Tutorial: How to build a dictionary file
Jürgen Wilbert
2023-04-23
build_dic_file.RmdIn the following examples I will lay out the construction of a dictionary file.
First, we need an example data.frame to work with (i.e. the
ex_scaledic_data example dataset):
| rel_1 | rel_2 | rel_3 | rel_4 | rel_5 | sui_1 | sui_2 | sui_3 | sui_4 | sui_5 | gender | age |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 6 | 1 | 2 | 1 | 2 | 2 | 2 | 3 | 0 | f | 10.5 |
| 3 | 1 | 1 | 2 | 1 | 4 | 1 | 4 | 1 | 0 | d | 6.5 |
| 1 | 66 | 2 | 3 | 4 | 3 | 2 | 1 | 3 | 3 | d | 10.5 |
| 2 | 2 | 5 | 3 | 1 | 0 | 2 | 1 | 2 | 3 | f | 13.0 |
| 5 | 5 | 4 | 2 | 3 | 1 | 3 | 1 | 1 | 3 | f | 8.0 |
| 3 | 4 | 5 | 5 | 1 | 3 | 2 | 3 | 1 | 1 | d | 6.0 |
| 3 | 2 | 2 | 2 | 3 | 0 | 4 | 3 | 4 | 2 | f | 6.0 |
| 4 | 3 | 4 | 3 | 3 | 0 | 0 | 4 | 3 | 3 | f | 10.0 |
| 2 | 6 | 2 | 11 | 1 | 1 | -999 | 4 | 2 | 1 | f | 8.5 |
| 6 | 6 | 4 | 5 | 3 | 55 | 4 | 0 | 3 | 1 | f | 8.5 |
| 6 | 1 | 3 | 1 | 66 | 4 | 0 | 2 | 3 | 4 | m | 7.5 |
| 6 | 1 | 2 | 5 | 3 | 1 | 3 | 1 | 3 | 0 | m | -999.0 |
| 3 | 1 | 5 | 5 | 5 | 2 | 2 | 1 | 3 | 0 | m | 7.0 |
| 6 | 1 | -999 | 1 | 1 | 4 | 2 | 3 | 3 | 0 | m | 7.5 |
| 2 | 2 | 1 | 5 | 3 | 2 | 3 | 2 | 2 | 0 | d | 8.5 |
| 3 | 5 | 5 | 4 | 2 | 1 | 0 | 0 | 1 | 2 | d | 13.0 |
| 4 | 4 | 5 | 5 | 1 | 3 | 1 | 4 | 66 | 0 | -999 | 10.5 |
| 5 | 5 | 4 | -999 | 4 | 1 | 4 | 2 | 0 | 1 | m | 10.0 |
| 6 | 5 | 4 | 2 | 4 | 0 | 0 | 2 | 3 | 3 | m | 8.0 |
| 2 | 3 | 5 | 2 | 3 | 1 | 3 | 2 | 4 | 3 | m | 6.0 |
Second, we need a dictionary file that is also a
data.frame. Each row of a dictionary file addresses a
variable (also called item in this context) and each column
holds a specific attribute. These attributes can be divided in two
classes. Firstly, predefined attributes with a specific name
and additional attributed that can have any name. The
predefined attributes are identified by their specific names.
Theses are: item_name, item_label,
values, value_labels, missing,
weight, and type1.
Additional attributes can have any name and are mainly used for selecting items or displaying additional information of an item (e.g., the scale and subscale an item belongs to; the reference and the author of an item; the translation of an item).
Building a dictionary step by step
We start the example with a very simple dictionary file containing
only item labels and the corresponding item names (i.e, the
corresponding variable names in our data frame)2. The dictionary file
has two columns, item_name and item_label:
| item_name | item_label |
|---|---|
| rel_1 | How often do you attend church or other religious meetings? |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life |
| rel_5 | I try hard to carry my religion over into all other dealings in life |
| sui_1 | Did you feel tense in the last week? |
| sui_2 | Did you feel blue in the last week? |
| sui_3 | Did you feel irritated in the last week? |
| sui_4 | Did you feel inferior in the last week? |
| sui_5 | Did you have problems falling asleep in the last week? |
| gender | gender |
| age | age |
We combine the data file and the dic file with the
apply_dic() function:
dat_dic <- apply_dic(ex_scaledic_data, dic_file)
1: 'type' attribute missing and replaced with an estimation (12x)We get the message that the dic file does not contain information of
the datatype (e.g. integer, factor,
character). The type attribute is used in
various scaledic functions. For example when the data is
checked for invalid values, missing values are imputed, or scales are
scored. A variable can be one of the following types:
integer for numbers without decimals, float
for numbers with decimals, character for variables with
text, and factor for variables with text or numbers that
are levels of a factor. Scaledic will estimate the data type
from the given data when the type attribute is not provided
in the dic file.
Let us add type information to the dic file:
| item_name | item_label | type |
|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | integer |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | integer |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | integer |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | integer |
| rel_5 | I try hard to carry my religion over into all other dealings in life | integer |
| sui_1 | Did you feel tense in the last week? | integer |
| sui_2 | Did you feel blue in the last week? | integer |
| sui_3 | Did you feel irritated in the last week? | integer |
| sui_4 | Did you feel inferior in the last week? | integer |
| sui_5 | Did you have problems falling asleep in the last week? | integer |
| gender | gender | factor |
| age | age | float |
Most item values are of type integer (the item has only
whole numbers) with the exception of gender which is of
type factor (i.e. it has a nominal scale with several
levels).
dat_dic <- apply_dic(ex_scaledic_data, dic_file)Now we can add weight information to the dic file. The
weight attribute tells various scaledic functions
a) whether the item is inverted or not, and b) how the values of an item
are weighted. If a weight value is unsigned (e.g. 1), the
item is not inverted. If a weight value has a negative sign
(e.g. -1), the item is inverted. If the (absolute) value is
not 1, the item will be weighted when calculating the item
scores (e.g. 1.5 will give an item a weight of 1.5).
| item_name | item_label | weight | type |
|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1 | integer |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1 | integer |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1 | integer |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1 | integer |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1 | integer |
| sui_1 | Did you feel tense in the last week? | 1 | integer |
| sui_2 | Did you feel blue in the last week? | 1 | integer |
| sui_3 | Did you feel irritated in the last week? | 1 | integer |
| sui_4 | Did you feel inferior in the last week? | 1 | integer |
| sui_5 | Did you have problems falling asleep in the last week? | 1 | integer |
| gender | gender | 1 | factor |
| age | age | 1 | float |
In this example, all weights are 1 and no
item is inverted.
Again, we join dic and data file:
dat_dic <- apply_dic(ex_scaledic_data, dic_file)Now we add the values attribute to the dic file.
values defines the valid values that a variable can take.
Explicitly defining the values makes it possible to
automatically identify invalid values in a data frame (e.g. typos).
values are also necessary to reverse score an inverted
item. For coding the values, you provide the possible
values separated with a comma (e.g., 1, 2, 3, 4 for the
integers 1 to 4). It is also possible to use a colon to define a range
of integers (e.g., 5:11 indicated all integers starting
with 5 and ending with 11). For type float, values
represent the maximum and the minimum of valid values
(e.g. 5, 11 for all values within 5 and 11 including
decimal numbers). For variables of type factor or
character you may want to provide text values. in that
case, put these values within quotes (e.g., 'm', 'f', 'd'
indicates three valid text entries).
Here is the dic file with added values:
| item_name | item_label | values | weight | type |
|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1:6 | 1 | integer |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1:6 | 1 | integer |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1:5 | 1 | integer |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1:5 | 1 | integer |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1:5 | 1 | integer |
| sui_1 | Did you feel tense in the last week? | 0:4 | 1 | integer |
| sui_2 | Did you feel blue in the last week? | 0:4 | 1 | integer |
| sui_3 | Did you feel irritated in the last week? | 0:4 | 1 | integer |
| sui_4 | Did you feel inferior in the last week? | 0:4 | 1 | integer |
| sui_5 | Did you have problems falling asleep in the last week? | 0:4 | 1 | integer |
| gender | gender | ‘m’, ‘f’, ‘d’ | 1 | factor |
| age | age | 5, 11 | 1 | float |
dat_dic <- apply_dic(ex_scaledic_data, dic_file)Invalid values can be replaced automatically with the
check_values() function.
dat_dic <- check_values(dat_dic, report = TRUE, replace = NA)
Found the following invalid values:
'rel_2'
Row: 3
Value: 66
'rel_3'
Row: 14
Value: -999
'rel_4'
Row: 9 18
Value: 11 -999
'rel_5'
Row: 11
Value: 66
'sui_1'
Row: 10
Value: 55
'sui_2'
Row: 9
Value: -999
'sui_4'
Row: 17
Value: 66
'age'
Row: 4 12 16
Value: 13 -999 13
dat_dic %>% kable(caption = "data frame with replaced invalid values")| rel_1 | rel_2 | rel_3 | rel_4 | rel_5 | sui_1 | sui_2 | sui_3 | sui_4 | sui_5 | gender | age |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 6 | 1 | 2 | 1 | 2 | 2 | 2 | 3 | 0 | f | 10.5 |
| 3 | 1 | 1 | 2 | 1 | 4 | 1 | 4 | 1 | 0 | d | 6.5 |
| 1 | NA | 2 | 3 | 4 | 3 | 2 | 1 | 3 | 3 | d | 10.5 |
| 2 | 2 | 5 | 3 | 1 | 0 | 2 | 1 | 2 | 3 | f | NA |
| 5 | 5 | 4 | 2 | 3 | 1 | 3 | 1 | 1 | 3 | f | 8.0 |
| 3 | 4 | 5 | 5 | 1 | 3 | 2 | 3 | 1 | 1 | d | 6.0 |
| 3 | 2 | 2 | 2 | 3 | 0 | 4 | 3 | 4 | 2 | f | 6.0 |
| 4 | 3 | 4 | 3 | 3 | 0 | 0 | 4 | 3 | 3 | f | 10.0 |
| 2 | 6 | 2 | NA | 1 | 1 | NA | 4 | 2 | 1 | f | 8.5 |
| 6 | 6 | 4 | 5 | 3 | NA | 4 | 0 | 3 | 1 | f | 8.5 |
| 6 | 1 | 3 | 1 | NA | 4 | 0 | 2 | 3 | 4 | m | 7.5 |
| 6 | 1 | 2 | 5 | 3 | 1 | 3 | 1 | 3 | 0 | m | NA |
| 3 | 1 | 5 | 5 | 5 | 2 | 2 | 1 | 3 | 0 | m | 7.0 |
| 6 | 1 | NA | 1 | 1 | 4 | 2 | 3 | 3 | 0 | m | 7.5 |
| 2 | 2 | 1 | 5 | 3 | 2 | 3 | 2 | 2 | 0 | d | 8.5 |
| 3 | 5 | 5 | 4 | 2 | 1 | 0 | 0 | 1 | 2 | d | NA |
| 4 | 4 | 5 | 5 | 1 | 3 | 1 | 4 | NA | 0 | NA | 10.5 |
| 5 | 5 | 4 | NA | 4 | 1 | 4 | 2 | 0 | 1 | m | 10.0 |
| 6 | 5 | 4 | 2 | 4 | 0 | 0 | 2 | 3 | 3 | m | 8.0 |
| 2 | 3 | 5 | 2 | 3 | 1 | 3 | 2 | 4 | 3 | m | 6.0 |
Next, we add value_labels. Value labels give longer
labels for all or some of the values. Value labels are coded in the form
value = label with a semicolon separating each entry.
m = male; f = female; d = diverse codes three value label
(quotes are not necessary).
Here is the dic file with added value_labels:
| item_name | item_label | values | value_labels | weight | type |
|---|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1:6 | 1 = Never; 2 = Once a year or less; 3 = A few times a year; 4 = A few times a month; 5 = Once a week; 6 = More than once/week | 1 | integer |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1:6 | 1 = Rarely or never; 2 = A few times a month; 3 = Once a week; 4 = Two or more times/week; 5 = Daily; 6 = More than once a day | 1 | integer |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | integer |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | integer |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | integer |
| sui_1 | Did you feel tense in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer |
| sui_2 | Did you feel blue in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer |
| sui_3 | Did you feel irritated in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer |
| sui_4 | Did you feel inferior in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer |
| sui_5 | Did you have problems falling asleep in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer |
| gender | gender | ‘m’, ‘f’, ‘d’ | m = male; f = female; d = diverse | 1 | factor |
| age | age | 5, 11 | 5 = min; 11 = max | 1 | float |
dat_dic <- apply_dic(ex_scaledic_data, dic_file) %>% check_values(replace = NA)Now lets see the coding for some of the variables:
dat_dic$rel_1
# How often do you attend church or other religious meetings?
# Data type is integer
# Valid values: 1:6
# Value labels:
# 1 = Never
# 2 = Once a year or less
# 3 = A few times a year
# 4 = A few times a month
# 5 = Once a week
# 6 = More than once/week
[1] 1 3 1 2 5 3 3 4 2 6 6 6 3 6 2 3 4 5 6 2Valid values are all integers from 1 to 6 and each value has a label.
dat_dic$sui_1
# Did you feel tense in the last week?
# Data type is integer
# Valid values: 0:4
# Value labels:
# 0 = not at all
# 4 = extremely
[1] 2 4 3 0 1 3 0 0 1 NA 4 1 2 4 2 1 3 1 0 1Here, valid values are 0, 1, 2, 3, 4 (short
0:4) but only the poles (0 and 4)
have labels.
dat_dic$gender
# gender
# Data type is factor
# Value labels:
# m = male
# f = female
# d = diverse
[1] f d d f f d f f f f m m m m d
[16] d <NA> m m m
Levels: m f dgender is of type factor. The valid values
'm', 'f', 'd' have been turned into three factor levels
with the corresponding labels.
Caution: The labels provided in the
value_labelsattribute are not automtically turned into the levels of a factor. This is because R internally would turn the values (here'm', 'f', 'd') into integers (here1, 2, 3)
dat_dic$age
# age
# Data type is float
# Valid values: From 5 to 11
# Value labels:
# 5 = min
# 11 = max
[1] 10.5 6.5 10.5 NA 8.0 6.0 6.0 10.0 8.5 8.5 7.5 NA 7.0 7.5 8.5
[16] NA 10.5 10.0 8.0 6.0age can take all numbers from 5 to 11 (minimum is 5 and
maximum is 11).
It is a common practice to code missing values with a specific number
(rather than just leaving an empty entry on a datasheet). In the example
dataset used here, -999 is used as a missing value. To
account for this, we add the missing attribute to the
dic file containing the missing number (e.g.,
-999). Multiple missing values are separated by commas
(e.g. -99, -77).
| item_name | item_label | values | value_labels | weight | type | missing |
|---|---|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1:6 | 1 = Never; 2 = Once a year or less; 3 = A few times a year; 4 = A few times a month; 5 = Once a week; 6 = More than once/week | 1 | integer | -999 |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1:6 | 1 = Rarely or never; 2 = A few times a month; 3 = Once a week; 4 = Two or more times/week; 5 = Daily; 6 = More than once a day | 1 | integer | -999 |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | integer | -999 |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | integer | -999 |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | integer | -999 |
| sui_1 | Did you feel tense in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer | -999 |
| sui_2 | Did you feel blue in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer | -999 |
| sui_3 | Did you feel irritated in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer | -999 |
| sui_4 | Did you feel inferior in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer | -999 |
| sui_5 | Did you have problems falling asleep in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | integer | -999 |
| gender | gender | ‘m’, ‘f’, ‘d’ | m = male; f = female; d = diverse | 1 | factor | |
| age | age | 5, 11 | 5 = min; 11 = max | 1 | float | -999 |
The values within the missing attributes are
automatically replaced with NA when joining a data with a dic file:
dat_dic <- apply_dic(ex_scaledic_data, dic_file)
1: Missing values replaced with NATo turn off this behavior, set the argument
replace_missing = FALSE. Default, values are not checked
for invalid entries. To do this, either set
check_values = TRUE or apply the
check_values() function.
dat_dic <- apply_dic(ex_scaledic_data, dic_file, check_values = TRUE)
1: Invalid values replaced with NA
2: Missing values replaced with NA
dat_dic %>%
slice(9:20) %>%
kable(caption = "Extract from the data frame with replaced missing values and checked values")| rel_1 | rel_2 | rel_3 | rel_4 | rel_5 | sui_1 | sui_2 | sui_3 | sui_4 | sui_5 | gender | age |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 6 | 2 | NA | 1 | 1 | NA | 4 | 2 | 1 | f | 8.5 |
| 6 | 6 | 4 | 5 | 3 | NA | 4 | 0 | 3 | 1 | f | 8.5 |
| 6 | 1 | 3 | 1 | NA | 4 | 0 | 2 | 3 | 4 | m | 7.5 |
| 6 | 1 | 2 | 5 | 3 | 1 | 3 | 1 | 3 | 0 | m | NA |
| 3 | 1 | 5 | 5 | 5 | 2 | 2 | 1 | 3 | 0 | m | 7.0 |
| 6 | 1 | NA | 1 | 1 | 4 | 2 | 3 | 3 | 0 | m | 7.5 |
| 2 | 2 | 1 | 5 | 3 | 2 | 3 | 2 | 2 | 0 | d | 8.5 |
| 3 | 5 | 5 | 4 | 2 | 1 | 0 | 0 | 1 | 2 | d | NA |
| 4 | 4 | 5 | 5 | 1 | 3 | 1 | 4 | NA | 0 | NA | 10.5 |
| 5 | 5 | 4 | NA | 4 | 1 | 4 | 2 | 0 | 1 | m | 10.0 |
| 6 | 5 | 4 | 2 | 4 | 0 | 0 | 2 | 3 | 3 | m | 8.0 |
| 2 | 3 | 5 | 2 | 3 | 1 | 3 | 2 | 4 | 3 | m | 6.0 |
In the last step of this tutorial we will add information about the
scales that the items belong to. Therefore, we add a new attribute
called scale to the dic file and another attribute
scale_label for a longer description (we could have named
these attributes in any other way as they are not predefined
attributes):
| item_name | item_label | scale | scale_label | values | value_labels | type | weight | missing |
|---|---|---|---|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | rel | Religious beliefs | 1:6 | 1 = Never; 2 = Once a year or less; 3 = A few times a year; 4 = A few times a month; 5 = Once a week; 6 = More than once/week | integer | 1 | -999 |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | rel | Religious beliefs | 1:6 | 1 = Rarely or never; 2 = A few times a month; 3 = Once a week; 4 = Two or more times/week; 5 = Daily; 6 = More than once a day | integer | 1 | -999 |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | rel | Religious beliefs | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | integer | 1 | -999 |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | rel | Religious beliefs | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | integer | 1 | -999 |
| rel_5 | I try hard to carry my religion over into all other dealings in life | rel | Religious beliefs | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | integer | 1 | -999 |
| sui_1 | Did you feel tense in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | integer | 1 | -999 |
| sui_2 | Did you feel blue in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | integer | 1 | -999 |
| sui_3 | Did you feel irritated in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | integer | 1 | -999 |
| sui_4 | Did you feel inferior in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | integer | 1 | -999 |
| sui_5 | Did you have problems falling asleep in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | integer | 1 | -999 |
| gender | gender | misc | Miscellaneous | ‘m’, ‘f’, ‘d’ | m = male; f = female; d = diverse | factor | 1 | |
| age | age | misc | Miscellaneous | 5, 11 | 5 = min; 11 = max | float | 1 | -999 |
dat_dic <- apply_dic(ex_scaledic_data, dic_file, check_values = TRUE)
1: Invalid values replaced with NA
2: Missing values replaced with NAWhen can use the scale attribute to select items:
dat_dic %>%
select_items(scale == "rel") %>%
descriptives()
name valid missing mean sd min max range median mad
1 rel_1 20 0 3.65 1.76 1 6 5 3 1.48
2 rel_2 19 1 3.32 1.92 1 6 5 3 2.97
3 rel_3 19 1 3.37 1.54 1 5 4 4 1.48
4 rel_4 18 2 3.17 1.50 1 5 4 3 1.48
5 rel_5 19 1 2.47 1.31 1 5 4 3 1.48and the rename_items() function to get the item
labels:
dat_dic %>%
select_items(scale == "rel") %>%
rename_items() %>%
descriptives() %>%
kable()| name | valid | missing | mean | sd | min | max | range | median | mad |
|---|---|---|---|---|---|---|---|---|---|
| How often do you attend church or other religious meetings? | 20 | 0 | 3.65 | 1.76 | 1 | 6 | 5 | 3 | 1.48 |
| How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 19 | 1 | 3.32 | 1.92 | 1 | 6 | 5 | 3 | 2.97 |
| In my life, I experience the presence of the Divine (i.e., God) | 19 | 1 | 3.37 | 1.54 | 1 | 5 | 4 | 4 | 1.48 |
| My religious beliefs are what really lie behind my whole approach to life | 18 | 2 | 3.17 | 1.50 | 1 | 5 | 4 | 3 | 1.48 |
| I try hard to carry my religion over into all other dealings in life | 19 | 1 | 2.47 | 1.31 | 1 | 5 | 4 | 3 | 1.48 |