In this vignette, you can see how to use the metadata that is often already stored in SPSS and Stata files. It’s easy. All we need is the rio::import function. For files with the right file extension, we can automatically pick the right way to import the data. Here, we’re downloading straight from the Open Science Framework, so we have to specify the file extension.

We select a subset of variables, just to keep it short. The data were shared by Emanuel Jauk in a project called How alluring are dark personalities? The Dark Triad and attractiveness in speed dating.

Often, files imported from SPSS or Stata to R will not have their missings coded properly. Here, that is not the case, but if you find yourself with such a dataset, the detect_missing function makes it easy to recognise common ways to specify missing data (e.g. negative values, labelled values, 99/999).

darktriad <- rio::import("https://osf.io/j4fcb/download", format = "sav")
if (!knit_by_pkgdown) {
  darktriad <- darktriad %>%
  select(DG, sex, relStat, education, NPI_avg)
}
metadata(darktriad)$name <- "How alluring are dark personalities? The Dark Triad and attractiveness in speed dating"
metadata(darktriad)$description <- paste0("The data to this speed dating study comes in two different formats: Personwise (one record for each individual) and dyadic (pairwise; one record for each date). The respective SPSS files are named \"DarkTriadDate_person.sav\" and \"DarkTriadDate_dyad.sav\".

### Download link
[Open Science Framework](https://osf.io/j4fcb/download)

### Personwise datafile 
The personwise datafile contains individual differences variables and perceiver and target effects according to the social relations model. These are centered marginal means that were calculated according to the formulae provided by Kenny, Kashy, and Cook (2006). These effects are not (!) based on multilevel analyses.

### Preprocessing
All rating variables (i.e., actual choice, friendship, short-term relationship etc.) were corrected for prior acquaintance, which means that dates wih prior acquaintance were excluded (set to missing) on a dyadic basis.

Variables are labeled in SPSS. 

### A list of important abbreviations, prefixes and suffixes:

* _acq = acquaintance (i.e., variables with this suffix are controlled for prior * acquaintance)
* avg = average
* _rat = rating variable
* _z = z-standardized score
* BC = booty call
* DG = dating group (three groups in this study)
* FIPI = five item personality inventory
* FS = friendship
* FWB = friends-with-benefits
* Int = Intelligence
* Like = Likeability
* LTR = long-term relationship
* MACHIV = mach-iv machiavellianism questionnaire
* N, E, O, A, C = Big5
* NPI = narcissistic personality inventory
* ONS = one night stand
* P = perceiver
* PA = physical attractiveness
* PercEff = perceiver effect
* SD = speed dating
* SRM = social relations model
* SRP = self-report psychopathy scale
* STR = short-term relationship
* T = target
* TargEff = target effect


")
metadata(darktriad)$identifier <- "https://osf.io/jvk3u/"
metadata(darktriad)$datePublished <- "2015-10-07"
metadata(darktriad)$creator <- list(
      "@type" = "Person",
      givenName = "Emanuel", familyName = "Jauk",
      email = "emanuel.jauk@uni‐graz.at", 
      affiliation = list("@type" = "Organization",
        name = "Karl‐Franzens‐Universität Graz, Austria"))
metadata(darktriad)$citation <- "Jauk, E., Neubauer, A. C., Mairunteregger, T., Pemp, S., Sieber, K. P., & Rauthmann, J. F. (2016). How alluring are dark personalities? The Dark Triad and attractiveness in speed dating. European Journal of Personality, 30(2), 125-138."
metadata(darktriad)$url <- "https://osf.io/j4fcb/"
metadata(darktriad)$temporalCoverage <- "2015" 
metadata(darktriad)$spatialCoverage <- "Graz, Austria" 
metadata(darktriad)$distribution = list(
  list("@type" = "DataDownload",
       "requiresSubscription" = "http://schema.org/True",
       "encodingFormat" = "https://www.loc.gov/preservation/digital/formats/fdd/fdd000469.shtml",
       contentUrl = "https://osf.io/j4fcb/download")
)

Now, we can immediately generate a codebook.

Metadata

Description

Dataset name: How alluring are dark personalities? The Dark Triad and attractiveness in speed dating

The data to this speed dating study comes in two different formats: Personwise (one record for each individual) and dyadic (pairwise; one record for each date). The respective SPSS files are named “DarkTriadDate_person.sav” and “DarkTriadDate_dyad.sav”.

Personwise datafile

The personwise datafile contains individual differences variables and perceiver and target effects according to the social relations model. These are centered marginal means that were calculated according to the formulae provided by Kenny, Kashy, and Cook (2006). These effects are not (!) based on multilevel analyses.

Preprocessing

All rating variables (i.e., actual choice, friendship, short-term relationship etc.) were corrected for prior acquaintance, which means that dates wih prior acquaintance were excluded (set to missing) on a dyadic basis.

Variables are labeled in SPSS.

A list of important abbreviations, prefixes and suffixes:

  • _acq = acquaintance (i.e., variables with this suffix are controlled for prior * acquaintance)
  • avg = average
  • _rat = rating variable
  • _z = z-standardized score
  • BC = booty call
  • DG = dating group (three groups in this study)
  • FIPI = five item personality inventory
  • FS = friendship
  • FWB = friends-with-benefits
  • Int = Intelligence
  • Like = Likeability
  • LTR = long-term relationship
  • MACHIV = mach-iv machiavellianism questionnaire
  • N, E, O, A, C = Big5
  • NPI = narcissistic personality inventory
  • ONS = one night stand
  • P = perceiver
  • PA = physical attractiveness
  • PercEff = perceiver effect
  • SD = speed dating
  • SRM = social relations model
  • SRP = self-report psychopathy scale
  • STR = short-term relationship
  • T = target
  • TargEff = target effect

  • Temporal Coverage: 2015
  • Spatial Coverage: Graz, Austria
  • Citation: Jauk, E., Neubauer, A. C., Mairunteregger, T., Pemp, S., Sieber, K. P., & Rauthmann, J. F. (2016). How alluring are dark personalities? The Dark Triad and attractiveness in speed dating. European Journal of Personality, 30(2), 125-138.
  • URL: https://osf.io/j4fcb/
  • Identifier: https://osf.io/jvk3u/
  • Date published: 2015-10-07

  • Creator:

    • @type: Person
    • givenName: Emanuel
    • familyName: Jauk
    • email: ‐graz.at
    • affiliation:

      • @type: Organization
      • name: Karl‐Franzens‐Universität Graz, Austria
  • distribution:

  • keywords: SD_Code, DG, DG_size, DG_size_acq, age, sex, height, weight, relStat, relStat_other, education, contracept, date, NPI_avg, SRP_avg, MACHIV_avg, BFI_N_avg, BFI_E_avg, BFI_O_avg, BFI_A_avg, BFI_C_avg, PA_R1, PA_R2, PA_R3, PA_R4, PA_avg, BMI, SOI_R_B_avg, SOI_R_A_avg, SOI_R_D_avg, _TargEff__choice_relFrequ_acq_, _TargEff__FS_avg_acq_, _TargEff__ONS_avg_acq_, _TargEff__BC_avg_acq_, _TargEff__FWB_avg_acq_, _TargEff__STR_avg_acq_, _TargEff__LTR_avg_acq_, _TargEff__PA_avg_acq_, _TargEff__Like_avg_acq_, _TargEff__Int_avg_acq_, _TargEff__FIPI_N_avg_acq_, _TargEff__FIPI_E_avg_acq_, _TargEff__FIPI_O_avg_acq_, _TargEff__FIPI_A_avg_acq_, _TargEff__FIPI_C_avg_acq_, _PercEff__choice_relFrequ_acq_, _PercEff__FS_avg_acq_, _PercEff__ONS_avg_acq_, _PercEff__BC_avg_acq_, _PercEff__FWB_avg_acq_, _PercEff__STR_avg_acq_, _PercEff__LTR_avg_acq_, _PercEff__PA_avg_acq_, _PercEff__Like_avg_acq_, _PercEff__Int_avg_acq_, _PercEff__FIPI_N_avg_acq_, _PercEff__FIPI_E_avg_acq_, _PercEff__FIPI_O_avg_acq_, _PercEff__FIPI_A_avg_acq_ and _PercEff__FIPI_C_avg_acq_

Variables

SD_Code

speed dating code

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss
SD_Code speed dating code numeric 0 90 90 172.63 52.26 101 123.25 145.5 223.75 246 ▇▇▃▁▁▃▇▇ F8.0

DG

dating group

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss
DG dating group numeric 0 90 90 2.03 0.77 1 1 2 3 3 ▆▁▁▇▁▁▁▆ F8.0

DG_size

dating group size

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss
DG_size dating group size numeric 0 90 90 15.36 2.89 11 13 15 18 19 ▆▆▅▆▁▁▇▇ F8.0

DG_size_acq

dating group size, corrected for prior acquaintance

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
DG_size_acq dating group size, corrected for prior acquaintance numeric 0 90 90 14.22 2.94 7 12 14 17 19 ▁▂▆▇▃▇▃▇ F8.0 11

age

age

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
age age numeric 0 90 90 22.87 3.09 18 21 22 25 32 ▃▇▆▅▂▂▁▁ F3.0 5

sex

sex

Distribution

0 missing values.

Summary statistics

name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
sex sex numeric 1. female,
2. male
0 90 90 1.49 0.5 1 1 1 2 2 ▇▁▁▁▁▁▁▇ F1.0 5

Value labels

  • female: 1
  • male: 2

height

height

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
height height numeric 0 90 90 174.54 9.26 156 168 173.5 180 196 ▂▅▇▇▇▅▃▁ F3.0 5

weight

weight

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
weight weight numeric 0 90 90 68.91 11.28 46 60 70 76 106 ▂▆▃▇▃▂▁▁ F3.0 5

relStat

relationship status

Distribution

1 missing values.

Summary statistics

name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
relStat relationship status numeric 1. single,
2. in a relationship,
3. living separately / divorced
1 89 90 1.09 0.32 1 1 1 1 3 ▇▁▁▁▁▁▁▁ F8.0 10

Value labels

  • single: 1
  • in a relationship: 2
  • living separately / divorced: 3

relStat_other

other relationship status

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n empty n_unique min max format.spss display_width
relStat_other other relationship status character 0 90 90 89 2 0 25 A234 5

education

highest educational attainment

Distribution

1 missing values.

Summary statistics

name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
education highest educational attainment numeric 1. nine years schooling only,
2. professional training,
3. vocational school,
4. university-entrance diploma,
5. academic degree
1 89 90 4.17 0.38 4 4 4 4 5 ▇▁▁▁▁▁▁▂ F1.0 5

Value labels

  • nine years schooling only: 1
  • professional training: 2
  • vocational school: 3
  • university-entrance diploma: 4
  • academic degree: 5

contracept

hormonal contraception

Distribution

44 missing values.

Summary statistics

name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
contracept hormonal contraception numeric 1. yes,
2. no
44 46 90 1.65 0.48 1 1 2 2 2 ▅▁▁▁▁▁▁▇ F1.0 5

Value labels

  • yes: 1
  • no: 2

date

past experience with speed dating

Distribution

0 missing values.

Summary statistics

name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
date past experience with speed dating numeric 1. yes,
2. no
0 90 90 1.9 0.3 1 2 2 2 2 ▁▁▁▁▁▁▁▇ F1.0 5

Value labels

  • yes: 1
  • no: 2

NPI_avg

narcissistic personality inventory - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
NPI_avg narcissistic personality inventory - average numeric 0 90 90 2.61 0.35 1.7 2.42 2.6 2.82 3.65 ▁▂▃▇▆▂▁▁ F8.2 10

SRP_avg

self-report psychopathy scale - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
SRP_avg self-report psychopathy scale - average numeric 0 90 90 2.06 0.37 1.35 1.82 2.06 2.3 3.5 ▃▆▇▇▁▂▁▁ F8.2 10

MACHIV_avg

mach-iv - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
MACHIV_avg mach-iv - average numeric 0 90 90 2.75 0.67 1.39 2.33 2.72 3.17 4.83 ▃▂▇▇▅▂▁▁ F8.2 12

BFI_N_avg

big five inventory: neuroticism - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
BFI_N_avg big five inventory: neuroticism - average numeric 0 90 90 2.79 0.93 1 2 2.75 3.5 4.75 ▃▅▇▇▇▆▃▂ F8.2 11

BFI_E_avg

big five inventory: extraversion - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
BFI_E_avg big five inventory: extraversion - average numeric 0 90 90 3.72 0.8 1 3.25 3.75 4.25 5 ▁▁▂▅▅▇▃▅ F8.2 11

BFI_O_avg

big five inventory: openness - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
BFI_O_avg big five inventory: openness - average numeric 0 90 90 4.07 0.76 1 3.75 4.25 4.75 5 ▁▁▁▂▂▇▇▇ F8.2 11

BFI_A_avg

big five inventory: agreeableness - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
BFI_A_avg big five inventory: agreeableness - average numeric 0 90 90 3.2 0.69 1.5 2.75 3.25 3.75 4.75 ▁▃▃▇▇▇▅▁ F8.2 11

BFI_C_avg

big five inventory: conscientiousness - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
BFI_C_avg big five inventory: conscientiousness - average numeric 0 90 90 3.35 0.77 1.75 2.75 3.5 4 4.75 ▂▅▁▃▃▇▂▂ F8.2 11

PA_R1

physical attractiveness - rater1(f)

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PA_R1 physical attractiveness - rater1(f) numeric 0 90 90 3.63 1.13 1 3 4 4 7 ▁▂▆▇▁▂▁▁ F8.2 14

PA_R2

physical attractiveness - rater2(f)

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss
PA_R2 physical attractiveness - rater2(f) numeric 0 90 90 4.7 1.22 2 4 5 5 7 ▁▅▁▅▇▁▃▂ F8.2

PA_R3

physical attractiveness - rater3(m)

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss
PA_R3 physical attractiveness - rater3(m) numeric 0 90 90 3.16 1.51 1 2 3 4 7 ▆▆▇▆▁▅▂▁ F8.2

PA_R4

physical attractiveness - rater4(m)

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss
PA_R4 physical attractiveness - rater4(m) numeric 0 90 90 4.14 1.63 1 3 4 5.75 7 ▁▆▇▃▁▆▆▂ F8.2

PA_avg

physical attractiveness - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PA_avg physical attractiveness - average numeric 0 90 90 3.91 1.14 1.75 3 3.75 4.75 6.5 ▂▆▆▅▃▇▂▁ F8.2 19

BMI

body mass index

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
BMI body mass index numeric 0 90 90 22.55 2.75 17.96 20.44 22.65 24.45 30.64 ▅▇▅▆▅▃▁▁ F8.2 10

SOI_R_B_avg

sociosexual orientation inventory revised: behavior - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
SOI_R_B_avg sociosexual orientation inventory revised: behavior - average numeric 0 90 90 3.63 2.11 1 2 3 5 8.67 ▇▇▅▃▅▂▁▂ F8.2 16

SOI_R_A_avg

sociosexual orientation inventory revised: attitude - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
SOI_R_A_avg sociosexual orientation inventory revised: attitude - average numeric 0 90 90 6.48 2.1 1.67 5 6.83 8.33 9 ▁▂▂▅▂▅▃▇ F8.2 17

SOI_R_D_avg

sociosexual orientation inventory revised: desire - average

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
SOI_R_D_avg sociosexual orientation inventory revised: desire - average numeric 0 90 90 5.11 2.09 1 3.67 5.33 6.67 9 ▅▅▃▇▆▇▆▂ F8.2 15

TargEff__choice_relFrequ_acq {#TargEff__choice_relFrequ_acq .tabset}

SRM target effect: actual choice

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__choice_relFrequ_acq SRM target effect: actual choice numeric 0 90 90 1.8e-05 0.26 -0.39 -0.21 -0.0056 0.17 0.61 ▇▇▅▇▆▅▂▂ F8.2 34

TargEff__FS_avg_acq {#TargEff__FS_avg_acq .tabset}

SRM target effect: friendship

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FS_avg_acq SRM target effect: friendship numeric 0 90 90 2.5e-05 0.67 -1.53 -0.45 -0.065 0.57 1.25 ▂▃▃▇▇▃▅▃ F8.2 29

TargEff__ONS_avg_acq {#TargEff__ONS_avg_acq .tabset}

SRM target effect: one night stand

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__ONS_avg_acq SRM target effect: one night stand numeric 0 90 90 2.2e-05 1.42 -1.98 -1.29 -0.22 0.96 3.11 ▇▆▅▅▃▃▃▂ F8.2 21

TargEff__BC_avg_acq {#TargEff__BC_avg_acq .tabset}

SRM target effect: booty call

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__BC_avg_acq SRM target effect: booty call numeric 0 90 90 -4.1e-05 1.33 -1.63 -1.15 -0.13 0.88 3.3 ▇▅▃▃▂▂▁▁ F8.2 28

TargEff__FWB_avg_acq {#TargEff__FWB_avg_acq .tabset}

SRM target effect: friends-with-benefits

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FWB_avg_acq SRM target effect: friends-with-benefits numeric 0 90 90 -2.8e-05 1.24 -1.63 -1.11 -0.16 0.79 3.23 ▇▃▅▃▃▂▁▁ F8.2 30

TargEff__STR_avg_acq {#TargEff__STR_avg_acq .tabset}

SRM target effect: short-term relationship (aggregated)

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__STR_avg_acq SRM target effect: short-term relationship (aggregated) numeric 0 90 90 -1.6e-05 1.32 -1.72 -1.21 -0.23 0.99 3.21 ▇▅▅▃▃▂▁▁ F8.2 13

TargEff__LTR_avg_acq {#TargEff__LTR_avg_acq .tabset}

SRM target effect: long-term relationship

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__LTR_avg_acq SRM target effect: long-term relationship numeric 0 90 90 -1.7e-05 0.94 -1.4 -0.73 -0.066 0.52 3.33 ▇▆▇▆▃▁▁▁ F8.2 27

TargEff__PA_avg_acq {#TargEff__PA_avg_acq .tabset}

SRM target effect: physical attractiveness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__PA_avg_acq SRM target effect: physical attractiveness numeric 0 90 90 -2.6e-05 1.18 -2.22 -0.88 0.22 0.86 2.52 ▅▅▅▅▇▇▂▁ F8.2 28

TargEff__Like_avg_acq {#TargEff__Like_avg_acq .tabset}

SRM target effect: likeability

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__Like_avg_acq SRM target effect: likeability numeric 0 90 90 4.4e-05 0.71 -1.59 -0.44 0.092 0.5 1.69 ▃▂▃▇▇▆▂▁ F8.2 31

TargEff__Int_avg_acq {#TargEff__Int_avg_acq .tabset}

SRM target effect: intelligence

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__Int_avg_acq SRM target effect: intelligence numeric 0 90 90 2.5e-05 0.46 -1.24 -0.33 -0.0082 0.31 1.01 ▁▂▂▇▇▅▃▂ F8.2 29

TargEff__FIPI_N_avg_acq {#TargEff__FIPI_N_avg_acq .tabset}

SRM target effect: neuroticism

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FIPI_N_avg_acq SRM target effect: neuroticism numeric 0 90 90 1.6e-05 0.72 -1.67 -0.53 -0.093 0.55 1.79 ▁▃▇▇▆▅▂▂ F8.2 28

TargEff__FIPI_E_avg_acq {#TargEff__FIPI_E_avg_acq .tabset}

SRM target effect: extraversion

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FIPI_E_avg_acq SRM target effect: extraversion numeric 0 90 90 -1.4e-05 0.85 -2.4 -0.5 0.00083 0.64 1.66 ▁▁▂▆▇▇▇▂ F8.2 28

TargEff__FIPI_O_avg_acq {#TargEff__FIPI_O_avg_acq .tabset}

SRM target effect: openness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FIPI_O_avg_acq SRM target effect: openness numeric 0 90 90 5.2e-06 0.65 -1.52 -0.37 -0.032 0.39 1.56 ▂▃▃▇▇▃▅▁ F8.2 28

TargEff__FIPI_A_avg_acq {#TargEff__FIPI_A_avg_acq .tabset}

SRM target effect: agreeableness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FIPI_A_avg_acq SRM target effect: agreeableness numeric 0 90 90 -4.5e-05 0.58 -1.62 -0.29 0.076 0.39 1.26 ▁▁▂▃▇▆▃▁ F8.2 31

TargEff__FIPI_C_avg_acq {#TargEff__FIPI_C_avg_acq .tabset}

SRM target effect: conscientiousness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
TargEff__FIPI_C_avg_acq SRM target effect: conscientiousness numeric 0 90 90 -3.9e-05 0.58 -1.8 -0.35 0.03 0.28 1.43 ▁▁▂▃▇▆▁▁ F8.2 29

PercEff__choice_relFrequ_acq {#PercEff__choice_relFrequ_acq .tabset}

SRM perceiver effect: actual choice

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__choice_relFrequ_acq SRM perceiver effect: actual choice numeric 0 90 90 4e-05 0.23 -0.38 -0.17 -0.016 0.12 0.62 ▃▅▇▇▃▂▂▁ F8.2 34

PercEff__FS_avg_acq {#PercEff__FS_avg_acq .tabset}

SRM perceiver effect: friendship

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FS_avg_acq SRM perceiver effect: friendship numeric 0 90 90 -3.7e-05 0.96 -2.71 -0.53 0.096 0.59 2.5 ▁▁▃▇▇▅▂▁ F8.2 29

PercEff__ONS_avg_acq {#PercEff__ONS_avg_acq .tabset}

SRM perceiver effect: one night stand

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__ONS_avg_acq SRM perceiver effect: one night stand numeric 0 90 90 1.6e-05 1.38 -1.96 -1.18 -0.23 1.09 3.03 ▇▆▆▃▅▅▂▂ F8.2 21

PercEff__BC_avg_acq {#PercEff__BC_avg_acq .tabset}

SRM perceiver effect: booty call

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__BC_avg_acq SRM perceiver effect: booty call numeric 0 90 90 -1.2e-05 1.29 -1.81 -1.16 -0.24 1.08 2.77 ▇▅▆▃▃▃▂▂ F8.2 28

PercEff__FWB_avg_acq {#PercEff__FWB_avg_acq .tabset}

SRM perceiver effect: friends-with-benefits

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FWB_avg_acq SRM perceiver effect: friends-with-benefits numeric 0 90 90 -1e-05 1.27 -1.82 -1.03 -0.24 0.87 2.97 ▇▅▇▃▅▂▃▁ F8.2 30

PercEff__STR_avg_acq {#PercEff__STR_avg_acq .tabset}

SRM perceiver effect: short-term relationship (aggregated)

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__STR_avg_acq SRM perceiver effect: short-term relationship (aggregated) numeric 0 90 90 -2.4e-06 1.26 -1.8 -1.03 -0.3 1.03 2.83 ▇▆▇▃▅▅▂▂ F8.2 14

PercEff__LTR_avg_acq {#PercEff__LTR_avg_acq .tabset}

SRM perceiver effect: long-term relationship

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__LTR_avg_acq SRM perceiver effect: long-term relationship numeric 0 90 90 -2e-06 1.08 -1.6 -0.9 -0.16 0.6 2.68 ▇▆▆▇▃▅▁▂ F8.2 27

PercEff__PA_avg_acq {#PercEff__PA_avg_acq .tabset}

SRM perceiver effect: physical attractiveness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__PA_avg_acq SRM perceiver effect: physical attractiveness numeric 0 90 90 -3.4e-05 1.18 -2.52 -0.95 0.16 0.97 2.53 ▂▃▆▅▇▆▅▁ F8.2 28

PercEff__Like_avg_acq {#PercEff__Like_avg_acq .tabset}

SRM perceiver effect: likeability

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__Like_avg_acq SRM perceiver effect: likeability numeric 0 90 90 3.6e-05 0.94 -2.5 -0.42 0.063 0.62 2.08 ▁▁▂▃▇▅▂▁ F8.2 31

PercEff__Int_avg_acq {#PercEff__Int_avg_acq .tabset}

SRM perceiver effect: intelligence

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__Int_avg_acq SRM perceiver effect: intelligence numeric 0 90 90 -3.9e-05 0.97 -2.36 -0.53 0.043 0.58 2.03 ▁▂▃▇▇▇▂▃ F8.2 29

PercEff__FIPI_N_avg_acq {#PercEff__FIPI_N_avg_acq .tabset}

SRM perceiver effect: neuroticism

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FIPI_N_avg_acq SRM perceiver effect: neuroticism numeric 0 90 90 3.2e-05 0.87 -2.25 -0.47 0.12 0.67 2.49 ▁▂▃▇▇▆▁▁ F8.2 28

PercEff__FIPI_E_avg_acq {#PercEff__FIPI_E_avg_acq .tabset}

SRM perceiver effect: extraversion

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FIPI_E_avg_acq SRM perceiver effect: extraversion numeric 0 90 90 -4.4e-05 0.73 -1.56 -0.49 0.12 0.51 1.71 ▃▂▆▃▇▇▁▁ F8.2 28

PercEff__FIPI_O_avg_acq {#PercEff__FIPI_O_avg_acq .tabset}

SRM perceiver effect: openness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FIPI_O_avg_acq SRM perceiver effect: openness numeric 0 90 90 -1.9e-06 0.67 -2.17 -0.5 0.064 0.48 1.47 ▁▁▂▆▇▇▅▂ F8.2 28

PercEff__FIPI_A_avg_acq {#PercEff__FIPI_A_avg_acq .tabset}

SRM perceiver effect: agreeableness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FIPI_A_avg_acq SRM perceiver effect: agreeableness numeric 0 90 90 4e-05 0.86 -2.17 -0.59 -0.046 0.51 1.73 ▁▁▅▇▆▆▃▃ F8.2 31

PercEff__FIPI_C_avg_acq {#PercEff__FIPI_C_avg_acq .tabset}

SRM perceiver effect: conscientiousness

Distribution

0 missing values.

Summary statistics

name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist format.spss display_width
PercEff__FIPI_C_avg_acq SRM perceiver effect: conscientiousness numeric 0 90 90 4.3e-05 0.7 -2.2 -0.51 -0.0029 0.49 1.56 ▁▁▃▆▇▆▅▂ F8.2 29

Missingness report

Among those who finished the survey. Only variables that have missing values are shown.

## Warning: Could not figure out who finished the surveys, because the
## variables expired and ended were missing.

Codebook table