In this vignette, you can how to add metadata to a dataset when it isn’t yet 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
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
## 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
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
## 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
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
##
## $class
## [1] "labelled"
# We could also assign the attributes manually, but then there's no error checking.
attributes(bfi$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
##
## $class
## [1] "labelled"
Aggregating scales
Now, we can form scale aggregates. The codebook
function aggregate_and_document_scale
does this for us and automatically sets the correct attributes.
bfi$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")))
knitr::opts_chunk$set(warning = TRUE, message = TRUE, error = TRUE, echo = FALSE)
Finally, we can generate our codebook.
Items
gender
Self-reported gender
Distribution

0 missings.
Summary statistics
gender |
Self-reported gender |
labelled |
FALSE |
1. male, |
|
|
|
|
|
2. female |
0 2800 |
2800 2 |
2: |
1881, 1: 919, NA: 0 |
|
|
|
|
|
education
Highest degree
Distribution

223 missings.
Summary statistics
education |
Highest degree |
labelled |
FALSE |
1. in high school, |
|
|
|
|
|
2. finished |
high school, |
|
|
|
|
|
|
|
|
3. some coll |
ege, |
|
|
|
|
|
|
|
|
4. college g |
raduate, |
|
|
|
|
|
|
|
|
5. graduate |
degree 223 |
2577 |
2800 5 |
3: 1249, 5: 418, 4: 394, 2: 292 |
|
|
|
|
|
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 missings.
Summary statistics
age |
age in years |
integer |
0 |
2800 |
2800 |
28.78 |
11.13 |
3 |
20 |
26 |
35 |
86 |
▁▇▆▃▂▁▁▁ |
Scale: consc
Overview
Reliability: Cronbach’s α [95% CI] = 0.73 [0.71;0.74].
Missings: 93.


Reliability details
Reliability
95% Confidence Interval
0.7267 |
0.7301 |
0.6942 |
0.351 |
2.705 |
0.008117 |
4.266 |
0.9513 |
0.34 |
Reliability if an item is dropped:
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 |
Item statistics
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 |
Non missing response frequency for each item
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 |
Summary statistics
C1 |
Conscientiousness: Am exacting in my work. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
21 |
2779 |
2800 |
6 |
5: 1018, 4: 655, 6: 597, 3: 275 |
C2 |
Conscientiousness: Continue until everything is perfect. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
24 |
2776 |
2800 |
6 |
5: 962, 4: 643, 6: 550, 3: 296 |
C3 |
Conscientiousness: Do things according to a plan. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
20 |
2780 |
2800 |
6 |
5: 942, 4: 741, 6: 472, 3: 293 |
C4R |
Conscientiousness: Do things in a half-way manner. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
26 |
2774 |
2800 |
6 |
5: 794, 6: 769, 4: 472, 3: 448 |
C5R |
Conscientiousness: Waste my time. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
16 |
2784 |
2800 |
6 |
3: 614, 5: 567, 6: 504, 2: 466 |
Scale: open
Overview
Reliability: Cronbach’s α [95% CI] = 0.6 [0.58;0.62].
Missings: 74.


Reliability details
Reliability
95% Confidence Interval
0.6002 |
0.6073 |
0.5681 |
0.2362 |
1.546 |
0.01186 |
4.587 |
0.8084 |
0.2261 |
Reliability if an item is dropped:
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 |
Item statistics
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 |
Non missing response frequency for each item
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 |
Summary statistics
O1 |
Openness: Am full of ideas. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
22 |
2778 |
2800 |
6 |
5: 925, 6: 912, 4: 606, 3: 210 |
O2R |
Openness: Avoid difficult reading material. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
0 |
2800 |
2800 |
6 |
6: 805, 5: 717, 3: 435, 4: 388 |
O3 |
Openness: Carry the conversation to a higher level. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
28 |
2772 |
2800 |
6 |
5: 943, 4: 775, 6: 541, 3: 292 |
O4 |
Openness: Spend time reflecting on things. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
14 |
2786 |
2800 |
6 |
6: 1084, 5: 887, 4: 481, 3: 154 |
O5R |
Openness: Will not probe deeply into a subject. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
20 |
2780 |
2800 |
6 |
5: 883, 6: 746, 4: 526, 3: 364 |
Scale: agree
Overview
Reliability: Cronbach’s α [95% CI] = 0.7 [0.69;0.72].
Missings: 91.


Reliability details
Reliability
95% Confidence Interval
0.703 |
0.713 |
0.6828 |
0.332 |
2.485 |
0.008952 |
4.652 |
0.8984 |
0.3376 |
Reliability if an item is dropped:
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 |
Item statistics
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 |
Non missing response frequency for each item
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 |
Summary statistics
A1R |
Agreeableness: Am indifferent to the feelings of others. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
16 |
2784 |
2800 |
6 |
6: 922, 5: 818, 4: 402, 3: 337 |
A2 |
Agreeableness: Inquire about others’ well-being. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
27 |
2773 |
2800 |
6 |
5: 1023, 6: 873, 4: 553, 3: 151 |
A3 |
Agreeableness: Know how to comfort others. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
26 |
2774 |
2800 |
6 |
5: 986, 6: 755, 4: 564, 3: 207 |
A4 |
Agreeableness: Love children. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
19 |
2781 |
2800 |
6 |
6: 1147, 5: 654, 4: 451, 2: 215 |
A5 |
Agreeableness: Make people feel at ease. |
labelled |
FALSE |
1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
16 |
2784 |
2800 |
6 |
5: 973, 6: 695, 4: 617, 3: 254 |
Scale: neuro
Overview
Reliability: Cronbach’s α [95% CI] = 0.81 [0.8;0.82].
Missings: 106.


Reliability details
Reliability
95% Confidence Interval
0.814 |
0.8147 |
0.7991 |
0.4679 |
4.396 |
0.005607 |
3.838 |
1.196 |
0.4137 |
Reliability if an item is dropped:
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 |
Item statistics
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 |
Non missing response frequency for each item
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 |
Summary statistics
N1R |
Emotional Stability: Get angry easily. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
22 |
2778 |
2800 |
6 |
5: 654, 6: 654, 3: 515, 4: 427 |
N2R |
Emotional Stability: Get irritated easily. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
21 |
2779 |
2800 |
6 |
3: 709, 5: 535, 2: 510, 4: 411 |
N3R |
Emotional Stability: Have frequent mood swings. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
11 |
2789 |
2800 |
6 |
5: 638, 3: 591, 6: 499, 2: 439 |
N4R |
Emotional Stability: Often feel blue. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
36 |
2764 |
2800 |
6 |
5: 655, 3: 608, 6: 472, 4: 401 |
N5R |
Emotional Stability: Panic easily. |
labelled |
FALSE |
6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
29 |
2771 |
2800 |
6 |
5: 660, 6: 654, 3: 507, 4: 382 |
Missingness report
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.
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 |