In this vignette, you can see how to add metadata to a dataset when it isn’t already stored in its attributes. For this example, we’ll use the bfi and bfi.dictionary datasets from the psych package. We use functions from the labelled package to set the relevant attributes with convenience functions.

knit_by_pkgdown <- !is.null(knitr::opts_chunk$get("fig.retina"))
library(dplyr)
library(codebook)
library(labelled)
pander::panderOptions("table.split.table", Inf)
ggplot2::theme_set(ggplot2::theme_bw())

data("bfi", package = 'psych')
bfi <- bfi %>% tbl_df()
data("bfi.dictionary", package = 'psych')
bfi.dictionary <- tibble::rownames_to_column(bfi.dictionary, "variable") %>% 
  tbl_df()

Let’s start by getting an overview of our dataset

head(bfi, 20)

## # A tibble: 20 x 28
##       A1    A2    A3    A4    A5    C1    C2    C3    C4    C5    E1    E2
##    <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
##  1     2     4     3     4     4     2     3     3     4     4     3     3
##  2     2     4     5     2     5     5     4     4     3     4     1     1
##  3     5     4     5     4     4     4     5     4     2     5     2     4
##  4     4     4     6     5     5     4     4     3     5     5     5     3
##  5     2     3     3     4     5     4     4     5     3     2     2     2
##  6     6     6     5     6     5     6     6     6     1     3     2     1
##  7     2     5     5     3     5     5     4     4     2     3     4     3
##  8     4     3     1     5     1     3     2     4     2     4     3     6
##  9     4     3     6     3     3     6     6     3     4     5     5     3
## 10     2     5     6     6     5     6     5     6     2     1     2     2
## 11     4     4     5     6     5     4     3     5     3     2     1     3
## 12     2     5     5     5     5     5     4     5     4     5     3     3
## 13     5     5     5     6     4     5     4     3     2     2     3     3
## 14     5     5     5     6     6     4     4     4     2     1     2     2
## 15     4     5     2     2     1     5     5     5     2     2     3     4
## 16     4     3     6     6     3     5     5     5     3     5     1     1
## 17     4     6     6     2     5     4     4     4     4     4     1     2
## 18     5     5     5     4     5     5     5     5     4     3     2     2
## 19     4     4     5     4     3     5     4     5     4     6     1     2
## 20     4     4     6     5     5     1     1     1     5     6     1     1
## # … with 16 more variables: E3 <int>, E4 <int>, E5 <int>, N1 <int>,
## #   N2 <int>, N3 <int>, N4 <int>, N5 <int>, O1 <int>, O2 <int>, O3 <int>,
## #   O4 <int>, O5 <int>, gender <int>, education <int>, age <int>

and our data dictionary.

bfi.dictionary

## # A tibble: 28 x 8
##    variable ItemLabel Item         Giant3  Big6    Little12  Keying IPIP100
##    <chr>    <fct>     <fct>        <fct>   <fct>   <fct>      <int> <fct>  
##  1 A1       q_146     Am indiffer… Cohesi… Agreea… Compassi…     -1 B5:A   
##  2 A2       q_1162    Inquire abo… Cohesi… Agreea… Compassi…      1 B5:A   
##  3 A3       q_1206    Know how to… Cohesi… Agreea… Compassi…      1 B5:A   
##  4 A4       q_1364    Love childr… Cohesi… Agreea… Compassi…      1 B5:A   
##  5 A5       q_1419    Make people… Cohesi… Agreea… Compassi…      1 B5:A   
##  6 C1       q_124     Am exacting… Stabil… Consci… Orderlin…      1 B5:C   
##  7 C2       q_530     Continue un… Stabil… Consci… Orderlin…      1 B5:C   
##  8 C3       q_619     Do things a… Stabil… Consci… Orderlin…      1 B5:C   
##  9 C4       q_626     Do things i… Stabil… Consci… Industri…     -1 B5:C   
## 10 C5       q_1949    Waste my ti… Stabil… Consci… Industri…     -1 B5:C   
## # … with 18 more rows

How to add variable and value labels

Using the var_label function from the labelled package, we can easily assign a label to a variable (or a list of labels to a dataset).

# First, let's see what we know about these variables.
bfi <- bfi %>% # here we use the pipe (feeding the bfi argument into the pipe)
  mutate(education = as.double(education), # the labelled class is a bit picky and doesn't like integers
         gender = as.double(gender))

bfi.dictionary %>% tail(3)

## # A tibble: 3 x 8
##   variable  ItemLabel Item             Giant3 Big6  Little12 Keying IPIP100
##   <chr>     <fct>     <fct>            <fct>  <fct> <fct>     <int> <fct>  
## 1 gender    gender    males=1, female… <NA>   <NA>  <NA>         NA <NA>   
## 2 education education in HS, fin HS, … <NA>   <NA>  <NA>         NA <NA>   
## 3 age       age       age in years     <NA>   <NA>  <NA>         NA <NA>

var_label(bfi$gender) <- "Self-reported gender"
attributes(bfi$gender) # check what we're doing

## $label
## [1] "Self-reported gender"

var_label(bfi) <- list(age = "age in years", education = "Highest degree")

# or using dplyr syntax
bfi <- bfi %>% set_variable_labels(
  age = "age in years", 
  education = "Highest degree")

Now, we saw that the value labels were encoded in the variable label. This is not what we want. Instead, we assign value labels.

bfi <- bfi %>% 
  add_value_labels(
    gender = c("male" = 1, "female" = 2),
    education = c("in high school" = 1, "finished high school" = 2,
                  "some college" = 3, "college graduate" = 4, 
                  "graduate degree" = 5) # dont use abbreviations if you can avoid it
    )
attributes(bfi$gender) # check what we're doing

## $label
## [1] "Self-reported gender"
## 
## $labels
##   male female 
##      1      2 
## 
## $class
## [1] "haven_labelled"

# We could also assign the attributes manually, but then there's no error checking.
attributes(bfi$gender) <- list(
  label = "Self-reported gender", 
  labels = c(male = 1L, female = 2L), 
  class = "haven_labelled")

As we see, adding value labels turned the variable gender into a different type (from a simple integer to a labelled class).

This is all pretty tedious, and we have the data we need in a nice dictionary already. With a few easy steps, we can transform it.

First, we take only the personality items. We did the rest already.

dict <- bfi.dictionary %>% 
  filter(!variable %in% c("gender", "education", "age")) %>% # we did those already
  mutate(label = paste0(Big6, ": ", Item)) %>% # make sure we name the construct in the label
  select(variable, label, Keying)

Now, we turn this data.frame with variable and label columns into a named list and assign it as variable labels.

var_label(bfi) <- dict_to_list(dict)

Now, we want to assign value labels to all likert items. First, we need to define a named vector.

value_labels <- c("Very Inaccurate" = 1, 
                  "Moderately Inaccurate" = 2, 
                  "Slightly Inaccurate" = 3,
                  "Slightly Accurate" = 4,
                  "Moderately Accurate" = 5,
                  "Very Accurate" = 6)

We’re going to be using these labels many times, so let’s put the step of assigning them into a function.

add_likert_label <- function(x) {
  val_labels(x) <- value_labels
  x
}

Now, for all personality items (we get them from our data dictionary), we assign these value labels.

personality_items <- dict %>% pull(variable)
bfi <- bfi %>% 
  mutate_at(personality_items, 
                         add_likert_label)

However, some of our items are reverse-coded. This information is contained in the Keying variable in our data dictionary. We’ll use it to rename the variables to end with the letter R.

# reverse underlying values for the reverse-keyed items
reverse_coded_items <- dict %>% filter(Keying == -1) %>% pull(variable)

bfi <- bfi %>% 
  rename_at(reverse_coded_items, add_R)

Next, we can conveniently call the reverse_labelled_values function on all variables ending with a number and R.

head(bfi$A1R, 3)

## <Labelled integer>: Agreeableness: Am indifferent to the feelings of others.
## [1] 2 2 5
## 
## Labels:
##  value                 label
##      1       Very Inaccurate
##      2 Moderately Inaccurate
##      3   Slightly Inaccurate
##      4     Slightly Accurate
##      5   Moderately Accurate
##      6         Very Accurate

labelled::val_labels(bfi$A1R)

##       Very Inaccurate Moderately Inaccurate   Slightly Inaccurate 
##                     1                     2                     3 
##     Slightly Accurate   Moderately Accurate         Very Accurate 
##                     4                     5                     6

bfi <- bfi %>% 
  mutate_at(
    vars(matches("[0-9]_?R$")), # only for variables that end in 1R 2_R 3R etc
    reverse_labelled_values)
labelled::val_labels(bfi$A1R)

##       Very Inaccurate Moderately Inaccurate   Slightly Inaccurate 
##                     6                     5                     4 
##     Slightly Accurate   Moderately Accurate         Very Accurate 
##                     3                     2                     1

head(bfi$A1R, 3)

## <Labelled double>: Agreeableness: Am indifferent to the feelings of others.
## [1] 5 5 2
## 
## Labels:
##  value                 label
##      6       Very Inaccurate
##      5 Moderately Inaccurate
##      4   Slightly Inaccurate
##      3     Slightly Accurate
##      2   Moderately Accurate
##      1         Very Accurate

As you can see, the underlying numeric values have changed, but the labels are still the way the participant answered them.

Aggregating scales

Now, we can form scale aggregates. The codebook function aggregate_and_document_scale does this for us and automatically sets the correct attributes. For some select calls her,e we have to set ignore.case to FALSE, or it would match the age and education variables.

bfi$consc <- aggregate_and_document_scale(bfi %>% select(starts_with("C")))
bfi$extra <- aggregate_and_document_scale(bfi %>% select(starts_with("E", ignore.case = F)))
bfi$open <- aggregate_and_document_scale(bfi %>% select(starts_with("O")))
bfi$agree <- aggregate_and_document_scale(bfi %>% select(starts_with("A", ignore.case = F)))
bfi$neuro <- aggregate_and_document_scale(bfi %>% select(starts_with("N")))

Last, we can assign some metadata to the dataset itself. We might want to give it a meaningful name and description, for example.

metadata(bfi)$name <- "25 Personality items representing 5 factors"
metadata(bfi)$description <- "25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 2800 subjects are included here as a demonstration set for scale construction, factor analysis, and Item Response Theory analysis. Three additional demographic variables (sex, education, and age) are also included.

The first 25 items are organized by five putative factors: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Opennness. The item data were collected using a 6 point response scale: 1 Very Inaccurate 2 Moderately Inaccurate 3 Slightly Inaccurate 4 Slightly Accurate 5 Moderately Accurate 6 Very Accurate

To see an example of the data collection technique, visit https://SAPA-project.org or the International Cognitive Ability Resource at https://icar-project.com. The items given were sampled from the International Personality Item Pool of Lewis Goldberg using the sampling technique of SAPA. This is a sample data set taken from the much larger SAPA data bank."
metadata(bfi)$identifier <- "https://CRAN.R-project.org/package=psych"
metadata(bfi)$datePublished <- "2010-01-01"
metadata(bfi)$creator <- list(
      "@type" = "Person",
      givenName = "William", familyName = "Revelle",
      email = "revelle@northwestern.edu", 
      affiliation = list("@type" = "Organization",
        name = "Northwestern University"))
metadata(bfi)$citation <- "Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer."
metadata(bfi)$url <- "https://CRAN.R-project.org/package=psych"
metadata(bfi)$temporalCoverage <- "Spring 2010" 
metadata(bfi)$spatialCoverage <- "Online"

# We don't want to look at the code in the codebook.
knitr::opts_chunk$set(warning = TRUE, message = TRUE, echo = FALSE)

Finally, we can generate our codebook.

Metadata

Description

Dataset name: 25 Personality items representing 5 factors

25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 2800 subjects are included here as a demonstration set for scale construction, factor analysis, and Item Response Theory analysis. Three additional demographic variables (sex, education, and age) are also included.

The first 25 items are organized by five putative factors: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Opennness. The item data were collected using a 6 point response scale: 1 Very Inaccurate 2 Moderately Inaccurate 3 Slightly Inaccurate 4 Slightly Accurate 5 Moderately Accurate 6 Very Accurate

To see an example of the data collection technique, visit https://SAPA-project.org or the International Cognitive Ability Resource at https://icar-project.com. The items given were sampled from the International Personality Item Pool of Lewis Goldberg using the sampling technique of SAPA. This is a sample data set taken from the much larger SAPA data bank.

Metadata for search engines

Temporal Coverage: Spring 2010
Spatial Coverage: Online
Citation: Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.
URL: https://CRAN.R-project.org/package=psych
Identifier: https://CRAN.R-project.org/package=psych
Date published: 2010-01-01
Creator:
- @type: Person
- givenName: William
- familyName: Revelle
- email: revelle@northwestern.edu
- affiliation:
  - @type: Organization
  - name: Northwestern University

keywords: A1R, A2, A3, A4, A5, C1, C2, C3, C4R, C5R, E1R, E2R, E3, E4, E5, N1R, N2R, N3R, N4R, N5R, O1, O2R, O3, O4, O5R, gender, education, age, consc, extra, open, agree and neuro

Variables

gender

Self-reported gender

Distribution

0 missing values.

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
gender	Self-reported gender	numeric	1. male, 2. female	0	2800	2800	1.67	0.47	1	1	2	2	2	▃▁▁▁▁▁▁▇

Value labels

male: 1
female: 2

education

Highest degree

Distribution

223 missing values.

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
education	Highest degree	numeric	1. in high school, 2. finished high school, 3. some college, 4. college graduate, 5. graduate degree	223	2577	2800	3.19	1.11	1	3	3	4	5	▂▂▁▇▁▂▁▃

Value labels

in high school: 1
finished high school: 2
some college: 3
college graduate: 4
graduate degree: 5

age

age in years

Distribution

0 missing values.

Summary statistics

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
age	age in years	integer	0	2800	2800	28.78	11.13	3	20	26	35	86	▁▇▆▃▂▁▁▁

Scale: consc

Overview

Reliability: ω_ordinal [95% CI] = 0.77 [0.76;0.78].

Missing: 93.

Reliability details

Reliability

Information about this analysis:

Observations: 2707

Positive correlations: 10 out of 10 (100%)

Estimates assuming interval level:

Index	Estimate
Omega	0.7330223
Omega Psych Tot	0.7710807
Omega Psych H	0.6184374
Omega Ordinal	0.7706569
Cronbach Alpha	0.7292772
Greatest Lower Bound	0.7661976
Alpha Ordinal	0.7695237

Scatter matrix

Detailed output

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: C1, C2, C3, C4R, C5R
##               Observations: 2707
##      Positive correlations: 10 out of 10 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.73
##       Omega (hierarchical): 0.62
##    Revelle's omega (total): 0.77
## Greatest Lower Bound (GLB): 0.77
##              Coefficient H: 0.74
##           Cronbach's alpha: 0.73
## Confidence intervals:
##              Omega (total): [0.72, 0.75]
##           Cronbach's alpha: [0.71, 0.75]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.77
##  Ordinal Omega (hierarch.): 0.77
##   Ordinal Cronbach's alpha: 0.77
## Confidence intervals:
##      Ordinal Omega (total): [0.76, 0.78]
##   Ordinal Cronbach's alpha: [0.76, 0.78]
## 
## 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 ('?scaleStructure') for more information.
## 
## Eigen values: 2.42, 0.827, 0.682, 0.566, 0.504
## Loadings:
##     PC1  
## C1  0.666
## C2  0.715
## C3  0.669
## C4R 0.745
## C5R 0.680
## 
##                  PC1
## SS loadings    2.420
## Proportion Var 0.484
## 
##     vars    n mean   sd median trimmed  mad min max range  skew kurtosis
## C1     1 2707 4.51 1.24      5    4.65 1.48   1   6     5 -0.86     0.32
## C2     2 2707 4.36 1.32      5    4.50 1.48   1   6     5 -0.74    -0.14
## C3     3 2707 4.30 1.29      5    4.41 1.48   1   6     5 -0.69    -0.12
## C4R    4 2707 4.45 1.37      5    4.59 1.48   1   6     5 -0.60    -0.62
## C5R    5 2707 3.69 1.63      4    3.74 1.48   1   6     5 -0.06    -1.22
##       se
## C1  0.02
## C2  0.03
## C3  0.02
## C4R 0.03
## C5R 0.03

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
C1	Conscientiousness: Am exacting in my work.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	21	2779	2800	4.5	1.24	1	4	5	5	6	▁▁▁▂▅▁▇▅
C2	Conscientiousness: Continue until everything is perfect.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	24	2776	2800	4.37	1.32	1	4	5	5	6	▁▂▁▂▆▁▇▅
C3	Conscientiousness: Do things according to a plan.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	20	2780	2800	4.3	1.29	1	4	5	5	6	▁▂▁▂▆▁▇▅
C4R	Conscientiousness: Do things in a half-way manner.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	26	2774	2800	4.45	1.38	1	3	5	6	6	▁▂▁▅▅▁▇▇
C5R	Conscientiousness: Waste my time.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	16	2784	2800	3.7	1.63	1	2	4	5	6	▃▆▁▇▅▁▇▆

Scale: extra

Overview

Reliability: ω_ordinal [95% CI] = 0.8 [0.78;0.81].

Missing: 87.

Reliability details

Reliability

Information about this analysis:

Observations: 2713

Positive correlations: 10 out of 10 (100%)

Estimates assuming interval level:

Index	Estimate
Omega	0.7673336
Omega Psych Tot	0.7950195
Omega Psych H	0.6522418
Omega Ordinal	0.7950197
Cronbach Alpha	0.7609326
Greatest Lower Bound	0.7977921
Alpha Ordinal	0.7928599

Scatter matrix

Detailed output

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: E1R, E2R, E3, E4, E5
##               Observations: 2713
##      Positive correlations: 10 out of 10 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.77
##       Omega (hierarchical): 0.65
##    Revelle's omega (total): 0.8
## Greatest Lower Bound (GLB): 0.8
##              Coefficient H: 0.78
##           Cronbach's alpha: 0.76
## Confidence intervals:
##              Omega (total): [0.75, 0.78]
##           Cronbach's alpha: [0.75, 0.78]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.8
##  Ordinal Omega (hierarch.): 0.79
##   Ordinal Cronbach's alpha: 0.79
## Confidence intervals:
##      Ordinal Omega (total): [0.78, 0.81]
##   Ordinal Cronbach's 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 ('?scaleStructure') for more information.
## 
## Eigen values: 2.565, 0.768, 0.643, 0.561, 0.464
## Loadings:
##     PC1  
## E1R 0.700
## E2R 0.780
## E3  0.691
## E4  0.758
## E5  0.644
## 
##                  PC1
## SS loadings    2.565
## Proportion Var 0.513
## 
##     vars    n mean   sd median trimmed  mad min max range  skew kurtosis
## E1R    1 2713 4.03 1.63      4    4.14 1.48   1   6     5 -0.38    -1.09
## E2R    2 2713 3.86 1.61      4    3.93 1.48   1   6     5 -0.22    -1.15
## E3     3 2713 4.00 1.35      4    4.07 1.48   1   6     5 -0.47    -0.46
## E4     4 2713 4.42 1.46      5    4.59 1.48   1   6     5 -0.83    -0.31
## E5     5 2713 4.42 1.34      5    4.57 1.48   1   6     5 -0.78    -0.09
##       se
## E1R 0.03
## E2R 0.03
## E3  0.03
## E4  0.03
## E5  0.03

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
E1R	Extraversion: Don’t talk a lot.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	23	2777	2800	4.03	1.63	1	3	4	5	6	▃▅▁▆▅▁▇▇
E2R	Extraversion: Find it difficult to approach others.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	16	2784	2800	3.86	1.61	1	3	4	5	6	▃▅▁▇▅▁▇▆
E3	Extraversion: Know how to captivate people.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	25	2775	2800	4	1.35	1	3	4	5	6	▂▃▁▃▇▁▇▃
E4	Extraversion: Make friends easily.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	9	2791	2800	4.42	1.46	1	4	5	6	6	▁▂▁▂▃▁▇▆
E5	Extraversion: Take charge.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	21	2779	2800	4.42	1.33	1	4	5	5	6	▁▂▁▂▅▁▇▅

Scale: open

Overview

Reliability: ω_ordinal [95% CI] = 0.68 [0.66;0.7].

Missing: 74.

Reliability details

Reliability

Information about this analysis:

Observations: 2726

Positive correlations: 10 out of 10 (100%)

Estimates assuming interval level:

Index	Estimate
Omega	0.6103741
Omega Psych Tot	0.6633845
Omega Psych H	0.5147633
Omega Ordinal	0.6825024
Cronbach Alpha	0.6025464
Greatest Lower Bound	0.6696422
Alpha Ordinal	0.6750928

Scatter matrix

Detailed output

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: O1, O2R, O3, O4, O5R
##               Observations: 2726
##      Positive correlations: 10 out of 10 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.61
##       Omega (hierarchical): 0.51
##    Revelle's omega (total): 0.66
## Greatest Lower Bound (GLB): 0.67
##              Coefficient H: 0.65
##           Cronbach's alpha: 0.6
## Confidence intervals:
##              Omega (total): [0.59, 0.63]
##           Cronbach's alpha: [0.58, 0.63]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.68
##  Ordinal Omega (hierarch.): 0.68
##   Ordinal Cronbach's alpha: 0.68
## Confidence intervals:
##      Ordinal Omega (total): [0.66, 0.7]
##   Ordinal Cronbach's alpha: [0.66, 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 ('?scaleStructure') for more information.
## 
## Eigen values: 1.98, 0.936, 0.825, 0.664, 0.595
## Loadings:
##     PC1  
## O1  0.666
## O2R 0.604
## O3  0.730
## O4  0.432
## O5R 0.673
## 
##                  PC1
## SS loadings    1.980
## Proportion Var 0.396
## 
##     vars    n mean   sd median trimmed  mad min max range  skew kurtosis
## O1     1 2726 4.82 1.13      5    4.96 1.48   1   6     5 -0.90     0.42
## O2R    2 2726 4.30 1.56      5    4.45 1.48   1   6     5 -0.60    -0.79
## O3     3 2726 4.44 1.22      5    4.56 1.48   1   6     5 -0.77     0.30
## O4     4 2726 4.90 1.22      5    5.10 1.48   1   6     5 -1.21     1.07
## O5R    5 2726 4.52 1.33      5    4.67 1.48   1   6     5 -0.74    -0.24
##       se
## O1  0.02
## O2R 0.03
## O3  0.02
## O4  0.02
## O5R 0.03

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
O1	Openness: Am full of ideas.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	22	2778	2800	4.82	1.13	1	4	5	6	6	▁▁▁▂▅▁▇▇
O2R	Openness: Avoid difficult reading material.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	0	2800	2800	4.29	1.57	1	3	5	6	6	▂▃▁▅▃▁▇▇
O3	Openness: Carry the conversation to a higher level.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	28	2772	2800	4.44	1.22	1	4	5	5	6	▁▁▁▂▆▁▇▅
O4	Openness: Spend time reflecting on things.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	14	2786	2800	4.89	1.22	1	4	5	6	6	▁▁▁▁▃▁▆▇
O5R	Openness: Will not probe deeply into a subject.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	20	2780	2800	4.51	1.33	1	4	5	6	6	▁▂▁▃▅▁▇▇

Scale: agree

Overview

Reliability: ω_ordinal [95% CI] = 0.77 [0.76;0.78].

Missing: 91.

Reliability details

Reliability

Information about this analysis:

Observations: 2709

Positive correlations: 10 out of 10 (100%)

Estimates assuming interval level:

Index	Estimate
Omega	0.7121290
Omega Psych Tot	0.7573212
Omega Psych H	0.6830031
Omega Ordinal	0.7696341
Cronbach Alpha	0.7037559
Greatest Lower Bound	0.7470268
Alpha Ordinal	0.7596084

Scatter matrix

Detailed output

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: A1R, A2, A3, A4, A5
##               Observations: 2709
##      Positive correlations: 10 out of 10 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.71
##       Omega (hierarchical): 0.68
##    Revelle's omega (total): 0.76
## Greatest Lower Bound (GLB): 0.75
##              Coefficient H: 0.77
##           Cronbach's alpha: 0.7
## Confidence intervals:
##              Omega (total): [0.7, 0.73]
##           Cronbach's alpha: [0.69, 0.72]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.77
##  Ordinal Omega (hierarch.): 0.77
##   Ordinal Cronbach's alpha: 0.76
## Confidence intervals:
##      Ordinal Omega (total): [0.76, 0.78]
##   Ordinal Cronbach's alpha: [0.75, 0.77]
## 
## 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 ('?scaleStructure') for more information.
## 
## Eigen values: 2.369, 0.891, 0.717, 0.558, 0.465
## Loadings:
##     PC1  
## A1R 0.509
## A2  0.764
## A3  0.798
## A4  0.614
## A5  0.716
## 
##                  PC1
## SS loadings    2.369
## Proportion Var 0.474
## 
##     vars    n mean   sd median trimmed  mad min max range  skew kurtosis
## A1R    1 2709 4.59 1.40      5    4.77 1.48   1   6     5 -0.83    -0.30
## A2     2 2709 4.80 1.18      5    4.97 1.48   1   6     5 -1.12     1.04
## A3     3 2709 4.60 1.30      5    4.78 1.48   1   6     5 -1.00     0.43
## A4     4 2709 4.68 1.49      5    4.91 1.48   1   6     5 -1.01     0.00
## A5     5 2709 4.55 1.26      5    4.70 1.48   1   6     5 -0.84     0.15
##       se
## A1R 0.03
## A2  0.02
## A3  0.03
## A4  0.03
## A5  0.02

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
A1R	Agreeableness: Am indifferent to the feelings of others.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	16	2784	2800	4.59	1.41	1	4	5	6	6	▁▂▁▃▃▁▇▇
A2	Agreeableness: Inquire about others’ well-being.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	27	2773	2800	4.8	1.17	1	4	5	6	6	▁▁▁▁▅▁▇▇
A3	Agreeableness: Know how to comfort others.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	26	2774	2800	4.6	1.3	1	4	5	6	6	▁▂▁▂▅▁▇▆
A4	Agreeableness: Love children.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	19	2781	2800	4.7	1.48	1	4	5	6	6	▁▂▁▁▃▁▅▇
A5	Agreeableness: Make people feel at ease.	integer	1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate	16	2784	2800	4.56	1.26	1	4	5	5	6	▁▂▁▂▅▁▇▆

Scale: neuro

Overview

Reliability: ω_ordinal [95% CI] = 0.84 [0.83;0.85].

Missing: 106.

Reliability details

Reliability

Information about this analysis:

Observations: 2694

Positive correlations: 10 out of 10 (100%)

Estimates assuming interval level:

Index	Estimate
Omega	0.8128444
Omega Psych Tot	0.8508430
Omega Psych H	0.7284528
Omega Ordinal	0.8388007
Cronbach Alpha	0.8133031
Greatest Lower Bound	0.8509286
Alpha Ordinal	0.8393180

Scatter matrix

Detailed output

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: N1R, N2R, N3R, N4R, N5R
##               Observations: 2694
##      Positive correlations: 10 out of 10 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.81
##       Omega (hierarchical): 0.73
##    Revelle's omega (total): 0.85
## Greatest Lower Bound (GLB): 0.85
##              Coefficient H: 0.85
##           Cronbach's alpha: 0.81
## Confidence intervals:
##              Omega (total): [0.8, 0.82]
##           Cronbach's alpha: [0.8, 0.82]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.84
##  Ordinal Omega (hierarch.): 0.82
##   Ordinal Cronbach's alpha: 0.84
## Confidence intervals:
##      Ordinal Omega (total): [0.83, 0.85]
##   Ordinal Cronbach's alpha: [0.83, 0.85]
## 
## 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 ('?scaleStructure') for more information.
## 
## Eigen values: 2.886, 0.78, 0.612, 0.428, 0.294
## Loadings:
##     PC1  
## N1R 0.818
## N2R 0.806
## N3R 0.813
## N4R 0.699
## N5R 0.646
## 
##                  PC1
## SS loadings    2.886
## Proportion Var 0.577
## 
##     vars    n mean   sd median trimmed  mad min max range  skew kurtosis
## N1R    1 2694 4.07 1.57      4    4.17 1.48   1   6     5 -0.38    -1.01
## N2R    2 2694 3.49 1.53      3    3.49 1.48   1   6     5  0.08    -1.05
## N3R    3 2694 3.78 1.60      4    3.84 1.48   1   6     5 -0.15    -1.18
## N4R    4 2694 3.81 1.57      4    3.88 1.48   1   6     5 -0.20    -1.10
## N5R    5 2694 4.03 1.62      4    4.14 1.48   1   6     5 -0.37    -1.07
##       se
## N1R 0.03
## N2R 0.03
## N3R 0.03
## N4R 0.03
## N5R 0.03

Summary statistics

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
N1R	Emotional Stability: Get angry easily.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	22	2778	2800	4.07	1.57	1	3	4	5	6	▂▅▁▆▅▁▇▇
N2R	Emotional Stability: Get irritated easily.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	21	2779	2800	3.49	1.53	1	2	3	5	6	▃▆▁▇▅▁▆▃
N3R	Emotional Stability: Have frequent mood swings.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	11	2789	2800	3.78	1.6	1	3	4	5	6	▃▆▁▇▅▁▇▆
N4R	Emotional Stability: Often feel blue.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	36	2764	2800	3.81	1.57	1	3	4	5	6	▃▅▁▇▅▁▇▆
N5R	Emotional Stability: Panic easily.	numeric	6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate	29	2771	2800	4.03	1.62	1	3	4	5	6	▃▃▁▆▅▁▇▇

Missingness report

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": "25 Personality items representing 5 factors",
  "description": "25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 2800 subjects are included here as a demonstration set for scale construction, factor analysis, and Item Response Theory analysis. Three additional demographic variables (sex, education, and age) are also included.\n\nThe first 25 items are organized by five putative factors: Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Opennness. The item data were collected using a 6 point response scale: 1 Very Inaccurate 2 Moderately Inaccurate 3 Slightly Inaccurate 4 Slightly Accurate 5 Moderately Accurate 6 Very Accurate\n\nTo see an example of the data collection technique, visit https://SAPA-project.org or the International Cognitive Ability Resource at https://icar-project.com. The items given were sampled from the International Personality Item Pool of Lewis Goldberg using the sampling technique of SAPA. This is a sample data set taken from the much larger SAPA data bank.\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name      |label                                                    |data_type |value_labels                                                                                                                         |scale_item_names        |missing |complete |n    |mean  |sd    |p0  |p25 |p50 |p75 |p100 |hist     |\n|:---------|:--------------------------------------------------------|:---------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------------------|:-------|:--------|:----|:-----|:-----|:---|:---|:---|:---|:----|:--------|\n|A1R       |Agreeableness: Am indifferent to the feelings of others. |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |16      |2784     |2800 |4.59  |1.41  |1   |4   |5   |6   |6    |▁▂▁▃▃▁▇▇ |\n|A2        |Agreeableness: Inquire about others' well-being.         |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |27      |2773     |2800 |4.8   |1.17  |1   |4   |5   |6   |6    |▁▁▁▁▅▁▇▇ |\n|A3        |Agreeableness: Know how to comfort others.               |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |26      |2774     |2800 |4.6   |1.3   |1   |4   |5   |6   |6    |▁▂▁▂▅▁▇▆ |\n|A4        |Agreeableness: Love children.                            |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |19      |2781     |2800 |4.7   |1.48  |1   |4   |5   |6   |6    |▁▂▁▁▃▁▅▇ |\n|A5        |Agreeableness: Make people feel at ease.                 |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |16      |2784     |2800 |4.56  |1.26  |1   |4   |5   |5   |6    |▁▂▁▂▅▁▇▆ |\n|C1        |Conscientiousness: Am exacting in my work.               |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |21      |2779     |2800 |4.5   |1.24  |1   |4   |5   |5   |6    |▁▁▁▂▅▁▇▅ |\n|C2        |Conscientiousness: Continue until everything is perfect. |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |24      |2776     |2800 |4.37  |1.32  |1   |4   |5   |5   |6    |▁▂▁▂▆▁▇▅ |\n|C3        |Conscientiousness: Do things according to a plan.        |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |20      |2780     |2800 |4.3   |1.29  |1   |4   |5   |5   |6    |▁▂▁▂▆▁▇▅ |\n|C4R       |Conscientiousness: Do things in a half-way manner.       |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |26      |2774     |2800 |4.45  |1.38  |1   |3   |5   |6   |6    |▁▂▁▅▅▁▇▇ |\n|C5R       |Conscientiousness: Waste my time.                        |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |16      |2784     |2800 |3.7   |1.63  |1   |2   |4   |5   |6    |▃▆▁▇▅▁▇▆ |\n|E1R       |Extraversion: Don't talk a lot.                          |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |23      |2777     |2800 |4.03  |1.63  |1   |3   |4   |5   |6    |▃▅▁▆▅▁▇▇ |\n|E2R       |Extraversion: Find it difficult to approach others.      |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |16      |2784     |2800 |3.86  |1.61  |1   |3   |4   |5   |6    |▃▅▁▇▅▁▇▆ |\n|E3        |Extraversion: Know how to captivate people.              |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |25      |2775     |2800 |4     |1.35  |1   |3   |4   |5   |6    |▂▃▁▃▇▁▇▃ |\n|E4        |Extraversion: Make friends easily.                       |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |9       |2791     |2800 |4.42  |1.46  |1   |4   |5   |6   |6    |▁▂▁▂▃▁▇▆ |\n|E5        |Extraversion: Take charge.                               |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |21      |2779     |2800 |4.42  |1.33  |1   |4   |5   |5   |6    |▁▂▁▂▅▁▇▅ |\n|N1R       |Emotional Stability: Get angry easily.                   |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |22      |2778     |2800 |4.07  |1.57  |1   |3   |4   |5   |6    |▂▅▁▆▅▁▇▇ |\n|N2R       |Emotional Stability: Get irritated easily.               |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |21      |2779     |2800 |3.49  |1.53  |1   |2   |3   |5   |6    |▃▆▁▇▅▁▆▃ |\n|N3R       |Emotional Stability: Have frequent mood swings.          |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |11      |2789     |2800 |3.78  |1.6   |1   |3   |4   |5   |6    |▃▆▁▇▅▁▇▆ |\n|N4R       |Emotional Stability: Often feel blue.                    |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |36      |2764     |2800 |3.81  |1.57  |1   |3   |4   |5   |6    |▃▅▁▇▅▁▇▆ |\n|N5R       |Emotional Stability: Panic easily.                       |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |29      |2771     |2800 |4.03  |1.62  |1   |3   |4   |5   |6    |▃▃▁▆▅▁▇▇ |\n|O1        |Openness: Am full of ideas.                              |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |22      |2778     |2800 |4.82  |1.13  |1   |4   |5   |6   |6    |▁▁▁▂▅▁▇▇ |\n|O2R       |Openness: Avoid difficult reading material.              |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |0       |2800     |2800 |4.29  |1.57  |1   |3   |5   |6   |6    |▂▃▁▅▃▁▇▇ |\n|O3        |Openness: Carry the conversation to a higher level.      |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |28      |2772     |2800 |4.44  |1.22  |1   |4   |5   |5   |6    |▁▁▁▂▆▁▇▅ |\n|O4        |Openness: Spend time reflecting on things.               |integer   |1. Very Inaccurate, - 2. Moderately Inaccurate, - 3. Slightly Inaccurate, - 4. Slightly Accurate, - 5. Moderately Accurate, - 6. Very Accurate |NA                      |14      |2786     |2800 |4.89  |1.22  |1   |4   |5   |6   |6    |▁▁▁▁▃▁▆▇ |\n|O5R       |Openness: Will not probe deeply into a subject.          |numeric   |6. Very Inaccurate, - 5. Moderately Inaccurate, - 4. Slightly Inaccurate, - 3. Slightly Accurate, - 2. Moderately Accurate, - 1. Very Accurate |NA                      |20      |2780     |2800 |4.51  |1.33  |1   |4   |5   |6   |6    |▁▂▁▃▅▁▇▇ |\n|gender    |Self-reported gender                                     |numeric   |1. male, - 2. female                                                                                                                   |NA                      |0       |2800     |2800 |1.67  |0.47  |1   |1   |2   |2   |2    |▃▁▁▁▁▁▁▇ |\n|education |Highest degree                                           |numeric   |1. in high school, - 2. finished high school, - 3. some college, - 4. college graduate, - 5. graduate degree                                 |NA                      |223     |2577     |2800 |3.19  |1.11  |1   |3   |3   |4   |5    |▂▂▁▇▁▂▁▃ |\n|age       |age in years                                             |integer   |NA                                                                                                                                   |NA                      |0       |2800     |2800 |28.78 |11.13 |3   |20  |26  |35  |86   |▁▇▆▃▂▁▁▁ |\n|consc     |5 C items aggregated by rowMeans                         |numeric   |NA                                                                                                                                   |C1, C2, C3, C4R, C5R    |93      |2707     |2800 |4.26  |0.95  |1   |3.6 |4.4 |5   |6    |▁▁▂▅▇▇▇▅ |\n|extra     |5 E items aggregated by rowMeans                         |numeric   |NA                                                                                                                                   |E1R, E2R, E3, E4, E5    |87      |2713     |2800 |4.14  |1.06  |1   |3.4 |4.2 |5   |6    |▁▁▃▅▇▇▇▆ |\n|open      |5 O items aggregated by rowMeans                         |numeric   |NA                                                                                                                                   |O1, O2R, O3, O4, O5R    |74      |2726     |2800 |4.59  |0.81  |1.2 |4   |4.6 |5.2 |6    |▁▁▁▂▇▇▇▅ |\n|agree     |5 A items aggregated by rowMeans                         |numeric   |NA                                                                                                                                   |A1R, A2, A3, A4, A5     |91      |2709     |2800 |4.64  |0.9   |1   |4.2 |4.8 |5.4 |6    |▁▁▁▂▃▆▇▇ |\n|neuro     |5 N items aggregated by rowMeans                         |numeric   |NA                                                                                                                                   |N1R, N2R, N3R, N4R, N5R |106     |2694     |2800 |3.84  |1.19  |1   |3   |4   |4.8 |6    |▂▃▆▇▇▇▇▅ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.7.6.9000).",
  "identifier": "https://CRAN.R-project.org/package=psych",
  "datePublished": "2010-01-01",
  "creator": {
    "@type": "Person",
    "givenName": "William",
    "familyName": "Revelle",
    "email": "revelle@northwestern.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Northwestern University"
    }
  },
  "citation": "Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.",
  "url": "https://CRAN.R-project.org/package=psych",
  "temporalCoverage": "Spring 2010",
  "spatialCoverage": "Online",
  "keywords": ["A1R", "A2", "A3", "A4", "A5", "C1", "C2", "C3", "C4R", "C5R", "E1R", "E2R", "E3", "E4", "E5", "N1R", "N2R", "N3R", "N4R", "N5R", "O1", "O2R", "O3", "O4", "O5R", "gender", "education", "age", "consc", "extra", "open", "agree", "neuro"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "A1R",
      "description": "Agreeableness: Am indifferent to the feelings of others.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "A2",
      "description": "Agreeableness: Inquire about others' well-being.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "A3",
      "description": "Agreeableness: Know how to comfort others.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "A4",
      "description": "Agreeableness: Love children.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "A5",
      "description": "Agreeableness: Make people feel at ease.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "C1",
      "description": "Conscientiousness: Am exacting in my work.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "C2",
      "description": "Conscientiousness: Continue until everything is perfect.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "C3",
      "description": "Conscientiousness: Do things according to a plan.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "C4R",
      "description": "Conscientiousness: Do things in a half-way manner.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "C5R",
      "description": "Conscientiousness: Waste my time.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "E1R",
      "description": "Extraversion: Don't talk a lot.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "E2R",
      "description": "Extraversion: Find it difficult to approach others.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "E3",
      "description": "Extraversion: Know how to captivate people.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "E4",
      "description": "Extraversion: Make friends easily.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "E5",
      "description": "Extraversion: Take charge.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "N1R",
      "description": "Emotional Stability: Get angry easily.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "N2R",
      "description": "Emotional Stability: Get irritated easily.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "N3R",
      "description": "Emotional Stability: Have frequent mood swings.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "N4R",
      "description": "Emotional Stability: Often feel blue.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "N5R",
      "description": "Emotional Stability: Panic easily.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "O1",
      "description": "Openness: Am full of ideas.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "O2R",
      "description": "Openness: Avoid difficult reading material.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "O3",
      "description": "Openness: Carry the conversation to a higher level.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "O4",
      "description": "Openness: Spend time reflecting on things.",
      "value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "O5R",
      "description": "Openness: Will not probe deeply into a subject.",
      "value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
      "maxValue": 6,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Self-reported gender",
      "value": "1. male,\n2. female",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "education",
      "description": "Highest degree",
      "value": "1. in high school,\n2. finished high school,\n3. some college,\n4. college graduate,\n5. graduate degree",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "age in years",
      "@type": "propertyValue"
    },
    {
      "name": "consc",
      "description": "5 C items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "extra",
      "description": "5 E items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "open",
      "description": "5 O items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "agree",
      "description": "5 A items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "neuro",
      "description": "5 N items aggregated by rowMeans",
      "@type": "propertyValue"
    }
  ]
}`

Codebook example with manual labelling

Ruben Arslan

2019-02-19

How to add variable and value labels

Aggregating scales

Metadata

Description

Variables

gender

Distribution

Summary statistics

Value labels

education

Distribution

Summary statistics

Value labels

age

Distribution

Summary statistics

Scale: consc

Overview

Reliability details

Reliability

Information about this analysis:

Estimates assuming interval level:

Scatter matrix

Summary statistics

Scale: extra

Overview

Reliability details

Reliability

Information about this analysis:

Estimates assuming interval level:

Scatter matrix

Summary statistics

Scale: open

Overview

Reliability details

Reliability

Information about this analysis:

Estimates assuming interval level:

Scatter matrix

Summary statistics

Scale: agree

Overview

Reliability details

Reliability

Information about this analysis:

Estimates assuming interval level:

Scatter matrix

Summary statistics

Scale: neuro

Overview

Reliability details

Reliability

Information about this analysis:

Estimates assuming interval level:

Scatter matrix

Summary statistics

Missingness report

Codebook table

Contents