Syntactic Boostrapping MA - action
Data V3!!
Anjie Cao and Molly Lewis
2020-08-07
options(knitr.duplicate.label = 'allow')DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data.csv") # make all variables (i.e. things that might change) as capital letters at the top of the scripts
ma_data <- read_csv(DATA_PATH) %>% filter(paradigm_type == "action_matching")Data Overview
effect size & paper
n_effect_sizes <- ma_data %>%
filter(!is.na(d_calc)) %>%
nrow()
n_papers <- ma_data %>%
distinct(unique_id) %>%
nrow()There are 78 effect sizes collected from 21 different papers.
Here are the papers in this analysis:
ma_data %>%
count(short_cite) %>%
arrange(-n) %>%
DT::datatable()ma_model <- rma(ma_data$d_calc, ma_data$d_var_calc)
ma_model##
## Random-Effects Model (k = 78; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.6593 (SE = 0.1277)
## tau (square root of estimated tau^2 value): 0.8120
## I^2 (total heterogeneity / total variability): 88.29%
## H^2 (total variability / sampling variability): 8.54
##
## Test for Heterogeneity:
## Q(df = 77) = 408.5756, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.5485 0.1016 5.3977 <.0001 0.3494 0.7477 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(ma_model,
header = T,
slab = ma_data$unique_id,
col = "red",
cex = .7)
## funnel plot {.tabset} ### vanilla
ma_data %>%
mutate(color = ifelse(sentence_structure == "transitive", "red", "blue"),
color_sample_size = ifelse(n_1 < 10, "red", ifelse(n_1 < 20, "orange", "yellow")),
color_confidence = ifelse(inclusion_certainty == 1, "red", "black"))-> ma_data_funnel
ma_data_funnel %>% filter (abs(d_calc) < 5) -> ma_data_funnel_no_outlier
ss_colors <- ma_data_funnel$color
ss_colors_no_outlier <- ma_data_funnel_no_outlier$color
ma_model_funnel <- rma(ma_data_funnel$d_calc, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier <- rma(ma_data_funnel_no_outlier$d_calc, ma_data_funnel_no_outlier$d_var_calc)
f1<- funnel(ma_model_funnel, xlab = "Effect Size", col = ss_colors)
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "All effect sizes break down by sentence structure")
f2<- funnel(ma_model_funnel_no_outlier, xlab = "Effect Size", col = ss_colors_no_outlier)
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "effect sizes excluded outliers (abs <5) break down by sentence structure")
take into account sentence structure?
ma_model_sentence_structure <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure, ma_data_funnel$d_var_calc)
ma_model_no_outlier_ss <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure, ma_data_funnel_no_outlier$d_var_calc)
f3 <- funnel(ma_model_sentence_structure, xlab = "effect size", col = ss_colors)
f3_b <- funnel(ma_model_no_outlier_ss, xlab = "effect size", col = ss_colors_no_outlier)
take into account sentence structure and age?
ma_model_ss_age <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure + ma_data_funnel $mean_age, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier_ss_age <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure+ma_data_funnel_no_outlier$mean_age, ma_data_funnel_no_outlier$d_var_calc)
f4 <- funnel(ma_model_ss_age, xlab = "effect size", col = ss_colors)
f4_b <- funnel(ma_model_funnel_no_outlier_ss_age, xlab = "effect size", col = ss_colors_no_outlier)
Variable Summary
Distribution with years:
ma_data %>%
ggplot(aes(x = publication_year, y = d_calc, size = n_1)) +
xlim(1990,2020) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("publication year") +
ggtitle("Syntactical Bootstrapping effect size with year") +
theme(legend.position = "none") 
m_year <- rma.mv(d_calc ~ publication_year, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_year)##
## Multivariate Meta-Analysis Model (k = 76; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.1620 324.3240 330.3240 337.2362 330.6668
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2547 0.5047 20 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 406.1270, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1310, p-val = 0.7174
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5348 0.3810 1.4037 0.1604 -0.2119 1.2815
## publication_year -0.0001 0.0002 -0.3620 0.7174 -0.0005 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Continuous Variables
CONTINUOUS_VARS <- c("n_1", "x_1", "sd_1", "d_calc", "d_var_calc", "mean_age","n_train_test_pair","n_test_trial_per_pair","productive_vocab_mean", "productive_vocab_median")
long_continuous <- ma_data %>%
pivot_longer(cols = CONTINUOUS_VARS)
long_continuous %>%
ggplot(aes(x = value)) +
geom_histogram() +
facet_wrap(~ name, scale = "free_x") +
labs(title = "Distribution of continuous measures")
long_continuous %>%
group_by(name) %>%
summarize(mean = mean(value),
sd = sd(value)) %>%
kable()| name | mean | sd |
|---|---|---|
| d_calc | 0.5634565 | 1.4158104 |
| d_var_calc | 0.2061217 | 0.3714431 |
| mean_age | 901.5634974 | 337.3459361 |
| n_1 | 14.8589744 | 6.2267216 |
| n_test_trial_per_pair | 1.7435897 | 0.6123384 |
| n_train_test_pair | 2.4358974 | 1.3919037 |
| productive_vocab_mean | NA | NA |
| productive_vocab_median | NA | NA |
| sd_1 | 0.1401252 | 0.0742438 |
| x_1 | 0.5664519 | 0.0956078 |
Categorical Variables
CATEGORICAL_VARS <- c("sentence_structure", "agent_argument_type_clean", "patient_argument_type_clean", "stimuli_actor","agent_argument_number","transitive_event_type","intransitive_event_type",
"presentation_type", "character_identification",
"test_mass_or_distributed", "practice_phase", "test_method")
long_categorical <- ma_data %>%
pivot_longer(cols = CATEGORICAL_VARS) %>%
count(name, value) # this is a short cut for group_by() %>% summarize(count = n())
long_categorical %>%
ggplot(aes(x = value, y = n)) +
facet_wrap(~ name, scale = "free_x") +
geom_col(position = 'dodge',width=0.4) +
theme(text = element_text(size=8),
axis.text.x = element_text(angle = 90, hjust = 1)) # rotate x-axis text
Prep for Moderators
ma_data_young <- ma_data_young <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_old <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36)
ma_data_vocab <- ma_data %>%
filter(!is.na(productive_vocab_median)) no moderators
all
m1 <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m1)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.9086 325.8171 329.8171 334.5047 329.9793
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2245 0.4739 21 no short_cite
##
## Test for Heterogeneity:
## Q(df = 77) = 408.5756, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3968 0.1125 3.5273 0.0004 0.1763 0.6173 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36
m_young <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.7270 231.4539 235.4539 239.5046 235.6803
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2940 0.5422 17 no short_cite
##
## Test for Heterogeneity:
## Q(df = 56) = 302.2290, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4071 0.1414 2.8791 0.0040 0.1299 0.6842 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36
m_old <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.9329 85.8658 89.8658 91.8573 90.5717
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0315 0.1775 5 no short_cite
##
## Test for Heterogeneity:
## Q(df = 20) = 106.3466, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3286 0.1037 3.1691 0.0015 0.1254 0.5319 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Participants characteristics:
age only
colored by unique_id
ma_data %>%
ggplot(aes(x = mean_age/30.44, y = d_calc,color = unique_id)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
all
ma_data %>%
ggplot(aes(x = mean_age/30.44, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
theme(legend.position = "none") 
m_simple <- rma.mv(d_calc ~ 1, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_log <- rma.mv(d_calc ~ log(mean_age), V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_simple)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.9086 325.8171 329.8171 334.5047 329.9793
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2245 0.4739 21 no short_cite
##
## Test for Heterogeneity:
## Q(df = 77) = 408.5756, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3968 0.1125 3.5273 0.0004 0.1763 0.6173 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.6433 319.2865 325.2865 332.2787 325.6199
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2306 0.4802 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 407.7223, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.9754, p-val = 0.0145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7864 0.1957 4.0180 <.0001 0.4028 1.1700 ***
## mean_age -0.0004 0.0002 -2.4445 0.0145 -0.0008 -0.0001 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_log)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.6477 319.2955 325.2955 332.2877 325.6288
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2273 0.4768 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 407.1479, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.0877, p-val = 0.0136
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 3.0449 1.0792 2.8216 0.0048 0.9298 5.1601 **
## log(mean_age) -0.3918 0.1588 -2.4673 0.0136 -0.7031 -0.0806 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(m_age,
header = T,
slab = ma_data$unique_id,
col = "red",
cex = .7
)
funnel(m_age)
young, < 36
ma_data_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
theme(legend.position = "none") 
m_age_young <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.3058 230.6116 236.6116 242.6336 237.0822
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2908 0.5393 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 55) = 296.5941, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3081, p-val = 0.5788
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6132 0.3973 1.5433 0.1228 -0.1656 1.3920
## mean_age -0.0003 0.0005 -0.5551 0.5788 -0.0012 0.0007
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(m_age_young,
header = T,
slab = ma_data_young$unique_id,
col = "red",
cex = .7
)
old, >= 36
ma_data_old %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
theme(legend.position = "none") 
m_age_old <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.8038 85.6077 91.6077 94.4410 93.2077
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0359 0.1895 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 106.1716, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9372, p-val = 0.3330
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7695 0.4683 1.6433 0.1003 -0.1483 1.6873
## mean_age -0.0003 0.0003 -0.9681 0.3330 -0.0010 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Median Vocabulary Size") +
ggtitle("Syntactical Bootstrapping effect size vs. Median Vocabulary (months)") +
theme(legend.position = "none") 
m_vocab <- rma.mv(d_calc ~ productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -76.1570 152.3139 158.3139 162.8930 159.1139
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2602 0.5101 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 34) = 160.5343, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7042, p-val = 0.0096
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8285 0.2557 3.2401 0.0012 0.3273 1.3297
## productive_vocab_median -0.0063 0.0024 -2.5892 0.0096 -0.0111 -0.0015
##
## intrcpt **
## productive_vocab_median **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_vocab_with_age <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_vocab_with_age)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -76.1407 152.2813 158.2813 162.8604 159.0813
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2767 0.5261 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 34) = 161.6858, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.1130, p-val = 0.0077
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9727 0.2867 3.3922 0.0007 0.4107 1.5347 ***
## mean_age -0.0006 0.0002 -2.6670 0.0077 -0.0010 -0.0002 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linguistic Stimuli
Sentence structure
SS only
m_SS <- rma.mv(d_calc ~ sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_SS)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -153.7787 307.5573 313.5573 320.5495 313.8907
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2924 0.5407 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 403.6155, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 18.9541, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1879 0.1352 1.3899 0.1646 -0.0771 0.4529
## sentence_structuretransitive 0.3504 0.0805 4.3536 <.0001 0.1926 0.5081
##
## intrcpt
## sentence_structuretransitive ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all with age
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
m_age_sentence <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -151.9921 303.9843 311.9843 321.2542 312.5557
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2864 0.5351 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 402.0777, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 21.8831, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4857 0.2180 2.2277 0.0259 0.0584
## mean_age -0.0003 0.0002 -1.7311 0.0834 -0.0007
## sentence_structuretransitive 0.3253 0.0817 3.9832 <.0001 0.1652
## ci.ub
## intrcpt 0.9131 *
## mean_age 0.0000 .
## sentence_structuretransitive 0.4854 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_sentence <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -148.2914 296.5829 306.5829 318.1032 307.4652
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2662 0.5159 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 381.3786, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 29.6044, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7336 0.2324 3.1567 0.0016
## mean_age -0.0006 0.0002 -2.8561 0.0043
## sentence_structuretransitive -0.2651 0.2241 -1.1833 0.2367
## mean_age:sentence_structuretransitive 0.0006 0.0002 2.8173 0.0048
## ci.lb ci.ub
## intrcpt 0.2781 1.1891 **
## mean_age -0.0010 -0.0002 **
## sentence_structuretransitive -0.7043 0.1740
## mean_age:sentence_structuretransitive 0.0002 0.0011 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young, age
ma_data_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, , color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), with sentence_structure") 
m_age_sentence_young <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_sentence_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.4871 228.9742 236.9742 244.9301 237.7905
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3256 0.5706 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 293.9865, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.2202, p-val = 0.3295
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4825 0.4118 1.1716 0.2413 -0.3247
## mean_age -0.0002 0.0005 -0.3992 0.6898 -0.0012
## sentence_structuretransitive 0.1316 0.0946 1.3915 0.1641 -0.0538
## ci.ub
## intrcpt 1.2897
## mean_age 0.0008
## sentence_structuretransitive 0.3169
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_sentence_young <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_sentence_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -111.3248 222.6496 232.6496 242.5010 233.9262
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3002 0.5479 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 281.8953, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 8.8233, p-val = 0.0317
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.1283 0.4318 0.2971 0.7664
## mean_age 0.0003 0.0005 0.6080 0.5432
## sentence_structuretransitive 1.3145 0.4685 2.8058 0.0050
## mean_age:sentence_structuretransitive -0.0016 0.0006 -2.5857 0.0097
## ci.lb ci.ub
## intrcpt -0.7181 0.9747
## mean_age -0.0007 0.0014
## sentence_structuretransitive 0.3963 2.2328 **
## mean_age:sentence_structuretransitive -0.0029 -0.0004 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old, age
ma_data_old %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
m_age_sentence_old <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -26.1658 52.3317 60.3317 63.8932 63.4086
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 68.0107, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 38.3360, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1760 0.4126 0.4265 0.6697 -0.6326
## mean_age -0.0002 0.0003 -0.7331 0.4635 -0.0008
## sentence_structuretransitive 0.7111 0.1151 6.1774 <.0001 0.4855
## ci.ub
## intrcpt 0.9846
## mean_age 0.0004
## sentence_structuretransitive 0.9367 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_sentence_old_interaction <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -26.1658 52.3317 60.3317 63.8932 63.4086
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 68.0107, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 38.3360, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1760 0.4126 0.4265 0.6697 -0.6326
## mean_age -0.0002 0.0003 -0.7331 0.4635 -0.0008
## sentence_structuretransitive 0.7111 0.1151 6.1774 <.0001 0.4855
## ci.ub
## intrcpt 0.9846
## mean_age 0.0004
## sentence_structuretransitive 0.9367 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all with vocab
ma_data_vocab %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Median Productive Vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. Median Productive Vocab")
m_age_sentence_vocab <- rma.mv(d_calc ~ productive_vocab_median + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_sentence_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -67.5932 135.1863 143.1863 149.1724 144.6149
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4741 0.6886 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 153.8355, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 23.8757, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.3683 0.3381 1.0893 0.2760 -0.2943
## productive_vocab_median -0.0022 0.0026 -0.8363 0.4030 -0.0074
## sentence_structuretransitive 0.5993 0.1431 4.1886 <.0001 0.3189
## ci.ub
## intrcpt 1.0309
## productive_vocab_median 0.0030
## sentence_structuretransitive 0.8798 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_sentence_vocab_interaction <- rma.mv(d_calc ~ productive_vocab_median * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_sentence_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -67.5932 135.1863 143.1863 149.1724 144.6149
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4741 0.6886 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 153.8355, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 23.8757, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.3683 0.3381 1.0893 0.2760 -0.2943
## productive_vocab_median -0.0022 0.0026 -0.8363 0.4030 -0.0074
## sentence_structuretransitive 0.5993 0.1431 4.1886 <.0001 0.3189
## ci.ub
## intrcpt 1.0309
## productive_vocab_median 0.0030
## sentence_structuretransitive 0.8798 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Agent Arguement Type
m_AAT <- rma.mv(d_calc ~ agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_AAT_young <- rma.mv(d_calc ~ agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_AAT)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -160.6370 321.2741 331.2741 342.7944 332.1564
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2257 0.4750 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 402.0555, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.5091, p-val = 0.6802
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.3032 0.1459 2.0773 0.0378
## agent_argument_type_cleannoun_phrase 0.1096 0.1599 0.6853 0.4931
## agent_argument_type_cleanpronoun 0.1375 0.2706 0.5080 0.6115
## agent_argument_type_cleanvarying_agent 0.2570 0.2450 1.0488 0.2943
## ci.lb ci.ub
## intrcpt 0.0171 0.5892 *
## agent_argument_type_cleannoun_phrase -0.2039 0.4231
## agent_argument_type_cleanpronoun -0.3929 0.6678
## agent_argument_type_cleanvarying_agent -0.2233 0.7373
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_AAT_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.4664 226.9328 236.9328 246.7843 238.2094
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2798 0.5290 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 287.7308, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9991, p-val = 0.5726
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.2615 0.1745 1.4990 0.1339
## agent_argument_type_cleannoun_phrase 0.1088 0.1613 0.6749 0.4997
## agent_argument_type_cleanpronoun 0.3198 0.3613 0.8852 0.3761
## agent_argument_type_cleanvarying_agent 0.3819 0.2936 1.3006 0.1934
## ci.lb ci.ub
## intrcpt -0.0804 0.6035
## agent_argument_type_cleannoun_phrase -0.2072 0.4249
## agent_argument_type_cleanpronoun -0.3883 1.0280
## agent_argument_type_cleanvarying_agent -0.1936 0.9574
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument") 
m_age_aa <- rma.mv(d_calc ~ mean_age + agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_aa)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -157.6119 315.2237 327.2237 340.9665 328.4964
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2320 0.4817 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 73) = 398.5696, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 7.4154, p-val = 0.1155
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.6832 0.2147 3.1824 0.0015
## mean_age -0.0004 0.0002 -2.4363 0.0148
## agent_argument_type_cleannoun_phrase 0.0939 0.1602 0.5861 0.5578
## agent_argument_type_cleanpronoun 0.2373 0.2763 0.8589 0.3904
## agent_argument_type_cleanvarying_agent 0.2712 0.2478 1.0945 0.2738
## ci.lb ci.ub
## intrcpt 0.2625 1.1040 **
## mean_age -0.0008 -0.0001 *
## agent_argument_type_cleannoun_phrase -0.2200 0.4078
## agent_argument_type_cleanpronoun -0.3042 0.7788
## agent_argument_type_cleanvarying_agent -0.2145 0.7568
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_aa_interaction <- rma.mv(d_calc ~ mean_age * agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_aa_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -153.6657 307.3313 325.3313 345.5678 328.3313
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2807 0.5298 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 70) = 388.7685, p-val < .0001
##
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 13.4983, p-val = 0.0609
##
## Model Results:
##
## estimate se zval
## intrcpt 0.0834 0.8021 0.1040
## mean_age 0.0003 0.0009 0.2974
## agent_argument_type_cleannoun_phrase -3.1937 1.7073 -1.8706
## agent_argument_type_cleanpronoun 0.7706 0.8960 0.8601
## agent_argument_type_cleanvarying_agent 0.9560 0.8482 1.1272
## mean_age:agent_argument_type_cleannoun_phrase 0.0041 0.0021 1.9533
## mean_age:agent_argument_type_cleanpronoun -0.0006 0.0010 -0.6473
## mean_age:agent_argument_type_cleanvarying_agent -0.0008 0.0009 -0.8967
## pval ci.lb ci.ub
## intrcpt 0.9172 -1.4887 1.6555
## mean_age 0.7662 -0.0015 0.0021
## agent_argument_type_cleannoun_phrase 0.0614 -6.5399 0.1525 .
## agent_argument_type_cleanpronoun 0.3897 -0.9855 2.5266
## agent_argument_type_cleanvarying_agent 0.2597 -0.7063 2.6184
## mean_age:agent_argument_type_cleannoun_phrase 0.0508 -0.0000 0.0083 .
## mean_age:agent_argument_type_cleanpronoun 0.5175 -0.0025 0.0013
## mean_age:agent_argument_type_cleanvarying_agent 0.3699 -0.0027 0.0010
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument, young only") 
m_age_aa_young <- rma.mv(d_calc ~ mean_age + agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_aa_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.2113 226.4225 238.4225 250.1300 240.2892
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2808 0.5299 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 52) = 287.1833, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 2.1333, p-val = 0.7113
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.4067 0.4258 0.9550 0.3396
## mean_age -0.0002 0.0005 -0.3737 0.7086
## agent_argument_type_cleannoun_phrase 0.1050 0.1616 0.6496 0.5159
## agent_argument_type_cleanpronoun 0.3167 0.3618 0.8752 0.3814
## agent_argument_type_cleanvarying_agent 0.3689 0.2961 1.2456 0.2129
## ci.lb ci.ub
## intrcpt -0.4279 1.2413
## mean_age -0.0011 0.0008
## agent_argument_type_cleannoun_phrase -0.2117 0.4217
## agent_argument_type_cleanpronoun -0.3924 1.0257
## agent_argument_type_cleanvarying_agent -0.2115 0.9493
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_aa_young_interaction <- rma.mv(d_calc ~ mean_age * agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_aa_young_interaction)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -109.6876 219.3752 237.3752 254.4016 241.9906
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3500 0.5916 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 49) = 286.9180, p-val < .0001
##
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 7.1890, p-val = 0.4095
##
## Model Results:
##
## estimate se zval
## intrcpt 0.6759 1.3631 0.4958
## mean_age -0.0005 0.0017 -0.2936
## agent_argument_type_cleannoun_phrase -3.7829 1.8653 -2.0280
## agent_argument_type_cleanpronoun -2.9237 11.9582 -0.2445
## agent_argument_type_cleanvarying_agent 0.2196 1.4386 0.1527
## mean_age:agent_argument_type_cleannoun_phrase 0.0048 0.0023 2.1025
## mean_age:agent_argument_type_cleanpronoun 0.0050 0.0186 0.2685
## mean_age:agent_argument_type_cleanvarying_agent 0.0002 0.0018 0.0880
## pval ci.lb ci.ub
## intrcpt 0.6200 -1.9958 3.3476
## mean_age 0.7690 -0.0038 0.0028
## agent_argument_type_cleannoun_phrase 0.0426 -7.4389 -0.1270 *
## agent_argument_type_cleanpronoun 0.8068 -26.3613 20.5138
## agent_argument_type_cleanvarying_agent 0.8787 -2.5999 3.0392
## mean_age:agent_argument_type_cleannoun_phrase 0.0355 0.0003 0.0093 *
## mean_age:agent_argument_type_cleanpronoun 0.7883 -0.0314 0.0414
## mean_age:agent_argument_type_cleanvarying_agent 0.9299 -0.0033 0.0036
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>=36, old
ma_data_old %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument, old only") 
m_age_aa_old <- rma.mv(d_calc ~ mean_age + agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_aa_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.4821 82.9641 92.9641 97.1302 98.4187
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0080 0.0893 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 97.4690, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.3151, p-val = 0.0972
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.9225 0.4653 1.9826 0.0474
## mean_age -0.0003 0.0004 -0.8856 0.3758
## agent_argument_type_cleanpronoun -0.0791 0.2023 -0.3911 0.6957
## agent_argument_type_cleanvarying_agent -0.4824 0.2190 -2.2031 0.0276
## ci.lb ci.ub
## intrcpt 0.0105 1.8345 *
## mean_age -0.0011 0.0004
## agent_argument_type_cleanpronoun -0.4756 0.3174
## agent_argument_type_cleanvarying_agent -0.9116 -0.0532 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_aa_old_interaction <- rma.mv(d_calc ~ mean_age * agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_aa_old_interaction)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.2722 84.5444 98.5444 103.5007 114.5444
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0236 0.1535 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 96.6569, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 5.1338, p-val = 0.3998
##
## Model Results:
##
## estimate se zval
## intrcpt -0.0821 3.1149 -0.0263
## mean_age 0.0005 0.0026 0.1971
## agent_argument_type_cleanpronoun 0.8915 3.1665 0.2815
## agent_argument_type_cleanvarying_agent 5.3278 6.7887 0.7848
## mean_age:agent_argument_type_cleanpronoun -0.0008 0.0027 -0.3183
## mean_age:agent_argument_type_cleanvarying_agent -0.0047 0.0055 -0.8545
## pval ci.lb ci.ub
## intrcpt 0.9790 -6.1871 6.0230
## mean_age 0.8438 -0.0046 0.0057
## agent_argument_type_cleanpronoun 0.7783 -5.3146 7.0976
## agent_argument_type_cleanvarying_agent 0.4326 -7.9777 18.6334
## mean_age:agent_argument_type_cleanpronoun 0.7503 -0.0061 0.0044
## mean_age:agent_argument_type_cleanvarying_agent 0.3928 -0.0155 0.0061
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Vocab
ma_data_vocab %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument, with vocab") 
m_age_aa_vocab <- rma.mv(d_calc ~ productive_vocab_median + agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_aa_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -70.4045 140.8089 152.8089 161.4129 156.3089
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2733 0.5228 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 31) = 147.8094, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 14.1191, p-val = 0.0069
##
## Model Results:
##
## estimate se zval pval
## intrcpt 2.1664 0.6841 3.1668 0.0015
## productive_vocab_median -0.0063 0.0025 -2.5118 0.0120
## agent_argument_type_cleannoun_phrase -1.4510 0.5649 -2.5684 0.0102
## agent_argument_type_cleanpronoun -1.3660 0.7608 -1.7955 0.0726
## agent_argument_type_cleanvarying_agent -1.4355 0.7283 -1.9709 0.0487
## ci.lb ci.ub
## intrcpt 0.8256 3.5073 **
## productive_vocab_median -0.0112 -0.0014 *
## agent_argument_type_cleannoun_phrase -2.5582 -0.3437 *
## agent_argument_type_cleanpronoun -2.8571 0.1251 .
## agent_argument_type_cleanvarying_agent -2.8630 -0.0080 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_aa_vocab_interaction <- rma.mv(d_calc ~ productive_vocab_median * agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_aa_vocab_interaction)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -68.3197 136.6395 154.6395 166.6293 164.6395
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2728 0.5223 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 28) = 145.8023, p-val < .0001
##
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 16.0482, p-val = 0.0247
##
## Model Results:
##
## estimate
## intrcpt -11.4787
## productive_vocab_median 0.4712
## agent_argument_type_cleannoun_phrase -3.4860
## agent_argument_type_cleanpronoun 12.0958
## agent_argument_type_cleanvarying_agent 12.2202
## productive_vocab_median:agent_argument_type_cleannoun_phrase 0.0735
## productive_vocab_median:agent_argument_type_cleanpronoun -0.4723
## productive_vocab_median:agent_argument_type_cleanvarying_agent -0.4777
## se
## intrcpt 17.0548
## productive_vocab_median 0.5963
## agent_argument_type_cleannoun_phrase 22.5875
## agent_argument_type_cleanpronoun 17.0648
## agent_argument_type_cleanvarying_agent 17.0572
## productive_vocab_median:agent_argument_type_cleannoun_phrase 0.7915
## productive_vocab_median:agent_argument_type_cleanpronoun 0.5964
## productive_vocab_median:agent_argument_type_cleanvarying_agent 0.5963
## zval pval
## intrcpt -0.6730 0.5009
## productive_vocab_median 0.7902 0.4294
## agent_argument_type_cleannoun_phrase -0.1543 0.8773
## agent_argument_type_cleanpronoun 0.7088 0.4784
## agent_argument_type_cleanvarying_agent 0.7164 0.4737
## productive_vocab_median:agent_argument_type_cleannoun_phrase 0.0929 0.9260
## productive_vocab_median:agent_argument_type_cleanpronoun -0.7919 0.4284
## productive_vocab_median:agent_argument_type_cleanvarying_agent -0.8011 0.4231
## ci.lb
## intrcpt -44.9055
## productive_vocab_median -0.6975
## agent_argument_type_cleannoun_phrase -47.7566
## agent_argument_type_cleanpronoun -21.3507
## agent_argument_type_cleanvarying_agent -21.2112
## productive_vocab_median:agent_argument_type_cleannoun_phrase -1.4779
## productive_vocab_median:agent_argument_type_cleanpronoun -1.6413
## productive_vocab_median:agent_argument_type_cleanvarying_agent -1.6464
## ci.ub
## intrcpt 21.9481
## productive_vocab_median 1.6399
## agent_argument_type_cleannoun_phrase 40.7847
## agent_argument_type_cleanpronoun 45.5422
## agent_argument_type_cleanvarying_agent 45.6517
## productive_vocab_median:agent_argument_type_cleannoun_phrase 1.6249
## productive_vocab_median:agent_argument_type_cleanpronoun 0.6967
## productive_vocab_median:agent_argument_type_cleanvarying_agent 0.6910
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Patient Argument Type
m_PAT <- rma.mv(d_calc ~ patient_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_PAT)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -140.2934 280.5868 292.5868 306.3296 293.8595
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2701 0.5197 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 73) = 371.1429, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 44.2842, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.2581 0.1323 1.9513 0.0510
## patient_argument_type_cleannoun 0.1727 0.0986 1.7516 0.0798
## patient_argument_type_cleannoun_phrase 0.6142 0.1790 3.4318 0.0006
## patient_argument_type_cleanpronoun 1.3817 0.2474 5.5855 <.0001
## patient_argument_type_cleanvarying_patient 0.0693 0.2316 0.2991 0.7649
## ci.lb ci.ub
## intrcpt -0.0011 0.5174 .
## patient_argument_type_cleannoun -0.0205 0.3660 .
## patient_argument_type_cleannoun_phrase 0.2634 0.9649 ***
## patient_argument_type_cleanpronoun 0.8969 1.8665 ***
## patient_argument_type_cleanvarying_patient -0.3846 0.5231
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% group_by(patient_argument_type_clean) %>% count()## # A tibble: 5 x 2
## # Groups: patient_argument_type_clean [5]
## patient_argument_type_clean n
## <chr> <int>
## 1 intransitive 33
## 2 noun 25
## 3 noun_phrase 12
## 4 pronoun 4
## 5 varying_patient 4Agent Argument Numbers
m_AAN <- rma.mv(d_calc ~ agent_argument_number, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_AAN)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.5568 323.1136 331.1136 340.3835 331.6850
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2143 0.4630 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 405.9925, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.3036, p-val = 0.5211
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3419 0.1205 2.8377 0.0045 0.1057 0.5780
## agent_argument_number2 0.1032 0.1588 0.6500 0.5157 -0.2081 0.4145
## agent_argument_numbervarying 0.1752 0.1792 0.9772 0.3285 -0.1762 0.5265
##
## intrcpt **
## agent_argument_number2
## agent_argument_numbervarying
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% group_by(agent_argument_number) %>% count()## # A tibble: 3 x 2
## # Groups: agent_argument_number [3]
## agent_argument_number n
## <chr> <int>
## 1 1 43
## 2 2 9
## 3 varying 26Number of sentence reptition
ma_data %>% ggplot(aes(x = n_repetitions_sentence)) +
geom_histogram() 
### all
ma_data %>%
ggplot(aes(x = n_repetitions_sentence, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of reptition in sentence") +
ggtitle("Syntactical Bootstrapping effect size vs. number of sentence repetition") 
m_rep <- rma.mv(d_calc ~ n_repetitions_sentence, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.6279 323.2557 329.2557 336.2479 329.5891
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2447 0.4947 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 408.4233, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5374, p-val = 0.2150
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2674 0.1571 1.7023 0.0887 -0.0405 0.5754 .
## n_repetitions_sentence 0.0154 0.0124 1.2399 0.2150 -0.0089 0.0397
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_age <- rma.mv(d_calc ~ n_repetitions_sentence + mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep_age)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.1553 318.3107 326.3107 335.5806 326.8821
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2401 0.4900 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 406.6686, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.0671, p-val = 0.0481
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7352 0.2690 2.7331 0.0063 0.2080 1.2625 **
## n_repetitions_sentence 0.0039 0.0135 0.2880 0.7734 -0.0225 0.0303
## mean_age -0.0004 0.0002 -2.1317 0.0330 -0.0008 -0.0000 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_age_interaction <- rma.mv(d_calc ~ n_repetitions_sentence * mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep_age_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.1721 316.3442 326.3442 337.8645 327.2266
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2240 0.4733 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 393.4213, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.8389, p-val = 0.0495
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4159 0.3576 1.1632 0.2448 -0.2849
## n_repetitions_sentence 0.0351 0.0270 1.2994 0.1938 -0.0178
## mean_age 0.0000 0.0004 0.0419 0.9666 -0.0007
## n_repetitions_sentence:mean_age -0.0000 0.0000 -1.3408 0.1800 -0.0001
## ci.ub
## intrcpt 1.1167
## n_repetitions_sentence 0.0880
## mean_age 0.0007
## n_repetitions_sentence:mean_age 0.0000
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young only
ma_data_young %>%
ggplot(aes(x = n_repetitions_sentence, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of reptition in sentence") +
ggtitle("Syntactical Bootstrapping effect size vs. number of sentence repetition, young only") 
m_rep_young <- rma.mv(d_calc ~ n_repetitions_sentence, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.0254 230.0508 236.0508 242.0727 236.5213
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3061 0.5533 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 55) = 301.9470, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4143, p-val = 0.5198
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3186 0.1995 1.5974 0.1102 -0.0723 0.7095
## n_repetitions_sentence 0.0090 0.0140 0.6437 0.5198 -0.0184 0.0365
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_age_young <- rma.mv(d_calc ~ n_repetitions_sentence + mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_age_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.6909 229.3817 237.3817 245.3376 238.1980
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3048 0.5521 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 296.5847, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5337, p-val = 0.7658
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4734 0.4891 0.9681 0.3330 -0.4851 1.4320
## n_repetitions_sentence 0.0072 0.0149 0.4859 0.6271 -0.0220 0.0364
## mean_age -0.0002 0.0005 -0.3465 0.7290 -0.0012 0.0008
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_age_interaction_young <- rma.mv(d_calc ~ n_repetitions_sentence * mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_age_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -112.7315 225.4630 235.4630 245.3145 236.7396
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3118 0.5584 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 293.9522, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.5403, p-val = 0.3156
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -1.3840 1.1765 -1.1764 0.2395 -3.6899
## n_repetitions_sentence 0.1412 0.0786 1.7958 0.0725 -0.0129
## mean_age 0.0024 0.0016 1.5307 0.1258 -0.0007
## n_repetitions_sentence:mean_age -0.0002 0.0001 -1.7343 0.0829 -0.0004
## ci.ub
## intrcpt 0.9219
## n_repetitions_sentence 0.2952 .
## mean_age 0.0056
## n_repetitions_sentence:mean_age 0.0000 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old only
ma_data_old %>%
ggplot(aes(x = n_repetitions_sentence, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of reptition in sentence") +
ggtitle("Syntactical Bootstrapping effect size vs. number of sentence repetition, old only") 
m_rep_old <- rma.mv(d_calc ~ n_repetitions_sentence, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.0872 82.1744 88.1744 91.0077 89.7744
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 98.8189, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.5277, p-val = 0.0061
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5724 0.0941 6.0814 <.0001 0.3880 0.7569
## n_repetitions_sentence -0.0481 0.0175 -2.7437 0.0061 -0.0825 -0.0137
##
## intrcpt ***
## n_repetitions_sentence **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_age_old <- rma.mv(d_calc ~ n_repetitions_sentence + mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_age_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.8188 81.6376 89.6376 93.1991 92.7145
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 97.2874, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 9.0593, p-val = 0.0108
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1226 0.4544 2.4705 0.0135 0.2320 2.0132 *
## n_repetitions_sentence -0.0544 0.0183 -2.9806 0.0029 -0.0902 -0.0186 **
## mean_age -0.0004 0.0003 -1.2376 0.2159 -0.0010 0.0002
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_age_interaction_old <- rma.mv(d_calc ~ n_repetitions_sentence * mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_age_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.3381 80.6762 90.6762 94.8422 96.1307
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 95.2713, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 11.0754, p-val = 0.0113
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -1.4789 1.8877 -0.7834 0.4334 -5.1786
## n_repetitions_sentence 0.7936 0.5975 1.3281 0.1841 -0.3776
## mean_age 0.0017 0.0015 1.1368 0.2556 -0.0012
## n_repetitions_sentence:mean_age -0.0007 0.0005 -1.4199 0.1556 -0.0016
## ci.ub
## intrcpt 2.2209
## n_repetitions_sentence 1.9648
## mean_age 0.0047
## n_repetitions_sentence:mean_age 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1add vocab in
m_rep_v_r <- rma.mv(d_calc ~ n_repetitions_sentence + productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_rep_v_r)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.7330 151.4661 159.4661 165.4521 160.8947
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2836 0.5326 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.3444, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.8381, p-val = 0.0327
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6930 0.4049 1.7117 0.0870 -0.1005 1.4866 .
## n_repetitions_sentence 0.0084 0.0188 0.4458 0.6557 -0.0285 0.0453
## productive_vocab_median -0.0056 0.0029 -1.9416 0.0522 -0.0113 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_rep_v_r_interaction <- rma.mv(d_calc ~ n_repetitions_sentence *productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_rep_v_r)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.7330 151.4661 159.4661 165.4521 160.8947
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2836 0.5326 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.3444, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.8381, p-val = 0.0327
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6930 0.4049 1.7117 0.0870 -0.1005 1.4866 .
## n_repetitions_sentence 0.0084 0.0188 0.4458 0.6557 -0.0285 0.0453
## productive_vocab_median -0.0056 0.0029 -1.9416 0.0522 -0.0113 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Visual stimuli
stimuli_modality(video vs animation)
single moderator
m_SM <- rma.mv(d_calc ~ stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_SM)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -160.0196 320.0392 326.0392 333.0314 326.3726
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2362 0.4861 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 405.7192, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.9853, p-val = 0.0256
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7046 0.1793 3.9297 <.0001 0.3532 1.0561 ***
## stimuli_modalityvideo -0.3359 0.1504 -2.2328 0.0256 -0.6308 -0.0410 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality") 
m_stim_mod<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_stim_mod)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.6951 317.3902 325.3902 334.6602 325.9616
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2331 0.4828 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 403.1084, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6119, p-val = 0.0367
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8196 0.2004 4.0903 <.0001 0.4269 1.2123 ***
## mean_age -0.0003 0.0002 -1.2782 0.2012 -0.0008 0.0002
## stimuli_modalityvideo -0.1615 0.2028 -0.7963 0.4259 -0.5589 0.2360
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_mod_interaction<- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_stim_mod_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.0371 316.0741 326.0741 337.5945 326.9565
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2414 0.4913 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 401.1491, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.6140, p-val = 0.0853
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8050 0.3028 2.6589 0.0078 0.2116
## mean_age -0.0003 0.0003 -0.9355 0.3495 -0.0009
## stimuli_modalityvideo -0.1230 0.6059 -0.2029 0.8392 -1.3104
## mean_age:stimuli_modalityvideo -0.0000 0.0006 -0.0674 0.9463 -0.0012
## ci.ub
## intrcpt 1.3984 **
## mean_age 0.0003
## stimuli_modalityvideo 1.0645
## mean_age:stimuli_modalityvideo 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young only
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality, young") 
m_stim_mod_young<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_stim_mod_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.3211 228.6423 236.6423 244.5982 237.4586
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3085 0.5555 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 295.5997, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.4061, p-val = 0.8162
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5570 0.4303 1.2944 0.1955 -0.2864 1.4004
## mean_age 0.0000 0.0010 0.0137 0.9890 -0.0019 0.0019
## stimuli_modalityvideo -0.1690 0.5071 -0.3333 0.7389 -1.1628 0.8249
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_mod_interaction_young <- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_stim_mod_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.5329 229.0657 239.0657 248.9172 240.3423
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3156 0.5617 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 295.5042, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.4654, p-val = 0.9264
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.7876 1.0585 0.7441 0.4568 -1.2870
## mean_age -0.0004 0.0020 -0.1999 0.8416 -0.0043
## stimuli_modalityvideo -0.5095 1.4791 -0.3445 0.7305 -3.4085
## mean_age:stimuli_modalityvideo 0.0006 0.0023 0.2416 0.8091 -0.0039
## ci.ub
## intrcpt 2.8622
## mean_age 0.0035
## stimuli_modalityvideo 2.3895
## mean_age:stimuli_modalityvideo 0.0050
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old only
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality, old") 
m_stim_mod_old<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_stim_mod_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.1965 84.3930 92.3930 95.9545 95.4699
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0369 0.1921 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.8392, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7115, p-val = 0.4250
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0341 0.5567 1.8574 0.0633 -0.0571 2.1252 .
## mean_age -0.0004 0.0004 -1.1622 0.2451 -0.0011 0.0003
## stimuli_modalityvideo -0.2197 0.2500 -0.8788 0.3795 -0.7098 0.2703
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_mod_interaction_old <- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_stim_mod_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.1137 84.2273 94.2273 98.3934 99.6819
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0520 0.2280 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 101.0787, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0317, p-val = 0.5658
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8948 0.5997 1.4921 0.1357 -0.2806
## mean_age -0.0003 0.0004 -0.8408 0.4005 -0.0011
## stimuli_modalityvideo 0.9144 1.5946 0.5734 0.5664 -2.2111
## mean_age:stimuli_modalityvideo -0.0009 0.0012 -0.7191 0.4721 -0.0033
## ci.ub
## intrcpt 2.0702
## mean_age 0.0004
## stimuli_modalityvideo 4.0398
## mean_age:stimuli_modalityvideo 0.0015
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = stimuli_modality)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("median vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. median vocab, breakdown by stimuli modality, vocab") 
m_stim_mod_v <- rma.mv(d_calc ~ productive_vocab_median + stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_v)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.4588 150.9176 158.9176 164.9036 160.3462
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2748 0.5242 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 157.9163, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6714, p-val = 0.0356
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8217 0.2738 3.0015 0.0027 0.2851 1.3583 **
## productive_vocab_median -0.0068 0.0056 -1.2116 0.2257 -0.0178 0.0042
## stimuli_modalityvideo 0.0335 0.3609 0.0929 0.9260 -0.6739 0.7410
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_mod_intereaction_v <- rma.mv(d_calc ~ productive_vocab_median * stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_intereaction_v)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.4841 150.9683 160.9683 168.2970 163.2760
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2683 0.5180 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 157.2698, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.1748, p-val = 0.0665
##
## Model Results:
##
## estimate se zval
## intrcpt 0.3538 0.7239 0.4888
## productive_vocab_median 0.0164 0.0339 0.4842
## stimuli_modalityvideo 0.5379 0.8058 0.6676
## productive_vocab_median:stimuli_modalityvideo -0.0239 0.0344 -0.6956
## pval ci.lb ci.ub
## intrcpt 0.6250 -1.0650 1.7727
## productive_vocab_median 0.6282 -0.0500 0.0829
## stimuli_modalityvideo 0.5044 -1.0413 2.1172
## productive_vocab_median:stimuli_modalityvideo 0.4867 -0.0913 0.0435
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab age comparison
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality, age compare vocab") 
m_stim_mod_v_a<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_v_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -74.8182 149.6365 157.6365 163.6225 159.0650
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2710 0.5206 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.2098, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.7842, p-val = 0.0204
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0151 0.2898 3.5023 0.0005 0.4470 1.5831 ***
## mean_age -0.0011 0.0007 -1.6082 0.1078 -0.0025 0.0003
## stimuli_modalityvideo 0.4087 0.5031 0.8125 0.4165 -0.5773 1.3948
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_mod_interaction_v_a <- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_interaction_v_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.0369 150.0739 160.0739 167.4026 162.3816
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2728 0.5223 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 159.1169, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.9359, p-val = 0.0474
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.6030 1.0757 0.5606 0.5751 -1.5052
## mean_age -0.0004 0.0020 -0.1999 0.8416 -0.0043
## stimuli_modalityvideo 0.9083 1.3540 0.6709 0.5023 -1.7454
## mean_age:stimuli_modalityvideo -0.0009 0.0021 -0.3979 0.6907 -0.0050
## ci.ub
## intrcpt 2.7113
## mean_age 0.0035
## stimuli_modalityvideo 3.5621
## mean_age:stimuli_modalityvideo 0.0033
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1stimuli_actor (person vs non-person)
single moderator
m_SA <- rma.mv(d_calc ~ stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_SA)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -160.7069 321.4139 327.4139 334.4061 327.7472
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2478 0.4978 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 404.1002, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.7006, p-val = 0.0544
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5452 0.1401 3.8906 <.0001 0.2706 0.8199 ***
## stimuli_actorperson -0.2569 0.1335 -1.9237 0.0544 -0.5186 0.0048 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, all") 
m_stim_actor_age <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_stim_actor_age)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.9327 317.8654 325.8654 335.1353 326.4368
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2380 0.4879 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 402.0529, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.3334, p-val = 0.0421
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7749 0.1980 3.9136 <.0001 0.3868 1.1630 ***
## mean_age -0.0004 0.0002 -1.6349 0.1021 -0.0008 0.0001
## stimuli_actorperson -0.0971 0.1639 -0.5925 0.5535 -0.4184 0.2241
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_actor_age_interaction <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_stim_actor_age_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.0494 316.0987 326.0987 337.6191 326.9811
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2337 0.4834 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 393.2653, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.7602, p-val = 0.0799
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.6378 0.2873 2.2195 0.0265 0.0746
## mean_age -0.0002 0.0003 -0.7799 0.4354 -0.0008
## stimuli_actorperson 0.2851 0.6064 0.4702 0.6382 -0.9034
## mean_age:stimuli_actorperson -0.0004 0.0006 -0.6543 0.5129 -0.0016
## ci.ub
## intrcpt 1.2009 *
## mean_age 0.0003
## stimuli_actorperson 1.4737
## mean_age:stimuli_actorperson 0.0008
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young only
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, young") 
m_stim_actor_age_young <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_stim_actor_age_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.4670 228.9340 236.9340 244.8899 237.7503
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3085 0.5554 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 296.5893, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.3155, p-val = 0.8541
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6312 0.4263 1.4808 0.1387 -0.2042 1.4667
## mean_age -0.0003 0.0006 -0.5047 0.6138 -0.0016 0.0009
## stimuli_actorperson 0.0387 0.2706 0.1429 0.8864 -0.4918 0.5691
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_actor_age_interaction_young <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_stim_actor_age_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.4216 226.8432 236.8432 246.6946 238.1198
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3492 0.5910 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 290.5912, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.6425, p-val = 0.8866
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 1.0220 0.7973 1.2819 0.1999 -0.5407
## mean_age -0.0008 0.0011 -0.7531 0.4514 -0.0030
## stimuli_actorperson -0.8348 1.4629 -0.5706 0.5683 -3.7021
## mean_age:stimuli_actorperson 0.0011 0.0019 0.6016 0.5474 -0.0026
## ci.ub
## intrcpt 2.5847
## mean_age 0.0013
## stimuli_actorperson 2.0325
## mean_age:stimuli_actorperson 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old only
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, old") 
m_stim_actor_age_old <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_stim_actor_age_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.0417 80.0835 88.0835 91.6450 91.1604
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 95.8384, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.5082, p-val = 0.0052
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8301 0.4191 1.9805 0.0476 0.0086 1.6515 *
## mean_age -0.0003 0.0003 -0.9066 0.3646 -0.0009 0.0003
## stimuli_actorperson -0.4097 0.1275 -3.2145 0.0013 -0.6595 -0.1599 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_actor_age_interaction_old <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_stim_actor_age_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.3879 80.7757 90.7757 94.9418 96.2303
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 95.5607, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.7860, p-val = 0.0129
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8938 0.4362 2.0490 0.0405 0.0388
## mean_age -0.0003 0.0003 -1.0180 0.3087 -0.0009
## stimuli_actorperson -1.1870 1.4804 -0.8018 0.4227 -4.0885
## mean_age:stimuli_actorperson 0.0006 0.0011 0.5270 0.5982 -0.0016
## ci.ub
## intrcpt 1.7487 *
## mean_age 0.0003
## stimuli_actorperson 1.7145
## mean_age:stimuli_actorperson 0.0028
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, vocab") 
m_stim_actor_vocab <- rma.mv(d_calc ~ productive_vocab_median + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_actor_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.4588 150.9176 158.9176 164.9036 160.3462
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2748 0.5242 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 157.9163, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6714, p-val = 0.0356
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8217 0.2738 3.0015 0.0027 0.2851 1.3583 **
## productive_vocab_median -0.0068 0.0056 -1.2116 0.2257 -0.0178 0.0042
## stimuli_actorperson 0.0335 0.3609 0.0929 0.9260 -0.6739 0.7410
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_actor_vocab_interaction <- rma.mv(d_calc ~ productive_vocab_median * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_actor_vocab_interaction)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.4841 150.9683 160.9683 168.2970 163.2760
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2683 0.5180 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 157.2698, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.1748, p-val = 0.0665
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.3538 0.7239 0.4888 0.6250
## productive_vocab_median 0.0164 0.0339 0.4842 0.6282
## stimuli_actorperson 0.5379 0.8058 0.6676 0.5044
## productive_vocab_median:stimuli_actorperson -0.0239 0.0344 -0.6956 0.4867
## ci.lb ci.ub
## intrcpt -1.0650 1.7727
## productive_vocab_median -0.0500 0.0829
## stimuli_actorperson -1.0413 2.1172
## productive_vocab_median:stimuli_actorperson -0.0913 0.0435
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab age comparison
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, age vocab comparison") 
m_stim_actor_vocab_a <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_actor_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -74.8182 149.6365 157.6365 163.6225 159.0650
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2710 0.5206 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.2098, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.7842, p-val = 0.0204
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0151 0.2898 3.5023 0.0005 0.4470 1.5831 ***
## mean_age -0.0011 0.0007 -1.6082 0.1078 -0.0025 0.0003
## stimuli_actorperson 0.4087 0.5031 0.8125 0.4165 -0.5773 1.3948
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_stim_actor_vocab_interaction_a <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_actor_vocab_interaction_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.0369 150.0739 160.0739 167.4026 162.3816
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2728 0.5223 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 159.1169, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.9359, p-val = 0.0474
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.6030 1.0757 0.5606 0.5751 -1.5052
## mean_age -0.0004 0.0020 -0.1999 0.8416 -0.0043
## stimuli_actorperson 0.9083 1.3540 0.6709 0.5023 -1.7454
## mean_age:stimuli_actorperson -0.0009 0.0021 -0.3979 0.6907 -0.0050
## ci.ub
## intrcpt 2.7113
## mean_age 0.0035
## stimuli_actorperson 3.5621
## mean_age:stimuli_actorperson 0.0033
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1transitive_event (indirect_caused_action vs direct caused action)
single moderator
m_TE <- rma.mv(d_calc ~ transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_TE)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.9968 323.9935 331.9935 341.2635 332.5649
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2284 0.4779 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 403.7172, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.4418, p-val = 0.8018
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.4141 0.1185 3.4949 0.0005
## transitive_event_typeindirect_caused_action -0.0307 0.1562 -0.1965 0.8442
## transitive_event_typeminimal_contact -0.1583 0.2464 -0.6424 0.5206
## ci.lb ci.ub
## intrcpt 0.1819 0.6464 ***
## transitive_event_typeindirect_caused_action -0.3368 0.2754
## transitive_event_typeminimal_contact -0.6412 0.3246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% count(transitive_event_type)## # A tibble: 3 x 2
## transitive_event_type n
## <chr> <int>
## 1 direct_caused_action 43
## 2 indirect_caused_action 31
## 3 minimal_contact 4all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = transitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by transitive_event_type") 
m_age_vs_tran <- rma.mv(d_calc ~ mean_age + transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_vs_tran)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.5969 317.1937 327.1937 338.7141 328.0761
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2210 0.4701 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 396.1966, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.8382, p-val = 0.0772
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.8127 0.1965 4.1353 <.0001
## mean_age -0.0005 0.0002 -2.5277 0.0115
## transitive_event_typeindirect_caused_action 0.0920 0.1628 0.5653 0.5719
## transitive_event_typeminimal_contact -0.1784 0.2457 -0.7261 0.4678
## ci.lb ci.ub
## intrcpt 0.4275 1.1979 ***
## mean_age -0.0008 -0.0001 *
## transitive_event_typeindirect_caused_action -0.2270 0.4110
## transitive_event_typeminimal_contact -0.6599 0.3032
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_transtivity_interaction <- rma.mv(d_calc ~ mean_age * transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_data_transtivity_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.3724 316.7448 330.7448 346.6815 332.4948
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2343 0.4840 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 72) = 395.3532, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 7.1213, p-val = 0.2118
##
## Model Results:
##
## estimate se zval
## intrcpt 0.8056 0.2304 3.4971
## mean_age -0.0005 0.0002 -1.9720
## transitive_event_typeindirect_caused_action 0.0848 0.3514 0.2413
## transitive_event_typeminimal_contact 1.9332 3.7097 0.5211
## mean_age:transitive_event_typeindirect_caused_action 0.0000 0.0004 0.0144
## mean_age:transitive_event_typeminimal_contact -0.0026 0.0046 -0.5697
## pval ci.lb ci.ub
## intrcpt 0.0005 0.3541 1.2571
## mean_age 0.0486 -0.0009 -0.0000
## transitive_event_typeindirect_caused_action 0.8093 -0.6040 0.7736
## transitive_event_typeminimal_contact 0.6023 -5.3378 9.2041
## mean_age:transitive_event_typeindirect_caused_action 0.9885 -0.0007 0.0007
## mean_age:transitive_event_typeminimal_contact 0.5689 -0.0115 0.0063
##
## intrcpt ***
## mean_age *
## transitive_event_typeindirect_caused_action
## transitive_event_typeminimal_contact
## mean_age:transitive_event_typeindirect_caused_action
## mean_age:transitive_event_typeminimal_contact
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1young < 36
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = transitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by transitive_event_type") 
m_age_vs_tran_young <- rma.mv(d_calc ~ mean_age + transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_vs_tran_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.7278 227.4557 237.4557 247.3071 238.7323
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2654 0.5152 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 285.9610, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0324, p-val = 0.5657
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.5905 0.3973 1.4862 0.1372
## mean_age -0.0003 0.0005 -0.5288 0.5970
## transitive_event_typeindirect_caused_action 0.2558 0.2230 1.1471 0.2513
## transitive_event_typeminimal_contact -0.1477 0.2507 -0.5890 0.5559
## ci.lb ci.ub
## intrcpt -0.1882 1.3691
## mean_age -0.0012 0.0007
## transitive_event_typeindirect_caused_action -0.1812 0.6928
## transitive_event_typeminimal_contact -0.6391 0.3437
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_transtivity_interaction_young <- rma.mv(d_calc ~ mean_age * transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_data_transtivity_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -111.0851 222.1702 236.1702 249.6930 238.7749
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2644 0.5142 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 51) = 278.5834, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 6.0090, p-val = 0.3053
##
## Model Results:
##
## estimate se zval
## intrcpt 0.7226 0.4088 1.7678
## mean_age -0.0005 0.0005 -0.9112
## transitive_event_typeindirect_caused_action -1.9246 1.1897 -1.6177
## transitive_event_typeminimal_contact 2.3568 3.7760 0.6241
## mean_age:transitive_event_typeindirect_caused_action 0.0035 0.0019 1.8688
## mean_age:transitive_event_typeminimal_contact -0.0031 0.0046 -0.6647
## pval ci.lb ci.ub
## intrcpt 0.0771 -0.0786 1.5239
## mean_age 0.3622 -0.0014 0.0005
## transitive_event_typeindirect_caused_action 0.1057 -4.2564 0.4071
## transitive_event_typeminimal_contact 0.5325 -5.0441 9.7577
## mean_age:transitive_event_typeindirect_caused_action 0.0616 -0.0002 0.0072
## mean_age:transitive_event_typeminimal_contact 0.5062 -0.0121 0.0060
##
## intrcpt .
## mean_age
## transitive_event_typeindirect_caused_action
## transitive_event_typeminimal_contact
## mean_age:transitive_event_typeindirect_caused_action .
## mean_age:transitive_event_typeminimal_contact
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1old > 36
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = transitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by transitive_event_type") 
m_age_vs_tran_old <- rma.mv(d_calc ~ mean_age + transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_vs_tran_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.9543 85.9086 93.9086 97.4701 96.9855
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0458 0.2140 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 105.9616, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.1174, p-val = 0.5719
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7602 0.4770 1.5936 0.1110
## mean_age -0.0003 0.0004 -0.8078 0.4192
## transitive_event_typeindirect_caused_action -0.0836 0.2046 -0.4085 0.6829
## ci.lb ci.ub
## intrcpt -0.1747 1.6951
## mean_age -0.0010 0.0004
## transitive_event_typeindirect_caused_action -0.4847 0.3175
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_transtivity_interaction_old <- rma.mv(d_calc ~ mean_age * transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_data_transtivity_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -43.1206 86.2412 96.2412 100.4073 101.6958
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0565 0.2377 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 105.0587, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.3965, p-val = 0.7064
##
## Model Results:
##
## estimate se zval
## intrcpt 2.2852 3.0396 0.7518
## mean_age -0.0016 0.0025 -0.6195
## transitive_event_typeindirect_caused_action -1.6640 3.1208 -0.5332
## mean_age:transitive_event_typeindirect_caused_action 0.0013 0.0026 0.5050
## pval ci.lb ci.ub
## intrcpt 0.4522 -3.6723 8.2428
## mean_age 0.5356 -0.0065 0.0034
## transitive_event_typeindirect_caused_action 0.5939 -7.7806 4.4525
## mean_age:transitive_event_typeindirect_caused_action 0.6136 -0.0037 0.0063
##
## intrcpt
## mean_age
## transitive_event_typeindirect_caused_action
## mean_age:transitive_event_typeindirect_caused_action
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = transitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("median vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. median vocab, breakdown by transitive_event_type") 
m_age_vs_tran_vocab <- rma.mv(d_calc ~ productive_vocab_median + transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_vs_tran_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.9543 85.9086 93.9086 97.4701 96.9855
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0458 0.2140 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 105.9616, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.1174, p-val = 0.5719
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7602 0.4770 1.5936 0.1110
## mean_age -0.0003 0.0004 -0.8078 0.4192
## transitive_event_typeindirect_caused_action -0.0836 0.2046 -0.4085 0.6829
## ci.lb ci.ub
## intrcpt -0.1747 1.6951
## mean_age -0.0010 0.0004
## transitive_event_typeindirect_caused_action -0.4847 0.3175
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_transtivity_interaction_vocab <- rma.mv(d_calc ~ productive_vocab_median * transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_data_transtivity_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.6234 151.2468 161.2468 168.5755 163.5545
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2476 0.4976 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 156.6708, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.6262, p-val = 0.0544
##
## Model Results:
##
## estimate
## intrcpt 0.7493
## productive_vocab_median -0.0057
## transitive_event_typeindirect_caused_action 0.3145
## productive_vocab_median:transitive_event_typeindirect_caused_action -0.0045
## se
## intrcpt 0.2667
## productive_vocab_median 0.0029
## transitive_event_typeindirect_caused_action 0.3352
## productive_vocab_median:transitive_event_typeindirect_caused_action 0.0056
## zval
## intrcpt 2.8096
## productive_vocab_median -1.9843
## transitive_event_typeindirect_caused_action 0.9385
## productive_vocab_median:transitive_event_typeindirect_caused_action -0.8111
## pval
## intrcpt 0.0050
## productive_vocab_median 0.0472
## transitive_event_typeindirect_caused_action 0.3480
## productive_vocab_median:transitive_event_typeindirect_caused_action 0.4173
## ci.lb
## intrcpt 0.2266
## productive_vocab_median -0.0113
## transitive_event_typeindirect_caused_action -0.3423
## productive_vocab_median:transitive_event_typeindirect_caused_action -0.0154
## ci.ub
## intrcpt 1.2720
## productive_vocab_median -0.0001
## transitive_event_typeindirect_caused_action 0.9714
## productive_vocab_median:transitive_event_typeindirect_caused_action 0.0064
##
## intrcpt **
## productive_vocab_median *
## transitive_event_typeindirect_caused_action
## productive_vocab_median:transitive_event_typeindirect_caused_action
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab age comparsion
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = transitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by transitive_event_type") 
m_age_vs_tran_vocab <- rma.mv(d_calc ~ mean_age + transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_vs_tran_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.9543 85.9086 93.9086 97.4701 96.9855
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0458 0.2140 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 105.9616, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.1174, p-val = 0.5719
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7602 0.4770 1.5936 0.1110
## mean_age -0.0003 0.0004 -0.8078 0.4192
## transitive_event_typeindirect_caused_action -0.0836 0.2046 -0.4085 0.6829
## ci.lb ci.ub
## intrcpt -0.1747 1.6951
## mean_age -0.0010 0.0004
## transitive_event_typeindirect_caused_action -0.4847 0.3175
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_transtivity_interaction_vocab <- rma.mv(d_calc ~ mean_age * transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_data_transtivity_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.6083 151.2166 161.2166 168.5453 163.5243
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2580 0.5079 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 157.2607, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 8.2242, p-val = 0.0416
##
## Model Results:
##
## estimate se zval
## intrcpt 0.8727 0.2981 2.9271
## mean_age -0.0005 0.0002 -2.1340
## transitive_event_typeindirect_caused_action 0.5385 0.5198 1.0360
## mean_age:transitive_event_typeindirect_caused_action -0.0006 0.0006 -0.9761
## pval ci.lb ci.ub
## intrcpt 0.0034 0.2883 1.4571
## mean_age 0.0328 -0.0010 -0.0000
## transitive_event_typeindirect_caused_action 0.3002 -0.4803 1.5574
## mean_age:transitive_event_typeindirect_caused_action 0.3290 -0.0017 0.0006
##
## intrcpt **
## mean_age *
## transitive_event_typeindirect_caused_action
## mean_age:transitive_event_typeindirect_caused_action
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1intransitive_event (one vs parallel)
single moderator
m_IE <- rma.mv(d_calc ~ intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_IE)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.2170 324.4341 332.4341 341.7040 333.0055
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2257 0.4751 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 406.9308, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.3141, p-val = 0.8547
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.2857 0.2306 1.2390 0.2154
## intransitive_event_typeone_action 0.1394 0.2551 0.5463 0.5848
## intransitive_event_typeparallel_actions 0.1213 0.2274 0.5335 0.5937
## ci.lb ci.ub
## intrcpt -0.1663 0.7376
## intransitive_event_typeone_action -0.3607 0.6394
## intransitive_event_typeparallel_actions -0.3243 0.5670
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% count(intransitive_event_type) ## # A tibble: 3 x 2
## intransitive_event_type n
## <chr> <int>
## 1 minimal_contact 5
## 2 one_action 32
## 3 parallel_actions 41all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = intransitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by intransitive_event_type") 
m_age_vs_intran <- rma.mv(d_calc ~ mean_age + intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_vs_intran)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.7897 317.5794 327.5794 339.0997 328.4618
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2298 0.4794 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 405.4220, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.6031, p-val = 0.0857
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7165 0.2879 2.4886 0.0128
## mean_age -0.0005 0.0002 -2.5093 0.0121
## intransitive_event_typeone_action 0.0513 0.2582 0.1986 0.8426
## intransitive_event_typeparallel_actions 0.1308 0.2281 0.5734 0.5663
## ci.lb ci.ub
## intrcpt 0.1522 1.2809 *
## mean_age -0.0008 -0.0001 *
## intransitive_event_typeone_action -0.4548 0.5573
## intransitive_event_typeparallel_actions -0.3162 0.5778
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_intranstivity_interaction <- rma.mv(d_calc ~ mean_age * intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_data_intranstivity_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.4174 316.8347 330.8347 346.7714 332.5847
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2393 0.4892 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 72) = 399.0172, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 7.5372, p-val = 0.1837
##
## Model Results:
##
## estimate se zval
## intrcpt 11.8173 16.5979 0.7120
## mean_age -0.0134 0.0194 -0.6930
## intransitive_event_typeone_action -11.1563 16.5890 -0.6725
## intransitive_event_typeparallel_actions -10.8851 16.5877 -0.6562
## mean_age:intransitive_event_typeone_action 0.0131 0.0194 0.6778
## mean_age:intransitive_event_typeparallel_actions 0.0129 0.0194 0.6635
## pval ci.lb ci.ub
## intrcpt 0.4765 -20.7141 44.3487
## mean_age 0.4883 -0.0515 0.0246
## intransitive_event_typeone_action 0.5013 -43.6702 21.3575
## intransitive_event_typeparallel_actions 0.5117 -43.3963 21.6262
## mean_age:intransitive_event_typeone_action 0.4979 -0.0249 0.0512
## mean_age:intransitive_event_typeparallel_actions 0.5070 -0.0251 0.0509
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1young < 36
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = intransitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by intransitive_event_type, young") 
m_age_vs_intran_young <- rma.mv(d_calc ~ mean_age + intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_vs_intran_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.4875 228.9750 238.9750 248.8264 240.2516
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2853 0.5341 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 293.8195, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.0801, p-val = 0.7819
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.6223 0.4850 1.2830 0.1995
## mean_age -0.0004 0.0005 -0.7078 0.4791
## intransitive_event_typeone_action -0.0161 0.2774 -0.0579 0.9538
## intransitive_event_typeparallel_actions 0.1000 0.2382 0.4198 0.6746
## ci.lb ci.ub
## intrcpt -0.3283 1.5728
## mean_age -0.0013 0.0006
## intransitive_event_typeone_action -0.5597 0.5276
## intransitive_event_typeparallel_actions -0.3668 0.5668
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_intranstivity_interaction_young <- rma.mv(d_calc ~ mean_age * intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_data_intranstivity_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.1896 228.3791 242.3791 255.9019 244.9838
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3058 0.5530 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 51) = 291.4716, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 1.5639, p-val = 0.9056
##
## Model Results:
##
## estimate se zval
## intrcpt 12.6185 16.8680 0.7481
## mean_age -0.0144 0.0197 -0.7286
## intransitive_event_typeone_action -11.8976 16.8540 -0.7059
## intransitive_event_typeparallel_actions -11.9272 16.8699 -0.7070
## mean_age:intransitive_event_typeone_action 0.0138 0.0197 0.7011
## mean_age:intransitive_event_typeparallel_actions 0.0141 0.0197 0.7130
## pval ci.lb ci.ub
## intrcpt 0.4544 -20.4422 45.6793
## mean_age 0.4663 -0.0530 0.0243
## intransitive_event_typeone_action 0.4802 -44.9308 21.1355
## intransitive_event_typeparallel_actions 0.4796 -44.9916 21.1373
## mean_age:intransitive_event_typeone_action 0.4832 -0.0248 0.0525
## mean_age:intransitive_event_typeparallel_actions 0.4759 -0.0246 0.0528
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1old > 36
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = intransitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by intransitive_event_type, old") 
m_age_vs_intran_old <- rma.mv(d_calc ~ mean_age + intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_vs_intran_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.1965 84.3930 92.3930 95.9545 95.4699
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0369 0.1921 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.8392, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7115, p-val = 0.4250
##
## Model Results:
##
## estimate se zval pval
## intrcpt 1.0341 0.5567 1.8574 0.0633
## mean_age -0.0004 0.0004 -1.1622 0.2451
## intransitive_event_typeparallel_actions -0.2197 0.2500 -0.8788 0.3795
## ci.lb ci.ub
## intrcpt -0.0571 2.1252 .
## mean_age -0.0011 0.0003
## intransitive_event_typeparallel_actions -0.7098 0.2703
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_intranstivity_interaction_old <- rma.mv(d_calc ~ mean_age * intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_data_intranstivity_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.1137 84.2273 94.2273 98.3934 99.6819
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0520 0.2280 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 101.0787, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0317, p-val = 0.5658
##
## Model Results:
##
## estimate se zval
## intrcpt 0.8948 0.5997 1.4921
## mean_age -0.0003 0.0004 -0.8408
## intransitive_event_typeparallel_actions 0.9144 1.5946 0.5734
## mean_age:intransitive_event_typeparallel_actions -0.0009 0.0012 -0.7191
## pval ci.lb ci.ub
## intrcpt 0.1357 -0.2806 2.0702
## mean_age 0.4005 -0.0011 0.0004
## intransitive_event_typeparallel_actions 0.5664 -2.2111 4.0398
## mean_age:intransitive_event_typeparallel_actions 0.4721 -0.0033 0.0015
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = intransitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("median vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. median vocab, breakdown by intransitive_event_type") 
m_age_vs_intran_vocab <- rma.mv(d_calc ~ productive_vocab_median + intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_vs_intran_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -73.3406 146.6811 156.6811 164.0098 158.9888
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.1112 0.3335 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 148.7457, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 13.5650, p-val = 0.0036
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.1141 0.4358 -0.2618 0.7935
## productive_vocab_median -0.0059 0.0025 -2.3337 0.0196
## intransitive_event_typeone_action 1.0413 0.4558 2.2847 0.0223
## intransitive_event_typeparallel_actions 1.0825 0.4432 2.4422 0.0146
## ci.lb ci.ub
## intrcpt -0.9681 0.7400
## productive_vocab_median -0.0108 -0.0009 *
## intransitive_event_typeone_action 0.1480 1.9347 *
## intransitive_event_typeparallel_actions 0.2138 1.9512 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_intranstivity_interaction_vocab <- rma.mv(d_calc ~ productive_vocab_median * intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_data_intranstivity_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -71.8293 143.6586 155.6586 164.2625 159.1586
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0624 0.2498 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 31) = 140.5713, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 19.4022, p-val = 0.0007
##
## Model Results:
##
## estimate se
## intrcpt 0.0457 0.3870
## productive_vocab_median -0.0082 0.0029
## intransitive_event_typeone_action 0.4977 0.4844
## intransitive_event_typeparallel_actions 0.9089 0.3785
## productive_vocab_median:intransitive_event_typeone_action 0.0122 0.0070
## zval pval
## intrcpt 0.1181 0.9060
## productive_vocab_median -2.8646 0.0042
## intransitive_event_typeone_action 1.0276 0.3041
## intransitive_event_typeparallel_actions 2.4012 0.0163
## productive_vocab_median:intransitive_event_typeone_action 1.7269 0.0842
## ci.lb ci.ub
## intrcpt -0.7128 0.8042
## productive_vocab_median -0.0138 -0.0026 **
## intransitive_event_typeone_action -0.4516 1.4471
## intransitive_event_typeparallel_actions 0.1670 1.6508 *
## productive_vocab_median:intransitive_event_typeone_action -0.0016 0.0260 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab age comparsion
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = intransitive_event_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by intransitive_event_type") 
m_age_vs_intran_vocab <- rma.mv(d_calc ~ mean_age + intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_vs_intran_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -72.5639 145.1279 155.1279 162.4566 157.4356
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0869 0.2949 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 145.2508, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 16.6301, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.0324 0.4211 0.0769 0.9387
## mean_age -0.0007 0.0002 -2.6978 0.0070
## intransitive_event_typeone_action 1.0548 0.4173 2.5276 0.0115
## intransitive_event_typeparallel_actions 1.1726 0.4047 2.8975 0.0038
## ci.lb ci.ub
## intrcpt -0.7930 0.8577
## mean_age -0.0011 -0.0002 **
## intransitive_event_typeone_action 0.2369 1.8727 *
## intransitive_event_typeparallel_actions 0.3794 1.9658 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_data_intranstivity_interaction_vocab <- rma.mv(d_calc ~ mean_age * intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_data_intranstivity_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -71.6511 143.3023 155.3023 163.9062 158.8023
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0592 0.2434 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 31) = 139.9367, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 19.9652, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.1266 0.3950 0.3204 0.7486
## mean_age -0.0008 0.0003 -2.9401 0.0033
## intransitive_event_typeone_action 0.2640 0.7856 0.3360 0.7369
## intransitive_event_typeparallel_actions 1.0856 0.3645 2.9783 0.0029
## mean_age:intransitive_event_typeone_action 0.0012 0.0011 1.1070 0.2683
## ci.lb ci.ub
## intrcpt -0.6476 0.9008
## mean_age -0.0013 -0.0003 **
## intransitive_event_typeone_action -1.2758 1.8038
## intransitive_event_typeparallel_actions 0.3712 1.8000 **
## mean_age:intransitive_event_typeone_action -0.0009 0.0034
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Visual Stimuli Pair
m_VSP <- rma.mv(d_calc ~ visual_stimuli_pair, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_VSP)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.1614 318.3229 334.3229 352.4243 336.6454
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2432 0.4931 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 71) = 401.4556, p-val < .0001
##
## Test of Moderators (coefficients 2:7):
## QM(df = 6) = 2.3168, p-val = 0.8884
##
## Model Results:
##
## estimate se
## intrcpt 0.1162 0.3069
## visual_stimuli_pairdirect_caused_action_one_action 0.3999 0.3537
## visual_stimuli_pairdirect_caused_action_parallel_actions 0.3648 0.3379
## visual_stimuli_pairindirect_caused_action_one_action 0.3058 0.4189
## visual_stimuli_pairindirect_caused_action_parallel_actions 0.3304 0.3630
## visual_stimuli_pairminimal_contact_one_action -0.2604 0.6641
## visual_stimuli_pairminimal_contact_parallel_actions -0.0178 0.2746
## zval pval
## intrcpt 0.3787 0.7049
## visual_stimuli_pairdirect_caused_action_one_action 1.1304 0.2583
## visual_stimuli_pairdirect_caused_action_parallel_actions 1.0798 0.2802
## visual_stimuli_pairindirect_caused_action_one_action 0.7299 0.4654
## visual_stimuli_pairindirect_caused_action_parallel_actions 0.9102 0.3627
## visual_stimuli_pairminimal_contact_one_action -0.3921 0.6950
## visual_stimuli_pairminimal_contact_parallel_actions -0.0648 0.9484
## ci.lb ci.ub
## intrcpt -0.4853 0.7177
## visual_stimuli_pairdirect_caused_action_one_action -0.2934 1.0932
## visual_stimuli_pairdirect_caused_action_parallel_actions -0.2974 1.0270
## visual_stimuli_pairindirect_caused_action_one_action -0.5153 1.1268
## visual_stimuli_pairindirect_caused_action_parallel_actions -0.3811 1.0420
## visual_stimuli_pairminimal_contact_one_action -1.5621 1.0412
## visual_stimuli_pairminimal_contact_parallel_actions -0.5561 0.5205
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% count()## # A tibble: 1 x 1
## n
## <int>
## 1 78n_repetition_video (??)
single moderator
m_RV <- rma.mv(d_calc ~ n_repetitions_video, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_RV)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.2758 324.5515 330.5515 337.5437 330.8849
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2358 0.4856 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 408.5592, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0016, p-val = 0.9685
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3900 0.2206 1.7679 0.0771 -0.0424 0.8223 .
## n_repetitions_video 0.0020 0.0512 0.0395 0.9685 -0.0982 0.1023
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% ggplot(aes(x = n_repetitions_video)) +
geom_histogram() 
Testing Procedure
test_method (point vs look)
single moderator
m_TM <- rma.mv(d_calc ~ test_method, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_TM)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.2886 322.5772 328.5772 335.5694 328.9105
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2262 0.4756 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 404.3146, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0662, p-val = 0.3018
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3484 0.1223 2.8496 0.0044 0.1088 0.5880 **
## test_methodpoint 0.3283 0.3179 1.0326 0.3018 -0.2948 0.9514
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% count(test_method)## # A tibble: 2 x 2
## test_method n
## <chr> <int>
## 1 look 60
## 2 point 18presentation_type
single moderator
m_PT <- rma.mv(d_calc ~ presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_PT)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.4484 322.8968 330.8968 340.1668 331.4683
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2420 0.4919 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 401.1312, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.4256, p-val = 0.8083
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4512 0.1583 2.8508 0.0044 0.1410
## presentation_typeimmediate_after -0.0024 0.1809 -0.0135 0.9892 -0.3571
## presentation_typesimultaneous -0.1627 0.2581 -0.6307 0.5282 -0.6685
## ci.ub
## intrcpt 0.7614 **
## presentation_typeimmediate_after 0.3522
## presentation_typesimultaneous 0.3430
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% count(presentation_type)## # A tibble: 3 x 2
## presentation_type n
## <chr> <int>
## 1 asynchronous 35
## 2 immediate_after 32
## 3 simultaneous 11all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by presentation type") 
m_age_pt <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_pt)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.3684 316.7367 326.7367 338.2571 327.6191
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2472 0.4972 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 395.5180, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.1952, p-val = 0.1025
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8056 0.2170 3.7132 0.0002 0.3804
## mean_age -0.0004 0.0002 -2.4028 0.0163 -0.0008
## presentation_typeimmediate_after 0.0337 0.1822 0.1851 0.8531 -0.3234
## presentation_typesimultaneous -0.0903 0.2621 -0.3445 0.7305 -0.6039
## ci.ub
## intrcpt 1.2308 ***
## mean_age -0.0001 *
## presentation_typeimmediate_after 0.3908
## presentation_typesimultaneous 0.4234
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pt_interaction <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_pt_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -156.6564 313.3127 327.3127 343.2494 329.0627
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2427 0.4927 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 72) = 386.7953, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 8.4349, p-val = 0.1338
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.9230 0.2310 3.9946 <.0001
## mean_age -0.0006 0.0002 -2.7687 0.0056
## presentation_typeimmediate_after -0.2755 0.3380 -0.8152 0.4150
## presentation_typesimultaneous -0.8890 0.7631 -1.1650 0.2440
## mean_age:presentation_typeimmediate_after 0.0004 0.0004 1.1051 0.2691
## mean_age:presentation_typesimultaneous 0.0009 0.0007 1.1733 0.2407
## ci.lb ci.ub
## intrcpt 0.4701 1.3758 ***
## mean_age -0.0010 -0.0002 **
## presentation_typeimmediate_after -0.9380 0.3869
## presentation_typesimultaneous -2.3846 0.6066
## mean_age:presentation_typeimmediate_after -0.0003 0.0012
## mean_age:presentation_typesimultaneous -0.0006 0.0023
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by presentation type, young only") 
m_data_pt_young <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_data_pt_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.7351 227.4702 237.4702 247.3216 238.7468
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3094 0.5563 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 288.8728, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.9596, p-val = 0.8110
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.6876 0.4111 1.6723 0.0945 -0.1183
## mean_age -0.0003 0.0005 -0.5649 0.5721 -0.0012
## presentation_typeimmediate_after -0.0114 0.1933 -0.0588 0.9531 -0.3902
## presentation_typesimultaneous -0.2853 0.3519 -0.8107 0.4175 -0.9750
## ci.ub
## intrcpt 1.4934 .
## mean_age 0.0007
## presentation_typeimmediate_after 0.3675
## presentation_typesimultaneous 0.4044
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pt_interaction_young <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_pt_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -111.4862 222.9724 236.9724 250.4952 239.5771
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3468 0.5889 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 51) = 285.0784, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 3.1468, p-val = 0.6774
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.7573 0.4214 1.7969 0.0723
## mean_age -0.0005 0.0005 -0.8913 0.3727
## presentation_typeimmediate_after -1.5472 1.0826 -1.4292 0.1530
## presentation_typesimultaneous -1.5010 2.6833 -0.5594 0.5759
## mean_age:presentation_typeimmediate_after 0.0022 0.0016 1.4364 0.1509
## mean_age:presentation_typesimultaneous 0.0017 0.0036 0.4806 0.6308
## ci.lb ci.ub
## intrcpt -0.0687 1.5833 .
## mean_age -0.0015 0.0005
## presentation_typeimmediate_after -3.6691 0.5746
## presentation_typesimultaneous -6.7602 3.7582
## mean_age:presentation_typeimmediate_after -0.0008 0.0053
## mean_age:presentation_typesimultaneous -0.0053 0.0087
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by presentation type, young only") 
vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("median vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. median vocab, breakdown by presentation type") 
m_pt_vocab <- rma.mv(d_calc ~ productive_vocab_median + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_pt_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.8093 151.6187 159.6187 165.6047 161.0473
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2698 0.5194 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.4768, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6946, p-val = 0.0352
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8330 0.2604 3.1985 0.0014 0.3225
## productive_vocab_median -0.0063 0.0025 -2.5368 0.0112 -0.0111
## presentation_typeimmediate_after -0.0293 0.2181 -0.1342 0.8932 -0.4567
## ci.ub
## intrcpt 1.3434 **
## productive_vocab_median -0.0014 *
## presentation_typeimmediate_after 0.3981
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_pt_vocab_interaction <- rma.mv(d_calc ~ productive_vocab_median * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_pt_vocab_interaction)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.5923 151.1847 161.1847 168.5133 163.4924
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2753 0.5247 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 159.3692, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.0166, p-val = 0.0714
##
## Model Results:
##
## estimate se
## intrcpt 0.8516 0.2641
## productive_vocab_median -0.0066 0.0025
## presentation_typeimmediate_after -0.2922 0.5020
## productive_vocab_median:presentation_typeimmediate_after 0.0076 0.0130
## zval pval
## intrcpt 3.2246 0.0013
## productive_vocab_median -2.5983 0.0094
## presentation_typeimmediate_after -0.5821 0.5605
## productive_vocab_median:presentation_typeimmediate_after 0.5800 0.5619
## ci.lb ci.ub
## intrcpt 0.3340 1.3692 **
## productive_vocab_median -0.0116 -0.0016 **
## presentation_typeimmediate_after -1.2761 0.6917
## productive_vocab_median:presentation_typeimmediate_after -0.0180 0.0331
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab age comparsion
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("mean age ") +
ggtitle("Syntactical Bootstrapping effect size vs. mean age, breakdown by presentation type") 
m_pt_vocab_a <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_pt_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.7201 151.4402 159.4402 165.4262 160.8687
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2951 0.5432 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 160.9167, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.2702, p-val = 0.0264
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.9904 0.2947 3.3609 0.0008 0.4128
## mean_age -0.0006 0.0002 -2.6438 0.0082 -0.0010
## presentation_typeimmediate_after -0.0943 0.2165 -0.4358 0.6630 -0.5186
## ci.ub
## intrcpt 1.5680 ***
## mean_age -0.0002 **
## presentation_typeimmediate_after 0.3299
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_pt_vocab_a_interaction <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_pt_vocab_a_interaction)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.3289 150.6579 160.6579 167.9865 162.9656
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3168 0.5628 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 160.8706, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.5832, p-val = 0.0555
##
## Model Results:
##
## estimate se zval pval
## intrcpt 1.0042 0.3016 3.3293 0.0009
## mean_age -0.0006 0.0002 -2.6520 0.0080
## presentation_typeimmediate_after -6.2613 10.6254 -0.5893 0.5557
## mean_age:presentation_typeimmediate_after 0.0096 0.0166 0.5803 0.5617
## ci.lb ci.ub
## intrcpt 0.4130 1.5953 ***
## mean_age -0.0010 -0.0002 **
## presentation_typeimmediate_after -27.0866 14.5640
## mean_age:presentation_typeimmediate_after -0.0229 0.0422
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1character_identification
single moderator
m_CI <- rma.mv(d_calc ~ character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_CI)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.9787 323.9575 329.9575 336.9497 330.2908
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2398 0.4897 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 406.5372, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0083, p-val = 0.9273
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3872 0.1622 2.3872 0.0170 0.0693 0.7051
## character_identificationyes 0.0211 0.2315 0.0912 0.9273 -0.4326 0.4748
##
## intrcpt *
## character_identificationyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% count(character_identification)## # A tibble: 2 x 2
## character_identification n
## <chr> <int>
## 1 no 41
## 2 yes 37all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification") 
m_age_ci <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_ci)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.6843 317.3686 325.3686 334.6385 325.9400
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2392 0.4891 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 401.9015, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.3295, p-val = 0.0422
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.7386 0.2140 3.4517 0.0006 0.3192
## mean_age -0.0005 0.0002 -2.5142 0.0119 -0.0008
## character_identificationyes 0.1388 0.2359 0.5884 0.5563 -0.3236
## ci.ub
## intrcpt 1.1581 ***
## mean_age -0.0001 *
## character_identificationyes 0.6012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_stimuli_ci_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_stimuli_ci_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.2438 316.4875 326.4875 338.0078 327.3699
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2409 0.4909 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 396.5392, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.0462, p-val = 0.0704
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.8215 0.2355 3.4877 0.0005
## mean_age -0.0006 0.0002 -2.5468 0.0109
## character_identificationyes -0.1707 0.4352 -0.3922 0.6949
## mean_age:character_identificationyes 0.0003 0.0004 0.8472 0.3969
## ci.lb ci.ub
## intrcpt 0.3598 1.2831 ***
## mean_age -0.0010 -0.0001 *
## character_identificationyes -1.0236 0.6822
## mean_age:character_identificationyes -0.0004 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification, young only") 
m_age_ci_young <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_ci_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.3668 228.7337 236.7337 244.6896 237.5500
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3144 0.5607 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 294.3994, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.2973, p-val = 0.8619
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6038 0.4060 1.4874 0.1369 -0.1919 1.3995
## mean_age -0.0003 0.0005 -0.5450 0.5857 -0.0012 0.0007
## character_identificationyes 0.0241 0.3052 0.0791 0.9369 -0.5740 0.6223
##
## intrcpt
## mean_age
## character_identificationyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_ci_interaction_young <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_ci_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.5404 227.0809 237.0809 246.9323 238.3575
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3441 0.5866 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 291.6547, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.6826, p-val = 0.8773
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.6697 0.4253 1.5747 0.1153
## mean_age -0.0004 0.0005 -0.6893 0.4906
## character_identificationyes -0.8853 1.4630 -0.6051 0.5451
## mean_age:character_identificationyes 0.0011 0.0018 0.6359 0.5249
## ci.lb ci.ub
## intrcpt -0.1638 1.5032
## mean_age -0.0014 0.0007
## character_identificationyes -3.7528 1.9823
## mean_age:character_identificationyes -0.0023 0.0046
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification, old only") 
m_age_ci_old <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_ci_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.9362 81.8724 89.8724 93.4339 92.9494
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 97.5418, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 8.8048, p-val = 0.0122
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5093 0.4086 1.2463 0.2127 -0.2916 1.3102
## mean_age -0.0004 0.0003 -1.2527 0.2103 -0.0010 0.0002
## character_identificationyes 0.4526 0.1541 2.9376 0.0033 0.1506 0.7546
##
## intrcpt
## mean_age
## character_identificationyes **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_ci_interaction_old <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_ci_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.2030 82.4060 92.4060 96.5721 97.8606
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 96.9220, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 9.4246, p-val = 0.0241
##
## Model Results:
##
## estimate se zval pval
## intrcpt 5.2458 6.0299 0.8700 0.3843
## mean_age -0.0042 0.0048 -0.8658 0.3866
## character_identificationyes -4.3056 6.0456 -0.7122 0.4764
## mean_age:character_identificationyes 0.0038 0.0048 0.7873 0.4311
## ci.lb ci.ub
## intrcpt -6.5726 17.0642
## mean_age -0.0136 0.0053
## character_identificationyes -16.1547 7.5436
## mean_age:character_identificationyes -0.0057 0.0133
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("median vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. median voca, breakdown by character_identification, old only") 
m_age_ci_vocab <- rma.mv(d_calc ~ productive_vocab_median + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_ci_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -74.7434 149.4869 157.4869 163.4729 158.9154
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3002 0.5479 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.1196, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.1087, p-val = 0.0286
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.9497 0.3159 3.0059 0.0026 0.3305
## productive_vocab_median -0.0063 0.0025 -2.5534 0.0107 -0.0111
## character_identificationyes -0.3698 0.5179 -0.7141 0.4752 -1.3850
## ci.ub
## intrcpt 1.5689 **
## productive_vocab_median -0.0015 *
## character_identificationyes 0.6453
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_ci_interaction_vocab <- rma.mv(d_calc ~ productive_vocab_median * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_ci_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -72.2209 144.4419 154.4419 161.7706 156.7496
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0981 0.3132 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 144.1155, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 14.6682, p-val = 0.0021
##
## Model Results:
##
## estimate se zval
## intrcpt 0.8596 0.2155 3.9879
## productive_vocab_median -0.0055 0.0024 -2.2628
## character_identificationyes 1.3618 0.7828 1.7397
## productive_vocab_median:character_identificationyes -0.0335 0.0139 -2.4204
## pval ci.lb ci.ub
## intrcpt <.0001 0.4371 1.2820
## productive_vocab_median 0.0236 -0.0103 -0.0007
## character_identificationyes 0.0819 -0.1724 2.8960
## productive_vocab_median:character_identificationyes 0.0155 -0.0607 -0.0064
##
## intrcpt ***
## productive_vocab_median *
## character_identificationyes .
## productive_vocab_median:character_identificationyes *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age w/ vocab comparison
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification, old only") 
m_age_ci_vocab_a <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_ci_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -74.6833 149.3666 157.3666 163.3526 158.7951
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3121 0.5587 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 158.9186, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.6098, p-val = 0.0223
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 1.1017 0.3427 3.2151 0.0013 0.4301
## mean_age -0.0006 0.0002 -2.6533 0.0080 -0.0010
## character_identificationyes -0.3928 0.5264 -0.7463 0.4555 -1.4246
## ci.ub
## intrcpt 1.7733 **
## mean_age -0.0002 **
## character_identificationyes 0.6389
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_ci_interaction_vocab_a <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_ci_interaction_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -71.6005 143.2010 153.2010 160.5297 155.5087
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0626 0.2503 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 141.1022, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 18.9790, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval
## intrcpt 1.0062 0.2270 4.4334 <.0001
## mean_age -0.0006 0.0002 -2.5574 0.0105
## character_identificationyes 4.6529 1.7021 2.7336 0.0063
## mean_age:character_identificationyes -0.0067 0.0022 -2.9986 0.0027
## ci.lb ci.ub
## intrcpt 0.5614 1.4510 ***
## mean_age -0.0010 -0.0001 *
## character_identificationyes 1.3169 7.9889 **
## mean_age:character_identificationyes -0.0110 -0.0023 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1practice_phase
single moderator
m_PP <- rma.mv(d_calc ~ practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_PP)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.7818 323.5636 329.5636 336.5558 329.8969
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2539 0.5039 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 408.2426, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8756, p-val = 0.1708
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4740 0.1309 3.6211 0.0003 0.2175 0.7306 ***
## practice_phaseyes -0.1612 0.1177 -1.3695 0.1708 -0.3919 0.0695
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase") 
m_age_pp <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_pp)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.6843 317.3686 325.3686 334.6385 325.9400
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2392 0.4891 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 401.9015, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.3295, p-val = 0.0422
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.7386 0.2140 3.4517 0.0006 0.3192
## mean_age -0.0005 0.0002 -2.5142 0.0119 -0.0008
## character_identificationyes 0.1388 0.2359 0.5884 0.5563 -0.3236
## ci.ub
## intrcpt 1.1581 ***
## mean_age -0.0001 *
## character_identificationyes 0.6012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pp_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_pp_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.2438 316.4875 326.4875 338.0078 327.3699
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2409 0.4909 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 396.5392, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.0462, p-val = 0.0704
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.8215 0.2355 3.4877 0.0005
## mean_age -0.0006 0.0002 -2.5468 0.0109
## character_identificationyes -0.1707 0.4352 -0.3922 0.6949
## mean_age:character_identificationyes 0.0003 0.0004 0.8472 0.3969
## ci.lb ci.ub
## intrcpt 0.3598 1.2831 ***
## mean_age -0.0010 -0.0001 *
## character_identificationyes -1.0236 0.6822
## mean_age:character_identificationyes -0.0004 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase, young only") 
m_age_pp_young <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_pp_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.5234 229.0468 237.0468 245.0027 237.8631
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2749 0.5243 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 290.5875, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.8004, p-val = 0.6702
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7492 0.4393 1.7055 0.0881 -0.1118 1.6103 .
## mean_age -0.0005 0.0006 -0.8686 0.3851 -0.0017 0.0007
## practice_phaseyes 0.1335 0.1928 0.6921 0.4889 -0.2445 0.5114
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pp_interaction_young <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_pp_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.3324 226.6648 236.6648 246.5162 237.9414
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3045 0.5518 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 288.4535, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9467, p-val = 0.5835
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3243 0.6888 1.9225 0.0545 -0.0258 2.6743
## mean_age -0.0014 0.0010 -1.3847 0.1662 -0.0033 0.0006
## practice_phaseyes -0.9787 0.9969 -0.9817 0.3263 -2.9326 0.9753
## mean_age:practice_phaseyes 0.0016 0.0014 1.1256 0.2603 -0.0012 0.0044
##
## intrcpt .
## mean_age
## practice_phaseyes
## mean_age:practice_phaseyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase, old only") 
m_age_pp_old <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_pp_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.5234 229.0468 237.0468 245.0027 237.8631
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2749 0.5243 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 290.5875, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.8004, p-val = 0.6702
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7492 0.4393 1.7055 0.0881 -0.1118 1.6103 .
## mean_age -0.0005 0.0006 -0.8686 0.3851 -0.0017 0.0007
## practice_phaseyes 0.1335 0.1928 0.6921 0.4889 -0.2445 0.5114
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pp_interaction_old <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_pp_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.8420 85.6839 95.6839 99.8500 101.1385
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0511 0.2260 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 104.1628, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9800, p-val = 0.5766
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0821 3.7856 -0.0217 0.9827 -7.5016 7.3375
## mean_age 0.0005 0.0032 0.1622 0.8712 -0.0057 0.0068
## practice_phaseyes 0.6518 3.8229 0.1705 0.8646 -6.8409 8.1446
## mean_age:practice_phaseyes -0.0008 0.0032 -0.2397 0.8106 -0.0071 0.0055
##
## intrcpt
## mean_age
## practice_phaseyes
## mean_age:practice_phaseyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("productive vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. productive vocab, breakdown by practice_phase, old only") 
m_age_pp_vocab <- rma.mv(d_calc ~ productive_vocab_median + practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_pp_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.6422 151.2843 159.2843 165.2704 160.7129
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2436 0.4935 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 156.3412, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.0094, p-val = 0.0301
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8217 0.2503 3.2832 0.0010 0.3312 1.3122 **
## productive_vocab_median -0.0086 0.0051 -1.6985 0.0894 -0.0185 0.0013 .
## practice_phaseyes 0.1368 0.2722 0.5026 0.6153 -0.3966 0.6702
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pp_interaction_vocab <- rma.mv(d_calc ~ productive_vocab_median * practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_pp_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.5823 151.1647 161.1647 168.4934 163.4724
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2483 0.4983 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 155.9780, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.4501, p-val = 0.0589
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.4265 0.6339 0.6728 0.5011
## productive_vocab_median 0.0133 0.0325 0.4085 0.6829
## practice_phaseyes 0.5138 0.6197 0.8291 0.4071
## productive_vocab_median:practice_phaseyes -0.0215 0.0316 -0.6801 0.4964
## ci.lb ci.ub
## intrcpt -0.8160 1.6690
## productive_vocab_median -0.0504 0.0769
## practice_phaseyes -0.7009 1.7285
## productive_vocab_median:practice_phaseyes -0.0834 0.0404
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age w/ vocab comparsion
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("mean age") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase, old only") 
m_age_pp_vocab_a <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_pp_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.7665 151.5330 159.5330 165.5190 160.9615
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2702 0.5198 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 159.9395, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.2472, p-val = 0.0267
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9924 0.2905 3.4157 0.0006 0.4230 1.5619 ***
## mean_age -0.0007 0.0004 -1.7594 0.0785 -0.0015 0.0001 .
## practice_phaseyes 0.0836 0.2398 0.3486 0.7274 -0.3865 0.5537
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_pp_interaction_vocab_a <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_pp_interaction_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -75.6884 151.3768 161.3768 168.7054 163.6845
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2650 0.5148 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 159.3021, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.4131, p-val = 0.0598
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5875 1.0760 0.5460 0.5851 -1.5214 2.6964
## mean_age 0.0000 0.0019 0.0105 0.9917 -0.0038 0.0038
## practice_phaseyes 0.5752 1.2784 0.4499 0.6528 -1.9305 3.0808
## mean_age:practice_phaseyes -0.0008 0.0021 -0.3907 0.6960 -0.0050 0.0034
##
## intrcpt
## mean_age
## practice_phaseyes
## mean_age:practice_phaseyes
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1test_or_mass_distributed
single moderator
m_TMD <- rma.mv(d_calc ~ test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_TMD)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.8809 323.7618 329.7618 336.7540 330.0951
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2394 0.4893 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 408.0962, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0229, p-val = 0.8797
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4085 0.1364 2.9943 0.0028 0.1411
## test_mass_or_distributedmass -0.0389 0.2571 -0.1514 0.8797 -0.5428
## ci.ub
## intrcpt 0.6759 **
## test_mass_or_distributedmass 0.4649
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test type") 
m_age_tt <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_tt)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -158.6671 317.3342 325.3342 334.6042 325.9056
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2454 0.4954 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 406.6032, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.0183, p-val = 0.0493
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8016 0.2116 3.7888 0.0002 0.3869
## mean_age -0.0004 0.0002 -2.4487 0.0143 -0.0008
## test_mass_or_distributedmass -0.0448 0.2598 -0.1723 0.8632 -0.5540
## ci.ub
## intrcpt 1.2163 ***
## mean_age -0.0001 *
## test_mass_or_distributedmass 0.4645
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_tt_interaction <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_tt_interaction)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -157.7668 315.5336 325.5336 337.0540 326.4160
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2382 0.4880 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 74) = 396.2509, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.3336, p-val = 0.0620
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.5269 0.3178 1.6579 0.0973
## mean_age -0.0001 0.0003 -0.4123 0.6801
## test_mass_or_distributedmass 0.3503 0.4294 0.8159 0.4146
## mean_age:test_mass_or_distributedmass -0.0004 0.0004 -1.1489 0.2506
## ci.lb ci.ub
## intrcpt -0.0960 1.1497 .
## mean_age -0.0008 0.0005
## test_mass_or_distributedmass -0.4912 1.1919
## mean_age:test_mass_or_distributedmass -0.0012 0.0003
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test_mass_or_distributed, young only") 
m_age_tt_young <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_tt_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -114.3078 228.6157 236.6157 244.5716 237.4320
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3143 0.5607 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 296.5626, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.3138, p-val = 0.8548
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5878 0.4247 1.3838 0.1664 -0.2447
## mean_age -0.0003 0.0005 -0.5162 0.6057 -0.0012
## test_mass_or_distributedmass 0.0487 0.3230 0.1508 0.8801 -0.5843
## ci.ub
## intrcpt 1.4203
## mean_age 0.0007
## test_mass_or_distributedmass 0.6817
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_tt_interaction_young <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_tt_interaction_young)##
## Multivariate Meta-Analysis Model (k = 57; method: REML)
##
## logLik Deviance AIC BIC AICc
## -113.2307 226.4615 236.4615 246.3130 237.7381
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3410 0.5840 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 53) = 296.3776, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.3688, p-val = 0.7129
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.5465 1.1727 -0.4660 0.6412
## mean_age 0.0012 0.0015 0.8064 0.4200
## test_mass_or_distributedmass 1.3089 1.2612 1.0378 0.2993
## mean_age:test_mass_or_distributedmass -0.0016 0.0016 -1.0351 0.3006
## ci.lb ci.ub
## intrcpt -2.8450 1.7521
## mean_age -0.0017 0.0040
## test_mass_or_distributedmass -1.1629 3.7807
## mean_age:test_mass_or_distributedmass -0.0046 0.0014
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test_mass_or_distributed, old only") 
m_age_tt_old <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_tt_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.0417 80.0835 88.0835 91.6450 91.1604
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 95.8384, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.5082, p-val = 0.0052
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8301 0.4191 1.9805 0.0476 0.0086
## mean_age -0.0003 0.0003 -0.9066 0.3646 -0.0009
## test_mass_or_distributedmass -0.4097 0.1275 -3.2145 0.0013 -0.6595
## ci.ub
## intrcpt 1.6515 *
## mean_age 0.0003
## test_mass_or_distributedmass -0.1599 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_tt_interaction_old <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_tt_interaction_old)##
## Multivariate Meta-Analysis Model (k = 21; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.3879 80.7757 90.7757 94.9418 96.2303
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0000 0.0000 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 95.5607, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.7860, p-val = 0.0129
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.8938 0.4362 2.0490 0.0405
## mean_age -0.0003 0.0003 -1.0180 0.3087
## test_mass_or_distributedmass -1.1870 1.4804 -0.8018 0.4227
## mean_age:test_mass_or_distributedmass 0.0006 0.0011 0.5270 0.5982
## ci.lb ci.ub
## intrcpt 0.0388 1.7487 *
## mean_age -0.0009 0.0003
## test_mass_or_distributedmass -4.0885 1.7145
## mean_age:test_mass_or_distributedmass -0.0016 0.0028
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("median vocab") +
ggtitle("Syntactical Bootstrapping effect size vs. median voca, breakdown by test_mass_or_distributed, old only") 
m_age_tt_vocab <- rma.mv(d_calc ~ productive_vocab_median + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_tt_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -74.5086 149.0173 157.0173 163.0033 158.4458
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2681 0.5178 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 155.1572, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.4670, p-val = 0.0239
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 1.3009 0.5916 2.1988 0.0279 0.1413
## productive_vocab_median -0.0061 0.0025 -2.5009 0.0124 -0.0110
## test_mass_or_distributedmass -0.5685 0.6413 -0.8864 0.3754 -1.8253
## ci.ub
## intrcpt 2.4604 *
## productive_vocab_median -0.0013 *
## test_mass_or_distributedmass 0.6884
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_tt_interaction_vocab <- rma.mv(d_calc ~ productive_vocab_median * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_tt_interaction_vocab)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -72.7496 145.4992 155.4992 162.8279 157.8069
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2680 0.5177 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 152.8033, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 9.8104, p-val = 0.0202
##
## Model Results:
##
## estimate se
## intrcpt -15.7524 11.1567
## productive_vocab_median 0.5920 0.3908
## test_mass_or_distributedmass 16.4860 11.1603
## productive_vocab_median:test_mass_or_distributedmass -0.5982 0.3908
## zval pval ci.lb
## intrcpt -1.4119 0.1580 -37.6192
## productive_vocab_median 1.5149 0.1298 -0.1739
## test_mass_or_distributedmass 1.4772 0.1396 -5.3877
## productive_vocab_median:test_mass_or_distributedmass -1.5307 0.1259 -1.3642
## ci.ub
## intrcpt 6.1144
## productive_vocab_median 1.3580
## test_mass_or_distributedmass 38.3597
## productive_vocab_median:test_mass_or_distributedmass 0.1678
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age w/ vocab comparison
ma_data_vocab %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (months)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test_mass_or_distributed, old only") 
m_age_tt_vocab_a <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_tt_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -74.3962 148.7924 156.7924 162.7785 158.2210
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2753 0.5247 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 155.8851, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 8.0512, p-val = 0.0179
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 1.4935 0.6099 2.4486 0.0143 0.2980
## mean_age -0.0006 0.0002 -2.6203 0.0088 -0.0010
## test_mass_or_distributedmass -0.6252 0.6464 -0.9672 0.3334 -1.8922
## ci.ub
## intrcpt 2.6889 *
## mean_age -0.0001 **
## test_mass_or_distributedmass 0.6418
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_age_tt_interaction_vocab_a <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_tt_interaction_vocab_a)##
## Multivariate Meta-Analysis Model (k = 36; method: REML)
##
## logLik Deviance AIC BIC AICc
## -72.6519 145.3038 155.3038 162.6325 157.6115
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2753 0.5247 6 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 153.5680, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.3647, p-val = 0.0157
##
## Model Results:
##
## estimate se zval pval
## intrcpt -90.6459 60.5818 -1.4963 0.1346
## mean_age 0.1459 0.0963 1.5149 0.1298
## test_mass_or_distributedmass 91.5147 60.5826 1.5106 0.1309
## mean_age:test_mass_or_distributedmass -0.1465 0.0963 -1.5210 0.1283
## ci.lb ci.ub
## intrcpt -209.3841 28.0924
## mean_age -0.0429 0.3346
## test_mass_or_distributedmass -27.2250 210.2544
## mean_age:test_mass_or_distributedmass -0.3352 0.0423
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1n_train_test_pair
single moderator
m_TTP <- rma.mv(d_calc ~ n_train_test_pair, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_TTP)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -161.9138 323.8277 329.8277 336.8199 330.1610
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2398 0.4897 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 408.5743, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0432, p-val = 0.8354
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4464 0.2619 1.7044 0.0883 -0.0669 0.9598 .
## n_train_test_pair -0.0182 0.0876 -0.2078 0.8354 -0.1900 0.1536
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% ggplot(aes(x = n_train_test_pair)) +
geom_histogram() 
n_test_trial
single moderator
m_TeT <- rma.mv(d_calc ~ n_test_trial_per_pair, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_TeT)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.5225 325.0450 331.0450 338.0372 331.3783
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2262 0.4756 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 408.2600, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4382, p-val = 0.5080
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2960 0.1896 1.5612 0.1185 -0.0756 0.6677
## n_test_trial_per_pair 0.0581 0.0878 0.6620 0.5080 -0.1139 0.2301
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ma_data %>% ggplot(aes(x = n_test_trial_per_pair)) +
geom_histogram() 
Single Moderator
summary(m_age)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -159.6433 319.2865 325.2865 332.2787 325.6199
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2306 0.4802 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 407.7223, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.9754, p-val = 0.0145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7864 0.1957 4.0180 <.0001 0.4028 1.1700 ***
## mean_age -0.0004 0.0002 -2.4445 0.0145 -0.0008 -0.0001 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1s_age <- coef(summary(m_age)) %>% rownames_to_column(var = "coefficient_name")
s_vocab <- coef(summary(m_vocab)) %>% rownames_to_column(var = "coefficient_name")
s_rep <- coef(summary(m_rep)) %>% rownames_to_column(var = "coefficient_name")
s_SS <- coef(summary(m_SS)) %>% rownames_to_column(var = "coefficient_name")
s_AAT <- coef(summary(m_AAT)) %>% rownames_to_column(var = "coefficient_name")
s_PAT <- coef(summary(m_PAT)) %>% rownames_to_column(var = "coefficient_name")
s_AAN <- coef(summary(m_AAN)) %>% rownames_to_column(var = "coefficient_name")
s_SM <- coef(summary(m_SM)) %>% rownames_to_column(var = "coefficient_name")
s_SA <- coef(summary(m_SA)) %>% rownames_to_column(var = "coefficient_name")
s_TE <- coef(summary(m_TE)) %>% rownames_to_column(var = "coefficient_name")
s_IE <- coef(summary(m_IE)) %>% rownames_to_column(var = "coefficient_name")
s_VSP <- coef(summary(m_VSP)) %>% rownames_to_column(var = "coefficient_name")
s_RV <- coef(summary(m_RV)) %>% rownames_to_column(var = "coefficient_name")
s_TM <- coef(summary(m_TM)) %>% rownames_to_column(var = "coefficient_name")
s_PT <- coef(summary(m_PT)) %>% rownames_to_column(var = "coefficient_name")
s_CI <- coef(summary(m_CI)) %>% rownames_to_column(var = "coefficient_name")
s_PP <- coef(summary(m_PP)) %>% rownames_to_column(var = "coefficient_name")
s_TMD <- coef(summary(m_TMD)) %>% rownames_to_column(var = "coefficient_name")
s_TTP <- coef(summary(m_TTP)) %>% rownames_to_column(var = "coefficient_name")
s_TeT <- coef(summary(m_TeT)) %>% rownames_to_column(var = "coefficient_name")
s_moderators <- bind_rows(s_age, s_vocab, s_SS,s_AAT,s_PAT,s_AAN,s_SM,s_SA,s_TE,s_IE,s_VSP,s_RV,s_TM,s_PT,s_CI,s_PP,s_TMD,s_TTP,s_TeT
) %>% filter(coefficient_name != "intrcpt") %>%
mutate(moderator_types = case_when(
coefficient_name == "mean_age" ~ "Participants_Characteristics",
coefficient_name == "productive_vocab_median" ~ "Participants_Characteristics",
coefficient_name == "sentence_structuretransitive" ~ "Linguistic_Stimuli",
coefficient_name == "agent_argument_type_cleannoun_phrase" ~ "Linguistic_Stimuli",
coefficient_name == "agent_argument_type_cleanpronoun" ~ "Linguistic_Stimuli",
coefficient_name == "agent_argument_type_cleanvarying_agent" ~ "Linguistic_Stimuli",
coefficient_name == "patient_argument_type_cleannoun"~ "Linguistic_Stimuli",
coefficient_name == "patient_argument_type_cleannoun_phrase"~ "Linguistic_Stimuli",
coefficient_name == "patient_argument_type_cleanpronoun"~ "Linguistic_Stimuli",
coefficient_name == "patient_argument_type_cleanvarying_patient"~ "Linguistic_Stimuli",
coefficient_name == "agent_argument_number2"~ "Linguistic_Stimuli",
coefficient_name == "agent_argument_numbervarying"~ "Linguistic_Stimuli",
coefficient_name == "n_repetitions_sentence"~ "Linguistic_Stimuli",
coefficient_name == "patient_argument_type_cleanvarying_patient"~ "Linguistic_Stimuli",
coefficient_name == "n_repetitions_video"~ "Visual_Stimuli",
coefficient_name == "stimuli_modalityvideo" ~ "Visual_Stimuli",
coefficient_name == "stimuli_actorperson" ~ "Visual_Stimuli",
coefficient_name == "transitive_event_typeindirect_caused_action" ~ "Visual_Stimuli",
coefficient_name == "transitive_event_typeminimal_contact" ~ "Visual_Stimuli",
coefficient_name == "intransitive_event_typeone_action" ~ "Visual_Stimuli",
coefficient_name == "intransitive_event_typeparallel_actions" ~ "Visual_Stimuli",
coefficient_name == "visual_stimuli_pairdirect_caused_action_one_action" ~ "Visual_Stimuli",
coefficient_name == "visual_stimuli_pairdirect_caused_action_parallel_actions" ~ "Visual_Stimuli",
coefficient_name == "visual_stimuli_pairindirect_caused_action_one_action" ~ "Visual_Stimuli",
coefficient_name == "visual_stimuli_pairindirect_caused_action_parallel_actions" ~ "Visual_Stimuli",
coefficient_name == "visual_stimuli_pairminimal_contact_one_action" ~ "Visual_Stimuli",
coefficient_name == "visual_stimuli_pairminimal_contact_parallel_actions" ~ "Visual_Stimuli",
coefficient_name == "test_methodpoint" ~ "Test_Procedure",
coefficient_name == "presentation_typeimmediate_after" ~ "Test_Procedure",
coefficient_name == "presentation_typesimultaneous" ~ "Test_Procedure",
coefficient_name == "character_identificationyes" ~ "Test_Procedure",
coefficient_name == "practice_phaseyes" ~ "Test_Procedure",
coefficient_name == "test_mass_or_distributedmass" ~ "Test_Procedure",
coefficient_name == "n_train_test_pair" ~ "Test_Procedure",
coefficient_name == "n_test_trial_per_pair" ~ "Test_Procedure",
)
)s_moderators %>% filter(moderator_types == "Participants_Characteristics") %>%
ggplot(aes(x = coefficient_name, y = estimate)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = ci.lb, ymax = ci.ub)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
s_moderators %>% filter(moderator_types == "Linguistic_Stimuli") %>%
ggplot(aes(x = coefficient_name, y = estimate)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = ci.lb, ymax = ci.ub)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
s_moderators %>% filter(moderator_types == "Visual_Stimuli") %>%
ggplot(aes(x = coefficient_name, y = estimate)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = ci.lb, ymax = ci.ub)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
s_moderators %>% filter(moderator_types == "Test_Procedure") %>%
ggplot(aes(x = coefficient_name, y = estimate)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = ci.lb, ymax = ci.ub)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
s_moderators %>%
ggplot(aes(x = coefficient_name, y = estimate, color = moderator_types)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = ci.lb, ymax = ci.ub)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) 
Reasonalbe Moderators (??)
null <- rma.mv(d_calc~1, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(null)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -162.9086 325.8171 329.8171 334.5047 329.9793
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2245 0.4739 21 no short_cite
##
## Test for Heterogeneity:
## Q(df = 77) = 408.5756, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3968 0.1125 3.5273 0.0004 0.1763 0.6173 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_sink <- rma.mv(d_calc ~ mean_age + sentence_structure + agent_argument_type_clean + agent_argument_number + n_repetitions_sentence + test_method + character_identification + practice_phase + test_mass_or_distributed , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_sink)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -140.7492 281.4984 305.4984 331.9547 311.2761
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3106 0.5573 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 67) = 369.4530, p-val < .0001
##
## Test of Moderators (coefficients 2:11):
## QM(df = 10) = 34.6318, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.2322 0.3767 0.6164 0.5376
## mean_age -0.0004 0.0003 -1.5174 0.1292
## sentence_structuretransitive 0.4804 0.0968 4.9621 <.0001
## agent_argument_type_cleannoun_phrase 0.5466 0.1888 2.8948 0.0038
## agent_argument_type_cleanpronoun 0.4004 0.9589 0.4176 0.6762
## agent_argument_type_cleanvarying_agent 0.6418 0.8419 0.7622 0.4459
## n_repetitions_sentence -0.0016 0.0184 -0.0879 0.9300
## test_methodpoint 0.2833 0.7440 0.3808 0.7033
## character_identificationyes 0.0214 0.3000 0.0713 0.9432
## practice_phaseyes 0.1691 0.2103 0.8042 0.4213
## test_mass_or_distributedmass -0.4297 0.7543 -0.5697 0.5689
## ci.lb ci.ub
## intrcpt -0.5062 0.9706
## mean_age -0.0010 0.0001
## sentence_structuretransitive 0.2907 0.6702 ***
## agent_argument_type_cleannoun_phrase 0.1765 0.9167 **
## agent_argument_type_cleanpronoun -1.4790 2.2798
## agent_argument_type_cleanvarying_agent -1.0084 2.2920
## n_repetitions_sentence -0.0377 0.0345
## test_methodpoint -1.1748 1.7415
## character_identificationyes -0.5666 0.6094
## practice_phaseyes -0.2430 0.5812
## test_mass_or_distributedmass -1.9081 1.0486
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m_sink_2 <- rma.mv(d_calc ~ mean_age + sentence_structure + n_repetitions_sentence + test_method + character_identification + practice_phase + test_mass_or_distributed , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_sink_2)##
## Multivariate Meta-Analysis Model (k = 78; method: REML)
##
## logLik Deviance AIC BIC AICc
## -146.9178 293.8356 311.8356 332.0721 314.8356
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3056 0.5529 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 70) = 381.0954, p-val < .0001
##
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 25.1224, p-val = 0.0007
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.3630 0.3324 1.0919 0.2749 -0.2886
## mean_age -0.0005 0.0003 -1.8436 0.0652 -0.0010
## sentence_structuretransitive 0.3343 0.0828 4.0372 <.0001 0.1720
## n_repetitions_sentence 0.0099 0.0155 0.6379 0.5235 -0.0205
## test_methodpoint 0.5372 0.3794 1.4157 0.1569 -0.2065
## character_identificationyes 0.0797 0.2881 0.2766 0.7821 -0.4849
## practice_phaseyes 0.1400 0.1764 0.7941 0.4272 -0.2056
## test_mass_or_distributedmass -0.0696 0.3080 -0.2259 0.8212 -0.6732
## ci.ub
## intrcpt 1.0145
## mean_age 0.0000 .
## sentence_structuretransitive 0.4966 ***
## n_repetitions_sentence 0.0402
## test_methodpoint 1.2808
## character_identificationyes 0.6443
## practice_phaseyes 0.4857
## test_mass_or_distributedmass 0.5340
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1