Goals

The goals are 1) to create a word list that is informative about both English and Spanish vocabulary size and 2) to ensure that there are sufficient doublets to estimate lexical overlap. On an IRT view, we can’t perfectly assess 2 (at least not without better bilingual CDI data), but we can assess criterion 1 - that is, we can look at whether the reduced word list is a good sub-test for the full CDI in each language. Our original concern was that the current DLL-ES test might not perform well for older or high ability kids due to the lack of abstract words, and we can test this formally.

The DLL lists are meant to be used together with the original English and Spanish MCDI short forms.

Now we need to match the DLL words to the wordbank items for which we have IRT parameters.

Matching DLL short Level 1

# use with EN WG short form (12-18 mos)
#dll1ENshort <- read_csv(here("DLL/DLL-ES1-short-English.csv")) # 79 items (81 after splitting defs)

dll1short <- dll1short_raw %>% # this is now 168 items...it has the WG short form included
  mutate(english = tolower(english)) %>%
  mutate(english = case_when(english == 'i' ~ 'I',
                          english == 'tv (television)' ~ 'TV',
                          english == 'water' ~ 'water (beverage)',
                          english == 'grandma (or word used in your family)' ~ 'grandma*',
                          english == 'mommy (or word used in your family)' ~ 'mommy*',
                          english == 'choo choo (train sound)' ~ 'choo choo',
                          english == 'patty cake' ~ 'pattycake',
                          english == 'bye or bye bye' ~ 'bye',
                          english == 'teddy bear' ~ 'teddybear',
                          english == 'his/her' ~ 'his', # hers ? (and should DLL say 'his/hers' ?)
                          english == 'to have' ~ 'have',
                          english == 'shh' ~ 'shh/shush/hush',
                          english == 'to sit' ~ 'sit',
                          english == 'to be' ~ 'be',
                          english == 'put, put on' ~ 'put',
                          english == 'to write' ~ 'write',
                          english == 'arms' ~ 'arm',
                          english == 'church (or word used in your family)' ~ 'church*',
                          english == 'want' ~ 'wanna/want to',
                          english == 'in' ~ 'inside/in', # into ?
                          TRUE ~ english))

dll1ENshort_num_matching = length(intersect(dll1short$english, coefs$en$definition)) 
# 75/81 (GK version)
# 160/168 (Sandy)
#setdiff(dll1short$english, coefs$en$definition) 

# are these items also on the short form CDIs?
#length(intersect(dll1short$english, wg_short_en$word)) # all of the WG
#length(intersect(dll1short$english, ws_short_enA$word)) # 36 of the WS
# use with SP WG short form (12-18 mos)
#dll1SPshort <- read_csv(here("DLL/DLL-ES1-short-Spanish.csv")) # 67 items

dll1short <- dll1short %>% # this is now 168 items...it has the WG short form included
  mutate(spanish = tolower(spanish)) %>%
  separate(col = spanish, into = c("spanish", NA), sep=" \\(") %>%
  mutate(spanish = case_when(spanish == 'mamá/mami' ~ 'mamá',
                             spanish == 'calcetines' ~ 'calcetín',
                             spanish == 'tomar baño / bañarse' ~ 'baño', # verb -> noun.. tomar(se) ?
                             spanish == 'espera' ~ 'esperar(se)', # close enough ?
                             spanish == 'acabar(se)' ~ 'acabar',
                             spanish == 'no hay más' ~ 'no hay', # or "más" ?
                             spanish == 'rapido' ~ 'rápido (descriptive)', # or rápido (quantifiers)
                             spanish == 'lastimado' ~ 'lastimar(se)', # close enough ?
                             spanish == 'otro' ~ 'otro/otra vez', # close enough ?
                             spanish == 'quiquiriqui' ~ 'quiquiriquí', # DLL missing acent
                             spanish == 'brazos' ~ 'brazo',
                             spanish == 'manos' ~ 'mano',
                             spanish == 'vaso' ~ 'vasos',
                             spanish == 'llaves' ~ 'llave',
                             spanish == 'adiós/byebye' ~ 'adíos/byebye', # wordbank accent is incorrect
                             spanish == 'uno, dos, tres' ~ 'uno dos tres...',
                             spanish == 'shh' ~ 'shhh',
                             spanish == 'ver' ~ 'ver(se)',
                             spanish == '¿dónde está?' ~ 'dónde', # close enough ? 
                             TRUE ~ spanish))

dll1SPshort_num_matching = length(intersect(dll1short$spanish, coefs$sp$definition)) # was 59 on GK version
# 158 / 168
#setdiff(dll1short$spanish, coefs$sp$definition)
# no match: tostada, alimentar, sonreír, algunos, también

# are these items also on the short form CDIs?
#length(intersect(dll1short$spanish, wg_short_sp$word)) # all of the WG
#length(intersect(dll1short$spanish, ws_short_sp$word)) # 63 of the WS

Matching DLL short Level 2

## [1] 153

Matching DLL Extended Level 1

dll1long <- dll1long_raw %>% mutate(english = tolower(english)) %>%
  mutate(english = case_when(english == 'daddy (or word used in your family)' ~ 'daddy*',
                          english == 'toy' ~ 'toy (object)',
                          english == 'swing' ~ 'swing (object)', # or swing (action) ?
                          english == 'dress' ~ 'dress (object)',
                          TRUE ~ english))


dll1long_EN_num_matching = length(intersect(dll1long$english, coefs$en$definition)) # 74 / 74 match

dll1long <- dll1long %>% mutate(spanish = tolower(spanish)) %>%
  separate(col = spanish, into = c("spanish", NA), sep=" \\(") %>%
  mutate(spanish = case_when(spanish == 'pipi' ~ 'pipí', # is this a match?
                             spanish == 'orejas' ~ 'oreja',
                             spanish == 'dedos' ~ 'dedo',
                             spanish == 'escalera' ~ 'escaleras', 
                             spanish == 'bolsa' ~ 'bolsa (clothing)', # or bolsa (household) ?
                             spanish == 'papá/papi' ~ 'papá', 
                             spanish == 'cosquillita' ~ 'cosquillitas', 
                             spanish == 'hacer la meme' ~ 'siesta', # close enough ?  or hacer?
                             TRUE ~ spanish))

dll1long_SP_num_matching = length(intersect(dll1long$spanish, coefs$sp$definition)) # 74 / 74 match

Matching DLL Extended Level 2

dll2long <- dll2long_raw %>% mutate(english = tolower(english)) %>%
  mutate(english = case_when(english == 'daddy (or name/word used in your family)' ~ 'daddy*',
                          english == 'teddy bear' ~ 'teddybear',
                          english == 'patty cake' ~ 'pattycake', # or swing (action) ?
                          english == 'dress' ~ 'dress (object)',
                          english == 'i' ~ 'I',
                          english == 'penis (or word used in your family)' ~ 'penis*',
                          english == 'water' ~ 'water (beverage)',
                          english == 'orange' ~ 'orange (food)',
                          english == 'clock/watch' ~ 'clock', # or watch (object)
                          english == 'drink' ~ 'drink (action)',
                          english == 'feet' ~ 'foot',
                          english == 'picture (\"or photo\")' ~ 'picture',
                          english == 'buttocks/bottom (or word used in your family)' ~ 'buttocks/bottom*',
                          TRUE ~ english))

dll2long <- dll2long %>% mutate(spanish = tolower(spanish)) %>%
  separate(col = spanish, into = c("spanish", NA), sep=" \\(") %>%
  mutate(spanish = case_when(spanish == 'pipi' ~ 'pipí',
                          spanish == 'shh' ~ 'shhh',
                          spanish == 'bolsa' ~ 'bolsa (clothing)', # or bolsa (household) ?
                          spanish == 'qué' ~ 'qué (question_words)',
                          TRUE ~ spanish))

#length(intersect(dll2long$spanish, coefs$sp$definition)) 
#setdiff(dll2long$spanish, coefs$sp$definition)

English DLL items not in our wordbank IRT model: one, two, three, family, drum, good morning, also, and many. Spanish DLL items not in our wordbank IRT model: tostada, algunos, alimentar, sonreír, no hay más (although we have no and no hay, as well as más), and lastimado (but we have lastimar(se)).

Does the DLL short form recover full form scores?

English DLL Level 1: Production

Using data from 3717 English-speaking children 12-18 month of age from Wordbank, we test how well sumscores from the DLL-ES1 matched form + CDI:WG short form predict children’s English production scores from the long form (LF) CDI (WG/WS). The left panel shows LF CDI scores vs. the DLL-ES1 Matched + CDI:WG short score, and the right panel shows the full CDI scores vs. just the CDI:WG short form score.

## Warning: The dot-dot notation (`..r.label..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(r.label)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Overall, the correlation of children’s CDI:WG short + DLL scores and their full CDI production scores is quite high (\(r=0.98\)), but as shown above, for small vocabulary sizes the DLL score overestimates full CDI:WG production scores, while for higher full CDI:WG scores the DLL underestimates vocab size (dotted line has slope \(=160 / 395\)). However, the CDI:WG short form alone (middle panel) shows a similar (and more extreme) overestimation for small vocabulary sizes.

English DLL Level 1: Comprehension

Now we do the same for comprehension (receptive vocabulary) using Wordbank’s CDI:WG English data.

The correlation of children’s DLL1 scores and their CDI:WG LF comprehension scores is quite high (\(r=0.99\)), and extrapolating from the DLL-ES1 to full CDI:WG scores shows very little overestimation (less than when using the CDI:WG short form alone).

English DLL Level 2 (Production)

Using data from 6411 English-speaking children 16-30 month of age from Wordbank, we test how well sumscores from the DLL-ES2 matched form predict children’s full production scores from the CDI:WS. The left panel shows full CDI scores vs. the DLL-ES2 matched inventory (A) score, and the right panel shows the full CDI scores vs. just the CDI:WG short form (A) score.

Overall, the correlation of children’s CDI:WS short + DLL2 scores and their full CDI production scores is quite high (\(r=0.99\)), but as shown above, the DLL2 again mostly overestimates production scores on the full CDI (dotted line has slope \(=162 / 680\)). In comparison, the CDI:WS short form (A) score only overestimates full CDI scores for smaller vocabulary sizes (<400).

Spanish DLL Level 1

Now we look at overestimation for Spanish DLLs + CDI short forms.

Using Wordbank data from 731 Spanish-speaking children aged 12-18 months, we test how well sumscores from the DLL-ES1 Matched form correlate with children’s full CDI:WG production scores.

As for English, the correlation of Spanish-speaking children’s DLL scores and their full CDI:WG production scores is quite high (\(r=0.99\)), but as shown above, their DLL score overestimates the production score on the full CDI at smaller vocabulary sizes (dotted line has slope \(=161 / 428\)). Do note that few children in this dataset have large productive vocabularies.

Spanish DLL Level 1: Comprehension

Now we do the same for comprehension (receptive vocabulary) using Wordbank’s CDI:WG Spanish data.

The correlation of children’s DLL1 Matched scores and their full CDI:WG comprehension scores is quite high (\(r=0.99\)), and extrapolating from DLL to full CDI:WG scores shows very little overestimation–similar to the level shown by using the CDI:WG alone.

Spanish DLL Level 2 (Production)

Full CDI vs. Short CDI / DLL Correlations

Language Full.Form.Score N r.with.CDI.short.form r.with.DLL.ES.matched r.with.DLL.ES.extended
English CDI:WG production 3696 0.963 0.978 0.985
English CDI:WG comprehension 3696 0.986 0.993 0.994
English CDI:WS production 6411 0.989 0.992 0.988
Spanish CDI:WG production 731 0.983 0.988 0.992
Spanish CDI:WG comprehension 731 0.986 0.988 0.984
Spanish CDI:WS production 1383 0.983 0.989 0.982

Recommendations

Overall, it seems that many of the items on the DLL are somewhat easier than average, and thus these forms tend to overestimate children’s full CDI scores (indeed, for English items on the DLL1 Matched form, the average easiness is -0.95, while the mean easiness of items not on the DLL is -2.57). This is also true of the CDI:WG short English form: the average easiness is -0.64 and the average ease of items not on the WG short form is -2.42. The CDI:WS short English form (A) is less biased towards easy items: average easiness is -1.73 vs. -2.27 for items not on the short WS. The histograms below show the distribution of easiness parameters for English (left) and Spanish (right) CDI words. Solid lines show the average ease of DLL items (DLL 1 = red, DLL 2 = orange), and dashed lines show the average of non-DLL items.

Spanish DLL1 items have an average ease of -0.81, while other items on the full CDI have a mean ease of -2.27.

Spanish DLL2 items have an average ease of -1.29, while other CDI items have a mean of -2.35.

English DLL2 items have an average ease of -1.67, while other items on the full CDI have a mean ease of -2.35.

We recommend bringing the overall mean estimated IRT difficulty of the words selected for the DLLs closer to the mean difficulty of the words on the rest of the CDI.

To start, we examine IRT easiness parameters for the doublets on the existing DLL lists, looking for items with large mismatch between their English and Spanish ease.

Do doublets have similar difficulties?

We want to whether assess doublet items have similar difficulty (operationalized by their IRT parameters) in English and in Spanish. For example, consider if “perro” was for some reason much more difficult than “dog”, then you wouldn’t want to include it because it wouldn’t be a good item for estimating vocabulary overlap!

Below are shown the parameters for items from the DLL-ES1 Matched form: en_d = English easiness, sp_d = Spanish easiness), ordered by the most to least discrepant (difficulty difference squared); (_a1 columns show item discriminations (slopes), and en_sp_d_diff simply shows the difference (English - Spanish) in the easiness parameter).

DLL Level 1 Short Form

For the Matched form, we merely want to identify items that have very different difficulty in English and Spanish, and recommend that researchers interested in estimating conceptual overlap in bilinguals not include these items in their calculations due to the bias. Below is shown the distribution of the squared difference in difficulty for doublets from the DLL Level 1 short form.

The mean squared difference in difficulty of doublets on the DLL-ES1 Matched form is 2.16 (SD=3.33), and as shown above this distribution is highly skewed: most doublets are fairly well-matched (median d_diff_sq=0.99), but a few items are extremely mismatched. We recommend that the 13 items (shown below) with a squared difficulty difference of 7.17 (mean + 1.5 * SD) or more be excluded from calculations of conceptual overlap.

english spanish sp_a1 sp_d en_a1 en_d en_sp_d_diff d_diff_sq
street calle 3.24 0.78 4.39 -3.38 -4.16 17.35
book libro 3.80 -1.55 2.88 2.55 4.10 16.83
on encima 2.96 -4.38 2.93 -0.45 3.93 15.46
none no hay 2.71 -0.29 2.37 -3.85 -3.55 12.63
hat sombrero 3.28 -2.14 3.15 1.37 3.51 12.33
don’t no 1.50 1.81 1.95 -1.61 -3.42 11.70
water (beverage) agua 2.58 4.38 2.80 0.99 -3.38 11.46
other otro/otra vez 2.34 -0.84 3.09 -4.05 -3.22 10.34
dish plato 4.57 0.17 2.99 -2.90 -3.06 9.39
babysitter nana 0.97 -1.36 2.41 -4.31 -2.94 8.67
bread pan 2.59 2.50 3.33 -0.35 -2.85 8.10
finish acabar 2.51 -0.93 3.13 -3.68 -2.75 7.55
today hoy 2.04 -2.29 3.79 -5.01 -2.72 7.40

DLL Level 2 Matched Form

The mean squared difference in difficulty of doublets on the DLL-ES2 Matched form is 2.63 (SD=4.19), and once again while most doublets are fairly well-matched, several items are extremely mismatched. We recommend that the 10 items (shown below) with a squared difficulty difference of 8.22 (mean + 1.5 * SD) or more be excluded from calculations of conceptual overlap.

english spanish sp_a1 sp_d en_a1 en_d en_sp_d_diff d_diff_sq
if que (connection word) 1.50 -0.82 3.12 -6.53 -5.71 32.63
street calle 3.24 0.78 4.39 -3.38 -4.16 17.35
book libro 3.80 -1.55 2.88 2.55 4.10 16.83
to a 1.00 -0.15 3.43 -3.80 -3.65 13.32
none no hay 2.71 -0.29 2.37 -3.85 -3.55 12.63
hat sombrero 3.28 -2.14 3.15 1.37 3.51 12.33
hear oír 1.83 -0.94 4.49 -4.35 -3.41 11.61
bench banco (outside) 2.94 -2.18 3.59 -5.43 -3.25 10.57
much mucho 3.33 -1.80 3.10 -5.04 -3.24 10.48
potato papas 2.54 1.11 2.90 -2.07 -3.18 10.12

Now we examine the doublets on the long supplemental DLL forms, and make recommendations of mismatched items to swap out.

DLL Level 1 Extended

Below are shown the parameters for items from the DLL Level 1 extended form. Clearly some of these items have quite different difficulty levels in English and Spanish, and we should try to find items that are more equivalent and swap them onto the long supplement.

english spanish sp_a1 sp_d en_a1 en_d en_sp_d_diff d_diff_sq
give dar 2.25 -0.59 3.88 -3.13 -2.54 6.45
ear oreja 4.68 -0.46 3.68 1.69 2.16 4.65
cheese queso 3.93 -0.63 2.89 1.45 2.08 4.33
crib cuna 2.92 -0.51 3.09 -2.42 -1.90 3.62
banana plátano/banana 2.95 0.01 2.26 1.89 1.88 3.54
shirt playera 3.47 -2.44 4.14 -0.58 1.85 3.43
chicken (food) pollo 3.94 0.72 3.46 -1.01 -1.73 2.99
moo muu 1.22 0.01 1.87 1.70 1.69 2.85
nap siesta 1.98 -2.71 3.79 -1.05 1.67 2.78
apple manzana 3.76 -0.07 3.22 1.55 1.62 2.62
stop parar(se) 3.76 -2.43 2.80 -0.81 1.62 2.62
drawer cajón 4.17 -2.32 3.67 -3.85 -1.53 2.35
face cara 4.25 -0.36 3.92 -1.83 -1.48 2.18
swing (object) columpio 3.80 -1.96 3.21 -0.49 1.47 2.16
belly button ombligo 3.33 -1.23 2.68 0.22 1.45 2.11
quack quack cuacuá 1.26 -0.28 1.80 1.10 1.38 1.89
picture fotos 4.46 -0.97 4.50 -2.34 -1.37 1.87
go ir(se) 2.36 -0.71 2.43 0.60 1.32 1.73
bunny conejo 3.41 -1.06 2.75 0.23 1.29 1.67
trash basura 4.54 -0.03 2.20 -1.28 -1.25 1.56
pen lápiz 3.71 -0.10 2.79 -1.34 -1.24 1.54
bite morder 3.72 -1.77 2.82 -0.54 1.23 1.51
comb peine 3.94 -0.68 2.92 -1.90 -1.23 1.50
baa baa bee/mee 0.83 -0.30 1.17 0.90 1.20 1.45
paper papel 4.18 -0.02 4.19 -1.22 -1.19 1.43
window ventana 4.28 -1.11 3.45 -2.29 -1.18 1.39
store tienda/mercado 3.66 -0.95 4.33 -2.06 -1.10 1.22
dance bailar 3.18 -0.40 3.38 -1.39 -0.99 0.98
tree árbol 3.78 -0.53 3.18 0.45 0.98 0.96
hit pegar(se) 3.48 -1.39 3.89 -2.35 -0.97 0.93
finger dedo 4.43 0.22 4.32 -0.73 -0.96 0.91
purse bolsa (clothing) 3.93 -0.83 2.72 -1.75 -0.93 0.86
daddy* papá 1.48 2.96 1.70 3.87 0.91 0.84
bear oso 2.96 -0.09 2.48 0.81 0.91 0.83
touch tocar 3.18 -2.17 3.75 -3.03 -0.86 0.73
door puerta 4.68 -0.28 3.37 0.52 0.80 0.64
clock reloj 3.80 -1.51 2.40 -0.73 0.78 0.61
tongue lengua 3.87 -0.50 3.16 -1.27 -0.78 0.60
open abrir 3.41 0.22 3.57 -0.55 -0.78 0.60
balloon globo/bomba 2.35 1.12 2.56 1.78 0.65 0.43
medicine medicina 4.31 -1.38 3.55 -1.98 -0.60 0.35
cry llorar 3.81 -1.08 4.08 -1.67 -0.59 0.35
bathtub tina 3.07 -1.03 2.79 -0.45 0.58 0.33
vroom pipí 1.76 1.19 0.93 0.61 -0.58 0.33
box caja 4.25 -1.28 3.30 -0.71 0.57 0.33
pajamas pijama 3.51 -2.06 3.82 -1.50 0.57 0.32
foot pies 3.78 0.65 3.86 0.10 -0.55 0.30
bicycle bicicleta 2.93 -0.90 3.02 -0.36 0.54 0.29
towel toalla 4.89 -2.02 4.12 -1.51 0.52 0.27
refrigerator refrigerador 3.96 -2.52 3.75 -3.02 -0.50 0.25
dress (object) vestido 3.29 -1.69 2.96 -2.17 -0.48 0.23
button botón 2.97 -0.96 2.69 -0.49 0.47 0.22
cake pastel 3.64 -0.35 3.53 -0.72 -0.37 0.13
throw tirar 3.82 -2.11 4.16 -2.46 -0.34 0.12
horse caballo 2.99 0.65 3.34 0.32 -0.34 0.11
stairs escaleras 4.20 -1.84 3.28 -2.12 -0.28 0.08
wash lavar(se) 4.22 -1.68 4.15 -1.96 -0.28 0.08
man señor 3.96 -1.76 2.79 -2.04 -0.27 0.07
glasses lentes 3.93 -1.45 3.20 -1.20 0.25 0.06
bib babero 3.01 -1.16 2.19 -0.92 0.24 0.06
close cerrar 4.06 -1.68 3.38 -1.92 -0.24 0.06
airplane avión 2.81 0.24 2.97 0.48 0.24 0.06
say decir 3.84 -3.28 3.46 -3.50 -0.22 0.05
telephone teléfono 3.18 -0.25 3.47 -0.04 0.21 0.04
brush cepillo 4.80 -0.24 3.13 -0.44 -0.20 0.04
thank you gracias 2.27 1.35 2.04 1.17 -0.18 0.03
pillow almohada 3.89 -1.14 4.09 -0.96 0.18 0.03
light luz 3.38 0.50 2.72 0.38 -0.12 0.01
diaper pañal 3.79 1.00 3.00 0.89 -0.10 0.01
hair pelo 5.15 0.85 4.09 0.95 0.10 0.01
toy (object) juguete 3.58 -0.39 3.57 -0.33 0.07 0.00
tickle cosquillitas 2.06 -0.92 2.47 -0.87 0.05 0.00
walk caminar 3.26 -1.10 3.59 -1.07 0.03 0.00
blow soplar 2.67 -1.58 3.18 -1.59 -0.01 0.00

DLL Level 2 Extended

english spanish sp_a1 sp_d en_a1 en_d en_sp_d_diff d_diff_sq
bubbles burbujas 3.02 -2.63 2.61 1.30 3.94 15.50
water (beverage) agua 2.58 4.38 2.80 0.99 -3.38 11.46
soup sopa 2.41 1.36 2.98 -1.98 -3.34 11.15
bread pan 2.59 2.50 3.33 -0.35 -2.85 8.10
table mesa 4.61 0.34 5.01 -2.22 -2.56 6.55
coat abrigo 2.63 -3.09 2.70 -0.80 2.29 5.22
pencil lápiz 3.71 -0.10 3.23 -2.31 -2.21 4.90
bee abeja 2.62 -2.19 2.53 -0.08 2.10 4.43
chocolate chocolate 3.35 -0.04 3.11 -2.12 -2.09 4.35
cheese queso 3.93 -0.63 2.89 1.45 2.08 4.33
crib cuna 2.92 -0.51 3.09 -2.42 -1.90 3.62
motorcycle moto 2.75 -0.24 2.85 -2.12 -1.89 3.56
girl niña 2.35 0.45 2.79 -1.40 -1.85 3.42
soda/pop soda/refresco 2.13 0.31 1.59 -1.51 -1.82 3.31
chicken (food) pollo 3.94 0.72 3.46 -1.01 -1.73 2.99
nose nariz 4.20 0.45 3.42 2.18 1.73 2.99
drink (action) tomar(se) 2.87 -1.83 3.05 -0.15 1.68 2.82
cup taza 3.53 -0.84 3.39 0.83 1.67 2.79
knee rodilla 4.52 -2.51 3.06 -0.85 1.65 2.74
apple manzana 3.76 -0.07 3.22 1.55 1.62 2.62
sing cantar 3.07 -0.79 4.05 -2.37 -1.58 2.51
bug bicho 1.58 -1.80 2.74 -0.22 1.58 2.50
house casa 4.22 0.55 3.31 -1.03 -1.58 2.49
blanket cobija 4.49 -1.29 3.26 0.29 1.57 2.48
face cara 4.25 -0.36 3.92 -1.83 -1.48 2.18
belly button ombligo 3.33 -1.23 2.68 0.22 1.45 2.11
stick palo 3.25 -0.50 3.30 -1.90 -1.40 1.96
quack quack cuacuá 1.26 -0.28 1.80 1.10 1.38 1.89
picture fotos 4.46 -0.97 4.50 -2.34 -1.37 1.87
I yo 2.25 0.23 2.11 -1.11 -1.34 1.79
ice hielo 3.19 -2.07 2.48 -0.73 1.33 1.77
go ir(se) 2.36 -0.71 2.43 0.60 1.32 1.73
park parque 3.57 -2.44 2.98 -1.14 1.30 1.70
bunny conejo 3.41 -1.06 2.75 0.23 1.29 1.67
potty bacinica 2.12 -1.43 3.20 -0.18 1.25 1.57
help ayudar 4.23 -2.32 3.36 -1.07 1.25 1.55
eye ojos 4.42 1.03 3.31 2.26 1.23 1.52
head cabeza 4.55 0.77 4.14 -0.43 -1.19 1.43
paper papel 4.18 -0.02 4.19 -1.22 -1.19 1.43
you tú 2.00 -0.22 2.65 -1.41 -1.19 1.41
window ventana 4.28 -1.11 3.45 -2.29 -1.18 1.39
room cuarto 4.28 -1.48 4.19 -2.64 -1.17 1.36
money dinero 3.67 -0.12 3.04 -1.27 -1.14 1.31
store tienda/mercado 3.66 -0.95 4.33 -2.06 -1.10 1.22
cereal cereal 2.37 -1.67 3.13 -0.57 1.10 1.21
run correr 3.97 -1.25 4.78 -2.26 -1.01 1.02
mouth boca 4.58 1.45 3.63 0.45 -0.99 0.99
dance bailar 3.18 -0.40 3.38 -1.39 -0.99 0.98
tree árbol 3.78 -0.53 3.18 0.45 0.98 0.96
hit pegar(se) 3.48 -1.39 3.89 -2.35 -0.97 0.93
finger dedo 4.43 0.22 4.32 -0.73 -0.96 0.91
that eso 2.35 -1.52 1.48 -0.57 0.95 0.90
rock piedra 4.04 -1.49 3.16 -0.55 0.94 0.89
purse bolsa (clothing) 3.93 -0.83 2.72 -1.75 -0.93 0.86
butterfly mariposa 3.67 -2.08 3.21 -1.15 0.92 0.85
daddy* papá 1.48 2.96 1.70 3.87 0.91 0.84
touch tocar 3.18 -2.17 3.75 -3.03 -0.86 0.73
ice cream helado/nieve 3.16 -1.45 3.64 -0.61 0.84 0.71
monkey mono 2.28 -1.16 3.59 -0.33 0.83 0.69
chair silla 4.92 0.91 4.39 0.11 -0.81 0.65
clock reloj 3.80 -1.51 2.40 -0.73 0.78 0.61
tongue lengua 3.87 -0.50 3.16 -1.27 -0.78 0.60
open abrir 3.41 0.22 3.57 -0.55 -0.78 0.60
raisin pasas 2.79 -2.29 2.86 -1.53 0.76 0.58
kick patear 3.52 -2.69 3.35 -1.93 0.76 0.57
tummy panza 4.03 0.40 3.65 -0.34 -0.74 0.55
beans frijoles 3.19 -0.75 2.46 -1.47 -0.72 0.52
kitchen cocina 5.10 -1.85 4.93 -2.54 -0.70 0.48
buttocks/bottom* nalgas 3.15 -1.33 3.07 -0.63 0.70 0.48
lady señora 4.01 -2.39 2.73 -3.08 -0.69 0.47
orange (food) naranja 3.76 -0.43 3.57 -1.10 -0.67 0.45
balloon globo/bomba 2.35 1.12 2.56 1.78 0.65 0.43
coffee café 3.18 -1.17 2.72 -1.82 -0.65 0.42
spaghetti espagueti 2.30 -2.46 3.21 -1.84 0.62 0.38
pattycake tortillitas 1.81 -0.78 1.94 -1.40 -0.62 0.38
medicine medicina 4.31 -1.38 3.55 -1.98 -0.60 0.35
cry llorar 3.81 -1.08 4.08 -1.67 -0.59 0.35
plant planta 3.89 -2.22 3.12 -2.80 -0.58 0.34
bathtub tina 3.07 -1.03 2.79 -0.45 0.58 0.33
vroom pipí 1.76 1.19 0.93 0.61 -0.58 0.33
box caja 4.25 -1.28 3.30 -0.71 0.57 0.33
foot pies 3.78 0.65 3.86 0.10 -0.55 0.30
bicycle bicicleta 2.93 -0.90 3.02 -0.36 0.54 0.29
popcorn palomitas 3.05 -1.80 2.52 -1.28 0.52 0.27
see ver(se) 2.58 -1.30 2.55 -0.79 0.51 0.26
spoon cuchara 4.17 0.04 3.42 0.54 0.50 0.25
teddybear osito 3.02 -0.22 2.22 -0.70 -0.48 0.23
dress (object) vestido 3.29 -1.69 2.96 -2.17 -0.48 0.23
button botón 2.97 -0.96 2.69 -0.49 0.47 0.22
grass pasto 4.08 -2.13 3.95 -1.66 0.46 0.22
hungry hambre 3.16 -1.50 3.50 -1.96 -0.46 0.21
yucky fuchi 1.66 0.16 1.87 -0.30 -0.46 0.21
ant hormiga 3.26 -1.18 2.54 -1.63 -0.45 0.21
what qué (question_words) 1.90 -0.41 1.74 -0.82 -0.41 0.17
good bueno 2.48 -1.64 2.63 -1.25 0.39 0.15
cake pastel 3.64 -0.35 3.53 -0.72 -0.37 0.13
cookie galleta 2.95 1.21 2.92 1.56 0.36 0.13
keys llave 4.14 -0.26 1.73 0.08 0.34 0.11
cloud nube 3.67 -2.06 3.23 -2.39 -0.33 0.11
cheek cachete 3.71 -1.64 3.18 -1.33 0.32 0.10
french fries papitas 2.61 -1.21 2.75 -0.90 0.31 0.10
tooth dientes 4.67 0.01 2.82 -0.29 -0.31 0.09
wash lavar(se) 4.22 -1.68 4.15 -1.96 -0.28 0.08
strawberry fresa 3.12 -1.93 3.15 -1.66 0.28 0.08
moon luna 3.02 -0.19 2.43 0.09 0.27 0.08
man señor 3.96 -1.76 2.79 -2.04 -0.27 0.07
glasses lentes 3.93 -1.45 3.20 -1.20 0.25 0.06
bib babero 3.01 -1.16 2.19 -0.92 0.24 0.06
close cerrar 4.06 -1.68 3.38 -1.92 -0.24 0.06
eat comer(se) 3.10 0.21 3.47 0.44 0.22 0.05
telephone teléfono 3.18 -0.25 3.47 -0.04 0.21 0.04
brush cepillo 4.80 -0.24 3.13 -0.44 -0.20 0.04
baby bebé 2.25 2.15 2.44 2.34 0.19 0.04
pillow almohada 3.89 -1.14 4.09 -0.96 0.18 0.03
shh/shush/hush shhh 1.18 0.44 1.73 0.61 0.16 0.03
lion león 3.09 -1.28 3.32 -1.12 0.16 0.03
penis* pene 1.75 -1.51 1.30 -1.35 0.15 0.02
couch sillón 3.76 -2.07 3.78 -2.22 -0.15 0.02
yum yum ¡am! 0.45 0.69 1.00 0.81 0.12 0.02
grapes uvas 2.49 -0.74 3.36 -0.62 0.11 0.01
diaper pañal 3.79 1.00 3.00 0.89 -0.10 0.01
hair pelo 5.15 0.85 4.09 0.95 0.10 0.01
sweater suéter 3.86 -1.20 2.14 -1.29 -0.08 0.01
corn elote 3.69 -1.72 3.49 -1.78 -0.06 0.00
stove estufa 4.61 -2.63 2.90 -2.57 0.06 0.00
train tren 2.62 -0.16 3.51 -0.10 0.06 0.00
tickle cosquillitas 2.06 -0.92 2.47 -0.87 0.05 0.00
yogurt yoghurt 2.59 -1.03 2.29 -1.00 0.03 0.00
walk caminar 3.26 -1.10 3.59 -1.07 0.03 0.00
blow soplar 2.67 -1.58 3.18 -1.59 -0.01 0.00
doll muñeca 2.59 -0.30 2.11 -0.30 0.00 0.00

DLL-ES1 Extended Form Swaps

The average discrepancy on the original DLL-ES1 Extended was 1.12, with the Spanish items being on average more difficult than their English equivalents (English - Spanish ease: 0.09). After substituting 15 of the items with the largest discrepancy, the average discrepancy was 0.6, and the overall difference between languages was closer to zero (English - Spanish ease: 0.01).

DLL-ES2 Extended Form Swaps

Now we evaluate the swaps for the DLL-ES2 Extended form.

The average discrepancy on the original DLL-ES2 Extended was 1.26, with the Spanish items being on average more difficult than their English equivalents (English - Spanish ease: -0.15). After substituting the 15 most discrepant items, the average discrepancy was 0.88, and the overall difference between languages was closer to zero (English - Spanish ease: 0.05).

New DLL vs. Full CDI Correlations

Finally, we re-evaluate the correlations between full CDI scores and DLL scores after the above swaps on the DLL-ES1 and DLL-ES2 Extended forms.

New DLL-ES1 Extended vs. English CDI:WG LF comprehension scores: \(r = 0.995\). New DLL-ES1 Extended vs. Spanish CDI:WG LF comprehension scores: \(r = 0.989\).

New DLL-ES1 Extended vs. English CDI:WG LF production scores: \(r = 0.987\). New DLL-ES1 Extended vs. Spanish CDI:WG LF production scores: \(r = 0.993\).

New DLL-ES2 Extended vs. English CDI:WS LF production scores: \(r = 0.993\). New DLL-ES2 Extended vs. Spanish CDI:WS LF production scores: \(r = 0.989\).