The bvq_vocabulary() function allows to extract
vocabulary sizes for individual responses to any of the questionnaires.
It takes the output of the bvq_responses() function as an
argument, and returns several measures of vocabulary size base on such
dataset.
To compute vocabulary size, we first need to run
bvq_responses() (although if this argument is not provided,
bvq_responses() is run under the hood):
library(bvq)
# vocabularies will be computed from these datasets
participants <- bvq_participants()
responses <- bvq_responses(participants = participants)
bvq_vocabulary(participants, responses)#> # A tibble: 34,465 × 16
#> child_id response_id time version version_list date_birth date_started
#> <chr> <chr> <dbl> <chr> <chr> <date> <date>
#> 1 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 2 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 3 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 4 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 5 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 6 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 7 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 8 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 9 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> 10 58298 BL1879 2 bvq-lockdown C 2020-04-27 2022-10-08
#> # ℹ 34,455 more rows
#> # ℹ 9 more variables: date_finished <date>, item <chr>, response <int>,
#> # sex <chr>, doe_catalan <dbl>, doe_spanish <dbl>, doe_others <dbl>,
#> # edu_parent1 <chr>, edu_parent2 <chr>The bvq_vocabulary() computes four measures of
vocabulary size.
-
Total (
total_*): total number of item the child was reported to know, summing both languages together. -
L1 (
l1_*): number of word the child was reported to know in their dominant language (e.g., Catalan words for a child whose language of most exposure is Catalan). -
L2 (
l2_*): number of word the child was reported to know in their non-dominant language (e.g., Spanish words for a child whose language of most exposure is Catalan) -
Conceptual (
concept_*): number of concepts the child know at least one word for, regardless of the language the word belongs to. -
TE (
te_*): number of translation equivalents the child knows, i.e., or how many concepts the child know one word in each language for.
Vocabulary sizes are, by default, computed in two different scales:
-
Proportion (
*_prop): proportion of the items the child was reported to known, from the total of items that were included in the questionnaire, and caregivers answered to. -
Counts (
*_count): sum of the total number of items the child was reported to know.
The scale returned by bvq_vocabulary() can be modified
with the .scale argument, which takes "prop"
for proportions (default), and "count" for counts. Both can
be computed using .scale = c("prop", "count"). For instance
we can get vocabulary sizes as proportions running:
#> # A tibble: 76 × 9
#> child_id response_id type total_prop l1_prop l2_prop concept_prop te_prop
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 58298 BL1879 underst… 0.518 0.924 0.141 0.881 0.114
#> 2 58298 BL1879 produces 0.430 0.784 0.103 0.743 0.0838
#> 3 58361 BL1863 underst… 0.703 0.805 0.601 0.826 0.574
#> 4 58361 BL1863 produces 0.476 0.573 0.379 0.617 0.331
#> 5 58298 BL1848 underst… 0.370 0.702 0.0623 0.662 0.0486
#> 6 58298 BL1848 produces 0.00985 0.0205 0 0.0189 0
#> 7 58068 BL1833 underst… 0.737 0.850 0.633 0.846 0.563
#> 8 58068 BL1833 produces 0.328 0.528 0.146 0.526 0.102
#> 9 57177 BL1748 underst… 0.837 0.814 0.857 0.887 0.714
#> 10 57177 BL1748 produces 0.568 0.678 0.466 0.757 0.329
#> # ℹ 66 more rows
#> # ℹ 1 more variable: contents <list>To get vocabulary sizes as counts, we can run this instead:
#> # A tibble: 76 × 9
#> child_id response_id type total_count l1_count l2_count concept_count
#> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 58298 BL1879 understands 368 316 52 326
#> 2 58298 BL1879 produces 306 268 38 275
#> 3 58361 BL1863 understands 490 281 209 289
#> 4 58361 BL1863 produces 332 200 132 216
#> 5 58298 BL1848 understands 263 240 23 245
#> 6 58298 BL1848 produces 7 7 0 7
#> 7 58068 BL1833 understands 523 288 235 314
#> 8 58068 BL1833 produces 233 179 54 195
#> 9 57177 BL1748 understands 594 276 318 329
#> 10 57177 BL1748 produces 403 230 173 281
#> # ℹ 66 more rows
#> # ℹ 2 more variables: te_count <int>, contents <list>Finally, two types of vocabulary sizes are computed:
-
Comprehension (
understands): number of items the child understands. -
Production: (
produces) number of items the child says.
These two measures are returned in the long format under the
type column.
Vocabulary contents
In additional to the vocabulary size scores,
bvq_vocabulary() also returns the column
contents. This column is a list containing the items marked
as acquired for comprehension or production. For instance:
Conditional vocabulary size: the ... extra
arguments
We can also compute vocabulary sizes conditional to some variables at
the item or participant level, such as semantic/functional
(semantic_category) or language profile (lp),
using the argument ... argument. Just take a look at the
variables included in the data frame returned by bvq_logs()
or in the pool dataset. For each participant, vocabulary
sizes are computed for each level or combination of levels of the
variables included in the columns included in .... You can
use this argument to preserve participant-level information in the
output data frame. For instance, we can keep information about the
language profile (lp) of the participant:
bvq_vocabulary(participants, responses, lp)We can also can also preserve information about the items, like the
semantic/functional category of the words
(semantic_category). In this case, the vocabulary sizes
will be computed for each level of the semantic_category
variable:
bvq_vocabulary(participants, responses, semantic_category)
#> # A tibble: 1,970 × 10
#> child_id response_id type semantic_category total_prop l1_prop l2_prop
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 58298 BL1879 understands Adventures 0.75 1 0.5
#> 2 58298 BL1879 produces Adventures 0.7 0.9 0.5
#> 3 58298 BL1879 understands Animals 0.516 0.968 0.0645
#> 4 58298 BL1879 produces Animals 0.452 0.839 0.0645
#> 5 58298 BL1879 understands Parts of animals 0.409 0.818 0
#> 6 58298 BL1879 produces Parts of animals 0.136 0.273 0
#> 7 58298 BL1879 understands Parts of things 0.375 0.75 0
#> 8 58298 BL1879 produces Parts of things 0.125 0.25 0
#> 9 58298 BL1879 understands Question words 0.5 1 0
#> 10 58298 BL1879 produces Question words 0.5 1 0
#> # ℹ 1,960 more rows
#> # ℹ 3 more variables: concept_prop <dbl>, te_prop <dbl>, contents <list>Finally, we can preserve more than one variable, including
combinations of participant-level and item-level variables, such as
language profile (lp), age (age) and
grammatical class (class):
bvq_vocabulary(participants, responses, age, lp, semantic_category)
#> # A tibble: 1,970 × 12
#> child_id response_id type age lp semantic_category total_prop l1_prop
#> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 58298 BL1879 unders… 29.4 Mono… Adventures 0.75 1
#> 2 58298 BL1879 produc… 29.4 Mono… Adventures 0.7 0.9
#> 3 58298 BL1879 unders… 29.4 Mono… Animals 0.516 0.968
#> 4 58298 BL1879 produc… 29.4 Mono… Animals 0.452 0.839
#> 5 58298 BL1879 unders… 29.4 Mono… Parts of animals 0.409 0.818
#> 6 58298 BL1879 produc… 29.4 Mono… Parts of animals 0.136 0.273
#> 7 58298 BL1879 unders… 29.4 Mono… Parts of things 0.375 0.75
#> 8 58298 BL1879 produc… 29.4 Mono… Parts of things 0.125 0.25
#> 9 58298 BL1879 unders… 29.4 Mono… Question words 0.5 1
#> 10 58298 BL1879 produc… 29.4 Mono… Question words 0.5 1
#> # ℹ 1,960 more rows
#> # ℹ 4 more variables: l2_prop <dbl>, concept_prop <dbl>, te_prop <dbl>,
#> # contents <list>