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 = future::value(future({
      reliabilities_diary <- codebook::compute_reliabilities(s3_daily, 'repeated_many')
      reliabilities_s2_initial <- codebook::compute_reliabilities(s2_initial)
      reliabilities_s4_followup <- codebook::compute_reliabilities(s4_followup)
      reliabilities <- list(
        diary = reliabilities_diary,
        s2_initial = reliabilities_s2_initial,
        s4_followup = reliabilities_s4_followup
      )
      save(reliabilities, file = "codings/reliabilities_new.rdata")
      reliabilities
  }))
  save(reliabilities, 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

How did this study come to your attention?

Distribution

Distribution of values for info_study

Distribution of values for info_study

21 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
info_study How did this study come to your attention? haven_labelled 21 0.9873 13 0 4 NA 43 0 6

Value labels

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

lab_code

Are you also taking part in our laboratory study Attraktivitätsbeurteilungen in Göttingen and have received a code for this study? If you have, please enter your personalized code here. If you haven’t you can leave it blank.

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 Are you also taking part in our laboratory study Attraktivitätsbeurteilungen in Göttingen and have received a code for this study? If you have, please enter your personalized code here. If you haven’t you can leave it blank. 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

Your age

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

Your sex

Distribution

Distribution of values for gender

Distribution of values for gender

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
gender Your sex haven_labelled 21 0.9873 1 1 2 1.002 0.0428 3 ▇▁▁▁▁▁▁▁

Value labels

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

gender_other

Other:

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 Other: 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

How many years of education have you had? (High school, studies at a university or community college, NOT training school)

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

What is the highest degree you’ve earned?

Distribution

Distribution of values for education_level

Distribution of values for education_level

21 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
education_level What is the highest degree you’ve earned? haven_labelled 21 0.9873 13 0 8 NA 29 0 13

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

Do you have a degree that doesn’t fit into one of these categories? If this is the case you can write your degree in here.

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 Do you have a degree that doesn’t fit into one of these categories? If this is the case you can write your degree in here. 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

What professional status do you have right now?

Distribution

Distribution of values for occupational_status

Distribution of values for occupational_status

21 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
occupational_status What professional status do you have right now? haven_labelled 21 0.9873 25 0 5 NA 36 0 7

Value labels

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

occupation

What profession do you practice?

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 What profession do you practice? 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

How much money do you have at your disposition each month (net)?

Distribution

Distribution of values for net_income

Distribution of values for net_income

21 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
net_income How much money do you have at your disposition each month (net)? haven_labelled 21 0.9873 6 0 9 NA 14 0 6

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

What are you currently studying or what did you study?

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 What are you currently studying or what did you study? 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

What are the first three digits of your zip code?

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 What are the first three digits of your zip code? 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

Which religion or belief system do you consider yourself a part of?

Distribution

Distribution of values for religion

Distribution of values for religion

21 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
religion Which religion or belief system do you consider yourself a part of? haven_labelled 21 0.9873 13 0 5 NA 13 0 7

Value labels

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

religiosity

How religious would you describe yourself?

Distribution

Distribution of values for religiosity

Distribution of values for religiosity

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
religiosity How religious would you describe yourself? haven_labelled 21 0.9873 1 2 6 2.223 1.347 7 ▇▅▁▂▂▁▁▁

Value labels

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

sex_orientation

What best describes your sexual orientation?

Distribution

Distribution of values for sex_orientation

Distribution of values for sex_orientation

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
sex_orientation What best describes your sexual orientation? haven_labelled 21 0.9873 1 1 8 1.367 0.794 9 ▇▃▁▁▁▁▁▁

Value labels

Response choices
name value
Solely heterosexual 1
Mostly heterosexual, only occasionally homosexual 2
Mostly heterosexual, but more than occasionally homosexual 3
In equal measure heterosexual and homosexual 4
Mostly homosexual, but more than occasionally heterosexual 5
Mostly homosexual, only occasionally heterosexual 6
Solely homosexual 7
asexual or aromantic 8
Item was never rendered for this user. NA

sex_orientation_special

If the above mentioned categories don’t suffice, you can specify here.

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 If the above mentioned categories don’t suffice, you can specify here. 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

What is your relationship status?

Distribution

Distribution of values for relationship_status

Distribution of values for relationship_status

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
relationship_status What is your relationship status? haven_labelled 21 0.9873 1 3 6 2.521 1.254 7 ▅▁▁▇▁▁▂▁

Value labels

Response choices
name value
single 1
loose relationship 2
committed relationship 3
engaged 4
married 5
other 6
Item was never rendered for this user. NA

relationship_count

Are you..

Distribution

Distribution of values for relationship_count

Distribution of values for relationship_count

549 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
relationship_count Are you.. haven_labelled 549 0.6693 1 1 4 1.171 0.5909 6 ▇▁▁▁▁▁▁▁

Missing value types

Plot of missing values for relationship_count

Plot of missing values for relationship_count

Value labels

Response choices
name value
monogamous 1
polygamous 2
in an open relationship 3
other 4
Item was not shown to this user. NA
Item was never rendered for this user. NA

relationship_details

You have selected a type of relationship which might not be concepted well in our questionnaire . Please describe your type of relationship here.

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 You have selected a type of relationship which might not be concepted well in our questionnaire . Please describe your type of relationship here. 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

Years

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

Months

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

What sex does your partner have?

Distribution

Distribution of values for partner_gender

Distribution of values for partner_gender

549 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
partner_gender What sex does your partner have? haven_labelled 549 0.6693 1 2 2 1.992 0.0897 5 ▁▁▁▁▁▁▁▇

Missing value types

Plot of missing values for partner_gender

Plot of missing values for partner_gender

Value labels

Response choices
name value
female 1
male 2
NA 3
Item was not shown to this user. NA
Item was never rendered for this user. NA

partner_gender_other

Other:

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 Other: 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

How old is your 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

Do you live with your partner?

Distribution

Distribution of values for abode_with_partner

Distribution of values for abode_with_partner

549 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
abode_with_partner Do you live with your partner? haven_labelled 549 0.6693 0 0 1 0.4572 0.4984 4 ▇▁▁▁▁▁▁▇

Missing value types

Plot of missing values for abode_with_partner

Plot of missing values for abode_with_partner

Value labels

Response choices
name value
No 0
Yes 1
Item was not shown to this user. NA
Item was never rendered for this user. NA

abode_flat_share

Do you live in a household with other persons (other than your partner)?

Distribution

Distribution of values for abode_flat_share

Distribution of values for abode_flat_share

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
abode_flat_share Do you live in a household with other persons (other than your partner)? haven_labelled 21 0.9873 1 1 3 1.843 0.942 4 ▇▁▁▁▁▁▁▆

Value labels

Response choices
name value
no 1
with parents/grandparents 2
yes 3
Item was never rendered for this user. NA

abode_flat_share_description

Who do you live with?

Please use this open XX to give us information about your roommates. It is easiest for us if you just indicate sex, age, and possibly relation to the person, e.g. as follows:

f, 55, mother f, 26, sister m, 30, brother

You don’t have to indicate yourself or your partner.

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 Who do you live with?

Please use this open XX to give us information about your roommates.
It is easiest for us if you just indicate sex, age, and possibly relation to the person, e.g. as follows:

f, 55, mother
f, 26, sister
m, 30, brother

You don’t have to indicate yourself or your partner.
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

Do you live by yourself?

Distribution

Distribution of values for abode_alone

Distribution of values for abode_alone

1455 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
abode_alone Do you live by yourself? haven_labelled 1455 0.1235 0 1 1 0.9171 0.2764 4 ▁▁▁▁▁▁▁▇

Missing value types

Plot of missing values for abode_alone

Plot of missing values for abode_alone

Value labels

Response choices
name value
No 0
Yes 1
Item was not shown to this user. NA
Item was never rendered for this user. NA

abode_special_case_description

Please specify your living situation further.

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 Please specify your living situation further. 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

Do you have a long distance relationship?

Distribution

Distribution of values for long_distance_relationship

Distribution of values for long_distance_relationship

1057 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
long_distance_relationship Do you have a long distance relationship? haven_labelled 1057 0.3633 0 0 1 0.4345 0.4961 4 ▇▁▁▁▁▁▁▆

Missing value types

Plot of missing values for long_distance_relationship

Plot of missing values for long_distance_relationship

Value labels

Response choices
name value
No 0
Yes 1
Item was not shown to this user. NA
Item was never rendered for this user. NA

distance_partner

How far away from your partner do you live?

Distribution

Distribution of values for distance_partner

Distribution of values for distance_partner

1397 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
distance_partner How far away from your partner do you live? haven_labelled 1397 0.1584 1 3 7 3.502 1.505 9 ▂▇▇▇▁▅▁▂

Missing value types

Plot of missing values for distance_partner

Plot of missing values for distance_partner

Value labels

Response choices
name value
less than an hour 1
1-2 hours 2
2-3 hours 3
3-5 hours 4
5-9 hours 5
9-12 hours 6
more than 12 hours 7
Item was not shown to this user. NA
Item was never rendered for this user. NA

days_same_place_partner_monthly

How may days a month do you spend at the same place as your 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

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
days_same_place_partner_monthly How may days a month do you spend at the same place as your partner? haven_labelled 549 0.6693 1 6 7 5.199 1.663 9 ▁▁▃▅▁▃▇▇

Missing value types

Plot of missing values for days_same_place_partner_monthly

Plot of missing values for days_same_place_partner_monthly

Value labels

Response choices
name value
less than three days 1
3-4 days 2
5-6 days 3
7-14 days 4
14-21 days 5
21-29 days 6
more than 29 days 7
Item was not shown to this user. NA
Item was never rendered for this user. NA

days_with_partner

How many days per week on average do you spend at the same place as your partner?

Distribution

Distribution of values for days_with_partner

Distribution of values for days_with_partner

549 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
days_with_partner How many days per week on average do you spend at the same place as your partner? haven_labelled 549 0.6693 0 5 7 4.5 2.234 10 ▁▂▅▃▂▂▅▇

Missing value types

Plot of missing values for days_with_partner

Plot of missing values for days_with_partner

Value labels

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

nights_with_partner

How many nights per week on average do you spend at the same place as your partner?

Distribution

Distribution of values for nights_with_partner

Distribution of values for nights_with_partner

549 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
nights_with_partner How many nights per week on average do you spend at the same place as your partner? haven_labelled 549 0.6693 0 4 7 4.223 2.37 10 ▂▃▃▃▂▂▃▇

Missing value types

Plot of missing values for nights_with_partner

Plot of missing values for nights_with_partner

Value labels

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

has_children

Do you have children?

Distribution

Distribution of values for has_children

Distribution of values for has_children

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
has_children Do you have children? haven_labelled 21 0.9873 0 0 1 0.1086 0.3112 3 ▇▁▁▁▁▁▁▁

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

nr_children

How many biological children do you have?

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

Is your current partner the father of your children?

Distribution

Distribution of values for partner_father

Distribution of values for partner_father

1482 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
partner_father Is your current partner the father of your children? haven_labelled 1482 0.1072 0 1 1 0.6404 0.4812 4 ▅▁▁▁▁▁▁▇

Missing value types

Plot of missing values for partner_father

Plot of missing values for partner_father

Value labels

Response choices
name value
No 0
Yes 1
Item was not shown to this user. NA
Item was never rendered for this user. NA

family_description_optional

You can add other information about your family relations, which aren’t included in our questionnaire, here (e.g. adopted children).

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 You can add other information about your family relations, which aren’t included in our questionnaire, here (e.g. adopted children). 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

Are you pregnant or have been in the last three months?

Distribution

Distribution of values for pregnant

Distribution of values for pregnant

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
pregnant Are you pregnant or have been in the last three months? haven_labelled 21 0.9873 0 0 1 0.014 0.1177 3 ▇▁▁▁▁▁▁▁

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

breast_feeding

Are you currently breastfeeding or have you breastfed in the last three months?

Distribution

Distribution of values for breast_feeding

Distribution of values for breast_feeding

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
breast_feeding Are you currently breastfeeding or have you breastfed in the last three months?
haven_labelled 21 0.9873 0 0 1 0.0171 0.1296 3 ▇▁▁▁▁▁▁▁

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

pregnant_stress

If you were to find out today that you are pregnant that would be…

Distribution

Distribution of values for pregnant_stress

Distribution of values for pregnant_stress

59 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
pregnant_stress If you were to find out today that you are pregnant that would be… haven_labelled 59 0.9645 1 2 6 2.659 1.483 8 ▇▇▁▅▅▁▂▁

Missing value types

Plot of missing values for pregnant_stress

Plot of missing values for pregnant_stress

Value labels

Response choices
name value
1: severly burdensome 1
2 2
3 3
4 4
5 5
6: very delightful 6
Item was not shown to this user. NA
Item was never rendered for this user. NA

pregnant_trying

Are you currently trying to fall pregnant?

Distribution

Distribution of values for pregnant_trying

Distribution of values for pregnant_trying

59 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
pregnant_trying Are you currently trying to fall pregnant?
haven_labelled 59 0.9645 1 1 6 1.278 0.8549 8 ▇▁▁▁▁▁▁▁

Missing value types

Plot of missing values for pregnant_trying

Plot of missing values for pregnant_trying

Value labels

Response choices
name value
1: I’m trying to avoid it. 1
2 2
3 3
4 4
5 5
6: I’m trying to conceive. 6
Item was not shown to this user. NA
Item was never rendered for this user. NA

wish_for_children

Why not?

Distribution

Distribution of values for wish_for_children

Distribution of values for wish_for_children

120 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
wish_for_children Why not? haven_labelled 120 0.9277 7 0 12 NA 29 0 7

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

Do you generally use contraception (e.g. the pill, condoms, rhythm method, pull-out method, etc.) when having sex?

Distribution

Distribution of values for contraception_at_all

Distribution of values for contraception_at_all

59 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
contraception_at_all Do you generally use contraception (e.g. the pill, condoms, rhythm method, pull-out method, etc.) when having sex? haven_labelled 59 0.9645 1 1 6 1.661 1.416 8 ▇▁▁▁▁▁▁▁

Missing value types

Plot of missing values for contraception_at_all

Plot of missing values for contraception_at_all

Value labels

Response choices
name value
yes 1
most of the time 2
ja, aber ich lasse es auch etwas “drauf ankommen” 3
nein, ich lasse es “drauf ankommen” 4
No, I don’t currently have sex at all. 5
No, I have other reasons. 6
Item was not shown to this user. NA
Item was never rendered for this user. NA

contraception_other_reasons_not_to

You can state other reasons here.

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 You can state other reasons here. 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

What kind of contraception are you currently using? You can name more than one method of contraception.

Distribution

Distribution of values for contraception_method

Distribution of values for contraception_method

273 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
contraception_method What kind of contraception are you currently using? You can name more than one method of contraception. haven_labelled 273 0.8355 70 0 10 NA 123 0 19

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

Which other method, that is not listed here, do you use?

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 Which other method, that is not listed here, do you use? 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

You have stated that you combine different contraceptives. What is your reason or are your reasons for doing so?

Distribution

Distribution of values for contraception_combi

Distribution of values for contraception_combi

1197 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
contraception_combi You have stated that you combine different contraceptives. What is your reason or are your reasons for doing so? haven_labelled 1197 0.2789 38 0 5 NA 123 0 7

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

Other reason:

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 Other reason: 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

You have stated that you use a form of contraception in which you determine your fertile window. How do you normally behave when in your fertile window?

Distribution

Distribution of values for contraception_calendar_abstinence

Distribution of values for contraception_calendar_abstinence

1520 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
contraception_calendar_abstinence You have stated that you use a form of contraception in which you determine your fertile window. How do you normally behave when in your fertile window? character 1520 0.0843 10 0 8 38 0

contraception_hormonal_pill

What is the name of your contraceptive pill? In case you are taking a “Replica with the identical active substances, generic drug…” and it’s missing in our list, you are welcome to state the trademark here.

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 What is the name of your contraceptive pill? In case you are taking a “Replica with the identical active substances, generic drug…” and it’s missing in our list, you are welcome to state the trademark here. haven_labelled 1086 0.3458 69 0 3 NA 19 0 97

other_pill_name

You have stated that your pill isn’t on this list. Please state the name of your pill here.

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 You have stated that your pill isn’t on this list. Please state the name of your pill here. 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

What hormonal contraceptive do you use?

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 What hormonal contraceptive do you use? 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

You have stated that your hormonal contraceptive isn’t on this list. Please state the name of your hormonal contraceptive here.

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 You have stated that your hormonal contraceptive isn’t on this list. Please state the name of your hormonal contraceptive here. 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

Please look up how many mircograms (µg) of oestrogen your pill contains by reading the package insert of your contraceptive pill. Most contraceptive pills contain between 0 and 50 micrograms of oestrogen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (µg).

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

Please look up what type of gestagen is used in your contraceptive pill by reading the package insert.

Distribution

Distribution of values for contraception_pill_gestagen_type

Distribution of values for contraception_pill_gestagen_type

1581 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
contraception_pill_gestagen_type Please look up what type of gestagen is used in your contraceptive pill by reading the package insert. haven_labelled 1581 0.0476 9 0 3 NA 14 0 12

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

Please look up how many mircograms (µg) of gestagen your pill contains by reading the package insert of your contraceptive pill. Most contraceptive pills contain between 50 and 3000 micrograms of oestrogen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (µg).

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

Please look up how many mircograms (µg) of oestrogen your contraceptive contains by reading the package insert of your contraceptive. Most contraceptives contain between 0 and 50 micrograms of oestrogen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (µg).

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

Please look up what type of gestagen is used in your contraceptive by reading the package insert.

Distribution

Distribution of values for contraception_other_gestagen_type

Distribution of values for contraception_other_gestagen_type

1581 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
contraception_other_gestagen_type Please look up what type of gestagen is used in your contraceptive by reading the package insert. haven_labelled 1581 0.0476 10 0 3 NA 14 0 12

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

Please look up how many mircograms (µg) of gestagen your contraceptive contains by reading the package insert of your contraceptive. Most contraceptives contain between 50 and 3000 micrograms of gestagen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (µg).

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

Do you use a cycle-app?

Distribution

Distribution of values for contraception_app

Distribution of values for contraception_app

36 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
contraception_app Do you use a cycle-app? haven_labelled 36 0.9783 0 0 1 0.2531 0.4349 3 ▇▁▁▁▁▁▁▃

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

contraception_app_name

What is this app called?

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 What is this app called? 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

Have you used hormonal contraception in the last three months?

Distribution

Distribution of values for hormonal_contraception_last3m

Distribution of values for hormonal_contraception_last3m

36 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
hormonal_contraception_last3m Have you used hormonal contraception in the last three months? haven_labelled 36 0.9783 0 0 1 0.431 0.4954 3 ▇▁▁▁▁▁▁▆

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

contraception_meeting_partner

Where you using hormonal contraception (e.g. contraceptive pill) when you met your partner?

Distribution

Distribution of values for contraception_meeting_partner

Distribution of values for contraception_meeting_partner

560 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
contraception_meeting_partner Where you using hormonal contraception (e.g. contraceptive pill) when you met your partner? haven_labelled 560 0.6627 0 0 1 0.4945 0.5002 4 ▇▁▁▁▁▁▁▇

Missing value types

Plot of missing values for contraception_meeting_partner

Plot of missing values for contraception_meeting_partner

Value labels

Response choices
name value
No 0
Yes 1
Item was not shown to this user. NA
Item was never rendered for this user. NA

self_rated_health

Please determine your general health at this moment in time.

Distribution

Distribution of values for self_rated_health

Distribution of values for self_rated_health

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
self_rated_health Please determine your general health at this moment in time. haven_labelled 53 0.9681 1 2 4 1.78 0.7117 6 ▆▁▇▁▁▂▁▁

Value labels

Response choices
name value
very good 1
good 2
partly good/ partly poor 3
poor 4
very poor 5
Item was never rendered for this user. NA

height

Please state your height in cm (without shoes).

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

Please state your weight in kg (without clothes).

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

Have you taken any hormonal medication (besides contraception) in the last three months?

Distribution

Distribution of values for hormonal_med

Distribution of values for hormonal_med

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
hormonal_med Have you taken any hormonal medication (besides contraception) in the last three months? haven_labelled 53 0.9681 0 0 1 0.0697 0.2547 3 ▇▁▁▁▁▁▁▁

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

hormonal_med_name

Which have you taken?

Distribution

Distribution of values for hormonal_med_name

Distribution of values for hormonal_med_name

1548 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
hormonal_med_name Which have you taken? haven_labelled 1548 0.0675 29 0 2 NA 24 0 4

Value labels

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

psychoactive_med

Do you take psychoactive medication (e.g. against depression)?

Distribution

Distribution of values for psychoactive_med

Distribution of values for psychoactive_med

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
psychoactive_med Do you take psychoactive medication (e.g. against depression)? haven_labelled 53 0.9681 0 0 1 0.0342 0.1819 3 ▇▁▁▁▁▁▁▁

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

psychoactive_med_name

Which do you take?

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 Which do you take? 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

How many glasses of alcohol do you averagely ingest in a week?

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

How many cigaretts do you averagely smoke in a day?

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

How many hours a week do you work out (causing you to sweat)?

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

Distribution

Distribution of values for sport_kinds

Distribution of values for sport_kinds

53 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
sport_kinds haven_labelled 53 0.9681 525 0 1 NA 122 3 7

Value labels

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

meat_eating

Do you eat meat or animal products?

Distribution

Distribution of values for meat_eating

Distribution of values for meat_eating

53 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
meat_eating Do you eat meat or animal products? haven_labelled 53 0.9681 7 0 7 NA 14 0 7

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

How old were you when you had your first period?

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

How sure are you about this statement?

Distribution

Distribution of values for menarche_certainty

Distribution of values for menarche_certainty

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menarche_certainty How sure are you about this statement? haven_labelled 53 0.9681 1 2 7 1.967 0.9227 8 ▇▆▆▁▁▁▁▁

Value labels

Response choices
name value
completely sure 1
±0.5 years 2
±1 year 3
±2 years 4
±3 years 5
±4 years 6
more doubtful 7
Item was never rendered for this user. NA

number_sexual_partner

With how many people have you had sexual intercourse with throughout your life?

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

How sure are you about this statement?

Distribution

Distribution of values for number_sexual_partner_certainty

Distribution of values for number_sexual_partner_certainty

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
number_sexual_partner_certainty How sure are you about this statement? haven_labelled 53 0.9681 1 1 8 1.646 1.225 9 ▇▂▁▁▁▁▁▁

Value labels

Response choices
name value
completely sure 1
NA 2
NA 3
NA 4
NA 5
NA 6
NA 7
more doubtful 8
Item was never rendered for this user. NA

first_time

How old were you when you had sex for the first time?

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

How sure are you about this statement?

Distribution

Distribution of values for first_time_certainty

Distribution of values for first_time_certainty

134 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
first_time_certainty How sure are you about this statement? haven_labelled 134 0.9193 1 1 7 1.227 0.5712 9 ▇▁▁▁▁▁▁▁

Missing value types

Plot of missing values for first_time_certainty

Plot of missing values for first_time_certainty

Value labels

Response choices
name value
completely sure 1
±0.5 years 2
±1 year 3
±2 years 4
±3 years 5
±4 years 6
more doubtful 7
Item was not shown to this user. NA
Item was never rendered for this user. NA

mother_menopause_yes

Has your mother reached her menopause?

Distribution

Distribution of values for mother_menopause_yes

Distribution of values for mother_menopause_yes

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
mother_menopause_yes Has your mother reached her menopause? haven_labelled 53 0.9681 1 2 4 1.772 0.8523 5 ▇▁▅▁▁▃▁▁

Value labels

Response choices
name value
Yes 1
No, she’s currently in her menopause. 2
No, not yet. 3
No, she died before she could have had it. 4
Item was never rendered for this user. NA

mother_menopause_age

How old was your mother when her menopause began? If you can’t name the point of time exactly, try to remember when it was that your mother expressed having typical menopausal symptoms (hot flashes etc.). Idealy try to ask your mother (via telephone), but you are free to carry on without doing this if it’s not possible at the moment.

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

How sure are you about this statement?

Distribution

Distribution of values for mother_menopause_certainty

Distribution of values for mother_menopause_certainty

895 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
mother_menopause_certainty How sure are you about this statement? haven_labelled 895 0.4608 1 4 7 4.546 1.889 9 ▂▂▆▇▁▃▂▇

Missing value types

Plot of missing values for mother_menopause_certainty

Plot of missing values for mother_menopause_certainty

Value labels

Response choices
name value
completely sure (I asked her) 1
±0.5 years 2
±1 year 3
±2 years 4
±3 years 5
±4 years 6
more doubtful (I guessed) 7
Item was not shown to this user. NA
Item was never rendered for this user. NA

menopause_yes

Have you reached menopause?

Distribution

Distribution of values for menopause_yes

Distribution of values for menopause_yes

1597 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menopause_yes Have you reached menopause? haven_labelled 1597 0.038 1 2 3 1.984 0.8518 5 ▇▁▁▆▁▁▁▇

Missing value types

Plot of missing values for menopause_yes

Plot of missing values for menopause_yes

Value labels

Response choices
name value
Yes 1
No, I’m currently in my menopause. 2
No, not yet. 3
Item was not shown to this user. NA
Item was never rendered for this user. NA

menopause_age

How old were you when your menopause began?

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

How sure are you about this statement?

Distribution

Distribution of values for menopause_certainty

Distribution of values for menopause_certainty

1637 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menopause_certainty How sure are you about this statement? haven_labelled 1637 0.0139 1 1 8 1.826 1.614 10 ▇▂▂▁▁▁▁▁

Missing value types

Plot of missing values for menopause_certainty

Plot of missing values for menopause_certainty

Value labels

Response choices
name value
completely sure 1
±0.5 years 2
±1 year 3
±2 years 4
±3 years 5
±4 years 6
±7 years 7
more doubtful 8
Item was not shown to this user. NA
Item was never rendered for this user. NA

menstruation_regular

Do you currently have a regular period (roughly every month)?

Distribution

Distribution of values for menstruation_regular

Distribution of values for menstruation_regular

53 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menstruation_regular Do you currently have a regular period (roughly every month)? haven_labelled 53 0.9681 0 1 1 0.8121 0.3908 3 ▂▁▁▁▁▁▁▇

Value labels

Response choices
name value
No 0
Yes 1
Item was never rendered for this user. NA

menstruation_last

Please state the first day (start) of your last period. If possible check the date in your calendar.

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 Please state the first day (start) of your last period. If possible check the date in your calendar. 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

How sure are you about this statement?

Distribution

Distribution of values for menstruation_last_certainty

Distribution of values for menstruation_last_certainty

355 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menstruation_last_certainty How sure are you about this statement? haven_labelled 355 0.7861 1 1 8 1.628 1.292 10 ▇▂▁▁▁▁▁▁

Missing value types

Plot of missing values for menstruation_last_certainty

Plot of missing values for menstruation_last_certainty

Value labels

Response choices
name value
completely sure 1
±1 day 2
±2 days 3
±3 days 4
±4 days 5
±5 days 6
±6 days 7
more doubtful 8
Item was not shown to this user. NA
Item was never rendered for this user. NA

menstruation_length

How long does your menstrual cycle last (amout of days between the start of one period to the next; mostly between 25-35 days)?

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

How sure are you about this statement?

Distribution

Distribution of values for menstruation_length_certainty

Distribution of values for menstruation_length_certainty

355 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menstruation_length_certainty How sure are you about this statement? haven_labelled 355 0.7861 1 2 8 2.607 1.508 10 ▆▇▇▃▁▁▁▁

Missing value types

Plot of missing values for menstruation_length_certainty

Plot of missing values for menstruation_length_certainty

Value labels

Response choices
name value
completely sure 1
±1 day 2
±2 days 3
±3 days 4
±4 days 5
±5 days 6
±6 days 7
more doubtful 8
Item was not shown to this user. NA
Item was never rendered for this user. NA

menstruation_regularity

How regular has your period been in the last three months?

Distribution

Distribution of values for menstruation_regularity

Distribution of values for menstruation_regularity

355 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menstruation_regularity How regular has your period been in the last three months? haven_labelled 355 0.7861 1 2 4 2.134 0.7791 6 ▃▁▇▁▁▃▁▁

Missing value types

Plot of missing values for menstruation_regularity

Plot of missing values for menstruation_regularity

Value labels

Response choices
name value
completely (no fluctuations) 1
Very (max. 1-2 days fluctuation) 2
Somewhat (3-5 days fluctuation) 3
Hardly (more than 5 days fluctuation) 4
Item was not shown to this user. NA
Item was never rendered for this user. NA

menstruation_regularity_certainty

How sure are you about this statement?

Distribution

Distribution of values for menstruation_regularity_certainty

Distribution of values for menstruation_regularity_certainty

355 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
menstruation_regularity_certainty How sure are you about this statement? haven_labelled 355 0.7861 1 1 8 1.566 1.083 10 ▇▃▁▁▁▁▁▁

Missing value types

Plot of missing values for menstruation_regularity_certainty

Plot of missing values for menstruation_regularity_certainty

Value labels

Response choices
name value
completely sure 1
±1 days 2
±2 days 3
±3 days 4
±4 days 5
±5 days 6
±6 days 7
more doubtful 8
Item was not shown to this user. NA
Item was never rendered for this user. NA

free_not_covered

In case you were unable to answer some questions or only able to answer the incorrectly or apart from this you are under the impression that we have missed something important, please state this here.

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 In case you were unable to answer some questions or only able to answer the incorrectly or apart from this you are under the impression that we have missed something important, please state this here. 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 label data_type n_missing complete_rate min median max mean sd hist
hetero_relationship numeric 36 0.9783 0 1 1 0.6718 0.4697 ▃▁▁▁▇

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": "How did this study come to your attention?",
      "value": "1. Invitation by the study team,\n2. Posters at the University of Goettingen,\n3. private contact,\n4. www.psytests.de,\n5. media,\n6. other",
      "maxValue": "6",
      "minValue": "1",
      "@type": "propertyValue"
    },
    {
      "name": "lab_code",
      "description": "Are you also taking part in our laboratory study _Attraktivitätsbeurteilungen_ in Göttingen and have received a code for this study? If you have, please enter your personalized code here. If you haven't you can leave it blank.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Your age",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Your sex",
      "value": "1. female,\n2. anderes,\nNA. Item was never rendered for this user.",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "gender_other",
      "description": "Other:",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "education_years",
      "description": "How many years of education have you had? (High school, studies at a university or community college, NOT training school)",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "education_level",
      "description": "What is the highest degree you've earned?",
      "value": "0_not_finished. without a school-leaving qualification,\n1_hauptschule. basic school certificate (9 years of schooling),\n2_realschule. secondary school certificate (10 years of schooling),\n3_polytechnic. graduation of polytechnical secondary school,\n4_professional_school. vocational qualification,\n5_fachabitur. advanced technical college certificate,\n6_abitur. higher education entrance qualification,\n7_vocational_uni_bachelor. polytechnic bachelors degree,\n8_uni_bachelor. bachelors degree,\n9_vocational_uni_master_level. polytechnic master's degree/diploma,\n10_uni_master_level. master's degree/diploma,\n11_uni_doctorate. Phd/doctoral degree,\n12_uni_habilitation. postdoctoral lecture qualification/habilitation",
      "maxValue": "9_vocational_uni_master_level",
      "minValue": "0_not_finished",
      "@type": "propertyValue"
    },
    {
      "name": "education_level_special",
      "description": "Do you have a degree that doesn't fit into one of these categories? If this is the case you can write your degree in here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "occupational_status",
      "description": "What professional status do you have right now?",
      "value": "not_working. unemployed,\npupil. schoolar,\ntrainee. apprentice,\nstudent. student,\nhomemaker. Haus- und Familienarbeit ,\nemployed. employed,\nintern. intern",
      "maxValue": "trainee",
      "minValue": "employed",
      "@type": "propertyValue"
    },
    {
      "name": "occupation",
      "description": "What profession do you practice?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "net_income",
      "description": "How much money do you have at your disposition each month (net)?",
      "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. I do not want to state",
      "maxValue": "euro_lt_500",
      "minValue": "dont_tell",
      "@type": "propertyValue"
    },
    {
      "name": "study_major",
      "description": "What are you currently studying or what did you study?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "postal_code",
      "description": "What are the first three digits of your zip code?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "religion",
      "description": "Which religion or belief system do you consider yourself a part of?",
      "value": "1. Christentum ,\n2. Islam,\n3. Buddhism,\n4. Hinduism,\n5. Judentum ,\n6. andere ,\n7. not religious",
      "maxValue": "7",
      "minValue": "1",
      "@type": "propertyValue"
    },
    {
      "name": "religiosity",
      "description": "How religious would you describe yourself?",
      "value": "1. 1: non-religious,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6: religious,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "sex_orientation",
      "description": "What best describes your sexual orientation?",
      "value": "1. Solely heterosexual,\n2. Mostly heterosexual, only occasionally homosexual,\n3. Mostly heterosexual, but more than occasionally homosexual,\n4. In equal measure heterosexual and homosexual,\n5. Mostly homosexual, but more than occasionally heterosexual,\n6. Mostly homosexual, only occasionally heterosexual,\n7. Solely homosexual,\n8. asexual or aromantic,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "sex_orientation_special",
      "description": "If the above mentioned categories don't suffice, you can specify here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "relationship_status",
      "description": "What is your relationship status?",
      "value": "1. single,\n2. loose relationship,\n3. committed relationship,\n4. engaged,\n5. married,\n6. other,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "relationship_count",
      "description": "Are you..",
      "value": "1. monogamous,\n2. polygamous,\n3. in an open relationship,\n4. other,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "relationship_details",
      "description": "You have selected a type of relationship which might not be concepted well in our questionnaire . Please describe your type of relationship here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "duration_relationship_years",
      "description": "Years",
      "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": "Months",
      "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": "What sex does your partner have?",
      "value": "1. female,\n2. male,\n3. NA,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 3,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "partner_gender_other",
      "description": "Other:",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "age_partner",
      "description": "How old is your 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": "Do you live with your partner?",
      "value": "0. No,\n1. Yes,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "abode_flat_share",
      "description": "Do you live in a household with other persons (other than your partner)?",
      "value": "1. no,\n2. with parents/grandparents,\n3. yes,\nNA. Item was never rendered for this user.",
      "maxValue": 3,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "abode_flat_share_description",
      "description": "Who do you live with? \n\nPlease use this open XX to give us information about your roommates. \nIt is easiest for us if you just indicate sex, age, and possibly relation to the person, e.g. as follows:\n\nf, 55, mother\nf, 26, sister\nm, 30, brother\n\nYou don't have to indicate yourself or your partner.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "abode_alone",
      "description": "Do you live by yourself?",
      "value": "0. No,\n1. Yes,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "abode_special_case_description",
      "description": "Please specify your living situation further.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "long_distance_relationship",
      "description": "Do you have a long distance relationship?",
      "value": "0. No,\n1. Yes,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "distance_partner",
      "description": "How far away from your partner do you live?",
      "value": "1. less than an hour,\n2. 1-2 hours,\n3. 2-3 hours,\n4. 3-5 hours,\n5. 5-9 hours,\n6. 9-12 hours,\n7. more than 12 hours,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "days_same_place_partner_monthly",
      "description": "How may days a month do you spend at the same place as your partner?",
      "value": "1. less than three days,\n2. 3-4 days,\n3. 5-6 days,\n4. 7-14 days,\n5. 14-21 days,\n6. 21-29 days,\n7. more than 29 days,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "days_with_partner",
      "description": "How many days per week on average do you spend at the same place as your partner?",
      "value": "0. 0: days,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7: days,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "nights_with_partner",
      "description": "How many nights per week on average do you spend at the same place as your partner?",
      "value": "0. 0: nights,\n1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6,\n7. 7: nights,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "has_children",
      "description": "Do you have children?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "nr_children",
      "description": "How many biological children do you have?",
      "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": "Is your current partner the father of your children?",
      "value": "0. No,\n1. Yes,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "family_description_optional",
      "description": "You can add other information about your family relations, which aren't included in our questionnaire, here (e.g. adopted children).",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "pregnant",
      "description": "Are you pregnant or have been in the last three months?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "breast_feeding",
      "description": "Are you currently breastfeeding or have you breastfed in the last three months?\n",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "pregnant_stress",
      "description": "If you were to find out today that you are pregnant that would be...",
      "value": "1. 1: severly burdensome,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6: very delightful,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "pregnant_trying",
      "description": "Are you currently trying to fall pregnant?\n",
      "value": "1. 1: I'm trying to avoid it.,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6: I'm trying to conceive.,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "wish_for_children",
      "description": "Why not?",
      "value": "not_actively_trying. I can imagine falling pregnant in the near future, but I am currently not trying.,\nnot_in_current_life_situation. `r ifelse(relationship_status == 1, 'I can imagine becoming pregnant, but rather not in my current life situation.', 'I can imagine conceiving a child with my current partner, but rather not in my current life situation.'),\nnot_with_this_partner. `r ifelse(relationship_status == 1, \"\", 'I can imagine becoming pregnant, but not with my current partner.'),\ndont_have_partner. `r ifelse(relationship_status == 1, 'I could imagine becoming pregnant, but I don't have a partner who would come into question.') ,\npartner_doesnt_want. `r ifelse(relationship_status == 1, \"\", 'I can imagine becoming pregnant, but my partner doesn't want to.'),\nrather_adopt. I want childern, but I rather want to adopt/foster children.,\ncant_imagine_having_kids. I can imagine ever having children.",
      "maxValue": "rather_adopt",
      "minValue": "cant_imagine_having_kids",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_at_all",
      "description": "Do you generally use contraception (e.g. the pill, condoms, rhythm method, pull-out method, etc.) when having sex?",
      "value": "1. yes,\n2. most of the time,\n3. ja, aber ich lasse es auch etwas \"drauf ankommen\",\n4. nein, ich lasse es \"drauf ankommen\",\n5. No, I don't currently have sex at all.,\n6. No, I have other reasons.,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_reasons_not_to",
      "description": "You can state other reasons here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method",
      "description": "What kind of contraception are you currently using? You can name more than one method of contraception.",
      "value": "hormonal_pill. Oral contraceptives (pill),\nhormonal_other. other hormonal contraceptive (depot injections, hormonal implant, vaginal ring, hormone plasters, hormonal intrauterine device),\nbarrier_condoms. condoms,\nbarrier_other. Other barrier methods (diaphragm, female condom, cervical cap),\nbarrier_intrauterine_pessar. copper spiral or chain (IUD),\nbarrier_coitus_interruptus. coitus interruptus,\nbarrier_no_penetrative_sex. waive penatrative sexual intercourse\n,\nawareness_calendar. calender method,\nawareness_temperature_billings. temperature- / Billings ovulation-/ symptothermal method,\nawareness_computer. fertility monitor,\nbarrier_spermicide. spermicide,\nbarrier_chemical. chemical contraception,\nhormonal_morning_after_pill. \"Pille danach\",\nbreast_feeding. breastfeeding,\ninfertile. I am infertile,\npartner_infertile. My partner is infertile,\nsterilised. I have been sterilized,\npartner_sterilised. My partner has been sterilized,\nnot_listed. Other",
      "maxValue": "sterilised",
      "minValue": "awareness_calendar",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method_other",
      "description": "Which other method, that is not listed here, do you use?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_combi",
      "description": "You have stated that you combine different contraceptives. What is your reason or are your reasons for doing so?",
      "value": "multiple_to_decrease_infection_risk. Always more than one (contraception + prevent sexually transmitted diseases),\nmultiple_to_decrease_conception_risk. Always more than one (increase contraception safety),\nfallback_if_forgotten. fall back on second method, if my parter or I forget to take/ buy the first (e.g. pill+condoms, or condoms+coitus interruptus),\nfallback_if_fertile. fall back on second method, when I am fertile (e.g. calendermethod + condoms or coitus interruptus while in my fertile window),\nbarrier_if_partner_sick. one method of contraception, others to prevent non-sexual infection (e.g. pill + abstinence, if partner is currently ill),\ndifferent_methods_for_different_partners. one method for certain partners, other method for other partners (e.g. pill when having sex with a acquainted/trustworthy person, condoms when having sex with less acquainted/well-known person),\nother. other reason",
      "maxValue": "other",
      "minValue": "barrier_if_partner_sick",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_method_combination_other",
      "description": "Other reason:",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_calendar_abstinence",
      "description": "You have stated that you use a form of contraception in which you determine your fertile window. How do you normally behave when in your fertile window?",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_pill",
      "description": "What is the name of your contraceptive pill?  In case you are taking a \"Replica with the identical active substances, generic drug...\" and it's missing in our list, you are welcome to state the trademark here.",
      "value": "other. My pill is not in the list.,\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": "You have stated that your pill isn't on this list. Please state the name of your pill here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_other",
      "description": "What hormonal contraceptive do you use?",
      "value": "nuvaring. NuvaRing,\ncirclet. Circlet,\nevra. Evra,\nmirena. Mirena,\ndepo_provera. Depo Provera,\ndepo_clinovir. Depo Clinovir,\nimplanon. Implanon NXT,\nother. My contraceptive is not in this list.",
      "maxValue": "other",
      "minValue": "circlet",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_hormonal_other_name",
      "description": "You have stated that your hormonal contraceptive isn't on this list. Please state the name of your hormonal contraceptive here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_pill_estrogen",
      "description": "Please look up how many mircograms (*µ*g) of oestrogen your pill contains by reading the package insert of your contraceptive pill. Most contraceptive pills contain between 0 and 50 micrograms of oestrogen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (*µ*g).",
      "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": "Please look up what type of gestagen is used in your contraceptive pill by reading the package insert.",
      "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. Not in this list.",
      "maxValue": "other_gestagen",
      "minValue": "CMA",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_pill_gestagen",
      "description": "Please look up how many mircograms (*µ*g) of gestagen your pill contains by reading the package insert of your contraceptive pill. Most contraceptive pills contain between 50 and 3000 micrograms of oestrogen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (*µ*g).",
      "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": "Please look up how many mircograms (*µ*g) of oestrogen your contraceptive contains by reading the package insert of your contraceptive. Most contraceptives contain between 0 and 50 micrograms of oestrogen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (*µ*g).",
      "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": "Please look up what type of gestagen is used in your contraceptive by reading the package insert.",
      "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. Not in this list.",
      "maxValue": "other_gestagen",
      "minValue": "CMA",
      "@type": "propertyValue"
    },
    {
      "name": "contraception_other_gestagen",
      "description": "Please look up how many mircograms (*µ*g) of gestagen your contraceptive contains by reading the package insert of your contraceptive. Most contraceptives contain between 50 and 3000 micrograms of gestagen. In some instances the weight indication is made in milligrams (mg). You have to multiply this number by 1000 in order to recieve the weight indication in micrograms (*µ*g).",
      "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": "Do you use a cycle-app?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "contraception_app_name",
      "description": "What is this app called?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_contraception_last3m",
      "description": "Have you used hormonal contraception in the last three months?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "contraception_meeting_partner",
      "description": "Where you using hormonal contraception (e.g. contraceptive pill) when you met your partner?",
      "value": "0. No,\n1. Yes,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "self_rated_health",
      "description": "Please determine your general health at this moment in time.",
      "value": "1. very good,\n2. good,\n3. partly good/ partly poor,\n4. poor,\n5. very poor,\nNA. Item was never rendered for this user.",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "height",
      "description": "Please state your height in cm (without shoes).",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "weight",
      "description": "Please state your weight in kg (without clothes).",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_med",
      "description": "Have you taken any hormonal medication (besides contraception) in the last three months?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "hormonal_med_name",
      "description": "Which have you taken?",
      "value": "1. insulin,\n2. thyroxine,\n3. escitalopram,\n4. monk's pepper",
      "maxValue": "4",
      "minValue": "1",
      "@type": "propertyValue"
    },
    {
      "name": "psychoactive_med",
      "description": "Do you take psychoactive medication (e.g. against depression)?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "psychoactive_med_name",
      "description": "Which do you take?",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "alcohol_weekly",
      "description": "How many glasses of alcohol do you averagely ingest in a week?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "cigarettes_daily",
      "description": "How many cigaretts do you averagely smoke in a day?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "sport_weekly",
      "description": "How many hours a week do you work out (causing you to sweat)?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "sport_kinds",
      "description": "",
      "value": "1. running,\n2. swimming,\n3. fitness-/strenght training,\n4. aerobic (e.g. step aerobic, zumba),\n5. climbing,\n6. yoga,\n7. teamsports (volleyball, soccer...)",
      "maxValue": "7",
      "minValue": "1",
      "@type": "propertyValue"
    },
    {
      "name": "meat_eating",
      "description": "Do you eat meat or animal products?",
      "value": "1_every_day. yes, a great deal,\n2_frequently. yes, frequently,\n3_rarely. yes, seldomly,\n4_only_poultry. only poultry,\n5_only_fish. only fish,\n6_vegetarian. no, vegetarian,\n7_vegan. no, vegan",
      "maxValue": "7_vegan",
      "minValue": "1_every_day",
      "@type": "propertyValue"
    },
    {
      "name": "menarche",
      "description": "How old were you when you had your first period?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menarche_certainty",
      "description": "How sure are you about this statement?",
      "value": "1. completely sure\n,\n2. ±0.5 years,\n3. ±1 year,\n4. ±2 years,\n5. ±3 years,\n6. ±4 years,\n7. more doubtful\n,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "number_sexual_partner",
      "description": "With how many people have you had sexual intercourse with throughout your life?",
      "value": "NA. Item was never rendered for this user.",
      "maxValue": "-Inf",
      "minValue": "Inf",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "number_sexual_partner_certainty",
      "description": "How sure are you about this statement?",
      "value": "1. completely sure\n,\n2. NA,\n3. NA,\n4. NA,\n5. NA,\n6. NA,\n7. NA,\n8. more doubtful\n,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "first_time",
      "description": "How old were you when you had sex for the first time?",
      "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": "How sure are you about this statement?",
      "value": "1. completely sure\n,\n2. ±0.5 years,\n3. ±1 year,\n4. ±2 years,\n5. ±3 years,\n6. ±4 years,\n7. more doubtful\n,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "mother_menopause_yes",
      "description": "Has your mother reached her menopause?",
      "value": "1. Yes,\n2. No, she's currently in her menopause.,\n3. No, not yet.,\n4. No, she died before she could have had it.,\nNA. Item was never rendered for this user.",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "mother_menopause_age",
      "description": "How old was your mother when her menopause began? *If you can't name the point of time exactly, try to remember when it was that your mother expressed having typical menopausal symptoms (hot flashes etc.). Idealy try to ask your mother (via telephone), but you are free to carry on without doing this if it's not possible at the moment.*",
      "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": "How sure are you about this statement?",
      "value": "1. completely sure (I asked her),\n2. ±0.5 years,\n3. ±1 year,\n4. ±2 years,\n5. ±3 years,\n6. ±4 years,\n7. more doubtful (I guessed),\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "menopause_yes",
      "description": "Have you reached menopause?",
      "value": "1. Yes,\n2. No, I'm currently in my menopause.,\n3. No, not yet.,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 3,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "menopause_age",
      "description": "How old were you when your menopause began?",
      "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": "How sure are you about this statement?",
      "value": "1. completely sure,\n2. ±0.5 years,\n3. ±1 year,\n4. ±2 years,\n5. ±3 years,\n6. ±4 years,\n7. ±7 years,\n8. more doubtful,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_regular",
      "description": "Do you currently have a regular period (roughly every month)?",
      "value": "0. No,\n1. Yes,\nNA. Item was never rendered for this user.",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_last",
      "description": "Please state the first day (start) of your last period. If possible check the date in your calendar.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_last_certainty",
      "description": "How sure are you about this statement?",
      "value": "1. completely sure,\n2. ±1 day,\n3. ±2 days,\n4. ±3 days,\n5. ±4 days,\n6. ±5 days,\n7. ±6 days,\n8. more doubtful,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_length",
      "description": "How long does your menstrual cycle last (amout of days between the start of one period to the next; mostly between 25-35 days)?",
      "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": "How sure are you about this statement?",
      "value": "1. completely sure,\n2. ±1 day,\n3. ±2 days,\n4. ±3 days,\n5. ±4 days,\n6. ±5 days,\n7. ±6 days,\n8. more doubtful,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_regularity",
      "description": "How regular has your period been in the last three months?",
      "value": "1. completely (no fluctuations),\n2. Very (max. 1-2 days fluctuation),\n3. Somewhat (3-5 days fluctuation)\n,\n4. Hardly (more than 5 days fluctuation),\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 4,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "menstruation_regularity_certainty",
      "description": "How sure are you about this statement?",
      "value": "1. completely sure,\n2. ±1 days,\n3. ±2 days,\n4. ±3 days,\n5. ±4 days,\n6. ±5 days,\n7. ±6 days,\n8. more doubtful,\nNA. Item was not shown to this user.,\nNA. Item was never rendered for this user.",
      "maxValue": 8,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "free_not_covered",
      "description": "In case you were unable to answer some questions or only able to answer the incorrectly or apart from this you are under the impression that we have missed something important, please state this here.",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "hetero_relationship",
      "description": "",
      "@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

I feel safe in my neighbourhood when I am out and about alone at night.

Distribution

Distribution of values for feel_safe_walking_dark

Distribution of values for feel_safe_walking_dark

93 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
feel_safe_walking_dark I feel safe in my neighbourhood when I am out and about alone at night. haven_labelled 93 0.9385 1 4 5 3.429 1.21 6 ▂▅▁▅▁▇▁▅

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was never rendered for this user. NA

feel_safe_violent_crime

I feel safe from violent crimes in my neighbourhood.

Distribution

Distribution of values for feel_safe_violent_crime

Distribution of values for feel_safe_violent_crime

93 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
feel_safe_violent_crime I feel safe from violent crimes in my neighbourhood. haven_labelled 93 0.9385 1 4 5 3.82 1.042 6 ▁▂▁▃▁▇▁▆

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was never rendered for this user. NA

feel_safe_sexual_assault

I feel safe from sexual assaults in my neighbourhood.

Distribution

Distribution of values for feel_safe_sexual_assault

Distribution of values for feel_safe_sexual_assault

93 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
feel_safe_sexual_assault I feel safe from sexual assaults in my neighbourhood. haven_labelled 93 0.9385 1 4 5 3.858 1.032 6 ▁▂▁▃▁▇▁▆

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was never rendered for this user. NA

feel_safe_theft

I feel safe from theft in my neighbourhood.

Distribution

Distribution of values for feel_safe_theft

Distribution of values for feel_safe_theft

93 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
feel_safe_theft I feel safe from theft in my neighbourhood. haven_labelled 93 0.9385 1 4 5 3.692 1.081 6 ▁▂▁▅▁▇▁▅

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was never rendered for this user. NA

satisfaction_sexual_intercourse

The sex with my partner is very satisfying.

Distribution

Distribution of values for satisfaction_sexual_intercourse

Distribution of values for satisfaction_sexual_intercourse

570 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
satisfaction_sexual_intercourse The sex with my partner is very satisfying. haven_labelled 570 0.623 1 4 5 3.996 1.064 7 ▁▁▁▃▁▆▁▇

Missing value types

Plot of missing values for satisfaction_sexual_intercourse

Plot of missing values for satisfaction_sexual_intercourse

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

satisfaction_single_life

I am happy with my single life.

Distribution

Distribution of values for satisfaction_single_life

Distribution of values for satisfaction_single_life

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
satisfaction_single_life I am happy with my single life. haven_labelled 1041 0.3115 1 3 5 3.076 1.164 7 ▂▇▁▇▁▇▁▃

Missing value types

Plot of missing values for satisfaction_single_life

Plot of missing values for satisfaction_single_life

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

investment_potential_partner

I invest a lot in order to get to know someone.

Distribution

Distribution of values for investment_potential_partner

Distribution of values for investment_potential_partner

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
investment_potential_partner I invest a lot in order to get to know someone. haven_labelled 1041 0.3115 1 2 5 2.501 1.139 7 ▆▇▁▆▁▅▁▁

Missing value types

Plot of missing values for investment_potential_partner

Plot of missing values for investment_potential_partner

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

timeperiod_potential_partner

I am currently looking for the following kind of partnership.

Distribution

Distribution of values for timeperiod_potential_partner

Distribution of values for timeperiod_potential_partner

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
timeperiod_potential_partner I am currently looking for the following kind of partnership. haven_labelled 1041 0.3115 4 0 4 NA 11 0 4

Value labels

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

characteristics_potential_partner

A partner that I would some to question should have following traits.

Distribution

Distribution of values for characteristics_potential_partner

Distribution of values for characteristics_potential_partner

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
characteristics_potential_partner A partner that I would some to question should have following traits. haven_labelled 1041 0.3115 101 0 5 NA 95 0 9

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

There are many acceptable singles in my surrounding area.

Distribution

Distribution of values for quantity_potential_partner

Distribution of values for quantity_potential_partner

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
quantity_potential_partner There are many acceptable singles in my surrounding area. haven_labelled 1041 0.3115 1 3 5 2.639 1.144 7 ▅▇▁▆▁▅▁▂

Missing value types

Plot of missing values for quantity_potential_partner

Plot of missing values for quantity_potential_partner

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

sexual_partner

If I am sexually active then usually with men…

Distribution

Distribution of values for sexual_partner

Distribution of values for sexual_partner

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
sexual_partner If I am sexually active then usually with men… haven_labelled 1041 0.3115 4 0 3 NA 10 0 4

Value labels

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

fling_frequency

In a month I averagely have sexual contact with another person about this often.

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

In a month I averagley have the possibility of having sexual contact with another person about this often.

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

The relationship with my partner is very important to me.

Distribution

Distribution of values for relationship_importance

Distribution of values for relationship_importance

570 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
relationship_importance The relationship with my partner is very important to me. haven_labelled 570 0.623 1 5 5 4.634 0.7117 7 ▁▁▁▁▁▂▁▇

Missing value types

Plot of missing values for relationship_importance

Plot of missing values for relationship_importance

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

relationship_importance_partner

The relationship with me is very important to my partner.

Distribution

Distribution of values for relationship_importance_partner

Distribution of values for relationship_importance_partner

570 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
relationship_importance_partner The relationship with me is very important to my partner. haven_labelled 570 0.623 1 5 5 4.624 0.7149 7 ▁▁▁▁▁▂▁▇

Missing value types

Plot of missing values for relationship_importance_partner

Plot of missing values for relationship_importance_partner

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 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

I feel very secure when I am with my partner.

Distribution

Distribution of values for attractiveness_warmth

Distribution of values for attractiveness_warmth

570 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
attractiveness_warmth I feel very secure when I am with my partner. haven_labelled 570 0.623 1 5 5 4.54 0.7891 7 ▁▁▁▁▁▂▁▇

Missing value types

Plot of missing values for attractiveness_warmth

Plot of missing values for attractiveness_warmth

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 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

What amount of money does your partner have at his disposal every month (netto)?

Distribution

Distribution of values for net_income_partner

Distribution of values for net_income_partner

570 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
net_income_partner What amount of money does your partner have at his disposal every month (netto)? haven_labelled 570 0.623 7 0 9 NA 14 0 7

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

My partner is physically stronger than a lot of other men.

Distribution

Distribution of values for partner_strength

Distribution of values for partner_strength

570 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
partner_strength My partner is physically stronger than a lot of other men. haven_labelled 570 0.623 1 3 5 3.074 1.202 7 ▃▆▁▇▁▇▁▃

Missing value types

Plot of missing values for partner_strength

Plot of missing values for partner_strength

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

partner_feel_safe

My partner makes my feel very safe.

Distribution

Distribution of values for partner_feel_safe

Distribution of values for partner_feel_safe

570 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
partner_feel_safe My partner makes my feel very safe. haven_labelled 570 0.623 1 5 5 4.487 0.8294 7 ▁▁▁▁▁▃▁▇

Missing value types

Plot of missing values for partner_feel_safe

Plot of missing values for partner_feel_safe

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

meet_potential_partner

I do the following in order to get to know a potential partner.

Distribution

Distribution of values for meet_potential_partner

Distribution of values for meet_potential_partner

1041 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min median max whitespace n_value_labels
meet_potential_partner I do the following in order to get to know a potential partner. haven_labelled 1041 0.3115 56 0 5 NA 62 0 9

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

What is the height of your partner (in cm)?

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

Other:

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 Other: 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

What is the weight of your partner in kg (without clothes)?

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

With how many different people have you had sexual intercourse in the last 12 months?

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

With how many different people have you had sexualy intercourse with only once in your life?

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

How many different people have you had sexual intercourse with, without being interested in having a long-term relationship with that person?

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

There are many problems in my relationship.

Distribution

Distribution of values for relationship_problems

Distribution of values for relationship_problems

572 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
relationship_problems There are many problems in my relationship. haven_labelled 572 0.6217 1 2 5 2.137 1.16 7 ▇▇▁▃▁▂▁▁

Missing value types

Plot of missing values for relationship_problems

Plot of missing values for relationship_problems

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

relationship_conflict

There are often quarrels/disagreements in my partnership.

Distribution

Distribution of values for relationship_conflict

Distribution of values for relationship_conflict

572 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd n_value_labels hist
relationship_conflict There are often quarrels/disagreements in my partnership. haven_labelled 572 0.6217 1 2 5 2.311 1.153 7 ▇▇▁▅▁▃▁▁

Missing value types

Plot of missing values for relationship_conflict

Plot of missing values for relationship_conflict

Value labels

Response choices
name value
1: does not apply at all 1
2 2
3 3
4 4
5: fully applies 5
Item was not shown to this user. NA
Item was never rendered for this user. NA

free_not_covered

In case you were unable to answer some questions or only able to answer the incorrectly or apart from this you are under the impression that we have missed something important, please state this here. <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 In case you were unable to answer some questions or only able to answer the incorrectly or apart from this you are under the impression that we have missed something important, please state this here. <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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
narq_5 I enjoy my successes very much. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 3.687 0.9881 6 ▁▂▁▆▁▇▁▅
narq_3 I show other how special I am. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 2.728 1.0411 6 ▃▇▁▇▁▅▁▁
narq_15 Being a very special person gives me a lot of strength. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 2.742 1.1093 6 ▃▇▁▇▁▆▁▂
narq_2 I will someday be famous. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 1 5 1.681 0.8954 6 ▇▅▁▂▁▁▁▁
narq_11 I often get annoyed when I am criticized. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 2.949 1.0344 6 ▂▇▁▇▁▆▁▂
narq_18 Mostly, I am very adept at dealing with other people. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 3.143 0.9427 6 ▁▃▁▇▁▆▁▁
narq_16 I manage to be the center of attention with my outstanding contributions. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 2.678 1.0777 6 ▃▇▁▇▁▅▁▁
narq_1 I am great. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 2.978 1.0576 6 ▂▃▁▇▁▅▁▂
narq_14 Other people are worth nothing. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 1 5 1.109 0.4168 6 ▇▁▁▁▁▁▁▁
narq_17 Most people are somehow losers. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 1 5 1.467 0.7928 6 ▇▂▁▁▁▁▁▁
narq_4 I react annoyed if another person steals the show from me. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 2 5 2.027 1.0408 6 ▇▇▁▃▁▂▁▁
narq_13 Most people won’t achieve anything. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 1 5 1.675 0.9333 6 ▇▃▁▂▁▁▁▁
narq_6 I secretly take pleasure in the failures of my rivals. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 2 5 2.541 1.2570 6 ▇▇▁▆▁▅▁▂
narq_7 Most of the time I am able to draw people’s attention to myself in conversations. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 3 5 3.001 1.0213 6 ▂▅▁▇▁▅▁▂
narq_12 I can barely stand it if another person is at the center of events. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 2 5 1.760 0.9111 6 ▇▆▁▂▁▁▁▁
narq_8 I deserve to be seen as a great personality. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 2 5 2.242 1.0841 6 ▇▇▁▇▁▂▁▁
narq_9 I want my rivals to fail. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 2 5 2.054 1.1276 6 ▇▆▁▃▁▂▁▁
narq_10 I enjoy it when another person is inferior to me. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
bfi_open_1 I see myself as someone who is original, comes up with new ideas. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 3.406 1.0195 6 ▁▃▁▆▁▇▁▃
bfi_open_2 I see myself as someone who is curious about many different things. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 4.246 0.8279 6 ▁▁▁▂▁▇▁▇
bfi_open_3 I see myself as someone who is ingenious, a deep thinker. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 5 5 4.293 0.8522 6 ▁▁▁▂▁▅▁▇
bfi_open_4 I see myself as someone who has an active imagination. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 4 5 3.998 1.0148 6 ▁▂▁▃▁▇▁▇
bfi_open_5 I see myself as someone who is inventive. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 4 5 3.491 1.0089 6 ▁▃▁▇▁▇▁▃
bfi_open_6 I see myself as someone who values artistic, aesthetic experiences. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 4 5 3.972 1.0658 6 ▁▂▁▃▁▇▁▇
bfi_open_7r I see myself as someone who preferes work that is routine. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 3 5 3.395 1.0881 6 ▁▃▁▇▁▇▁▃
bfi_open_8 I see myself as someone who likes to reflect, play with ideas. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 4 5 3.956 0.9561 6 ▁▂▁▃▁▇▁▆
bfi_open_9r I see myself as someone who has few artistic interests. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 4 5 3.999 1.1394 6 ▁▂▁▂▁▅▁▇
bfi_open_10 I see myself as someone who is sophisticated in art, music and/or literature. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
bfi_extra_1 I see myself as someone who is talkative. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 3.803 1.0278 6 ▁▂▁▅▁▇▁▆
bfi_extra_3 I see myself as someone who is full of energy. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 3.427 0.9308 6 ▁▂▁▇▁▇▁▂
bfi_extra_2r I see myself as someone who is reserved. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 3.276 1.1523 6 ▂▆▁▆▁▇▁▃
bfi_extra_4 I see myself as someone who generates a lot of enthusiasm. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 4 5 3.706 1.0140 6 ▁▂▁▅▁▇▁▅
bfi_extra_5r I see myself as someone who tends to be quiet. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 4 5 3.769 1.1299 6 ▁▃▁▅▁▇▁▇
bfi_extra_6 I see myself as someone who has an assertive personality. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 3.360 1.0313 6 ▁▃▁▇▁▇▁▃
bfi_extra_7r I see myself as someone who is sometimes shy, inhibited. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 2 5 2.684 1.2292 6 ▅▇▁▅▁▅▁▂
bfi_extra_8 I see myself as someone who is outgoing, sociable. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
bfi_agree_2 I see myself as someone who is helpful and unselfish with others. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 3.897 0.8435 6 ▁▁▁▃▁▇▁▃
bfi_agree_3r I see myself as someone who starts quarrels with others. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 5 5 4.282 0.9002 6 ▁▁▁▂▁▅▁▇
bfi_agree_1r I see myself as someone who tends to find fault with others. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 3.096 1.0858 6 ▂▆▁▇▁▇▁▂
bfi_agree_4 I see myself as someone who has a forgiving nature. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 3.311 1.1652 6 ▂▅▁▆▁▇▁▃
bfi_agree_5 I see myself as someone who is generally trusting. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 4 5 3.857 0.9915 6 ▁▂▁▃▁▇▁▅
bfi_agree_6r I see myself as someone who can be cold and aloof. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 3.003 1.2897 6 ▅▇▁▇▁▇▁▅
bfi_agree_7 I see myself as someone who is considerate and kind to almost everyone. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 4 5 4.247 0.7914 6 ▁▁▁▂▁▇▁▇
bfi_agree_8r I see myself as someone who is sometimes rude to others. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
93 0.9385 1 3 5 3.273 1.2767 6 ▃▆▁▆▁▇▁▆
bfi_agree_9 I see myself as someone who likes to cooperate with others. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
soi_r_attitude_6r I do not want to have sex with a person until I am sure that we will have a long-term, serious relationship. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 3.527 1.405 6 ▃▃▁▃▁▆▁▇
soi_r_attitude_4 Sex without love is OK. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 4 5 3.565 1.371 6 ▂▃▁▃▁▅▁▇
soi_r_attitude_5 I can imagine myself being comfortable and enjoying “casual” sex with different partners. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 I see myself as someone who can be somewhat careless. NA NA haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 50 0.9669 1 4 5 3.559 1.0744 6 ▁▃▁▅▁▇▁▅
bfi_consc_3 I see myself as someone who is a reliable worker. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 50 0.9669 1 4 5 4.198 0.8225 6 ▁▁▁▂▁▇▁▇
bfi_consc_1 I see myself as someone who does a thorough job. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 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 I see myself as someone who tends to be disorganized. NA NA haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 73 0.9517 1 3 5 2.942 1.1755 6 ▃▇▁▇▁▇▁▃
bfi_consc_5 I see myself as someone who perserveres until the task is finished. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 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 I see myself as someone who does things efficiently. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 93 0.9385 1 4 5 3.790 0.9447 6 ▁▂▁▅▁▇▁▅
bfi_consc_8r I see myself as someone who is easily distracted. NA NA haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
asendorpf_shyness_4r It is easy for me to get in touch with strangers. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 2.672 1.196 6 ▅▇▁▆▁▅▁▂
asendorpf_shyness_2 I feel inhibited when I am with other people. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 3 5 2.672 1.055 6 ▃▇▁▇▁▅▁▁
asendorpf_shyness_5 I feel uneasy at parties and in large groups. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 2 5 2.643 1.257 6 ▆▇▁▆▁▅▁▂
asendorpf_shyness_3r I easily approach others. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 2 5 2.596 1.117 6 ▃▇▁▆▁▅▁▁
asendorpf_shyness_1 I feel shy in the presence of others. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 I see myself as someone who is relaxed, handles stress well. NA NA haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 50 0.9669 1 3 5 3.222 1.111 6 ▂▆▁▇▁▇▁▃
bfi_neuro_1 I see myself as someone who is depressed, blue. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 50 0.9669 1 2 5 2.269 1.123 6 ▇▇▁▆▁▃▁▁
bfi_neuro_3 I see myself as someone who an be tense. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 73 0.9517 1 3 5 2.889 1.088 6 ▂▇▁▇▁▆▁▂
bfi_neuro_4 I see myself as someone who worries a lot. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 73 0.9517 1 4 5 3.660 1.115 6 ▁▃▁▅▁▇▁▆
bfi_neuro_5r I see myself as someone who is emotionally stable, not easily upset. NA NA haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 73 0.9517 1 3 5 2.894 1.065 6 ▂▇▁▇▁▆▁▂
bfi_neuro_8 I see myself as someone who gets nervous easily. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
NA NA NA NA 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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
pvd_germ_aversion_2 I don’t like using a pencil which another person has obviously chewed on. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 2.842 1.426 6 ▇▇▁▆▁▆▁▆
pvd_germ_aversion_3R I don’t mind sharing a water bottle with a friend. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 1 5 1.616 1.061 6 ▇▂▁▁▁▁▁▁
pvd_germ_aversion_1 I preferably wash my hands directly after shaking someones hand. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 1 5 1.717 1.012 6 ▇▃▁▂▁▁▁▁
pvd_germ_aversion_4R I don’t worry when I’m surrounded by people who are ill.  haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
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 data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
pvd_infectability_1 I am generally very prone to colds, flus and other infections. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
50 0.9669 1 2 5 2.235 1.168 6 ▇▇▁▅▁▃▁▁
pvd_infectability_3R My immune system protects me from most illnesses which other people get. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
73 0.9517 1 3 5 2.712 1.106 6 ▃▇▁▇▁▅▁▂
pvd_infectability_2 If there is an illness “going around” I’ll probably catch it too. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
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 consistency) estimates
Scale structure
Information about this scale
Dataframe: res$dat
Items: spms_partner_1, spms_partner_2 & spms_partner_3R
Observations: 942
Positive correlations: 3
Number of correlations: 3
Percentage positive correlations: 100
Estimates assuming interval level
Omega (total): 0.82
Omega (hierarchical): 0.04
Revelle’s Omega (total): 0.83
Greatest Lower Bound (GLB): 0.84
Coefficient H: 0.84
Coefficient Alpha: 0.82

Confidence intervals

Omega (total): [0.8; 0.84]
Coefficient Alpha [0.8; 0.84]
Estimates assuming ordinal level
Ordinal Omega (total): 0.86
Ordinal Omega (hierarch.): 0.85
Ordinal Coefficient Alpha: 0.85

Confidence intervals

Ordinal Omega (total): [0.84; 0.87]
Ordinal Coefficient Alpha [0.84; 0.87]

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

2.211, 0.473 & 0.316

Factor analysis (reproducing only shared variance)
ML1
spms_partner_1 0.782
spms_partner_2 0.865
spms_partner_3R 0.691
Component analysis (reproducing full covariance matrix)
PC1
spms_partner_1 0.863
spms_partner_2 0.888
spms_partner_3R 0.823
Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
spms_partner_1 3.64012738853503 4 0.980874797782546 0.990391234706036 1 0.0322686901318675 1 3 5 5 -0.375584802582975 -0.383073123912641 0.150743099787686 942 0 942
spms_partner_2 3.38428874734607 3 0.963748643422659 0.981707004876026 1 0.0319857426343549 1 2 4 5 -0.133924268400622 -0.401762661093455 0.154458598726115 942 0 942
spms_partner_3R 3.25796178343949 3 1.19055822170479 1.09112704196385 1 0.0355508400900614 1 2 4 5 -0.20508575201181 -0.605903805394255 0.14968152866242 942 0 942
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
spms_partner_1 Women take notice of my partner. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
570 0.623 1 4 5 3.640 0.9904 7 ▁▂▁▆▁▇▁▅
spms_partner_2 Women feel attracted towards my partner. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
570 0.623 1 3 5 3.384 0.9817 7 ▁▃▁▇▁▆▁▃
spms_partner_3R My partner seldomly recieves compliments from women. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
570 0.623 1 3 5 3.258 1.0911 7 ▂▅▁▇▁▇▁▃

Scale: spms_self

Overview

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

Missing: 99.

Likert plot of scale spms_self items

Likert plot of scale spms_self items

Distribution of scale spms_self

Distribution of scale spms_self

Reliability details

Scale diagnosis
Reliability (internal consistency) estimates
Scale structure
Information about this scale
Dataframe: res$dat
Items: spms_self_1, spms_self_2 & spms_self_3R
Observations: 1413
Positive correlations: 3
Number of correlations: 3
Percentage positive correlations: 100
Estimates assuming interval level
Omega (total): 0.85
Omega (hierarchical): 0.04
Revelle’s Omega (total): 0.86
Greatest Lower Bound (GLB): 0.87
Coefficient H: 0.87
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): 0.89
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

2.329, 0.4 & 0.271

Factor analysis (reproducing only shared variance)
ML1
spms_self_1 0.821
spms_self_2 0.881
spms_self_3R 0.746
Component analysis (reproducing full covariance matrix)
PC1
spms_self_1 0.885
spms_self_2 0.903
spms_self_3R 0.854
Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
spms_self_1 3.6135881104034 4 1.06021183305967 1.02966588418752 1 0.0273920945851273 1 3 5 5 -0.604890632650613 -0.0677967009074301 0.128096249115357 1413 0 1413
spms_self_2 3.4069355980184 3 1.03896036199676 1.01929405080024 1 0.0271161737786514 1 2 4 5 -0.371789608268124 -0.331087106000438 0.159235668789809 1413 0 1413
spms_self_3R 3.47487615003539 4 1.33453123465032 1.15521912841258 1 0.0307321745024066 1 3 5 5 -0.551497243292905 -0.486209335381379 0.114295824486907 1413 0 1413
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
spms_self_1 Men notice me. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
99 0.9345 1 4 5 3.614 1.030 6 ▁▂▁▅▁▇▁▃
spms_self_2 Men feel attracted towards me. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was never rendered for this user.
99 0.9345 1 3 5 3.407 1.019 6 ▁▃▁▇▁▇▁▃
spms_self_3R I seldomly recieve compliments from men. haven_labelled
  1. 1: does not apply at all,
    4. 2,
    3. 3,
    2. 4,
    1. 5: fully applies,
    NA. Item was never rendered for this user.
99 0.9345 1 4 5 3.475 1.155 6 ▂▃▁▅▁▇▁▃

Scale: relationship_satisfaction

Overview

Reliability: ωordinal [95% CI] = 0.91 [0.9;0.92].

Missing: 572.

Likert plot of scale relationship_satisfaction items

Likert plot of scale relationship_satisfaction items

Distribution of scale relationship_satisfaction

Distribution of scale relationship_satisfaction

Reliability details

Scale diagnosis
Reliability (internal consistency) estimates
Scale structure
Information about this scale
Dataframe: res$dat
Items: relationship_problems_R, relationship_satisfaction_overall, relationship_conflict_R, relationship_satisfaction_2 & relationship_satisfaction_3
Observations: 940
Positive correlations: 10
Number of correlations: 10
Percentage positive correlations: 100
Estimates assuming interval level
Omega (total): 0.87
Omega (hierarchical): 0.83
Revelle’s Omega (total): 0.92
Greatest Lower Bound (GLB): 0.93
Coefficient H: 0.92
Coefficient Alpha: 0.87

Confidence intervals

Omega (total): [0.86; 0.88]
Coefficient Alpha [0.86; 0.89]
Estimates assuming ordinal level
Ordinal Omega (total): 0.91
Ordinal Omega (hierarch.): 0.89
Ordinal Coefficient Alpha: 0.91

Confidence intervals

Ordinal Omega (total): [0.9; 0.92]
Ordinal Coefficient Alpha [0.9; 0.92]

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

3.394, 0.775, 0.358, 0.288 & 0.185

Factor analysis (reproducing only shared variance)
ML1
relationship_problems_R 0.743
relationship_satisfaction_overall 0.902
relationship_conflict_R 0.543
relationship_satisfaction_2 0.758
relationship_satisfaction_3 0.883
Component analysis (reproducing full covariance matrix)
PC1
relationship_problems_R 0.846
relationship_satisfaction_overall 0.888
relationship_conflict_R 0.687
relationship_satisfaction_2 0.804
relationship_satisfaction_3 0.878
Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
relationship_problems_R 3.86276595744681 4 1.34536401332336 1.15989827714475 2 0.0378317104033922 1 3 5 5 -0.900347569518517 0.000429579103411686 0.162765957446809 940 0 940
relationship_satisfaction_overall 4.31170212765957 5 0.97089592821698 0.985340513841271 1 0.0321382639347783 1 4 NA 5 -1.49931275549617 1.71392745421107 0.122872340425532 940 0 940
relationship_conflict_R 3.68936170212766 4 1.32832121088528 1.15252818225208 2 0.0375913244134142 1 3 5 5 -0.636781319254214 -0.429155240165173 0.143085106382979 940 0 940
relationship_satisfaction_2 3.97127659574468 4 1.10141050914282 1.04948106659569 2 0.0342302980939217 1 3 5 5 -1.0007097688853 0.451406982452771 0.182446808510638 940 0 940
relationship_satisfaction_3 4.23191489361702 4.5 0.898234880928104 0.947752542031992 1 0.0309122794737616 1 4 5 5 -1.21421240117257 0.951124684814117 0.15531914893617 940 0 940
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
relationship_problems_R Es gibt viele Probleme in meiner Beziehung. 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 not shown to this user.,
    NA. Item was never rendered for this user.
0 s1_demo\(hetero_relationship == 1 </td> <td style="text-align:left;"> </td> <td style="text-align:left;"> 119 </td> <td style="text-align:right;"> 572 </td> <td style="text-align:right;"> 0.6217 </td> <td style="text-align:left;"> 1 </td> <td style="text-align:left;"> 4.0 </td> <td style="text-align:left;"> 5 </td> <td style="text-align:right;"> 3.863 </td> <td style="text-align:right;"> 1.1599 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> ▁▂▁▃▁▇▁▇ </td> </tr> <tr> <td style="text-align:left;"> relationship_satisfaction_overall </td> <td style="text-align:left;"> All in all I am happy with my relationship. </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> haven_labelled </td> <td style="text-align:left;"> 1. 1: does not apply at all,<br>2. 2,<br>3. 3,<br>4. 4,<br>5. 5: fully applies,<br>NA. Item was not shown to this user.,<br>NA. Item was never rendered for this user. </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:left;"> NA </td> <td style="text-align:right;"> 572 </td> <td style="text-align:right;"> 0.6217 </td> <td style="text-align:left;"> 1 </td> <td style="text-align:left;"> 5.0 </td> <td style="text-align:left;"> 5 </td> <td style="text-align:right;"> 4.312 </td> <td style="text-align:right;"> 0.9853 </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> ▁▁▁▂▁▃▁▇ </td> </tr> <tr> <td style="text-align:left;"> relationship_conflict_R </td> <td style="text-align:left;"> Es gibt oft Streit/Meinungsverschiedenheiten in meiner Partnerschaft. </td> <td style="text-align:left;"> rating_button </td> <td style="text-align:left;"> 5 agree </td> <td style="text-align:left;"> haven_labelled </td> <td style="text-align:left;"> 1. 1: trifft gar nicht zu,<br>2. 2,<br>3. 3,<br>4. 4,<br>5. 5: trifft völlig zu,<br>NA. Item was not shown to this user.,<br>NA. Item was never rendered for this user. </td> <td style="text-align:left;"> 0 </td> <td style="text-align:left;"> s1_demo\)hetero_relationship == 1 119 572 0.6217 1 4.0 5 3.689 1.1525 7 ▁▃▁▅▁▇▁▇
relationship_satisfaction_2 My needs (e.g. intimacy, quality time, …) are met in our relationship. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
NA NA NA NA 572 0.6217 1 4.0 5 3.971 1.0495 7 ▁▂▁▃▁▇▁▇
relationship_satisfaction_3 Our relationship makes me very happy. NA NA haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
NA NA NA NA 572 0.6217 1 4.5 5 4.232 0.9478 7 ▁▁▁▂▁▅▁▇

Scale: alternatives

Overview

Reliability: ωordinal [95% CI] = 0.8 [0.78;0.82].

Missing: 572.

Likert plot of scale alternatives items

Likert plot of scale alternatives items

Distribution of scale alternatives

Distribution of scale alternatives

Reliability details

Scale diagnosis
Reliability (internal consistency) estimates
Scale structure
Information about this scale
Dataframe: res$dat
Items: alternatives_1, alternatives_2, alternatives_3, alternatives_4, alternatives_5 & alternatives_6
Observations: 940
Positive correlations: 15
Number of correlations: 15
Percentage positive correlations: 100
Estimates assuming interval level
Omega (total): 0.77
Omega (hierarchical): 0.70
Revelle’s Omega (total): 0.83
Greatest Lower Bound (GLB): 0.77
Coefficient H: 0.79
Coefficient Alpha: 0.76

Confidence intervals

Omega (total): [0.74; 0.79]
Coefficient Alpha [0.74; 0.79]
Estimates assuming ordinal level
Ordinal Omega (total): 0.8
Ordinal Omega (hierarch.): 0.8
Ordinal Coefficient Alpha: 0.8

Confidence intervals

Ordinal Omega (total): [0.78; 0.82]
Ordinal Coefficient Alpha [0.78; 0.82]

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

2.76, 0.851, 0.778, 0.582, 0.569 & 0.461

Factor analysis (reproducing only shared variance)
ML1
alternatives_1 0.747
alternatives_2 0.590
alternatives_3 0.518
alternatives_4 0.562
alternatives_5 0.658
alternatives_6 0.473
Component analysis (reproducing full covariance matrix)
PC1
alternatives_1 0.782
alternatives_2 0.677
alternatives_3 0.608
alternatives_4 0.662
alternatives_5 0.736
alternatives_6 0.584
Item descriptives
mean median var sd IQR se min q1 q3 max skew kurt dip n NA valid
alternatives_1 2.84042553191489 3 1.70826818933678 1.30700734096514 2 0.0426298790099248 1 2 4 5 0.145178500616055 -1.13053745866452 0.107446808510638 940 0 940
alternatives_2 2.37340425531915 2 1.47170937846963 1.21314029628466 2 0.0395682735909426 1 1 3 5 0.592976300318238 -0.600252158334668 0.14468085106383 940 0 940
alternatives_3 2.27872340425532 2 1.56014320349852 1.24905692564371 2 0.0407397448719609 1 1 4 5 0.690425659216733 -0.633597346881776 0.148936170212766 940 0 940
alternatives_4 2.21595744680851 2 1.24724242630231 1.11680008340898 2 0.0364260023198053 1 1 3 5 0.775968210024259 -0.100470206751446 0.152659574468085 940 0 940
alternatives_5 3.47340425531915 4 1.58502141254843 1.25897633518205 2 0.0410632803374597 1 2 5 5 -0.43560043704709 -0.836934296914847 0.128723404255319 940 0 940
alternatives_6 2.68085106382979 3 1.60410577119163 1.2665329728008 2 0.0413097506802896 1 2 4 5 0.250420452627273 -0.946842805704418 0.118617021276596 940 0 940
Scattermatrix
Scatterplot

Scatterplot

Summary statistics

name label data_type value_labels n_missing complete_rate min median max mean sd n_value_labels hist
alternatives_1 I think other people besides my partner, that I could have a relationship with, are very attractive. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
572 0.6217 1 3 5 2.840 1.307 7 ▆▇▁▆▁▆▁▃
alternatives_2 Alternatives to our relationship (e.g. other relationship, spending time with friends, spending time alone…) are very appealing to me. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
572 0.6217 1 2 5 2.373 1.213 7 ▇▇▁▆▁▃▁▂
alternatives_3 I flirt with men without them knowing that I am in a relationship. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
572 0.6217 1 2 5 2.279 1.249 7 ▇▇▁▃▁▃▁▂
alternatives_4 My needs (e.g. intimacy, quality time, …) can easily be fulfilled in another relationship. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
572 0.6217 1 2 5 2.216 1.117 7 ▇▇▁▅▁▂▁▁
alternatives_5 I am very aware of the fact that there are many other attractive partners “out there” for me. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
572 0.6217 1 4 5 3.473 1.259 7 ▂▅▁▆▁▇▁▇
alternatives_6 It would be easy for me to find a partner for a new relationship. haven_labelled
  1. 1: does not apply at all,
    2. 2,
    3. 3,
    4. 4,
    5. 5: fully applies,
    NA. Item was not shown to this user.,
    NA. Item was never rendered for this user.
572 0.6217 1 3 5 2.681 1.266 7 ▆▇▁▇▁▅▁▃

Scale: investment

Overview

Reliability: ωordinal [95% CI] = 0.67 [0.61;0.73].

Missing: 572.

Likert plot of scale investment items

Likert plot of scale investment items

Distribution of scale investment

Distribution of scale investment

Reliability details

Scale diagnosis
Reliability (internal consistency) estimates
Scale structure
Information about this scale
Dataframe: res$dat
Items: investment_1, investment_2 & investment_3
Observations: 940
Positive correlations: 3
Number of correlations: 3
Percentage positive correlations: 100
Estimates assuming interval level
Omega (total): 0.65
Omega (hierarchical): 0.03
Revelle’s Omega (total): 0.62
Greatest Lower Bound (GLB): 0.65
Coefficient H: 0.94
Coefficient Alpha: 0.55

Confidence intervals

Omega (total): [0.58; 0.72]
Coefficient Alpha [0.5; 0.6]
Estimates assuming ordinal level
Ordinal Omega (total): 0.67
Ordinal Omega (hierarch.): 0.67
Ordinal Coefficient Alpha: 0.58

Confidence intervals

Ordinal Omega (total): [0.61; 0.73]
Ordinal Coefficient Alpha [0.53; 0.63]