2a. Models predicting word knowledge by measure
Christina
2020-11-11
complete_predictors <- read_csv("complete_predictors_all_vars.csv") %>%
pivot_longer(cols = c("prop_say_naive_combined","wordbank_production_24","kuperman_inv",
"picture_naming_inv", "wordbank_threshold_inv"),
names_to = "measure", values_to = "value") %>%
select(num_item_id, word, category, wordnet_pos, preschoolness, helpfulness, pos_scale_hypernyms,
frequency=childes_adult_log_freq, concreteness, mean_generality, measure, value)
complete_predictors_wordbank <- read_csv("complete_predictors_wordbank.csv") %>%
pivot_longer(cols = c("prop_say_naive_combined","wordbank_production_24","kuperman_inv",
"picture_naming_inv", "wordbank_threshold_inv"),
names_to = "measure", values_to = "value") %>%
select(num_item_id, word, category, wordnet_pos, preschoolness, helpfulness, pos_scale_hypernyms,
frequency=childes_adult_log_freq, concreteness, mean_generality, measure, value)Codebook:
wordbank_threshold_inv: 1/wordbank_threshold_aoa so that interpretation is consistent with naive proportion data
kuperman_inv: 1/KupermanAoA
picture_naming_inv: 1/picture_naming_aoa
prop_say_naive_combined: Proportion of children estimated to produce the word at 18/24 months (combined across age group because r = .95), from a survey of naive adults (N=78).
wordbank_production_24: Proportion of children reported in wordbank to produce word at 24 months (for comparison to naive data)
preschoolness: on a scale of 1-5, how much is the word associated with preschoolers (MTurk)
helpfulness: on a scale of 1-5, how helpful would it be for a preschooler to know this word (MTurk)
frequency: log frequency based on adult speech in CHILDES
concreteness: concreteness norms from Brysbaert et al. - adults asked to rate on a scale of 1-5
1 Analyses including picture-naming data (fewer words)
omnibus <- lmer(value ~ frequency*measure + preschoolness*measure + helpfulness*measure + concreteness*measure +
(1|word) + (1|measure), data=complete_predictors)
summary(omnibus)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ frequency * measure + preschoolness * measure + helpfulness *
## measure + concreteness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors
##
## REML criterion at convergence: -839.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2867 -0.4548 0.0056 0.4600 3.2330
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.003992 0.06318
## measure (Intercept) 0.002702 0.05198
## Residual 0.008532 0.09237
## Number of obs: 575, groups: word, 115; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.032364 0.506887 395.321127
## frequency 0.018467 0.010129 391.071727
## measurepicture_naming_inv -0.858187 0.593128 440.000014
## measureprop_say_naive_combined -0.827351 0.593128 440.000002
## measurewordbank_production_24 -1.327427 0.593128 440.000033
## measurewordbank_threshold_inv -0.433963 0.593128 439.999921
## preschoolness 0.024440 0.015551 391.071714
## helpfulness 0.003877 0.017657 391.071724
## concreteness 0.003582 0.103011 391.073868
## frequency:measurepicture_naming_inv 0.050988 0.011823 440.000562
## frequency:measureprop_say_naive_combined 0.112545 0.011823 440.000563
## frequency:measurewordbank_production_24 0.090134 0.011823 440.000564
## frequency:measurewordbank_threshold_inv 0.028126 0.011823 440.000563
## measurepicture_naming_inv:preschoolness -0.052245 0.018152 440.000565
## measureprop_say_naive_combined:preschoolness 0.033581 0.018152 440.000565
## measurewordbank_production_24:preschoolness 0.015016 0.018152 440.000566
## measurewordbank_threshold_inv:preschoolness -0.002265 0.018152 440.000566
## measurepicture_naming_inv:helpfulness -0.057527 0.020611 440.000562
## measureprop_say_naive_combined:helpfulness 0.050272 0.020611 440.000561
## measurewordbank_production_24:helpfulness 0.018728 0.020611 440.000562
## measurewordbank_threshold_inv:helpfulness -0.006590 0.020611 440.000561
## measurepicture_naming_inv:concreteness 0.190003 0.120241 440.000007
## measureprop_say_naive_combined:concreteness 0.021116 0.120241 439.999995
## measurewordbank_production_24:concreteness 0.198285 0.120241 440.000027
## measurewordbank_threshold_inv:concreteness 0.113484 0.120241 439.999915
## t value Pr(>|t|)
## (Intercept) 0.064 0.94912
## frequency 1.823 0.06903 .
## measurepicture_naming_inv -1.447 0.14864
## measureprop_say_naive_combined -1.395 0.16375
## measurewordbank_production_24 -2.238 0.02572 *
## measurewordbank_threshold_inv -0.732 0.46477
## preschoolness 1.572 0.11685
## helpfulness 0.220 0.82633
## concreteness 0.035 0.97228
## frequency:measurepicture_naming_inv 4.313 1.99e-05 ***
## frequency:measureprop_say_naive_combined 9.519 < 2e-16 ***
## frequency:measurewordbank_production_24 7.624 1.53e-13 ***
## frequency:measurewordbank_threshold_inv 2.379 0.01779 *
## measurepicture_naming_inv:preschoolness -2.878 0.00419 **
## measureprop_say_naive_combined:preschoolness 1.850 0.06499 .
## measurewordbank_production_24:preschoolness 0.827 0.40854
## measurewordbank_threshold_inv:preschoolness -0.125 0.90078
## measurepicture_naming_inv:helpfulness -2.791 0.00548 **
## measureprop_say_naive_combined:helpfulness 2.439 0.01512 *
## measurewordbank_production_24:helpfulness 0.909 0.36404
## measurewordbank_threshold_inv:helpfulness -0.320 0.74931
## measurepicture_naming_inv:concreteness 1.580 0.11478
## measureprop_say_naive_combined:concreteness 0.176 0.86068
## measurewordbank_production_24:concreteness 1.649 0.09985 .
## measurewordbank_threshold_inv:concreteness 0.944 0.34579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues1.1 Frequency
1.1.1 Model
frequency <- lmer(value ~ frequency*measure + (1|word) + (1|measure), data = complete_predictors)
summary(frequency)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ frequency * measure + (1 | word) + (1 | measure)
## Data: complete_predictors
##
## REML criterion at convergence: -856.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2919 -0.4661 0.0155 0.4740 2.7426
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.004076 0.06384
## measure (Intercept) 0.012449 0.11157
## Residual 0.009372 0.09681
## Number of obs: 575, groups: word, 115; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.078849 0.132114 546.735616
## frequency 0.024056 0.009786 413.184438
## measurepicture_naming_inv -0.125861 0.178533 451.999768
## measureprop_say_naive_combined -0.563196 0.178533 451.999770
## measurewordbank_production_24 -0.297916 0.178533 451.999770
## measurewordbank_threshold_inv 0.100316 0.178533 451.999773
## frequency:measurepicture_naming_inv 0.035496 0.011554 451.999898
## frequency:measureprop_say_naive_combined 0.123886 0.011554 451.999899
## frequency:measurewordbank_production_24 0.095312 0.011554 451.999899
## frequency:measurewordbank_threshold_inv 0.027352 0.011554 451.999899
## t value Pr(>|t|)
## (Intercept) 0.597 0.55087
## frequency 2.458 0.01438 *
## measurepicture_naming_inv -0.705 0.48119
## measureprop_say_naive_combined -3.155 0.00171 **
## measurewordbank_production_24 -1.669 0.09587 .
## measurewordbank_threshold_inv 0.562 0.57447
## frequency:measurepicture_naming_inv 3.072 0.00225 **
## frequency:measureprop_say_naive_combined 10.722 < 2e-16 ***
## frequency:measurewordbank_production_24 8.249 1.76e-15 ***
## frequency:measurewordbank_threshold_inv 2.367 0.01834 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) frqncy msrp__ msr___ ms__24 msrw__ frqncy:msrp__ fr:___
## frequency -0.529
## msrpctr_nm_ -0.676 0.273
## msrprp_sy__ -0.676 0.273 0.500
## msrwrdb__24 -0.676 0.273 0.500 0.500
## msrwrdbnk__ -0.676 0.273 0.500 0.500 0.500
## frqncy:msrp__ 0.312 -0.590 -0.462 -0.231 -0.231 -0.231
## frqncy:m___ 0.312 -0.590 -0.231 -0.462 -0.231 -0.231 0.500
## frqncy:__24 0.312 -0.590 -0.231 -0.231 -0.462 -0.231 0.500 0.500
## frqncy:msrw__ 0.312 -0.590 -0.231 -0.231 -0.231 -0.462 0.500 0.500
## f:__24
## frequency
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## frqncy:msrp__
## frqncy:m___
## frqncy:__24
## frqncy:msrw__ 0.500
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues1.1.2 Plot
ggplot(complete_predictors, aes(x = frequency, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
1.2 Concreteness
1.2.1 Model
concreteness <- lmer(value ~ concreteness*measure + (1|word) + (1|measure), data = complete_predictors)
summary(concreteness)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ concreteness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors
##
## REML criterion at convergence: -652.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3158 -0.5284 0.0835 0.5190 2.3111
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 1.128e-02 0.106196
## measure (Intercept) 1.294e-05 0.003597
## Residual 1.256e-02 0.112089
## Number of obs: 575, groups: word, 115; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.15257 0.69119 298.16049
## concreteness 0.02005 0.14116 298.14883
## measurepicture_naming_inv -0.59503 0.70960 452.00025
## measureprop_say_naive_combined -0.13590 0.70960 452.00026
## measurewordbank_production_24 -0.78449 0.70960 452.00028
## measurewordbank_threshold_inv -0.27084 0.70960 452.00029
## concreteness:measurepicture_naming_inv 0.14765 0.14492 452.00025
## concreteness:measureprop_say_naive_combined 0.09352 0.14492 452.00026
## concreteness:measurewordbank_production_24 0.23850 0.14492 452.00028
## concreteness:measurewordbank_threshold_inv 0.11574 0.14492 452.00029
## t value Pr(>|t|)
## (Intercept) 0.221 0.825
## concreteness 0.142 0.887
## measurepicture_naming_inv -0.839 0.402
## measureprop_say_naive_combined -0.192 0.848
## measurewordbank_production_24 -1.106 0.270
## measurewordbank_threshold_inv -0.382 0.703
## concreteness:measurepicture_naming_inv 1.019 0.309
## concreteness:measureprop_say_naive_combined 0.645 0.519
## concreteness:measurewordbank_production_24 1.646 0.101
## concreteness:measurewordbank_threshold_inv 0.799 0.425
##
## Correlation of Fixed Effects:
## (Intr) cncrtn msrp__ msr___ ms__24 msrw__ cncrtnss:msrp__
## concretenss -1.000
## msrpctr_nm_ -0.513 0.513
## msrprp_sy__ -0.513 0.513 0.500
## msrwrdb__24 -0.513 0.513 0.500 0.500
## msrwrdbnk__ -0.513 0.513 0.500 0.500 0.500
## cncrtnss:msrp__ 0.513 -0.513 -1.000 -0.500 -0.500 -0.500
## cncrtns:___ 0.513 -0.513 -0.500 -1.000 -0.500 -0.500 0.500
## cncrtn:__24 0.513 -0.513 -0.500 -0.500 -1.000 -0.500 0.500
## cncrtnss:msrw__ 0.513 -0.513 -0.500 -0.500 -0.500 -1.000 0.500
## cn:___ c:__24
## concretenss
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## cncrtnss:msrp__
## cncrtns:___
## cncrtn:__24 0.500
## cncrtnss:msrw__ 0.500 0.500
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues1.2.2 Plot
ggplot(complete_predictors, aes(x = concreteness, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
1.3 Preschoolness
1.3.1 Model
preschoolness <- lmer(value ~ preschoolness*measure + (1|word) + (1|measure), data = complete_predictors)
summary(preschoolness)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ preschoolness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors
##
## REML criterion at convergence: -692.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8599 -0.5478 0.0776 0.5891 2.7042
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.00966 0.09828
## measure (Intercept) 0.02229 0.14930
## Residual 0.01142 0.10685
## Number of obs: 575, groups: word, 115; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.722e-01 1.561e-01 1.776e-08
## preschoolness 3.414e-02 1.899e-02 3.070e+02
## measurepicture_naming_inv 1.874e-01 2.164e-01 1.639e-08
## measureprop_say_naive_combined 1.072e-01 2.164e-01 1.639e-08
## measurewordbank_production_24 2.365e-01 2.164e-01 1.639e-08
## measurewordbank_threshold_inv 2.658e-01 2.164e-01 1.639e-08
## preschoolness:measurepicture_naming_inv -2.595e-02 1.976e-02 4.520e+02
## preschoolness:measureprop_say_naive_combined 9.341e-02 1.976e-02 4.520e+02
## preschoolness:measurewordbank_production_24 6.376e-02 1.976e-02 4.520e+02
## preschoolness:measurewordbank_threshold_inv 1.301e-02 1.976e-02 4.520e+02
## t value Pr(>|t|)
## (Intercept) 1.103 1.00000
## preschoolness 1.798 0.07318 .
## measurepicture_naming_inv 0.866 1.00000
## measureprop_say_naive_combined 0.495 1.00000
## measurewordbank_production_24 1.092 1.00000
## measurewordbank_threshold_inv 1.228 1.00000
## preschoolness:measurepicture_naming_inv -1.313 0.18983
## preschoolness:measureprop_say_naive_combined 4.726 3.06e-06 ***
## preschoolness:measurewordbank_production_24 3.226 0.00135 **
## preschoolness:measurewordbank_threshold_inv 0.658 0.51082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) prschl msrp__ msr___ ms__24 msrw__ prschlnss:msrp__
## prescholnss -0.280
## msrpctr_nm_ -0.693 0.109
## msrprp_sy__ -0.693 0.109 0.500
## msrwrdb__24 -0.693 0.109 0.500 0.500
## msrwrdbnk__ -0.693 0.109 0.500 0.500 0.500
## prschlnss:msrp__ 0.145 -0.520 -0.210 -0.105 -0.105 -0.105
## prschln:___ 0.145 -0.520 -0.105 -0.210 -0.105 -0.105 0.500
## prschl:__24 0.145 -0.520 -0.105 -0.105 -0.210 -0.105 0.500
## prschlnss:msrw__ 0.145 -0.520 -0.105 -0.105 -0.105 -0.210 0.500
## pr:___ p:__24
## prescholnss
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## prschlnss:msrp__
## prschln:___
## prschl:__24 0.500
## prschlnss:msrw__ 0.500 0.5001.3.2 Plot
ggplot(complete_predictors, aes(x = preschoolness, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
1.4 Helpfulness
1.4.1 Model
helpfulness <- lmer(value ~ helpfulness*measure + (1|word) + (1|measure), data = complete_predictors)
summary(helpfulness)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ helpfulness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors
##
## REML criterion at convergence: -664.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.13284 -0.55334 0.07433 0.57322 2.49015
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.01135 0.1065
## measure (Intercept) 0.02711 0.1646
## Residual 0.01180 0.1086
## Number of obs: 575, groups: word, 115; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.218199 0.182432 537.718327
## helpfulness 0.009931 0.023608 288.029876
## measurepicture_naming_inv 0.266237 0.245982 452.000033
## measureprop_say_naive_combined 0.051262 0.245982 452.000030
## measurewordbank_production_24 0.227823 0.245982 452.000032
## measurewordbank_threshold_inv 0.285912 0.245982 452.000030
## helpfulness:measurepicture_naming_inv -0.042299 0.023835 452.000000
## helpfulness:measureprop_say_naive_combined 0.082657 0.023835 452.000000
## helpfulness:measurewordbank_production_24 0.047405 0.023835 451.999999
## helpfulness:measurewordbank_threshold_inv 0.002999 0.023835 451.999999
## t value Pr(>|t|)
## (Intercept) 1.196 0.232202
## helpfulness 0.421 0.674303
## measurepicture_naming_inv 1.082 0.279675
## measureprop_say_naive_combined 0.208 0.835012
## measurewordbank_production_24 0.926 0.354848
## measurewordbank_threshold_inv 1.162 0.245714
## helpfulness:measurepicture_naming_inv -1.775 0.076632 .
## helpfulness:measureprop_say_naive_combined 3.468 0.000575 ***
## helpfulness:measurewordbank_production_24 1.989 0.047320 *
## helpfulness:measurewordbank_threshold_inv 0.126 0.899922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hlpfln msrp__ msr___ ms__24 msrw__ hlpflnss:msrp__
## helpfulness -0.424
## msrpctr_nm_ -0.674 0.160
## msrprp_sy__ -0.674 0.160 0.500
## msrwrdb__24 -0.674 0.160 0.500 0.500
## msrwrdbnk__ -0.674 0.160 0.500 0.500 0.500
## hlpflnss:msrp__ 0.214 -0.505 -0.317 -0.159 -0.159 -0.159
## hlpflns:___ 0.214 -0.505 -0.159 -0.317 -0.159 -0.159 0.500
## hlpfln:__24 0.214 -0.505 -0.159 -0.159 -0.317 -0.159 0.500
## hlpflnss:msrw__ 0.214 -0.505 -0.159 -0.159 -0.159 -0.317 0.500
## hl:___ h:__24
## helpfulness
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## hlpflnss:msrp__
## hlpflns:___
## hlpfln:__24 0.500
## hlpflnss:msrw__ 0.500 0.500
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues1.4.2 Plot
ggplot(complete_predictors, aes(x = helpfulness, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
2 Analyses excluding picture-naming data (more words)
omnibus_wb <- lmer(value ~ frequency*measure + preschoolness*measure + helpfulness*measure + concreteness*measure +
(1|word) + (1|measure), data=complete_predictors_wordbank)
summary(omnibus_wb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ frequency * measure + preschoolness * measure + helpfulness *
## measure + concreteness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors_wordbank
##
## REML criterion at convergence: -2658
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5969 -0.5035 0.0355 0.4832 3.8609
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.005365 0.07325
## measure (Intercept) 0.009546 0.09771
## Residual 0.008012 0.08951
## Number of obs: 1691, groups: word, 395; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -2.300e-03 1.283e-01 7.187e-08
## frequency 1.352e-02 4.621e-03 1.087e+03
## measurepicture_naming_inv -6.374e-01 4.634e-01 3.053e-06
## measureprop_say_naive_combined -7.736e-01 1.658e-01 5.003e-08
## measurewordbank_production_24 -6.863e-01 1.655e-01 4.967e-08
## measurewordbank_threshold_inv 3.203e-02 1.655e-01 4.967e-08
## preschoolness 2.170e-02 6.967e-03 1.087e+03
## helpfulness -8.057e-04 9.312e-03 1.087e+03
## concreteness 2.145e-02 1.087e-02 1.087e+03
## frequency:measurepicture_naming_inv 3.746e-02 9.384e-03 1.376e+03
## frequency:measureprop_say_naive_combined 7.738e-02 5.113e-03 1.279e+03
## frequency:measurewordbank_production_24 5.686e-02 5.058e-03 1.274e+03
## frequency:measurewordbank_threshold_inv 1.489e-02 5.058e-03 1.274e+03
## measurepicture_naming_inv:preschoolness -4.435e-02 1.438e-02 1.377e+03
## measureprop_say_naive_combined:preschoolness 5.957e-02 7.655e-03 1.276e+03
## measurewordbank_production_24:preschoolness 2.908e-02 7.625e-03 1.274e+03
## measurewordbank_threshold_inv:preschoolness -3.056e-03 7.625e-03 1.274e+03
## measurepicture_naming_inv:helpfulness -7.138e-02 1.668e-02 1.364e+03
## measureprop_say_naive_combined:helpfulness 1.193e-02 1.023e-02 1.276e+03
## measurewordbank_production_24:helpfulness -2.459e-02 1.019e-02 1.274e+03
## measurewordbank_threshold_inv:helpfulness -8.049e-03 1.019e-02 1.274e+03
## measurepicture_naming_inv:concreteness 1.676e-01 8.990e-02 1.413e+03
## measureprop_say_naive_combined:concreteness 7.058e-02 1.198e-02 1.277e+03
## measurewordbank_production_24:concreteness 1.293e-01 1.190e-02 1.274e+03
## measurewordbank_threshold_inv:concreteness 3.639e-02 1.190e-02 1.274e+03
## t value Pr(>|t|)
## (Intercept) -0.018 1.000000
## frequency 2.926 0.003504 **
## measurepicture_naming_inv -1.376 0.999978
## measureprop_say_naive_combined -4.666 0.999999
## measurewordbank_production_24 -4.147 0.999999
## measurewordbank_threshold_inv 0.194 1.000000
## preschoolness 3.114 0.001891 **
## helpfulness -0.087 0.931068
## concreteness 1.973 0.048746 *
## frequency:measurepicture_naming_inv 3.992 6.89e-05 ***
## frequency:measureprop_say_naive_combined 15.134 < 2e-16 ***
## frequency:measurewordbank_production_24 11.241 < 2e-16 ***
## frequency:measurewordbank_threshold_inv 2.944 0.003295 **
## measurepicture_naming_inv:preschoolness -3.085 0.002074 **
## measureprop_say_naive_combined:preschoolness 7.781 1.47e-14 ***
## measurewordbank_production_24:preschoolness 3.814 0.000143 ***
## measurewordbank_threshold_inv:preschoolness -0.401 0.688586
## measurepicture_naming_inv:helpfulness -4.279 2.01e-05 ***
## measureprop_say_naive_combined:helpfulness 1.166 0.243737
## measurewordbank_production_24:helpfulness -2.412 0.015987 *
## measurewordbank_threshold_inv:helpfulness -0.790 0.429805
## measurepicture_naming_inv:concreteness 1.864 0.062548 .
## measureprop_say_naive_combined:concreteness 5.894 4.83e-09 ***
## measurewordbank_production_24:concreteness 10.871 < 2e-16 ***
## measurewordbank_threshold_inv:concreteness 3.058 0.002272 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 12.1 Frequency
2.1.1 Model
frequency_wb <- lmer(value ~ frequency*measure + (1|word) + (1|measure), data = complete_predictors_wordbank)
summary(frequency_wb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ frequency * measure + (1 | word) + (1 | measure)
## Data: complete_predictors_wordbank
##
## REML criterion at convergence: -2380.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4013 -0.4967 0.0338 0.5026 3.7290
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.007714 0.08783
## measure (Intercept) 0.007290 0.08538
## Residual 0.009703 0.09850
## Number of obs: 1691, groups: word, 395; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.667e-01 9.161e-02 1.472e+03
## frequency 1.088e-02 4.655e-03 1.015e+03
## measurepicture_naming_inv 1.468e-02 1.392e-01 1.308e+03
## measureprop_say_naive_combined -2.034e-01 1.259e-01 1.284e+03
## measurewordbank_production_24 7.236e-02 1.257e-01 1.284e+03
## measurewordbank_threshold_inv 2.197e-01 1.257e-01 1.284e+03
## frequency:measurepicture_naming_inv 1.209e-02 9.594e-03 1.378e+03
## frequency:measureprop_say_naive_combined 6.955e-02 4.995e-03 1.290e+03
## frequency:measurewordbank_production_24 3.081e-02 4.913e-03 1.284e+03
## frequency:measurewordbank_threshold_inv 6.533e-03 4.913e-03 1.284e+03
## t value Pr(>|t|)
## (Intercept) 1.820 0.0689 .
## frequency 2.338 0.0196 *
## measurepicture_naming_inv 0.105 0.9160
## measureprop_say_naive_combined -1.615 0.1065
## measurewordbank_production_24 0.576 0.5650
## measurewordbank_threshold_inv 1.747 0.0808 .
## frequency:measurepicture_naming_inv 1.260 0.2080
## frequency:measureprop_say_naive_combined 13.923 < 2e-16 ***
## frequency:measurewordbank_production_24 6.271 4.88e-10 ***
## frequency:measurewordbank_threshold_inv 1.330 0.1839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) frqncy msrp__ msr___ ms__24 msrw__ frqncy:msrp__ fr:___
## frequency -0.355
## msrpctr_nm_ -0.620 0.130
## msrprp_sy__ -0.685 0.144 0.451
## msrwrdb__24 -0.686 0.144 0.452 0.499
## msrwrdbnk__ -0.686 0.144 0.452 0.499 0.500
## frqncy:msrp__ 0.096 -0.270 -0.491 -0.071 -0.070 -0.070
## frqncy:m___ 0.184 -0.519 -0.123 -0.277 -0.134 -0.134 0.255
## frqncy:__24 0.187 -0.528 -0.123 -0.136 -0.273 -0.137 0.256 0.492
## frqncy:msrw__ 0.187 -0.528 -0.123 -0.136 -0.137 -0.273 0.256 0.492
## f:__24
## frequency
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## frqncy:msrp__
## frqncy:m___
## frqncy:__24
## frqncy:msrw__ 0.500
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues2.1.2 Plot
ggplot(complete_predictors_wordbank, aes(x = frequency, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
2.2 Concreteness
2.2.1 Model
concreteness_wb <- lmer(value ~ concreteness*measure + (1|word) + (1|measure), data = complete_predictors_wordbank)
summary(concreteness_wb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ concreteness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors_wordbank
##
## REML criterion at convergence: -2155.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4631 -0.5165 0.0485 0.5095 2.7199
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.009993 0.09997
## measure (Intercept) 0.051321 0.22654
## Residual 0.010915 0.10447
## Number of obs: 1695, groups: word, 396; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.330e-01 2.327e-01 9.388e-07
## concreteness 2.107e-03 1.158e-02 9.635e+02
## measurepicture_naming_inv -2.734e-01 6.046e-01 1.070e-05
## measureprop_say_naive_combined 4.074e-01 3.250e-01 8.939e-07
## measurewordbank_production_24 -2.956e-02 3.249e-01 8.927e-07
## measurewordbank_threshold_inv 1.569e-01 3.249e-01 8.927e-07
## concreteness:measurepicture_naming_inv 7.657e-02 1.047e-01 1.404e+03
## concreteness:measureprop_say_naive_combined -2.761e-02 1.196e-02 1.296e+03
## concreteness:measurewordbank_production_24 6.990e-02 1.183e-02 1.293e+03
## concreteness:measurewordbank_threshold_inv 2.392e-02 1.183e-02 1.293e+03
## t value Pr(>|t|)
## (Intercept) 1.002 1.0000
## concreteness 0.182 0.8557
## measurepicture_naming_inv -0.452 0.9999
## measureprop_say_naive_combined 1.253 1.0000
## measurewordbank_production_24 -0.091 1.0000
## measurewordbank_threshold_inv 0.483 1.0000
## concreteness:measurepicture_naming_inv 0.731 0.4649
## concreteness:measureprop_say_naive_combined -2.308 0.0211 *
## concreteness:measurewordbank_production_24 5.907 4.46e-09 ***
## concreteness:measurewordbank_threshold_inv 2.021 0.0435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cncrtn msrp__ msr___ ms__24 msrw__ cncrtnss:msrp__
## concretenss -0.226
## msrpctr_nm_ -0.375 0.045
## msrprp_sy__ -0.698 0.084 0.269
## msrwrdb__24 -0.698 0.084 0.269 0.500
## msrwrdbnk__ -0.698 0.084 0.269 0.500 0.500
## cncrtnss:msrp__ 0.013 -0.058 -0.848 -0.009 -0.009 -0.009
## cncrtns:___ 0.114 -0.505 -0.044 -0.167 -0.082 -0.082 0.056
## cncrtn:__24 0.115 -0.511 -0.044 -0.083 -0.165 -0.083 0.056
## cncrtnss:msrw__ 0.115 -0.511 -0.044 -0.083 -0.083 -0.165 0.056
## cn:___ c:__24
## concretenss
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## cncrtnss:msrp__
## cncrtns:___
## cncrtn:__24 0.495
## cncrtnss:msrw__ 0.495 0.5002.2.2 Plot
ggplot(complete_predictors_wordbank, aes(x = concreteness, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
2.3 Preschoolness
2.3.1 Model
preschoolness_wb <- lmer(value ~ preschoolness*measure + (1|word) + (1|measure), data = complete_predictors_wordbank)
summary(preschoolness_wb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ preschoolness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors_wordbank
##
## REML criterion at convergence: -2224
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7029 -0.5189 0.0505 0.5337 2.9908
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.009036 0.09506
## measure (Intercept) 0.065892 0.25670
## Residual 0.010573 0.10283
## Number of obs: 1695, groups: word, 396; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.853e-01 2.577e-01 1.420e-06
## preschoolness 2.214e-02 8.255e-03 9.926e+02
## measurepicture_naming_inv 1.835e-01 3.650e-01 1.429e-06
## measureprop_say_naive_combined 9.957e-02 3.638e-01 1.410e-06
## measurewordbank_production_24 2.268e-01 3.638e-01 1.410e-06
## measurewordbank_threshold_inv 2.784e-01 3.638e-01 1.410e-06
## preschoolness:measurepicture_naming_inv -3.894e-02 1.567e-02 1.376e+03
## preschoolness:measureprop_say_naive_combined 7.057e-02 8.602e-03 1.293e+03
## preschoolness:measurewordbank_production_24 2.345e-02 8.573e-03 1.292e+03
## preschoolness:measurewordbank_threshold_inv -5.059e-03 8.573e-03 1.292e+03
## t value Pr(>|t|)
## (Intercept) 0.719 0.99999
## preschoolness 2.682 0.00744 **
## measurepicture_naming_inv 0.503 0.99999
## measureprop_say_naive_combined 0.274 0.99999
## measurewordbank_production_24 0.623 0.99999
## measurewordbank_threshold_inv 0.765 0.99999
## preschoolness:measurepicture_naming_inv -2.485 0.01306 *
## preschoolness:measureprop_say_naive_combined 8.204 5.56e-16 ***
## preschoolness:measurewordbank_production_24 2.736 0.00631 **
## preschoolness:measurewordbank_threshold_inv -0.590 0.55524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) prschl msrp__ msr___ ms__24 msrw__ prschlnss:msrp__
## prescholnss -0.083
## msrpctr_nm_ -0.703 0.032
## msrprp_sy__ -0.706 0.032 0.498
## msrwrdb__24 -0.706 0.032 0.498 0.500
## msrwrdbnk__ -0.706 0.032 0.498 0.500 0.500
## prschlnss:msrp__ 0.024 -0.284 -0.100 -0.017 -0.017 -0.017
## prschln:___ 0.043 -0.517 -0.030 -0.061 -0.030 -0.030 0.273
## prschl:__24 0.043 -0.519 -0.030 -0.030 -0.061 -0.030 0.274
## prschlnss:msrw__ 0.043 -0.519 -0.030 -0.030 -0.030 -0.061 0.274
## pr:___ p:__24
## prescholnss
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## prschlnss:msrp__
## prschln:___
## prschl:__24 0.498
## prschlnss:msrw__ 0.498 0.5002.3.2 Plot
ggplot(complete_predictors_wordbank, aes(x = preschoolness, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()
2.4 Helpfulness
2.4.1 Model
helpfulness_wb <- lmer(value ~ helpfulness*measure + (1|word) + (1|measure), data = complete_predictors_wordbank)
summary(helpfulness_wb)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ helpfulness * measure + (1 | word) + (1 | measure)
## Data: complete_predictors_wordbank
##
## REML criterion at convergence: -2124.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.05043 -0.50957 0.05925 0.54186 2.70666
##
## Random effects:
## Groups Name Variance Std.Dev.
## word (Intercept) 0.01020 0.1010
## measure (Intercept) 0.05226 0.2286
## Residual 0.01108 0.1053
## Number of obs: 1695, groups: word, 396; measure, 5
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.361e-01 2.319e-01 1.337e+03
## helpfulness 1.888e-03 1.115e-02 9.607e+02
## measurepicture_naming_inv 3.273e-01 3.297e-01 1.295e+03
## measureprop_say_naive_combined 1.751e-01 3.258e-01 1.292e+03
## measurewordbank_production_24 4.083e-01 3.258e-01 1.292e+03
## measurewordbank_threshold_inv 3.113e-01 3.258e-01 1.292e+03
## helpfulness:measurepicture_naming_inv -7.017e-02 1.927e-02 1.366e+03
## helpfulness:measureprop_say_naive_combined 3.086e-02 1.141e-02 1.293e+03
## helpfulness:measurewordbank_production_24 -3.498e-02 1.138e-02 1.292e+03
## helpfulness:measurewordbank_threshold_inv -1.329e-02 1.138e-02 1.292e+03
## t value Pr(>|t|)
## (Intercept) 1.018 0.308956
## helpfulness 0.169 0.865584
## measurepicture_naming_inv 0.993 0.321086
## measureprop_say_naive_combined 0.538 0.590925
## measurewordbank_production_24 1.253 0.210262
## measurewordbank_threshold_inv 0.956 0.339488
## helpfulness:measurepicture_naming_inv -3.641 0.000282 ***
## helpfulness:measureprop_say_naive_combined 2.706 0.006902 **
## helpfulness:measurewordbank_production_24 -3.074 0.002159 **
## helpfulness:measurewordbank_threshold_inv -1.168 0.243146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hlpfln msrp__ msr___ ms__24 msrw__ hlpflnss:msrp__
## helpfulness -0.166
## msrpctr_nm_ -0.694 0.061
## msrprp_sy__ -0.702 0.062 0.494
## msrwrdb__24 -0.702 0.062 0.494 0.500
## msrwrdbnk__ -0.702 0.062 0.494 0.500 0.500
## hlpflnss:msrp__ 0.050 -0.301 -0.193 -0.036 -0.036 -0.036
## hlpflns:___ 0.085 -0.509 -0.060 -0.121 -0.060 -0.060 0.295
## hlpfln:__24 0.085 -0.510 -0.060 -0.060 -0.121 -0.060 0.295
## hlpflnss:msrw__ 0.085 -0.510 -0.060 -0.060 -0.060 -0.121 0.295
## hl:___ h:__24
## helpfulness
## msrpctr_nm_
## msrprp_sy__
## msrwrdb__24
## msrwrdbnk__
## hlpflnss:msrp__
## hlpflns:___
## hlpfln:__24 0.499
## hlpflnss:msrw__ 0.499 0.500
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues2.4.2 Plot
ggplot(complete_predictors_wordbank, aes(x = helpfulness, y = value, color = as.factor(measure)))+
geom_point()+
geom_smooth(method="lm")+
scale_color_brewer(palette = "Set1")+
theme_classic()