# Libraries
library(tidyverse)
library(igraph)
library(ggraph)
library(tidygraph)
library(haven)
library(stargazer)
library(statnet)
library(intergraph)
library(metafor)
# Parameters
baseline_network_file <- "baseline_network.dta"
endline_network_file <- "endline_network.dta"
#===============================================================================
Creating networks for each classroom
classids <- read_dta(endline_network_file) %>% select(classid) %>% arrange(classid) %>% distinct() %>% unlist()
b_netlist <- list()
e_netlist <- list()
for (class in classids) {
print(str_c("Now at class: ", class))
b_nodes <-
read_dta(baseline_network_file) %>%
filter(classid == class) %>%
select(-b_node2) %>%
mutate(
reservation = recode(reservation, `Non-reservation` = "0", `Reservation` = "1", .default = NA_character_) %>% as.integer()
) %>%
distinct()
b_edges <-
read_dta(baseline_network_file) %>%
filter(classid == class) %>%
select(b_node1, b_node2) %>%
filter(b_node2 != "")
e_nodes <-
read_dta(endline_network_file) %>%
filter(classid == class) %>%
select(-e_node2) %>%
mutate(
reservation = recode(reservation, `Non-reservation` = "0", `Reservation` = "1", .default = NA_character_) %>% as.integer()
) %>%
distinct()
e_edges <-
read_dta(endline_network_file) %>%
filter(classid == class) %>%
select(e_node1, e_node2) %>%
filter(e_node2 != "")
b_netlist[[class]] <-
graph_from_data_frame(b_edges, b_nodes, directed = TRUE) %>%
asNetwork()
e_netlist[[class]] <-
graph_from_data_frame(e_edges, e_nodes, directed = TRUE) %>%
asNetwork()
}
Running STERGMs
results <- tibble()
for (class in classids) {
tryCatch(
{
print(str_c("Now at class: ", class))
model <-
stergm(
list(b_netlist[[class]], e_netlist[[class]]),
formation =
~ edges + nodefactor("reservation") + nodematch("reservation") + nodefactor("female") + nodematch("female") + nodefactor("ses") + nodematch("ses") + nodefactor("area") + nodematch("area") + nodecov("score") + absdiff("score") + mutual + gwesp(0.25),
dissolution =
~ edges + nodefactor("reservation") + nodematch("reservation") + nodefactor("female") + nodematch("female") + nodefactor("ses") + nodematch("ses") + nodefactor("area") + nodematch("area") + nodecov("score") + absdiff("score") + mutual + gwesp(0.25),
estimate = "CMLE",
times = 1:2,
control = control.stergm(seed = 100)
)
summary <- model %>% summary()
results <-
bind_rows(
results,
bind_cols(
rep(
class, nrow(summary[["formation"]][["coefs"]][["Estimate"]] %>% as_tibble())
) %>%
as_tibble() %>%
select(classid = value),
model[["formation.fit"]][["coef"]] %>% names() %>% as_tibble() %>% select(name = value),
summary[["formation"]][["coefs"]][["Estimate"]] %>% as_tibble() %>% select(form_coef = value),
summary[["formation"]][["coefs"]][["Std. Error"]] %>% as_tibble() %>% select(form_se = value),
summary[["dissolution"]][["coefs"]][["Estimate"]] %>% as_tibble() %>% select(diss_coef = value),
summary[["dissolution"]][["coefs"]][["Std. Error"]] %>% as_tibble() %>% select(diss_se = value)
)
)
},
error = function(e){cat("ERROR :", conditionMessage(e), "\n")})
}
results %>% write_csv("results_v2.csv")
Level 1 Formation
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.reservation.1") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0021 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0457
## I^2 (total heterogeneity / total variability): 38.41%
## H^2 (total variability / sampling variability): 1.62
##
## Test for Heterogeneity:
## Q(df = 152) = 247.4486, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0095 0.0066 1.4307 0.1525 -0.0035 0.0224
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.female.1") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 152; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0044 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0667
## I^2 (total heterogeneity / total variability): 47.35%
## H^2 (total variability / sampling variability): 1.90
##
## Test for Heterogeneity:
## Q(df = 151) = 296.6546, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0443 0.0093 -4.7605 <.0001 -0.0625 -0.0261 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.area.2") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 155; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0401
## I^2 (total heterogeneity / total variability): 27.81%
## H^2 (total variability / sampling variability): 1.39
##
## Test for Heterogeneity:
## Q(df = 154) = 235.8977, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0056 0.0067 -0.8375 0.4023 -0.0188 0.0076
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.ses.2") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0347
## I^2 (total heterogeneity / total variability): 21.53%
## H^2 (total variability / sampling variability): 1.27
##
## Test for Heterogeneity:
## Q(df = 155) = 221.1349, p-val = 0.0004
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0160 0.0064 -2.4939 0.0126 -0.0287 -0.0034 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.ses.3") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 146; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0260
## I^2 (total heterogeneity / total variability): 11.01%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 145) = 188.7776, p-val = 0.0085
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0202 0.0068 -2.9960 0.0027 -0.0335 -0.0070 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodecov.score") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0054 (SE = 0.0020)
## tau (square root of estimated tau^2 value): 0.0738
## I^2 (total heterogeneity / total variability): 30.79%
## H^2 (total variability / sampling variability): 1.44
##
## Test for Heterogeneity:
## Q(df = 155) = 242.3327, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0355 0.0114 -3.1012 0.0019 -0.0579 -0.0131 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0507
## I^2 (total heterogeneity / total variability): 21.57%
## H^2 (total variability / sampling variability): 1.28
##
## Test for Heterogeneity:
## Q(df = 152) = 204.0432, p-val = 0.0031
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0212 0.0093 -2.2821 0.0225 -0.0393 -0.0030 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.female") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 152; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0062 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0788
## I^2 (total heterogeneity / total variability): 38.75%
## H^2 (total variability / sampling variability): 1.63
##
## Test for Heterogeneity:
## Q(df = 151) = 243.5342, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2208 0.0115 -19.1419 <.0001 -0.2434 -0.1982 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.area") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 155; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0253
## I^2 (total heterogeneity / total variability): 5.58%
## H^2 (total variability / sampling variability): 1.06
##
## Test for Heterogeneity:
## Q(df = 154) = 198.2115, p-val = 0.0094
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0007 0.0087 -0.0778 0.9380 -0.0178 0.0165
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.ses") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0009 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0294
## I^2 (total heterogeneity / total variability): 7.97%
## H^2 (total variability / sampling variability): 1.09
##
## Test for Heterogeneity:
## Q(df = 155) = 182.9541, p-val = 0.0620
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0171 0.0085 -2.0110 0.0443 -0.0338 -0.0004 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "absdiff.score") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0156 (SE = 0.0066)
## tau (square root of estimated tau^2 value): 0.1250
## I^2 (total heterogeneity / total variability): 25.89%
## H^2 (total variability / sampling variability): 1.35
##
## Test for Heterogeneity:
## Q(df = 155) = 221.9955, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0209 0.0208 1.0063 0.3143 -0.0198 0.0616
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "mutual") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0992 (SE = 0.0143)
## tau (square root of estimated tau^2 value): 0.3150
## I^2 (total heterogeneity / total variability): 83.80%
## H^2 (total variability / sampling variability): 6.17
##
## Test for Heterogeneity:
## Q(df = 155) = 869.4830, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.3046 0.0287 45.4406 <.0001 1.2484 1.3609 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "gwesp") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0312 (SE = 0.0051)
## tau (square root of estimated tau^2 value): 0.1768
## I^2 (total heterogeneity / total variability): 79.33%
## H^2 (total variability / sampling variability): 4.84
##
## Test for Heterogeneity:
## Q(df = 155) = 790.6077, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.5227 0.0175 29.8393 <.0001 0.4883 0.5570 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "gwesp.decay") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 156; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0534 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.2310
## I^2 (total heterogeneity / total variability): 77.94%
## H^2 (total variability / sampling variability): 4.53
##
## Test for Heterogeneity:
## Q(df = 155) = 552.6290, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.8757 0.0236 37.1248 <.0001 0.8295 0.9219 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Level 1 persistence
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.reservation.1") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0068)
## 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 = 137) = 126.4267, p-val = 0.7309
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0018 0.0224 0.0823 0.9344 -0.0421 0.0458
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.female.1") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 145; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0059 (SE = 0.0050)
## tau (square root of estimated tau^2 value): 0.0768
## I^2 (total heterogeneity / total variability): 11.99%
## H^2 (total variability / sampling variability): 1.14
##
## Test for Heterogeneity:
## Q(df = 144) = 169.0677, p-val = 0.0752
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0238 0.0191 1.2417 0.2143 -0.0137 0.0613
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.area.2") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 139; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0084)
## tau (square root of estimated tau^2 value): 0.0487
## I^2 (total heterogeneity / total variability): 2.74%
## H^2 (total variability / sampling variability): 1.03
##
## Test for Heterogeneity:
## Q(df = 138) = 152.8217, p-val = 0.1836
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0067 0.0251 -0.2684 0.7884 -0.0560 0.0425
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.ses.2") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 145; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0086)
## 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 = 144) = 158.5095, p-val = 0.1930
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0102 0.0254 0.4016 0.6880 -0.0396 0.0601
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodefactor.ses.3") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0148 (SE = 0.0153)
## tau (square root of estimated tau^2 value): 0.1217
## I^2 (total heterogeneity / total variability): 10.42%
## H^2 (total variability / sampling variability): 1.12
##
## Test for Heterogeneity:
## Q(df = 137) = 164.7158, p-val = 0.0534
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0137 0.0327 -0.4175 0.6763 -0.0779 0.0505
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodecov.score") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 145; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0263)
## tau (square root of estimated tau^2 value): 0.0014
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 144) = 143.9231, p-val = 0.4861
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0263 0.0425 0.6190 0.5359 -0.0570 0.1096
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0127)
## 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 = 137) = 109.6581, p-val = 0.9587
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0546 0.0299 -1.8236 0.0682 -0.1133 0.0041 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.female") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 145; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0188 (SE = 0.0241)
## tau (square root of estimated tau^2 value): 0.1372
## I^2 (total heterogeneity / total variability): 7.89%
## H^2 (total variability / sampling variability): 1.09
##
## Test for Heterogeneity:
## Q(df = 144) = 156.8294, p-val = 0.2196
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1957 0.0413 -4.7357 <.0001 -0.2767 -0.1147 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.area") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 139; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0149)
## tau (square root of estimated tau^2 value): 0.0357
## I^2 (total heterogeneity / total variability): 0.87%
## H^2 (total variability / sampling variability): 1.01
##
## Test for Heterogeneity:
## Q(df = 138) = 133.5736, p-val = 0.5906
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0054 0.0325 0.1675 0.8669 -0.0583 0.0692
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "nodematch.ses") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 144; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0266 (SE = 0.0171)
## tau (square root of estimated tau^2 value): 0.1631
## I^2 (total heterogeneity / total variability): 17.27%
## H^2 (total variability / sampling variability): 1.21
##
## Test for Heterogeneity:
## Q(df = 143) = 162.5088, p-val = 0.1263
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0464 0.0335 -1.3840 0.1663 -0.1120 0.0193
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "absdiff.score") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 145; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0781)
## 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 = 144) = 108.2767, p-val = 0.9884
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1026 0.0719 1.4272 0.1535 -0.0383 0.2434
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "mutual") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 130; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2927 (SE = 0.0834)
## tau (square root of estimated tau^2 value): 0.5410
## I^2 (total heterogeneity / total variability): 44.59%
## H^2 (total variability / sampling variability): 1.80
##
## Test for Heterogeneity:
## Q(df = 129) = 228.3006, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 1.6359 0.0737 22.2018 <.0001 1.4915 1.7803 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "gwesp") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 121; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0726 (SE = 0.0247)
## tau (square root of estimated tau^2 value): 0.2694
## I^2 (total heterogeneity / total variability): 39.57%
## H^2 (total variability / sampling variability): 1.65
##
## Test for Heterogeneity:
## Q(df = 120) = 194.9123, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6392 0.0424 15.0638 <.0001 0.5560 0.7224 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
filter(name == "gwesp.decay") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML")
##
## Random-Effects Model (k = 99; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2412 (SE = 0.0980)
## tau (square root of estimated tau^2 value): 0.4911
## I^2 (total heterogeneity / total variability): 74.08%
## H^2 (total variability / sampling variability): 3.86
##
## Test for Heterogeneity:
## Q(df = 98) = 830.8170, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.6471 0.0990 -6.5395 <.0001 -0.8410 -0.4531 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Level 2 formation
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ elite)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0517
## I^2 (residual heterogeneity / unaccounted variability): 22.20%
## H^2 (unaccounted variability / sampling variability): 1.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 204.0355, p-val = 0.0026
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0005, p-val = 0.9817
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0211 0.0103 -2.0512 0.0403 -0.0413 -0.0009 *
## elite -0.0006 0.0242 -0.0229 0.9817 -0.0480 0.0469
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ stu_res_actual)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0026 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0514
## I^2 (residual heterogeneity / unaccounted variability): 22.03%
## H^2 (unaccounted variability / sampling variability): 1.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 203.3212, p-val = 0.0029
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7102, p-val = 0.3994
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0211 0.0093 -2.2708 0.0232 -0.0394 -0.0029 *
## stu_res_actual 0.0095 0.0112 0.8427 0.3994 -0.0125 0.0314
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ fac_res_actual)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0503
## I^2 (residual heterogeneity / unaccounted variability): 21.29%
## H^2 (unaccounted variability / sampling variability): 1.27
## R^2 (amount of heterogeneity accounted for): 1.56%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 201.7628, p-val = 0.0036
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8931, p-val = 0.1689
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0212 0.0093 -2.2925 0.0219 -0.0393 -0.0031 *
## fac_res_actual 0.0132 0.0096 1.3759 0.1689 -0.0056 0.0319
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ hostel)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0517
## I^2 (residual heterogeneity / unaccounted variability): 22.19%
## H^2 (unaccounted variability / sampling variability): 1.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 204.0017, p-val = 0.0026
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0001, p-val = 0.9931
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0214 0.0172 -1.2449 0.2132 -0.0550 0.0123
## hostel 0.0002 0.0204 0.0087 0.9931 -0.0399 0.0402
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ section)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0026 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0508
## I^2 (residual heterogeneity / unaccounted variability): 21.61%
## H^2 (unaccounted variability / sampling variability): 1.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 203.1087, p-val = 0.0030
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4145, p-val = 0.5197
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0099 0.0199 -0.4970 0.6192 -0.0488 0.0290
## section -0.0145 0.0225 -0.6439 0.5197 -0.0585 0.0296
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ seminars)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0493
## I^2 (residual heterogeneity / unaccounted variability): 20.58%
## H^2 (unaccounted variability / sampling variability): 1.26
## R^2 (amount of heterogeneity accounted for): 5.39%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 200.6125, p-val = 0.0043
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.5814, p-val = 0.1081
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0055 0.0134 -0.4085 0.6829 -0.0317 0.0207
## seminars -0.0296 0.0184 -1.6067 0.1081 -0.0657 0.0065
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = form_coef, var = form_se * form_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ mentors)
##
## Mixed-Effects Model (k = 153; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0013)
## tau (square root of estimated tau^2 value): 0.0521
## I^2 (residual heterogeneity / unaccounted variability): 22.50%
## H^2 (unaccounted variability / sampling variability): 1.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 151) = 204.0233, p-val = 0.0026
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1662, p-val = 0.6835
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0276 0.0181 -1.5214 0.1282 -0.0632 0.0080
## mentors 0.0086 0.0212 0.4077 0.6835 -0.0329 0.0501
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Level 2 persistence
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ elite)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 109.0585, p-val = 0.9568
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5996, p-val = 0.4387
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0642 0.0324 -1.9811 0.0476 -0.1278 -0.0007 *
## elite 0.0653 0.0844 0.7744 0.4387 -0.1000 0.2307
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ stu_res_actual)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 109.4536, p-val = 0.9542
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2045, p-val = 0.6511
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0528 0.0302 -1.7498 0.0802 -0.1120 0.0063 .
## stu_res_actual -0.0174 0.0385 -0.4522 0.6511 -0.0928 0.0580
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ fac_res_actual)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 109.3991, p-val = 0.9546
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2590, p-val = 0.6108
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0543 0.0299 -1.8138 0.0697 -0.1130 0.0044 .
## fac_res_actual -0.0151 0.0296 -0.5089 0.6108 -0.0731 0.0429
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ hostel)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 109.3149, p-val = 0.9551
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3432, p-val = 0.5580
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0303 0.0512 -0.5914 0.5543 -0.1306 0.0700
## hostel -0.0370 0.0631 -0.5859 0.5580 -0.1606 0.0867
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ section)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 109.6234, p-val = 0.9530
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0347, p-val = 0.8522
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0443 0.0626 -0.7088 0.4785 -0.1670 0.0783
## section -0.0133 0.0713 -0.1863 0.8522 -0.1529 0.1264
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ seminars)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 108.6269, p-val = 0.9595
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0313, p-val = 0.3099
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0212 0.0444 -0.4776 0.6329 -0.1083 0.0659
## seminars -0.0611 0.0601 -1.0155 0.3099 -0.1789 0.0568
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
read_csv("results_v2.csv") %>%
left_join(read_rds("controls.Rds"), by = "classid") %>%
filter(name == "nodematch.reservation") %>%
mutate(coef = diss_coef, var = diss_se * diss_se) %>%
filter(is.finite(coef), is.finite(var)) %>%
rma(coef, var, data = ., method = "REML", mods = ~ mentors)
##
## Mixed-Effects Model (k = 138; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0128)
## 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 = 136) = 109.3920, p-val = 0.9546
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2661, p-val = 0.6059
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0788 0.0557 -1.4157 0.1569 -0.1879 0.0303
## mentors 0.0341 0.0660 0.5159 0.6059 -0.0953 0.1635
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1