Gesture and Language Meta-Analysis
2021-08-12
These are the analyses for the entire dataset. For analyses including only effect sizes based on Pearson’s R, click here. See R Markdown file for code used to open data set and calculated effect size variances etc. It prints kind of messy, and I have suppressed it from this HTML file.
1 Determining Baseline Model
Fit baseline model to the full data set
m1 <- rma.mv(yi, vi, random=~1|Article/Comp, data=EffectSizes_short)
summary(m1)##
## Multivariate Meta-Analysis Model (k = 95; method: REML)
##
## logLik Deviance AIC BIC AICc
## -15.2654 30.5308 36.5308 44.1607 36.7975
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0530 0.2302 30 no Article
## sigma^2.2 0.0027 0.0522 95 no Article/Comp
##
## Test for Heterogeneity:
## Q(df = 94) = 249.8259, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.2904 0.0501 5.7965 <.0001 0.1922 0.3886 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Look at residuals
boxplot(resid(m1, method="rstandard"))
hist(resid(m1, method="rstandard"))
These residuals aren’t great.
Summary, controlling for correlated sampling error.
summary(robust(m1, cluster=EffectSizes_short$Article))##
## Number of outcomes: 95
## Number of clusters: 30
## Outcomes per cluster: 1-8 (mean: 3.17, median: 2)
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.2904 0.0500 5.8039 29 <.0001 0.1880 0.3927 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Tests of publication bias.
regtest(rma(yi, vi, data=EffectSizes_short))##
## Regression Test for Funnel Plot Asymmetry
##
## Model: mixed-effects meta-regression model
## Predictor: standard error
##
## Test for Funnel Plot Asymmetry: z = 2.4505, p = 0.0143
## Limit Estimate (as sei -> 0): b = 0.1186 (CI: 0.0168, 0.2203)funnel(rma(yi, vi, data=EffectSizes_short))
There are a lot of effect size variances that are quite large because a subset of the correlaions were calculated on very small sample sizes. These variances could potentially strongly influence the slope of the corrections for effect sizes. If we look at a histogram of the effect size standard errors, we see that all of the especially large effect sizes standard errors come from studies with sample sizes < = 10.
EffectSizes_short %>%
mutate(
small_n = ifelse(n <= 10, yes="N <= 10", no = "N > 10")
) %>%
ggplot(aes(x = sei, fill=small_n)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Given that, these effect sizes could strongly influence the publication bias correction, and that effect sizes based on 10 participants are likely not very informative to begin with. I have decided to drop these corrections from subsequent analyses.
Here I fit the model again, without those effect sizes.
m2 <- rma.mv(yi, vi, random=~1|Article/Comp, data=EffectSizes_short2)
summary(m2)##
## Multivariate Meta-Analysis Model (k = 77; method: REML)
##
## logLik Deviance AIC BIC AICc
## 11.8113 -23.6227 -17.6227 -10.6305 -17.2894
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0443 0.2104 25 no Article
## sigma^2.2 0.0025 0.0502 77 no Article/Comp
##
## Test for Heterogeneity:
## Q(df = 76) = 200.6366, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.2726 0.0489 5.5736 <.0001 0.1767 0.3684 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1and test for publication bias again
regtest(rma(yi, vi, data=EffectSizes_short2))##
## Regression Test for Funnel Plot Asymmetry
##
## Model: mixed-effects meta-regression model
## Predictor: standard error
##
## Test for Funnel Plot Asymmetry: z = 3.4964, p = 0.0005
## Limit Estimate (as sei -> 0): b = 0.0249 (CI: -0.0930, 0.1428)funnel(rma(yi, vi, data=EffectSizes_short2))
The funnel plots and regression test still suggest publication bias, but there aren’t the disproporionately large effect sizes that would influence the publication bias correction.
boxplot(resid(m2, method="rstandard"))
hist(resid(m2, method="rstandard"))
The residuals look much better, if still a bit skewed.
Here’s a forest plot
forest(m2, slab=EffectSizes_short2$Article, xlim=c(-2.8,2.8))
Add variance to correct for publication bias
m3 <- rma.mv(yi, vi, mods=vi, random=~1|Article/Comp, data=EffectSizes_short2)
summary(m3)##
## Multivariate Meta-Analysis Model (k = 77; method: REML)
##
## logLik Deviance AIC BIC AICc
## 12.5781 -25.1561 -17.1561 -7.8862 -16.5847
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0433 0.2080 25 no Article
## sigma^2.2 0.0025 0.0504 77 no Article/Comp
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 161.7020, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2157, p-val = 0.2702
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1965 0.0841 2.3355 0.0195 0.0316 0.3614 *
## mods 2.0704 1.8778 1.1026 0.2702 -1.6099 5.7508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1boxplot(resid(m3, method="rstandard"))
hist(resid(m3, method="rstandard"))
Summarize the above model, with cluster robust variance estimation, by article, to account for the correlated sampling errors within articles.
summary(robust(m3, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 23) = 0.8505, p-val = 0.3660
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1965 0.0887 2.2147 23 0.0370 0.0130 0.3801 *
## mods 2.0704 2.2450 0.9222 23 0.3660 -2.5738 6.7146
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1predict(robust(m3, cluster=EffectSizes_short2$Article), 0,transf=transf.ztor) # get estimates on scale of r. ##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1940 0.0130 0.3628 -0.2754 0.58882 Tests of the Moderators
Here are all of the analyses of the moderator variables. The code for each moderator is basically the same, so I have annotated it for the model testing the effect of modality only.
2.1 Modality: Comprehension or Production
m_modal <- rma.mv(yi, vi, mods= ~ Modalitys*vi, random=~1|Article/Comp, data=EffectSizes_short2) # fit multilevel model.
summary(robust(m_modal, cluster=EffectSizes_short2$Article)) # get robust standard errors##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 21) = 5.6576, p-val = 0.0053
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.2071 0.0912 2.2719 21 0.0337 0.0175 0.3967 *
## Modalitys1 0.0648 0.0166 3.9075 21 0.0008 0.0303 0.0993 ***
## vi 1.8178 2.3850 0.7622 21 0.4544 -3.1420 6.7776
## Modalitys1:vi -1.5354 0.5995 -2.5610 21 0.0182 -2.7822 -0.2886 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_modal, cluster=EffectSizes_short2$Article), btt=2) # F test with robust standard errors##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 21) = 15.2682, p-val = 0.0008predict(robust(m_modal, cluster=EffectSizes_short2$Article), c(1, 0, 0), transf=transf.ztor) # get means and CIs for level 1 (assuming 0 se)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.2654 0.0836 0.4301 -0.2018 0.6342predict(robust(m_modal, cluster=EffectSizes_short2$Article), c(-1, 0, 0), transf=transf.ztor) # get means and CIs for level -1 (assuming 0 se)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1413 -0.0548 0.3270 -0.3256 0.5528table(EffectSizes_short2$Modalitys)##
## comp prod
## 30 47contrasts(EffectSizes_short2$Modalitys) # Just a sanity check: which is -1 and which is 1## [,1]
## comp 1
## prod -1EffectSizes_short2 %>% # get descriptive statistics for each level of the variable.
group_by(Modalitys) %>%
summarise(
N_part = sum(n),
K = n()
)2.2 Measure: Vocab vs Other
m_measure <- rma.mv(yi, vi, mods= ~ Measures*vi, random=~1|Article/Comp, data=EffectSizes_short2)
summary(robust(m_measure, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 21) = 3.5611, p-val = 0.0316
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.2041 0.0929 2.1978 21 0.0393 0.0110 0.3972 *
## Measures1 0.0317 0.0108 2.9434 21 0.0078 0.0093 0.0541 **
## vi 2.0221 2.1402 0.9448 21 0.3555 -2.4288 6.4729
## Measures1:vi -0.3147 0.7604 -0.4139 21 0.6832 -1.8961 1.2667
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_measure, cluster=EffectSizes_short2$Article), btt=2)##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 21) = 8.6639, p-val = 0.0078predict(robust(m_measure, cluster=EffectSizes_short2$Article), c(1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.2315 0.0506 0.3977 -0.2540 0.6239predict(robust(m_measure, cluster=EffectSizes_short2$Article), c(-1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1707 -0.0309 0.3590 -0.3186 0.5882table(EffectSizes_short2$Measures)##
## Other Vocab
## 27 50contrasts(EffectSizes_short2$Measures)## [,1]
## Other 1
## Vocab -1EffectSizes_short2 %>%
group_by(Measures) %>%
summarise(
N_part = sum(n),
K = n()
)2.3 Pointing Context: Gesture Alone vs Gesture and Speech Combination
m_context <- rma.mv(yi, vi, mods= ~ Just_Pointings*vi, random=~1|Article/Comp, data=EffectSizes_short2)
summary(robust(m_context, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 21) = 13.7663, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0699 0.0898 0.7780 21 0.4452 -0.1169 0.2566
## Just_Pointings1 0.1319 0.0334 3.9532 21 0.0007 0.0625 0.2013
## vi 6.3399 2.1775 2.9116 21 0.0083 1.8116 10.8681
## Just_Pointings1:vi -4.6652 1.6018 -2.9126 21 0.0083 -7.9963 -1.3342
##
## intrcpt
## Just_Pointings1 ***
## vi **
## Just_Pointings1:vi **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_context, cluster=EffectSizes_short2$Article), btt=2)##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 21) = 15.6282, p-val = 0.0007table(EffectSizes_short2$Just_Pointings)##
## pointing_alone Gesture_Speech_Combination
## 70 7predict(robust(m_context, cluster=EffectSizes_short2$Article), c(1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1991 0.0021 0.3812 -0.2989 0.6119predict(robust(m_context, cluster=EffectSizes_short2$Article), c(-1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## -0.0620 -0.2551 0.1359 -0.5167 0.4200contrasts(EffectSizes_short2$Just_Pointings)## [,1]
## pointing_alone 1
## Gesture_Speech_Combination -1EffectSizes_short2 %>%
group_by(Just_Pointings) %>%
summarise(
N_part = sum(n),
K = n()
)2.4 Age of Pointing. < 18 month or > 18 month.
m_pointing <- rma.mv(yi, vi, mods= ~ Age_Pointings*vi , random=~1|Article/Comp, data=EffectSizes_short2)
summary(robust(m_pointing, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 21) = 31.2276, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.2733 0.0934 2.9273 21 0.0080 0.0791 0.4674 **
## Age_Pointings1 0.0544 0.0077 7.0699 21 <.0001 0.0384 0.0704 ***
## vi 1.4229 2.1185 0.6717 21 0.5091 -2.9827 5.8284
## Age_Pointings1:vi 0.9696 0.6970 1.3910 21 0.1788 -0.4799 2.4190
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_pointing, cluster=EffectSizes_short2$Article), btt=2)##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 21) = 49.9831, p-val < .0001predict(robust(m_pointing, cluster=EffectSizes_short2$Article), c(1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.3165 0.1382 0.4749 -0.1598 0.6732predict(robust(m_pointing, cluster=EffectSizes_short2$Article), c(-1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.2155 0.0181 0.3966 -0.2681 0.6123contrasts(EffectSizes_short2$Age_Pointings)## [,1]
## Over_18 1
## Under 18 -1EffectSizes_short2 %>%
group_by(Age_Pointings) %>%
summarise(
N_part = sum(n),
K = n()
)2.5 Language: English, Mixed, Other
m_language <- rma.mv(yi, vi, mods= ~ Languages*vi, random=~1|Article/Comp, data=EffectSizes_short2)## Warning: Redundant predictors dropped from the model.summary(robust(m_language, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 20) = 12.9868, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1071 0.0614 1.7453 20 0.0963 -0.0209 0.2352 .
## Languages1 0.1804 0.0893 2.0193 20 0.0571 -0.0060 0.3667 .
## Languages2 -0.0914 0.0541 -1.6881 20 0.1069 -0.2043 0.0215
## vi 2.7234 1.9068 1.4282 20 0.1687 -1.2542 6.7010
## Languages1:vi -1.2896 1.9068 -0.6763 20 0.5066 -5.2672 2.6880
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_language, cluster=EffectSizes_short2$Article), btt=c(2, 3))##
## Test of Moderators (coefficients 2:3):
## F(df1 = 2, df2 = 20) = 2.9318, p-val = 0.0765table(EffectSizes_short2$Languages)##
## English Mixed Other
## 37 8 32predict(robust(m_language, cluster=EffectSizes_short2$Article), c(-1, -1, 0, 0), transf=transf.ztor) #Other##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.0181 -0.2510 0.2847 -0.4645 0.4925predict(robust(m_language, cluster=EffectSizes_short2$Article), c(1, 0, 0, 0), transf=transf.ztor) #English##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.2799 0.0413 0.4882 -0.2158 0.6608predict(robust(m_language, cluster=EffectSizes_short2$Article), c(0, 1, 0, 0), transf=transf.ztor) #Mixed##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.0157 -0.0021 0.0336 -0.4033 0.4293contrasts(EffectSizes_short2$Languages)## [,1] [,2]
## English 1 0
## Mixed 0 1
## Other -1 -1EffectSizes_short2 %>%
group_by(Languages) %>%
summarise(
N_part = sum(n),
K = n(),
mean = median(yi)
)2.6 Country: North America/Other
m_country <- rma.mv(yi, vi, mods= ~ Countrys*vi , random=~1|Article/Comp, data=EffectSizes_short2)## Warning: Redundant predictors dropped from the model.summary(robust(m_country, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 21) = 1.9682, p-val = 0.1497
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1465 0.0929 1.5773 21 0.1297 -0.0467 0.3397
## Countrys1 0.1825 0.0929 1.9645 21 0.0628 -0.0107 0.3756 .
## vi 3.0190 2.1642 1.3949 21 0.1776 -1.4818 7.5197
## Countrys1:vi -2.7592 2.1642 -1.2749 21 0.2163 -7.2599 1.7415
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_country, cluster=EffectSizes_short2$Article), btt=2)##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 21) = 3.8593, p-val = 0.0628predict(robust(m_country, cluster=EffectSizes_short2$Article), c(1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.3176 0.0673 0.5303 -0.1597 0.6746predict(robust(m_country, cluster=EffectSizes_short2$Article), c(-1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## -0.0360 -0.3097 0.2433 -0.4919 0.4354contrasts(EffectSizes_short2$Countrys)## [,1] [,2]
## North America 1 0
## Mixed 0 1
## Other -1 -1EffectSizes_short2 %>%
group_by(Countrys) %>%
summarise(
N_part = sum(n),
K = n()
)2.7 Pointing Measure: Frequency/Onset
m_fo <- rma.mv(yi, vi, mods= ~ Pointing_Measures + vi, random=~1|Article/Comp, data=EffectSizes_short2)
summary(robust(m_fo, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:3):
## F(df1 = 2, df2 = 22) = 0.4055, p-val = 0.6715
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.1829 0.1135 1.6113 22 0.1214 -0.0525 0.4182
## Pointing_Measures1 0.0189 0.0656 0.2874 22 0.7765 -0.1172 0.1549
## vi 2.1323 2.3753 0.8977 22 0.3791 -2.7938 7.0584
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(robust(m_fo, cluster=EffectSizes_short2$Article), btt=2)##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 22) = 0.0826, p-val = 0.7765predict(robust(m_fo, cluster=EffectSizes_short2$Article), c(1, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1990 0.0212 0.3647 -0.2805 0.5991predict(robust(m_fo, cluster=EffectSizes_short2$Article), c(-1, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1626 -0.1736 0.4648 -0.3834 0.6243contrasts(EffectSizes_short2$Pointing_Measures)## [,1]
## frequency 1
## onset -1EffectSizes_short2 %>%
group_by(Pointing_Measures) %>%
summarise(
N_part = sum(n),
K = n(),
S = sum(as.integer(Pointing_Measures)),
mean(yi)
)# I got strange predicted values when I allowed the interaction between variance and condition, so I removed the interaction. 2.8 Design: Concurrent/Longitudinal
m_design <- rma.mv(yi, vi, mods= ~ Designs*vi, random=~1|Article/Comp, data=EffectSizes_short2)
summary(robust(m_design, cluster=EffectSizes_short2$Article))##
## Number of outcomes: 77
## Number of clusters: 25
## Outcomes per cluster: 1-8 (mean: 3.08, median: 2)
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 21) = 2.5581, p-val = 0.0825
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.2084 0.0890 2.3412 21 0.0292 0.0233 0.3935 *
## Designs1 0.0198 0.0159 1.2454 21 0.2267 -0.0133 0.0528
## vi 1.9085 2.0413 0.9349 21 0.3605 -2.3367 6.1537
## Designs1:vi -0.0697 1.1315 -0.0616 21 0.9515 -2.4227 2.2834
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1table(EffectSizes_short2$Designs)##
## Concurrent Longitudinal
## 24 53anova(robust(m_design, cluster=EffectSizes_short2$Article), btt=2)##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 21) = 1.5511, p-val = 0.2267predict(robust(m_design, cluster=EffectSizes_short2$Article), c(1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.2243 0.0209 0.4099 -0.2601 0.6185predict(robust(m_design, cluster=EffectSizes_short2$Article), c(-1, 0, 0), transf=transf.ztor)##
## pred ci.lb ci.ub pi.lb pi.ub
## 0.1864 0.0221 0.3409 -0.2823 0.5833contrasts(EffectSizes_short2$Designs)## [,1]
## Concurrent 1
## Longitudinal -1EffectSizes_short2 %>%
group_by(Designs) %>%
summarise(
N_part = sum(n),
K = n()
)sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Dutch_Netherlands.1252 LC_CTYPE=Dutch_Netherlands.1252
## [3] LC_MONETARY=Dutch_Netherlands.1252 LC_NUMERIC=C
## [5] LC_TIME=Dutch_Netherlands.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
## [5] tidyr_1.1.3 tibble_3.1.3 ggplot2_3.3.5 tidyverse_1.3.1
## [9] metafor_3.0-2 Matrix_1.3-3 readr_2.0.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 lubridate_1.7.10 lattice_0.20-44 assertthat_0.2.1
## [5] digest_0.6.27 utf8_1.2.2 R6_2.5.0 cellranger_1.1.0
## [9] backports_1.2.1 reprex_2.0.1 evaluate_0.14 highr_0.9
## [13] httr_1.4.2 pillar_1.6.2 rlang_0.4.11 readxl_1.3.1
## [17] rstudioapi_0.13 jquerylib_0.1.4 rmarkdown_2.9 mathjaxr_1.4-0
## [21] labeling_0.4.2 bit_4.0.4 munsell_0.5.0 broom_0.7.9
## [25] compiler_4.1.0 modelr_0.1.8 xfun_0.24 pkgconfig_2.0.3
## [29] htmltools_0.5.1.1 tidyselect_1.1.1 fansi_0.5.0 crayon_1.4.1
## [33] tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2 grid_4.1.0
## [37] nlme_3.1-152 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0
## [41] DBI_1.1.1 magrittr_2.0.1 scales_1.1.1 cli_3.0.1
## [45] stringi_1.7.3 vroom_1.5.3 farver_2.1.0 fs_1.5.0
## [49] xml2_1.3.2 bslib_0.2.5.1 ellipsis_0.3.2 generics_0.1.0
## [53] vctrs_0.3.8 tools_4.1.0 bit64_4.0.5 glue_1.4.2
## [57] hms_1.1.0 parallel_4.1.0 yaml_2.2.1 colorspace_2.0-2
## [61] rvest_1.0.1 knitr_1.33 haven_2.4.3 sass_0.4.0