Meta-analysis Script
Saurabh Khanna
2020-06-14
- Reading in data
- Calculate effect sizes
- Synthesizing effect sizes
- Delayed Tests Analysis
- Moderator Analysis
- Vocabulary - overall
- Vocabulary - overall (Controlling for type)
- Vocabulary - VR
- Vocabulary - VS
- Listening Comprehension - overall
- Listening Comprehension - overall (Controlling for type)
- Listening Comprehension - LR
- Listening Comprehension - LS
- Reading Comprehension - overall
- Reading Comprehension - overall (Controlling for type)
- Reading Comprehension - RR
- Reading Comprehension - RS
- EL vs EO
- Publication Bias
- Risk of bias analysis
- Sensitivity analyses
- MetaForest plots
# Libraries
library(tidyverse)
library(readxl)
library(metafor)
library(MAd)
library(metaforest)
library(dmetar)
library(DT)
library(robumeta)
# Parameters
data_file <- here::here("data", "L&L Data Set Means SDs.xlsx")Reading in data
# join checks
read_xlsx(data_file, sheet = "VR") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 1 x 1
## AUTYR
## <chr>
## 1 Nelson11FUread_xlsx(data_file, sheet = "VS") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 1 x 1
## AUTYR
## <chr>
## 1 Nelson11FUread_xlsx(data_file, sheet = "LR") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 0 x 1
## # ... with 1 variable: AUTYR <chr>read_xlsx(data_file, sheet = "LS") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 0 x 1
## # ... with 1 variable: AUTYR <chr>read_xlsx(data_file, sheet = "RR") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 0 x 1
## # ... with 1 variable: AUTYR <chr>read_xlsx(data_file, sheet = "RS") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 0 x 1
## # ... with 1 variable: AUTYR <chr>read_xlsx(data_file, sheet = "DS") %>%
select(AUTYR) %>%
drop_na(AUTYR) %>%
anti_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
select(AUTYR) %>%
drop_na(AUTYR),
by = "AUTYR"
)## # A tibble: 0 x 1
## # ... with 1 variable: AUTYR <chr>All good!
Calculate effect sizes
Post only
df_post <-
bind_rows(
"VR" = read_xlsx(data_file, sheet = "VR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VR", "")),
"VS" = read_xlsx(data_file, sheet = "VS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VS", "")),
"RR" = read_xlsx(data_file, sheet = "RR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RR", "")),
"RS" = read_xlsx(data_file, sheet = "RS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RS", "")),
"LR" = read_xlsx(data_file, sheet = "LR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LR", "")),
"LS" = read_xlsx(data_file, sheet = "LS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LS", "")),
"MR" = read_xlsx(data_file, sheet = "MR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MR", "")),
#"MS" = read_xlsx(data_file, sheet = "MS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MS", "")),
#"SR" = read_xlsx(data_file, sheet = "SR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SR", "")),
"SS" = read_xlsx(data_file, sheet = "SS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SS", "")),
"AS" = read_xlsx(data_file, sheet = "AS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "AS", "")),
"DS" = read_xlsx(data_file, sheet = "DS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "DS", "")),
.id = "type"
) %>%
drop_na(AUTYR) %>%
filter(is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
select(AUTYR, type, sort(current_vars()))
for (m in 1:5) {
if (
!(str_glue("TM{m}post") %in% colnames(df_post)) |
!(str_glue("CM{m}post") %in% colnames(df_post))
) {
next
}
df_post <-
escalc(
data = df_post,
measure = "SMD",
m1i = df_post[, str_c("TM", m, "post")] %>% unlist(),
m2i = df_post[, str_c("CM", m, "post")] %>% unlist(),
sd1i = df_post[, str_c("TS", m, "post")] %>% unlist(),
sd2i = df_post[, str_c("CS", m, "post")] %>% unlist(),
n1i = df_post[, str_c("TN", m, "post")] %>% unlist(),
n2i = df_post[, str_c("CN", m, "post")] %>% unlist(),
var.names = c(str_glue("ES_{m}"), str_glue("EV_{m}"))
)
}
df_post <-
df_post %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_post %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)Pre and Post
df_prepost <-
bind_rows(
"VR" = read_xlsx(data_file, sheet = "VR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VR", "")),
"VS" = read_xlsx(data_file, sheet = "VS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VS", "")),
"RR" = read_xlsx(data_file, sheet = "RR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RR", "")),
"RS" = read_xlsx(data_file, sheet = "RS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RS", "")),
"LR" = read_xlsx(data_file, sheet = "LR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LR", "")),
"LS" = read_xlsx(data_file, sheet = "LS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LS", "")),
"MR" = read_xlsx(data_file, sheet = "MR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MR", "")),
# "MS" = read_xlsx(data_file, sheet = "MS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MS", "")),
# "SR" = read_xlsx(data_file, sheet = "SR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SR", "")),
"SS" = read_xlsx(data_file, sheet = "SS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SS", "")),
"AS" = read_xlsx(data_file, sheet = "AS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "AS", "")),
"DS" = read_xlsx(data_file, sheet = "DS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "DS", "")),
.id = "type"
) %>%
drop_na(AUTYR) %>%
filter(!is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
select(AUTYR, type, sort(current_vars())) %>%
mutate(
TN1post = if_else(is.na(TN1post) & !is.na(TN1pre), TN1pre, TN1post),
TN2post = if_else(is.na(TN2post) & !is.na(TN2pre), TN2pre, TN2post),
CN1post = if_else(is.na(CN1post) & !is.na(CN1pre), CN1pre, CN1post),
CN2post = if_else(is.na(CN2post) & !is.na(CN2pre), CN2pre, CN2post)
)
# treatment (post-pre)
for (mt in 1:4) {
if (
!(str_glue("TM{mt}post") %in% colnames(df_prepost)) |
!(str_glue("TM{mt}pre") %in% colnames(df_prepost))
) {
next
}
df_prepost <-
escalc(
data = df_prepost,
measure = "SMCR",
m1i = df_prepost[, str_c("TM", mt, "post")] %>% unlist(),
m2i = df_prepost[, str_c("TM", mt, "pre")] %>% unlist(),
sd1i = df_prepost[, str_c("TS", mt, "pre")] %>% unlist(),
ni = df_prepost[, str_c("TN", mt, "post")] %>% unlist(),
ri = rep(0.5, 94),
var.names = c(str_glue("TES_TM{mt}"), str_glue("TEV_TM{mt}"))
)
}
# control (post-pre)
for (mc in 1:4) {
if (
!(str_glue("CM{mc}post") %in% colnames(df_prepost)) |
!(str_glue("CM{mc}pre") %in% colnames(df_prepost))
) {
next
}
df_prepost <-
escalc(
data = df_prepost,
measure = "SMCR",
m1i = df_prepost[, str_c("CM", mc, "post")] %>% unlist(),
m2i = df_prepost[, str_c("CM", mc, "pre")] %>% unlist(),
sd1i = df_prepost[, str_c("CS", mc, "pre")] %>% unlist(),
ni = df_prepost[, str_c("CN", mc, "post")] %>% unlist(),
ri = rep(0.5, 94),
var.names = c(str_glue("CES_CM{mc}"), str_glue("CEV_CM{mc}"))
)
}
# ES and EV taken together
for (m in 1:4) {
if (
!(str_glue("TES_TM{m}") %in% colnames(df_prepost)) |
!(str_glue("TEV_TM{m}") %in% colnames(df_prepost)) |
!(str_glue("CES_CM{m}") %in% colnames(df_prepost)) |
!(str_glue("CEV_CM{m}") %in% colnames(df_prepost))
) {
next
}
# subtracting effect size
df_prepost[, str_c("ES_", m)] <-
(df_prepost[, str_c("TES_TM", m)] %>% unlist()) -
(df_prepost[, str_c("CES_CM", m)] %>% unlist())
# adding variance
df_prepost[, str_c("EV_", m)] <-
(df_prepost[, str_c("TEV_TM", m)] %>% unlist()) +
(df_prepost[, str_c("CEV_CM", m)] %>% unlist())
}
df_prepost <-
df_prepost %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_prepost %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)Directly entered ES
df_es_direct <-
tribble(
~AUTYR, ~type, ~ES, ~EV,
# "Apthorp12_K", "VR", 0.98, 0,
# "Apthorp12_1", "VR", 1.00, 0,
# "Apthorp12_3", "VR", 0.95, 0,
# "Apthorp12_4", "VR", 1.24, 0,
# "Apthorp12_P", "VS", 0.06, 0,
# "Apthorp12_I", "VS", -0.14, 0,
"Gersten10", "VS", 0.33, 0.05,
"Jayanthi18", "VS", -0.043, 0.001,
# "Apthorp12_K", "LR", 0.24, 0,
# "Apthorp12_1", "LR", 0.21, 0,
#
# "Apthorp12_P", "LS", 0.05, 0,
#
# "Apthorp12_3", "RR", 0.09, 0,
# "Apthorp12_4", "RR", 0.44, 0,
#
# "Apthorp12_I", "RS", -0.11, 0,
"Gersten10", "RS", 0.13, 0.04
)Combining all
df_append <-
bind_rows(df_post, df_prepost, df_es_direct) %>%
left_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
drop_na(AUTYR) %>%
fill(Performance, Detection, Attrition, Reporting),
by = "AUTYR"
) %>%
mutate(
LowIncome = recode(LowIncome, "0" = "0", "1" = "1", .default = NA_character_) %>% as.integer(),
Hours = Hours %>% parse_number(),
CONT = recode(CONT, "BAU" = "0", "ALT" = "1") %>% as.integer()
) %>%
arrange(type, stdid) %>%
select(type, stdid, AUTYR, everything())
cor_es <-
df_append %>%
unite("type_stdid", c("type", "stdid")) %>%
agg(id = type_stdid, es = ES, var = EV, method = "BHHR", data = .) %>%
separate(id, c("type", "stdid")) %>%
rename(ES = es, EV = var)
df_clean <-
df_append %>%
group_by(type, stdid) %>%
summarize_at(vars(Content:Hours), ~ round(mean(.))) %>%
ungroup() %>%
left_join(cor_es, by = c("type", "stdid")) %>%
select(type, stdid, ES, EV, everything()) %>%
mutate(
design = case_when(
RCT == 1 ~ "RCT",
QED == 1 ~ "QED",
WSD == 1 ~ "WSD",
TRUE ~ NA_character_
) %>% as_factor(),
grade = case_when(
(GradeK + Grade1 + Grade2) > 0 & (Grade3 + Grade4 + Grade5) == 0 ~ "K-2",
(Grade3 + Grade4 + Grade5) > 0 & (GradeK + Grade1 + Grade2) == 0 ~ "3-5",
TRUE ~ "Both"
) %>% as_factor(),
grouping = case_when(
(WholeCl == 1) & (SmallGr == 0) & (Indiv == 0) ~ 1,
TRUE ~ 0
),
CONT = CONT %>% factor(labels = c("BAU", "ALT")),
TCOM = if_else(TLC == 1 | TRC == 1, 1, 0)
) %>%
select(-c(RCT, QED, WSD, WholeCl, SmallGr, Indiv), -starts_with("Grade", ignore.case = F)) %>%
left_join(
read_xlsx(data_file, sheet = "citations"),
by = c("type", "stdid")
) %>%
mutate(
type = type %>% as_factor(),
citation = if_else(str_detect(type, "S$"), str_c(citation, " "), citation)
)
rm(df_post, df_prepost, df_es_direct)
df_clean %>% summary()## type stdid ES EV
## VR :44 Length:119 Min. :-0.89878 Min. :0.00100
## VS :24 Class :character 1st Qu.: 0.06143 1st Qu.:0.01204
## RS :17 Mode :character Median : 0.26546 Median :0.02606
## DS :10 Mean : 0.59503 Mean :0.05630
## LR : 6 3rd Qu.: 0.82537 3rd Qu.:0.06699
## LS : 6 Max. : 7.00093 Max. :0.51430
## (Other):12
## Content PD LowIncome EL
## Min. :0.0000 Min. :0.00000 Min. :0.00 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00 1st Qu.:0.0000
## Median :0.0000 Median :0.00000 Median :1.00 Median :0.0000
## Mean :0.2941 Mean :0.05042 Mean :0.69 Mean :0.3193
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.00 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.00000 Max. :1.00 Max. :1.0000
## NA's :19
## TMULT TVOC TSYN TMOR
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.3445 Mean :0.9244 Mean :0.2017 Mean :0.3782
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## TCOG TLC TRC TPAD
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1345 Mean :0.3697 Mean :0.2017 Mean :0.1429
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## TDD TTEC TSTR TWRT CONT
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000 BAU:87
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 ALT:32
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.1933 Mean :0.2941 Mean :0.1345 Mean :0.05882
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
##
## Duration Hours design grade grouping
## Min. :0.0000 Min. : 1.00 RCT:84 3-5 :46 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.: 13.00 QED:32 K-2 :70 1st Qu.:0.0000
## Median :1.0000 Median : 22.00 WSD: 3 Both: 3 Median :0.0000
## Mean :0.6471 Mean : 29.83 Mean :0.3193
## 3rd Qu.:1.0000 3rd Qu.: 35.75 3rd Qu.:1.0000
## Max. :1.0000 Max. :100.00 Max. :1.0000
## NA's :3
## TCOM citation
## Min. :0.0000 Length:119
## 1st Qu.:0.0000 Class :character
## Median :0.0000 Mode :character
## Mean :0.4202
## 3rd Qu.:1.0000
## Max. :1.0000
## df_clean %>% datatable()Summary stats
df_clean %>% count(stdid)## # A tibble: 58 x 2
## stdid n
## <chr> <int>
## 1 Apel1 3
## 2 Apel2 3
## 3 ApelK 2
## 4 Apth1 1
## 5 Apth3 1
## 6 ApthK 1
## 7 ApthP 2
## 8 Arth1 1
## 9 Arth2 1
## 10 Arth3 1
## # ... with 48 more rows#df_clean %>% arrange(type, ES) %>% writexl::write_xlsx("df_clean.xlsx")
#df_clean %>% select(stdid, type) %>% arrange(stdid, type) %>% writexl::write_xlsx("temp.xlsx")
df_clean %>% count(type, stdid)## # A tibble: 119 x 3
## type stdid n
## <fct> <chr> <int>
## 1 AS Jones1 1
## 2 AS Jones2 1
## 3 AS Proc19 1
## 4 DS Apel1 1
## 5 DS Apel2 1
## 6 DS ApelK 1
## 7 DS Conn183 1
## 8 DS Conn184 1
## 9 DS Fillippini 1
## 10 DS Morris 1
## # ... with 109 more rowsdf_v <- df_clean %>% filter(type %in% c("VR", "VS"))
df_l <- df_clean %>% filter(type %in% c("LR", "LS"))
df_r <- df_clean %>% filter(type %in% c("RR", "RS"))
df_m <- df_clean %>% filter(type %in% c("MR", "MS"))
df_s <- df_clean %>% filter(type %in% c("SR", "SS"))
df_a <- df_clean %>% filter(type == "AS")
df_d <- df_clean %>% filter(type == "DS")
# changes for rebecca
# df_v %>% filter(stdid %in% c("Coyne10", "Coyne19", "Puhal", "Pullen"), type == "VR") %>% rma(
# yi = ES,
# vi = EV,
# data = .,
# method = "REML",
# slab = citation
# )Synthesizing effect sizes
Vocabulary
model_1 <-
df_v %>%
arrange(desc(type), desc(ES)) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
)
model_2 <-
df_v %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
subset = (type == "VR"),
slab = stdid
)
model_3 <-
df_v %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
subset = (type == "VS"),
slab = stdid
)
model_1##
## Random-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.2617 (SE = 0.2273)
## tau (square root of estimated tau^2 value): 1.1233
## I^2 (total heterogeneity / total variability): 99.28%
## H^2 (total variability / sampling variability): 138.33
##
## Test for Heterogeneity:
## Q(df = 67) = 2339.9370, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.8504 0.1392 6.1083 <.0001 0.5775 1.1233 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_2##
## Random-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.5141 (SE = 0.3407)
## tau (square root of estimated tau^2 value): 1.2305
## I^2 (total heterogeneity / total variability): 99.33%
## H^2 (total variability / sampling variability): 148.48
##
## Test for Heterogeneity:
## Q(df = 43) = 1578.6988, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.2728 0.1896 6.7116 <.0001 0.9011 1.6445 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_3##
## Random-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0406
## I^2 (total heterogeneity / total variability): 15.87%
## H^2 (total variability / sampling variability): 1.19
##
## Test for Heterogeneity:
## Q(df = 23) = 31.8193, p-val = 0.1040
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0313 0.0228 1.3730 0.1698 -0.0134 0.0759
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_v %>% count(type)## # A tibble: 2 x 2
## type n
## <fct> <int>
## 1 VR 44
## 2 VS 24#boxplot(df_v$ES, plot=FALSE)$outforest(
model_1,
xlab = "Vocabulary",
addcred = T,
header = T,
xlim = c(-15, 18),
ylim = c(-1, 85),
rows = c(3:26, 34:77),
pch = 21,
bg = "grey",
lwd = 1.5
)
op <- par(cex = 0.75, font = 4)
text(-15, c(29, 80), pos = 4, c("Standardized Measure", "Custom Measure"))
addpoly(model_2, row = 32, cex = 1.25, col = "white", lwd = 3)
addpoly(model_3, row = 1.5, cex = 1.25, col = "white", lwd = 3)
Listening Comprehension
# Combined
model_1 <-
df_l %>%
arrange(desc(type), desc(ES)) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
)
# Custom (all distal)
model_2 <-
df_l %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
subset = (type == "LR"),
slab = stdid
)
# Standardized
model_3 <-
df_l %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
subset = (type == "LS"),
slab = stdid
)
model_1##
## Random-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0633
## I^2 (total heterogeneity / total variability): 21.15%
## H^2 (total variability / sampling variability): 1.27
##
## Test for Heterogeneity:
## Q(df = 11) = 12.6457, p-val = 0.3171
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0968 0.0417 2.3245 0.0201 0.0152 0.1785 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_2##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_3##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_l %>% count(type)## # A tibble: 2 x 2
## type n
## <fct> <int>
## 1 LR 6
## 2 LS 6forest(
model_1,
xlab = "Listening Comprehension",
addcred = T,
header = T,
ylim = c(-1, 26),
rows = c(3:8, 15:20),
pch = 21,
bg = "grey",
lwd = 1.5
)
op <- par(cex = 0.75, font = 4)
text(-2.5, c(10, 22), pos = 4, c("Standardized Measure", "Custom Measure"))
addpoly(model_2, row = 13, cex = 1.25, col = "white", lwd = 3)
addpoly(model_3, row = 1.5, cex = 1.25, col = "white", lwd = 3)
Reading Comprehension
model_1 <-
df_r %>%
arrange(desc(type), desc(ES)) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
)
# Custom (all)
model_2 <-
df_r %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
subset = (type == "RR"),
slab = stdid
)
# Custom (proximal)
model_3 <-
df_r %>%
filter(type == "RR", stdid != "Silver17a4") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)
# Custom (distal)
model_4 <-
df_r %>%
filter(type == "RR", stdid == "Silver17a4") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)
# Standardized
model_5 <-
df_r %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
subset = (type == "RS"),
slab = stdid
)
model_1 #overall##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0925 (SE = 0.0378)
## tau (square root of estimated tau^2 value): 0.3041
## I^2 (total heterogeneity / total variability): 90.52%
## H^2 (total variability / sampling variability): 10.55
##
## Test for Heterogeneity:
## Q(df = 19) = 102.0868, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1887 0.0770 2.4498 0.0143 0.0377 0.3397 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_2 #custom##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_3 #proximal custom##
## Random-Effects Model (k = 2; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.4043 (SE = 0.6130)
## tau (square root of estimated tau^2 value): 0.6359
## I^2 (total heterogeneity / total variability): 93.27%
## H^2 (total variability / sampling variability): 14.86
##
## Test for Heterogeneity:
## Q(df = 1) = 14.8551, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6030 0.4653 1.2958 0.1950 -0.3090 1.5150
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_4 #distal custom##
## Fixed-Effects Model (k = 1)
##
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.8508 0.1245 6.8366 <.0001 0.6069 1.0948 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_5 #standardized##
## Random-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0164 (SE = 0.0109)
## tau (square root of estimated tau^2 value): 0.1279
## I^2 (total heterogeneity / total variability): 64.68%
## H^2 (total variability / sampling variability): 2.83
##
## Test for Heterogeneity:
## Q(df = 16) = 44.7441, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0839 0.0449 1.8672 0.0619 -0.0042 0.1719 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_r %>% count(type)## # A tibble: 2 x 2
## type n
## <fct> <int>
## 1 RR 3
## 2 RS 17forest(
model_1,
xlab = "Reading Comprehension",
addcred = T,
header = T,
ylim = c(-1, 35),
rows = c(3:19, 27:29),
pch = 21,
bg = "grey",
lwd = 1.5
)
op <- par(cex = 0.75, font = 4)
text(-3.6, c(21, 31), pos = 4, c("Standardized Measure", "Custom Measure"))
addpoly(model_2, row = 24, cex = 1.25, col = "white", lwd = 3)
addpoly(model_5, row = 1, cex = 1.25, col = "white", lwd = 3)
Morphology
df_m %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.4450 (SE = 0.4987)
## tau (square root of estimated tau^2 value): 0.6671
## I^2 (total heterogeneity / total variability): 77.35%
## H^2 (total variability / sampling variability): 4.41
##
## Test for Heterogeneity:
## Q(df = 3) = 10.4334, p-val = 0.0152
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.1371 0.3909 2.9092 0.0036 0.3710 1.9033 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Custom (distal)
df_m %>%
filter(stdid != "Brimo") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.6306 (SE = 0.7526)
## tau (square root of estimated tau^2 value): 0.7941
## I^2 (total heterogeneity / total variability): 86.06%
## H^2 (total variability / sampling variability): 7.17
##
## Test for Heterogeneity:
## Q(df = 2) = 9.9807, p-val = 0.0068
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.2856 0.5003 2.5696 0.0102 0.3050 2.2662 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Custom (proximal)
df_m %>%
filter(stdid == "Brimo") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)##
## Fixed-Effects Model (k = 1)
##
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.5860 0.6145 0.9538 0.3402 -0.6183 1.7904
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_m %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
) %>%
forest(
order = "obs",
xlab = "Morphology",
addcred = T,
header = T,
pch = 21,
bg = "grey",
lwd = 1.5
)
Syntax
df_s %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)##
## Random-Effects Model (k = 2; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0178)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 1) = 0.7719, p-val = 0.3796
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0115 0.0793 0.1452 0.8846 -0.1438 0.1668
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_s %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
) %>%
forest(
order = "obs",
xlab = "Syntax",
addcred = T,
header = T,
pch = 21,
bg = "grey",
lwd = 1.5
)
Academic Learning
df_a %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.0380
## I^2 (total heterogeneity / total variability): 30.44%
## H^2 (total variability / sampling variability): 1.44
##
## Test for Heterogeneity:
## Q(df = 2) = 2.2530, p-val = 0.3242
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0792 0.0392 2.0170 0.0437 0.0022 0.1561 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_a %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
) %>%
forest(
order = "obs",
xlab = "Academic Learning",
addcred = T,
header = T,
pch = 21,
bg = "grey",
lwd = 1.5
)
Decoding measures
df_d %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 10; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.1722 (SE = 0.1049)
## tau (square root of estimated tau^2 value): 0.4150
## I^2 (total heterogeneity / total variability): 82.93%
## H^2 (total variability / sampling variability): 5.86
##
## Test for Heterogeneity:
## Q(df = 9) = 42.3448, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0526 0.1496 0.3515 0.7252 -0.2407 0.3458
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_d %>%
left_join(read_xlsx(data_file, sheet = "citations") %>% distinct(stdid, citation), by = "stdid") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation.y
) %>%
forest(
order = "obs",
xlab = "Decoding Measures",
addcred = T,
header = T,
pch = 21,
bg = "grey",
xlim = c(-5, 4),
lwd = 1.5
)
Delayed Tests Analysis
Calculate ES
Delay only
df_delay <-
bind_rows(
"VR" = read_xlsx(data_file, sheet = "VR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VR", "")),
"VS" = read_xlsx(data_file, sheet = "VS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VS", "")),
"RR" = read_xlsx(data_file, sheet = "RR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RR", "")),
"RS" = read_xlsx(data_file, sheet = "RS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RS", "")),
"LR" = read_xlsx(data_file, sheet = "LR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LR", "")),
"LS" = read_xlsx(data_file, sheet = "LS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LS", "")),
"MR" = read_xlsx(data_file, sheet = "MR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MR", "")),
#"MS" = read_xlsx(data_file, sheet = "MS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MS", "")),
#"SR" = read_xlsx(data_file, sheet = "SR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SR", "")),
"SS" = read_xlsx(data_file, sheet = "SS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SS", "")),
"AS" = read_xlsx(data_file, sheet = "AS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "AS", "")),
.id = "type"
) %>%
drop_na(AUTYR) %>%
filter(!is.na(TM1delay) & is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
select(AUTYR, type, sort(current_vars()))
for (m in 1:4) {
if (
!(str_glue("TM{m}delay") %in% colnames(df_delay)) |
!(str_glue("CM{m}delay") %in% colnames(df_delay))
) {
next
}
df_delay <-
escalc(
data = df_delay,
measure = "SMD",
m1i = df_delay[, str_c("TM", m, "delay")] %>% unlist(),
m2i = df_delay[, str_c("CM", m, "delay")] %>% unlist(),
sd1i = df_delay[, str_c("TS", m, "delay")] %>% unlist(),
sd2i = df_delay[, str_c("CS", m, "delay")] %>% unlist(),
n1i = df_delay[, str_c("TN", m, "delay")] %>% unlist(),
n2i = df_delay[, str_c("CN", m, "delay")] %>% unlist(),
var.names = c(str_glue("ES_{m}"), str_glue("EV_{m}"))
)
}
df_delay <-
df_delay %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_delay %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)Pre-Delay
df_predelay <-
bind_rows(
"VR" = read_xlsx(data_file, sheet = "VR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VR", "")),
"VS" = read_xlsx(data_file, sheet = "VS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "VS", "")),
"RR" = read_xlsx(data_file, sheet = "RR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RR", "")),
"RS" = read_xlsx(data_file, sheet = "RS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "RS", "")),
"LR" = read_xlsx(data_file, sheet = "LR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LR", "")),
"LS" = read_xlsx(data_file, sheet = "LS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "LS", "")),
"MR" = read_xlsx(data_file, sheet = "MR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MR", "")),
#"MS" = read_xlsx(data_file, sheet = "MS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "MS", "")),
#"SR" = read_xlsx(data_file, sheet = "SR") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SR", "")),
"SS" = read_xlsx(data_file, sheet = "SS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "SS", "")),
"AS" = read_xlsx(data_file, sheet = "AS") %>% rename_at(vars(-AUTYR), ~ str_replace(., "AS", "")),
.id = "type"
) %>%
drop_na(AUTYR) %>%
filter(!is.na(TM1delay)) %>%
select_if(~ any(!is.na(.))) %>%
select(AUTYR, type, sort(current_vars()))
# treatment (delay-pre)
for (mt in 1:4) {
if (
!(str_glue("TM{mt}delay") %in% colnames(df_predelay)) |
!(str_glue("TM{mt}pre") %in% colnames(df_predelay))
) {
next
}
df_predelay <-
escalc(
data = df_predelay,
measure = "SMCR",
m1i = df_predelay[, str_c("TM", mt, "delay")] %>% unlist(),
m2i = df_predelay[, str_c("TM", mt, "pre")] %>% unlist(),
sd1i = df_predelay[, str_c("TS", mt, "pre")] %>% unlist(),
ni = df_predelay[, str_c("TN", mt, "delay")] %>% unlist(),
ri = rep(0.5, 13),
var.names = c(str_glue("TES_TM{mt}"), str_glue("TEV_TM{mt}"))
)
}
# control (delay-pre)
for (mc in 1:4) {
if (
!(str_glue("CM{mc}delay") %in% colnames(df_predelay)) |
!(str_glue("CM{mc}pre") %in% colnames(df_predelay))
) {
next
}
df_predelay <-
escalc(
data = df_predelay,
measure = "SMCR",
m1i = df_predelay[, str_c("CM", mc, "delay")] %>% unlist(),
m2i = df_predelay[, str_c("CM", mc, "pre")] %>% unlist(),
sd1i = df_predelay[, str_c("CS", mc, "pre")] %>% unlist(),
ni = df_predelay[, str_c("CN", mc, "delay")] %>% unlist(),
ri = rep(0.5, 13),
var.names = c(str_glue("CES_CM{mc}"), str_glue("CEV_CM{mc}"))
)
}
# ES and EV taken together
for (m in 1:4) {
if (
!(str_glue("TES_TM{m}") %in% colnames(df_predelay)) |
!(str_glue("TEV_TM{m}") %in% colnames(df_predelay)) |
!(str_glue("CES_CM{m}") %in% colnames(df_predelay)) |
!(str_glue("CEV_CM{m}") %in% colnames(df_predelay))
) {
next
}
# subtracting effect size
df_predelay[, str_c("ES_", m)] <-
(df_predelay[, str_c("TES_TM", m)] %>% unlist()) -
(df_predelay[, str_c("CES_CM", m)] %>% unlist())
# adding variance
df_predelay[, str_c("EV_", m)] <-
(df_predelay[, str_c("TEV_TM", m)] %>% unlist()) +
(df_predelay[, str_c("CEV_CM", m)] %>% unlist())
}
df_predelay <-
df_predelay %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_predelay %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)Combining ES
df_clean_delay <-
bind_rows(df_delay, df_predelay) %>%
left_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
drop_na(AUTYR),
by = "AUTYR"
) %>%
mutate(stdid = if_else(is.na(stdid), "Nelson11FU", stdid)) %>%
arrange(type, stdid) %>%
select(type, stdid, ES, EV) %>%
unite("type_stdid", c("type", "stdid")) %>%
agg(id = type_stdid, es = ES, var = EV, method = "BHHR", data = .) %>%
separate(id, c("type", "stdid")) %>%
rename(ES = es, EV = var) %>%
left_join(
read_xlsx(data_file, sheet = "citations"),
by = c("type", "stdid")
) %>%
mutate(
citation = if_else(str_detect(type, "S$"), str_c(citation, " "), citation)
)
df_clean_delay %>% summary()## type stdid ES EV
## Length:11 Length:11 Min. :0.002612 Min. :0.01543
## Class :character Class :character 1st Qu.:0.198064 1st Qu.:0.02676
## Mode :character Mode :character Median :0.338542 Median :0.03848
## Mean :0.766881 Mean :0.10418
## 3rd Qu.:1.057405 3rd Qu.:0.07843
## Max. :2.921499 Max. :0.64887
## citation
## Length:11
## Class :character
## Mode :character
##
##
## rm(df_delay, df_predelay)Forest plots - Vocabulary
model_1 <-
df_clean_delay %>%
filter(type %in% c("VR", "VS")) %>%
arrange(desc(type), desc(ES)) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
)
model_2 <-
df_clean_delay %>%
filter(type == "VR") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)
model_3 <-
df_clean_delay %>%
filter(type == "VS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = stdid
)
model_1##
## Random-Effects Model (k = 9; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.6038 (SE = 0.3442)
## tau (square root of estimated tau^2 value): 0.7770
## I^2 (total heterogeneity / total variability): 92.99%
## H^2 (total variability / sampling variability): 14.27
##
## Test for Heterogeneity:
## Q(df = 8) = 82.0792, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.7348 0.2779 2.6438 0.0082 0.1901 1.2795 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_2##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.8172 (SE = 0.5954)
## tau (square root of estimated tau^2 value): 0.9040
## I^2 (total heterogeneity / total variability): 92.05%
## H^2 (total variability / sampling variability): 12.59
##
## Test for Heterogeneity:
## Q(df = 5) = 47.8553, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.0512 0.3974 2.6451 0.0082 0.2723 1.8302 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_3##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0496 (SE = 0.0805)
## tau (square root of estimated tau^2 value): 0.2227
## I^2 (total heterogeneity / total variability): 63.83%
## H^2 (total variability / sampling variability): 2.76
##
## Test for Heterogeneity:
## Q(df = 2) = 4.8472, p-val = 0.0886
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1651 0.1631 1.0124 0.3113 -0.1546 0.4848
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(
model_1,
xlab = "Vocabulary - Delayed effects",
addcred = T,
header = T,
xlim = c(-9, 9),
ylim = c(-1, 23),
rows = c(3:5, 12:17),
pch = 21,
bg = "grey",
lwd = 1.5
)
op <- par(cex = 0.75, font = 4)
text(-9, c(7, 19), pos = 4, c("Standardized Measure", "Custom Measure"))
addpoly(model_2, row = 10, cex = 1.25, col = "white", lwd = 3)
addpoly(model_3, row = 1.5, cex = 1.25, col = "white", lwd = 3)
Forest plots - Reading Comprehension
df_clean_delay %>%
filter(type %in% c("RR", "RS")) %>%
arrange(desc(type), desc(ES)) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
)##
## Random-Effects Model (k = 2; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0329)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 1) = 0.0341, p-val = 0.8534
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3518 0.1016 3.4638 0.0005 0.1527 0.5508 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_clean_delay %>%
filter(type %in% c("RR", "RS")) %>%
arrange(desc(type), desc(ES)) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML",
slab = citation
) %>%
forest(
xlab = "Reading Comprehension - Delayed effects",
addcred = T,
header = T,
pch = 21,
bg = "grey",
lwd = 1.5
)
Moderator Analysis
Vocabulary - overall
rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9450 (SE = 0.1736)
## tau (square root of estimated tau^2 value): 0.9721
## I^2 (residual heterogeneity / unaccounted variability): 98.99%
## H^2 (unaccounted variability / sampling variability): 98.73
## R^2 (amount of heterogeneity accounted for): 25.10%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 1610.5180, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 21.2255, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0992 0.2021 0.4908 0.6236 -0.2970 0.4954
## typeVR 1.1640 0.2527 4.6071 <.0001 0.6688 1.6592 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ Content)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2743 (SE = 0.2313)
## tau (square root of estimated tau^2 value): 1.1288
## I^2 (residual heterogeneity / unaccounted variability): 99.26%
## H^2 (unaccounted variability / sampling variability): 134.32
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2331.1913, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5844, p-val = 0.4446
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7841 0.1647 4.7611 <.0001 0.4613 1.1069 ***
## Content 0.2385 0.3120 0.7645 0.4446 -0.3730 0.8501
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ PD)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2658 (SE = 0.2298)
## tau (square root of estimated tau^2 value): 1.1251
## I^2 (residual heterogeneity / unaccounted variability): 99.19%
## H^2 (unaccounted variability / sampling variability): 123.80
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2133.9554, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8414, p-val = 0.3590
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8780 0.1426 6.1555 <.0001 0.5985 1.1576 ***
## PD -0.6208 0.6768 -0.9173 0.3590 -1.9472 0.7056
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TMULT)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2175 (SE = 0.2213)
## tau (square root of estimated tau^2 value): 1.1034
## I^2 (residual heterogeneity / unaccounted variability): 99.25%
## H^2 (unaccounted variability / sampling variability): 133.16
## R^2 (amount of heterogeneity accounted for): 3.51%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2234.8060, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.1209, p-val = 0.0773
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6607 0.1737 3.8027 0.0001 0.3201 1.0012 ***
## TMULT 0.4982 0.2820 1.7666 0.0773 -0.0545 1.0510 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TVOC)##
## Random-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.2617 (SE = 0.2273)
## tau (square root of estimated tau^2 value): 1.1233
## I^2 (total heterogeneity / total variability): 99.28%
## H^2 (total variability / sampling variability): 138.33
##
## Test for Heterogeneity:
## Q(df = 67) = 2339.9370, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.8504 0.1392 6.1083 <.0001 0.5775 1.1233 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TSYN)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2302 (SE = 0.2235)
## tau (square root of estimated tau^2 value): 1.1092
## I^2 (residual heterogeneity / unaccounted variability): 99.26%
## H^2 (unaccounted variability / sampling variability): 135.93
## R^2 (amount of heterogeneity accounted for): 2.50%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2188.6887, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.2488, p-val = 0.1337
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7518 0.1523 4.9363 <.0001 0.4533 1.0503 ***
## TSYN 0.5318 0.3546 1.4996 0.1337 -0.1632 1.2267
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TMOR)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.1913 (SE = 0.2168)
## tau (square root of estimated tau^2 value): 1.0915
## I^2 (residual heterogeneity / unaccounted variability): 99.23%
## H^2 (unaccounted variability / sampling variability): 130.32
## R^2 (amount of heterogeneity accounted for): 5.58%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2234.9404, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.7133, p-val = 0.0299
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6505 0.1635 3.9791 <.0001 0.3301 0.9709 ***
## TMOR 0.6337 0.2919 2.1710 0.0299 0.0616 1.2058 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TLC)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2596 (SE = 0.2286)
## tau (square root of estimated tau^2 value): 1.1223
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 138.58
## R^2 (amount of heterogeneity accounted for): 0.17%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2263.1441, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0053, p-val = 0.3160
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7592 0.1662 4.5678 <.0001 0.4334 1.0849 ***
## TLC 0.3045 0.3037 1.0026 0.3160 -0.2907 0.8997
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TRC)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2539 (SE = 0.2278)
## tau (square root of estimated tau^2 value): 1.1198
## I^2 (residual heterogeneity / unaccounted variability): 99.26%
## H^2 (unaccounted variability / sampling variability): 135.10
## R^2 (amount of heterogeneity accounted for): 0.62%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2337.5229, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.4890, p-val = 0.2224
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9175 0.1493 6.1439 <.0001 0.6248 1.2102 ***
## TRC -0.4940 0.4049 -1.2203 0.2224 -1.2876 0.2995
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TCOM)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2735 (SE = 0.2311)
## tau (square root of estimated tau^2 value): 1.1285
## I^2 (residual heterogeneity / unaccounted variability): 99.27%
## H^2 (unaccounted variability / sampling variability): 137.53
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2274.7248, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3833, p-val = 0.5358
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7856 0.1749 4.4922 <.0001 0.4428 1.1283 ***
## TCOM 0.1803 0.2913 0.6191 0.5358 -0.3905 0.7512
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TPAD)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2456 (SE = 0.2262)
## tau (square root of estimated tau^2 value): 1.1161
## I^2 (residual heterogeneity / unaccounted variability): 99.27%
## H^2 (unaccounted variability / sampling variability): 137.71
## R^2 (amount of heterogeneity accounted for): 1.28%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2334.4529, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9135, p-val = 0.1666
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9249 0.1485 6.2266 <.0001 0.6338 1.2161 ***
## TPAD -0.5647 0.4082 -1.3833 0.1666 -1.3647 0.2354
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ LowIncome)##
## Mixed-Effects Model (k = 54; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4100 (SE = 0.2854)
## tau (square root of estimated tau^2 value): 1.1874
## I^2 (residual heterogeneity / unaccounted variability): 99.41%
## H^2 (unaccounted variability / sampling variability): 168.26
## R^2 (amount of heterogeneity accounted for): 6.04%
##
## Test for Residual Heterogeneity:
## QE(df = 52) = 2038.7842, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.1870, p-val = 0.0407
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.5017 0.3245 4.6277 <.0001 0.8657 2.1377 ***
## LowIncome -0.7699 0.3763 -2.0462 0.0407 -1.5073 -0.0325 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ EL)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2342 (SE = 0.2243)
## tau (square root of estimated tau^2 value): 1.1110
## I^2 (residual heterogeneity / unaccounted variability): 99.22%
## H^2 (unaccounted variability / sampling variability): 128.36
## R^2 (amount of heterogeneity accounted for): 2.18%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2300.8444, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.5989, p-val = 0.1069
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0213 0.1740 5.8699 <.0001 0.6803 1.3624 ***
## EL -0.4592 0.2848 -1.6121 0.1069 -1.0174 0.0991
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TDD)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2571 (SE = 0.2283)
## tau (square root of estimated tau^2 value): 1.1212
## I^2 (residual heterogeneity / unaccounted variability): 99.24%
## H^2 (unaccounted variability / sampling variability): 132.35
## R^2 (amount of heterogeneity accounted for): 0.36%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2321.3572, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.3252, p-val = 0.2497
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9253 0.1535 6.0285 <.0001 0.6245 1.2262 ***
## TDD -0.4162 0.3616 -1.1512 0.2497 -1.1249 0.2924
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TTEC)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2811 (SE = 0.2325)
## tau (square root of estimated tau^2 value): 1.1318
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 139.74
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2303.9109, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0736, p-val = 0.7862
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8240 0.1713 4.8104 <.0001 0.4883 1.1597 ***
## TTEC 0.0809 0.2983 0.2713 0.7862 -0.5038 0.6656
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TSTR)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2566 (SE = 0.2282)
## tau (square root of estimated tau^2 value): 1.1210
## I^2 (residual heterogeneity / unaccounted variability): 99.22%
## H^2 (unaccounted variability / sampling variability): 127.96
## R^2 (amount of heterogeneity accounted for): 0.40%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2247.8002, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.4612, p-val = 0.2267
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.7797 0.1507 5.1731 <.0001 0.4843 1.0751 ***
## TSTR 0.4702 0.3890 1.2088 0.2267 -0.2922 1.2326
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TWRT)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2827 (SE = 0.2327)
## tau (square root of estimated tau^2 value): 1.1326
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability): 141.49
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2316.1017, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0134, p-val = 0.9079
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8466 0.1447 5.8518 <.0001 0.5631 1.1302 ***
## TWRT 0.0687 0.5943 0.1156 0.9079 -1.0960 1.2334
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ CONT)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2830 (SE = 0.2328)
## tau (square root of estimated tau^2 value): 1.1327
## I^2 (residual heterogeneity / unaccounted variability): 99.27%
## H^2 (unaccounted variability / sampling variability): 137.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2201.2157, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0023, p-val = 0.9614
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8554 0.1707 5.0120 <.0001 0.5209 1.1899 ***
## CONTALT -0.0145 0.2999 -0.0484 0.9614 -0.6023 0.5733
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ Duration)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2827 (SE = 0.2327)
## tau (square root of estimated tau^2 value): 1.1326
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability): 140.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2288.6694, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0000, p-val = 0.9962
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8516 0.2305 3.6937 0.0002 0.3997 1.3034 ***
## Duration -0.0014 0.2906 -0.0047 0.9962 -0.5709 0.5681
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ Hours)##
## Mixed-Effects Model (k = 66; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.3081 (SE = 0.2410)
## tau (square root of estimated tau^2 value): 1.1437
## I^2 (residual heterogeneity / unaccounted variability): 99.18%
## H^2 (unaccounted variability / sampling variability): 122.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 64) = 2093.3441, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0650, p-val = 0.7988
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8272 0.2304 3.5897 0.0003 0.3756 1.2789 ***
## Hours 0.0017 0.0066 0.2550 0.7988 -0.0112 0.0146
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ design)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.1673 (SE = 0.2142)
## tau (square root of estimated tau^2 value): 1.0804
## I^2 (residual heterogeneity / unaccounted variability): 99.22%
## H^2 (unaccounted variability / sampling variability): 128.61
## R^2 (amount of heterogeneity accounted for): 7.48%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1893.1333, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.6456, p-val = 0.0361
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8561 0.1678 5.1032 <.0001 0.5273 1.1848 ***
## designQED -0.2380 0.2918 -0.8157 0.4147 -0.8100 0.3339
## designWSD 1.4979 0.6518 2.2979 0.0216 0.2203 2.7755 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ grade)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.2877 (SE = 0.2355)
## tau (square root of estimated tau^2 value): 1.1348
## I^2 (residual heterogeneity / unaccounted variability): 99.27%
## H^2 (unaccounted variability / sampling variability): 136.36
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 2258.9643, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.7894, p-val = 0.6739
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8990 0.2508 3.5841 0.0003 0.4074 1.3907 ***
## gradeK-2 -0.0922 0.3040 -0.3033 0.7617 -0.6879 0.5036
## gradeBoth 0.9013 1.1729 0.7685 0.4422 -1.3975 3.2002
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ grouping)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.1269 (SE = 0.2055)
## tau (square root of estimated tau^2 value): 1.0616
## I^2 (residual heterogeneity / unaccounted variability): 99.16%
## H^2 (unaccounted variability / sampling variability): 118.51
## R^2 (amount of heterogeneity accounted for): 10.69%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 2339.7215, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.0283, p-val = 0.0046
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5465 0.1695 3.2242 0.0013 0.2143 0.8787 **
## grouping 0.7645 0.2698 2.8334 0.0046 0.2357 1.2933 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Vocabulary - overall (Controlling for type)
rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ Content + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9515 (SE = 0.1761)
## tau (square root of estimated tau^2 value): 0.9754
## I^2 (residual heterogeneity / unaccounted variability): 98.96%
## H^2 (unaccounted variability / sampling variability): 95.73
## R^2 (amount of heterogeneity accounted for): 24.59%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1551.2785, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 21.9934, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0237 0.2179 0.1088 0.9133 -0.4033 0.4507
## Content 0.2578 0.2715 0.9494 0.3424 -0.2744 0.7899
## typeVR 1.1700 0.2536 4.6144 <.0001 0.6731 1.6670 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ PD + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9606 (SE = 0.1777)
## tau (square root of estimated tau^2 value): 0.9801
## I^2 (residual heterogeneity / unaccounted variability): 98.90%
## H^2 (unaccounted variability / sampling variability): 90.77
## R^2 (amount of heterogeneity accounted for): 23.87%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1607.2580, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 20.9905, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0773 0.2176 0.3550 0.7226 -0.3493 0.5039
## PD 0.1780 0.6190 0.2876 0.7737 -1.0352 1.3912
## typeVR 1.1863 0.2659 4.4618 <.0001 0.6652 1.7075 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TMULT + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9108 (SE = 0.1689)
## tau (square root of estimated tau^2 value): 0.9543
## I^2 (residual heterogeneity / unaccounted variability): 98.95%
## H^2 (unaccounted variability / sampling variability): 94.80
## R^2 (amount of heterogeneity accounted for): 27.82%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1471.6344, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 24.9239, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0429 0.2150 -0.1996 0.8418 -0.4644 0.3785
## TMULT 0.4240 0.2462 1.7218 0.0851 -0.0586 0.9065 .
## typeVR 1.1344 0.2488 4.5595 <.0001 0.6468 1.6220 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TVOC + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9450 (SE = 0.1736)
## tau (square root of estimated tau^2 value): 0.9721
## I^2 (residual heterogeneity / unaccounted variability): 98.99%
## H^2 (unaccounted variability / sampling variability): 98.73
## R^2 (amount of heterogeneity accounted for): 25.10%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 1610.5180, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 21.2255, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0992 0.2021 0.4908 0.6236 -0.2970 0.4954
## typeVR 1.1640 0.2527 4.6071 <.0001 0.6688 1.6592 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TSYN + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9304 (SE = 0.1723)
## tau (square root of estimated tau^2 value): 0.9646
## I^2 (residual heterogeneity / unaccounted variability): 98.98%
## H^2 (unaccounted variability / sampling variability): 97.94
## R^2 (amount of heterogeneity accounted for): 26.26%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1474.8382, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 23.0012, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0525 0.2043 0.2568 0.7973 -0.3480 0.4529
## TSYN 0.3803 0.3137 1.2123 0.2254 -0.2345 0.9951
## typeVR 1.1281 0.2525 4.4681 <.0001 0.6333 1.6230 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TMOR + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.8797 (SE = 0.1635)
## tau (square root of estimated tau^2 value): 0.9379
## I^2 (residual heterogeneity / unaccounted variability): 98.91%
## H^2 (unaccounted variability / sampling variability): 91.60
## R^2 (amount of heterogeneity accounted for): 30.28%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1470.1825, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 28.1941, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0768 0.2091 -0.3674 0.7133 -0.4867 0.3330
## TMOR 0.5934 0.2525 2.3504 0.0188 0.0986 1.0882 *
## typeVR 1.1452 0.2443 4.6882 <.0001 0.6664 1.6239 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TLC + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9252 (SE = 0.1714)
## tau (square root of estimated tau^2 value): 0.9619
## I^2 (residual heterogeneity / unaccounted variability): 98.97%
## H^2 (unaccounted variability / sampling variability): 96.87
## R^2 (amount of heterogeneity accounted for): 26.68%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1480.8369, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 23.6015, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0206 0.2176 -0.0948 0.9245 -0.4472 0.4059
## TLC 0.3680 0.2631 1.3989 0.1619 -0.1476 0.8837
## typeVR 1.1801 0.2504 4.7133 <.0001 0.6894 1.6709 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TRC + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9616 (SE = 0.1780)
## tau (square root of estimated tau^2 value): 0.9806
## I^2 (residual heterogeneity / unaccounted variability): 98.98%
## H^2 (unaccounted variability / sampling variability): 98.29
## R^2 (amount of heterogeneity accounted for): 23.79%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1580.5157, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 20.9406, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1204 0.2240 0.5376 0.5908 -0.3186 0.5595
## TRC -0.0837 0.3675 -0.2278 0.8198 -0.8039 0.6365
## typeVR 1.1492 0.2633 4.3651 <.0001 0.6332 1.6652 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TCOM + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9378 (SE = 0.1737)
## tau (square root of estimated tau^2 value): 0.9684
## I^2 (residual heterogeneity / unaccounted variability): 98.96%
## H^2 (unaccounted variability / sampling variability): 96.19
## R^2 (amount of heterogeneity accounted for): 25.67%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1483.4381, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 22.6218, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0168 0.2266 -0.0743 0.9408 -0.4610 0.4274
## TCOM 0.2823 0.2528 1.1164 0.2642 -0.2133 0.7778
## typeVR 1.1871 0.2526 4.6996 <.0001 0.6920 1.6822 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TPAD + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9387 (SE = 0.1739)
## tau (square root of estimated tau^2 value): 0.9689
## I^2 (residual heterogeneity / unaccounted variability): 98.99%
## H^2 (unaccounted variability / sampling variability): 98.82
## R^2 (amount of heterogeneity accounted for): 25.60%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1610.4114, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 22.8803, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1721 0.2100 0.8196 0.4125 -0.2395 0.5836
## TPAD -0.4415 0.3577 -1.2343 0.2171 -1.1427 0.2596
## typeVR 1.1412 0.2525 4.5195 <.0001 0.6463 1.6361 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ LowIncome + type)##
## Mixed-Effects Model (k = 54; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.0551 (SE = 0.2176)
## tau (square root of estimated tau^2 value): 1.0272
## I^2 (residual heterogeneity / unaccounted variability): 99.16%
## H^2 (unaccounted variability / sampling variability): 118.43
## R^2 (amount of heterogeneity accounted for): 29.69%
##
## Test for Residual Heterogeneity:
## QE(df = 51) = 1361.5232, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 22.4661, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6281 0.3520 1.7845 0.0743 -0.0617 1.3180 .
## LowIncome -0.6715 0.3282 -2.0457 0.0408 -1.3148 -0.0281 *
## typeVR 1.2317 0.2990 4.1195 <.0001 0.6457 1.8177 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ EL + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9509 (SE = 0.1760)
## tau (square root of estimated tau^2 value): 0.9751
## I^2 (residual heterogeneity / unaccounted variability): 98.95%
## H^2 (unaccounted variability / sampling variability): 95.48
## R^2 (amount of heterogeneity accounted for): 24.64%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1578.1614, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 21.9171, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2156 0.2404 0.8971 0.3697 -0.2555 0.6868
## EL -0.2312 0.2565 -0.9015 0.3673 -0.7339 0.2715
## typeVR 1.1175 0.2586 4.3211 <.0001 0.6106 1.6244 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TDD + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9286 (SE = 0.1721)
## tau (square root of estimated tau^2 value): 0.9636
## I^2 (residual heterogeneity / unaccounted variability): 98.93%
## H^2 (unaccounted variability / sampling variability): 93.40
## R^2 (amount of heterogeneity accounted for): 26.40%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1446.0999, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 23.6596, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1745 0.2071 0.8427 0.3994 -0.2314 0.5805
## TDD -0.4512 0.3122 -1.4451 0.1484 -1.0632 0.1608
## typeVR 1.1737 0.2506 4.6828 <.0001 0.6824 1.6649 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TTEC + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9596 (SE = 0.1776)
## tau (square root of estimated tau^2 value): 0.9796
## I^2 (residual heterogeneity / unaccounted variability): 99.00%
## H^2 (unaccounted variability / sampling variability): 99.65
## R^2 (amount of heterogeneity accounted for): 23.95%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1583.7454, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 21.0551, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0683 0.2215 0.3085 0.7577 -0.3657 0.5024
## TTEC 0.0923 0.2595 0.3556 0.7222 -0.4164 0.6009
## typeVR 1.1649 0.2545 4.5770 <.0001 0.6661 1.6637 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TSTR + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9283 (SE = 0.1721)
## tau (square root of estimated tau^2 value): 0.9635
## I^2 (residual heterogeneity / unaccounted variability): 98.91%
## H^2 (unaccounted variability / sampling variability): 91.61
## R^2 (amount of heterogeneity accounted for): 26.43%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1605.1423, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 24.0102, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0095 0.2085 0.0458 0.9635 -0.3991 0.4182
## TSTR 0.5242 0.3361 1.5597 0.1188 -0.1345 1.1829
## typeVR 1.1799 0.2507 4.7062 <.0001 0.6885 1.6714 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ TWRT + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9619 (SE = 0.1780)
## tau (square root of estimated tau^2 value): 0.9808
## I^2 (residual heterogeneity / unaccounted variability): 99.01%
## H^2 (unaccounted variability / sampling variability): 101.07
## R^2 (amount of heterogeneity accounted for): 23.77%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1590.0347, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 20.8928, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1016 0.2051 0.4952 0.6205 -0.3004 0.5035
## TWRT -0.0536 0.5182 -0.1034 0.9176 -1.0694 0.9621
## typeVR 1.1658 0.2552 4.5687 <.0001 0.6657 1.6659 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ CONT + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9608 (SE = 0.1778)
## tau (square root of estimated tau^2 value): 0.9802
## I^2 (residual heterogeneity / unaccounted variability): 98.98%
## H^2 (unaccounted variability / sampling variability): 97.84
## R^2 (amount of heterogeneity accounted for): 23.85%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1512.5971, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 20.9843, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1209 0.2176 0.5557 0.5784 -0.3056 0.5474
## CONTALT -0.0742 0.2615 -0.2836 0.7767 -0.5867 0.4384
## typeVR 1.1681 0.2550 4.5806 <.0001 0.6683 1.6679 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ Duration + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9490 (SE = 0.1757)
## tau (square root of estimated tau^2 value): 0.9742
## I^2 (residual heterogeneity / unaccounted variability): 98.99%
## H^2 (unaccounted variability / sampling variability): 98.80
## R^2 (amount of heterogeneity accounted for): 24.78%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1594.3164, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 21.9660, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0776 0.2811 -0.2761 0.7824 -0.6285 0.4733
## Duration 0.2332 0.2570 0.9076 0.3641 -0.2705 0.7370
## typeVR 1.2098 0.2581 4.6868 <.0001 0.7039 1.7157 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ Hours + type)##
## Mixed-Effects Model (k = 66; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9919 (SE = 0.1863)
## tau (square root of estimated tau^2 value): 0.9959
## I^2 (residual heterogeneity / unaccounted variability): 98.88%
## H^2 (unaccounted variability / sampling variability): 89.49
## R^2 (amount of heterogeneity accounted for): 22.97%
##
## Test for Residual Heterogeneity:
## QE(df = 63) = 1448.0226, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 19.6022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0013 0.2744 0.0049 0.9961 -0.5364 0.5391
## Hours 0.0032 0.0058 0.5590 0.5761 -0.0081 0.0146
## typeVR 1.1775 0.2666 4.4174 <.0001 0.6550 1.6999 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ design + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.8945 (SE = 0.1674)
## tau (square root of estimated tau^2 value): 0.9458
## I^2 (residual heterogeneity / unaccounted variability): 98.93%
## H^2 (unaccounted variability / sampling variability): 93.76
## R^2 (amount of heterogeneity accounted for): 29.10%
##
## Test for Residual Heterogeneity:
## QE(df = 64) = 1279.7340, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 27.3859, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1772 0.2139 0.8283 0.4075 -0.2421 0.5965
## designQED -0.2404 0.2575 -0.9335 0.3505 -0.7451 0.2643
## designWSD 1.0952 0.5801 1.8878 0.0591 -0.0419 2.2322 .
## typeVR 1.0803 0.2495 4.3300 <.0001 0.5913 1.5693 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ grade + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.9739 (SE = 0.1816)
## tau (square root of estimated tau^2 value): 0.9868
## I^2 (residual heterogeneity / unaccounted variability): 98.99%
## H^2 (unaccounted variability / sampling variability): 98.71
## R^2 (amount of heterogeneity accounted for): 22.82%
##
## Test for Residual Heterogeneity:
## QE(df = 64) = 1516.6008, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 20.9680, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1305 0.2773 0.4704 0.6380 -0.4131 0.6741
## gradeK-2 -0.0444 0.2658 -0.1669 0.8675 -0.5653 0.4766
## gradeBoth 0.5199 1.0269 0.5063 0.6127 -1.4928 2.5325
## typeVR 1.1500 0.2576 4.4643 <.0001 0.6451 1.6549 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", mods = ~ grouping + type)##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.8586 (SE = 0.1597)
## tau (square root of estimated tau^2 value): 0.9266
## I^2 (residual heterogeneity / unaccounted variability): 98.84%
## H^2 (unaccounted variability / sampling variability): 86.49
## R^2 (amount of heterogeneity accounted for): 31.95%
##
## Test for Residual Heterogeneity:
## QE(df = 65) = 1564.4234, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 29.5679, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0801 0.2056 -0.3896 0.6968 -0.4831 0.3229
## grouping 0.6064 0.2399 2.5275 0.0115 0.1362 1.0766 *
## typeVR 1.0683 0.2442 4.3753 <.0001 0.5898 1.5469 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Vocabulary - VR
rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ Content)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5269 (SE = 0.3476)
## tau (square root of estimated tau^2 value): 1.2357
## I^2 (residual heterogeneity / unaccounted variability): 99.27%
## H^2 (unaccounted variability / sampling variability): 137.76
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1486.7688, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8528, p-val = 0.3558
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1658 0.2230 5.2275 <.0001 0.7287 1.6029 ***
## Content 0.3957 0.4285 0.9235 0.3558 -0.4441 1.2354
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ PD)##
## Random-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.5141 (SE = 0.3407)
## tau (square root of estimated tau^2 value): 1.2305
## I^2 (total heterogeneity / total variability): 99.33%
## H^2 (total variability / sampling variability): 148.48
##
## Test for Heterogeneity:
## Q(df = 43) = 1578.6988, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.2728 0.1896 6.7116 <.0001 0.9011 1.6445 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TMULT)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4194 (SE = 0.3240)
## tau (square root of estimated tau^2 value): 1.1914
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 139.45
## R^2 (amount of heterogeneity accounted for): 6.25%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1309.1288, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.3726, p-val = 0.0663
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9930 0.2384 4.1655 <.0001 0.5258 1.4603 ***
## TMULT 0.6878 0.3745 1.8365 0.0663 -0.0462 1.4219 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TVOC)##
## Random-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.5141 (SE = 0.3407)
## tau (square root of estimated tau^2 value): 1.2305
## I^2 (total heterogeneity / total variability): 99.33%
## H^2 (total variability / sampling variability): 148.48
##
## Test for Heterogeneity:
## Q(df = 43) = 1578.6988, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.2728 0.1896 6.7116 <.0001 0.9011 1.6445 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TSYN)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5066 (SE = 0.3430)
## tau (square root of estimated tau^2 value): 1.2274
## I^2 (residual heterogeneity / unaccounted variability): 99.33%
## H^2 (unaccounted variability / sampling variability): 149.43
## R^2 (amount of heterogeneity accounted for): 0.49%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1387.4803, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9435, p-val = 0.3314
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1751 0.2142 5.4851 <.0001 0.7552 1.5950 ***
## TSYN 0.4436 0.4566 0.9714 0.3314 -0.4514 1.3386
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TMOR)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.3094 (SE = 0.3000)
## tau (square root of estimated tau^2 value): 1.1443
## I^2 (residual heterogeneity / unaccounted variability): 99.22%
## H^2 (unaccounted variability / sampling variability): 128.74
## R^2 (amount of heterogeneity accounted for): 13.51%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1297.0458, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.1994, p-val = 0.0073
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9419 0.2151 4.3783 <.0001 0.5202 1.3635 ***
## TMOR 1.0146 0.3781 2.6832 0.0073 0.2735 1.7557 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TLC)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4826 (SE = 0.3378)
## tau (square root of estimated tau^2 value): 1.2176
## I^2 (residual heterogeneity / unaccounted variability): 99.32%
## H^2 (unaccounted variability / sampling variability): 146.91
## R^2 (amount of heterogeneity accounted for): 2.08%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1340.6034, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6238, p-val = 0.2026
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1213 0.2221 5.0484 <.0001 0.6860 1.5566 ***
## TLC 0.5297 0.4157 1.2743 0.2026 -0.2850 1.3445
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TRC)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5461 (SE = 0.3521)
## tau (square root of estimated tau^2 value): 1.2434
## I^2 (residual heterogeneity / unaccounted variability): 99.32%
## H^2 (unaccounted variability / sampling variability): 146.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1544.2135, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2304, p-val = 0.6312
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.2986 0.1987 6.5347 <.0001 0.9091 1.6880 ***
## TRC -0.3588 0.7475 -0.4800 0.6312 -1.8238 1.1062
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TCOM)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5090 (SE = 0.3436)
## tau (square root of estimated tau^2 value): 1.2284
## I^2 (residual heterogeneity / unaccounted variability): 99.31%
## H^2 (unaccounted variability / sampling variability): 144.57
## R^2 (amount of heterogeneity accounted for): 0.34%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1382.8962, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9908, p-val = 0.3195
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1397 0.2318 4.9166 <.0001 0.6853 1.5940 ***
## TCOM 0.4000 0.4018 0.9954 0.3195 -0.3876 1.1875
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TPAD)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4917 (SE = 0.3399)
## tau (square root of estimated tau^2 value): 1.2214
## I^2 (residual heterogeneity / unaccounted variability): 99.33%
## H^2 (unaccounted variability / sampling variability): 148.92
## R^2 (amount of heterogeneity accounted for): 1.47%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1578.6983, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6845, p-val = 0.1943
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3602 0.2000 6.7996 <.0001 0.9682 1.7523 ***
## TPAD -0.7691 0.5926 -1.2979 0.1943 -1.9305 0.3923
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ LowIncome)##
## Mixed-Effects Model (k = 35; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.6388 (SE = 0.4163)
## tau (square root of estimated tau^2 value): 1.2802
## I^2 (residual heterogeneity / unaccounted variability): 99.41%
## H^2 (unaccounted variability / sampling variability): 169.64
## R^2 (amount of heterogeneity accounted for): 8.80%
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 1230.4220, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.9508, p-val = 0.0468
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 2.0888 0.4152 5.0308 <.0001 1.2750 2.9025 ***
## LowIncome -0.9730 0.4895 -1.9877 0.0468 -1.9325 -0.0136 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ EL)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5238 (SE = 0.3470)
## tau (square root of estimated tau^2 value): 1.2344
## I^2 (residual heterogeneity / unaccounted variability): 99.32%
## H^2 (unaccounted variability / sampling variability): 146.14
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1477.6432, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8623, p-val = 0.3531
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3886 0.2274 6.1071 <.0001 0.9430 1.8342 ***
## EL -0.3855 0.4151 -0.9286 0.3531 -1.1991 0.4281
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TDD)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4886 (SE = 0.3394)
## tau (square root of estimated tau^2 value): 1.2201
## I^2 (residual heterogeneity / unaccounted variability): 99.26%
## H^2 (unaccounted variability / sampling variability): 134.96
## R^2 (amount of heterogeneity accounted for): 1.68%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1383.4488, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7166, p-val = 0.1901
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3907 0.2086 6.6666 <.0001 0.9818 1.7995 ***
## TDD -0.6323 0.4826 -1.3102 0.1901 -1.5782 0.3136
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TTEC)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5471 (SE = 0.3521)
## tau (square root of estimated tau^2 value): 1.2438
## I^2 (residual heterogeneity / unaccounted variability): 99.33%
## H^2 (unaccounted variability / sampling variability): 150.05
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1537.5818, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1515, p-val = 0.6971
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.2214 0.2333 5.2345 <.0001 0.7641 1.6787 ***
## TTEC 0.1592 0.4089 0.3893 0.6971 -0.6423 0.9607
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TSTR)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4378 (SE = 0.3282)
## tau (square root of estimated tau^2 value): 1.1991
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability): 141.80
## R^2 (amount of heterogeneity accounted for): 5.04%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1501.0757, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.3562, p-val = 0.0670
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1359 0.1994 5.6975 <.0001 0.7451 1.5266 ***
## TSTR 0.9802 0.5351 1.8320 0.0670 -0.0685 2.0289 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ TWRT)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5536 (SE = 0.3535)
## tau (square root of estimated tau^2 value): 1.2465
## I^2 (residual heterogeneity / unaccounted variability): 99.35%
## H^2 (unaccounted variability / sampling variability): 153.68
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1547.6905, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0062, p-val = 0.9371
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.2774 0.1990 6.4201 <.0001 0.8874 1.6674 ***
## TWRT -0.0599 0.7591 -0.0790 0.9371 -1.5478 1.4279
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ CONT)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5511 (SE = 0.3529)
## tau (square root of estimated tau^2 value): 1.2454
## I^2 (residual heterogeneity / unaccounted variability): 99.30%
## H^2 (unaccounted variability / sampling variability): 143.87
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1448.4606, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0690, p-val = 0.7928
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3095 0.2364 5.5384 <.0001 0.8461 1.7730 ***
## CONTALT -0.1062 0.4046 -0.2626 0.7928 -0.8992 0.6867
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ Duration)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5162 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 1.2314
## I^2 (residual heterogeneity / unaccounted variability): 99.32%
## H^2 (unaccounted variability / sampling variability): 147.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1561.1366, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9624, p-val = 0.3266
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0641 0.2851 3.7319 0.0002 0.5052 1.6230 ***
## Duration 0.3748 0.3821 0.9810 0.3266 -0.3740 1.1236
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ Hours)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5499 (SE = 0.3528)
## tau (square root of estimated tau^2 value): 1.2449
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability): 139.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1406.3902, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1181, p-val = 0.7311
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.1948 0.2981 4.0088 <.0001 0.6107 1.7790 ***
## Hours 0.0030 0.0087 0.3437 0.7311 -0.0140 0.0200
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ design)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.4577 (SE = 0.3366)
## tau (square root of estimated tau^2 value): 1.2073
## I^2 (residual heterogeneity / unaccounted variability): 99.30%
## H^2 (unaccounted variability / sampling variability): 143.11
## R^2 (amount of heterogeneity accounted for): 3.72%
##
## Test for Residual Heterogeneity:
## QE(df = 41) = 1244.7195, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 3.4672, p-val = 0.1766
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3211 0.2367 5.5821 <.0001 0.8572 1.7849 ***
## designQED -0.3913 0.4095 -0.9556 0.3393 -1.1939 0.4113
## designWSD 1.0337 0.7413 1.3944 0.1632 -0.4192 2.4867
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ grade)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.5868 (SE = 0.3654)
## tau (square root of estimated tau^2 value): 1.2597
## I^2 (residual heterogeneity / unaccounted variability): 99.32%
## H^2 (unaccounted variability / sampling variability): 146.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 41) = 1444.9920, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.2239, p-val = 0.8941
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3220 0.3412 3.8743 0.0001 0.6532 1.9907 ***
## gradeK-2 -0.0910 0.4171 -0.2182 0.8272 -0.9086 0.7265
## gradeBoth 0.4784 1.3146 0.3639 0.7159 -2.0982 3.0550
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VR"), mods = ~ grouping)##
## Mixed-Effects Model (k = 44; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 1.3286 (SE = 0.3041)
## tau (square root of estimated tau^2 value): 1.1526
## I^2 (residual heterogeneity / unaccounted variability): 99.17%
## H^2 (unaccounted variability / sampling variability): 120.95
## R^2 (amount of heterogeneity accounted for): 12.25%
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 1479.2581, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.0361, p-val = 0.0140
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8735 0.2405 3.6321 0.0003 0.4021 1.3449 ***
## grouping 0.8795 0.3580 2.4569 0.0140 0.1779 1.5812 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Vocabulary - VS
rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ Content)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0426
## I^2 (residual heterogeneity / unaccounted variability): 16.86%
## H^2 (unaccounted variability / sampling variability): 1.20
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.3242, p-val = 0.0897
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1953, p-val = 0.6586
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0270 0.0259 1.0415 0.2977 -0.0238 0.0777
## Content 0.0254 0.0575 0.4419 0.6586 -0.0873 0.1382
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ PD)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0011
## I^2 (residual heterogeneity / unaccounted variability): 0.01%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 99.93%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 28.5592, p-val = 0.1579
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.2562, p-val = 0.0712
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0385 0.0217 1.7719 0.0764 -0.0041 0.0810 .
## PD -0.0685 0.0380 -1.8045 0.0712 -0.1430 0.0059 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TMULT)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0493
## I^2 (residual heterogeneity / unaccounted variability): 21.41%
## H^2 (unaccounted variability / sampling variability): 1.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.7488, p-val = 0.0818
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4384, p-val = 0.5079
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0436 0.0277 1.5742 0.1154 -0.0107 0.0978
## TMULT -0.0384 0.0579 -0.6621 0.5079 -0.1519 0.0752
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TVOC)##
## Random-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0406
## I^2 (total heterogeneity / total variability): 15.87%
## H^2 (total variability / sampling variability): 1.19
##
## Test for Heterogeneity:
## Q(df = 23) = 31.8193, p-val = 0.1040
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0313 0.0228 1.3730 0.1698 -0.0134 0.0759
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TSYN)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0401
## I^2 (residual heterogeneity / unaccounted variability): 15.88%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 2.11%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.1752, p-val = 0.0926
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4470, p-val = 0.5038
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0278 0.0232 1.1983 0.2308 -0.0177 0.0733
## TSYN 0.0739 0.1105 0.6686 0.5038 -0.1426 0.2904
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TMOR)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0518
## I^2 (residual heterogeneity / unaccounted variability): 23.09%
## H^2 (unaccounted variability / sampling variability): 1.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.5781, p-val = 0.0849
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8643, p-val = 0.3526
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0482 0.0281 1.7122 0.0869 -0.0070 0.1033 .
## TMOR -0.0550 0.0591 -0.9297 0.3526 -0.1708 0.0609
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TLC)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0323
## I^2 (residual heterogeneity / unaccounted variability): 10.66%
## H^2 (unaccounted variability / sampling variability): 1.12
## R^2 (amount of heterogeneity accounted for): 36.68%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 29.3875, p-val = 0.1341
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7767, p-val = 0.1826
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0161 0.0230 0.7018 0.4828 -0.0289 0.0612
## TLC 0.0824 0.0618 1.3329 0.1826 -0.0388 0.2036
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TRC)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0203
## I^2 (residual heterogeneity / unaccounted variability): 4.25%
## H^2 (unaccounted variability / sampling variability): 1.04
## R^2 (amount of heterogeneity accounted for): 75.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 27.3143, p-val = 0.1995
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.8447, p-val = 0.0499
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0022 0.0219 0.1011 0.9195 -0.0406 0.0450
## TRC 0.0938 0.0478 1.9608 0.0499 0.0000 0.1876 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TCOM)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0192
## I^2 (residual heterogeneity / unaccounted variability): 3.82%
## H^2 (unaccounted variability / sampling variability): 1.04
## R^2 (amount of heterogeneity accounted for): 77.65%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 27.5094, p-val = 0.1925
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.6907, p-val = 0.0547
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0006 0.0221 0.0280 0.9776 -0.0427 0.0439
## TCOM 0.0871 0.0454 1.9211 0.0547 -0.0018 0.1760 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TPAD)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0442
## I^2 (residual heterogeneity / unaccounted variability): 18.57%
## H^2 (unaccounted variability / sampling variability): 1.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.6601, p-val = 0.0834
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3156, p-val = 0.5743
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0363 0.0242 1.4987 0.1340 -0.0112 0.0838
## TPAD -0.0523 0.0931 -0.5617 0.5743 -0.2348 0.1302
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ LowIncome)##
## Mixed-Effects Model (k = 19; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 12.6768, p-val = 0.7575
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.6493, p-val = 0.0561
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0882 0.0450 1.9581 0.0502 -0.0001 0.1765 .
## LowIncome -0.0946 0.0495 -1.9103 0.0561 -0.1916 0.0025 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ EL)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0424
## I^2 (residual heterogeneity / unaccounted variability): 14.87%
## H^2 (unaccounted variability / sampling variability): 1.17
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 30.6978, p-val = 0.1025
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3204, p-val = 0.5713
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0448 0.0323 1.3865 0.1656 -0.0185 0.1082
## EL -0.0261 0.0462 -0.5661 0.5713 -0.1166 0.0643
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TDD)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0459
## I^2 (residual heterogeneity / unaccounted variability): 19.40%
## H^2 (unaccounted variability / sampling variability): 1.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.6659, p-val = 0.0833
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3610, p-val = 0.5479
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0386 0.0252 1.5318 0.1256 -0.0108 0.0879
## TDD -0.0450 0.0748 -0.6009 0.5479 -0.1916 0.1017
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TTEC)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0450
## I^2 (residual heterogeneity / unaccounted variability): 18.66%
## H^2 (unaccounted variability / sampling variability): 1.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.7825, p-val = 0.0812
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0163, p-val = 0.8986
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0347 0.0265 1.3073 0.1911 -0.0173 0.0866
## TTEC -0.0074 0.0577 -0.1275 0.8986 -0.1204 0.1057
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TSTR)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0020)
## tau (square root of estimated tau^2 value): 0.0023
## I^2 (residual heterogeneity / unaccounted variability): 0.05%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 99.67%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 26.5603, p-val = 0.2284
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.2422, p-val = 0.0220
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0499 0.0231 2.1576 0.0310 0.0046 0.0953 *
## TSTR -0.0831 0.0363 -2.2896 0.0220 -0.1542 -0.0120 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ TWRT)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0427
## I^2 (residual heterogeneity / unaccounted variability): 17.55%
## H^2 (unaccounted variability / sampling variability): 1.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.7993, p-val = 0.0809
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0003, p-val = 0.9868
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0321 0.0235 1.3672 0.1716 -0.0139 0.0781
## TWRT 0.0023 0.1397 0.0165 0.9868 -0.2715 0.2761
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ CONT)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0353
## I^2 (residual heterogeneity / unaccounted variability): 12.16%
## H^2 (unaccounted variability / sampling variability): 1.14
## R^2 (amount of heterogeneity accounted for): 24.49%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 30.5664, p-val = 0.1053
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5454, p-val = 0.4602
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0214 0.0241 0.8877 0.3747 -0.0258 0.0686
## CONTALT 0.0422 0.0571 0.7385 0.4602 -0.0697 0.1540
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ Duration)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0431
## I^2 (residual heterogeneity / unaccounted variability): 17.55%
## H^2 (unaccounted variability / sampling variability): 1.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.1361, p-val = 0.0934
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5145, p-val = 0.4732
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0825 0.0738 1.1189 0.2632 -0.0620 0.2271
## Duration -0.0557 0.0777 -0.7173 0.4732 -0.2081 0.0966
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ Hours)##
## Mixed-Effects Model (k = 22; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0011
## I^2 (residual heterogeneity / unaccounted variability): 0.01%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 20) = 23.5048, p-val = 0.2647
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5507, p-val = 0.2130
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0346 0.0643 -0.5386 0.5902 -0.1607 0.0914
## Hours 0.0026 0.0021 1.2453 0.2130 -0.0015 0.0066
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ design)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0442
## I^2 (residual heterogeneity / unaccounted variability): 18.21%
## H^2 (unaccounted variability / sampling variability): 1.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 31.7929, p-val = 0.0810
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0063, p-val = 0.9367
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0336 0.0256 1.3139 0.1889 -0.0165 0.0837
## designQED -0.0050 0.0635 -0.0794 0.9367 -0.1294 0.1193
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ grade)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0290
## I^2 (residual heterogeneity / unaccounted variability): 8.23%
## H^2 (unaccounted variability / sampling variability): 1.09
## R^2 (amount of heterogeneity accounted for): 48.86%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 29.3135, p-val = 0.1361
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5962, p-val = 0.2064
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0663 0.0381 1.7399 0.0819 -0.0084 0.1410 .
## gradeK-2 -0.0574 0.0455 -1.2634 0.2064 -0.1466 0.0317
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_v, method = "REML", subset = (type == "VS"), mods = ~ grouping)##
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0194
## I^2 (residual heterogeneity / unaccounted variability): 3.89%
## H^2 (unaccounted variability / sampling variability): 1.04
## R^2 (amount of heterogeneity accounted for): 77.08%
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 28.2798, p-val = 0.1666
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8999, p-val = 0.0886
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0671 0.0331 2.0288 0.0425 0.0023 0.1318 *
## grouping -0.0694 0.0407 -1.7029 0.0886 -0.1492 0.0105 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Listening Comprehension - overall
rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0053)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 7.9677, p-val = 0.6320
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6781, p-val = 0.0306
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## typeLR 0.1551 0.0717 2.1629 0.0306 0.0145 0.2956 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ Content)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0060 (SE = 0.0098)
## tau (square root of estimated tau^2 value): 0.0775
## I^2 (residual heterogeneity / unaccounted variability): 27.40%
## H^2 (unaccounted variability / sampling variability): 1.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.4870, p-val = 0.2538
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5495, p-val = 0.4585
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1206 0.0502 2.4012 0.0163 0.0222 0.2190 *
## Content -0.0833 0.1124 -0.7413 0.4585 -0.3035 0.1369
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ PD)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0039 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0626
## I^2 (residual heterogeneity / unaccounted variability): 22.09%
## H^2 (unaccounted variability / sampling variability): 1.28
## R^2 (amount of heterogeneity accounted for): 1.97%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.3024, p-val = 0.2653
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2188, p-val = 0.6400
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0939 0.0419 2.2420 0.0250 0.0118 0.1760 *
## PD 0.1480 0.3164 0.4678 0.6400 -0.4721 0.7681
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TMULT)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0051)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 9.4427, p-val = 0.4907
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.2030, p-val = 0.0735
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0380 0.0323 1.1790 0.2384 -0.0252 0.1013
## TMULT 0.1353 0.0756 1.7897 0.0735 -0.0129 0.2834 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TVOC)##
## Random-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0633
## I^2 (total heterogeneity / total variability): 21.15%
## H^2 (total variability / sampling variability): 1.27
##
## Test for Heterogeneity:
## Q(df = 11) = 12.6457, p-val = 0.3171
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0968 0.0417 2.3245 0.0201 0.0152 0.1785 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TSYN)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0048)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 8.7385, p-val = 0.5571
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.9072, p-val = 0.0481
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0393 0.0315 1.2491 0.2116 -0.0224 0.1010
## TSYN 0.1657 0.0838 1.9767 0.0481 0.0014 0.3301 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TMOR)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0051)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 9.4427, p-val = 0.4907
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.2030, p-val = 0.0735
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0380 0.0323 1.1790 0.2384 -0.0252 0.1013
## TMOR 0.1353 0.0756 1.7897 0.0735 -0.0129 0.2834 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TLC)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0095)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 9.1475, p-val = 0.5182
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4982, p-val = 0.0614
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0176 0.0378 0.4657 0.6414 -0.0565 0.0918
## TLC 0.1111 0.0594 1.8704 0.0614 -0.0053 0.2276 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TRC)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0064 (SE = 0.0103)
## tau (square root of estimated tau^2 value): 0.0802
## I^2 (residual heterogeneity / unaccounted variability): 27.85%
## H^2 (unaccounted variability / sampling variability): 1.39
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.5827, p-val = 0.2479
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4798, p-val = 0.4885
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1228 0.0522 2.3532 0.0186 0.0205 0.2251 *
## TRC -0.0740 0.1068 -0.6927 0.4885 -0.2833 0.1353
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TCOM)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0095)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 9.1475, p-val = 0.5182
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4982, p-val = 0.0614
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0176 0.0378 0.4657 0.6414 -0.0565 0.0918
## TCOM 0.1111 0.0594 1.8704 0.0614 -0.0053 0.2276 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TPAD)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0039 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0626
## I^2 (residual heterogeneity / unaccounted variability): 22.09%
## H^2 (unaccounted variability / sampling variability): 1.28
## R^2 (amount of heterogeneity accounted for): 1.97%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.3024, p-val = 0.2653
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2188, p-val = 0.6400
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0939 0.0419 2.2420 0.0250 0.0118 0.1760 *
## TPAD 0.1480 0.3164 0.4678 0.6400 -0.4721 0.7681
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ LowIncome)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0034 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.0585
## I^2 (residual heterogeneity / unaccounted variability): 15.43%
## H^2 (unaccounted variability / sampling variability): 1.18
## R^2 (amount of heterogeneity accounted for): 14.41%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 11.1661, p-val = 0.3447
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4458, p-val = 0.5044
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1245 0.0609 2.0455 0.0408 0.0052 0.2437 *
## LowIncome -0.0545 0.0816 -0.6677 0.5044 -0.2145 0.1055
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ EL)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0051 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.0711
## I^2 (residual heterogeneity / unaccounted variability): 25.44%
## H^2 (unaccounted variability / sampling variability): 1.34
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.5743, p-val = 0.2485
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0000, p-val = 0.9987
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1009 0.0456 2.2110 0.0270 0.0115 0.1903 *
## EL 0.0003 0.1500 0.0017 0.9987 -0.2938 0.2943
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TDD)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0055 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.0741
## I^2 (residual heterogeneity / unaccounted variability): 26.98%
## H^2 (unaccounted variability / sampling variability): 1.37
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.6455, p-val = 0.2442
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0585, p-val = 0.8090
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1054 0.0459 2.2963 0.0217 0.0154 0.1954 *
## TDD -0.0406 0.1681 -0.2418 0.8090 -0.3701 0.2888
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TTEC)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0051)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 9.4427, p-val = 0.4907
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.2030, p-val = 0.0735
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0380 0.0323 1.1790 0.2384 -0.0252 0.1013
## TTEC 0.1353 0.0756 1.7897 0.0735 -0.0129 0.2834 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TSTR)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0055 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.0741
## I^2 (residual heterogeneity / unaccounted variability): 26.98%
## H^2 (unaccounted variability / sampling variability): 1.37
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.6455, p-val = 0.2442
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0585, p-val = 0.8090
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1054 0.0459 2.2963 0.0217 0.0154 0.1954 *
## TSTR -0.0406 0.1681 -0.2418 0.8090 -0.3701 0.2888
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TWRT)##
## Random-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0633
## I^2 (total heterogeneity / total variability): 21.15%
## H^2 (total variability / sampling variability): 1.27
##
## Test for Heterogeneity:
## Q(df = 11) = 12.6457, p-val = 0.3171
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0968 0.0417 2.3245 0.0201 0.0152 0.1785 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ CONT)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0038 (SE = 0.0079)
## tau (square root of estimated tau^2 value): 0.0617
## I^2 (residual heterogeneity / unaccounted variability): 20.05%
## H^2 (unaccounted variability / sampling variability): 1.25
## R^2 (amount of heterogeneity accounted for): 4.96%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 11.7523, p-val = 0.3020
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3814, p-val = 0.5368
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0863 0.0442 1.9526 0.0509 -0.0003 0.1729 .
## CONTALT 0.0767 0.1242 0.6176 0.5368 -0.1668 0.3202
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ Duration)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0074)
## tau (square root of estimated tau^2 value): 0.0601
## I^2 (residual heterogeneity / unaccounted variability): 20.89%
## H^2 (unaccounted variability / sampling variability): 1.26
## R^2 (amount of heterogeneity accounted for): 9.63%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 11.5842, p-val = 0.3138
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9019, p-val = 0.3423
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5157 0.4447 1.1595 0.2463 -0.3560 1.3874
## Duration -0.4242 0.4466 -0.9497 0.3423 -1.2996 0.4512
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ Hours)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0050)
## tau (square root of estimated tau^2 value): 0.0271
## I^2 (residual heterogeneity / unaccounted variability): 4.76%
## H^2 (unaccounted variability / sampling variability): 1.05
## R^2 (amount of heterogeneity accounted for): 81.65%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 9.1196, p-val = 0.5208
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.1496, p-val = 0.0759
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0915 0.0986 -0.9273 0.3538 -0.2848 0.1019
## Hours 0.0050 0.0028 1.7747 0.0759 -0.0005 0.0105 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ design)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0050 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.0707
## I^2 (residual heterogeneity / unaccounted variability): 25.20%
## H^2 (unaccounted variability / sampling variability): 1.34
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.5297, p-val = 0.2512
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0115, p-val = 0.9145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0993 0.0453 2.1928 0.0283 0.0105 0.1881 *
## designQED 0.0169 0.1574 0.1074 0.9145 -0.2916 0.3254
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ grade)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0058 (SE = 0.0099)
## tau (square root of estimated tau^2 value): 0.0760
## I^2 (residual heterogeneity / unaccounted variability): 25.42%
## H^2 (unaccounted variability / sampling variability): 1.34
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.3632, p-val = 0.2615
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0006, p-val = 0.9800
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1050 0.0838 1.2532 0.2101 -0.0592 0.2693
## gradeK-2 -0.0025 0.0990 -0.0251 0.9800 -0.1965 0.1915
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ grouping)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0065 (SE = 0.0105)
## tau (square root of estimated tau^2 value): 0.0807
## I^2 (residual heterogeneity / unaccounted variability): 27.51%
## H^2 (unaccounted variability / sampling variability): 1.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 12.6438, p-val = 0.2443
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3335, p-val = 0.5636
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0675 0.0799 0.8443 0.3985 -0.0892 0.2242
## grouping 0.0562 0.0974 0.5775 0.5636 -0.1346 0.2471
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Listening Comprehension - overall (Controlling for type)
rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ Content + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.4826, p-val = 0.5870
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.1632, p-val = 0.0757
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0361 0.0339 1.0656 0.2866 -0.0303 0.1025
## Content -0.0646 0.0928 -0.6965 0.4861 -0.2464 0.1172
## typeLR 0.1619 0.0724 2.2371 0.0253 0.0200 0.3037 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ PD + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.0025
## I^2 (residual heterogeneity / unaccounted variability): 0.04%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 99.84%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.4875, p-val = 0.5865
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.1515, p-val = 0.0761
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0278 0.0331 0.8406 0.4006 -0.0370 0.0926
## PD 0.2141 0.3091 0.6928 0.4884 -0.3916 0.8199
## typeLR 0.1575 0.0718 2.1929 0.0283 0.0167 0.2982 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TMULT + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.7324, p-val = 0.5613
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.9133, p-val = 0.0857
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## TMULT -0.0919 0.1894 -0.4850 0.6277 -0.4631 0.2794
## typeLR 0.2350 0.1797 1.3078 0.1909 -0.1172 0.5872
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TVOC + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0053)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 7.9677, p-val = 0.6320
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6781, p-val = 0.0306
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## typeLR 0.1551 0.0717 2.1629 0.0306 0.0145 0.2956 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TSYN + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.7696, p-val = 0.5575
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.8761, p-val = 0.0873
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## TSYN 0.0605 0.1359 0.4450 0.6563 -0.2059 0.3268
## typeLR 0.1144 0.1162 0.9843 0.3250 -0.1134 0.3421
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TMOR + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.7324, p-val = 0.5613
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.9133, p-val = 0.0857
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## TMOR -0.0919 0.1894 -0.4850 0.6277 -0.4631 0.2794
## typeLR 0.2350 0.1797 1.3078 0.1909 -0.1172 0.5872
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TLC + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0103)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.5217, p-val = 0.5830
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.1240, p-val = 0.0771
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0176 0.0378 0.4657 0.6414 -0.0565 0.0918
## TLC 0.0507 0.0760 0.6678 0.5043 -0.0982 0.1997
## typeLR 0.1169 0.0917 1.2751 0.2023 -0.0628 0.2966
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TRC + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.9230, p-val = 0.5419
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.7228, p-val = 0.0943
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0269 0.0363 0.7415 0.4584 -0.0442 0.0981
## TRC 0.0179 0.0849 0.2114 0.8326 -0.1484 0.1843
## typeLR 0.1584 0.0734 2.1586 0.0309 0.0146 0.3021 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TCOM + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0103)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.5217, p-val = 0.5830
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.1240, p-val = 0.0771
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0176 0.0378 0.4657 0.6414 -0.0565 0.0918
## TCOM 0.0507 0.0760 0.6678 0.5043 -0.0982 0.1997
## typeLR 0.1169 0.0917 1.2751 0.2023 -0.0628 0.2966
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TPAD + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.0025
## I^2 (residual heterogeneity / unaccounted variability): 0.04%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 99.84%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.4875, p-val = 0.5865
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.1515, p-val = 0.0761
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0278 0.0331 0.8406 0.4006 -0.0370 0.0926
## TPAD 0.2141 0.3091 0.6928 0.4884 -0.3916 0.8199
## typeLR 0.1575 0.0718 2.1929 0.0283 0.0167 0.2982 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ LowIncome + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0075)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.9009, p-val = 0.5442
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.7448, p-val = 0.0933
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0459 0.0690 0.6647 0.5062 -0.0894 0.1811
## LowIncome -0.0189 0.0733 -0.2583 0.7962 -0.1626 0.1247
## typeLR 0.1456 0.0806 1.8070 0.0708 -0.0123 0.3035 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ EL + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.7061, p-val = 0.5640
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.9396, p-val = 0.0846
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0310 0.0328 0.9445 0.3449 -0.0334 0.0954
## EL -0.0728 0.1423 -0.5114 0.6091 -0.3517 0.2062
## typeLR 0.1686 0.0764 2.2064 0.0274 0.0188 0.3183 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TDD + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.0923, p-val = 0.6275
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.5534, p-val = 0.0622
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## TDD -0.1500 0.1603 -0.9356 0.3495 -0.4643 0.1642
## typeLR 0.1846 0.0783 2.3565 0.0184 0.0311 0.3381 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TTEC + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.7324, p-val = 0.5613
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.9133, p-val = 0.0857
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## TTEC -0.0919 0.1894 -0.4850 0.6277 -0.4631 0.2794
## typeLR 0.2350 0.1797 1.3078 0.1909 -0.1172 0.5872
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TSTR + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.0923, p-val = 0.6275
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.5534, p-val = 0.0622
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## TSTR -0.1500 0.1603 -0.9356 0.3495 -0.4643 0.1642
## typeLR 0.1846 0.0783 2.3565 0.0184 0.0311 0.3381 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ TWRT + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0053)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 7.9677, p-val = 0.6320
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6781, p-val = 0.0306
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
## typeLR 0.1551 0.0717 2.1629 0.0306 0.0145 0.2956 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ CONT + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.4610, p-val = 0.5892
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.1847, p-val = 0.0748
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0255 0.0335 0.7606 0.4469 -0.0402 0.0911
## CONTALT 0.0797 0.1120 0.7118 0.4766 -0.1397 0.2991
## typeLR 0.1494 0.0721 2.0715 0.0383 0.0080 0.2908 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ Duration + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 6.7471, p-val = 0.6634
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.8986, p-val = 0.0524
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5157 0.4407 1.1702 0.2419 -0.3480 1.3794
## Duration -0.4882 0.4419 -1.1048 0.2693 -1.3543 0.3779
## typeLR 0.1578 0.0717 2.1993 0.0279 0.0172 0.2984 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ Hours + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.0024
## I^2 (residual heterogeneity / unaccounted variability): 0.04%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 99.85%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.0770, p-val = 0.6291
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.5624, p-val = 0.0620
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0584 0.0995 -0.5871 0.5572 -0.2536 0.1367
## Hours 0.0030 0.0031 0.9437 0.3453 -0.0032 0.0091
## typeLR 0.1173 0.0821 1.4283 0.1532 -0.0437 0.2783
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ design + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.7150, p-val = 0.5631
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.9307, p-val = 0.0850
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0306 0.0328 0.9329 0.3509 -0.0337 0.0949
## designQED -0.0758 0.1507 -0.5027 0.6152 -0.3712 0.2196
## typeLR 0.1696 0.0773 2.1943 0.0282 0.0181 0.3210 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ grade + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0078)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.6755, p-val = 0.5672
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.9702, p-val = 0.0833
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0649 0.0721 0.9000 0.3681 -0.0765 0.2063
## gradeK-2 -0.0419 0.0775 -0.5405 0.5888 -0.1939 0.1101
## typeLR 0.1552 0.0717 2.1651 0.0304 0.0147 0.2958 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", mods = ~ grouping + type)##
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0080)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 9) = 7.9637, p-val = 0.5378
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.6820, p-val = 0.0962
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0264 0.0687 0.3846 0.7005 -0.1082 0.1610
## grouping 0.0047 0.0743 0.0627 0.9500 -0.1410 0.1504
## typeLR 0.1551 0.0717 2.1633 0.0305 0.0146 0.2956 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Listening Comprehension - LR
rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ Content)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0213)
## tau (square root of estimated tau^2 value): 0.0689
## I^2 (residual heterogeneity / unaccounted variability): 15.77%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.6219, p-val = 0.3283
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7196, p-val = 0.3963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2151 0.0776 2.7729 0.0056 0.0631 0.3672 **
## Content -0.1504 0.1772 -0.8483 0.3963 -0.4978 0.1970
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ PD)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TMULT)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0071 (SE = 0.0215)
## tau (square root of estimated tau^2 value): 0.0840
## I^2 (residual heterogeneity / unaccounted variability): 23.17%
## H^2 (unaccounted variability / sampling variability): 1.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 5.2620, p-val = 0.2615
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1864, p-val = 0.6659
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2652 0.1956 1.3556 0.1752 -0.1182 0.6486
## TMULT -0.0909 0.2106 -0.4318 0.6659 -0.5037 0.3219
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TVOC)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TSYN)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0080 (SE = 0.0231)
## tau (square root of estimated tau^2 value): 0.0896
## I^2 (residual heterogeneity / unaccounted variability): 24.60%
## H^2 (unaccounted variability / sampling variability): 1.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 5.2992, p-val = 0.2579
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1260, p-val = 0.7226
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1494 0.1287 1.1607 0.2458 -0.1029 0.4017
## TSYN 0.0557 0.1569 0.3550 0.7226 -0.2518 0.3632
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TMOR)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0071 (SE = 0.0215)
## tau (square root of estimated tau^2 value): 0.0840
## I^2 (residual heterogeneity / unaccounted variability): 23.17%
## H^2 (unaccounted variability / sampling variability): 1.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 5.2620, p-val = 0.2615
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1864, p-val = 0.6659
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2652 0.1956 1.3556 0.1752 -0.1182 0.6486
## TMOR -0.0909 0.2106 -0.4318 0.6659 -0.5037 0.3219
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TLC)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TRC)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TCOM)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TPAD)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ LowIncome)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0080 (SE = 0.0231)
## tau (square root of estimated tau^2 value): 0.0896
## I^2 (residual heterogeneity / unaccounted variability): 24.60%
## H^2 (unaccounted variability / sampling variability): 1.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 5.2992, p-val = 0.2579
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1260, p-val = 0.7226
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2051 0.0897 2.2859 0.0223 0.0292 0.3809 *
## LowIncome -0.0557 0.1569 -0.3550 0.7226 -0.3632 0.2518
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ EL)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0213)
## tau (square root of estimated tau^2 value): 0.0689
## I^2 (residual heterogeneity / unaccounted variability): 15.77%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.6219, p-val = 0.3283
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7196, p-val = 0.3963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2151 0.0776 2.7729 0.0056 0.0631 0.3672 **
## EL -0.1504 0.1772 -0.8483 0.3963 -0.4978 0.1970
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TDD)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0213)
## tau (square root of estimated tau^2 value): 0.0689
## I^2 (residual heterogeneity / unaccounted variability): 15.77%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.6219, p-val = 0.3283
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7196, p-val = 0.3963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2151 0.0776 2.7729 0.0056 0.0631 0.3672 **
## TDD -0.1504 0.1772 -0.8483 0.3963 -0.4978 0.1970
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TTEC)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0071 (SE = 0.0215)
## tau (square root of estimated tau^2 value): 0.0840
## I^2 (residual heterogeneity / unaccounted variability): 23.17%
## H^2 (unaccounted variability / sampling variability): 1.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 5.2620, p-val = 0.2615
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1864, p-val = 0.6659
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2652 0.1956 1.3556 0.1752 -0.1182 0.6486
## TTEC -0.0909 0.2106 -0.4318 0.6659 -0.5037 0.3219
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TSTR)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0213)
## tau (square root of estimated tau^2 value): 0.0689
## I^2 (residual heterogeneity / unaccounted variability): 15.77%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.6219, p-val = 0.3283
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7196, p-val = 0.3963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2151 0.0776 2.7729 0.0056 0.0631 0.3672 **
## TSTR -0.1504 0.1772 -0.8483 0.3963 -0.4978 0.1970
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ TWRT)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ CONT)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0071 (SE = 0.0215)
## tau (square root of estimated tau^2 value): 0.0840
## I^2 (residual heterogeneity / unaccounted variability): 23.17%
## H^2 (unaccounted variability / sampling variability): 1.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 5.2620, p-val = 0.2615
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1864, p-val = 0.6659
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1742 0.0780 2.2326 0.0256 0.0213 0.3272 *
## CONTALT 0.0909 0.2106 0.4318 0.6659 -0.3219 0.5037
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ Duration)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0172)
## tau (square root of estimated tau^2 value): 0.0536
## I^2 (total heterogeneity / total variability): 10.50%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 5) = 5.4973, p-val = 0.3582
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1860 0.0674 2.7571 0.0058 0.0538 0.3181 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ Hours)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0062 (SE = 0.0222)
## tau (square root of estimated tau^2 value): 0.0785
## I^2 (residual heterogeneity / unaccounted variability): 19.58%
## H^2 (unaccounted variability / sampling variability): 1.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.8931, p-val = 0.2984
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4596, p-val = 0.4978
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0101 0.2700 0.0374 0.9702 -0.5190 0.5392
## Hours 0.0041 0.0061 0.6779 0.4978 -0.0078 0.0161
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ design)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0213)
## tau (square root of estimated tau^2 value): 0.0689
## I^2 (residual heterogeneity / unaccounted variability): 15.77%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.6219, p-val = 0.3283
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7196, p-val = 0.3963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2151 0.0776 2.7729 0.0056 0.0631 0.3672 **
## designQED -0.1504 0.1772 -0.8483 0.3963 -0.4978 0.1970
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ grade)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0181)
## tau (square root of estimated tau^2 value): 0.0340
## I^2 (residual heterogeneity / unaccounted variability): 4.49%
## H^2 (unaccounted variability / sampling variability): 1.05
## R^2 (amount of heterogeneity accounted for): 59.78%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.1814, p-val = 0.3820
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2517, p-val = 0.2632
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3482 0.1593 2.1852 0.0289 0.0359 0.6605 *
## gradeK-2 -0.1954 0.1747 -1.1188 0.2632 -0.5377 0.1469
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LR"), mods = ~ grouping)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0047 (SE = 0.0213)
## tau (square root of estimated tau^2 value): 0.0689
## I^2 (residual heterogeneity / unaccounted variability): 15.77%
## H^2 (unaccounted variability / sampling variability): 1.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 4.6219, p-val = 0.3283
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7196, p-val = 0.3963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0648 0.1594 0.4065 0.6844 -0.2476 0.3771
## grouping 0.1504 0.1772 0.8483 0.3963 -0.1970 0.4978
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Listening Comprehension - LS
rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ Content)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.4342, p-val = 0.6565
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0362, p-val = 0.8491
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0322 0.0344 0.9348 0.3499 -0.0353 0.0997
## Content -0.0216 0.1137 -0.1903 0.8491 -0.2445 0.2013
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ PD)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1.9902, p-val = 0.7376
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4801, p-val = 0.4884
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0278 0.0330 0.8411 0.4003 -0.0369 0.0924
## PD 0.2141 0.3090 0.6929 0.4884 -0.3916 0.8199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TMULT)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TVOC)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TSYN)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TMOR)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TLC)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0139)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.0244, p-val = 0.7313
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4460, p-val = 0.5043
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0176 0.0378 0.4657 0.6414 -0.0565 0.0918
## TLC 0.0507 0.0760 0.6678 0.5043 -0.0982 0.1997
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TRC)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0107)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.4257, p-val = 0.6580
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0447, p-val = 0.8326
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0269 0.0363 0.7415 0.4584 -0.0442 0.0981
## TRC 0.0179 0.0849 0.2114 0.8326 -0.1484 0.1843
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TCOM)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0139)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.0244, p-val = 0.7313
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4460, p-val = 0.5043
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0176 0.0378 0.4657 0.6414 -0.0565 0.0918
## TCOM 0.0507 0.0760 0.6678 0.5043 -0.0982 0.1997
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TPAD)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1.9902, p-val = 0.7376
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4801, p-val = 0.4884
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0278 0.0330 0.8411 0.4003 -0.0369 0.0924
## TPAD 0.2141 0.3090 0.6929 0.4884 -0.3916 0.8199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ LowIncome)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0104)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.4699, p-val = 0.6500
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0005, p-val = 0.9827
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0318 0.0792 0.4009 0.6885 -0.1235 0.1870
## LowIncome -0.0019 0.0870 -0.0217 0.9827 -0.1725 0.1687
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ EL)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1.9902, p-val = 0.7376
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4801, p-val = 0.4884
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0278 0.0330 0.8411 0.4003 -0.0369 0.0924
## EL 0.2141 0.3090 0.6929 0.4884 -0.3916 0.8199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TDD)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TTEC)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TSTR)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ TWRT)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 2.4704, p-val = 0.7809
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0328 0.9204 0.3574 -0.0341 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ CONT)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0065)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.1926, p-val = 0.7004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2777, p-val = 0.5982
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0259 0.0338 0.7641 0.4448 -0.0405 0.0922
## CONTALT 0.0731 0.1388 0.5270 0.5982 -0.1989 0.3452
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ Duration)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1.2498, p-val = 0.8698
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2205, p-val = 0.2693
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5157 0.4407 1.1702 0.2419 -0.3480 1.3794
## Duration -0.4882 0.4419 -1.1048 0.2693 -1.3543 0.3779
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ Hours)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0.0016
## I^2 (residual heterogeneity / unaccounted variability): 0.02%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.1067, p-val = 0.7161
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3637, p-val = 0.5465
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0396 0.1204 -0.3293 0.7420 -0.2756 0.1963
## Hours 0.0023 0.0039 0.6031 0.5465 -0.0053 0.0100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ design)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0057)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1.2498, p-val = 0.8698
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2205, p-val = 0.2693
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0275 0.0329 0.8355 0.4034 -0.0370 0.0920
## designQED 0.4882 0.4419 1.1048 0.2693 -0.3779 1.3543
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ grade)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0104)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.4699, p-val = 0.6500
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0005, p-val = 0.9827
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0318 0.0792 0.4009 0.6885 -0.1235 0.1870
## gradeK-2 -0.0019 0.0870 -0.0217 0.9827 -0.1725 0.1687
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_l, method = "REML", subset = (type == "LS"), mods = ~ grouping)##
## Mixed-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0108)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 2.2950, p-val = 0.6817
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1753, p-val = 0.6754
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0587 0.0756 0.7768 0.4373 -0.0894 0.2068
## grouping -0.0351 0.0839 -0.4187 0.6754 -0.1996 0.1293
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Reading Comprehension - overall
rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0468 (SE = 0.0225)
## tau (square root of estimated tau^2 value): 0.2164
## I^2 (residual heterogeneity / unaccounted variability): 83.06%
## H^2 (unaccounted variability / sampling variability): 5.90
## R^2 (amount of heterogeneity accounted for): 49.36%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 67.1025, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.3894, p-val = 0.0007
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0947 0.0642 1.4747 0.1403 -0.0312 0.2206
## typeRR 0.5572 0.1651 3.3748 0.0007 0.2336 0.8809 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ Content)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0891 (SE = 0.0378)
## tau (square root of estimated tau^2 value): 0.2985
## I^2 (residual heterogeneity / unaccounted variability): 89.46%
## H^2 (unaccounted variability / sampling variability): 9.49
## R^2 (amount of heterogeneity accounted for): 3.64%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.0264, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6522, p-val = 0.1987
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2748 0.1014 2.7093 0.0067 0.0760 0.4736 **
## Content -0.1965 0.1529 -1.2854 0.1987 -0.4961 0.1031
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ PD)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0986 (SE = 0.0406)
## tau (square root of estimated tau^2 value): 0.3140
## I^2 (residual heterogeneity / unaccounted variability): 91.38%
## H^2 (unaccounted variability / sampling variability): 11.60
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 102.0842, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0362, p-val = 0.8492
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1939 0.0825 2.3505 0.0187 0.0322 0.3555 *
## PD -0.0551 0.2899 -0.1901 0.8492 -0.6232 0.5130
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TMULT)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0976 (SE = 0.0407)
## tau (square root of estimated tau^2 value): 0.3123
## I^2 (residual heterogeneity / unaccounted variability): 90.74%
## H^2 (unaccounted variability / sampling variability): 10.80
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.9004, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8963, p-val = 0.3438
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1340 0.0980 1.3664 0.1718 -0.0582 0.3261
## TMULT 0.1558 0.1645 0.9467 0.3438 -0.1667 0.4782
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TVOC)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0971 (SE = 0.0398)
## tau (square root of estimated tau^2 value): 0.3116
## I^2 (residual heterogeneity / unaccounted variability): 91.31%
## H^2 (unaccounted variability / sampling variability): 11.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 102.0713, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0561, p-val = 0.8128
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1176 0.3127 0.3759 0.7070 -0.4953 0.7304
## TVOC 0.0765 0.3231 0.2369 0.8128 -0.5567 0.7097
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TSYN)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0780 (SE = 0.0337)
## tau (square root of estimated tau^2 value): 0.2793
## I^2 (residual heterogeneity / unaccounted variability): 89.14%
## H^2 (unaccounted variability / sampling variability): 9.21
## R^2 (amount of heterogeneity accounted for): 15.63%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 92.9203, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.5138, p-val = 0.0336
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1011 0.0824 1.2270 0.2198 -0.0604 0.2627
## TSYN 0.3587 0.1688 2.1246 0.0336 0.0278 0.6896 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TMOR)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0979 (SE = 0.0408)
## tau (square root of estimated tau^2 value): 0.3129
## I^2 (residual heterogeneity / unaccounted variability): 90.78%
## H^2 (unaccounted variability / sampling variability): 10.84
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.8809, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6419, p-val = 0.4230
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1357 0.1034 1.3126 0.1893 -0.0669 0.3384
## TMOR 0.1281 0.1599 0.8012 0.4230 -0.1852 0.4414
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TLC)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0892 (SE = 0.0377)
## tau (square root of estimated tau^2 value): 0.2987
## I^2 (residual heterogeneity / unaccounted variability): 90.20%
## H^2 (unaccounted variability / sampling variability): 10.20
## R^2 (amount of heterogeneity accounted for): 3.52%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 98.4077, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0292, p-val = 0.1543
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2647 0.0929 2.8478 0.0044 0.0825 0.4468 **
## TLC -0.2295 0.1611 -1.4245 0.1543 -0.5453 0.0863
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TRC)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0815 (SE = 0.0351)
## tau (square root of estimated tau^2 value): 0.2855
## I^2 (residual heterogeneity / unaccounted variability): 88.67%
## H^2 (unaccounted variability / sampling variability): 8.82
## R^2 (amount of heterogeneity accounted for): 11.85%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 93.0488, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8435, p-val = 0.0917
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2823 0.0924 3.0550 0.0023 0.1012 0.4634 **
## TRC -0.2554 0.1514 -1.6863 0.0917 -0.5522 0.0414 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TCOM)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0769 (SE = 0.0335)
## tau (square root of estimated tau^2 value): 0.2774
## I^2 (residual heterogeneity / unaccounted variability): 88.07%
## H^2 (unaccounted variability / sampling variability): 8.38
## R^2 (amount of heterogeneity accounted for): 16.81%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 92.2445, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.4106, p-val = 0.0357
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3218 0.0963 3.3424 0.0008 0.1331 0.5105 ***
## TCOM -0.3021 0.1439 -2.1001 0.0357 -0.5841 -0.0202 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TPAD)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0989 (SE = 0.0408)
## tau (square root of estimated tau^2 value): 0.3145
## I^2 (residual heterogeneity / unaccounted variability): 91.38%
## H^2 (unaccounted variability / sampling variability): 11.60
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.7734, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0015, p-val = 0.9695
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1885 0.0827 2.2790 0.0227 0.0264 0.3507 *
## TPAD 0.0109 0.2854 0.0382 0.9695 -0.5484 0.5702
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ LowIncome)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1131 (SE = 0.0497)
## tau (square root of estimated tau^2 value): 0.3364
## I^2 (residual heterogeneity / unaccounted variability): 92.38%
## H^2 (unaccounted variability / sampling variability): 13.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 100.6308, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1445, p-val = 0.2847
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3425 0.1508 2.2714 0.0231 0.0470 0.6380 *
## LowIncome -0.2011 0.1879 -1.0698 0.2847 -0.5694 0.1673
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ EL)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0960 (SE = 0.0401)
## tau (square root of estimated tau^2 value): 0.3098
## I^2 (residual heterogeneity / unaccounted variability): 90.22%
## H^2 (unaccounted variability / sampling variability): 10.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 102.0868, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0775, p-val = 0.2993
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1303 0.0966 1.3496 0.1771 -0.0589 0.3196
## EL 0.1709 0.1646 1.0380 0.2993 -0.1518 0.4936
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TDD)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1009 (SE = 0.0420)
## tau (square root of estimated tau^2 value): 0.3176
## I^2 (residual heterogeneity / unaccounted variability): 90.46%
## H^2 (unaccounted variability / sampling variability): 10.48
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 98.2325, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0333, p-val = 0.8553
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2010 0.1012 1.9861 0.0470 0.0026 0.3994 *
## TDD -0.0300 0.1646 -0.1823 0.8553 -0.3527 0.2926
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TTEC)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0781 (SE = 0.0338)
## tau (square root of estimated tau^2 value): 0.2794
## I^2 (residual heterogeneity / unaccounted variability): 88.81%
## H^2 (unaccounted variability / sampling variability): 8.93
## R^2 (amount of heterogeneity accounted for): 15.58%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 93.8813, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.3488, p-val = 0.0370
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0740 0.0900 0.8219 0.4111 -0.1024 0.2504
## TTEC 0.3125 0.1499 2.0854 0.0370 0.0188 0.6062 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TSTR)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0932 (SE = 0.0392)
## tau (square root of estimated tau^2 value): 0.3052
## I^2 (residual heterogeneity / unaccounted variability): 90.34%
## H^2 (unaccounted variability / sampling variability): 10.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 95.6324, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9613, p-val = 0.3269
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2287 0.0873 2.6189 0.0088 0.0575 0.3998 **
## TSTR -0.1838 0.1875 -0.9805 0.3269 -0.5513 0.1836
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TWRT)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0993 (SE = 0.0414)
## tau (square root of estimated tau^2 value): 0.3152
## I^2 (residual heterogeneity / unaccounted variability): 91.26%
## H^2 (unaccounted variability / sampling variability): 11.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.8903, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1753, p-val = 0.6755
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2010 0.0839 2.3946 0.0166 0.0365 0.3655 *
## TWRT -0.1074 0.2565 -0.4186 0.6755 -0.6102 0.3954
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ CONT)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0509 (SE = 0.0237)
## tau (square root of estimated tau^2 value): 0.2256
## I^2 (residual heterogeneity / unaccounted variability): 84.48%
## H^2 (unaccounted variability / sampling variability): 6.44
## R^2 (amount of heterogeneity accounted for): 44.97%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 75.4928, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 12.3023, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1006 0.0651 1.5441 0.1226 -0.0271 0.2282
## CONTALT 0.6534 0.1863 3.5075 0.0005 0.2883 1.0186 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ Duration)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0990 (SE = 0.0412)
## tau (square root of estimated tau^2 value): 0.3147
## I^2 (residual heterogeneity / unaccounted variability): 91.00%
## H^2 (unaccounted variability / sampling variability): 11.11
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 99.3515, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0174, p-val = 0.8951
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2068 0.1537 1.3455 0.1785 -0.0945 0.5082
## Duration -0.0237 0.1794 -0.1319 0.8951 -0.3753 0.3279
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ Hours)##
## Mixed-Effects Model (k = 19; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1072 (SE = 0.0452)
## tau (square root of estimated tau^2 value): 0.3274
## I^2 (residual heterogeneity / unaccounted variability): 91.06%
## H^2 (unaccounted variability / sampling variability): 11.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 101.4785, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0868, p-val = 0.7683
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2209 0.1261 1.7523 0.0797 -0.0262 0.4680 .
## Hours -0.0008 0.0027 -0.2946 0.7683 -0.0062 0.0046
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ design)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1004 (SE = 0.0417)
## tau (square root of estimated tau^2 value): 0.3169
## I^2 (residual heterogeneity / unaccounted variability): 91.08%
## H^2 (unaccounted variability / sampling variability): 11.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 101.8975, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1734, p-val = 0.6772
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2117 0.0958 2.2109 0.0270 0.0240 0.3994 *
## designQED -0.0719 0.1726 -0.4164 0.6772 -0.4102 0.2664
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ grade)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1041 (SE = 0.0436)
## tau (square root of estimated tau^2 value): 0.3227
## I^2 (residual heterogeneity / unaccounted variability): 92.08%
## H^2 (unaccounted variability / sampling variability): 12.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 101.4600, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5412, p-val = 0.7629
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2167 0.0921 2.3530 0.0186 0.0362 0.3971 *
## gradeK-2 -0.1532 0.2115 -0.7242 0.4689 -0.5678 0.2614
## gradeBoth 0.0198 0.3793 0.0521 0.9584 -0.7237 0.7632
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ grouping)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0946 (SE = 0.0398)
## tau (square root of estimated tau^2 value): 0.3075
## I^2 (residual heterogeneity / unaccounted variability): 89.96%
## H^2 (unaccounted variability / sampling variability): 9.96
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 94.6879, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7816, p-val = 0.3767
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2256 0.0881 2.5607 0.0104 0.0529 0.3984 *
## grouping -0.1654 0.1871 -0.8841 0.3767 -0.5322 0.2013
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Reading Comprehension - overall (Controlling for type)
rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ Content + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0462 (SE = 0.0231)
## tau (square root of estimated tau^2 value): 0.2150
## I^2 (residual heterogeneity / unaccounted variability): 81.70%
## H^2 (unaccounted variability / sampling variability): 5.46
## R^2 (amount of heterogeneity accounted for): 50.02%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.0554, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 13.2530, p-val = 0.0013
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1696 0.0853 1.9885 0.0468 0.0024 0.3368 *
## Content -0.1574 0.1185 -1.3286 0.1840 -0.3896 0.0748
## typeRR 0.5403 0.1648 3.2782 0.0010 0.2173 0.8634 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ PD + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0506 (SE = 0.0242)
## tau (square root of estimated tau^2 value): 0.2249
## I^2 (residual heterogeneity / unaccounted variability): 84.68%
## H^2 (unaccounted variability / sampling variability): 6.53
## R^2 (amount of heterogeneity accounted for): 45.29%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.0369, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.8642, p-val = 0.0044
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0917 0.0690 1.3288 0.1839 -0.0436 0.2270
## PD 0.0465 0.2399 0.1940 0.8462 -0.4236 0.5166
## typeRR 0.5622 0.1708 3.2906 0.0010 0.2273 0.8970 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TMULT + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0498 (SE = 0.0244)
## tau (square root of estimated tau^2 value): 0.2232
## I^2 (residual heterogeneity / unaccounted variability): 83.53%
## H^2 (unaccounted variability / sampling variability): 6.07
## R^2 (amount of heterogeneity accounted for): 46.12%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.0018, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.4973, p-val = 0.0019
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0373 0.0805 0.4630 0.6434 -0.1206 0.1951
## TMULT 0.1591 0.1274 1.2491 0.2116 -0.0905 0.4087
## typeRR 0.5747 0.1693 3.3950 0.0007 0.2429 0.9065 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TVOC + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0496 (SE = 0.0237)
## tau (square root of estimated tau^2 value): 0.2228
## I^2 (residual heterogeneity / unaccounted variability): 84.53%
## H^2 (unaccounted variability / sampling variability): 6.46
## R^2 (amount of heterogeneity accounted for): 46.32%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.1018, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.9668, p-val = 0.0042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1122 0.2718 0.4129 0.6797 -0.4205 0.6450
## TVOC -0.0179 0.2801 -0.0639 0.9491 -0.5669 0.5311
## typeRR 0.5591 0.1693 3.3017 0.0010 0.2272 0.8910 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TSYN + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0348 (SE = 0.0184)
## tau (square root of estimated tau^2 value): 0.1865
## I^2 (residual heterogeneity / unaccounted variability): 78.82%
## H^2 (unaccounted variability / sampling variability): 4.72
## R^2 (amount of heterogeneity accounted for): 62.38%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 60.2779, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 19.7038, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0235 0.0640 0.3675 0.7133 -0.1019 0.1489
## TSYN 0.3150 0.1285 2.4515 0.0142 0.0632 0.5669 *
## typeRR 0.5436 0.1495 3.6368 0.0003 0.2507 0.8366 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TMOR + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0496 (SE = 0.0243)
## tau (square root of estimated tau^2 value): 0.2228
## I^2 (residual heterogeneity / unaccounted variability): 83.47%
## H^2 (unaccounted variability / sampling variability): 6.05
## R^2 (amount of heterogeneity accounted for): 46.34%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.0091, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.5285, p-val = 0.0019
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0295 0.0842 0.3508 0.7258 -0.1354 0.1945
## TMOR 0.1554 0.1243 1.2501 0.2112 -0.0883 0.3992
## typeRR 0.5833 0.1697 3.4369 0.0006 0.2507 0.9160 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TLC + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0516 (SE = 0.0250)
## tau (square root of estimated tau^2 value): 0.2271
## I^2 (residual heterogeneity / unaccounted variability): 84.31%
## H^2 (unaccounted variability / sampling variability): 6.37
## R^2 (amount of heterogeneity accounted for): 44.24%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 65.4312, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 11.1893, p-val = 0.0037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1327 0.0846 1.5681 0.1169 -0.0332 0.2985
## TLC -0.0969 0.1372 -0.7064 0.4799 -0.3657 0.1719
## typeRR 0.5217 0.1786 2.9206 0.0035 0.1716 0.8718 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TRC + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0486 (SE = 0.0240)
## tau (square root of estimated tau^2 value): 0.2205
## I^2 (residual heterogeneity / unaccounted variability): 82.39%
## H^2 (unaccounted variability / sampling variability): 5.68
## R^2 (amount of heterogeneity accounted for): 47.42%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 62.8526, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.0437, p-val = 0.0024
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1514 0.0874 1.7313 0.0834 -0.0200 0.3227 .
## TRC -0.1264 0.1311 -0.9640 0.3351 -0.3833 0.1305
## typeRR 0.5015 0.1772 2.8308 0.0046 0.1543 0.8488 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TCOM + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0484 (SE = 0.0239)
## tau (square root of estimated tau^2 value): 0.2200
## I^2 (residual heterogeneity / unaccounted variability): 82.31%
## H^2 (unaccounted variability / sampling variability): 5.65
## R^2 (amount of heterogeneity accounted for): 47.66%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 62.4477, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.6139, p-val = 0.0018
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1777 0.0942 1.8855 0.0594 -0.0070 0.3624 .
## TCOM -0.1577 0.1302 -1.2109 0.2259 -0.4129 0.0975
## typeRR 0.4751 0.1804 2.6334 0.0085 0.1215 0.8287 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TPAD + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0495 (SE = 0.0239)
## tau (square root of estimated tau^2 value): 0.2225
## I^2 (residual heterogeneity / unaccounted variability): 84.35%
## H^2 (unaccounted variability / sampling variability): 6.39
## R^2 (amount of heterogeneity accounted for): 46.46%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 66.4886, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 11.2379, p-val = 0.0036
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0851 0.0687 1.2390 0.2153 -0.0495 0.2196
## TPAD 0.1174 0.2320 0.5060 0.6129 -0.3374 0.5722
## typeRR 0.5683 0.1696 3.3506 0.0008 0.2359 0.9007 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ LowIncome + type)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0243 (SE = 0.0151)
## tau (square root of estimated tau^2 value): 0.1560
## I^2 (residual heterogeneity / unaccounted variability): 72.82%
## H^2 (unaccounted variability / sampling variability): 3.68
## R^2 (amount of heterogeneity accounted for): 77.76%
##
## Test for Residual Heterogeneity:
## QE(df = 14) = 44.4327, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 26.7286, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1656 0.0874 1.8942 0.0582 -0.0058 0.3370 .
## LowIncome -0.1093 0.1055 -1.0365 0.3000 -0.3160 0.0974
## typeRR 0.8403 0.1667 5.0415 <.0001 0.5136 1.1670 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ EL + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0480 (SE = 0.0237)
## tau (square root of estimated tau^2 value): 0.2191
## I^2 (residual heterogeneity / unaccounted variability): 82.34%
## H^2 (unaccounted variability / sampling variability): 5.66
## R^2 (amount of heterogeneity accounted for): 48.11%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.0335, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 13.0631, p-val = 0.0015
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0347 0.0785 0.4423 0.6582 -0.1191 0.1886
## EL 0.1725 0.1267 1.3613 0.1734 -0.0759 0.4209
## typeRR 0.5731 0.1669 3.4333 0.0006 0.2459 0.9003 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TDD + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0529 (SE = 0.0256)
## tau (square root of estimated tau^2 value): 0.2300
## I^2 (residual heterogeneity / unaccounted variability): 83.45%
## H^2 (unaccounted variability / sampling variability): 6.04
## R^2 (amount of heterogeneity accounted for): 42.79%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 60.4284, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.5081, p-val = 0.0052
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0953 0.0841 1.1337 0.2569 -0.0695 0.2601
## TDD 0.0019 0.1267 0.0148 0.9882 -0.2465 0.2503
## typeRR 0.5590 0.1725 3.2411 0.0012 0.2210 0.8970 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TTEC + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0442 (SE = 0.0222)
## tau (square root of estimated tau^2 value): 0.2103
## I^2 (residual heterogeneity / unaccounted variability): 82.09%
## H^2 (unaccounted variability / sampling variability): 5.58
## R^2 (amount of heterogeneity accounted for): 52.18%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 65.5326, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 14.5038, p-val = 0.0007
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0311 0.0737 0.4215 0.6734 -0.1134 0.1755
## TTEC 0.2041 0.1246 1.6389 0.1012 -0.0400 0.4483
## typeRR 0.4912 0.1667 2.9468 0.0032 0.1645 0.8179 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TSTR + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0336 (SE = 0.0181)
## tau (square root of estimated tau^2 value): 0.1832
## I^2 (residual heterogeneity / unaccounted variability): 77.40%
## H^2 (unaccounted variability / sampling variability): 4.43
## R^2 (amount of heterogeneity accounted for): 63.69%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 52.0484, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 17.9917, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1410 0.0622 2.2674 0.0234 0.0191 0.2629 *
## TSTR -0.2561 0.1279 -2.0023 0.0453 -0.5069 -0.0054 *
## typeRR 0.5992 0.1496 4.0058 <.0001 0.3060 0.8923 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ TWRT + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0528 (SE = 0.0255)
## tau (square root of estimated tau^2 value): 0.2297
## I^2 (residual heterogeneity / unaccounted variability): 84.91%
## H^2 (unaccounted variability / sampling variability): 6.63
## R^2 (amount of heterogeneity accounted for): 42.95%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 67.0944, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.5280, p-val = 0.0052
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0965 0.0719 1.3425 0.1794 -0.0444 0.2374
## TWRT -0.0037 0.2016 -0.0181 0.9855 -0.3988 0.3915
## typeRR 0.5584 0.1741 3.2066 0.0013 0.2171 0.8997 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ CONT + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0217 (SE = 0.0129)
## tau (square root of estimated tau^2 value): 0.1473
## I^2 (residual heterogeneity / unaccounted variability): 70.26%
## H^2 (unaccounted variability / sampling variability): 3.36
## R^2 (amount of heterogeneity accounted for): 76.54%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 49.8196, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 30.3294, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0429 0.0506 0.8481 0.3964 -0.0563 0.1421
## CONTALT 0.5464 0.1536 3.5566 0.0004 0.2453 0.8475 ***
## typeRR 0.4669 0.1317 3.5439 0.0004 0.2087 0.7251 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ Duration + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0448 (SE = 0.0223)
## tau (square root of estimated tau^2 value): 0.2116
## I^2 (residual heterogeneity / unaccounted variability): 82.31%
## H^2 (unaccounted variability / sampling variability): 5.65
## R^2 (amount of heterogeneity accounted for): 51.60%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 62.5839, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 13.5896, p-val = 0.0011
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0810 0.1431 -0.5663 0.5712 -0.3616 0.1995
## Duration 0.2092 0.1533 1.3648 0.1723 -0.0912 0.5097
## typeRR 0.6782 0.1854 3.6589 0.0003 0.3149 1.0416 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ Hours + type)##
## Mixed-Effects Model (k = 19; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0538 (SE = 0.0266)
## tau (square root of estimated tau^2 value): 0.2319
## I^2 (residual heterogeneity / unaccounted variability): 83.82%
## H^2 (unaccounted variability / sampling variability): 6.18
## R^2 (amount of heterogeneity accounted for): 45.73%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 57.7967, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.8845, p-val = 0.0043
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0329 0.1107 0.2970 0.7665 -0.1841 0.2498
## Hours 0.0016 0.0022 0.7149 0.4747 -0.0027 0.0058
## typeRR 0.6034 0.1840 3.2789 0.0010 0.2427 0.9641 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ design + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0530 (SE = 0.0256)
## tau (square root of estimated tau^2 value): 0.2303
## I^2 (residual heterogeneity / unaccounted variability): 84.61%
## H^2 (unaccounted variability / sampling variability): 6.50
## R^2 (amount of heterogeneity accounted for): 42.66%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 66.6154, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 10.7842, p-val = 0.0046
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1177 0.0783 1.5040 0.1326 -0.0357 0.2712
## designQED -0.0727 0.1344 -0.5409 0.5886 -0.3361 0.1907
## typeRR 0.5641 0.1728 3.2640 0.0011 0.2254 0.9028 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ grade + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0547 (SE = 0.0264)
## tau (square root of estimated tau^2 value): 0.2339
## I^2 (residual heterogeneity / unaccounted variability): 86.16%
## H^2 (unaccounted variability / sampling variability): 7.22
## R^2 (amount of heterogeneity accounted for): 40.82%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 66.4138, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.5392, p-val = 0.0145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0941 0.0777 1.2117 0.2256 -0.0581 0.2464
## gradeK-2 -0.0302 0.1793 -0.1686 0.8661 -0.3816 0.3211
## gradeBoth 0.1423 0.3034 0.4690 0.6391 -0.4523 0.7369
## typeRR 0.5617 0.1785 3.1464 0.0017 0.2118 0.9115 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", mods = ~ grouping + type)##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0388 (SE = 0.0203)
## tau (square root of estimated tau^2 value): 0.1970
## I^2 (residual heterogeneity / unaccounted variability): 78.80%
## H^2 (unaccounted variability / sampling variability): 4.72
## R^2 (amount of heterogeneity accounted for): 58.05%
##
## Test for Residual Heterogeneity:
## QE(df = 17) = 55.6481, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 15.8232, p-val = 0.0004
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1408 0.0661 2.1298 0.0332 0.0112 0.2704 *
## grouping -0.2275 0.1314 -1.7312 0.0834 -0.4850 0.0301 .
## typeRR 0.5916 0.1563 3.7845 0.0002 0.2852 0.8979 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Reading Comprehension - RR
rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ Content)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0412)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9266, p-val = 0.3358
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 21.4317, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9126 0.1066 8.5594 <.0001 0.7036 1.1216 ***
## Content -0.7606 0.1643 -4.6294 <.0001 -1.0827 -0.4386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ PD)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TMULT)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.2286 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 0.4782
## I^2 (residual heterogeneity / unaccounted variability): 93.63%
## H^2 (unaccounted variability / sampling variability): 15.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.6957, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8597, p-val = 0.3538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5015 0.3494 1.4352 0.1512 -0.1833 1.1864
## TMULT 0.5816 0.6273 0.9272 0.3538 -0.6478 1.8110
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TVOC)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TSYN)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.2286 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 0.4782
## I^2 (residual heterogeneity / unaccounted variability): 93.63%
## H^2 (unaccounted variability / sampling variability): 15.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.6957, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8597, p-val = 0.3538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5015 0.3494 1.4352 0.1512 -0.1833 1.1864
## TSYN 0.5816 0.6273 0.9272 0.3538 -0.6478 1.8110
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TMOR)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.2286 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 0.4782
## I^2 (residual heterogeneity / unaccounted variability): 93.63%
## H^2 (unaccounted variability / sampling variability): 15.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.6957, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8597, p-val = 0.3538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5015 0.3494 1.4352 0.1512 -0.1833 1.1864
## TMOR 0.5816 0.6273 0.9272 0.3538 -0.6478 1.8110
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TLC)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TRC)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TCOM)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TPAD)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ LowIncome)## Error in rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == : Number of parameters to be estimated is larger than the number of observations.rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ EL)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.2286 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 0.4782
## I^2 (residual heterogeneity / unaccounted variability): 93.63%
## H^2 (unaccounted variability / sampling variability): 15.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.6957, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8597, p-val = 0.3538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5015 0.3494 1.4352 0.1512 -0.1833 1.1864
## EL 0.5816 0.6273 0.9272 0.3538 -0.6478 1.8110
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TDD)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.4043 (SE = 0.6130)
## tau (square root of estimated tau^2 value): 0.6359
## I^2 (residual heterogeneity / unaccounted variability): 93.27%
## H^2 (unaccounted variability / sampling variability): 14.86
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.8551, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0965, p-val = 0.7560
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6030 0.4653 1.2958 0.1950 -0.3090 1.5150
## TDD 0.2478 0.7977 0.3107 0.7560 -1.3156 1.8113
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TTEC)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0412)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9266, p-val = 0.3358
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 21.4317, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1520 0.1250 1.2158 0.2241 -0.0930 0.3970
## TTEC 0.7606 0.1643 4.6294 <.0001 0.4386 1.0827 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TSTR)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0412)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9266, p-val = 0.3358
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 21.4317, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9126 0.1066 8.5594 <.0001 0.7036 1.1216 ***
## TSTR -0.7606 0.1643 -4.6294 <.0001 -1.0827 -0.4386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ TWRT)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ CONT)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.2286 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 0.4782
## I^2 (residual heterogeneity / unaccounted variability): 93.63%
## H^2 (unaccounted variability / sampling variability): 15.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.6957, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8597, p-val = 0.3538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5015 0.3494 1.4352 0.1512 -0.1833 1.1864
## CONTALT 0.5816 0.6273 0.9272 0.3538 -0.6478 1.8110
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ Duration)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.2286 (SE = 0.3453)
## tau (square root of estimated tau^2 value): 0.4782
## I^2 (residual heterogeneity / unaccounted variability): 93.63%
## H^2 (unaccounted variability / sampling variability): 15.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.6957, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8597, p-val = 0.3538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5015 0.3494 1.4352 0.1512 -0.1833 1.1864
## Duration 0.5816 0.6273 0.9272 0.3538 -0.6478 1.8110
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ Hours)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1899 (SE = 0.2908)
## tau (square root of estimated tau^2 value): 0.4358
## I^2 (residual heterogeneity / unaccounted variability): 92.38%
## H^2 (unaccounted variability / sampling variability): 13.12
## R^2 (amount of heterogeneity accounted for): 10.23%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.1183, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1967, p-val = 0.2740
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0848 0.7481 -0.1134 0.9097 -1.5511 1.3815
## Hours 0.0601 0.0550 1.0939 0.2740 -0.0476 0.1678
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ design)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.4043 (SE = 0.6130)
## tau (square root of estimated tau^2 value): 0.6359
## I^2 (residual heterogeneity / unaccounted variability): 93.27%
## H^2 (unaccounted variability / sampling variability): 14.86
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.8551, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0965, p-val = 0.7560
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6030 0.4653 1.2958 0.1950 -0.3090 1.5150
## designQED 0.2478 0.7977 0.3107 0.7560 -1.3156 1.8113
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ grade)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2116 (SE = 0.2357)
## tau (square root of estimated tau^2 value): 0.4600
## I^2 (total heterogeneity / total variability): 90.86%
## H^2 (total variability / sampling variability): 10.94
##
## Test for Heterogeneity:
## Q(df = 2) = 22.3583, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6811 0.2802 2.4306 0.0151 0.1319 1.2302 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RR"), mods = ~ grouping)##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0412)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9266, p-val = 0.3358
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 21.4317, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.9126 0.1066 8.5594 <.0001 0.7036 1.1216 ***
## grouping -0.7606 0.1643 -4.6294 <.0001 -1.0827 -0.4386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Reading Comprehension - RS
rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ Content)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0221 (SE = 0.0142)
## tau (square root of estimated tau^2 value): 0.1488
## I^2 (residual heterogeneity / unaccounted variability): 69.35%
## H^2 (unaccounted variability / sampling variability): 3.26
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 43.5734, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1194, p-val = 0.7297
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1038 0.0703 1.4769 0.1397 -0.0340 0.2416
## Content -0.0342 0.0989 -0.3455 0.7297 -0.2280 0.1596
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ PD)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0181 (SE = 0.0118)
## tau (square root of estimated tau^2 value): 0.1346
## I^2 (residual heterogeneity / unaccounted variability): 68.05%
## H^2 (unaccounted variability / sampling variability): 3.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 44.6786, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0828, p-val = 0.7736
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0813 0.0478 1.7012 0.0889 -0.0124 0.1751 .
## PD 0.0562 0.1954 0.2877 0.7736 -0.3268 0.4392
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TMULT)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0232 (SE = 0.0146)
## tau (square root of estimated tau^2 value): 0.1523
## I^2 (residual heterogeneity / unaccounted variability): 71.57%
## H^2 (unaccounted variability / sampling variability): 3.52
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 43.8091, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3861, p-val = 0.5344
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0641 0.0623 1.0281 0.3039 -0.0581 0.1862
## TMULT 0.0653 0.1052 0.6213 0.5344 -0.1408 0.2715
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TVOC)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0176 (SE = 0.0115)
## tau (square root of estimated tau^2 value): 0.1325
## I^2 (residual heterogeneity / unaccounted variability): 67.53%
## H^2 (unaccounted variability / sampling variability): 3.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 44.7435, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0085, p-val = 0.9265
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1062 0.2401 0.4423 0.6583 -0.3644 0.5768
## TVOC -0.0226 0.2446 -0.0923 0.9265 -0.5020 0.4569
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TSYN)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0153 (SE = 0.0107)
## tau (square root of estimated tau^2 value): 0.1236
## I^2 (residual heterogeneity / unaccounted variability): 63.65%
## H^2 (unaccounted variability / sampling variability): 2.75
## R^2 (amount of heterogeneity accounted for): 6.63%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 41.3516, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.3167, p-val = 0.0377
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0395 0.0488 0.8097 0.4181 -0.0561 0.1351
## TSYN 0.2343 0.1128 2.0777 0.0377 0.0133 0.4554 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TMOR)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0231 (SE = 0.0145)
## tau (square root of estimated tau^2 value): 0.1520
## I^2 (residual heterogeneity / unaccounted variability): 71.49%
## H^2 (unaccounted variability / sampling variability): 3.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 43.8564, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4112, p-val = 0.5213
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0611 0.0643 0.9503 0.3420 -0.0650 0.1872
## TMOR 0.0658 0.1027 0.6413 0.5213 -0.1354 0.2671
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TLC)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0201 (SE = 0.0131)
## tau (square root of estimated tau^2 value): 0.1416
## I^2 (residual heterogeneity / unaccounted variability): 69.16%
## H^2 (unaccounted variability / sampling variability): 3.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 43.0729, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5829, p-val = 0.4452
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1121 0.0591 1.8961 0.0579 -0.0038 0.2279 .
## TLC -0.0770 0.1008 -0.7635 0.4452 -0.2745 0.1206
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TRC)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0174 (SE = 0.0120)
## tau (square root of estimated tau^2 value): 0.1318
## I^2 (residual heterogeneity / unaccounted variability): 64.09%
## H^2 (unaccounted variability / sampling variability): 2.78
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 40.4942, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.4465, p-val = 0.2291
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1331 0.0611 2.1776 0.0294 0.0133 0.2529 *
## TRC -0.1109 0.0922 -1.2027 0.2291 -0.2915 0.0698
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TCOM)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0182 (SE = 0.0124)
## tau (square root of estimated tau^2 value): 0.1350
## I^2 (residual heterogeneity / unaccounted variability): 65.17%
## H^2 (unaccounted variability / sampling variability): 2.87
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 40.0894, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9049, p-val = 0.1675
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1486 0.0656 2.2668 0.0234 0.0201 0.2771 *
## TCOM -0.1282 0.0929 -1.3802 0.1675 -0.3103 0.0539
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TPAD)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0170 (SE = 0.0113)
## tau (square root of estimated tau^2 value): 0.1302
## I^2 (residual heterogeneity / unaccounted variability): 66.51%
## H^2 (unaccounted variability / sampling variability): 2.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 44.1303, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5069, p-val = 0.4765
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0756 0.0470 1.6103 0.1073 -0.0164 0.1677
## TPAD 0.1312 0.1842 0.7120 0.4765 -0.2299 0.4922
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ LowIncome)##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0271 (SE = 0.0168)
## tau (square root of estimated tau^2 value): 0.1645
## I^2 (residual heterogeneity / unaccounted variability): 75.72%
## H^2 (unaccounted variability / sampling variability): 4.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 13) = 43.3565, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7442, p-val = 0.3883
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1606 0.0939 1.7114 0.0870 -0.0233 0.3446 .
## LowIncome -0.0996 0.1155 -0.8627 0.3883 -0.3260 0.1267
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ EL)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0220 (SE = 0.0141)
## tau (square root of estimated tau^2 value): 0.1485
## I^2 (residual heterogeneity / unaccounted variability): 69.45%
## H^2 (unaccounted variability / sampling variability): 3.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 44.6423, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6574, p-val = 0.4175
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0580 0.0606 0.9566 0.3388 -0.0608 0.1768
## EL 0.0847 0.1045 0.8108 0.4175 -0.1201 0.2895
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TDD)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0224 (SE = 0.0144)
## tau (square root of estimated tau^2 value): 0.1497
## I^2 (residual heterogeneity / unaccounted variability): 69.30%
## H^2 (unaccounted variability / sampling variability): 3.26
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 41.0104, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0222, p-val = 0.8816
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0931 0.0656 1.4182 0.1561 -0.0356 0.2217
## TDD -0.0149 0.1003 -0.1489 0.8816 -0.2116 0.1817
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TTEC)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0223 (SE = 0.0141)
## tau (square root of estimated tau^2 value): 0.1492
## I^2 (residual heterogeneity / unaccounted variability): 71.13%
## H^2 (unaccounted variability / sampling variability): 3.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 44.6014, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3642, p-val = 0.5462
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0671 0.0591 1.1348 0.2564 -0.0488 0.1830
## TTEC 0.0653 0.1083 0.6035 0.5462 -0.1469 0.2775
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TSTR)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0148 (SE = 0.0107)
## tau (square root of estimated tau^2 value): 0.1215
## I^2 (residual heterogeneity / unaccounted variability): 61.56%
## H^2 (unaccounted variability / sampling variability): 2.60
## R^2 (amount of heterogeneity accounted for): 9.71%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 39.3279, p-val = 0.0006
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1887, p-val = 0.2756
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1062 0.0484 2.1933 0.0283 0.0113 0.2011 *
## TSTR -0.1206 0.1107 -1.0903 0.2756 -0.3375 0.0962
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ TWRT)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0204 (SE = 0.0131)
## tau (square root of estimated tau^2 value): 0.1427
## I^2 (residual heterogeneity / unaccounted variability): 70.00%
## H^2 (unaccounted variability / sampling variability): 3.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 44.7361, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0018, p-val = 0.9662
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0850 0.0513 1.6571 0.0975 -0.0155 0.1855 .
## TWRT 0.0063 0.1480 0.0424 0.9662 -0.2838 0.2963
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ CONT)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0095 (SE = 0.0077)
## tau (square root of estimated tau^2 value): 0.0973
## I^2 (residual heterogeneity / unaccounted variability): 52.55%
## H^2 (unaccounted variability / sampling variability): 2.11
## R^2 (amount of heterogeneity accounted for): 42.15%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 33.9212, p-val = 0.0035
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 10.2617, p-val = 0.0014
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0495 0.0395 1.2522 0.2105 -0.0280 0.1270
## CONTALT 0.5129 0.1601 3.2034 0.0014 0.1991 0.8267 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ Duration)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0189 (SE = 0.0126)
## tau (square root of estimated tau^2 value): 0.1373
## I^2 (residual heterogeneity / unaccounted variability): 67.64%
## H^2 (unaccounted variability / sampling variability): 3.09
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 42.9838, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6034, p-val = 0.4373
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0046 0.1247 -0.0373 0.9703 -0.2490 0.2397
## Duration 0.1045 0.1346 0.7768 0.4373 -0.1592 0.3683
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ Hours)##
## Mixed-Effects Model (k = 16; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0146 (SE = 0.0110)
## tau (square root of estimated tau^2 value): 0.1209
## I^2 (residual heterogeneity / unaccounted variability): 60.16%
## H^2 (unaccounted variability / sampling variability): 2.51
## R^2 (amount of heterogeneity accounted for): 21.29%
##
## Test for Residual Heterogeneity:
## QE(df = 14) = 35.9596, p-val = 0.0011
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.3306, p-val = 0.2487
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0145 0.0729 0.1986 0.8425 -0.1284 0.1573
## Hours 0.0015 0.0013 1.1535 0.2487 -0.0011 0.0042
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ design)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0186 (SE = 0.0124)
## tau (square root of estimated tau^2 value): 0.1363
## I^2 (residual heterogeneity / unaccounted variability): 67.27%
## H^2 (unaccounted variability / sampling variability): 3.06
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 41.6019, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.3556, p-val = 0.2443
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1190 0.0552 2.1582 0.0309 0.0109 0.2272 *
## designQED -0.1209 0.1038 -1.1643 0.2443 -0.3244 0.0826
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ grade)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0199 (SE = 0.0128)
## tau (square root of estimated tau^2 value): 0.1409
## I^2 (residual heterogeneity / unaccounted variability): 70.99%
## H^2 (unaccounted variability / sampling variability): 3.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 14) = 44.0555, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.4765, p-val = 0.7880
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0804 0.0523 1.5377 0.1241 -0.0221 0.1828
## gradeK-2 -0.0149 0.1464 -0.1015 0.9192 -0.3018 0.2721
## gradeBoth 0.1561 0.2321 0.6724 0.5013 -0.2988 0.6109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1rma(yi = ES, vi = EV, data = df_r, method = "REML", subset = (type == "RS"), mods = ~ grouping)##
## Mixed-Effects Model (k = 17; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0182 (SE = 0.0125)
## tau (square root of estimated tau^2 value): 0.1350
## I^2 (residual heterogeneity / unaccounted variability): 64.81%
## H^2 (unaccounted variability / sampling variability): 2.84
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 40.3166, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6187, p-val = 0.4315
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1046 0.0528 1.9790 0.0478 0.0010 0.2082 *
## grouping -0.0872 0.1108 -0.7866 0.4315 -0.3044 0.1300
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1EL vs EO
EL
EL Post:
read_post_data <- function(type, category) {
read_xlsx(data_file, sheet = "ELs") %>% select(AUTYR = AUTH_YR, (ends_with(category) & contains(type))) %>%
rename_at(vars(-AUTYR), ~ str_replace(., type, "")) %>%
rename_at(vars(-AUTYR), ~ str_replace(., category, "")) %>%
drop_na(AUTYR) %>%
filter(is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
mutate(type = type) %>%
select(AUTYR, type, sort(current_vars()))
}
df_el_post <-
bind_rows(
read_post_data("VR", "EL"),
read_post_data("VS", "EL"),
read_post_data("RR", "EL"),
read_post_data("RS", "EL")
)
for (m in 1:4) {
if (
!(str_glue("TM{m}post") %in% colnames(df_el_post)) |
!(str_glue("CM{m}post") %in% colnames(df_el_post))
) {
next
}
df_el_post <-
escalc(
data = df_el_post,
measure = "SMD",
m1i = df_el_post[, str_c("TM", m, "post")] %>% unlist(),
m2i = df_el_post[, str_c("CM", m, "post")] %>% unlist(),
sd1i = df_el_post[, str_c("TS", m, "post")] %>% unlist(),
sd2i = df_el_post[, str_c("CS", m, "post")] %>% unlist(),
n1i = df_el_post[, str_c("TN", m, "post")] %>% unlist(),
n2i = df_el_post[, str_c("CN", m, "post")] %>% unlist(),
var.names = c(str_glue("ES_{m}"), str_glue("EV_{m}"))
)
}
df_el_post <-
df_el_post %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_el_post %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)EL prepost:
read_prepost_data <- function(type, category) {
read_xlsx(data_file, sheet = "ELs") %>% select(AUTYR = AUTH_YR, (ends_with(category) & contains(type))) %>%
rename_at(vars(-AUTYR), ~ str_replace(., type, "")) %>%
rename_at(vars(-AUTYR), ~ str_replace(., category, "")) %>%
drop_na(AUTYR) %>%
filter(!is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
mutate(type = type) %>%
select(AUTYR, type, sort(current_vars()))
}
df_el_prepost <-
bind_rows(
read_prepost_data("VR", "EL"),
read_prepost_data("VS", "EL"),
read_prepost_data("RR", "EL"),
read_prepost_data("RS", "EL")
) %>%
mutate(
TN1post = if_else(is.na(TN1post) & !is.na(TN1pre), TN1pre, TN1post),
TN2post = if_else(is.na(TN2post) & !is.na(TN2pre), TN2pre, TN2post),
CN1post = if_else(is.na(CN1post) & !is.na(CN1pre), CN1pre, CN1post),
CN2post = if_else(is.na(CN2post) & !is.na(CN2pre), CN2pre, CN2post)
)
# treatment (post-pre)
for (mt in 1:4) {
if (
!(str_glue("TM{mt}post") %in% colnames(df_el_prepost)) |
!(str_glue("TM{mt}pre") %in% colnames(df_el_prepost))
) {
next
}
df_el_prepost <-
escalc(
data = df_el_prepost,
measure = "SMCR",
m1i = df_el_prepost[, str_c("TM", mt, "post")] %>% unlist(),
m2i = df_el_prepost[, str_c("TM", mt, "pre")] %>% unlist(),
sd1i = df_el_prepost[, str_c("TS", mt, "pre")] %>% unlist(),
ni = df_el_prepost[, str_c("TN", mt, "post")] %>% unlist(),
ri = rep(0.5, 11),
var.names = c(str_glue("TES_TM{mt}"), str_glue("TEV_TM{mt}"))
)
}
# control (post-pre)
for (mc in 1:4) {
if (
!(str_glue("CM{mc}post") %in% colnames(df_el_prepost)) |
!(str_glue("CM{mc}pre") %in% colnames(df_el_prepost))
) {
next
}
df_el_prepost <-
escalc(
data = df_el_prepost,
measure = "SMCR",
m1i = df_el_prepost[, str_c("CM", mc, "post")] %>% unlist(),
m2i = df_el_prepost[, str_c("CM", mc, "pre")] %>% unlist(),
sd1i = df_el_prepost[, str_c("CS", mc, "pre")] %>% unlist(),
ni = df_el_prepost[, str_c("CN", mc, "post")] %>% unlist(),
ri = rep(0.5, 11),
var.names = c(str_glue("CES_CM{mc}"), str_glue("CEV_CM{mc}"))
)
}
# ES and EV taken together
for (m in 1:4) {
if (
!(str_glue("TES_TM{m}") %in% colnames(df_el_prepost)) |
!(str_glue("TEV_TM{m}") %in% colnames(df_el_prepost)) |
!(str_glue("CES_CM{m}") %in% colnames(df_el_prepost)) |
!(str_glue("CEV_CM{m}") %in% colnames(df_el_prepost))
) {
next
}
# subtracting effect size
df_el_prepost[, str_c("ES_", m)] <-
(df_el_prepost[, str_c("TES_TM", m)] %>% unlist()) -
(df_el_prepost[, str_c("CES_CM", m)] %>% unlist())
# adding variance
df_el_prepost[, str_c("EV_", m)] <-
(df_el_prepost[, str_c("TEV_TM", m)] %>% unlist()) +
(df_el_prepost[, str_c("CEV_CM", m)] %>% unlist())
}
df_el_prepost <-
df_el_prepost %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_el_prepost %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)EO
EO Post:
read_post_data <- function(type, category) {
read_xlsx(data_file, sheet = "ELs") %>% select(AUTYR = AUTH_YR, (ends_with(category) & contains(type))) %>%
rename_at(vars(-AUTYR), ~ str_replace(., type, "")) %>%
rename_at(vars(-AUTYR), ~ str_replace(., category, "")) %>%
drop_na(AUTYR) %>%
filter(is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
mutate(type = type) %>%
select(AUTYR, type, sort(current_vars()))
}
df_eo_post <-
bind_rows(
read_post_data("VR", "EO"),
read_post_data("VS", "EO"),
read_post_data("RR", "EO"),
read_post_data("RS", "EO")
)
for (m in 1:4) {
if (
!(str_glue("TM{m}post") %in% colnames(df_eo_post)) |
!(str_glue("CM{m}post") %in% colnames(df_eo_post))
) {
next
}
df_eo_post <-
escalc(
data = df_eo_post,
measure = "SMD",
m1i = df_eo_post[, str_c("TM", m, "post")] %>% unlist(),
m2i = df_eo_post[, str_c("CM", m, "post")] %>% unlist(),
sd1i = df_eo_post[, str_c("TS", m, "post")] %>% unlist(),
sd2i = df_eo_post[, str_c("CS", m, "post")] %>% unlist(),
n1i = df_eo_post[, str_c("TN", m, "post")] %>% unlist(),
n2i = df_eo_post[, str_c("CN", m, "post")] %>% unlist(),
var.names = c(str_glue("ES_{m}"), str_glue("EV_{m}"))
)
}
df_eo_post <-
df_eo_post %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_eo_post %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)EO prepost:
read_prepost_data <- function(type, category) {
read_xlsx(data_file, sheet = "ELs") %>% select(AUTYR = AUTH_YR, (ends_with(category) & contains(type))) %>%
rename_at(vars(-AUTYR), ~ str_replace(., type, "")) %>%
rename_at(vars(-AUTYR), ~ str_replace(., category, "")) %>%
drop_na(AUTYR) %>%
filter(!is.na(TM1pre)) %>%
select_if(~ any(!is.na(.))) %>%
mutate(type = type) %>%
select(AUTYR, type, sort(current_vars()))
}
df_eo_prepost <-
bind_rows(
read_prepost_data("VR", "EO"),
read_prepost_data("VS", "EO"),
read_prepost_data("RR", "EO"),
read_prepost_data("RS", "EO")
) %>%
mutate(
TN1post = if_else(is.na(TN1post) & !is.na(TN1pre), TN1pre, TN1post),
TN2post = if_else(is.na(TN2post) & !is.na(TN2pre), TN2pre, TN2post),
CN1post = if_else(is.na(CN1post) & !is.na(CN1pre), CN1pre, CN1post),
CN2post = if_else(is.na(CN2post) & !is.na(CN2pre), CN2pre, CN2post)
)
# treatment (post-pre)
for (mt in 1:4) {
if (
!(str_glue("TM{mt}post") %in% colnames(df_eo_prepost)) |
!(str_glue("TM{mt}pre") %in% colnames(df_eo_prepost))
) {
next
}
df_eo_prepost <-
escalc(
data = df_eo_prepost,
measure = "SMCR",
m1i = df_eo_prepost[, str_c("TM", mt, "post")] %>% unlist(),
m2i = df_eo_prepost[, str_c("TM", mt, "pre")] %>% unlist(),
sd1i = df_eo_prepost[, str_c("TS", mt, "pre")] %>% unlist(),
ni = df_eo_prepost[, str_c("TN", mt, "post")] %>% unlist(),
ri = rep(0.5, 11),
var.names = c(str_glue("TES_TM{mt}"), str_glue("TEV_TM{mt}"))
)
}
# control (post-pre)
for (mc in 1:4) {
if (
!(str_glue("CM{mc}post") %in% colnames(df_eo_prepost)) |
!(str_glue("CM{mc}pre") %in% colnames(df_eo_prepost))
) {
next
}
df_eo_prepost <-
escalc(
data = df_eo_prepost,
measure = "SMCR",
m1i = df_eo_prepost[, str_c("CM", mc, "post")] %>% unlist(),
m2i = df_eo_prepost[, str_c("CM", mc, "pre")] %>% unlist(),
sd1i = df_eo_prepost[, str_c("CS", mc, "pre")] %>% unlist(),
ni = df_eo_prepost[, str_c("CN", mc, "post")] %>% unlist(),
ri = rep(0.5, 11),
var.names = c(str_glue("CES_CM{mc}"), str_glue("CEV_CM{mc}"))
)
}
# ES and EV taken together
for (m in 1:4) {
if (
!(str_glue("TES_TM{m}") %in% colnames(df_eo_prepost)) |
!(str_glue("TEV_TM{m}") %in% colnames(df_eo_prepost)) |
!(str_glue("CES_CM{m}") %in% colnames(df_eo_prepost)) |
!(str_glue("CEV_CM{m}") %in% colnames(df_eo_prepost))
) {
next
}
# subtracting effect size
df_eo_prepost[, str_c("ES_", m)] <-
(df_eo_prepost[, str_c("TES_TM", m)] %>% unlist()) -
(df_eo_prepost[, str_c("CES_CM", m)] %>% unlist())
# adding variance
df_eo_prepost[, str_c("EV_", m)] <-
(df_eo_prepost[, str_c("TEV_TM", m)] %>% unlist()) +
(df_eo_prepost[, str_c("CEV_CM", m)] %>% unlist())
}
df_eo_prepost <-
df_eo_prepost %>%
select(AUTYR, type, starts_with("ES")) %>%
pivot_longer(cols = starts_with("ES"), names_to = "num", values_to = "ES", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)) %>%
left_join(
df_eo_prepost %>%
select(AUTYR, type, starts_with("EV")) %>%
pivot_longer(cols = starts_with("EV"), names_to = "num", values_to = "EV", values_drop_na = T) %>%
mutate(num = str_sub(num, 4)),
by = c("AUTYR", "type", "num")
) %>%
select(-num)Joining
# EL
df_el_clean <-
bind_rows(df_el_post, df_el_prepost) %>%
left_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
drop_na(AUTYR),
by = "AUTYR"
) %>%
arrange(type, stdid) %>%
select(type, stdid, AUTYR, ES, EV) %>%
unite("type_stdid", c("type", "stdid")) %>%
agg(id = type_stdid, es = ES, var = EV, method = "BHHR", data = .) %>%
separate(id, c("type", "stdid")) %>%
rename(ES = es, EV = var)
# EO
df_eo_clean <-
bind_rows(df_eo_post, df_eo_prepost) %>%
left_join(
read_xlsx(data_file, sheet = "StudyChar") %>%
drop_na(AUTYR),
by = "AUTYR"
) %>%
arrange(type, stdid) %>%
select(type, stdid, AUTYR, ES, EV) %>%
unite("type_stdid", c("type", "stdid")) %>%
agg(id = type_stdid, es = ES, var = EV, method = "BHHR", data = .) %>%
separate(id, c("type", "stdid")) %>%
rename(ES = es, EV = var)
rm(df_el_post, df_el_prepost, df_eo_post, df_eo_prepost)
df_el_clean %>% datatable()df_eo_clean %>% datatable()Calculating Effect Sizes
# EL
df_el_clean %>%
filter(type == "VR") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.3533 (SE = 0.2846)
## tau (square root of estimated tau^2 value): 0.5944
## I^2 (total heterogeneity / total variability): 83.25%
## H^2 (total variability / sampling variability): 5.97
##
## Test for Heterogeneity:
## Q(df = 5) = 22.8558, p-val = 0.0004
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.2167 0.2753 4.4198 <.0001 0.6771 1.7562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_el_clean %>%
filter(type == "VS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0362)
## tau (square root of estimated tau^2 value): 0.0006
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 5) = 4.2750, p-val = 0.5105
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1203 0.1008 1.1937 0.2326 -0.0772 0.3178
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_el_clean %>%
filter(type == "RR") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.1888 (SE = 0.3216)
## tau (square root of estimated tau^2 value): 0.4346
## I^2 (total heterogeneity / total variability): 60.98%
## H^2 (total variability / sampling variability): 2.56
##
## Test for Heterogeneity:
## Q(df = 2) = 4.6386, p-val = 0.0983
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.9002 0.3244 2.7752 0.0055 0.2644 1.5359 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_el_clean %>%
filter(type == "RS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0843)
## tau (square root of estimated tau^2 value): 0.0020
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 2.6406, p-val = 0.2670
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0664 0.1530 0.4341 0.6642 -0.2335 0.3663
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# EO
df_eo_clean %>%
filter(type == "VR") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2976 (SE = 0.2184)
## tau (square root of estimated tau^2 value): 0.5455
## I^2 (total heterogeneity / total variability): 87.88%
## H^2 (total variability / sampling variability): 8.25
##
## Test for Heterogeneity:
## Q(df = 5) = 34.2149, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.1729 0.2400 4.8862 <.0001 0.7024 1.6433 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_eo_clean %>%
filter(type == "VS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0290 (SE = 0.0441)
## tau (square root of estimated tau^2 value): 0.1704
## I^2 (total heterogeneity / total variability): 41.98%
## H^2 (total variability / sampling variability): 1.72
##
## Test for Heterogeneity:
## Q(df = 5) = 8.7224, p-val = 0.1207
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0993 0.1082 0.9178 0.3587 -0.1128 0.3114
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_eo_clean %>%
filter(type == "RR") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.1587 (SE = 0.2016)
## tau (square root of estimated tau^2 value): 0.3984
## I^2 (total heterogeneity / total variability): 79.49%
## H^2 (total variability / sampling variability): 4.88
##
## Test for Heterogeneity:
## Q(df = 2) = 10.9316, p-val = 0.0042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1562 0.2590 0.6030 0.5465 -0.3515 0.6638
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_eo_clean %>%
filter(type == "RS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0302 (SE = 0.0689)
## tau (square root of estimated tau^2 value): 0.1738
## I^2 (total heterogeneity / total variability): 44.19%
## H^2 (total variability / sampling variability): 1.79
##
## Test for Heterogeneity:
## Q(df = 2) = 3.5380, p-val = 0.1705
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.1507 -0.5560 0.5782 -0.3792 0.2116
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Combined
# EL
df_el_clean %>%
filter(type == "VR" | type == "VS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.4048 (SE = 0.2110)
## tau (square root of estimated tau^2 value): 0.6362
## I^2 (total heterogeneity / total variability): 86.16%
## H^2 (total variability / sampling variability): 7.22
##
## Test for Heterogeneity:
## Q(df = 11) = 70.0514, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.7220 0.2040 3.5388 0.0004 0.3221 1.1219 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_el_clean %>%
filter(type == "RR" | type == "RS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2306 (SE = 0.2182)
## tau (square root of estimated tau^2 value): 0.4802
## I^2 (total heterogeneity / total variability): 72.25%
## H^2 (total variability / sampling variability): 3.60
##
## Test for Heterogeneity:
## Q(df = 5) = 16.9677, p-val = 0.0046
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.5469 0.2414 2.2650 0.0235 0.0737 1.0201 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# EO
df_eo_clean %>%
filter(type == "VR" | type == "VS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.4452 (SE = 0.2094)
## tau (square root of estimated tau^2 value): 0.6672
## I^2 (total heterogeneity / total variability): 91.79%
## H^2 (total variability / sampling variability): 12.18
##
## Test for Heterogeneity:
## Q(df = 11) = 119.2246, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6516 0.2024 3.2199 0.0013 0.2550 1.0482 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_eo_clean %>%
filter(type == "RR" | type == "RS") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 6; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0906 (SE = 0.0828)
## tau (square root of estimated tau^2 value): 0.3010
## I^2 (total heterogeneity / total variability): 70.62%
## H^2 (total variability / sampling variability): 3.40
##
## Test for Heterogeneity:
## Q(df = 5) = 19.4352, p-val = 0.0016
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0492 0.1480 0.3327 0.7393 -0.2408 0.3393
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# # Difference
# bind_rows(
# df_el_clean %>% mutate(els = "EL"),
# df_eo_clean %>% mutate(els = "EO")
# ) %>%
# mutate(els = els %>% as_factor()) %>%
# filter(type == "RR"| type == "RS") %>%
# rma(
# yi = ES,
# vi = EV,
# data = .,
# mods = ~ els,
# method = "REML"
# )Publication Bias
Funnel plots
df_v %>%
rma(
yi = ES,
vi = EV,
data = .,
mods = ~ type * grouping * TMULT,
method = "REML"
) %>%
funnel(xlab = "Vocabulary Effect Size", pch = 21, bg ="grey")
df_l %>%
rma(
yi = ES,
vi = EV,
data = .,
mods = ~ type * grouping * TMULT,
method = "REML"
) %>%
funnel(xlab = "Listening Comprehension Effect Size", pch = 21, bg ="grey")
df_r %>%
rma(
yi = ES,
vi = EV,
data = .,
mods = ~ type * grouping * TMULT,
method = "REML"
) %>%
funnel(xlab = "Reading Comprehension Effect Size", pch = 21, bg ="grey")
Egger’s Test
# Vocabulary
df_v %>%
rma(
yi = ES,
vi = EV,
data = .,
mods = ~ type * grouping * TMULT,
method = "REML"
) %>%
regtest(model = "rma", predictor = "sei")##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = 1.5606, p = 0.1186# Listening Comp.
df_l %>%
rma(
yi = ES,
vi = EV,
data = .,
mods = ~ type * grouping * TMULT,
method = "REML"
) %>%
regtest(model = "rma", predictor = "sei")##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = 1.3100, p = 0.1902# Reading Comp.
df_r %>%
rma(
yi = ES,
vi = EV,
data = .,
mods = ~ type * grouping * TMULT,
method = "REML"
) %>%
regtest(model = "rma", predictor = "sei")##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = 0.4794, p = 0.6317P Curves
# Vocabulary
df_v %>%
mutate(
SE = sqrt(EV)
) %>%
meta::metagen(
ES,
SE,
data = .,
comb.fixed = F,
comb.random = T,
method.tau = "REML",
hakn = T,
sm = "SMD"
) %>%
pcurve()
## P-curve analysis
## -----------------------
## - Total number of provided studies: k = 68
## - Total number of p<0.05 studies included into the analysis: k = 39 (57.35%)
## - Total number of studies with p<0.025: k = 36 (52.94%)
##
## Results
## -----------------------
## pBinomial zFull pFull zHalf pHalf
## Right-skewness test 0 -31.216 0 -31.570 0
## Flatness test 1 28.320 1 31.259 1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.
## Power Estimate: 99% (99%-99%)
##
## Evidential value
## -----------------------
## - Evidential value present: yes
## - Evidential value absent/inadequate: no# Reading Comp
df_r %>%
mutate(
SE = sqrt(EV)
) %>%
meta::metagen(
ES,
SE,
data = .,
comb.fixed = F,
comb.random = T,
method.tau = "REML",
hakn = T,
sm = "SMD"
) %>%
pcurve()
## P-curve analysis
## -----------------------
## - Total number of provided studies: k = 20
## - Total number of p<0.05 studies included into the analysis: k = 4 (20%)
## - Total number of studies with p<0.025: k = 4 (20%)
##
## Results
## -----------------------
## pBinomial zFull pFull zHalf pHalf
## Right-skewness test 0.062 -9.718 0 -9.432 0
## Flatness test 1.000 7.509 1 7.895 1
## Note: p-values of 0 or 1 correspond to p<0.001 and p>0.999, respectively.
## Power Estimate: 99% (99%-99%)
##
## Evidential value
## -----------------------
## - Evidential value present: yes
## - Evidential value absent/inadequate: no# Listening Comp
df_l %>%
mutate(
SE = sqrt(EV)
) %>%
meta::metagen(
ES,
SE,
data = .,
comb.fixed = F,
comb.random = T,
method.tau = "REML",
hakn = T,
sm = "SMD"
) %>%
pcurve()## Error in pcurve(.): Two or less significant (p<0.05) effect sizes were detected, so p-curve analysis cannot be conducted.Risk of bias analysis
source("rob_summary.R")
df_append %>%
mutate(Selection = if_else(RCT == 1, "Low", "High")) %>%
distinct(stdid, Selection, Performance, Detection, Attrition, Reporting) %>%
select(stdid, Selection, Performance, Detection, Attrition, Reporting) %>%
mutate_at(vars(Selection:Reporting), ~ recode(., "H" = "High", "L" = "Low", "U" = "Unclear")) %>%
mutate_at(vars(Selection:Reporting), ~ factor(., levels = c("High", "Low", "Unclear"), ordered = T)) %>%
rename(Study = stdid, `Selection bias` = Selection, `Performance bias` = Performance, `Detection bias` = Detection, `Attrition bias` = Attrition, `Reporting bias` = Reporting) %>%
left_join(
cor_es %>%
group_by(stdid) %>%
summarise(EV = mean(EV, na.rm = T)) %>%
transmute(Study = stdid, Overall = factor("Low"), Weight = 1/EV %>% as.numeric()),
by = "Study"
) %>%
mutate(Study = Study %>% as_factor()) %>%
rob_summary(tool = "ROB1")
Sensitivity analyses
Exclude high selection risk studies
df_v %>%
filter(design == "RCT") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 43; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.5341 (SE = 0.3440)
## tau (square root of estimated tau^2 value): 1.2386
## I^2 (total heterogeneity / total variability): 99.56%
## H^2 (total variability / sampling variability): 227.55
##
## Test for Heterogeneity:
## Q(df = 42) = 1488.3931, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.8629 0.1915 4.5051 <.0001 0.4875 1.2383 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_l %>%
filter(design == "RCT") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 10; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0050 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.0709
## I^2 (total heterogeneity / total variability): 27.08%
## H^2 (total variability / sampling variability): 1.37
##
## Test for Heterogeneity:
## Q(df = 9) = 11.5834, p-val = 0.2378
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0994 0.0453 2.1926 0.0283 0.0105 0.1883 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_r %>%
filter(design == "RCT") %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 14; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0844 (SE = 0.0428)
## tau (square root of estimated tau^2 value): 0.2905
## I^2 (total heterogeneity / total variability): 90.91%
## H^2 (total variability / sampling variability): 11.00
##
## Test for Heterogeneity:
## Q(df = 13) = 59.2673, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.2080 0.0892 2.3302 0.0198 0.0330 0.3829 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Capping outliers
df_v %>%
mutate(
ES = if_else(
ES %in% boxplot(.$ES, plot = FALSE)$out,
quantile(.$ES, probs = 0.75, na.rm = T) + 1.5 * IQR(.$ES, na.rm = T),
ES
)
) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 68; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.5577 (SE = 0.1047)
## tau (square root of estimated tau^2 value): 0.7468
## I^2 (total heterogeneity / total variability): 98.38%
## H^2 (total variability / sampling variability): 61.70
##
## Test for Heterogeneity:
## Q(df = 67) = 1650.2998, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.7258 0.0947 7.6665 <.0001 0.5403 0.9114 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_l %>%
mutate(
ES = if_else(
ES %in% boxplot(.$ES, plot = FALSE)$out,
quantile(.$ES, probs = 0.75, na.rm = T) + 1.5 * IQR(.$ES, na.rm = T),
ES
)
) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 12; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0076)
## tau (square root of estimated tau^2 value): 0.0633
## I^2 (total heterogeneity / total variability): 21.15%
## H^2 (total variability / sampling variability): 1.27
##
## Test for Heterogeneity:
## Q(df = 11) = 12.6457, p-val = 0.3171
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0968 0.0417 2.3245 0.0201 0.0152 0.1785 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_r %>%
mutate(
ES = if_else(
ES %in% boxplot(.$ES, plot = FALSE)$out,
quantile(.$ES, probs = 0.75, na.rm = T) + 1.5 * IQR(.$ES, na.rm = T),
ES
)
) %>%
rma(
yi = ES,
vi = EV,
data = .,
method = "REML"
)##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0282 (SE = 0.0149)
## tau (square root of estimated tau^2 value): 0.1680
## I^2 (total heterogeneity / total variability): 74.46%
## H^2 (total variability / sampling variability): 3.91
##
## Test for Heterogeneity:
## Q(df = 19) = 57.3079, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1290 0.0495 2.6037 0.0092 0.0319 0.2261 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Change within study correlations
df_append %>%
filter(type %in% c("VR", "VS")) %>%
robu(
formula = ES ~ 1,
var.eff.size = EV,
studynum = stdid,
data = .,
modelweights = "CORR"
) %>%
sensitivity()## RVE: Correlated Effects Model with Small-Sample Corrections
## Model: ES ~ 1
##
## Sensitivity Analysis
##
## Rho = 0 Rho = 0.2 Rho = 0.4 Rho = 0.6 Rho = 0.8
## X.Intercept. Coefficient 0.904 0.904 0.904 0.904 0.904
## Std. Error 0.133 0.133 0.133 0.133 0.133
## Tau.sq Estimate 0.365 0.365 0.365 0.365 0.365
## Rho = 1
## 0.904
## 0.133
## 0.365df_append %>%
filter(type %in% c("RR", "RS")) %>%
robu(
formula = ES ~ 1,
var.eff.size = EV,
studynum = stdid,
data = .,
modelweights = "CORR"
) %>%
sensitivity()## RVE: Correlated Effects Model with Small-Sample Corrections
## Model: ES ~ 1
##
## Sensitivity Analysis
##
## Rho = 0 Rho = 0.2 Rho = 0.4 Rho = 0.6 Rho = 0.8
## X.Intercept. Coefficient 0.1290 0.1290 0.1290 0.1290 0.1290
## Std. Error 0.0539 0.0539 0.0539 0.0539 0.0539
## Tau.sq Estimate 0.0336 0.0336 0.0336 0.0336 0.0336
## Rho = 1
## 0.1290
## 0.0539
## 0.0336df_append %>%
filter(type %in% c("LR", "LS")) %>%
robu(
formula = ES ~ 1,
var.eff.size = EV,
studynum = stdid,
data = .,
modelweights = "CORR"
) %>%
sensitivity()## RVE: Correlated Effects Model with Small-Sample Corrections
## Model: ES ~ 1
##
## Sensitivity Analysis
##
## Rho = 0 Rho = 0.2 Rho = 0.4 Rho = 0.6 Rho = 0.8
## X.Intercept. Coefficient 0.08342 0.08370 0.08398 0.08425 0.08452
## Std. Error 0.04190 0.04188 0.04187 0.04185 0.04184
## Tau.sq Estimate 0.00477 0.00484 0.00491 0.00498 0.00505
## Rho = 1
## 0.08478
## 0.04182
## 0.00513MetaForest plots
Vocabulary
df_v %>%
rename(vi = EV) %>%
mutate(stdid = stdid %>% as_factor()) %>%
drop_na(Hours, LowIncome) %>%
MetaForest(
formula = ES ~ .,
data = .,
study = "stdid"
) %>%
preselect(replications = 500L) %>%
plot()
Listening Comprehension
df_l %>%
rename(vi = EV) %>%
mutate(stdid = stdid %>% as_factor()) %>%
drop_na(Hours, LowIncome) %>%
MetaForest(
formula = ES ~ .,
data = .,
study = "stdid"
) %>%
preselect(replications = 500L) %>%
plot()
Reading Comprehension
df_r %>%
rename(vi = EV) %>%
mutate(stdid = stdid %>% as_factor()) %>%
drop_na(Hours, LowIncome) %>%
MetaForest(
formula = ES ~ .,
data = .,
study = "stdid"
) %>%
preselect(replications = 500L) %>%
plot()