Der Sex mit meinem Partner ist sehr befriedigend.

Distribution

Distribution of values for satisfaction_sexual_intercourse

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for satisfaction_sexual_intercourse

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	satisfaction_sexual_intercourse	Der Sex mit meinem Partner ist sehr befriedigend.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

satisfaction_single_life

Ich bin zufrieden mit meinem Singleleben.

Distribution

Distribution of values for satisfaction_single_life

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for satisfaction_single_life

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	satisfaction_single_life	Ich bin zufrieden mit meinem Singleleben.	0	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

investment_potential_partner

Ich investiere viel, um jemanden kennenzulernen.

Distribution

Distribution of values for investment_potential_partner

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for investment_potential_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	investment_potential_partner	Ich investiere viel, um jemanden kennenzulernen.	0	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

timeperiod_potential_partner

Folgende Art von Partnerschaft, suche ich derzeit eher.

Distribution

Distribution of values for timeperiod_potential_partner

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc		timeperiod_potential_partner	Folgende Art von Partnerschaft, suche ich derzeit eher.	0	mc_vertical	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
1	no_interest
2	one_night
3	short
4	long

characteristics_potential_partner

Ein Partner, der für mich in Frage käme, sollte folgende Eigenschaften haben.

Distribution

Distribution of values for characteristics_potential_partner

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc_multiple_button		characteristics_potential_partner	Ein Partner, der für mich in Frage käme, sollte folgende Eigenschaften haben.	0	square square100	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
1	body
2	face
3	money
4	social_status
5	intelligence
6	humor
7	creativity
8	trustworthiness
9	personality

quantity_potential_partner

Es befinden sich viele annehmbare Singles in meiner Umgebung.

Distribution

Distribution of values for quantity_potential_partner

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for quantity_potential_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	quantity_potential_partner	Es befinden sich viele annehmbare Singles in meiner Umgebung.	0	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

sexual_partner

Wenn ich sexuell aktiv werde, dann üblicherweise mit Männern…

Distribution

Distribution of values for sexual_partner

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc		sexual_partner	Wenn ich sexuell aktiv werde, dann üblicherweise mit Männern…	0	mc_vertical	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
1	longterm
2	friends
3	met_before
4	new

fling_frequency

Im Monat habe ich durchschnittlich etwa so oft sexuellen Kontakt zu einer anderen Person.

Distribution

Distribution of values for fling_frequency

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for fling_frequency

Item

Item options
type	name	label	optional	showif	value	item_order
number	fling_frequency	Im Monat habe ich durchschnittlich etwa so oft sexuellen Kontakt zu einer anderen Person.	0	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

fling_frequency_2

Im Monat habe ich durchschnittlich etwa so oft die Möglichkeit zu sexuellen Kontakt zu einer anderen Person.

Distribution

Distribution of values for fling_frequency_2

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for fling_frequency_2

Item

Item options
type	name	label	optional	showif	value	item_order
number	fling_frequency_2	Im Monat habe ich durchschnittlich etwa so oft die Möglichkeit zu sexuellen Kontakt zu einer anderen Person.	0	s1_demo$hetero_relationship == 0		82

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

relationship_importance

Mir ist die Beziehung zu meinem Partner sehr wichtig.

Distribution

Distribution of values for relationship_importance

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for relationship_importance

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	relationship_importance	Mir ist die Beziehung zu meinem Partner sehr wichtig.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

relationship_importance_partner

Meinem Partner ist die Beziehung zu mir sehr wichtig.

Distribution

Distribution of values for relationship_importance_partner

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for relationship_importance_partner

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	relationship_importance_partner	Meinem Partner ist die Beziehung zu mir sehr wichtig.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

partner_attractiveness_longterm

Mein Partner ist sehr attraktiv für eine langfristige Beziehung (z.B. als möglicher Ehepartner).

Distribution

Distribution of values for partner_attractiveness_longterm

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_attractiveness_longterm

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	attractiveness_ltp	Mein Partner ist sehr attraktiv für eine langfristige Beziehung (z.B. als möglicher Ehepartner).	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

attractiveness_warmth

Ich fühle mich bei meinem Partner sehr geborgen.

Distribution

Distribution of values for attractiveness_warmth

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for attractiveness_warmth

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	attractiveness_warmth	Ich fühle mich bei meinem Partner sehr geborgen.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

partner_attractiveness_trust

Das Vertrauen zwischen meinem Partner und mir ist sehr stark.

Distribution

Distribution of values for partner_attractiveness_trust

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_attractiveness_trust

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	attractiveness_trustworthiness	Das Vertrauen zwischen meinem Partner und mir ist sehr stark.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

net_income_partner

Wie viel Geld hat Ihr Partner monatlich zur Verfügung (netto)?

Distribution

Distribution of values for net_income_partner

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
mc		net_income_partner	Wie viel Geld hat Ihr Partner monatlich zur Verfügung (netto)?	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1	euro_lt_500
2	euro_500_1000
3	euro_1000_2000
4	euro_2000_3000
5	euro_gt_3000
6	dont_know
7	dont_tell

partner_strength

Mein Partner ist körperlich stärker als viele andere Männer.

Distribution

Distribution of values for partner_strength

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_strength

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	partner_strength	Mein Partner ist körperlich stärker als viele andere Männer.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

partner_feel_safe

Mit meinem Partner fühle ich mich sehr sicher.

Distribution

Distribution of values for partner_feel_safe

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_feel_safe

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	partner_feel_safe	Mit meinem Partner fühle ich mich sehr sicher.	0	s1_demo$hetero_relationship == 1		82

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

meet_potential_partner

Folgendes tue ich, um einen potentiellen Partner kennen zu lernen.

Distribution

Distribution of values for meet_potential_partner

1041 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	type_options	name	label	optional	class	showif	value	item_order
mc_multiple		meet_potential_partner	Folgendes tue ich, um einen potentiellen Partner kennen zu lernen.	0	mc_vertical	s1_demo$hetero_relationship == 0		83

Value labels

Response choices
name	value
1	no_interest
2	dating_app
3	online_dating
4	contact_ad
5	going_out
6	sports
7	social_networks
8	friends
9	other

partner_height

Was ist die Körpergröße Ihres Partners in cm (ohne Schuhe)?

Distribution

Distribution of values for partner_height

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_height

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	80,230	partner_height	Was ist die Körpergröße Ihres Partners in cm (ohne Schuhe)?	0	s1_demo$hetero_relationship == 1		83

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

meet_potential_partner_other

Andere:

Distribution

Distribution of values for meet_potential_partner_other

1505 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meet_potential_partner_other	Andere:	text	character	0	meet_potential_partner == ‘other’		84	1505	0.0046	7	0	3	85	0

Item

Item options
type	name	label	optional	showif	value	item_order
text	meet_potential_partner_other	Andere:	0	meet_potential_partner == ‘other’		84

Value labels

Response choices
name	value

partner_weight

Was ist das Gewicht Ihres Partners in kg (ohne Kleidung)?

Distribution

Distribution of values for partner_weight

570 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for partner_weight

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
number	40,200	partner_weight	Was ist das Gewicht Ihres Partners in kg (ohne Kleidung)?	0	s1_demo$hetero_relationship == 1		84

Value labels

Response choices
name	value
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

soi_r_behavior_1

Mit wie vielen verschiedenen Personen haben Sie in den letzten 12 Monaten Geschlechtsverkehr gehabt?

Distribution

Distribution of values for soi_r_behavior_1

101 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
number	soi_r_behavior_1	Mit wie vielen verschiedenen Personen haben Sie in den letzten 12 Monaten Geschlechtsverkehr gehabt?	0			117

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

soi_r_behavior_2

Mit wie vielen verschiedenen Personen haben Sie in Ihrem Leben nur einmal Geschlechtsverkehr gehabt?

Distribution

Distribution of values for soi_r_behavior_2

101 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
number	soi_r_behavior_2	Mit wie vielen verschiedenen Personen haben Sie in Ihrem Leben nur einmal Geschlechtsverkehr gehabt?	0			117

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

soi_r_behavior_3

Mit wie vielen verschiedenen Personen hatten Sie schon Geschlechtsverkehr, ohne dabei ein Interesse an einer längerfristigen Beziehung mit dieser Person zu haben?

Distribution

Distribution of values for soi_r_behavior_3

101 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Item

Item options
type	name	label	optional	showif	value	item_order
number	soi_r_behavior_3	Mit wie vielen verschiedenen Personen hatten Sie schon Geschlechtsverkehr, ohne dabei ein Interesse an einer längerfristigen Beziehung mit dieser Person zu haben?	0			117

Value labels

Response choices
name	value
Item was never rendered for this user.	NA

relationship_problems

Es gibt viele Probleme in meiner Beziehung.

Distribution

Distribution of values for relationship_problems

572 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for relationship_problems

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	relationship_problems	Es gibt viele Probleme in meiner Beziehung.	0	s1_demo$hetero_relationship == 1		119

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

relationship_conflict

Es gibt oft Streit/Meinungsverschiedenheiten in meiner Partnerschaft.

Distribution

Distribution of values for relationship_conflict

572 missing values.

Summary statistics

## Error in as.data.frame.default(x[[i]], optional = TRUE): cannot coerce class '"haven_labelled"' to a data.frame

## Error in names(df) = html_item_name: names() applied to a non-vector

## Error in (skimr::skim_with(haven_labelled = haven_labelled_sfl, haven_labelled_spss = haven_labelled_sfl, : inherits(data, "data.frame") is not TRUE

## Error in cb_table$value_labels <- NULL: object 'cb_table' not found

## Error in exists("value_labels", metadata_table): object 'cb_table' not found

Missing value types

Plot of missing values for relationship_conflict

Item

Item options
type	type_options	name	label	optional	showif	value	item_order
rating_button	5 agree	relationship_conflict	Es gibt oft Streit/Meinungsverschiedenheiten in meiner Partnerschaft.	0	s1_demo$hetero_relationship == 1		119

Value labels

Response choices
name	value
1: trifft gar nicht zu	1
2	2
3	3
4	4
5: trifft völlig zu	5
Item was not shown to this user.	NA
Item was never rendered for this user.	NA

free_not_covered

Distribution

Distribution of values for free_not_covered

1419 missing values.

Summary statistics

name	label	type	data_type	optional	showif	value	item_order	n_missing	complete_rate	n_unique	empty	min	max	whitespace
free_not_covered	Falls Fragen für Sie nicht oder nur falsch beantwortbar waren, oder Sie ansonsten den Eindruck haben, dass uns hier etwas wichtiges entging, geben Sie es bitte in diesem offenen Feld an. <small>optional</small>	textarea	character	1			160	1419	0.0615	93	0	1	698	0

Item

Item options
type	name	label	optional	showif	value	item_order
textarea	free_not_covered	Falls Fragen für Sie nicht oder nur falsch beantwortbar waren, oder Sie ansonsten den Eindruck haben, dass uns hier etwas wichtiges entging, geben Sie es bitte in diesem offenen Feld an. <small>optional</small>	1			160

Value labels

Response choices
name	value

Scale: narq

Overview

Reliability: ω_ordinal [95% CI] = 0.86 [0.85;0.87].

Missing: 93.

Likert plot of scale narq items

Distribution of scale narq

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	narq_5, narq_3, narq_15, narq_2, narq_11, narq_18, narq_16, narq_1, narq_14, narq_17, narq_4, narq_13, narq_6, narq_7, narq_12, narq_8, narq_9 & narq_10
Observations:	1419
Positive correlations:	145
Number of correlations:	153
Percentage positive correlations:	95

Estimates assuming interval level


Omega (total):	0.84
Omega (hierarchical):	0.51
Revelle’s Omega (total):	0.88
Greatest Lower Bound (GLB):	0.90
Coefficient H:	0.85
Coefficient Alpha:	0.84

Confidence intervals


Omega (total):	[0.83; 0.85]
Coefficient Alpha	[0.82; 0.85]

Estimates assuming ordinal level


Ordinal Omega (total):	0.86
Ordinal Omega (hierarch.):	0.82
Ordinal Coefficient Alpha:	0.87

Confidence intervals


Ordinal Omega (total):	[0.85; 0.87]
Ordinal Coefficient Alpha	[0.86; 0.88]

Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help (‘?ufs::scaleStructure’) for more information.

Eigen values

4.877, 2.757, 1.394, 1.095, 0.974, 0.801, 0.736, 0.701, 0.669, 0.609, 0.578, 0.503, 0.465, 0.463, 0.394, 0.352, 0.329 & 0.305

Factor analysis (reproducing only shared variance)

	ML1	ML4	ML3	ML2
narq_5	0.248	0.366	-0.081	0.020
narq_3	0.107	0.565	-0.095	0.117
narq_15	-0.027	0.691	0.021	0.008
narq_2	0.027	0.388	0.118	0.129
narq_11	0.420	-0.053	-0.025	-0.034
narq_18	-0.092	0.076	-0.028	0.514
narq_16	0.047	0.189	-0.019	0.662
narq_1	-0.061	0.692	-0.031	-0.027
narq_14	0.060	-0.083	0.459	0.029
narq_17	-0.007	0.046	0.855	0.002
narq_4	0.548	0.073	0.021	0.063
narq_13	0.030	-0.028	0.781	0.004
narq_6	0.797	-0.033	-0.015	-0.029
narq_7	-0.008	-0.072	0.029	0.855
narq_12	0.535	0.007	-0.042	0.178
narq_8	0.037	0.641	0.131	0.023
narq_9	0.765	-0.015	0.028	-0.032
narq_10	0.631	0.063	0.100	-0.028

Component analysis (reproducing full covariance matrix)

	TC1	TC2	TC3	TC4
narq_5	0.240	0.599	-0.133	-0.115
narq_3	0.142	0.577	-0.127	0.219
narq_15	-0.033	0.698	0.021	0.118
narq_2	0.010	0.404	0.185	0.230
narq_11	0.627	-0.135	-0.101	-0.034
narq_18	-0.092	0.037	-0.010	0.706
narq_16	0.082	0.232	0.015	0.699
narq_1	-0.121	0.788	-0.032	0.010
narq_14	-0.035	-0.150	0.731	0.076
narq_17	0.049	0.061	0.825	0.013
narq_4	0.729	-0.025	0.002	0.143
narq_13	0.038	0.001	0.830	-0.011
narq_6	0.691	0.116	0.127	-0.181
narq_7	0.031	0.028	0.062	0.817
narq_12	0.737	-0.121	-0.067	0.299
narq_8	0.021	0.645	0.173	0.123
narq_9	0.663	0.122	0.172	-0.167
narq_10	0.613	0.146	0.205	-0.115

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
narq_5	3.69767441860465	4	0.971299242299997	0.985545149802888	1	0.0261628666254947	1	3	5	5	-0.441620454841714	-0.391039013913282	0.132840028188865	1419	1419
narq_3	2.72797744890768	3	1.08533095576754	1.04179218453948	1	0.0276560338012321	1	2	4	5	0.0590173018996267	-0.712225429009423	0.148343904157858	1419	1419
narq_15	2.74489076814658	3	1.23670695209384	1.11207326741265	2	0.0295217571502576	1	2	4	5	0.102532850953908	-0.787897328649723	0.140239605355884	1419	1419
narq_2	1.6814658210007	1	0.799733816003045	0.894278377242258	1	0.0237400446996593	1	NA	2	5	1.26743696571477	1.07344688038982	0.140944326990839	1419	1419
narq_11	2.95137420718816	3	1.07168181967277	1.03522066230962	2	0.0274815822708678	1	2	4	5	0.0629204773948583	-0.714527563706265	0.148343904157858	1419	1419
narq_18	3.14023960535588	3	0.889345781758942	0.943051314488741	1	0.025034800046351	1	2	4	5	-0.181440966587617	-0.222975211538846	0.146582100070472	1419	1419
narq_16	2.67441860465116	3	1.16472594876505	1.07922469799623	1	0.0286497395256445	1	2	4	5	0.141661818809007	-0.734389822854693	0.150105708245243	1419	1419
narq_1	2.97885835095137	3	1.12790250389883	1.0620275438513	2	0.0281932136624492	1	2	4	5	-0.0710697895635157	-0.453617785647023	0.11169837914024	1419	1419
narq_14	1.10852713178295	1	0.172980833360667	0.415909645669185	0	0.0110409843628939	1	NA	2	5	4.59713221965587	24.6656865134064	0.0260747004933051	1419	1419
narq_17	1.46723044397463	1	0.628509319918773	0.79278579699612	1	0.0210457624123505	1	NA	2	5	1.81281265549157	3.08135634069116	0.101127554615927	1419	1419
narq_4	2.02748414376321	2	1.08316510464967	1.04075218214985	2	0.0276284252803892	1	1	3	5	0.824629655574526	-0.127441250308918	0.171952078928823	1419	1419
narq_13	1.67512332628612	1	0.871108500294711	0.933331934680642	1	0.0247767836199573	1	NA	2	5	1.31816874619511	1.05068045797274	0.12015503875969	1419	1419
narq_6	2.54122621564482	2	1.57992825556049	1.25695197026795	3	0.0333677932049591	1	1	4	5	0.376042962170525	-0.962607258961765	0.126849894291755	1419	1419
narq_7	3.00070472163495	3	1.04301783870124	1.02128244805306	2	0.0271115701606515	1	2	4	5	-0.0769894738538039	-0.457500125874986	0.124383368569415	1419	1419
narq_12	1.75968992248062	2	0.830080580794	0.91108758129721	1	0.0241862718094557	1	1	3	5	1.1703057167437	0.890261941928853	0.167371388301621	1419	1419
narq_8	2.24242424242424	2	1.17532162243023	1.08412251264801	2	0.0287797598210278	1	1	3	5	0.465402337969955	-0.615637460736991	0.140591966173362	1419	1419
narq_9	2.05426356589147	2	1.27138343118925	1.12755639822993	2	0.0299327815326501	1	1	3	5	0.845004882000305	-0.225588617252515	0.140239605355884	1419	1419
narq_10	2.07117688513037	2	1.25938725994487	1.12222424672829	2	0.029791231073402	1	1	3	5	0.777093465856214	-0.434429447887318	0.145172656800564	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
narq_5	Ich genieße meine Erfolge sehr.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.687	0.9881	6	▁▂▁▆▁▇▁▅
narq_3	Ich zeige anderen, was für ein besonderer Mensch ich bin.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	2.728	1.0411	6	▃▇▁▇▁▅▁▁
narq_15	Ich ziehe viel Kraft daraus, eine ganz besondere Person zu sein.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	2.742	1.1093	6	▃▇▁▇▁▆▁▂
narq_2	Ich werde einmal berühmt sein.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	1	5	1.681	0.8954	6	▇▅▁▂▁▁▁▁
narq_11	Ich reagiere häufig gereizt auf Kritik.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	2.949	1.0344	6	▂▇▁▇▁▆▁▂
narq_18	Ich verhalte mich im Umgang mit anderen meist überaus gewandt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	3.143	0.9427	6	▁▃▁▇▁▆▁▁
narq_16	Mit meinen besonderen Beiträgen schaffe ich es im Mittelpunkt zu stehen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.678	1.0777	6	▃▇▁▇▁▅▁▁
narq_1	Ich bin großartig.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.978	1.0576	6	▂▃▁▇▁▅▁▂
narq_14	Andere Menschen sind nichts wert.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	1	5	1.109	0.4168	6	▇▁▁▁▁▁▁▁
narq_17	Die meisten Menschen sind ziemliche Versager.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	1	5	1.467	0.7928	6	▇▂▁▁▁▁▁▁
narq_4	Ich reagiere genervt, wenn eine andere Person mir die Schau stiehlt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.027	1.0408	6	▇▇▁▃▁▂▁▁
narq_13	Die meisten Menschen werden es zu nichts bringen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	1	5	1.675	0.9333	6	▇▃▁▂▁▁▁▁
narq_6	Es freut mich insgeheim, wenn meine Gegner scheitern.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.541	1.2570	6	▇▇▁▆▁▅▁▂
narq_7	In Gesprächen gelingt es mir meist, die Aufmerksamkeit der Anwesenden auf mich zu ziehen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	3.001	1.0213	6	▂▅▁▇▁▅▁▂
narq_12	Ich ertrage es nur schlecht, wenn eine andere Person Mittelpunkt des Geschehens ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	1.760	0.9111	6	▇▆▁▂▁▁▁▁
narq_8	Ich habe es verdient, als große Persönlichkeit angesehen zu werden.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.242	1.0841	6	▇▇▁▇▁▂▁▁
narq_9	Ich will, dass meine Konkurrenten scheitern.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.054	1.1276	6	▇▆▁▃▁▂▁▁
narq_10	Ich genieße es, wenn ein anderer Mensch mir unterlegen ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.071	1.1222	6	▇▆▁▃▁▂▁▁

Scale: bfi_open

Overview

Reliability: ω_ordinal [95% CI] = 0.85 [0.84;0.86].

Missing: 93.

Likert plot of scale bfi_open items

Distribution of scale bfi_open

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	bfi_open_1, bfi_open_2, bfi_open_3, bfi_open_4, bfi_open_5, bfi_open_6, bfi_open_7r, bfi_open_8, bfi_open_9r & bfi_open_10
Observations:	1419
Positive correlations:	45
Number of correlations:	45
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.82
Omega (hierarchical):	0.68
Revelle’s Omega (total):	0.86
Greatest Lower Bound (GLB):	0.86
Coefficient H:	0.84
Coefficient Alpha:	0.82

Confidence intervals


Omega (total):	[0.8; 0.83]
Coefficient Alpha	[0.8; 0.83]

Estimates assuming ordinal level


Ordinal Omega (total):	0.85
Ordinal Omega (hierarch.):	0.85
Ordinal Coefficient Alpha:	0.85

Confidence intervals


Ordinal Omega (total):	[0.84; 0.86]
Ordinal Coefficient Alpha	[0.84; 0.86]

Eigen values

3.872, 1.352, 0.973, 0.883, 0.692, 0.567, 0.56, 0.497, 0.311 & 0.293

Factor analysis (reproducing only shared variance)

	ML1	ML2
bfi_open_1	0.820	-0.055
bfi_open_2	0.253	0.275
bfi_open_3	0.215	0.234
bfi_open_4	0.553	0.149
bfi_open_5	0.863	-0.021
bfi_open_6	-0.001	0.802
bfi_open_7r	0.268	0.036
bfi_open_8	0.502	0.182
bfi_open_9r	-0.009	0.853
bfi_open_10	0.022	0.609

Component analysis (reproducing full covariance matrix)

	TC1	TC2
bfi_open_1	0.858	-0.081
bfi_open_2	0.347	0.315
bfi_open_3	0.288	0.302
bfi_open_4	0.655	0.117
bfi_open_5	0.858	-0.028
bfi_open_6	0.002	0.851
bfi_open_7r	0.454	-0.073
bfi_open_8	0.645	0.149
bfi_open_9r	0.024	0.854
bfi_open_10	-0.042	0.785

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
bfi_open_1	3.40803382663848	4	1.03578872664057	1.01773706164243	1	0.0270174522282406	1	3	5	5	-0.390767425608948	-0.345781846260282	0.15292459478506	1419	1419
bfi_open_2	4.24524312896406	4	0.688755564965097	0.829912986381763	1	0.0220313628227126	1	3	5	5	-1.0396439373525	0.872050032518053	0.195207892882311	1419	1419
bfi_open_3	4.29175475687104	5	0.728641417951616	0.853604954268434	1	0.0226603038672081	1	4	NA	5	-1.08727222413103	0.522611337685383	0.166666666666667	1419	1419
bfi_open_4	4.00211416490486	4	1.03031992771882	1.01504676134591	2	0.0269460339194452	1	3	5	5	-0.8915377187735	0.177320272044884	0.17723749119098	1419	1419
bfi_open_5	3.49189570119803	4	1.02161974651888	1.01075206975741	1	0.0268320244869499	1	3	5	5	-0.373864554773889	-0.337345707247325	0.155038759689922	1419	1419
bfi_open_6	3.97181113460183	4	1.13601723933997	1.06584109478851	2	0.0282944504495813	1	3	5	5	-0.916727479091988	0.134314901906509	0.169485553206483	1419	1419
bfi_open_7r	3.39464411557435	3	1.18406156225555	1.08814592875016	1	0.0288865678134171	1	2	4	5	-0.297649579752449	-0.617118281579566	0.145525017618041	1419	1419
bfi_open_8	3.95560253699789	4	0.914106459683263	0.956089148397399	2	0.025380910125332	1	3	5	5	-0.754910786061553	0.0768393133009771	0.162790697674419	1419	1419
bfi_open_9r	3.99929527836505	4	1.29830697833453	1.13943274410319	2	0.0302480580607144	1	3	5	5	-0.963895025126894	-0.0521401859214166	0.139887244538407	1419	1419
bfi_open_10	3.05778717406624	3	1.27733529740943	1.13019259306077	2	0.0300027635256333	1	2	4	5	-0.09895213483261	-0.770743981787494	0.137420718816068	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
bfi_open_1	Ich sehe mich selbst als jemanden, der originell ist, neue Ideen entwickelt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.406	1.0195	6	▁▃▁▆▁▇▁▃
bfi_open_2	Ich sehe mich selbst als jemanden, der vielseitig interessiert ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	4.246	0.8279	6	▁▁▁▂▁▇▁▇
bfi_open_3	Ich sehe mich selbst als jemanden, der tiefsinnig ist, gern über Sachen nachdenkt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	5	5	4.293	0.8522	6	▁▁▁▂▁▅▁▇
bfi_open_4	Ich sehe mich selbst als jemanden, der eine lebhafte Vorstellungskraft hat, fantasievoll ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.998	1.0148	6	▁▂▁▃▁▇▁▇
bfi_open_5	Ich sehe mich selbst als jemanden, der erfinderisch und einfallsreich ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.491	1.0089	6	▁▃▁▇▁▇▁▃
bfi_open_6	Ich sehe mich selbst als jemanden, der künstlerische und ästhetische Eindrücke schätzt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	3.972	1.0658	6	▁▂▁▃▁▇▁▇
bfi_open_7r	Ich sehe mich selbst als jemanden, der routinemäßige und einfache Aufgaben bevorzugt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	3.395	1.0881	6	▁▃▁▇▁▇▁▃
bfi_open_8	Ich sehe mich selbst als jemanden, der gerne Überlegungen anstellt, mit Ideen spielt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	3.956	0.9561	6	▁▂▁▃▁▇▁▆
bfi_open_9r	Ich sehe mich selbst als jemanden, der nur wenig künstlerisches Interesse hat.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	3.999	1.1394	6	▁▂▁▂▁▅▁▇
bfi_open_10	Ich sehe mich selbst als jemanden, der sich gut in Musik, Kunst und Literatur auskennt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	3.058	1.1302	6	▂▆▁▇▁▇▁▂

Scale: bfi_extra

Overview

Reliability: ω_ordinal [95% CI] = 0.9 [0.89;0.9].

Missing: 93.

Likert plot of scale bfi_extra items

Distribution of scale bfi_extra

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	bfi_extra_1, bfi_extra_3, bfi_extra_2r, bfi_extra_4, bfi_extra_5r, bfi_extra_6, bfi_extra_7r & bfi_extra_8
Observations:	1419
Positive correlations:	28
Number of correlations:	28
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.88
Omega (hierarchical):	0.77
Revelle’s Omega (total):	0.90
Greatest Lower Bound (GLB):	0.89
Coefficient H:	0.90
Coefficient Alpha:	0.88

Confidence intervals


Omega (total):	[0.87; 0.89]
Coefficient Alpha	[0.87; 0.89]

Estimates assuming ordinal level


Ordinal Omega (total):	0.90
Ordinal Omega (hierarch.):	0.88
Ordinal Coefficient Alpha:	0.89

Confidence intervals


Ordinal Omega (total):	[0.89; 0.9]
Ordinal Coefficient Alpha	[0.89; 0.9]

Eigen values

4.327, 0.946, 0.662, 0.584, 0.492, 0.374, 0.328 & 0.286

Factor analysis (reproducing only shared variance)

	ML1
bfi_extra_1	0.761
bfi_extra_3	0.522
bfi_extra_2r	0.801
bfi_extra_4	0.584
bfi_extra_5r	0.800
bfi_extra_6	0.474
bfi_extra_7r	0.683
bfi_extra_8	0.823

Component analysis (reproducing full covariance matrix)

	PC1
bfi_extra_1	0.782
bfi_extra_3	0.616
bfi_extra_2r	0.819
bfi_extra_4	0.672
bfi_extra_5r	0.811
bfi_extra_6	0.573
bfi_extra_7r	0.727
bfi_extra_8	0.836

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
bfi_extra_1	3.79985905567301	4	1.06569814655228	1.03232656972117	2	0.0274047539709076	1	3	5	5	-0.617076468950068	-0.252024227290298	0.144115574348132	1419	1419
bfi_extra_3	3.43128964059197	3	0.875917306035061	0.935904538954193	1	0.0248450775002555	1	2	4	5	-0.231640977780597	-0.231078630486507	0.178646934460888	1419	1419
bfi_extra_2r	3.27554615926709	3	1.33516322406669	1.15549263263194	2	0.0306743933957135	1	2	4	5	-0.202204762350853	-0.871710491165895	0.125792811839323	1419	1419
bfi_extra_4	3.71176885130374	4	1.02758354032668	1.01369795320237	1	0.0269102276577333	1	3	5	5	-0.60411549538113	-0.138748326473191	0.116983791402396	1419	1419
bfi_extra_5r	3.77096546863989	4	1.27825372165583	1.13059883321001	2	0.0300135478178035	1	3	5	5	-0.644319889395021	-0.4435887366287	0.156448202959831	1419	1419
bfi_extra_6	3.36645525017618	3	1.06025618470267	1.02968742087231	1	0.0273346935588084	1	2	4	5	-0.288935950107599	-0.506672382878023	0.154686398872445	1419	1419
bfi_extra_7r	2.68428470754052	2	1.51097288362352	1.22921636973461	2	0.0326315074876816	1	1	4	5	0.298853281069789	-0.954943565660508	0.103241719520789	1419	1419
bfi_extra_8	3.50599013389711	4	1.19372290822417	1.09257627112443	1	0.0290041783122021	1	3	5	5	-0.419804728357477	-0.527383004631509	0.132840028188865	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
bfi_extra_1	Ich sehe mich selbst als jemanden, der gesprächig ist, sich gerne unterhält.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.803	1.0278	6	▁▂▁▅▁▇▁▆
bfi_extra_3	Ich sehe mich selbst als jemanden, der voller Energie und Tatendrang ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	3.427	0.9308	6	▁▂▁▇▁▇▁▂
bfi_extra_2r	Ich sehe mich selbst als jemanden, der eher zurückhaltend und reserviert ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	3.276	1.1523	6	▂▆▁▆▁▇▁▃
bfi_extra_4	Ich sehe mich selbst als jemanden, der begeisterungsfähig ist, andere mitreißen kann.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.706	1.0140	6	▁▂▁▅▁▇▁▅
bfi_extra_5r	Ich sehe mich selbst als jemanden, der eher still und wortkarg ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.769	1.1299	6	▁▃▁▅▁▇▁▇
bfi_extra_6	Ich sehe mich selbst als jemanden, der durchsetzungsfähig und energisch ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	3.360	1.0313	6	▁▃▁▇▁▇▁▃
bfi_extra_7r	Ich sehe mich selbst als jemanden, der manchmal schüchtern und gehemmt ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.684	1.2292	6	▅▇▁▅▁▅▁▂
bfi_extra_8	Ich sehe mich selbst als jemanden, der aus sich heraus geht, gesellig ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	3.506	1.0926	6	▁▃▁▆▁▇▁▅

Scale: bfi_agree

Overview

Reliability: ω_ordinal [95% CI] = 0.79 [0.77;0.81].

Missing: 93.

Likert plot of scale bfi_agree items

Distribution of scale bfi_agree

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	bfi_agree_2, bfi_agree_3r, bfi_agree_1r, bfi_agree_4, bfi_agree_5, bfi_agree_6r, bfi_agree_7, bfi_agree_8r & bfi_agree_9
Observations:	1419
Positive correlations:	36
Number of correlations:	36
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.78
Omega (hierarchical):	0.55
Revelle’s Omega (total):	0.81
Greatest Lower Bound (GLB):	0.83
Coefficient H:	0.81
Coefficient Alpha:	0.76

Confidence intervals


Omega (total):	[0.76; 0.79]
Coefficient Alpha	[0.75; 0.78]

Estimates assuming ordinal level


Ordinal Omega (total):	0.79
Ordinal Omega (hierarch.):	0.76
Ordinal Coefficient Alpha:	0.80

Confidence intervals


Ordinal Omega (total):	[0.77; 0.81]
Ordinal Coefficient Alpha	[0.78; 0.81]

Eigen values

3.149, 1.209, 0.934, 0.9, 0.75, 0.625, 0.595, 0.518 & 0.318

Factor analysis (reproducing only shared variance)

	ML1	ML2
bfi_agree_2	-0.044	0.687
bfi_agree_3r	0.373	0.052
bfi_agree_1r	0.424	0.131
bfi_agree_4	0.258	0.195
bfi_agree_5	0.172	0.343
bfi_agree_6r	0.715	0.024
bfi_agree_7	0.019	0.664
bfi_agree_8r	0.901	-0.028
bfi_agree_9	0.091	0.389

Component analysis (reproducing full covariance matrix)

	TC1	TC2
bfi_agree_2	-0.076	0.832
bfi_agree_3r	0.748	-0.203
bfi_agree_1r	0.726	-0.037
bfi_agree_4	0.512	0.112
bfi_agree_5	0.193	0.504
bfi_agree_6r	0.623	0.186
bfi_agree_7	0.057	0.741
bfi_agree_8r	0.738	0.141
bfi_agree_9	0.186	0.471

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
bfi_agree_2	3.89781536293164	4	0.708168707775097	0.841527603691702	1	0.022339691348957	1	3	5	5	-0.722456245541199	0.753900461993496	0.114517265680056	1419	1419
bfi_agree_3r	4.28188865398168	5	0.810468644857073	0.900260320605697	1	0.0238988449194262	1	4	NA	5	-1.24633183902132	1.10474997908307	0.158914728682171	1419	1419
bfi_agree_1r	3.09654686398872	3	1.16909243979799	1.08124578140124	2	0.0287033924055507	1	2	4	5	-0.0281772400927308	-0.754622250283923	0.139887244538407	1419	1419
bfi_agree_4	3.31219168428471	3	1.36297537648933	1.16746536414976	2	0.0309922286343185	1	2	4	5	-0.314353246381306	-0.773956612144722	0.126497533474278	1419	1419
bfi_agree_5	3.85412262156448	4	0.983641313585224	0.991786929529334	2	0.0263285646156085	1	3	5	5	-0.812354008453773	0.210948895813709	0.135658914728682	1419	1419
bfi_agree_6r	2.99788583509514	3	1.66783755818426	1.29144785345141	2	0.0342835413987765	1	2	4	5	-0.00786896143849787	-1.12113175259572	0.119097956307259	1419	1419
bfi_agree_7	4.24735729386892	4	0.626359372251064	0.791428690566032	1	0.0210097358593975	1	3	5	5	-0.965280837638447	0.838676078037697	0.210711768851304	1419	1419
bfi_agree_8r	3.27272727272727	3	1.63008077958713	1.27674616881631	2	0.0338932617506496	1	2	4	5	-0.22700354883649	-1.05660178260991	0.107117688513037	1419	1419
bfi_agree_9	4.10852713178295	4	0.923333442669553	0.960902410585775	1	0.0255086858408263	1	3	5	5	-1.00618440692128	0.517498277574388	0.178999295278365	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
bfi_agree_2	Ich sehe mich selbst als jemanden, der hilfsbereit und selbstlos gegenüber anderen ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.897	0.8435	6	▁▁▁▃▁▇▁▃
bfi_agree_3r	Ich sehe mich selbst als jemanden, der häufig in Streitereien verwickelt ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	5	5	4.282	0.9002	6	▁▁▁▂▁▅▁▇
bfi_agree_1r	Ich sehe mich selbst als jemanden, der dazu neigt, andere zu kritisieren.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	3.096	1.0858	6	▂▆▁▇▁▇▁▂
bfi_agree_4	Ich sehe mich selbst als jemanden, der nicht nachtragend ist, anderen leicht vergibt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	3.311	1.1652	6	▂▅▁▆▁▇▁▃
bfi_agree_5	Ich sehe mich selbst als jemanden, der anderen Vertrauen schenkt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.857	0.9915	6	▁▂▁▃▁▇▁▅
bfi_agree_6r	Ich sehe mich selbst als jemanden, der sich kalt und distanziert verhalten kann.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	3.003	1.2897	6	▅▇▁▇▁▇▁▅
bfi_agree_7	Ich sehe mich selbst als jemanden, der rücksichtsvoll und einfühlsam zu anderen ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	4.247	0.7914	6	▁▁▁▂▁▇▁▇
bfi_agree_8r	Ich sehe mich selbst als jemanden, der schroff und abweisend zu anderen sein kann.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	3.273	1.2767	6	▃▆▁▆▁▇▁▆
bfi_agree_9	Ich sehe mich selbst als jemanden, der sich kooperativ verhält, Zusammenarbeit dem Wettbewerb vorzieht.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	4.109	0.9609	6	▁▁▁▃▁▇▁▇

Scale: soi_r_attitude

Overview

Reliability: ω_ordinal [95% CI] = 0.89 [0.88;0.9].

Missing: 50.

Likert plot of scale soi_r_attitude items

Distribution of scale soi_r_attitude

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	soi_r_attitude_6r, soi_r_attitude_4 & soi_r_attitude_5
Observations:	1462
Positive correlations:	3
Number of correlations:	3
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.85
Omega (hierarchical):	0.06
Revelle’s Omega (total):	0.85
Greatest Lower Bound (GLB):	0.84
Coefficient H:	0.85
Coefficient Alpha:	0.85

Confidence intervals


Omega (total):	[0.84; 0.87]
Coefficient Alpha	[0.84; 0.86]

Estimates assuming ordinal level


Ordinal Omega (total):	0.89
Ordinal Omega (hierarch.):	0.89
Ordinal Coefficient Alpha:	0.89

Confidence intervals


Ordinal Omega (total):	[0.88; 0.9]
Ordinal Coefficient Alpha	[0.88; 0.9]

Eigen values

2.314, 0.352 & 0.333

Factor analysis (reproducing only shared variance)

	ML1
soi_r_attitude_6r	0.822
soi_r_attitude_4	0.802
soi_r_attitude_5	0.809

Component analysis (reproducing full covariance matrix)

	PC1
soi_r_attitude_6r	0.882
soi_r_attitude_4	0.875
soi_r_attitude_5	0.878

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
soi_r_attitude_6r	3.52735978112175	4	1.97426804158462	1.40508648900508	3	0.0367476330848067	1	2	5	5	-0.530423406851316	-1.0613856360851	0.124829001367989	1462	1462
soi_r_attitude_4	3.56497948016416	4	1.88044281272033	1.37129238775701	3	0.0358638061867387	1	2	5	5	-0.528465357879277	-1.00413248783396	0.114569083447332	1462	1462
soi_r_attitude_5	2.9015047879617	3	2.12307781619883	1.45707852094485	2	0.0381073957243358	1	1	4	5	0.0704432848153881	-1.39044667493889	0.106475148198814	1462	1462

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
soi_r_attitude_6r	Ich möchte nicht eher Sex mit jemandem haben, solange ich mir nicht sicher bin, dass es sich um eine ernste Langzeitbeziehung handelt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.527	1.405	6	▃▃▁▃▁▆▁▇
soi_r_attitude_4	Ich finde, Sex ohne Liebe ist ok.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.565	1.371	6	▂▃▁▃▁▅▁▇
soi_r_attitude_5	Ich könnte mir vorstellen, dass ich “unverbindlichen” Sex mit verschiedenen Personen genieße und mich dabei wohl fühle.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	2.901	1.457	6	▇▇▁▅▁▇▁▆

Scale: bfi_consc

Overview

Reliability: ω_ordinal [95% CI] = 0.85 [0.84;0.86].

Missing: 93.

Likert plot of scale bfi_consc items

Distribution of scale bfi_consc

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	bfi_consc_2r, bfi_consc_3, bfi_consc_1, bfi_consc_9r, bfi_consc_4r, bfi_consc_5, bfi_consc_6, bfi_consc_7 & bfi_consc_8r
Observations:	1419
Positive correlations:	36
Number of correlations:	36
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.81
Omega (hierarchical):	0.70
Revelle’s Omega (total):	0.85
Greatest Lower Bound (GLB):	0.86
Coefficient H:	0.83
Coefficient Alpha:	0.81

Confidence intervals


Omega (total):	[0.8; 0.83]
Coefficient Alpha	[0.8; 0.83]

Estimates assuming ordinal level


Ordinal Omega (total):	0.85
Ordinal Omega (hierarch.):	0.85
Ordinal Coefficient Alpha:	0.85

Confidence intervals


Ordinal Omega (total):	[0.84; 0.86]
Ordinal Coefficient Alpha	[0.83; 0.86]

Eigen values

3.72, 0.942, 0.86, 0.733, 0.659, 0.622, 0.539, 0.499 & 0.426

Factor analysis (reproducing only shared variance)

	ML1
bfi_consc_2r	0.415
bfi_consc_3	0.637
bfi_consc_1	0.668
bfi_consc_9r	0.544
bfi_consc_4r	0.595
bfi_consc_5	0.618
bfi_consc_6	0.609
bfi_consc_7	0.577
bfi_consc_8r	0.563

Component analysis (reproducing full covariance matrix)

	PC1
bfi_consc_2r	0.485
bfi_consc_3	0.685
bfi_consc_1	0.710
bfi_consc_9r	0.612
bfi_consc_4r	0.663
bfi_consc_5	0.672
bfi_consc_6	0.664
bfi_consc_7	0.636
bfi_consc_8r	0.632

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
bfi_consc_2r	3.55109231853418	4	1.16011693011726	1.07708724350317	1	0.0285929973897504	1	3	5	5	-0.357876114899703	-0.760856616409921	0.114164904862579	1419	1419
bfi_consc_3	4.20577871740662	4	0.6670751865425	0.816746708926642	1	0.0216818430050954	1	3	5	5	-0.954410659665551	0.815192630283469	0.205778717406624	1419	1419
bfi_consc_1	4.02818886539817	4	0.694550384615002	0.833396895011616	1	0.0221238487294582	1	3	5	5	-0.689722406928276	0.20149625499385	0.151515151515152	1419	1419
bfi_consc_9r	2.90486257928118	3	1.73494812990336	1.31717429746536	2	0.0349664908543339	1	2	4	5	0.0977724687147142	-1.1458240895515	0.108527131782946	1419	1419
bfi_consc_4r	2.94291754756871	3	1.37544169347889	1.17279226356541	2	0.031133639668574	1	2	4	5	0.076728663360034	-0.885716708085572	0.136363636363636	1419	1419
bfi_consc_5	3.5877378435518	4	0.995646430520311	0.997820840892949	1	0.0264887444087594	1	3	5	5	-0.409055865658888	-0.423495355615039	0.134249471458774	1419	1419
bfi_consc_6	3.71952078928823	4	0.922686371041408	0.960565651603995	1	0.0254997460369756	1	3	5	5	-0.519344286595407	-0.169353461055322	0.126849894291755	1419	1419
bfi_consc_7	3.78999295278365	4	0.892396262291627	0.944667275971613	1	0.0250776983192072	1	3	5	5	-0.540847531162739	-0.100095392852032	0.126849894291755	1419	1419
bfi_consc_8r	3.12544045102185	3	1.31006459782659	1.14458053356965	2	0.0303847143359283	1	2	4	5	-0.159133929098284	-0.831804268254366	0.136715997181113	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
bfi_consc_2r	Ich sehe mich selbst als jemanden, der etwas achtlos sein kann.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	3.559	1.0744	6	▁▃▁▅▁▇▁▅
bfi_consc_3	Ich sehe mich selbst als jemanden, der zuverlässig ist und gewissenhaft.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	4.198	0.8225	6	▁▁▁▂▁▇▁▇
bfi_consc_1	Ich sehe mich selbst als jemanden, der Aufgaben gründlich erledigt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	4	5	4.019	0.8475	6	▁▁▁▃▁▇▁▅
bfi_consc_9r	Ich sehe mich selbst als jemanden, der dazu neigt, unordentlich zu sein.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.905	1.3206	6	▆▇▁▇▁▇▁▅
bfi_consc_4r	Ich sehe mich selbst als jemanden, der bequem ist und zur Faulheit neigt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.942	1.1755	6	▃▇▁▇▁▇▁▃
bfi_consc_5	Ich sehe mich selbst als jemanden, der nicht aufgibt, ehe die Aufgabe erledigt ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.591	1.0006	6	▁▂▁▆▁▇▁▃
bfi_consc_6	Ich sehe mich selbst als jemanden, der tüchtig ist und flott arbeitet.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	3.720	0.9606	6	▁▂▁▅▁▇▁▅
bfi_consc_7	Ich sehe mich selbst als jemanden, der Pläne macht und diese auch durchführt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	4	5	3.790	0.9447	6	▁▂▁▅▁▇▁▅
bfi_consc_8r	Ich sehe mich selbst als jemanden, der leicht ablenkbar ist, nicht bei der Sache bleibt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	3.125	1.1446	6	▂▆▁▇▁▇▁▃

Scale: asendorpf_shyness

Overview

Reliability: ω_ordinal [95% CI] = 0.88 [0.87;0.89].

Missing: 93.

Likert plot of scale asendorpf_shyness items

Distribution of scale asendorpf_shyness

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	asendorpf_shyness_4r, asendorpf_shyness_2, asendorpf_shyness_5, asendorpf_shyness_3r & asendorpf_shyness_1
Observations:	1419
Positive correlations:	10
Number of correlations:	10
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.86
Omega (hierarchical):	0.81
Revelle’s Omega (total):	0.89
Greatest Lower Bound (GLB):	0.89
Coefficient H:	0.88
Coefficient Alpha:	0.85

Confidence intervals


Omega (total):	[0.84; 0.87]
Coefficient Alpha	[0.84; 0.87]

Estimates assuming ordinal level


Ordinal Omega (total):	0.88
Ordinal Omega (hierarch.):	0.88
Ordinal Coefficient Alpha:	0.88

Confidence intervals


Ordinal Omega (total):	[0.87; 0.89]
Ordinal Coefficient Alpha	[0.87; 0.89]

Eigen values

3.205, 0.677, 0.526, 0.334 & 0.259

Factor analysis (reproducing only shared variance)

	ML1
asendorpf_shyness_4r	0.811
asendorpf_shyness_2	0.716
asendorpf_shyness_5	0.533
asendorpf_shyness_3r	0.849
asendorpf_shyness_1	0.785

Component analysis (reproducing full covariance matrix)

	PC1
asendorpf_shyness_4r	0.833
asendorpf_shyness_2	0.796
asendorpf_shyness_5	0.646
asendorpf_shyness_3r	0.860
asendorpf_shyness_1	0.848

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
asendorpf_shyness_4r	2.68217054263566	3	1.44545762674801	1.20227186058229	2	0.0319162225518445	1	2	4	5	0.331962201553692	-0.815730854463806	0.12262156448203	1419	1419
asendorpf_shyness_2	2.6800563777308	3	1.11195034942862	1.05449056393532	1	0.0279931325191952	1	2	4	5	0.261324523126014	-0.601816507955876	0.149048625792812	1419	1419
asendorpf_shyness_5	2.64411557434813	2	1.58905385405205	1.26057679419068	2	0.0334640199327251	1	1	4	5	0.337283548551323	-0.941045544066679	0.109936575052854	1419	1419
asendorpf_shyness_3r	2.5968992248062	2	1.25347415838445	1.11958660155633	1	0.0297212105788025	1	1	3	5	0.321254689711459	-0.747680262601273	0.122269203664553	1419	1419
asendorpf_shyness_1	2.72374911909796	3	1.31291230936982	1.14582385617067	2	0.0304177203158058	1	2	4	5	0.205058319475354	-0.764741164593884	0.145525017618041	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
asendorpf_shyness_4r	Ich finde es leicht, mit Fremden in Kontakt zu kommen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	2.672	1.196	6	▅▇▁▆▁▅▁▂
asendorpf_shyness_2	Ich fühle mich anderen gegenüber gehemmt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	2.672	1.055	6	▃▇▁▇▁▅▁▁
asendorpf_shyness_5	Ich fühle mich auf Parties und in anderen größeren Gruppen unwohl.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	2	5	2.643	1.257	6	▆▇▁▆▁▅▁▂
asendorpf_shyness_3r	Ich gehe ungezwungen auf andere Menschen zu.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	2	5	2.596	1.117	6	▃▇▁▆▁▅▁▁
asendorpf_shyness_1	Ich fühle mich in Gegenwart anderer schüchtern.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	2.724	1.146	6	▅▇▁▇▁▅▁▂

Scale: bfi_neuro

Overview

Reliability: ω_ordinal [95% CI] = 0.87 [0.86;0.88].

Missing: 93.

Likert plot of scale bfi_neuro items

Distribution of scale bfi_neuro

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	bfi_neuro_2r, bfi_neuro_1, bfi_neuro_3, bfi_neuro_4, bfi_neuro_5r, bfi_neuro_8, bfi_neuro_6r & bfi_neuro_7
Observations:	1419
Positive correlations:	28
Number of correlations:	28
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.85
Omega (hierarchical):	0.72
Revelle’s Omega (total):	0.89
Greatest Lower Bound (GLB):	0.89
Coefficient H:	0.86
Coefficient Alpha:	0.85

Confidence intervals


Omega (total):	[0.84; 0.86]
Coefficient Alpha	[0.84; 0.86]

Estimates assuming ordinal level


Ordinal Omega (total):	0.87
Ordinal Omega (hierarch.):	0.86
Ordinal Coefficient Alpha:	0.87

Confidence intervals


Ordinal Omega (total):	[0.86; 0.88]
Ordinal Coefficient Alpha	[0.86; 0.88]

Eigen values

3.913, 0.989, 0.761, 0.565, 0.549, 0.447, 0.415 & 0.361

Factor analysis (reproducing only shared variance)

	ML1
bfi_neuro_2r	0.725
bfi_neuro_1	0.527
bfi_neuro_3	0.690
bfi_neuro_4	0.560
bfi_neuro_5r	0.718
bfi_neuro_8	0.573
bfi_neuro_6r	0.725
bfi_neuro_7	0.611

Component analysis (reproducing full covariance matrix)

	PC1
bfi_neuro_2r	0.747
bfi_neuro_1	0.621
bfi_neuro_3	0.748
bfi_neuro_4	0.648
bfi_neuro_5r	0.741
bfi_neuro_8	0.651
bfi_neuro_6r	0.741
bfi_neuro_7	0.685

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
bfi_neuro_2r	3.22128259337562	3	1.24154955266577	1.11424842502279	2	0.0295795001754808	1	2	4	5	-0.140942561537846	-0.809411519300068	0.13953488372093	1419	1419
bfi_neuro_1	2.26708949964764	2	1.27346479522817	1.12847897420739	2	0.0299572727822444	1	1	3	5	0.61774597722199	-0.428104565751207	0.151867512332629	1419	1419
bfi_neuro_3	2.88794926004228	3	1.18983352069586	1.09079490313067	2	0.0289568890599126	1	2	4	5	0.121966287615833	-0.804177049117325	0.139182522903453	1419	1419
bfi_neuro_4	3.66243833685694	4	1.24492804185788	1.11576343454062	2	0.0296197185175418	1	3	5	5	-0.517933448329891	-0.610939951276621	0.132135306553911	1419	1419
bfi_neuro_5r	2.89006342494715	3	1.14023264759644	1.06781676686426	2	0.0283468978133819	1	2	4	5	0.125945433739933	-0.683648640706795	0.150105708245243	1419	1419
bfi_neuro_8	3.15151515151515	3	1.4290721032611	1.19543803823582	2	0.0317348078468673	1	2	4	5	-0.130231955048835	-0.949934787075819	0.123326286116984	1419	1419
bfi_neuro_6r	2.90909090909091	3	1.146172586229	1.0705945013071	2	0.0284206372009316	1	2	4	5	0.108958996073248	-0.700585148920632	0.147991543340381	1419	1419
bfi_neuro_7	2.93657505285412	3	1.31473325441246	1.14661818161603	2	0.0304388069506378	1	2	4	5	0.124186092821193	-0.84458445376297	0.139182522903453	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
bfi_neuro_2r	Ich sehe mich selbst als jemanden, der entspannt ist, sich durch Stress nicht aus der Ruhe bringen lässt.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	3	5	3.222	1.111	6	▂▆▁▇▁▇▁▃
bfi_neuro_1	Ich sehe mich selbst als jemanden, der deprimiert, niedergeschlagen ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	2	5	2.269	1.123	6	▇▇▁▆▁▃▁▁
bfi_neuro_3	Ich sehe mich selbst als jemanden, der leicht angespannt reagiert.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.889	1.088	6	▂▇▁▇▁▆▁▂
bfi_neuro_4	Ich sehe mich selbst als jemanden, der sich viele Sorgen macht.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	4	5	3.660	1.115	6	▁▃▁▅▁▇▁▆
bfi_neuro_5r	Ich sehe mich selbst als jemanden, der nicht leicht aus der Fassung zu bringen ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.894	1.065	6	▂▇▁▇▁▆▁▂
bfi_neuro_8	Ich sehe mich selbst als jemanden, der launisch sein kann, schwankende Stimmungen hat.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	3.152	1.197	6	▂▆▁▇▁▇▁▃
bfi_neuro_6r	Ich sehe mich selbst als jemanden, der ruhig bleibt, selbst in angespannten Situationen ausgeglichen ist.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	2.909	1.071	6	▂▇▁▇▁▆▁▂
bfi_neuro_7	Ich sehe mich selbst als jemanden, der leicht nervös und unsicher wird.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	2.937	1.147	6	▃▇▁▇▁▆▁▃

Scale: pvd_germ_aversion

Overview

Reliability: ω_ordinal [95% CI] = 0.67 [0.65;0.7].

Missing: 93.

Likert plot of scale pvd_germ_aversion items

Distribution of scale pvd_germ_aversion

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	pvd_germ_aversion_2, pvd_germ_aversion_3R, pvd_germ_aversion_1 & pvd_germ_aversion_4R
Observations:	1419
Positive correlations:	6
Number of correlations:	6
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.59
Omega (hierarchical):	0.56
Revelle’s Omega (total):	0.61
Greatest Lower Bound (GLB):	0.60
Coefficient H:	0.62
Coefficient Alpha:	0.58

Confidence intervals


Omega (total):	[0.55; 0.62]
Coefficient Alpha	[0.54; 0.61]

Estimates assuming ordinal level


Ordinal Omega (total):	0.67
Ordinal Omega (hierarch.):	0.67
Ordinal Coefficient Alpha:	0.67

Confidence intervals


Ordinal Omega (total):	[0.65; 0.7]
Ordinal Coefficient Alpha	[0.64; 0.69]

Eigen values

1.808, 0.848, 0.677 & 0.667

Factor analysis (reproducing only shared variance)

	ML1
pvd_germ_aversion_2	0.556
pvd_germ_aversion_3R	0.566
pvd_germ_aversion_1	0.590
pvd_germ_aversion_4R	0.360

Component analysis (reproducing full covariance matrix)

	PC1
pvd_germ_aversion_2	0.702
pvd_germ_aversion_3R	0.707
pvd_germ_aversion_1	0.723
pvd_germ_aversion_4R	0.541

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
pvd_germ_aversion_2	2.83298097251586	3	2.03625986635138	1.42697577637162	2	0.0378813460981358	1	1	4	5	0.155639071211968	-1.32319145942006	0.113460183227625	1419	1419
pvd_germ_aversion_3R	1.60887949260042	1	1.10855297488945	1.05287842360334	1	0.0279503356848791	1	NA	2	5	1.86259484414591	2.66040258046728	0.0937279774489077	1419	1419
pvd_germ_aversion_1	1.70754052149401	1	1.00820121045135	1.00409223204412	1	0.0266552284813331	1	NA	2	5	1.47672565774217	1.61387940238512	0.121564482029598	1419	1419
pvd_germ_aversion_4R	2.7815362931642	3	1.50654178482433	1.22741263836753	2	0.0325836245640078	1	2	4	5	0.204282128432199	-0.975514775224748	0.115574348132488	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
pvd_germ_aversion_2	Ich mag es nicht mit einem Stift zu schreiben, an dem offensichtlich eine andere Person gekaut hat.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.842	1.426	6	▇▇▁▆▁▆▁▆
pvd_germ_aversion_3R	Es macht mir nichts aus, mit einem Freund eine Wasserflasche zu teilen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	1	5	1.616	1.061	6	▇▂▁▁▁▁▁▁
pvd_germ_aversion_1	Am liebsten wasche ich meine Hände direkt, nachdem ich jemandem die Hände geschüttelt habe.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	1	5	1.717	1.012	6	▇▃▁▂▁▁▁▁
pvd_germ_aversion_4R	Es macht mir keine Sorgen, von kranken Menschen umgeben zu sein.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	3	5	2.781	1.227	6	▅▇▁▆▁▆▁▂

Scale: pvd_infectability

Overview

Reliability: ω_ordinal [95% CI] = 0.87 [0.86;0.88].

Missing: 93.

Likert plot of scale pvd_infectability items

Distribution of scale pvd_infectability

Reliability details

Scale diagnosis

Reliability (internal consistency) estimates

Scale structure

Information about this scale


Dataframe:	res$dat
Items:	pvd_infectability_1, pvd_infectability_3R & pvd_infectability_2
Observations:	1419
Positive correlations:	3
Number of correlations:	3
Percentage positive correlations:	100

Estimates assuming interval level


Omega (total):	0.84
Omega (hierarchical):	0.00
Revelle’s Omega (total):	0.84
Greatest Lower Bound (GLB):	0.85
Coefficient H:	0.85
Coefficient Alpha:	0.84

Confidence intervals


Omega (total):	[0.83; 0.85]
Coefficient Alpha	[0.82; 0.85]

Estimates assuming ordinal level


Ordinal Omega (total):	0.87
Ordinal Omega (hierarch.):	0.87
Ordinal Coefficient Alpha:	0.87

Confidence intervals


Ordinal Omega (total):	[0.86; 0.88]
Ordinal Coefficient Alpha	[0.86; 0.88]

Eigen values

2.264, 0.413 & 0.323

Factor analysis (reproducing only shared variance)

	ML1
pvd_infectability_1	0.853
pvd_infectability_3R	0.762
pvd_infectability_2	0.770

Component analysis (reproducing full covariance matrix)

	PC1
pvd_infectability_1	0.888
pvd_infectability_3R	0.857
pvd_infectability_2	0.860

Item descriptives

	mean	median	var	sd	IQR	se	min	q1	q3	max	skew	kurt	dip	n	valid
pvd_infectability_1	2.23255813953488	2	1.37324105356381	1.17185368266	2	0.031108723542661	1	1	3	5	0.738848789551805	-0.346777885499556	0.162085976039464	1419	1419
pvd_infectability_3R	2.71035940803383	3	1.22846101318893	1.10835960463603	1	0.0294231720535361	1	2	4	5	0.238125425211416	-0.627024708541569	0.151162790697674	1419	1419
pvd_infectability_2	2.17970401691332	2	1.13199963024478	1.0639547124971	2	0.0282443734254036	1	1	3	5	0.724390880896979	-0.11118863126463	0.15292459478506	1419	1419

Scattermatrix

Scatterplot

Summary statistics

name	label	type	type_options	data_type	value_labels	item_order	n_missing	complete_rate	min	median	max	mean	sd	n_value_labels	hist
pvd_infectability_1	Ich bin generell sehr anfällig für Erkältungen, Grippen und andere Infektionen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	5	50	0.9669	1	2	5	2.235	1.168	6	▇▇▁▅▁▃▁▁
pvd_infectability_3R	Mein Immunsystem schützt mich vor den meisten Krankheiten, die andere Leute bekommen.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 4. 2, 3. 3, 2. 4, 1. 5: trifft völlig zu, NA. Item was never rendered for this user.	28	73	0.9517	1	3	5	2.712	1.106	6	▃▇▁▇▁▅▁▂
pvd_infectability_2	Wenn eine Krankheit „gerade umgeht“, bekomme ich sie bestimmt auch.	rating_button	5 agree	haven_labelled	1: trifft gar nicht zu, 2. 2, 3. 3, 4. 4, 5. 5: trifft völlig zu, NA. Item was never rendered for this user.	55	93	0.9385	1	2	5	2.180	1.064	6	▇▇▁▅▁▂▁▁

Scale: spms_partner

Overview

Reliability: ω_ordinal [95% CI] = 0.86 [0.84;0.87].

Missing: 570.

Likert plot of scale spms_partner items

Distribution of scale spms_partner

Codebook GOCD2

Demographics

Metadata

Description

Survey overview

info_study

Distribution

Summary statistics

Item

Value labels

lab_code

Distribution

Summary statistics

Item

Value labels

age

Distribution

Summary statistics

Item

Value labels

gender

Distribution

Summary statistics

Item

Value labels

gender_other

Distribution

Summary statistics

Item

Value labels

education_years

Distribution

Summary statistics

Item

Value labels

education_level

Distribution

Summary statistics

Item

Value labels

education_level_special

Distribution

Summary statistics

Item

Value labels

occupational_status

Distribution

Summary statistics

Item

Value labels

occupation

Distribution

Summary statistics

Item

Value labels

net_income

Distribution

Summary statistics

Item

Value labels

study_major

Distribution

Summary statistics

Item

Value labels

postal_code

Distribution

Summary statistics

Item

Value labels

religion

Distribution

Summary statistics

Item

Value labels

religiosity

Distribution

Summary statistics

Item

Value labels