The function bvq_responses() retrieves participants’
responses to the Barcelona Vocabulary Questionnaire (BVQ) using the formr API, and returns them
along participant- and item-level information. This function returns a
tidy data frame in which each row is one participant’s response to an
individual item.
library(bvq)
library(dplyr)
participants <- bvq_participants()
responses <- bvq_responses(participants)
# select relevant variables
responses %>%
select(id, time, item, response, randomisation) %>%
filter(!is.na(response)) # drop unanswered items## # 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>Consulting participant-level information
Participant-level properties like language profile variables can be
extracted using the bvq_logs() function.
bvq_logs(participants, responses) %>%
select(child_id, time, age, lp, starts_with("doe_"))## # A tibble: 38 × 7
## child_id time age lp doe_spanish doe_catalan doe_others
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 58298 2 29.4 Monolingual 0 1 0
## 2 58361 1 24.4 Bilingual 0.75 0.25 0
## 3 58298 1 24.9 Monolingual 0.1 0.9 0
## 4 58068 2 26.2 Bilingual 0.25 0.75 0
## 5 57177 4 30.6 Bilingual 0.2 0.7 0.1
## 6 56911 4 32.2 Monolingual 0.15 0.85 0
## 7 57436 4 30.8 Bilingual 0.35 0.65 0
## 8 57534 1 21.5 Monolingual 0.05 0.95 0
## 9 57436 3 25.5 Bilingual 0.4 0.6 0
## 10 57336 1 21.2 Bilingual 0.4 0.6 0
## # ℹ 28 more rowsItem properties
Item-level properties can be consulted in the pool data
frame (see ?pool):
data("pool")
pool## # A tibble: 1,590 × 14
## item language te label xsampa n_lemmas is_multiword subtlex_lemma
## <chr> <chr> <int> <chr> <chr> <int> <lgl> <chr>
## 1 cat_pessigol… Catalan 1 (fer… "p@.s… 1 FALSE pessigolles
## 2 cat_abracar Catalan 2 abra… "@.B4… 1 FALSE abraçar
## 3 cat_obrir Catalan 3 obrir "u\"B… 1 FALSE obrir
## 4 cat_acabar Catalan 4 acab… "@.k@… 1 FALSE acabar
## 5 cat_llancar Catalan 5 llan… "L@n\… 1 FALSE llançar
## 6 cat_apagar Catalan 6 apag… "@.p@… 1 FALSE apagar
## 7 cat_aprendre Catalan 7 apre… "@\"p… 1 FALSE aprendre
## 8 cat_esgarrap… Catalan 8 esga… "@z.g… 1 FALSE esgarrapar
## 9 cat_ajudar Catalan 9 ajud… "@.Zu… 1 FALSE ajudar
## 10 cat_ballar Catalan 10 ball… "b@\"… 1 FALSE ballar
## # ℹ 1,580 more rows
## # ℹ 6 more variables: wordbank_lemma <chr>, childes_lemma <chr>,
## # semantic_category <chr>, class <chr>, version <list>, include <lgl>Computing vocabulary sizes
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
data frame.
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):
bvq_vocabulary(participants,
responses,
lp, # to keep participants' language profile
.scale = "prop" # to return estimates as proportions
)## # A tibble: 76 × 10
## child_id response_id type lp total_prop l1_prop l2_prop concept_prop
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 58298 BL1879 understan… Mono… 0.518 0.924 0.141 0.881
## 2 58298 BL1879 produces Mono… 0.430 0.784 0.103 0.743
## 3 58361 BL1863 understan… Bili… 0.703 0.805 0.601 0.826
## 4 58361 BL1863 produces Bili… 0.476 0.573 0.379 0.617
## 5 58298 BL1848 understan… Mono… 0.370 0.702 0.0623 0.662
## 6 58298 BL1848 produces Mono… 0.00985 0.0205 0 0.0189
## 7 58068 BL1833 understan… Bili… 0.737 0.850 0.633 0.846
## 8 58068 BL1833 produces Bili… 0.328 0.528 0.146 0.526
## 9 57177 BL1748 understan… Bili… 0.837 0.814 0.857 0.887
## 10 57177 BL1748 produces Bili… 0.568 0.678 0.466 0.757
## # ℹ 66 more rows
## # ℹ 2 more variables: te_prop <dbl>, contents <list>Computing word acquisition norms
The function bvq::bvq_norms() computes the proportion of
children in the sample that understand or produce each item, sometimes
called word prevalence. This function returns the
estimated probability of an average participant understanding or
producing each word. The bvq_norms() function allows to
condition this probability on the age, language profile or language
dominance of participants, among other variables. Proportions are
adjusted for zero- and one-inflation following Gelman, Hill, and Vehtari
(2020).
# items we want to compute norms for
bvq_norms(participants,
responses,
item = c("cat_gos", "cat_gat"),
age = c(12, 35)
)## # A tibble: 96 × 9
## te item label age type item_dominance .sum .n .prop
## <int> <chr> <chr> <dbl> <chr> <chr> <int> <int> <dbl>
## 1 173 cat_gat gat 12 produces L1 0 1 0.4
## 2 173 cat_gat gat 12 produces L2 0 3 0.286
## 3 173 cat_gat gat 13 produces L1 0 1 0.4
## 4 173 cat_gat gat 13 produces L2 0 1 0.4
## 5 173 cat_gat gat 14 produces L2 0 1 0.4
## 6 173 cat_gat gat 14 produces L1 0 1 0.4
## 7 173 cat_gat gat 15 produces L1 0 1 0.4
## 8 173 cat_gat gat 15 produces L2 0 1 0.4
## 9 173 cat_gat gat 17 produces L1 0 1 0.4
## 10 173 cat_gat gat 19 produces L2 0 1 0.4
## # ℹ 86 more rows