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

Starting date times

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

Duration people took for answering 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

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

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

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

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

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

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

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

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

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

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

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

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’) &gt; -1 || occupational_status.indexOf(‘student’) &gt; -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

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

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

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

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

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

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

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

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 &gt; 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

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 &gt; 5 | relationship_count &gt; 3 21

Value labels

Response choices
name value

duration_relationship_years

Jahre

Distribution

Distribution of values for duration_relationship_years

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

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 &gt; 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

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

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 &gt; 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

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

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 &gt; 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

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

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 &gt; 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

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

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 &gt; 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

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

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 &gt; 1 31

Value labels

Response choices
name value

abode_alone

Wohnen Sie also alleine?

Distribution

Distribution of values for abode_alone

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

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 &amp; 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

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

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

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

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

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
&lt; 1 Std. 1
1 - 2 Std. 2
2-3 Std. 3
3 - 5 Std. 4
5 - 9 Std. 5
9 - 12 Std. 6
&amp;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

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

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 &gt; 1 36

Value labels

Response choices
name value
&lt; 3 Tage 1
3 - 4 Tage 2
5 - 6 Tage 3
7 - 14 Tage 4
14-21 Tage 5
21-29 Tage 6
&amp;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

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

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 &gt; 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

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

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 &gt; 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

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

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

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

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

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

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

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

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

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

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

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

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

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 &lt; 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

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

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

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

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 &lt; 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 &gt; 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

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

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

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

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

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

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

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

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

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

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? <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>

Distribution

Distribution of values for sport_kinds

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? &lt;small&gt;&lt;em&gt;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.&lt;/em&gt;&lt;/small&gt; 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

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

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

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

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

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

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

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 &gt; 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

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

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 &gt; 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

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

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

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

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

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 &lt;small&gt;(ich habe sie gefragt)&lt;/small&gt; 1
±0.5 Jahre 2
±1 Jahr 3
±2 Jahre 4
±3 Jahre 5
±4 Jahre 6
unsicherer &lt;small&gt;(ich habe geraten)&lt;/small&gt; 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

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

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 &gt; 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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. <small>optional</small>

Distribution

Distribution of values for free_not_covered

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. &lt;small&gt;optional&lt;/small&gt; 1 107

Value labels

Response choices
name value

hetero_relationship

Distribution

Distribution of values for hetero_relationship

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

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

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

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

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

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

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

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

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

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

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

Starting date times

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

Duration people took for answering 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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>

Distribution

Distribution of values for free_not_covered

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. &lt;small&gt;optional&lt;/small&gt; 1 160

Value labels

Response choices
name value

Scale: narq

Overview

Reliability: ωordinal [95% CI] = 0.86 [0.85;0.87].

Missing: 93.

Likert plot of scale narq items

Likert plot of scale narq items

Distribution of scale narq

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 NA valid
narq_5 3.69767441860465 4 0.971299242299997 0.985545149802888 1 0.0261628666254947 1 3 5 5 -0.441620454841714 -0.391039013913282 0.132840028188865 1419 0 1419
narq_3 2.72797744890768 3 1.08533095576754 1.04179218453948 1 0.0276560338012321 1 2 4 5 0.0590173018996267 -0.712225429009423 0.148343904157858 1419 0 1419
narq_15 2.74489076814658 3 1.23670695209384 1.11207326741265 2 0.0295217571502576 1 2 4 5 0.102532850953908 -0.787897328649723 0.140239605355884 1419 0 1419
narq_2 1.6814658210007 1 0.799733816003045 0.894278377242258 1 0.0237400446996593 1 NA 2 5 1.26743696571477 1.07344688038982 0.140944326990839 1419 0 1419
narq_11 2.95137420718816 3 1.07168181967277 1.03522066230962 2 0.0274815822708678 1 2 4 5 0.0629204773948583 -0.714527563706265 0.148343904157858 1419 0 1419
narq_18 3.14023960535588 3 0.889345781758942 0.943051314488741 1 0.025034800046351 1 2 4 5 -0.181440966587617 -0.222975211538846 0.146582100070472 1419 0 1419
narq_16 2.67441860465116 3 1.16472594876505 1.07922469799623 1 0.0286497395256445 1 2 4 5 0.141661818809007 -0.734389822854693 0.150105708245243 1419 0 1419
narq_1 2.97885835095137 3 1.12790250389883 1.0620275438513 2 0.0281932136624492 1 2 4 5 -0.0710697895635157 -0.453617785647023 0.11169837914024 1419 0 1419
narq_14 1.10852713178295 1 0.172980833360667 0.415909645669185 0 0.0110409843628939 1 NA 2 5 4.59713221965587 24.6656865134064 0.0260747004933051 1419 0 1419
narq_17 1.46723044397463 1 0.628509319918773 0.79278579699612 1 0.0210457624123505 1 NA 2 5 1.81281265549157 3.08135634069116 0.101127554615927 1419 0 1419
narq_4 2.02748414376321 2 1.08316510464967 1.04075218214985 2 0.0276284252803892 1 1 3 5 0.824629655574526 -0.127441250308918 0.171952078928823 1419 0 1419
narq_13 1.67512332628612 1 0.871108500294711 0.933331934680642 1 0.0247767836199573 1 NA 2 5 1.31816874619511 1.05068045797274 0.12015503875969 1419 0 1419
narq_6 2.54122621564482 2 1.57992825556049 1.25695197026795 3 0.0333677932049591 1 1 4 5 0.376042962170525 -0.962607258961765 0.126849894291755 1419 0 1419
narq_7 3.00070472163495 3 1.04301783870124 1.02128244805306 2 0.0271115701606515 1 2 4 5 -0.0769894738538039 -0.457500125874986 0.124383368569415 1419 0 1419
narq_12 1.75968992248062 2 0.830080580794 0.91108758129721 1 0.0241862718094557 1 1 3 5 1.1703057167437 0.890261941928853 0.167371388301621 1419 0 1419
narq_8 2.24242424242424 2 1.17532162243023 1.08412251264801 2 0.0287797598210278 1 1 3 5 0.465402337969955 -0.615637460736991 0.140591966173362 1419 0 1419
narq_9 2.05426356589147 2 1.27138343118925 1.12755639822993 2 0.0299327815326501 1 1 3 5 0.845004882000305 -0.225588617252515 0.140239605355884 1419 0 1419
narq_10 2.07117688513037 2 1.25938725994487 1.12222424672829 2 0.029791231073402 1 1 3 5 0.777093465856214 -0.434429447887318 0.145172656800564 1419 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 28 73 0.9517 1 3 5 2.678 1.0777 6 ▃▇▁▇▁▅▁▁
narq_1 Ich bin großartig. rating_button 5 agree haven_labelled
  1. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 2 5 2.071 1.1222 6 ▇▆▁▃▁▂▁▁

Scale: bfi_open

Overview

Reliability: ωordinal [95% CI] = 0.85 [0.84;0.86].

Missing: 93.

Likert plot of scale bfi_open items

Likert plot of scale bfi_open items

Distribution of scale bfi_open

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]

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

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 NA 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 3 5 3.058 1.1302 6 ▂▆▁▇▁▇▁▂

Scale: bfi_extra

Overview

Reliability: ωordinal [95% CI] = 0.9 [0.89;0.9].

Missing: 93.

Likert plot of scale bfi_extra items

Likert plot of scale bfi_extra items

Distribution of scale bfi_extra

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]

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

4.327, 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 NA 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 4 5 3.506 1.0926 6 ▁▃▁▆▁▇▁▅

Scale: bfi_agree

Overview

Reliability: ωordinal [95% CI] = 0.79 [0.77;0.81].

Missing: 93.

Likert plot of scale bfi_agree items

Likert plot of scale bfi_agree items

Distribution of scale bfi_agree

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]

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

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 NA 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 4 5 4.109 0.9609 6 ▁▁▁▃▁▇▁▇

Scale: soi_r_attitude

Overview

Reliability: ωordinal [95% CI] = 0.89 [0.88;0.9].

Missing: 50.

Likert plot of scale soi_r_attitude items

Likert plot of scale soi_r_attitude items

Distribution of scale soi_r_attitude

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]

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

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 NA 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 0 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 0 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 0 1462
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 5 50 0.9669 1 3 5 2.901 1.457 6 ▇▇▁▅▁▇▁▆

Scale: bfi_consc

Overview

Reliability: ωordinal [95% CI] = 0.85 [0.84;0.86].

Missing: 93.

Likert plot of scale bfi_consc items

Likert plot of scale bfi_consc items

Distribution of scale bfi_consc

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]

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

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 NA 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 3 5 3.125 1.1446 6 ▂▆▁▇▁▇▁▃

Scale: asendorpf_shyness

Overview

Reliability: ωordinal [95% CI] = 0.88 [0.87;0.89].

Missing: 93.

Likert plot of scale asendorpf_shyness items

Likert plot of scale asendorpf_shyness items

Distribution of scale asendorpf_shyness

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]

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

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 NA 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 0 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 3 5 2.724 1.146 6 ▅▇▁▇▁▅▁▂

Scale: bfi_neuro

Overview

Reliability: ωordinal [95% CI] = 0.87 [0.86;0.88].

Missing: 93.

Likert plot of scale bfi_neuro items

Likert plot of scale bfi_neuro items

Distribution of scale bfi_neuro

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]

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

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 NA 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 0 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 0 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 0 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 0 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 3 5 2.937 1.147 6 ▃▇▁▇▁▆▁▃

Scale: pvd_germ_aversion

Overview

Reliability: ωordinal [95% CI] = 0.67 [0.65;0.7].

Missing: 93.

Likert plot of scale pvd_germ_aversion items

Likert plot of scale pvd_germ_aversion items

Distribution of scale pvd_germ_aversion

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]

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

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 NA 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 0 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 3 5 2.781 1.227 6 ▅▇▁▆▁▆▁▂

Scale: pvd_infectability

Overview

Reliability: ωordinal [95% CI] = 0.87 [0.86;0.88].

Missing: 93.

Likert plot of scale pvd_infectability items

Likert plot of scale pvd_infectability items

Distribution of scale pvd_infectability

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]

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

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 NA 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 0 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 0 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 0 1419
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label type type_options data_type value_labels optional showif value 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    4. 2,
    3. 3,
    2. 4,
    1. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 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. 1: trifft gar nicht zu,
    2. 2,
    3. 3,
    4. 4,
    5. 5: trifft völlig zu,
    NA. Item was never rendered for this user.
0 55 93 0.9385 1 2 5 2.180 1.064 6 ▇▇▁▅▁▂▁▁

Scale: spms_partner

Overview

Reliability: ωordinal [95% CI] = 0.86 [0.84;0.87].

Missing: 570.

Likert plot of scale spms_partner items

Likert plot of scale spms_partner items

Distribution of scale spms_partner

Distribution of scale spms_partner

Reliability details

Scale diagnosis
Reliability (internal consisten