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$get("fig.retina"))
library(dplyr)
library(codebook)
library(labelled)
pander::panderOptions("table.split.table", Inf)
ggplot2::theme_set(ggplot2::theme_bw())
data("bfi", package = 'psych')
bfi <- bfi %>% tbl_df()
data("bfi.dictionary", package = 'psych')
bfi.dictionary$variable = rownames(bfi.dictionary)
bfi.dictionary <- bfi.dictionary %>% tbl_df()
Let’s start by getting an overview of our dataset
## # 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.
## # A tibble: 28 x 8
## ItemLabel Item Giant3 Big6 Little12 Keying IPIP100 variable
## * <fct> <fct> <fct> <fct> <fct> <int> <fct> <chr>
## 1 q_146 Am indiffer… Cohes… Agreea… Compassi… -1 B5:A A1
## 2 q_1162 Inquire abo… Cohes… Agreea… Compassi… 1 B5:A A2
## 3 q_1206 Know how to… Cohes… Agreea… Compassi… 1 B5:A A3
## 4 q_1364 Love childr… Cohes… Agreea… Compassi… 1 B5:A A4
## 5 q_1419 Make people… Cohes… Agreea… Compassi… 1 B5:A A5
## 6 q_124 Am exacting… Stabi… Consci… Orderlin… 1 B5:C C1
## 7 q_530 Continue un… Stabi… Consci… Orderlin… 1 B5:C C2
## 8 q_619 Do things a… Stabi… Consci… Orderlin… 1 B5:C C3
## 9 q_626 Do things i… Stabi… Consci… Industri… -1 B5:C C4
## 10 q_1949 Waste my ti… Stabi… Consci… Industri… -1 B5:C C5
## # ... with 18 more rows
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
## ItemLabel Item Giant3 Big6 Little12 Keying IPIP100 variable
## <fct> <fct> <fct> <fct> <fct> <int> <fct> <chr>
## 1 gender males=1, female… <NA> <NA> <NA> NA <NA> gender
## 2 education in HS, fin HS, … <NA> <NA> <NA> NA <NA> educati…
## 3 age age in years <NA> <NA> <NA> NA <NA> age
## $label
## [1] "Self-reported gender"
var_label(bfi) <- list(age = "age in years", education = "Highest degree")
# or using dplyr syntax
bfi <- bfi %>% set_variable_labels(
age = "age in years",
education = "Highest degree")
Now, we saw that the value labels were encoded in the variable label. This is not what we want. Instead, we assign value labels.
bfi <- bfi %>%
add_value_labels(
gender = c("male" = 1, "female" = 2),
education = c("in high school" = 1, "finished high school" = 2,
"some college" = 3, "college graduate" = 4,
"graduate degree" = 5) # dont use abbreviations if you can avoid it
)
attributes(bfi$gender) # check what we're doing
## $label
## [1] "Self-reported gender"
##
## $labels
## male female
## 1 2
##
## $class
## [1] "labelled"
# We could also assign the attributes manually, but then there's no error checking.
attributes(bfi$gender) <- list(
label = "Self-reported gender",
labels = c(male = 1L, female = 2L),
class = "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.
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)
# turn the key-value data frame into a list
labels <- dict$label %>% as.character() %>% as.list() %>%
purrr::set_names(dict$variable)
# assign the list of labels to the bfi data frame
var_label(bfi) <- labels
# assign value labels to all likert items
value_labels <- c("Very Inaccurate" = 1,
"Moderately Inaccurate" = 2,
"Slightly Inaccurate" = 3,
"Slightly Accurate" = 4,
"Moderately Accurate" = 5,
"Very Accurate" = 6)
add_likert_label <- function(x) {
val_labels(x) <- value_labels
x
}
bfi <- bfi %>%
mutate_at(dict %>% pull(variable),
add_likert_label)
# reverse underlying values for the reverse-keyed items
bfi <- bfi %>%
mutate_at(dict %>% filter(Keying == -1) %>% pull(variable),
reverse_labelled_values) %>%
rename_at(dict %>% filter(Keying == -1) %>% pull(variable),
~ paste0(.,"R"))
attributes(bfi$A1R)
## $label
## [1] "Agreeableness: Am indifferent to the feelings of others."
##
## $labels
## Very Inaccurate Moderately Inaccurate Slightly Inaccurate
## 6 5 4
## Slightly Accurate Moderately Accurate Very Accurate
## 3 2 1
##
## $class
## [1] "labelled"
Now, we can form scale aggregates. The codebook
function aggregate_and_document_scale
does this for us and automatically sets the correct attributes.
bfi$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")))
Finally, we can generate our codebook.
Self-reported gender
0 missings.
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 | ▃▁▁▁▁▁▁▇ |
Highest degree
223 missings.
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 | ▂▂▁▇▁▂▁▃ |
age in years
## Error: `x` must be a double vector
0 missings.
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 | ▁▇▆▃▂▁▁▁ |
Reliability: Cronbach’s α [95% CI] = 0.73 [0.71;0.74].
Missings: 93.
lower | estimate | upper |
---|---|---|
0.7108 | 0.7267 | 0.7426 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | ase | mean | sd | median_r |
---|---|---|---|---|---|---|---|---|
0.7267 | 0.7301 | 0.6942 | 0.351 | 2.705 | 0.008117 | 4.266 | 0.9513 | 0.34 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | var.r | med.r | |
---|---|---|---|---|---|---|---|---|
C1 | 0.694 | 0.6964 | 0.6401 | 0.3645 | 2.294 | 0.009337 | 0.003733 | 0.3478 |
C2 | 0.6736 | 0.6749 | 0.6189 | 0.3416 | 2.076 | 0.009891 | 0.005605 | 0.3383 |
C3 | 0.6887 | 0.694 | 0.6443 | 0.3618 | 2.268 | 0.009564 | 0.007021 | 0.3597 |
C4R | 0.6538 | 0.6629 | 0.6028 | 0.3296 | 1.967 | 0.01066 | 0.003672 | 0.3237 |
C5R | 0.6897 | 0.6902 | 0.6283 | 0.3577 | 2.228 | 0.009562 | 0.001734 | 0.3476 |
n | raw.r | std.r | r.cor | r.drop | mean | sd | |
---|---|---|---|---|---|---|---|
C1 | 2779 | 0.6457 | 0.6702 | 0.5399 | 0.4502 | 4.502 | 1.241 |
C2 | 2776 | 0.6964 | 0.7097 | 0.6027 | 0.5046 | 4.37 | 1.318 |
C3 | 2780 | 0.6639 | 0.6748 | 0.5389 | 0.4642 | 4.304 | 1.289 |
C4R | 2774 | 0.7365 | 0.7306 | 0.6413 | 0.5525 | 4.447 | 1.375 |
C5R | 2784 | 0.7197 | 0.6819 | 0.566 | 0.4775 | 3.703 | 1.629 |
1 | 2 | 3 | 4 | 5 | 6 | miss | |
---|---|---|---|---|---|---|---|
C1 | 0.02627 | 0.05793 | 0.09896 | 0.2357 | 0.3663 | 0.2148 | 0.0075 |
C2 | 0.03206 | 0.08501 | 0.1066 | 0.2316 | 0.3465 | 0.1981 | 0.008571 |
C3 | 0.03022 | 0.08921 | 0.1054 | 0.2665 | 0.3388 | 0.1698 | 0.007143 |
C4R | 0.02271 | 0.08219 | 0.1615 | 0.1702 | 0.2862 | 0.2772 | 0.009286 |
C5R | 0.1024 | 0.1674 | 0.2205 | 0.125 | 0.2037 | 0.181 | 0.005714 |
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 | ▃▆▁▇▅▁▇▆ |
Reliability: Cronbach’s α [95% CI] = 0.76 [0.75;0.78].
Missings: 87.
lower | estimate | upper |
---|---|---|
0.748 | 0.7617 | 0.7755 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | ase | mean | sd | median_r |
---|---|---|---|---|---|---|---|---|
0.7617 | 0.7618 | 0.7266 | 0.3901 | 3.198 | 0.007027 | 4.145 | 1.061 | 0.3818 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | var.r | med.r | |
---|---|---|---|---|---|---|---|---|
E1R | 0.7257 | 0.7254 | 0.6731 | 0.3978 | 2.642 | 0.00837 | 0.004394 | 0.3818 |
E2R | 0.6902 | 0.6931 | 0.6342 | 0.3608 | 2.258 | 0.009509 | 0.002792 | 0.3546 |
E3 | 0.7279 | 0.7262 | 0.6737 | 0.3988 | 2.653 | 0.008241 | 0.007098 | 0.396 |
E4 | 0.7019 | 0.7032 | 0.6464 | 0.372 | 2.37 | 0.009073 | 0.003275 | 0.3767 |
E5 | 0.7436 | 0.7442 | 0.6914 | 0.4211 | 2.909 | 0.007824 | 0.004344 | 0.419 |
n | raw.r | std.r | r.cor | r.drop | mean | sd | |
---|---|---|---|---|---|---|---|
E1R | 2777 | 0.7238 | 0.7027 | 0.5882 | 0.5163 | 4.026 | 1.632 |
E2R | 2784 | 0.7797 | 0.7647 | 0.6936 | 0.6054 | 3.858 | 1.605 |
E3 | 2775 | 0.683 | 0.7011 | 0.5827 | 0.5046 | 4.001 | 1.353 |
E4 | 2791 | 0.7467 | 0.7459 | 0.6625 | 0.578 | 4.422 | 1.458 |
E5 | 2779 | 0.6432 | 0.6637 | 0.5229 | 0.4542 | 4.416 | 1.335 |
1 | 2 | 3 | 4 | 5 | 6 | miss | |
---|---|---|---|---|---|---|---|
E1R | 0.08678 | 0.1322 | 0.162 | 0.1455 | 0.2348 | 0.2387 | 0.008214 |
E2R | 0.09124 | 0.1383 | 0.2152 | 0.1232 | 0.2407 | 0.1915 | 0.005714 |
E3 | 0.05369 | 0.1056 | 0.1485 | 0.2977 | 0.2677 | 0.1268 | 0.008929 |
E4 | 0.05016 | 0.09387 | 0.0971 | 0.1612 | 0.3375 | 0.2601 | 0.003214 |
E5 | 0.03418 | 0.07953 | 0.1036 | 0.2227 | 0.3383 | 0.2217 | 0.0075 |
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 | ▁▂▁▂▅▁▇▅ |
Reliability: Cronbach’s α [95% CI] = 0.6 [0.58;0.62].
Missings: 74.
lower | estimate | upper |
---|---|---|
0.5769 | 0.6002 | 0.6234 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | ase | mean | sd | median_r |
---|---|---|---|---|---|---|---|---|
0.6002 | 0.6073 | 0.5681 | 0.2362 | 1.546 | 0.01186 | 4.587 | 0.8084 | 0.2261 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | var.r | med.r | |
---|---|---|---|---|---|---|---|---|
O1 | 0.5316 | 0.5341 | 0.4762 | 0.2227 | 1.146 | 0.01428 | 0.009206 | 0.2278 |
O2R | 0.5672 | 0.5701 | 0.5103 | 0.249 | 1.326 | 0.01334 | 0.007597 | 0.2164 |
O3 | 0.4974 | 0.5006 | 0.4418 | 0.2004 | 1.002 | 0.01527 | 0.007096 | 0.1961 |
O4 | 0.6115 | 0.6208 | 0.5603 | 0.2904 | 1.637 | 0.0119 | 0.00437 | 0.2854 |
O5R | 0.5117 | 0.528 | 0.4738 | 0.2185 | 1.118 | 0.01504 | 0.01154 | 0.2039 |
n | raw.r | std.r | r.cor | r.drop | mean | sd | |
---|---|---|---|---|---|---|---|
O1 | 2778 | 0.6151 | 0.6496 | 0.5156 | 0.3907 | 4.816 | 1.13 |
O2R | 2800 | 0.654 | 0.5991 | 0.4298 | 0.3321 | 4.287 | 1.565 |
O3 | 2772 | 0.6747 | 0.6926 | 0.5911 | 0.4505 | 4.438 | 1.221 |
O4 | 2786 | 0.4979 | 0.5193 | 0.2903 | 0.2179 | 4.892 | 1.221 |
O5R | 2780 | 0.6704 | 0.6577 | 0.5237 | 0.4162 | 4.51 | 1.328 |
1 | 2 | 3 | 4 | 5 | 6 | miss | |
---|---|---|---|---|---|---|---|
O1 | 0.007919 | 0.03708 | 0.07559 | 0.2181 | 0.333 | 0.3283 | 0.007857 |
O2R | 0.06393 | 0.09857 | 0.1554 | 0.1386 | 0.2561 | 0.2875 | 0 |
O3 | 0.02742 | 0.05231 | 0.1053 | 0.2796 | 0.3402 | 0.1952 | 0.01 |
O4 | 0.01974 | 0.04487 | 0.05528 | 0.1726 | 0.3184 | 0.3891 | 0.005 |
O5R | 0.02518 | 0.06871 | 0.1309 | 0.1892 | 0.3176 | 0.2683 | 0.007143 |
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 | ▁▂▁▃▅▁▇▇ |
Reliability: Cronbach’s α [95% CI] = 0.7 [0.69;0.72].
Missings: 91.
lower | estimate | upper |
---|---|---|
0.6855 | 0.703 | 0.7206 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | ase | mean | sd | median_r |
---|---|---|---|---|---|---|---|---|
0.703 | 0.713 | 0.6828 | 0.332 | 2.485 | 0.008952 | 4.652 | 0.8984 | 0.3376 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | var.r | med.r | |
---|---|---|---|---|---|---|---|---|
A1R | 0.7185 | 0.7255 | 0.673 | 0.3979 | 2.643 | 0.008725 | 0.00653 | 0.376 |
A2 | 0.6172 | 0.6256 | 0.5795 | 0.2946 | 1.671 | 0.0119 | 0.01695 | 0.2866 |
A3 | 0.6003 | 0.6129 | 0.5578 | 0.2836 | 1.584 | 0.01244 | 0.009431 | 0.3219 |
A4 | 0.6858 | 0.6935 | 0.6498 | 0.3613 | 2.263 | 0.009825 | 0.01586 | 0.3651 |
A5 | 0.643 | 0.6555 | 0.6051 | 0.3224 | 1.903 | 0.01115 | 0.0126 | 0.3376 |
n | raw.r | std.r | r.cor | r.drop | mean | sd | |
---|---|---|---|---|---|---|---|
A1R | 2784 | 0.5807 | 0.5664 | 0.3764 | 0.3084 | 4.587 | 1.408 |
A2 | 2773 | 0.728 | 0.748 | 0.6665 | 0.5636 | 4.802 | 1.172 |
A3 | 2774 | 0.7603 | 0.7674 | 0.7092 | 0.587 | 4.604 | 1.302 |
A4 | 2781 | 0.6542 | 0.6307 | 0.4712 | 0.3944 | 4.7 | 1.48 |
A5 | 2784 | 0.6866 | 0.6992 | 0.5957 | 0.4886 | 4.56 | 1.259 |
1 | 2 | 3 | 4 | 5 | 6 | miss | |
---|---|---|---|---|---|---|---|
A1R | 0.02945 | 0.0801 | 0.121 | 0.1444 | 0.2938 | 0.3312 | 0.005714 |
A2 | 0.01695 | 0.04544 | 0.05445 | 0.1994 | 0.3689 | 0.3148 | 0.009643 |
A3 | 0.03244 | 0.062 | 0.07462 | 0.2033 | 0.3554 | 0.2722 | 0.009286 |
A4 | 0.04639 | 0.07731 | 0.06652 | 0.1622 | 0.2352 | 0.4124 | 0.006786 |
A5 | 0.02119 | 0.06681 | 0.09124 | 0.2216 | 0.3495 | 0.2496 | 0.005714 |
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 | ▁▂▁▂▅▁▇▆ |
Reliability: Cronbach’s α [95% CI] = 0.81 [0.8;0.82].
Missings: 106.
lower | estimate | upper |
---|---|---|
0.803 | 0.814 | 0.825 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | ase | mean | sd | median_r |
---|---|---|---|---|---|---|---|---|
0.814 | 0.8147 | 0.7991 | 0.4679 | 4.396 | 0.005607 | 3.838 | 1.196 | 0.4137 |
raw_alpha | std.alpha | G6(smc) | average_r | S/N | alpha se | var.r | med.r | |
---|---|---|---|---|---|---|---|---|
N1R | 0.7581 | 0.7583 | 0.711 | 0.4396 | 3.138 | 0.007474 | 0.006093 | 0.4132 |
N2R | 0.7632 | 0.7634 | 0.7159 | 0.4465 | 3.226 | 0.007322 | 0.005421 | 0.4137 |
N3R | 0.7553 | 0.7567 | 0.7312 | 0.4374 | 3.11 | 0.007663 | 0.01787 | 0.3946 |
N4R | 0.7953 | 0.7969 | 0.7688 | 0.4952 | 3.924 | 0.006405 | 0.01817 | 0.489 |
N5R | 0.8126 | 0.8128 | 0.787 | 0.5205 | 4.343 | 0.005854 | 0.01374 | 0.5344 |
n | raw.r | std.r | r.cor | r.drop | mean | sd | |
---|---|---|---|---|---|---|---|
N1R | 2778 | 0.8 | 0.8025 | 0.7648 | 0.6672 | 4.071 | 1.571 |
N2R | 2779 | 0.7873 | 0.7917 | 0.7496 | 0.6526 | 3.492 | 1.526 |
N3R | 2789 | 0.8081 | 0.806 | 0.7425 | 0.6748 | 3.783 | 1.603 |
N4R | 2764 | 0.7152 | 0.7145 | 0.5985 | 0.5428 | 3.814 | 1.57 |
N5R | 2771 | 0.6806 | 0.6744 | 0.5318 | 0.4865 | 4.03 | 1.619 |
1 | 2 | 3 | 4 | 5 | 6 | miss | |
---|---|---|---|---|---|---|---|
N1R | 0.06983 | 0.1202 | 0.1854 | 0.1537 | 0.2354 | 0.2354 | 0.007857 |
N2R | 0.104 | 0.1835 | 0.2551 | 0.1479 | 0.1925 | 0.1169 | 0.0075 |
N3R | 0.09215 | 0.1574 | 0.2119 | 0.1309 | 0.2288 | 0.1789 | 0.003929 |
N4R | 0.08973 | 0.1375 | 0.22 | 0.1451 | 0.237 | 0.1708 | 0.01286 |
N5R | 0.08697 | 0.118 | 0.183 | 0.1379 | 0.2382 | 0.236 | 0.01036 |
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 | ▃▃▁▆▅▁▇▇ |
Among those who finished the survey. Only variables that have missings are shown.
## Warning: Could not figure out who finished the surveys, because the
## variables expired and ended were missing.
description | E4 | N3R | O4 | A1R | A5 | C5R | E2R | A4 | C3 | O5R | C1 | E5 | N2R | N1R | O1 | E1R | C2 | E3 | A3 | C4R | A2 | O3 | N5R | N4R | open | extra | agree | consc | neuro | education | var_miss | n_miss |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Missings in 0 variables | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 2236 |
Missings per variable | 9 | 11 | 14 | 16 | 16 | 16 | 16 | 19 | 20 | 20 | 21 | 21 | 21 | 22 | 22 | 23 | 24 | 25 | 26 | 26 | 27 | 28 | 29 | 36 | 74 | 87 | 91 | 93 | 106 | 223 | 1182 | 1182 |
Missings in 1 variables | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 200 |
99 other, less frequent patterns | 95 | 92 | 93 | 90 | 93 | 93 | 92 | 87 | 87 | 88 | 93 | 94 | 92 | 89 | 90 | 91 | 86 | 87 | 87 | 87 | 87 | 83 | 86 | 87 | 65 | 68 | 59 | 61 | 61 | 80 | 427 | 364 |
name | label | data_type | value_labels | scale_item_names | 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 |
NA | 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 |
NA | 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 |
NA | 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 |
NA | 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 |
NA | 16 | 2784 | 2800 | 4.56 | 1.26 | 1 | 4 | 5 | 5 | 6 | ▁▂▁▂▅▁▇▆ |
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 | ▁▁▁▂▅▁▇▅ |
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 | ▁▂▁▂▆▁▇▅ |
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 | ▁▂▁▂▆▁▇▅ |
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 | ▁▂▁▅▅▁▇▇ |
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 | ▃▆▁▇▅▁▇▆ |
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 | ▃▅▁▆▅▁▇▇ |
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 | ▃▅▁▇▅▁▇▆ |
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 | ▂▃▁▃▇▁▇▃ |
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 | ▁▂▁▂▃▁▇▆ |
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 | ▁▂▁▂▅▁▇▅ |
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 | ▂▅▁▆▅▁▇▇ |
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 | ▃▆▁▇▅▁▆▃ |
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 | ▃▆▁▇▅▁▇▆ |
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 | ▃▅▁▇▅▁▇▆ |
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 | ▃▃▁▆▅▁▇▇ |
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 | ▁▁▁▂▅▁▇▇ |
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 | ▂▃▁▅▃▁▇▇ |
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 | ▁▁▁▂▆▁▇▅ |
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 | ▁▁▁▁▃▁▆▇ |
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 | ▁▂▁▃▅▁▇▇ |
gender | Self-reported gender | numeric | 1. male, 2. female |
NA | 0 | 2800 | 2800 | 1.67 | 0.47 | 1 | 1 | 2 | 2 | 2 | ▃▁▁▁▁▁▁▇ |
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 | ▂▂▁▇▁▂▁▃ |
age | age in years | integer | NA | NA | 0 | 2800 | 2800 | 28.78 | 11.13 | 3 | 20 | 26 | 35 | 86 | ▁▇▆▃▂▁▁▁ |
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 | ▁▁▂▅▇▇▇▅ |
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 | ▁▁▃▅▇▇▇▆ |
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 | ▁▁▁▂▇▇▇▅ |
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 | ▁▁▁▂▃▆▇▇ |
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 | ▂▃▆▇▇▇▇▅ |