The function of this R package is to provide function for converting MacArthur-Bates Communicative Development Inventory (CDI) Words and Gestures (WG) scores collected on individuals outside the normed range (8-18 months) to appropriate Words and Sentences (WS) scores (see Fenson et al., 1994).
This was done in order to adapt scores collected as part of the Baby Connectome Project (BCP; Howell et al., 2018), which collected WG scores out of range for comparison with studies of intellectual and developmental disabilities.
For more information, see the preprint (Day et al., 2024).
All values are rounded to the nearest integer, and then truncated to the possible range. Values below 0 are set to 0, and values above the maximum are set to the maximum value.
wg2ws_total_age()
This function converts a total WG score (i.e., 0-396) to WS based on either WG total score alone or including the effect of age. The models are listed below:
\begin{align}
\hat{y} =
& -0.74(\textrm{age}) + 1.2(\textrm{WG}) - {} \\
& 0.014(\textrm{age}^2) - 0.00073(\textrm{WG}^2) + {} \\
& 0.0074(\textrm{age}^3) + (4.9 \times{} 10^{-6})(\textrm{WG}^3) + {} \\
& 0.012(\textrm{age})(\textrm{WG}) -
(5.2 \times{} 10^{-6})(\textrm{age}^2)(\textrm{WG}^2) +
(7.0 \times{} 10^{-10})(\textrm{age}^3)(\textrm{WG}^3)
\end{align}\hat{y} = 1.2(\textrm{WG}) - (6.9\times10^{-4})(\textrm{WG}^2) +
(4.99\times10^{-6})(\textrm{WG}^3)Because 14 items are not in the same category between WG and WS, in order to calculate WS category scores, the entire list of items is needed.
Those 14 items are (WG -> WS):
Thus the flow looks something like:
child_wg_words <- c("moo", "home", "run")
child_wg2ws <- wg2ws_items(child_wg_words) # returns a 22x2 data frame
\# Returns a 22x2 data frame
child_ws_cat_scores <- wg2ws_category_score(child_wg2ws)There is no need to estimate category scores if you don’t need them,
but the new simulated WS score could be found as
sum(child_ws_cat_scores$n).
wg2ws_items.R
This function takes a vector of items (in Wordbank format), which
includes entires like fish (food) and
fish (animal) and returns WS category totals (i.e., remaps
them).
Note that in and inside appear as two
distinct items on WG, but as one combined item (inside/in)
on WS. Thus, you can choose how to score it.
"either" (default): If either is
endorsed, treat inside/in as endorsed."both": Both must be endorsed to treat
inside/in as endorsed."in": Only in has to be endorsed to treat
inside/in as endorsed."inside": As above, but with inside.Example:
items <- c("beach", "radio", "her", "penguin", "see", "tiger", "party", "in")
wg2ws_items(items, in_inside = "either")
category n
1 action_words 1
2 animals 2
3 body_parts 0
4 clothing 0
5 connecting_words 0
6 descriptive_words 0
7 food_drink 0
8 furniture_rooms 0
9 games_routines 0
10 helping_verbs 0
11 household 1
12 locations 1
13 outside 0
14 people 0
15 places 2
16 pronouns 1
17 quantifiers 0
18 question_words 0
19 sounds 0
20 time_words 0
21 toys 0
22 vehicles 0wg2ws_category_score.R
Converts the WG total scores (calculated above) to WS scores using a set of models.
Because some models include total WG score, and because information
is not kept on how inside/in was chosen to be endorsed,
there is technically a minor discrepancy between true WG score and its
mapping to WS (which is the value used for prediction), of up to one
word.
In random simulations, with 3,297 faux inventories, 1,408 (43%) had a one-item discrepancy in the predicted result. Because the predicted result is out of 680, we consider this fairly minor, and provide a parameter to override the WG score used in modeling with the true WG score from the original instrument.
The function lets you model age and WG total score, either can be excluded, and the best available model will be used.
random <- sample(wg2ws_list_items("WG"), 380)
c("in", "inside") %in% random
test_table <- wg2ws_items(random)
test_result_noage <- wg2ws_category_score(test_table, age = NA, verbose = TRUE)
test_result_30mo <- wg2ws_category_score(test_table, age = 30, verbose = TRUE)
test_result_36mo <- wg2ws_category_score(test_table, age = 36, verbose = TRUE)
> head(test_result_noage)
category n new_value
1 action_words 56 98
2 animals 35 41
3 body_parts 19 23
4 clothing 18 24
5 connecting_words 0 4
6 descriptive_words 37 62
> head(test_result_30mo)
category n new_value
1 action_words 54 98
2 animals 35 41
3 body_parts 18 24
4 clothing 18 25
5 connecting_words 0 5
6 descriptive_words 35 60
> head(test_result_36mo)
category n new_value
1 action_words 54 101
2 animals 35 41
3 body_parts 18 25
4 clothing 18 26
5 connecting_words 0 6
6 descriptive_words 35 63wg2ws_list_items(instrument = c("WG", "WS")): List
items on WG or WS.wg2ws_get_cat_function(the_category, age): Returns the
function to do the projection, or with echo_only = TRUE,
returns the coefficients in a human-readable format.wg2ws_summarize_cat(category_scores): Take a table from
wg2ws_category_score and summarize the results into a total
score, and a lexical/syntactic score (Day & Elison, 2021).Fenson, L., Dale, P. S., Reznick, J. S., Bates, E., Thal, D. J., & Pethick, S. J. (1994). “Variability in Early Cognitive Development.” Monographs of the Society for Research in Child Development, 59(5).
Howell, B. R., … Elison, J. T. (2018). “The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.” NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.03.049
Day, T. K. M., Borovsky, A., Thal, D., & Elison, J. T. (2024). “The CDI in two longitudinal datasets: Methods and differences across decades.” PsyArXiV. https://osf.io/preprints/psyarxiv/rwhcy
Day, T. K. M., & Elison, J. T. (2021). “A broadened estimate of syntactic and lexical ability from the MB-CDI.” Journal of Child Language, 1–18. https://doi.org/10.1017/S0305000921000283