Exploratory Analyses

Library

library(formr)
library(effects)

## Lade nötiges Paket: carData

## lattice theme set by effectsTheme()
## See ?effectsTheme for details.

library(effectsize)
library(lme4)

## Lade nötiges Paket: Matrix

library(sjstats)

## 
## Attache Paket: 'sjstats'

## Die folgenden Objekte sind maskiert von 'package:effectsize':
## 
##     cohens_f, cramers_v, phi

library(lmerTest)

## 
## Attache Paket: 'lmerTest'

## Das folgende Objekt ist maskiert 'package:lme4':
## 
##     lmer

## Das folgende Objekt ist maskiert 'package:stats':
## 
##     step

library(ggplot2)
library(tidyr)

## 
## Attache Paket: 'tidyr'

## Die folgenden Objekte sind maskiert von 'package:Matrix':
## 
##     expand, pack, unpack

library(ggpubr)
library(RColorBrewer)
library(coefplot)
library(tibble)
library(purrr) # for running multiple regression
library(broom)

## 
## Attache Paket: 'broom'

## Das folgende Objekt ist maskiert 'package:sjstats':
## 
##     bootstrap

library(mvmeta)

## This is mvmeta 1.0.3. For an overview type: help('mvmeta-package').

library(lm.beta)
library(dplyr)

## 
## Attache Paket: 'dplyr'

## Die folgenden Objekte sind maskiert von 'package:formr':
## 
##     first, last

## Die folgenden Objekte sind maskiert von 'package:stats':
## 
##     filter, lag

## Die folgenden Objekte sind maskiert von 'package:base':
## 
##     intersect, setdiff, setequal, union

library(stringr)
library(tidyr)
library(knitr)

apatheme = theme_bw() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        axis.line = element_line(),
        legend.title = element_blank(),
        plot.title = element_text(hjust = 0.5))

Data

Load selected data based on 03_codebook

data_included_documented = read.csv(file = "data_included_documented.csv")[,-1]

Inclusion of Data

countries = as.data.frame(table(data_included_documented$country)) %>%
  arrange(-Freq)

kable(countries)

Var1	Freq
France	2013
Germany	1846
United States of America	1254
Mexico	1157
Italy	968
Brazil	806
Spain	562
United Kingdom	499
Denmark	395
Colombia	387
Canada	338
Japan	290
Switzerland	280
Argentina	217
Austria	197
Russia	155
Chile	154
Australia	133
Peru	119
Belgium	102
Ecuador	102
China	90
Venezuela	67
Guatemala	61
Ireland	59
Portugal	51
Costa Rica	47
India	45
Netherlands	44
Dominican Republic	41
Philippines	36
New Zealand	34
Finland	31
South Africa	29
Sweden	27
El Salvador	26
Singapore	26
Romania	24
Uruguay	24
Bolivia	23
Ukraine	21
Honduras	18
Indonesia	18
Malaysia	17
Morocco	16
Nicaragua	15
Panama	15
United Arab Emirates	14
Belarus	13
Israel	13
Luxembourg	13
Estonia	12
Norway	12
Czechia	11
Paraguay	11
Bulgaria	9
Hungary	9
Kazakhstan	9
Hong Kong	8
Latvia	8
Pakistan	8
Trinidad and Tobago	8
Turkey	8
Algeria	7
Iran	7
Poland	7
Taiwan	7
Tunisia	7
Andorra	6
Bosnia and Herzegovina	6
Croatia	6
Jamaica	6
Kenya	6
Nigeria	6
Saudi Arabia	6
Iceland	5
Senegal	5
Serbia	5
South Korea	5
Greece	4
Haiti	4
Namibia	4
Thailand	4
Bahrain	3
Cameroon	3
Egypt	3
Georgia	3
Ghana	3
Guyana	3
Lebanon	3
Lithuania	3
Slovakia	3
Slovenia	3
Sri Lanka	3
Albania	2
Antigua and Barbuda	2
Armenia	2
Benin	2
Central African Republic	2
Dominica	2
Jordan	2
Kyrgyzstan	2
Mali	2
Malta	2
Mauritius	2
Palestinian Territories	2
Qatar	2
Saint Lucia	2
Turkmenistan	2
Vietnam	2
Afghanistan	1
Aruba	1
Bahamas, The	1
Barbados	1
Belize	1
Botswana	1
Burma	1
Cote d’Ivoire	1
Cuba	1
East Timor (see Timor-Leste)	1
Ethiopia	1
Fiji	1
Grenada	1
Guinea-Bissau	1
Iraq	1
Kuwait	1
Liechtenstein	1
Macedonia	1
Madagascar	1
Maldives	1
Marshall Islands	1
Mauritania	1
Micronesia	1
Monaco	1
Montenegro	1
Nepal	1
Saint Vincent and the Grenadines	1
Sint Maarten	1
South Sudan	1
Swaziland	1
Syria	1
Tanzania	1
Uganda	1
Zimbabwe	1

We will include all countries with more than 500 participants. This allows us to show effect sizes for a diverse range of countries. Diversity of countries is indicated by:

• location: European (France, Germany, Italy, Spain); North American (United States of America); South American (Mexico, Brazil)
• language: French (France); German (Germany); English (United States of America); Spanish (Mexico, Spain); Italian (Italy); Portuguese (Brazil)
• culture: Western (France, Germany, Italy, Spain, United States of America); Non-Western (Mexico, Brazil)

Sample sizes of other countries are too small (n < 500) to reach any conclusions.

seven_countries = countries %>% filter(Freq > 500)
data_included_documented_rescon = data_included_documented %>%
  filter(country %in% seven_countries$Var1)

countries_rescon =
  data_included_documented_rescon %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)


countries_rescon

## # A tibble: 7 × 2
##   country                   Freq
##   <fct>                    <int>
## 1 France                    2013
## 2 Germany                   1846
## 3 United States of America  1254
## 4 Mexico                    1157
## 5 Italy                      968
## 6 Brazil                     806
## 7 Spain                      562

Analyses

Political, Ethnic, and Religious Similarity

H1a Preference for Similarity in Political Beliefs

H1a(1) Linear Effect

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_politicalsim, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_politicalsim = data_included_documented_rescon_wide %>%
  select(-pref_politicalsim) %>%
  map(~lm(scale(data_included_documented_rescon_wide$pref_politicalsim) ~ scale(.x),
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_politicalsim_lin_coef = models_pref_politicalsim %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname == "scale(.x)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_politicalsim_lin_se = models_pref_politicalsim %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_politicalsim_lin_analyses = left_join(models_pref_politicalsim_lin_coef,
                                            models_pref_politicalsim_lin_se,
                                            by = "name") %>%
  mutate(outcome = "H2a) Prefered Political Similarity - Linear Effect")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_politicalsim)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_politicalsim_lin_analyses$n = countries_rescon$Freq
data_included_documented_rescon %>% filter(!is.na(pref_politicalsim)) %>% nrow()

## [1] 8405

model = mvmeta(mean ~ 1, data = models_pref_politicalsim_lin_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_politicalsim_lin_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error         z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)   -0.1144      0.0103  -11.0926    0.0000   -0.1346   -0.0942  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 73.6827 (df = 6), p-value = 0.0000
## I-square statistic = 91.9%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
## -18.4545   38.9091   38.8550

H1a(2) Quadratic Effect: Regression 1 (x <= breaking_point)

data_included_documented_rescon_wide_reg1 = data_included_documented_rescon %>%
  dplyr::filter(political_orientation <= 3) %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_politicalsim, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_politicalsim_reg1 = data_included_documented_rescon_wide_reg1 %>%
  select(-pref_politicalsim) %>%
  map(~lm(scale(data_included_documented_rescon_wide_reg1$pref_politicalsim) ~
            scale(.x),
          data = data_included_documented_rescon_wide_reg1)) %>%
  map(lm.beta)

models_pref_politicalsim_quad_coef_reg1 = models_pref_politicalsim_reg1 %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname == "scale(.x)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)


models_pref_politicalsim_quad_se_reg1 = models_pref_politicalsim_reg1 %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_politicalsim_quad_analyses_reg1 = left_join(models_pref_politicalsim_quad_coef_reg1,
                                            models_pref_politicalsim_quad_se_reg1,
                                            by = "name") %>%
  mutate(outcome = "H2a(1)) Preferred Political Similarity - Quadratic Effect Regression 1")

models_pref_politicalsim_quad_analyses_reg1$n = countries_rescon$Freq

data_included_documented_rescon %>% filter(political_orientation <= 3, !is.na(pref_politicalsim)) %>% nrow()

## [1] 6907

model = mvmeta(mean ~ 1, data = models_pref_politicalsim_quad_analyses_reg1, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_politicalsim_quad_analyses_reg1, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error         z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)   -0.2570      0.0111  -23.2080    0.0000   -0.2787   -0.2353  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 43.9753 (df = 6), p-value = 0.0000
## I-square statistic = 86.4%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##  logLik      AIC      BIC  
## -4.1612  10.3223  10.2682

H1a(2) Quadratic Effect: Regression 2 (x >= breaking_point)

data_included_documented_rescon_wide_reg2 = data_included_documented_rescon %>%
  dplyr::filter(political_orientation >= 3) %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_politicalsim, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_politicalsim_reg2 = data_included_documented_rescon_wide_reg2 %>%
  select(-pref_politicalsim) %>%
  map(~lm(scale(data_included_documented_rescon_wide_reg2$pref_politicalsim) ~
            scale(.x),
          data = data_included_documented_rescon_wide_reg2)) %>%
  map(lm.beta)

models_pref_politicalsim_quad_coef_reg2 = models_pref_politicalsim_reg2 %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname == "scale(.x)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)


models_pref_politicalsim_quad_se_reg2 = models_pref_politicalsim_reg1 %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_politicalsim_quad_analyses_reg2 = left_join(models_pref_politicalsim_quad_coef_reg2,
                                            models_pref_politicalsim_quad_se_reg2,
                                            by = "name") %>%
  mutate(outcome = "H2a(1)) Preferred Political Similarity - Quadratic Effect Regression 2")

models_pref_politicalsim_quad_analyses_reg2$n = countries_rescon$Freq

data_included_documented_rescon %>% filter(political_orientation >= 3, !is.na(pref_politicalsim)) %>% nrow()

## [1] 4643

model = mvmeta(mean ~ 1, data = models_pref_politicalsim_quad_analyses_reg2, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_politicalsim_quad_analyses_reg2, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error        z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.1877      0.0111  16.9462    0.0000    0.1660    0.2094  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 99.7921 (df = 6), p-value = 0.0000
## I-square statistic = 94.0%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
## -32.0696   66.1392   66.0851

H1b Preference for Similarity in Ethnicity/Race

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_ethnicalsim, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_ethnicalsim = data_included_documented_rescon_wide %>%
  select(-pref_ethnicalsim) %>%
  map(~lm(data_included_documented_rescon_wide$pref_ethnicalsim ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_ethnicalsim_coef = models_pref_ethnicalsim %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_ethnicalsim_se = models_pref_ethnicalsim %>%
 map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_ethnicalsim_analyses = left_join(models_pref_ethnicalsim_coef,
                                            models_pref_ethnicalsim_se,
                                            by = "name") %>%
  mutate(outcome = "H2b) Preferred Ethnic Similarity")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_ethnicalsim)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_ethnicalsim_analyses$n = countries_rescon$Freq
sum(models_pref_ethnicalsim_analyses$n)

## [1] 5680

model = mvmeta(mean ~ 1, data = models_pref_ethnicalsim_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_ethnicalsim_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error        z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.1573      0.0143  10.9657    0.0000    0.1291    0.1854  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 33.3460 (df = 6), p-value = 0.0000
## I-square statistic = 82.0%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##  logLik      AIC      BIC  
## -0.4393   2.8786   2.8245

H1c Preference for Similarity in Religion

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_religioussim, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_religioussim = data_included_documented_rescon_wide %>%
  select(-pref_religioussim) %>%
  map(~lm(data_included_documented_rescon_wide$pref_religioussim ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_religioussim_coef = models_pref_religioussim %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_religioussim_se = models_pref_religioussim %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_religioussim_analyses = left_join(models_pref_religioussim_coef,
                                            models_pref_religioussim_se,
                                            by = "name") %>%
  mutate(outcome = "H2c) Preferred Religious Similarity")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_religioussim)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_religioussim_analyses$n = countries_rescon$Freq
sum(models_pref_religioussim_analyses$n)

## [1] 8396

model = mvmeta(mean ~ 1, data = models_pref_religioussim_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_religioussim_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.1439      0.0171  8.4172    0.0000    0.1104    0.1773  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 15.7427 (df = 6), p-value = 0.0152
## I-square statistic = 61.9%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
##   7.2453  -12.4906  -12.5446

Ideal Partner Preferences

H2a Preference for the Level of Financial Security- Successfulness

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_level_financially_secure_successful_ambitious, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_level_financially_secure_successful_ambitious = data_included_documented_rescon_wide %>%
  select(-pref_level_financially_secure_successful_ambitious) %>%
  map(~lm(data_included_documented_rescon_wide$pref_level_financially_secure_successful_ambitious ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_level_financially_secure_successful_ambitious_coef = models_pref_level_financially_secure_successful_ambitious %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_level_financially_secure_successful_ambitious_se = models_pref_level_financially_secure_successful_ambitious %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))

models_pref_level_financially_secure_successful_ambitious_analyses = left_join(models_pref_level_financially_secure_successful_ambitious_coef,
                                            models_pref_level_financially_secure_successful_ambitious_se,
                                            by = "name") %>%
  mutate(outcome = "H3a) Financial Security-Successfulness")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_level_financially_secure_successful_ambitious)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_level_financially_secure_successful_ambitious_analyses$n = countries_rescon$Freq
sum(models_pref_level_financially_secure_successful_ambitious_analyses$n)

## [1] 8139

model = mvmeta(mean ~ 1, data = models_pref_level_financially_secure_successful_ambitious_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_level_financially_secure_successful_ambitious_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error        z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.1426      0.0069  20.6251    0.0000    0.1291    0.1562  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 16.1524 (df = 6), p-value = 0.0130
## I-square statistic = 62.9%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
##  13.2641  -24.5282  -24.5823

H2b Preference for the Level of Confidence-Assertiveness

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_level_confident_assertive , France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_level_confident_assertive = data_included_documented_rescon_wide %>%
  select(-pref_level_confident_assertive) %>%
  map(~lm(data_included_documented_rescon_wide$pref_level_confident_assertive ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_level_confident_assertive_coef = models_pref_level_confident_assertive %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_level_confident_assertive_se = models_pref_level_confident_assertive %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_level_confident_assertive_analyses = left_join(models_pref_level_confident_assertive_coef,
                                            models_pref_level_confident_assertive_se,
                                            by = "name") %>%
  mutate(outcome = "H3d) Confidence-Assertiveness")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_level_confident_assertive)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_level_confident_assertive_analyses$n = countries_rescon$Freq
sum(models_pref_level_confident_assertive_analyses$n)

## [1] 8292

model = mvmeta(mean ~ 1, data = models_pref_level_confident_assertive_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_level_confident_assertive_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error        z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.0734      0.0060  12.2476    0.0000    0.0617    0.0852  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 107.7961 (df = 6), p-value = 0.0000
## I-square statistic = 94.4%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
## -31.4557   64.9114   64.8573

H2c Preference for the Level of Education-Intelligence

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_level_intelligence_educated, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_level_intelligence_educated = data_included_documented_rescon_wide %>%
  select(-pref_level_intelligence_educated) %>%
  map(~lm(data_included_documented_rescon_wide$pref_level_intelligence_educated ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_level_intelligence_educated_coef = models_pref_level_intelligence_educated %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_level_intelligence_educated_se = models_pref_level_intelligence_educated %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_level_intelligence_educated_analyses = left_join(models_pref_level_intelligence_educated_coef,
                                            models_pref_level_intelligence_educated_se,
                                            by = "name") %>%
  mutate(outcome = "H3e) Education-Intelligence")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_level_intelligence_educated)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_level_intelligence_educated_analyses$n = countries_rescon$Freq
sum(models_pref_level_intelligence_educated_analyses$n)

## [1] 8280

model = mvmeta(mean ~ 1, data = models_pref_level_intelligence_educated_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_level_intelligence_educated_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub   
## (Intercept)    0.0124      0.0062  1.9872    0.0469    0.0002    0.0246  *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 93.7652 (df = 6), p-value = 0.0000
## I-square statistic = 93.6%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
## -24.7569   51.5138   51.4597

H2d Preference for the Level of Kindness-Supportiveness

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_level_kind_supportive , France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_level_kind_supportive = data_included_documented_rescon_wide %>%
  select(-pref_level_kind_supportive) %>%
  map(~lm(data_included_documented_rescon_wide$pref_level_kind_supportive ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_level_kind_supportive_coef = models_pref_level_kind_supportive %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_level_kind_supportive_se = models_pref_level_kind_supportive %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_level_kind_supportive_analyses = left_join(models_pref_level_kind_supportive_coef,
                                            models_pref_level_kind_supportive_se,
                                            by = "name") %>%
  mutate(outcome = "H3b) Kindness-Supportiveness")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_level_kind_supportive)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_level_kind_supportive_analyses$n = countries_rescon$Freq
sum(models_pref_level_kind_supportive_analyses$n)

## [1] 8261

model = mvmeta(mean ~ 1, data = models_pref_level_kind_supportive_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_level_kind_supportive_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.0238      0.0055  4.3523    0.0000    0.0131    0.0345  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 36.0962 (df = 6), p-value = 0.0000
## I-square statistic = 83.4%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##  logLik      AIC      BIC  
##  4.9736  -7.9472  -8.0013

H2e Preference for the Level of Attractiveness

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(pref_level_attractiveness , France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_pref_level_attractiveness = data_included_documented_rescon_wide %>%
  select(-pref_level_attractiveness) %>%
  map(~lm(data_included_documented_rescon_wide$pref_level_attractiveness ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_pref_level_attractiveness_coef = models_pref_level_attractiveness %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_pref_level_attractiveness_se = models_pref_level_attractiveness %>%
 map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_pref_level_attractiveness_analyses = left_join(models_pref_level_attractiveness_coef,
                                            models_pref_level_attractiveness_se,
                                            by = "name") %>%
  mutate(outcome = "H3c) Attractiveness")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(pref_level_attractiveness)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_pref_level_attractiveness_analyses$n = countries_rescon$Freq
sum(models_pref_level_attractiveness_analyses$n)

## [1] 8153

model = mvmeta(mean ~ 1, data = models_pref_level_attractiveness_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_pref_level_attractiveness_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.0715      0.0073  9.7341    0.0000    0.0571    0.0859  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 22.7699 (df = 6), p-value = 0.0009
## I-square statistic = 73.6%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
##   9.5856  -17.1712  -17.2253

Ideal Age and Height

H3a(1) Ideal Age (Importance)

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(imp_age, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_imp_age = data_included_documented_rescon_wide %>%
  select(-imp_age) %>%
  map(~lm(data_included_documented_rescon_wide$imp_age ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_imp_age_coef = models_imp_age %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_imp_age_se = models_imp_age %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_imp_age_analyses = left_join(models_imp_age_coef,
                                            models_imp_age_se,
                                            by = "name") %>%
  mutate(outcome = "H4a(1)) Ideal Age (Importance)")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(imp_age)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_imp_age_analyses$n = countries_rescon$Freq
sum(models_imp_age_analyses$n)

## [1] 8517

model = mvmeta(mean ~ 1, data = models_imp_age_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_imp_age_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.0944      0.0111  8.5071    0.0000    0.0726    0.1161  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 19.3271 (df = 6), p-value = 0.0036
## I-square statistic = 69.0%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
##   8.4613  -14.9226  -14.9767

H3a(2) Ideal Age (Level)

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(ideal_age_rel, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_ideal_age_rel = data_included_documented_rescon_wide %>%
  select(-ideal_age_rel) %>%
  map(~lm(data_included_documented_rescon_wide$ideal_age_rel ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_ideal_age_rel_coef = models_ideal_age_rel %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_ideal_age_rel_se = models_ideal_age_rel %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_ideal_age_rel_analyses = left_join(models_ideal_age_rel_coef,
                                            models_ideal_age_rel_se,
                                            by = "name") %>%
  mutate(outcome = "H4a(2)) Ideal Age (Level)")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(ideal_age_rel)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_ideal_age_rel_analyses$n = countries_rescon$Freq
sum(models_ideal_age_rel_analyses$n)

## [1] 7771

model = mvmeta(mean ~ 1, data = models_ideal_age_rel_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_ideal_age_rel_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub   
## (Intercept)    0.0044      0.0246  0.1780    0.8587   -0.0438    0.0526   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 2.2741 (df = 6), p-value = 0.8928
## I-square statistic = 1.0%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
##  11.2587  -20.5174  -20.5715

H3b(1) Ideal Height (Importance)

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(imp_height, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_imp_height = data_included_documented_rescon_wide %>%
  select(-imp_height) %>%
  map(~lm(data_included_documented_rescon_wide$imp_height ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_imp_height_coef = models_imp_height %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_imp_height_se = models_imp_height %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_imp_height_analyses = left_join(models_imp_height_coef,
                                            models_imp_height_se,
                                            by = "name") %>%
  mutate(outcome = "H4b(1)) Ideal Height (Importance)")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(imp_height)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_imp_height_analyses$n = countries_rescon$Freq
sum(models_imp_height_analyses$n)

## [1] 8459

model = mvmeta(mean ~ 1, data = models_imp_height_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_imp_height_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.1147      0.0118  9.7572    0.0000    0.0917    0.1378  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 5.7811 (df = 6), p-value = 0.4481
## I-square statistic = 1.0%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
##  14.8191  -27.6381  -27.6922

H3b(2) Ideal Height (Level)

data_included_documented_rescon_wide = data_included_documented_rescon %>%
  pivot_wider(names_from = country, values_from = political_orientation) %>%
  select(ideal_height, France, Germany, `United States of America`, Mexico,
         Italy, Brazil, Spain)

models_ideal_height = data_included_documented_rescon_wide %>%
  select(-ideal_height) %>%
  map(~lm(data_included_documented_rescon_wide$ideal_height ~ .x,
      data = data_included_documented_rescon_wide)) %>%
  map(lm.beta)

models_ideal_height_coef = models_ideal_height %>%
  map(coef) %>%
  as.data.frame() %>%
  rownames_to_column(var = "rowname") %>%
  filter(rowname != "(Intercept)") %>%
  pivot_longer(cols = -rowname) %>%
  select(-rowname) %>%
  rename(mean = value)

models_ideal_height_se = models_ideal_height %>%
  map(tidy) %>%
  tibble(models_pref_politicalsim_lin_se = ., Names = names(.)) %>%
  hoist(models_pref_politicalsim_lin_se, coefficients = "std.error") %>%
  select(-models_pref_politicalsim_lin_se) %>%
  unnest_wider(., coefficients, names_sep = "_") %>%
  select(coefficients_2, Names) %>%
  rename("name" = "Names",
         "se" = "coefficients_2") %>%
  mutate(name = ifelse(name == "United States of America",
                       "United.States.of.America", name))


models_ideal_height_analyses = left_join(models_ideal_height_coef,
                                            models_ideal_height_se,
                                            by = "name") %>%
  mutate(outcome = "H4b(2)) Ideal Height (Level)")

countries_rescon =
  data_included_documented_rescon %>%
  filter(!is.na(ideal_height)) %>%
  select(country) %>%
  table() %>%
  as.data.frame() %>%
  arrange(-Freq)

models_ideal_height_analyses$n = countries_rescon$Freq
sum(models_ideal_height_analyses$n)

## [1] 8112

model = mvmeta(mean ~ 1, data = models_ideal_height_analyses, S = se^2,
               method = "fixed")
summary(model)

## Call:  mvmeta(formula = mean ~ 1, S = se^2, data = models_ideal_height_analyses, 
##     method = "fixed")
## 
## Univariate fixed-effects meta-analysis
## Dimension: 1
## 
## Fixed-effects coefficients
##              Estimate  Std. Error       z  Pr(>|z|)  95%ci.lb  95%ci.ub     
## (Intercept)    0.0125      0.0036  3.5174    0.0004    0.0055    0.0195  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Univariate Cochran Q-test for heterogeneity:
## Q = 89.4342 (df = 6), p-value = 0.0000
## I-square statistic = 93.3%
## 
## 7 studies, 7 observations, 1 fixed and 0 random-effects parameters
##   logLik       AIC       BIC  
## -18.6924   39.3848   39.3307