The function 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).
If argument item is left NULL (default), proportions are
computed for all items (currently 1,590). This may take time. If you
need to compute norms for specific items, you can provide the item
identifiers in the item argument. Available items can be
consulted in the pool data set:
library(bvq)
head(pool)
#> # A tibble: 6 × 14
#> item language te label xsampa n_lemmas is_multiword subtlex_lemma
#> <chr> <chr> <int> <chr> <chr> <int> <lgl> <chr>
#> 1 cat_pessigoll… Catalan 1 (fer… "[email protected]… 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
#> # ℹ 6 more variables: wordbank_lemma <chr>, childes_lemma <chr>,
#> # semantic_category <chr>, class <chr>, version <list>, include <lgl>Let’s go through an example of how to use the
bvq_norms() function for a specific set of items.
# norms will be computed from these datasets
participants <- bvq_participants()
responses <- bvq_responses(participants = participants)
# items we want to compute norms for
my_items <- c("cat_gos", "cat_gat")
norms <- bvq_norms(
participants = participants,
responses = responses,
item = my_items,
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 11 produces L2 0 1 0.4
#> 2 173 cat_gat gat 12 produces L1 0 1 0.4
#> 3 173 cat_gat gat 12 produces L2 0 3 0.286
#> 4 173 cat_gat gat 13 produces L1 0 2 0.333
#> 5 173 cat_gat gat 14 produces L1 0 2 0.333
#> 6 173 cat_gat gat 15 produces L1 0 1 0.4
#> 7 173 cat_gat gat 15 produces L2 0 1 0.4
#> 8 173 cat_gat gat 17 produces L1 0 1 0.4
#> 9 173 cat_gat gat 19 produces L2 0 1 0.4
#> 10 173 cat_gat gat 20 produces L2 1 2 0.5
#> # ℹ 86 more rowsIf we want to retrieve the acquisition norms of our item of interest,
but also those of its translation equivalent, we can use the argument
te. This argument can take the value TRUE if
we want to also include the norms of the translation of the items
specified in the item argument. For example:
my_items <- c("cat_gos", "cat_gat")
bvq_norms(
participants = participants,
responses = responses,
item = my_items,
te = TRUE,
age = c(15, 16)
)
#> # A tibble: 16 × 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 15 produ… L1 0 1 0.4
#> 2 173 cat_gat gat 15 produ… L2 0 1 0.4
#> 3 173 cat_gat gat 15 under… L1 0 1 0.4
#> 4 173 cat_gat gat 15 under… L2 0 1 0.4
#> 5 173 spa_gato gato / gatito 15 produ… L2 0 1 0.4
#> 6 173 spa_gato gato / gatito 15 produ… L1 0 1 0.4
#> 7 173 spa_gato gato / gatito 15 under… L2 0 1 0.4
#> 8 173 spa_gato gato / gatito 15 under… L1 1 1 0.6
#> 9 195 cat_gos gos / gosset 15 produ… L1 0 1 0.4
#> 10 195 cat_gos gos / gosset 15 produ… L2 0 1 0.4
#> 11 195 cat_gos gos / gosset 15 under… L1 1 1 0.6
#> 12 195 cat_gos gos / gosset 15 under… L2 0 1 0.4
#> 13 195 spa_perro perro / perrito 15 produ… L2 0 1 0.4
#> 14 195 spa_perro perro / perrito 15 produ… L1 0 1 0.4
#> 15 195 spa_perro perro / perrito 15 under… L2 0 1 0.4
#> 16 195 spa_perro perro / perrito 15 under… L1 1 1 0.6We can even indicate a specific translation equivalent in the
te argument to retrieve its norms, while leaving the
argument item blank:
bvq_norms(
participants = participants,
responses = responses,
te = 175,
age = c(25, 29)
)
#> # A tibble: 24 × 9
#> te item label age type item_dominance .sum .n .prop
#> <int> <chr> <chr> <dbl> <chr> <chr> <int> <int> <dbl>
#> 1 175 cat_cuc cuc 25 produces L1 1 2 0.5
#> 2 175 cat_cuc cuc 26 produces L1 1 1 0.6
#> 3 175 cat_cuc cuc 26 produces L2 0 1 0.4
#> 4 175 cat_cuc cuc 27 produces L1 0 1 0.4
#> 5 175 cat_cuc cuc 29 produces L1 1 1 0.6
#> 6 175 cat_cuc cuc 29 produces L2 0 1 0.4
#> 7 175 cat_cuc cuc 25 understands L1 1 2 0.5
#> 8 175 cat_cuc cuc 26 understands L1 1 1 0.6
#> 9 175 cat_cuc cuc 26 understands L2 0 1 0.4
#> 10 175 cat_cuc cuc 27 understands L1 1 1 0.6
#> # ℹ 14 more rowsWe may be interested in computing the acquisition norms of some items
of interest, while preserving some participant-level or item-level
characteristics. For instance, we can take a look at the acquisition
norms for the item “cat_casa” for monolinguals and bilinguals
separately. We can do this by adding the unquoted variable names of
interest to the arguments (lp, in this case):
bvq_norms(
participants = participants,
responses = responses,
item = "cat_casa",
age = c(22, 22),
lp
)
#> # A tibble: 2 × 10
#> te item label age type item_dominance lp .sum .n .prop
#> <int> <chr> <chr> <dbl> <chr> <chr> <chr> <int> <int> <dbl>
#> 1 591 cat_casa casa 22 produces L1 Monol… 1 1 0.6
#> 2 591 cat_casa casa 22 understands L1 Monol… 1 1 0.6To can get acquisition norms for the combination of multiple
variables of interest. For instance, we can get the acquisition norms
above, disaggregated by lp and dominance:
bvq_norms(
participants = participants,
responses = responses,
item = "cat_casa",
age = c(22, 22),
lp,
dominance
)
#> # A tibble: 2 × 11
#> te item label age type item_dominance lp dominance .sum .n .prop
#> <int> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <int> <int> <dbl>
#> 1 591 cat_… casa 22 prod… L1 Mono… Catalan 1 1 0.6
#> 2 591 cat_… casa 22 unde… L1 Mono… Catalan 1 1 0.6