Code
library(tidyverse)
library(gt)
library(gtsummary)
library(janitor)
library(sjPlot)
library(ggcorrplot)
library(haven)
library(readxl)
library(writexl)
library(broom)
library(labelled)
library(Hmisc)
library(rcompanion)Coding_articles_05
version 05
- Loading Packages
- Loading Data
Code
main_data_raw <- read_excel("data/Code_book_for_R_LIOR.xlsx", sheet = "coding main")
countries_data_raw <- read_excel("data/Code_book_for_R_LIOR.xlsx", sheet = "coding by countries")
slb_type_data_raw <- read_excel("data/Code_book_for_R_LIOR.xlsx", sheet = "coding by slb type ")
gender_data_raw <- read_excel("data/Code_book_for_R_LIOR.xlsx", sheet = "coding by gender")- Reshaping Data
Code
# Main data (filtered)
main_data <- main_data_raw[-c(1), ] %>%
janitor::clean_names() %>%
rename(rule_bending = behavioral_mechanisms_0_no_1_yes,
rule_breaking = x26,
instrumental_actions = x27,
prioritizing = x28,
personal_resources = x29,
routinizing = x30,
ratioining = x31,
rigid_rule_following = x32,
agression = x33,
new_behavioral_coping = x34,
cynicism = cognitive_mechanisms_0_no_1_yes,
compassion = x36,
emotional_detachment = x37,
empathy = x38,
new_cognitive_coping = x39) %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking, instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following, agression), as.numeric),
journal_article = replace_na(journal_article, 99),
language = replace_na(language, 99),
use = replace_na(use, 99),
empirical_study = replace_na(empirical_study, 99),
type_of_slb = na_if(type_of_slb, "NA"),
use = recode(number,
`71` = 0,
`172` = 0,
`178` = 0,
.default = use)) %>%
filter(journal_article != 0,
language != 0,
use != 0,
empirical_study != 0) %>%
mutate(across(c(journal_article, language, use, empirical_study), ~ na_if(., 99)),
type_of_analysis = recode(type_of_analysis,
"qualitative" = "Qualitative",
.default = type_of_analysis),
country = recode(country,
"Switzerlanmd, Germany, Sweden" = "Switzerland, Germany, Sweden",
"Switzerlanmd, Sweden" = "Switzerland, Sweden",
"Switzerlanmd, Sweden, Latvia, Lithuania" = "Switzerland, Sweden, Latvia, Lithuania",
"United Kindom" = "United Kingdom",
"United states" = "United States",
.default = country),
moving_towards = rowMeans(dplyr::select(., rule_bending, rule_breaking, instrumental_actions, prioritizing), na.rm = T),
moving_away = rowMeans(dplyr::select(., routinizing, ratioining), na.rm = T),
moving_against = rowMeans(dplyr::select(., rigid_rule_following, agression), na.rm = T),
moving_towards_bi = recode(moving_towards,
`0` = 0,
.default = 1),
moving_away_bi = recode(moving_away,
`0` = 0,
.default = 1),
moving_against_bi = recode(moving_against,
`0` = 0,
.default = 1))
# Main data (unfiltered)
main_data_unfiltered <- main_data_raw[-c(1), ] %>%
janitor::clean_names() %>%
rename(rule_bending = behavioral_mechanisms_0_no_1_yes,
rule_breaking = x26,
instrumental_actions = x27,
prioritizing = x28,
personal_resources = x29,
routinizing = x30,
ratioining = x31,
rigid_rule_following = x32,
agression = x33,
new_behavioral_coping = x34,
cynicism = cognitive_mechanisms_0_no_1_yes,
compassion = x36,
emotional_detachment = x37,
empathy = x38,
new_cognitive_coping = x39) %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking, instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following, agression), as.numeric),
journal_article = replace_na(journal_article, 99),
language = replace_na(language, 99),
use = replace_na(use, 99),
empirical_study = replace_na(empirical_study, 99),
type_of_slb = na_if(type_of_slb, "NA"),
use = recode(number,
`71` = 0,
`172` = 0,
`178` = 0,
.default = use)) %>%
mutate(across(c(journal_article, language, use, empirical_study), ~ na_if(., 99)),
type_of_analysis = recode(type_of_analysis,
"qualitative" = "Qualitative",
.default = type_of_analysis),
country = recode(country,
"Switzerlanmd, Germany, Sweden" = "Switzerland, Germany, Sweden",
"Switzerlanmd, Sweden" = "Switzerland, Sweden",
"Switzerlanmd, Sweden, Latvia, Lithuania" = "Switzerland, Sweden, Latvia, Lithuania",
"United Kindom" = "United Kingdom",
"United states" = "United States",
.default = country),
moving_towards = rowMeans(dplyr::select(., rule_bending, rule_breaking, instrumental_actions, prioritizing), na.rm = T),
moving_away = rowMeans(dplyr::select(., routinizing, ratioining), na.rm = T),
moving_against = rowMeans(dplyr::select(., rigid_rule_following, agression), na.rm = T),
moving_towards_bi = recode(moving_towards,
`0` = 0,
.default = 1),
moving_away_bi = recode(moving_away,
`0` = 0,
.default = 1),
moving_against_bi = recode(moving_against,
`0` = 0,
.default = 1))
# Countries Data
countries_data <- countries_data_raw %>%
janitor::clean_names() %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking, instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following, agression), as.numeric),
journal_article = replace_na(journal_article, 99),
language = replace_na(language, 99),
use = replace_na(use, 99),
empirical_study = replace_na(empirical_study, 99),
use = recode(number,
`71` = 0,
`172` = 0,
`178` = 0,
.default = use)) %>%
filter(journal_article != 0,
language != 0,
use != 0,
empirical_study != 0) %>%
mutate(type_of_analysis = recode(type_of_analysis,
"qualitative" = "Qualitative",
.default = type_of_analysis),
country_name = recode(country_name,
"The Netherlands" = "Netherlands",
"Switzerlanmd" = "Switzerland",
"United Kindom" = "United Kingdom",
"United states" = "United States",
.default = country_name),
across(c(journal_article, language, use, empirical_study), ~ na_if(., 99)),
date = as.character(date),
moving_towards = rowMeans(dplyr::select(., rule_bending, rule_breaking, instrumental_actions, prioritizing), na.rm = T),
moving_away = rowMeans(dplyr::select(., routinizing, ratioining), na.rm = T),
moving_against = rowMeans(dplyr::select(., rigid_rule_following, agression), na.rm = T),
moving_towards_bi = recode(moving_towards,
`0` = 0,
.default = 1),
moving_away_bi = recode(moving_away,
`0` = 0,
.default = 1),
moving_against_bi = recode(moving_against,
`0` = 0,
.default = 1))
# Countries Data (unfiltered)
countries_data_unfiltered <- countries_data_raw %>%
janitor::clean_names() %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking, instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following, agression), as.numeric),
journal_article = replace_na(journal_article, 99),
language = replace_na(language, 99),
use = replace_na(use, 99),
empirical_study = replace_na(empirical_study, 99),
use = recode(number,
`71` = 0,
`172` = 0,
`178` = 0,
.default = use)) %>%
mutate(type_of_analysis = recode(type_of_analysis,
"qualitative" = "Qualitative",
.default = type_of_analysis),
country_name = recode(country_name,
"The Netherlands" = "Netherlands",
"Switzerlanmd" = "Switzerland",
"United Kindom" = "United Kingdom",
"United states" = "United States",
.default = country_name),
across(c(journal_article, language, use, empirical_study), ~ na_if(., 99)),
date = as.character(date),
moving_towards = rowMeans(dplyr::select(., rule_bending, rule_breaking, instrumental_actions, prioritizing), na.rm = T),
moving_away = rowMeans(dplyr::select(., routinizing, ratioining), na.rm = T),
moving_against = rowMeans(dplyr::select(., rigid_rule_following, agression), na.rm = T),
moving_towards_bi = recode(moving_towards,
`0` = 0,
.default = 1),
moving_away_bi = recode(moving_away,
`0` = 0,
.default = 1),
moving_against_bi = recode(moving_against,
`0` = 0,
.default = 1))
# SLB Type Data
slb_type_data <- slb_type_data_raw %>%
janitor::clean_names() %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking, instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following, agression), as.numeric),
journal_article = replace_na(journal_article, 99),
language = replace_na(language, 99),
use = replace_na(use, 99),
empirical_study = replace_na(empirical_study, 99),
use = recode(number,
`71` = 0,
`172` = 0,
`178` = 0,
.default = use)) %>%
filter(journal_article != 0,
language != 0,
use != 0,
empirical_study != 0) %>%
mutate(type_of_analysis = recode(type_of_analysis,
"qualitative" = "Qualitative",
.default = type_of_analysis),
country_name = recode(country_name,
"The Netherlands" = "Netherlands",
"Switzerlanmd" = "Switzerland",
"United Kindom" = "United Kingdom",
"United states" = "United States",
.default = country_name),
type_of_slb = na_if(type_of_slb, "NA"),
type_of_slb = factor(type_of_slb, ordered = T, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")),
across(c(journal_article, language, use, empirical_study), ~ na_if(., 99)),
date = as.character(date),
moving_towards = rowMeans(dplyr::select(., rule_bending, rule_breaking, instrumental_actions, prioritizing), na.rm = T),
moving_away = rowMeans(dplyr::select(., routinizing, ratioining), na.rm = T),
moving_against = rowMeans(dplyr::select(., rigid_rule_following, agression), na.rm = T),
moving_towards_bi = recode(moving_towards,
`0` = 0,
.default = 1),
moving_away_bi = recode(moving_away,
`0` = 0,
.default = 1),
moving_against_bi = recode(moving_against,
`0` = 0,
.default = 1),
behavioral_mechanisms = (rule_bending + rule_breaking + instrumental_actions + prioritizing + personal_resources + routinizing + ratioining + rigid_rule_following + agression + new_behavioral_coping),
cognitive_mechanisms = (cynicism + compassion + emotional_detachment + empathy + new_cognitive_coping),
explanatory_factors = (personal_factors + organizational_factors + environmental_factors),
super_mechanisms = (moving_towards_bi + moving_away_bi + moving_against_bi))
# Gender Data
gender_data <- gender_data_raw %>%
janitor::clean_names() %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking, instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following, agression), as.numeric),
journal_article = replace_na(journal_article, 99),
language = replace_na(language, 99),
use = replace_na(use, 99),
empirical_study = replace_na(empirical_study, 99),
use = recode(number,
`71` = 0,
`172` = 0,
`178` = 0,
.default = use)) %>%
filter(journal_article != 0,
language != 0,
use != 0,
empirical_study != 0) %>%
mutate(type_of_analysis = recode(type_of_analysis,
"qualitative" = "Qualitative",
.default = type_of_analysis),
country_name = recode(country_name,
"The Netherlands" = "Netherlands",
"Switzerlanmd" = "Switzerland",
"United Kindom" = "United Kingdom",
"United states" = "United States",
.default = country_name),
date = as.character(date),
across(c(journal_article, language, use, empirical_study), ~ na_if(., 99)),
moving_towards = rowMeans(dplyr::select(., rule_bending, rule_breaking, instrumental_actions, prioritizing), na.rm = T),
moving_away = rowMeans(dplyr::select(., routinizing, ratioining), na.rm = T),
moving_against = rowMeans(dplyr::select(., rigid_rule_following, agression), na.rm = T),
moving_towards_bi = recode(moving_towards,
`0` = 0,
.default = 1),
moving_away_bi = recode(moving_away,
`0` = 0,
.default = 1),
moving_against_bi = recode(moving_against,
`0` = 0,
.default = 1),
behavioral_mechanisms = (rule_bending + rule_breaking + instrumental_actions + prioritizing + personal_resources + routinizing + ratioining + rigid_rule_following + agression + new_behavioral_coping),
cognitive_mechanisms = (cynicism + compassion + emotional_detachment + empathy + new_cognitive_coping),
explanatory_factors = (personal_factors + organizational_factors + environmental_factors),
super_mechanisms = (moving_towards_bi + moving_away_bi + moving_against_bi))- Export data (as an Excel file)
Code
New_Code_book_for_R_LIOR_05 <- list("coding main" = main_data,
"coding by countries" = countries_data,
"coding by SLB type" = slb_type_data,
"coding by gender" = gender_data)
write_xlsx(New_Code_book_for_R_LIOR_05, "New_Code_book_for_R_LIOR_05.xlsx")1. Coding - Main
Frequency Table (Numbers and Percentages): General
Code
main_data %>%
dplyr::select(-number, -rec_number, -title) %>%
mutate(across(c(journal_article, language, use, empirical_study, rule_bending, rule_breaking,
instrumental_actions, prioritizing, routinizing, ratioining, rigid_rule_following,
agression, moving_towards, moving_away, moving_against, year), ~ as.character(.))) %>%
gtsummary::tbl_summary(by = NULL,
statistic = all_categorical() ~ "{n} ({p}%)") %>%
bold_labels()| Characteristic | N = 1661 |
|---|---|
| coder_last_name | |
| Edri-Peer | 29 (17%) |
| Gilboa | 36 (22%) |
| Kadmiel | 101 (61%) |
| date | |
| 11.8.24 | 3 (7.7%) |
| 12.8.24 | 2 (5.1%) |
| 13.8.24 | 4 (10%) |
| 2.8.24 | 3 (7.7%) |
| 20.12.24 | 10 (26%) |
| 4.8.24 | 1 (2.6%) |
| 4.8.25 | 1 (2.6%) |
| 4.8.26 | 1 (2.6%) |
| 5.8.24 | 2 (5.1%) |
| 5.8.27 | 1 (2.6%) |
| 6.8.24 | 5 (13%) |
| 7.8.24 | 5 (13%) |
| 8.8.24 | 1 (2.6%) |
| Unknown | 127 |
| authors | |
| Agger, A. and Poulsen, B. | 1 (0.6%) |
| Agger, A. and Tortzen, A. | 1 (0.6%) |
| Ahern, E. C., Sadler, L. H., Lamb, M. E. and Gariglietti, G. M. | 1 (0.6%) |
| Alcadipani, R., Lotta, G. and Cohen, N. | 1 (0.6%) |
| Anagnostopoulos, D. | 1 (0.6%) |
| Andersen, S. C. and Guul, T. S. | 1 (0.6%) |
| Aniteye, P. and Mayhew, S. H. | 1 (0.6%) |
| Atinga, R. A., Agyepong, I. A. and Esena, R. K. | 1 (0.6%) |
| Barnes, C., Michener, J. and Rains, E. | 1 (0.6%) |
| Baviskar, S. | 1 (0.6%) |
| Baviskar, S. and Winter, S. C. | 1 (0.6%) |
| Bearss, B., Martin, A., Dorsey Vinton, S., Chaidez, V., Palmer-Wackerly, A. L., Mollard, E., Edison-Soe, L., Chan, N., Estrada Gonzalez, E., Carter, M., Coburn, K., Xia, Y. and Tippens, J. A. | 1 (0.6%) |
| Bell, E. and Jilke, S. | 1 (0.6%) |
| Bell, E. and Smith, K. | 1 (0.6%) |
| Bergen, A., & While, A. | 1 (0.6%) |
| Berlin, J., Szücs, S., Höjer, S. and Liljegren, A. | 1 (0.6%) |
| Bernards, B., Schmidt, E. and Groeneveld, S. | 1 (0.6%) |
| Binhas, A. | 1 (0.6%) |
| Bisgaard, M. and Pedersen, M. J. | 1 (0.6%) |
| Bishu, S. G., & Osei‐Kojo, A. | 1 (0.6%) |
| Bjerregaard, T. | 1 (0.6%) |
| Bjerregaard, T., & Klitmoller, A. | 1 (0.6%) |
| Blijleven, W. and van Hulst, M. | 2 (1.2%) |
| Borrelli, L. M. | 3 (1.8%) |
| Borrelli, L. M. and Trasciani, G. | 1 (0.6%) |
| Borry, E. L. and Henderson, A. C. | 1 (0.6%) |
| Breit, E., Egeland, C., Løberg, I. B. and Røhnebæk, M. T. | 1 (0.6%) |
| Brodkin, E. Z. | 2 (1.2%) |
| Campos, S. A. and Peeters, R. | 1 (0.6%) |
| Cecchini, M. and Harrits, G. S. | 1 (0.6%) |
| Chalhi, S., Koster, M. and Vermeulen, J. | 1 (0.6%) |
| Charbonneau, É, Boisvert, Y. and Bégin, L. | 1 (0.6%) |
| Civinskas, R., Dvorak, J. and Šumskas, G. | 1 (0.6%) |
| Cohen, N., Benish, A. and Shamriz-Ilouz, A. | 1 (0.6%) |
| Cohen, N., Klenk, T., Davidovitz, M. and Cardaun, S. | 1 (0.6%) |
| Davidovitz, M. | 1 (0.6%) |
| Davidovitz, M. and Cohen, N. | 4 (2.4%) |
| Davidovitz, M., Cardaun, S., Klenk, T. and Cohen, N. | 1 (0.6%) |
| de Boer, N. | 1 (0.6%) |
| De Boer, N., & Raaphorst, N. | 1 (0.6%) |
| de Boer, N., Eshuis, J. and Klijn, E. H. | 1 (0.6%) |
| Diab, H. and Cohen, N. | 1 (0.6%) |
| Dias, J. J., & Maynard-Moody, S. | 1 (0.6%) |
| Dolata, M., Schenk, B., Fuhrer, J., Marti, A. and Schwabe, G. | 1 (0.6%) |
| Döring, M. and Jilke, S. | 1 (0.6%) |
| Druckman, J. N., Levy, J. and Sands, N. | 1 (0.6%) |
| Dussuet, A., & Ledoux, C. | 1 (0.6%) |
| Edri-Peer, O. and Cohen, N. | 1 (0.6%) |
| Ellis, K., Davis, A., & Rummery, K. | 1 (0.6%) |
| Eriksson, E. and Johansson, K. | 1 (0.6%) |
| Esmark, A. and Liengaard, M. B. | 1 (0.6%) |
| Evans, T. | 1 (0.6%) |
| Falkenhain, M. and Hirseland, A. | 1 (0.6%) |
| Fee, M. | 1 (0.6%) |
| Fineman, S. | 1 (0.6%) |
| Gabay, G. and Tikva, S. S. | 1 (0.6%) |
| Gilstrap, C. M. | 1 (0.6%) |
| Gofen, A. | 1 (0.6%) |
| Gofen, A., Blomqvist, P., Needham, C. E., Warren, K. and Winblad, U. | 1 (0.6%) |
| Gore, R. | 1 (0.6%) |
| Guul, T. S., Pedersen, M. J. and Petersen, N. B. G. | 1 (0.6%) |
| Hand, L. C. | 2 (1.2%) |
| Hand, L. C. and Catlaw, T. J. | 1 (0.6%) |
| Hansen, L. S. | 1 (0.6%) |
| Hansen, P. | 1 (0.6%) |
| Harrits, G. S. | 1 (0.6%) |
| Harvey, J. and Attwell, K. | 1 (0.6%) |
| Hastings, A. and Gannon, M. | 1 (0.6%) |
| Henderson, A. C. | 1 (0.6%) |
| Høiland, G. and Willumsen, E. | 1 (0.6%) |
| Holstead, K., Russell, S. and Waylen, K. | 1 (0.6%) |
| Jensen, D. C., & Pedersen, L. B. | 1 (0.6%) |
| Jewell, C. J. | 1 (0.6%) |
| Jilke, S. and Tummers, L. | 1 (0.6%) |
| Jørgensen, B. and Schou, J. | 1 (0.6%) |
| Kang, I. and Jilke, S. | 1 (0.6%) |
| Karadaghi, G. and Willott, C. | 1 (0.6%) |
| Kelly, G. | 1 (0.6%) |
| Kelly, M. | 1 (0.6%) |
| Khelifi, S. and Triki, M. | 1 (0.6%) |
| Kipo-Sunyehzi, D. D. | 1 (0.6%) |
| Kipo-Sunyehzi, D. D., Brenya, E. and Fusheini, A. | 1 (0.6%) |
| Križ, K., & Skivenes, M. | 1 (0.6%) |
| Lameck, W. and Hulst, R. | 1 (0.6%) |
| Lavee, E. | 3 (1.8%) |
| Lavee, E. and Strier, R. | 1 (0.6%) |
| Leonardi, D., Paraciani, R. and Raspanti, D. | 1 (0.6%) |
| Lillvis, D. | 1 (0.6%) |
| Linnansaari, A., Schreuders, M., Kunst, A. E., Rimpelä, A., Kinnunen, J. M., Lorant, V., Grard, A., Mélard, N., Robert, P. O., Richter, M., Mlinarić, M., Hoffman, L., Clancy, L., Keogan, S., Breslin, E., Hanafin, J., Federico, B., Marandola, D., Marco, A., Nuyts, P., Kuipers, M., Perelman, J., Leão, T., Alves, J. and Lindfors, P. | 1 (0.6%) |
| Liu, N., Tang, S. Y., Lo, C. W. H. and Zhan, X. | 1 (0.6%) |
| Løberg, I. B. and Egeland, C. | 1 (0.6%) |
| Lotta, G., Krieger, M. G. M., Cohen, N. and Kirschbaum, C. | 1 (0.6%) |
| Lotta, G., Lima-Silva, F. and Favareto, A. | 1 (0.6%) |
| Lotta, G., Nieto-Morales, F. and Peeters, R. | 1 (0.6%) |
| Ludt, C., Bjørnholt, M. and Niklasson, B. | 1 (0.6%) |
| Maple, E. and Kebbell, M. | 1 (0.6%) |
| May, P. J., & Wood, R. S. | 1 (0.6%) |
| Maynard-Moody, S. & Musheno, M. | 1 (0.6%) |
| McDonald, C., & Marston, G. | 1 (0.6%) |
| Mekolle, J. E., Tshimwanga, K. E., Ongeh, N. J., Agbornkwai, A. N., Amadeus, O. A., Esa, I., Mekolle, K. E., Forbinake, N. A., Nkfusai, C. N. and Atanga, P. N. | 1 (0.6%) |
| Meza, O., Pérez-Chiqués, E., Campos, S. A. and Varela Castro, S. | 1 (0.6%) |
| Miranda, M. and Godwin, M. L. | 1 (0.6%) |
| Mock-Muñoz de Luna, C., Granberg, A., Krasnik, A. and Vitus, K. | 1 (0.6%) |
| Møller, M. Ø | 1 (0.6%) |
| Møller, M. Ø and Stensöta, H. O. | 1 (0.6%) |
| Monties, V. and Gagnon, S. | 1 (0.6%) |
| Mortensen, N. M. and Needham, C. | 1 (0.6%) |
| Mousa, M., Tarba, S., Arslan, A., & Cooper, S. C. | 1 (0.6%) |
| Mukuru, M., Kiwanuka, S. N., Gibson, L. and Ssengooba, F. | 1 (0.6%) |
| Munobwa, J. S., Öberg, P. and Ahmadi, F. | 1 (0.6%) |
| Mutereko, S. and Chitakunye, P. | 1 (0.6%) |
| Myers, G. and Kowal, C. | 1 (0.6%) |
| Namugumya, B. S., Candel, J. J. L., Talsma, E. F., Termeer, C. J. A. M. and Harris, J. | 1 (0.6%) |
| Nielsen, M. H. and Monrad, M. | 1 (0.6%) |
| Nielsen, V. L. | 1 (0.6%) |
| Nordesjö, K., Ulmestig, R. and Denvall, V. | 1 (0.6%) |
| Oberfield, Z. W. | 1 (0.6%) |
| Pare Toe, L. and Samuelsen, H. | 1 (0.6%) |
| Pedersen, K. Z. and Pors, A. S. | 1 (0.6%) |
| Petersen, N. B. G. | 1 (0.6%) |
| Pisoni, D. | 1 (0.6%) |
| Portillo, S. and Kras, K. R. | 1 (0.6%) |
| Potipiroon, W. | 1 (0.6%) |
| Precious, C., Baker, K. and Edwards, M. | 1 (0.6%) |
| Raaphorst, N. | 1 (0.6%) |
| Ramani, S., Gilson, L., Sivakami, M. and Gawde, N. | 1 (0.6%) |
| Rayner, J. and Lawton, A. | 1 (0.6%) |
| Ropes, E. and de Boer, N. | 1 (0.6%) |
| Sabbe, M., Schiffino, N. and Moyson, S. | 1 (0.6%) |
| Sager, F., Thomann, E., Zollinger, C., van der Heiden, N., & Mavrot, C. | 1 (0.6%) |
| Salifu, R. S. and Hlongwana, K. W. | 1 (0.6%) |
| Sandfort, J. R. | 1 (0.6%) |
| Savi, R. and Cepilovs, A. | 1 (0.6%) |
| Selekman, R. K. | 1 (0.6%) |
| Smith, B. D., & Donovan, S. E. | 1 (0.6%) |
| Soss, J., Fording, R., & Schram, S. F. | 1 (0.6%) |
| Stanhope, V., Henwood, B. F. and Padgett, D. K. | 1 (0.6%) |
| Summers, A. P. and Semrud-Clikeman, M. | 1 (0.6%) |
| Thunman, E. | 1 (0.6%) |
| Tier, M. V. D., Hermans, K. and Potting, M. | 1 (0.6%) |
| Trappenburg, M., Kampen, T. and Tonkens, E. | 2 (1.2%) |
| Tummers, L. | 1 (0.6%) |
| Tummers, L. and Rocco, P. | 1 (0.6%) |
| Tweheyo, R., Reed, C., Campbell, S., Davies, L. and Daker-White, G. | 1 (0.6%) |
| van der Meer, J., Vermeeren, B. and Steijn, B. | 1 (0.6%) |
| van Loon, N. M. and Jakobsen, M. L. | 1 (0.6%) |
| Van Parys, L., & Struyven, L. | 1 (0.6%) |
| Verhoeven, I. and Van Bochove, M. | 1 (0.6%) |
| Vilhena, S. | 1 (0.6%) |
| Visser, E. L. and van Hulst, M. | 1 (0.6%) |
| Volckmar-Eeg, M. G. and Vassenden, A. | 1 (0.6%) |
| Wunsch, J. S., Teply, L. L. and Zimmerman, J. | 1 (0.6%) |
| Xiao, M., Liu, N., Lo, C. W. H. and Zhan, X. | 1 (0.6%) |
| Yang, F., Huang, X. and Li, Z. | 1 (0.6%) |
| Zhang, Y. | 1 (0.6%) |
| year | |
| 1981 | 1 (0.6%) |
| 1994 | 1 (0.6%) |
| 1997 | 1 (0.6%) |
| 1998 | 1 (0.6%) |
| 1999 | 1 (0.6%) |
| 2000 | 2 (1.2%) |
| 2003 | 3 (1.8%) |
| 2005 | 1 (0.6%) |
| 2006 | 1 (0.6%) |
| 2007 | 2 (1.2%) |
| 2008 | 1 (0.6%) |
| 2009 | 2 (1.2%) |
| 2010 | 1 (0.6%) |
| 2011 | 3 (1.8%) |
| 2012 | 2 (1.2%) |
| 2013 | 3 (1.8%) |
| 2014 | 4 (2.4%) |
| 2015 | 2 (1.2%) |
| 2016 | 3 (1.8%) |
| 2017 | 9 (5.4%) |
| 2018 | 14 (8.4%) |
| 2019 | 11 (6.6%) |
| 2020 | 11 (6.6%) |
| 2021 | 23 (14%) |
| 2022 | 26 (16%) |
| 2023 | 27 (16%) |
| 2024 | 10 (6.0%) |
| journal | |
| International Journal of Public Administration | 1 (0.6%) |
| Administration and Society | 6 (3.6%) |
| Administrative Theory and Praxis | 2 (1.2%) |
| American Review of Public Administration | 4 (2.4%) |
| Australian Journal of Public Administration | 3 (1.8%) |
| Australian Social Work | 1 (0.6%) |
| BMC Health Services Research | 1 (0.6%) |
| BMC Public Health | 1 (0.6%) |
| BMJ Global Health | 1 (0.6%) |
| British Journal of Social Work | 2 (1.2%) |
| Child Abuse Review | 1 (0.6%) |
| Child and Family Social Work | 1 (0.6%) |
| Children and Youth Services Review | 1 (0.6%) |
| Computer Supported Cooperative Work: CSCW: An International Journal | 1 (0.6%) |
| Corvinus Journal of Sociology and Social Policy | 1 (0.6%) |
| Current Sociology | 1 (0.6%) |
| Economics of Education Review | 1 (0.6%) |
| Educational Policy | 1 (0.6%) |
| Environment and Planning C: Politics and Space | 1 (0.6%) |
| Environmental Policy and Governance | 1 (0.6%) |
| Ethnos | 1 (0.6%) |
| European Journal of Social Work | 2 (1.2%) |
| European Management Review | 1 (0.6%) |
| Geopolitics | 1 (0.6%) |
| Global Public Health | 1 (0.6%) |
| Governance | 1 (0.6%) |
| Health & social care in the community | 1 (0.6%) |
| Health Communication | 1 (0.6%) |
| Health Policy and Planning | 1 (0.6%) |
| Health Research Policy and Systems | 1 (0.6%) |
| Higher Education | 1 (0.6%) |
| Human Resources for Health | 1 (0.6%) |
| Human Service Organizations Management, Leadership and Governance | 1 (0.6%) |
| Implementation Science Communications | 1 (0.6%) |
| International Journal of Health Policy and Management | 2 (1.2%) |
| International Journal of Offender Therapy and Comparative Criminology | 1 (0.6%) |
| International Journal of Public Administration | 2 (1.2%) |
| International Journal of Qualitative Studies on Health and Well-being | 1 (0.6%) |
| International Journal of Social Welfare | 1 (0.6%) |
| International Journal of Sociology and Social Policy | 2 (1.2%) |
| International Public Management Journal | 3 (1.8%) |
| International Review of Administrative Sciences | 1 (0.6%) |
| International Social Security Review | 1 (0.6%) |
| Israel Affairs | 1 (0.6%) |
| Journal of Aggression, Conflict and Peace Research | 1 (0.6%) |
| Journal of Borderlands Studies | 1 (0.6%) |
| Journal of Comparative Policy Analysis: Research and Practice | 2 (1.2%) |
| Journal of Ethnic and Migration Studies | 1 (0.6%) |
| Journal of European Public Policy | 1 (0.6%) |
| Journal of Health Politics, Policy and Law | 1 (0.6%) |
| Journal of Immigrant and Refugee Studies | 1 (0.6%) |
| JOURNAL OF NURSING MANAGEMENT | 1 (0.6%) |
| Journal of Organizational Ethnography | 1 (0.6%) |
| Journal of public administration and theory | 1 (0.6%) |
| Journal of public administration research and theory | 5 (3.0%) |
| Journal of Public Administration Research and Theory | 9 (5.4%) |
| JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY | 1 (0.6%) |
| Journal of Social Policy | 2 (1.2%) |
| Journal of Social Work | 1 (0.6%) |
| Law & Policy | 1 (0.6%) |
| Law and Policy | 1 (0.6%) |
| Local Government Studies | 3 (1.8%) |
| Mediterranean Journal of Social Sciences | 1 (0.6%) |
| Migration Studies | 1 (0.6%) |
| Nordic Journal of Human Rights | 1 (0.6%) |
| Nordic Journal of Working Life Studies | 2 (1.2%) |
| Nordic Social Work Research | 2 (1.2%) |
| Organization Studies | 1 (0.6%) |
| Perspectives on Public Management and Governance | 1 (0.6%) |
| Policy and Society | 1 (0.6%) |
| Politiche Sociali | 1 (0.6%) |
| Professions and Professionalism | 1 (0.6%) |
| Psychiatric Services | 1 (0.6%) |
| Public Administration | 9 (5.4%) |
| Public Administration and Development | 2 (1.2%) |
| Public Administration Quarterly | 1 (0.6%) |
| Public Administration Review | 9 (5.4%) |
| Public Management Review | 12 (7.2%) |
| Public Performance and Management Review | 3 (1.8%) |
| Qualitative Health Research | 1 (0.6%) |
| Qualitative Research in Organizations and Management: An International Journal | 1 (0.6%) |
| Regulation and Governance | 1 (0.6%) |
| Scandinavian Political Studies | 1 (0.6%) |
| SCHOOL PSYCHOLOGY QUARTERLY | 1 (0.6%) |
| Scientific Reports | 1 (0.6%) |
| Social Policy & Administration | 1 (0.6%) |
| Social Policy and Administration | 2 (1.2%) |
| Social Science and Medicine | 2 (1.2%) |
| Social Sciences | 1 (0.6%) |
| Social Service Review | 4 (2.4%) |
| Social Theory and Health | 1 (0.6%) |
| Time and Society | 1 (0.6%) |
| Urban Affairs Review | 1 (0.6%) |
| Violence Against Women | 1 (0.6%) |
| Voluntas | 1 (0.6%) |
| journal_article | |
| 1 | 165 (100%) |
| Unknown | 1 |
| language | |
| 1 | 165 (100%) |
| Unknown | 1 |
| use | |
| 1 | 165 (100%) |
| Unknown | 1 |
| empirical_study | |
| 1 | 165 (100%) |
| Unknown | 1 |
| theoretical_study | |
| 0 | 165 (100%) |
| Unknown | 1 |
| policy_domain | |
| Agriculture | 1 (0.6%) |
| Economics | 1 (0.6%) |
| Education | 20 (12%) |
| Environment | 3 (1.8%) |
| Governance | 22 (13%) |
| Health | 37 (22%) |
| Other | 12 (7.3%) |
| Policing | 8 (4.8%) |
| Welfare | 61 (37%) |
| Unknown | 1 |
| no_of_countries | |
| 1 | 149 (90%) |
| 2 | 9 (5.5%) |
| 3 | 5 (3.0%) |
| 4 | 1 (0.6%) |
| 7 | 1 (0.6%) |
| Unknown | 1 |
| country | |
| Australia | 4 (2.4%) |
| Belgium | 3 (1.8%) |
| Belgium, Finland, Germany, Ireland, Italy, The Netherlands, and Portugal | 1 (0.6%) |
| Brazil | 3 (1.8%) |
| Brazil, Mexico | 1 (0.6%) |
| Burkina Faso | 1 (0.6%) |
| Cameroon | 1 (0.6%) |
| Canada | 1 (0.6%) |
| China | 4 (2.4%) |
| Denmark | 22 (13%) |
| Denmark, Sweden | 2 (1.2%) |
| Egypt | 1 (0.6%) |
| Estonia and Latvia | 1 (0.6%) |
| France | 2 (1.2%) |
| Germany | 4 (2.4%) |
| Germany, Israel | 2 (1.2%) |
| Germany, Netherlands, Belgium | 1 (0.6%) |
| Ghana | 5 (3.0%) |
| India | 2 (1.2%) |
| Israel | 15 (9.1%) |
| Italy | 1 (0.6%) |
| Kurdistan Region of Iraq | 1 (0.6%) |
| Lithuania | 1 (0.6%) |
| Mexico | 2 (1.2%) |
| Netherlands | 15 (9.1%) |
| Norway | 5 (3.0%) |
| Scotland | 1 (0.6%) |
| South Africa | 2 (1.2%) |
| South Korea | 1 (0.6%) |
| Sweden | 5 (3.0%) |
| Sweden, Denmark | 1 (0.6%) |
| Switzerland | 2 (1.2%) |
| Switzerland, Germany, Sweden | 1 (0.6%) |
| Switzerland, Sweden | 1 (0.6%) |
| Switzerland, Sweden, France | 1 (0.6%) |
| Switzerland, Sweden, Latvia, Lithuania | 1 (0.6%) |
| Thailand | 1 (0.6%) |
| Tunisia | 2 (1.2%) |
| Uganda | 3 (1.8%) |
| United Kingdom | 6 (3.6%) |
| United Kingdom, Israel, Sweden | 1 (0.6%) |
| United States | 34 (21%) |
| United States, Germany, Sweden | 1 (0.6%) |
| Unknown | 1 |
| developed_developing | |
| 0 | 141 (85%) |
| 1 | 24 (15%) |
| Unknown | 1 |
| vulnerable_clients | |
| 0 | 44 (27%) |
| 1 | 121 (73%) |
| Unknown | 1 |
| social_equity | |
| 0 | 141 (85%) |
| 1 | 24 (15%) |
| Unknown | 1 |
| covid_19 | |
| 0 | 156 (95%) |
| 1 | 9 (5.5%) |
| Unknown | 1 |
| type_of_analysis | |
| Mixed | 10 (6.1%) |
| Qualitative | 125 (76%) |
| Quantitative | 30 (18%) |
| Unknown | 1 |
| gender | |
| 0 | 3 (1.8%) |
| 0+1 | 54 (33%) |
| 1 | 15 (9.1%) |
| 2 | 92 (56%) |
| Unknown | 2 |
| sector | |
| 1 | 3 (1.8%) |
| 1+2 | 4 (2.4%) |
| 1+2+3 | 1 (0.6%) |
| 2 | 146 (88%) |
| 2+3 | 3 (1.8%) |
| 3 | 8 (4.8%) |
| Unknown | 1 |
| type_of_slb | |
| 1 | 11 (6.8%) |
| 1, 2 | 4 (2.5%) |
| 1, 2, 4 | 1 (0.6%) |
| 1, 3, 10 | 1 (0.6%) |
| 10 | 2 (1.2%) |
| 2 | 35 (22%) |
| 2, 10 | 1 (0.6%) |
| 2, 3 | 3 (1.9%) |
| 2, 3, 4 | 1 (0.6%) |
| 2, 8 | 5 (3.1%) |
| 2, 9 | 1 (0.6%) |
| 3 | 14 (8.6%) |
| 3, 4 ,8 | 1 (0.6%) |
| 3, 8 | 2 (1.2%) |
| 3, 9 | 1 (0.6%) |
| 3,4 | 1 (0.6%) |
| 4 | 7 (4.3%) |
| 4, 5 | 4 (2.5%) |
| 4, 5, 10 | 1 (0.6%) |
| 4, 5, 9 | 2 (1.2%) |
| 4. 5, 8 | 1 (0.6%) |
| 5 | 4 (2.5%) |
| 5, 9 | 1 (0.6%) |
| 7 | 7 (4.3%) |
| 8 | 36 (22%) |
| 9 | 14 (8.6%) |
| 9, 2, 3, 4 | 1 (0.6%) |
| Unknown | 4 |
| rule_bending | |
| 0 | 101 (61%) |
| 1 | 65 (39%) |
| rule_breaking | |
| 0 | 141 (85%) |
| 1 | 25 (15%) |
| instrumental_actions | |
| 0 | 137 (83%) |
| 1 | 29 (17%) |
| prioritizing | |
| 0 | 103 (62%) |
| 1 | 63 (38%) |
| personal_resources | |
| 0 | 141 (85%) |
| 1 | 25 (15%) |
| routinizing | |
| 0 | 114 (69%) |
| 1 | 52 (31%) |
| ratioining | |
| 0 | 107 (64%) |
| 1 | 59 (36%) |
| rigid_rule_following | |
| 0 | 121 (73%) |
| 1 | 45 (27%) |
| agression | |
| 0 | 136 (82%) |
| 1 | 29 (18%) |
| Unknown | 1 |
| new_behavioral_coping | |
| 0 | 99 (60%) |
| 1 | 66 (40%) |
| Unknown | 1 |
| cynicism | |
| 0 | 137 (83%) |
| 1 | 29 (17%) |
| compassion | |
| 0 | 121 (73%) |
| 1 | 45 (27%) |
| emotional_detachment | |
| 0 | 125 (76%) |
| 1 | 40 (24%) |
| Unknown | 1 |
| empathy | |
| 0 | 124 (75%) |
| 1 | 42 (25%) |
| new_cognitive_coping | |
| 0 | 140 (84%) |
| 1 | 26 (16%) |
| personal_factors | |
| 0 | 57 (35%) |
| 1 | 108 (65%) |
| Unknown | 1 |
| organizational_factors | |
| 0 | 25 (15%) |
| 1 | 140 (85%) |
| Unknown | 1 |
| environmental_factors | |
| 0 | 68 (41%) |
| 1 | 97 (59%) |
| Unknown | 1 |
| moving_towards | |
| 0 | 52 (31%) |
| 0.25 | 65 (39%) |
| 0.5 | 33 (20%) |
| 0.75 | 13 (7.8%) |
| 1 | 3 (1.8%) |
| moving_away | |
| 0 | 87 (52%) |
| 0.5 | 47 (28%) |
| 1 | 32 (19%) |
| moving_against | |
| 0 | 106 (64%) |
| 0.5 | 46 (28%) |
| 1 | 14 (8.4%) |
| moving_towards_bi | 114 (69%) |
| moving_away_bi | 79 (48%) |
| moving_against_bi | 60 (36%) |
| 1 n (%) | |
Frequency Table (Numbers and Percentages): Personal, Organizational, Environmental factors (Bi)/Moving towards, away, against (Table 1)
Code
moving_towards_tb <- main_data %>%
dplyr::select(moving_towards_bi, environmental_factors, organizational_factors, personal_factors) %>%
mutate(across(everything(), ~ as.numeric(.))) %>%
drop_na() %>%
group_by(moving_towards_bi) %>%
dplyr::summarize(environmental_factors_p = mean(environmental_factors) * 100,
environmental_factors_n = sum(environmental_factors),
organizational_factors_p = mean(organizational_factors) * 100,
organizational_factors_n = sum(organizational_factors),
personal_factors_p = mean(personal_factors) * 100,
personal_factors_n = sum(personal_factors)) %>%
mutate(environmental_factors = paste0(environmental_factors_n, " (", round(environmental_factors_p, 1), "%)"),
organizational_factors = paste0(organizational_factors_n, " (", round(organizational_factors_p, 1), "%)"),
personal_factors = paste0(personal_factors_n, " (", round(personal_factors_p, 1), "%)"),
var = "Moving Towards") %>%
filter(moving_towards_bi == 1) %>%
dplyr::select(var, environmental_factors, organizational_factors, personal_factors)
moving_away_tb <- main_data %>%
dplyr::select(moving_away_bi, environmental_factors, organizational_factors, personal_factors) %>%
mutate(across(everything(), ~ as.numeric(.))) %>%
drop_na() %>%
group_by(moving_away_bi) %>%
dplyr::summarize(environmental_factors_p = mean(environmental_factors) * 100,
environmental_factors_n = sum(environmental_factors),
organizational_factors_p = mean(organizational_factors) * 100,
organizational_factors_n = sum(organizational_factors),
personal_factors_p = mean(personal_factors) * 100,
personal_factors_n = sum(personal_factors)) %>%
mutate(environmental_factors = paste0(environmental_factors_n, " (", round(environmental_factors_p, 1), "%)"),
organizational_factors = paste0(organizational_factors_n, " (", round(organizational_factors_p, 1), "%)"),
personal_factors = paste0(personal_factors_n, " (", round(personal_factors_p, 1), "%)"),
var = "Moving Away") %>%
filter(moving_away_bi == 1) %>%
dplyr::select(var, environmental_factors, organizational_factors, personal_factors)
moving_against_tb <- main_data %>%
dplyr::select(moving_against_bi, environmental_factors, organizational_factors, personal_factors) %>%
mutate(across(everything(), ~ as.numeric(.))) %>%
drop_na() %>%
group_by(moving_against_bi) %>%
dplyr::summarize(environmental_factors_p = mean(environmental_factors) * 100,
environmental_factors_n = sum(environmental_factors),
organizational_factors_p = mean(organizational_factors) * 100,
organizational_factors_n = sum(organizational_factors),
personal_factors_p = mean(personal_factors) * 100,
personal_factors_n = sum(personal_factors)) %>%
mutate(environmental_factors = paste0(environmental_factors_n, " (", round(environmental_factors_p, 1), "%)"),
organizational_factors = paste0(organizational_factors_n, " (", round(organizational_factors_p, 1), "%)"),
personal_factors = paste0(personal_factors_n, " (", round(personal_factors_p, 1), "%)"),
var = "Moving Against") %>%
filter(moving_against_bi == 1) %>%
dplyr::select(var, environmental_factors, organizational_factors, personal_factors)
rbind(moving_towards_tb, moving_away_tb, moving_against_tb) %>%
rename(Variable = var) %>%
gt() %>%
tab_header(title = "Frequency Table") %>%
fmt_number(columns = -Variable,
decimals = 2) %>%
cols_label(environmental_factors = "Environmental Factors",
organizational_factors = "Organizational Factors",
personal_factors = "Personal Factors") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Frequency Table | |||
| Variable | Environmental Factors | Organizational Factors | Personal Factors |
|---|---|---|---|
| Moving Towards | 72 (63.7%) | 102 (90.3%) | 77 (68.1%) |
| Moving Away | 56 (71.8%) | 74 (94.9%) | 51 (65.4%) |
| Moving Against | 34 (56.7%) | 52 (86.7%) | 45 (75%) |
Frequency Table (Numbers and Percentages): Personal, Organizational, Environmental factors (Bi)/Moving towards, away, against (Table 2)
Code
# moving_towards_bi
moving_towards_tb <- main_data %>%
mutate(environmental_factors = as.numeric(environmental_factors),
organizational_factors = as.numeric(organizational_factors),
personal_factors = as.numeric(personal_factors)) %>%
group_by(moving_towards_bi) %>%
dplyr::summarize(environmental_factors = sum(environmental_factors, na.rm = T),
organizational_factors = sum(organizational_factors, na.rm = T),
personal_factors = sum(personal_factors, na.rm = T)) %>%
drop_na() %>%
mutate(total = rowSums(dplyr::select(., environmental_factors, organizational_factors, personal_factors)),
environmental_factors = (environmental_factors/total) * 100,
environmental_factors = round((environmental_factors), 1),
organizational_factors = (organizational_factors/total) * 100,
organizational_factors = round((organizational_factors), 1),
personal_factors = (personal_factors/total) * 100,
personal_factors = round((personal_factors), 1),
#total = 100,
var = "Moving Towards") %>%
filter(moving_towards_bi == 1) %>%
dplyr::select(-moving_towards_bi)
# moving_away_bi
moving_away_tb <- main_data %>%
mutate(environmental_factors = as.numeric(environmental_factors),
organizational_factors = as.numeric(organizational_factors),
personal_factors = as.numeric(personal_factors)) %>%
group_by(moving_away_bi) %>%
dplyr::summarize(environmental_factors = sum(environmental_factors, na.rm = T),
organizational_factors = sum(organizational_factors, na.rm = T),
personal_factors = sum(personal_factors, na.rm = T)) %>%
drop_na() %>%
mutate(total = rowSums(dplyr::select(., environmental_factors, organizational_factors, personal_factors)),
environmental_factors = (environmental_factors/total) * 100,
environmental_factors = round((environmental_factors), 1),
organizational_factors = (organizational_factors/total) * 100,
organizational_factors = round((organizational_factors), 1),
personal_factors = (personal_factors/total) * 100,
personal_factors = round((personal_factors), 1),
#total = 100,
var = "Moving Away") %>%
filter(moving_away_bi == 1) %>%
dplyr::select(-moving_away_bi)
# moving_against_bi
moving_against_tb <- main_data %>%
mutate(environmental_factors = as.numeric(environmental_factors),
organizational_factors = as.numeric(organizational_factors),
personal_factors = as.numeric(personal_factors)) %>%
group_by(moving_against_bi) %>%
dplyr::summarize(environmental_factors = sum(environmental_factors, na.rm = T),
organizational_factors = sum(organizational_factors, na.rm = T),
personal_factors = sum(personal_factors, na.rm = T)) %>%
drop_na() %>%
mutate(total = rowSums(dplyr::select(., environmental_factors, organizational_factors, personal_factors)),
environmental_factors = (environmental_factors/total) * 100,
environmental_factors = round((environmental_factors), 1),
organizational_factors = (organizational_factors/total) * 100,
organizational_factors = round((organizational_factors), 1),
personal_factors = (personal_factors/total) * 100,
personal_factors = round((personal_factors), 1),
#total = 100,
var = "Moving Against") %>%
filter(moving_against_bi == 1) %>%
dplyr::select(-moving_against_bi)
rbind(moving_towards_tb, moving_away_tb, moving_against_tb) %>%
dplyr::select(var, environmental_factors, organizational_factors, personal_factors) %>%
gt() %>%
tab_header(title = "Moving towards/away/against by factors") %>%
cols_label(var = "Variable",
environmental_factors = "Environmental Factors",
organizational_factors = "Organizational Factors",
personal_factors = "Personal Factors") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Moving towards/away/against by factors | |||
| Variable | Environmental Factors | Organizational Factors | Personal Factors |
|---|---|---|---|
| Moving Towards | 28.7 | 40.6 | 30.7 |
| Moving Away | 30.9 | 40.9 | 28.2 |
| Moving Against | 26.0 | 39.7 | 34.4 |
Correlations Tables
- Correlations cannot be calculated for the following variables —
journal_article,language,use,empirical_study,theoretical_study— either because they consist of only one category or one category is overwhelmingly dominant.
Correlations between Numerical/Binary Variables (Pearson)
Code
sjPlot::tab_corr(main_data %>%
dplyr::select(moving_towards, moving_away, moving_against, year, no_of_countries, developed_developing, vulnerable_clients, social_equity, covid_19, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors) %>%
mutate(across(everything(), as.numeric)),
triangle = "lower",
corr.method = "pearson",
p.numeric = T,
show.p = T,
na.deletion = "listwise")| moving_towards | moving_away | moving_against | year | no_of_countries | developed_developing | vulnerable_clients | social_equity | covid_19 | rule_breaking | instrumental_actions | prioritizing | personal_resources | routinizing | ratioining | rigid_rule_following | agression | new_behavioral_coping | cynicism | compassion | emotional_detachment | empathy | new_cognitive_coping | personal_factors | organizational_factors | environmental_factors | |
| moving_towards | ||||||||||||||||||||||||||
| moving_away | 0.100 (.205) |
|||||||||||||||||||||||||
| moving_against | -0.016 (.841) |
0.244 (.002) |
||||||||||||||||||||||||
| year | 0.087 (.273) |
-0.178 (.023) |
-0.198 (.012) |
|||||||||||||||||||||||
| no_of_countries | -0.036 (.645) |
-0.140 (.076) |
-0.004 (.959) |
0.025 (.751) |
||||||||||||||||||||||
| developed_developing | 0.046 (.562) |
0.171 (.029) |
0.181 (.021) |
0.176 (.025) |
-0.073 (.353) |
|||||||||||||||||||||
| vulnerable_clients | 0.175 (.026) |
0.017 (.832) |
-0.057 (.475) |
0.004 (.964) |
-0.008 (.917) |
-0.116 (.142) |
||||||||||||||||||||
| social_equity | -0.079 (.319) |
-0.048 (.544) |
-0.183 (.020) |
0.044 (.582) |
-0.076 (.333) |
-0.120 (.129) |
0.133 (.092) |
|||||||||||||||||||
| covid_19 | -0.080 (.314) |
-0.002 (.981) |
0.039 (.620) |
0.149 (.059) |
-0.017 (.829) |
0.133 (.092) |
-0.037 (.638) |
-0.025 (.749) |
||||||||||||||||||
| rule_breaking | 0.520 (<.001) |
0.048 (.545) |
-0.007 (.930) |
0.081 (.308) |
-0.107 (.174) |
0.071 (.369) |
0.025 (.756) |
-0.082 (.300) |
0.046 (.565) |
|||||||||||||||||
| instrumental_actions | 0.597 (<.001) |
0.031 (.697) |
-0.076 (.335) |
0.130 (.099) |
-0.065 (.412) |
0.133 (.092) |
0.135 (.087) |
-0.059 (.458) |
-0.043 (.587) |
0.291 (<.001) |
||||||||||||||||
| prioritizing | 0.570 (<.001) |
0.131 (.098) |
-0.108 (.172) |
0.100 (.208) |
-0.071 (.371) |
-0.024 (.761) |
0.092 (.244) |
0.106 (.178) |
-0.133 (.092) |
0.021 (.794) |
0.069 (.382) |
|||||||||||||||
| personal_resources | 0.161 (.041) |
0.070 (.376) |
-0.033 (.674) |
0.146 (.063) |
-0.052 (.514) |
0.120 (.128) |
0.179 (.022) |
0.014 (.857) |
-0.029 (.714) |
0.007 (.932) |
0.157 (.046) |
0.056 (.479) |
||||||||||||||
| routinizing | 0.035 (.656) |
0.816 (<.001) |
0.267 (.001) |
-0.050 (.527) |
-0.084 (.289) |
0.105 (.183) |
-0.014 (.860) |
-0.021 (.793) |
0.010 (.903) |
0.042 (.600) |
0.030 (.703) |
0.049 (.533) |
0.078 (.322) |
|||||||||||||
| ratioining | 0.128 (.105) |
0.829 (<.001) |
0.136 (.084) |
-0.240 (.002) |
-0.146 (.064) |
0.176 (.025) |
0.041 (.607) |
-0.058 (.466) |
-0.012 (.875) |
0.037 (.637) |
0.021 (.793) |
0.164 (.037) |
0.037 (.637) |
0.353 (<.001) |
||||||||||||
| rigid_rule_following | 0.033 (.675) |
0.275 (<.001) |
0.817 (<.001) |
-0.144 (.068) |
0.005 (.952) |
0.030 (.705) |
-0.073 (.356) |
-0.177 (.025) |
-0.027 (.734) |
-0.069 (.385) |
0.004 (.955) |
-0.016 (.837) |
-0.030 (.701) |
0.243 (.002) |
0.210 (.007) |
|||||||||||
| agression | -0.065 (.409) |
0.093 (.239) |
0.743 (<.001) |
-0.168 (.032) |
-0.012 (.875) |
0.271 (<.001) |
-0.011 (.889) |
-0.104 (.187) |
0.098 (.216) |
0.068 (.390) |
-0.134 (.089) |
-0.163 (.038) |
-0.021 (.789) |
0.169 (.032) |
-0.013 (.871) |
0.222 (.005) |
||||||||||
| new_behavioral_coping | 0.217 (.006) |
0.053 (.503) |
-0.006 (.943) |
0.105 (.183) |
0.040 (.610) |
0.100 (.205) |
-0.078 (.322) |
-0.164 (.037) |
-0.089 (.262) |
-0.001 (.989) |
0.242 (.002) |
0.066 (.407) |
0.034 (.670) |
0.042 (.598) |
0.045 (.566) |
0.010 (.902) |
-0.021 (.792) |
|||||||||
| cynicism | 0.034 (.670) |
0.087 (.268) |
0.186 (.018) |
-0.052 (.513) |
-0.035 (.657) |
0.001 (.988) |
0.090 (.255) |
-0.053 (.505) |
0.032 (.689) |
-0.060 (.450) |
-0.086 (.278) |
0.083 (.295) |
0.076 (.337) |
0.112 (.156) |
0.033 (.675) |
0.125 (.114) |
0.170 (.031) |
-0.008 (.921) |
||||||||
| compassion | 0.019 (.808) |
0.061 (.443) |
0.111 (.161) |
0.062 (.432) |
-0.018 (.822) |
-0.089 (.258) |
0.116 (.143) |
-0.177 (.025) |
0.034 (.671) |
0.085 (.283) |
-0.032 (.689) |
-0.102 (.196) |
0.123 (.118) |
0.124 (.116) |
-0.022 (.783) |
0.095 (.228) |
0.077 (.331) |
-0.075 (.342) |
-0.059 (.456) |
|||||||
| emotional_detachment | 0.012 (.884) |
0.315 (<.001) |
0.264 (.001) |
-0.222 (.005) |
-0.097 (.218) |
-0.110 (.164) |
0.117 (.137) |
-0.158 (.044) |
-0.076 (.334) |
-0.086 (.276) |
-0.118 (.135) |
0.087 (.272) |
-0.007 (.931) |
0.290 (<.001) |
0.229 (.003) |
0.294 (<.001) |
0.106 (.179) |
-0.089 (.260) |
0.268 (.001) |
0.101 (.201) |
||||||
| empathy | 0.181 (.021) |
0.008 (.924) |
0.055 (.485) |
-0.035 (.660) |
0.085 (.283) |
-0.115 (.146) |
0.093 (.241) |
-0.043 (.588) |
0.045 (.572) |
0.144 (.067) |
-0.013 (.874) |
0.134 (.090) |
0.066 (.406) |
0.095 (.231) |
-0.079 (.316) |
-0.004 (.956) |
0.099 (.212) |
-0.129 (.102) |
0.034 (.665) |
0.315 (<.001) |
0.193 (.014) |
|||||
| new_cognitive_coping | 0.123 (.120) |
0.098 (.217) |
0.241 (.002) |
-0.083 (.295) |
-0.055 (.486) |
0.063 (.425) |
0.149 (.059) |
-0.182 (.020) |
0.114 (.148) |
0.093 (.242) |
-0.029 (.717) |
0.077 (.332) |
0.046 (.561) |
0.030 (.709) |
0.129 (.101) |
0.187 (.017) |
0.191 (.015) |
-0.015 (.851) |
0.200 (.011) |
-0.002 (.976) |
0.296 (<.001) |
0.016 (.838) |
||||
| personal_factors | 0.181 (.021) |
-0.056 (.483) |
0.125 (.114) |
-0.102 (.195) |
-0.008 (.924) |
-0.002 (.982) |
0.151 (.055) |
-0.062 (.431) |
0.120 (.129) |
0.131 (.097) |
0.102 (.195) |
0.029 (.713) |
-0.013 (.871) |
0.018 (.824) |
-0.107 (.176) |
0.035 (.656) |
0.170 (.030) |
-0.093 (.237) |
0.161 (.041) |
0.210 (.007) |
0.115 (.144) |
0.304 (<.001) |
0.247 (.002) |
|||
| organizational_factors | 0.201 (.010) |
0.272 (<.001) |
0.075 (.340) |
0.052 (.514) |
0.020 (.802) |
0.170 (.031) |
-0.015 (.854) |
-0.071 (.372) |
0.101 (.200) |
0.130 (.099) |
0.013 (.865) |
0.109 (.168) |
0.178 (.023) |
0.208 (.008) |
0.239 (.002) |
0.098 (.213) |
0.013 (.865) |
-0.119 (.130) |
0.099 (.211) |
0.059 (.453) |
0.037 (.637) |
0.083 (.294) |
-0.244 (.002) |
-0.047 (.550) |
||
| environmental_factors | 0.276 (<.001) |
0.168 (.032) |
-0.024 (.758) |
-0.017 (.827) |
-0.017 (.833) |
0.193 (.014) |
0.014 (.863) |
-0.008 (.921) |
0.146 (.063) |
0.111 (.161) |
0.191 (.015) |
0.178 (.024) |
0.146 (.065) |
0.102 (.196) |
0.174 (.027) |
-0.002 (.979) |
-0.039 (.624) |
0.012 (.876) |
0.180 (.022) |
0.054 (.492) |
-0.021 (.796) |
0.020 (.797) |
0.123 (.119) |
0.058 (.466) |
0.149 (.058) |
|
| Computed correlation used pearson-method with listwise-deletion. | ||||||||||||||||||||||||||
Code
main_data_unfiltered %>%
dplyr::select(moving_towards, moving_away, moving_against, year, no_of_countries, developed_developing, vulnerable_clients, social_equity, covid_19, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors) %>%
mutate(across(everything(), as.numeric)) %>%
drop_na() %>%
{
cor_results <- Hmisc::rcorr(as.matrix(.),
type = "pearson")
cor_results$r %>%
as_tibble(rownames = "Variable") %>%
pivot_longer(-Variable, names_to = "Compared_Variable", values_to = "Correlation") %>%
left_join(cor_results$P %>%
as_tibble(rownames = "Variable") %>%
pivot_longer(-Variable, names_to = "Compared_Variable", values_to = "P_value"),
by = c("Variable", "Compared_Variable")) %>%
filter(Variable %in% c("moving_towards", "moving_away", "moving_against")) %>%
mutate(Correlation = round(Correlation, 3),
P_value = round(P_value, 3)) %>%
gt() %>%
tab_header(title = "Correlation Table with P-Values",
subtitle = "Key Variables: Moving Towards, Moving Away, Moving Against") %>%
fmt_number(columns = vars(Correlation),
decimals = 3) %>%
fmt_number(columns = vars(P_value),
decimals = 3) %>%
cols_label(Variable = "Key Variable",
Compared_Variable = "Compared To",
Correlation = "Correlation (Pearson)",
P_value = "P-Value") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))
}| Correlation Table with P-Values | |||
| Key Variables: Moving Towards, Moving Away, Moving Against | |||
| Key Variable | Compared To | Correlation (Pearson) | P-Value |
|---|---|---|---|
| moving_towards | moving_towards | 1.000 | NA |
| moving_towards | moving_away | 0.105 | 0.181 |
| moving_towards | moving_against | −0.011 | 0.889 |
| moving_towards | year | 0.084 | 0.284 |
| moving_towards | no_of_countries | −0.035 | 0.661 |
| moving_towards | developed_developing | 0.048 | 0.539 |
| moving_towards | vulnerable_clients | 0.184 | 0.018 |
| moving_towards | social_equity | −0.093 | 0.238 |
| moving_towards | covid_19 | −0.078 | 0.325 |
| moving_towards | rule_breaking | 0.521 | 0.000 |
| moving_towards | instrumental_actions | 0.597 | 0.000 |
| moving_towards | prioritizing | 0.572 | 0.000 |
| moving_towards | personal_resources | 0.163 | 0.038 |
| moving_towards | routinizing | 0.040 | 0.615 |
| moving_towards | ratioining | 0.132 | 0.092 |
| moving_towards | rigid_rule_following | 0.037 | 0.638 |
| moving_towards | agression | −0.062 | 0.433 |
| moving_towards | new_behavioral_coping | 0.221 | 0.005 |
| moving_towards | cynicism | 0.037 | 0.642 |
| moving_towards | compassion | 0.023 | 0.768 |
| moving_towards | emotional_detachment | 0.015 | 0.846 |
| moving_towards | empathy | 0.184 | 0.019 |
| moving_towards | new_cognitive_coping | 0.125 | 0.112 |
| moving_towards | personal_factors | 0.175 | 0.026 |
| moving_towards | organizational_factors | 0.197 | 0.012 |
| moving_towards | environmental_factors | 0.269 | 0.001 |
| moving_away | moving_towards | 0.105 | 0.181 |
| moving_away | moving_away | 1.000 | NA |
| moving_away | moving_against | 0.247 | 0.002 |
| moving_away | year | −0.179 | 0.022 |
| moving_away | no_of_countries | −0.138 | 0.078 |
| moving_away | developed_developing | 0.173 | 0.027 |
| moving_away | vulnerable_clients | 0.025 | 0.748 |
| moving_away | social_equity | −0.060 | 0.449 |
| moving_away | covid_19 | −0.001 | 0.994 |
| moving_away | rule_breaking | 0.050 | 0.526 |
| moving_away | instrumental_actions | 0.033 | 0.674 |
| moving_away | prioritizing | 0.134 | 0.088 |
| moving_away | personal_resources | 0.072 | 0.361 |
| moving_away | routinizing | 0.817 | 0.000 |
| moving_away | ratioining | 0.829 | 0.000 |
| moving_away | rigid_rule_following | 0.277 | 0.000 |
| moving_away | agression | 0.095 | 0.226 |
| moving_away | new_behavioral_coping | 0.057 | 0.469 |
| moving_away | cynicism | 0.090 | 0.255 |
| moving_away | compassion | 0.064 | 0.419 |
| moving_away | emotional_detachment | 0.317 | 0.000 |
| moving_away | empathy | 0.011 | 0.893 |
| moving_away | new_cognitive_coping | 0.100 | 0.206 |
| moving_away | personal_factors | −0.059 | 0.453 |
| moving_away | organizational_factors | 0.269 | 0.001 |
| moving_away | environmental_factors | 0.163 | 0.037 |
| moving_against | moving_towards | −0.011 | 0.889 |
| moving_against | moving_away | 0.247 | 0.002 |
| moving_against | moving_against | 1.000 | NA |
| moving_against | year | −0.199 | 0.011 |
| moving_against | no_of_countries | −0.003 | 0.969 |
| moving_against | developed_developing | 0.182 | 0.020 |
| moving_against | vulnerable_clients | −0.049 | 0.535 |
| moving_against | social_equity | −0.189 | 0.016 |
| moving_against | covid_19 | 0.040 | 0.610 |
| moving_against | rule_breaking | −0.005 | 0.948 |
| moving_against | instrumental_actions | −0.074 | 0.348 |
| moving_against | prioritizing | −0.104 | 0.186 |
| moving_against | personal_resources | −0.031 | 0.690 |
| moving_against | routinizing | 0.269 | 0.001 |
| moving_against | ratioining | 0.139 | 0.077 |
| moving_against | rigid_rule_following | 0.818 | 0.000 |
| moving_against | agression | 0.744 | 0.000 |
| moving_against | new_behavioral_coping | −0.002 | 0.978 |
| moving_against | cynicism | 0.187 | 0.017 |
| moving_against | compassion | 0.113 | 0.151 |
| moving_against | emotional_detachment | 0.266 | 0.001 |
| moving_against | empathy | 0.058 | 0.465 |
| moving_against | new_cognitive_coping | 0.242 | 0.002 |
| moving_against | personal_factors | 0.121 | 0.123 |
| moving_against | organizational_factors | 0.074 | 0.351 |
| moving_against | environmental_factors | −0.028 | 0.724 |
Correlations in which One Variable is Nominal (Cramer’s V)
Nominal variables: (1) coder_last_name; (2) date; (3) authors; (4) journal; (5) country; (6) type_of_analysis; (7) gender; (8) sector; (9) type_of_slb; (10) policy_domain.
Code
# 1. coder_last_name
tb1 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ environmental_factors)),
Compared_To = c("date", "authors", "journal", "country", "type_of_analysis", "gender", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "coder_last_name",
row_id = paste0("coder_last_name_", row_number()))
chi_sqrt_1 <-
rbind(chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ coder_last_name, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("coder_last_name_", row_number())) %>%
left_join(tb1, by = "row_id")
# 2. date
tb2 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ date, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ date, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "authors", "journal", "country", "type_of_analysis", "gender", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "date",
row_id = paste0("date_", row_number()))
chi_sqrt_2 <-
rbind(chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ date, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("date_", row_number())) %>%
left_join(tb2, by = "row_id")
# 3. authors
tb3 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ authors, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "country", "type_of_analysis", "gender", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "authors",
row_id = paste0("authors_", row_number()))
chi_sqrt_3 <-
rbind(chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ authors, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("authors_", row_number())) %>%
left_join(tb3, by = "row_id")
# 4. journal
tb4 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ journal, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "authors", "country", "type_of_analysis", "gender", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "journal",
row_id = paste0("journal_", row_number()))
chi_sqrt_4 <-
rbind(chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ journal, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("journal_", row_number())) %>%
left_join(tb4, by = "row_id")
# 5. country
tb5 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ country, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ country, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "authors", "type_of_analysis", "gender", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "country",
row_id = paste0("country_", row_number()))
chi_sqrt_5 <-
rbind(chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ country, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("country_", row_number())) %>%
left_join(tb5, by = "row_id")
# 6. type_of_analysis
tb6 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "authors", "country", "gender", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "type_of_analysis",
row_id = paste0("type_of_analysis_", row_number()))
chi_sqrt_6 <-
rbind(chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_analysis, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("type_of_analysis_", row_number())) %>%
left_join(tb6, by = "row_id")
# 7. gender
tb7 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ gender, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "authors", "country", "type_of_analysis", "sector", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "gender",
row_id = paste0("gender_", row_number()))
chi_sqrt_7 <-
rbind(chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ gender, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("gender_", row_number())) %>%
left_join(tb7, by = "row_id")
# 8. sector
tb8 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ sector, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "authors", "country", "type_of_analysis", "gender", "type_of_slb", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "sector",
row_id = paste0("sector_", row_number()))
chi_sqrt_8 <-
rbind(chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ sector, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("sector_", row_number())) %>%
left_join(tb8, by = "row_id")
# 9. type_of_slb
tb9 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ policy_domain),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "authors", "country", "type_of_analysis", "gender", "sector", "policy_domain", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "type_of_slb",
row_id = paste0("type_of_slb_", row_number()))
chi_sqrt_9 <-
rbind(chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ policy_domain) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ type_of_slb, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("type_of_slb_", row_number())) %>%
left_join(tb9, by = "row_id")
# 10. policy_domain
tb10 <- tibble(Cramer_V = c(cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ coder_last_name),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ date),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ authors),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ journal),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ country),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ type_of_analysis),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ gender),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ sector),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ type_of_slb),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ moving_towards),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ moving_away),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ moving_against),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ year),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ no_of_countries),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ developed_developing),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ vulnerable_clients),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ social_equity),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ covid_19),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ rule_breaking),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ instrumental_actions),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ prioritizing),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ personal_resources),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ routinizing),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ ratioining),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ rigid_rule_following),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ agression),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ new_behavioral_coping),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ cynicism),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ compassion),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ emotional_detachment),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ empathy),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ new_cognitive_coping),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ personal_factors),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ organizational_factors),
cramerV(main_data_unfiltered $ policy_domain, main_data_unfiltered $ environmental_factors)),
Compared_To = c("coder_last_name", "date", "journal", "authors", "country", "type_of_analysis", "gender", "sector", "type_of_slb", "moving_towards", "moving_away", "moving_against", "year", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors")) %>%
mutate(key_variable = "policy_domain",
row_id = paste0("policy_domain_", row_number()))
chi_sqrt_10 <-
rbind(chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ coder_last_name) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ date) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ journal) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ authors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ country) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ type_of_analysis) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ gender) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ sector) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ type_of_slb) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ moving_towards) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ moving_away) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ moving_against) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ year) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ no_of_countries) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ developed_developing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ vulnerable_clients) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ social_equity) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ covid_19) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ rule_breaking) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ instrumental_actions) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ prioritizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ personal_resources) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ routinizing) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ ratioining) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ rigid_rule_following) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ agression) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ cynicism) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ compassion) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ emotional_detachment) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ empathy) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ personal_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ organizational_factors) %>% broom::tidy(),
chisq.test(main_data_unfiltered $ policy_domain, main_data_unfiltered $ environmental_factors) %>% broom::tidy()) %>%
mutate(row_id = paste0("policy_domain_", row_number())) %>%
left_join(tb10, by = "row_id")
# rbind
rbind(chi_sqrt_1,
chi_sqrt_2,
chi_sqrt_3,
chi_sqrt_4,
chi_sqrt_5,
chi_sqrt_6,
chi_sqrt_7,
chi_sqrt_8,
chi_sqrt_9,
chi_sqrt_10) %>%
mutate(p.value = round((p.value), 3),
statistic = round((statistic), 3)) %>%
rename(chi_square = statistic) %>%
dplyr::select(key_variable, Compared_To, Cramer_V, chi_square, p.value) %>%
gt() %>%
tab_header(title = "Chi-Square and Cramer's V Correlations",
subtitle = "Key Variables and Their Comparisons") %>%
fmt_number(columns = vars(chi_square, Cramer_V, p.value),
decimals = 3) %>%
cols_label(key_variable = "Key Variable",
Compared_To = "Compared To",
Cramer_V = "Cramer's V",
chi_square = "Chi-Square",
p.value = "P-Value") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Chi-Square and Cramer's V Correlations | ||||
| Key Variables and Their Comparisons | ||||
| Key Variable | Compared To | Cramer's V | Chi-Square | P-Value |
|---|---|---|---|---|
| coder_last_name | date | 0.328 | 40.000 | 0.000 |
| coder_last_name | authors | 1.000 | 372.000 | 0.180 |
| coder_last_name | journal | 0.805 | 240.890 | 0.084 |
| coder_last_name | country | 0.512 | 97.698 | 0.146 |
| coder_last_name | type_of_analysis | 0.101 | 3.756 | 0.440 |
| coder_last_name | gender | 0.378 | 53.210 | 0.000 |
| coder_last_name | sector | 0.165 | 10.079 | 0.434 |
| coder_last_name | type_of_slb | 0.469 | 81.853 | 0.005 |
| coder_last_name | policy_domain | 0.392 | 57.273 | 0.000 |
| coder_last_name | moving_towards | 0.160 | 9.586 | 0.295 |
| coder_last_name | moving_away | 0.225 | 18.907 | 0.001 |
| coder_last_name | moving_against | 0.141 | 7.421 | 0.115 |
| coder_last_name | year | 0.345 | 44.316 | 0.870 |
| coder_last_name | no_of_countries | 0.181 | 12.226 | 0.141 |
| coder_last_name | developed_developing | 0.079 | 2.307 | 0.315 |
| coder_last_name | vulnerable_clients | 0.044 | 0.726 | 0.695 |
| coder_last_name | social_equity | 0.128 | 6.103 | 0.047 |
| coder_last_name | covid_19 | 0.027 | 0.275 | 0.872 |
| coder_last_name | rule_breaking | 0.063 | 1.483 | 0.476 |
| coder_last_name | instrumental_actions | 0.065 | 1.594 | 0.451 |
| coder_last_name | prioritizing | 0.123 | 5.652 | 0.059 |
| coder_last_name | personal_resources | 0.054 | 1.080 | 0.583 |
| coder_last_name | routinizing | 0.127 | 6.038 | 0.049 |
| coder_last_name | ratioining | 0.118 | 5.220 | 0.074 |
| coder_last_name | rigid_rule_following | 0.166 | 10.286 | 0.006 |
| coder_last_name | agression | 0.029 | 0.308 | 0.857 |
| coder_last_name | new_behavioral_coping | 0.072 | 1.922 | 0.382 |
| coder_last_name | cynicism | 0.098 | 3.560 | 0.169 |
| coder_last_name | compassion | 0.157 | 9.143 | 0.010 |
| coder_last_name | emotional_detachment | 0.132 | 6.496 | 0.039 |
| coder_last_name | empathy | 0.116 | 5.030 | 0.081 |
| coder_last_name | new_cognitive_coping | 0.159 | 9.431 | 0.009 |
| coder_last_name | personal_factors | 0.251 | 23.374 | 0.000 |
| coder_last_name | organizational_factors | 0.086 | 2.739 | 0.254 |
| coder_last_name | environmental_factors | 0.132 | 6.475 | 0.039 |
| date | coder_last_name | 0.328 | 40.000 | 0.000 |
| date | authors | 0.434 | 456.000 | 0.020 |
| date | journal | 0.361 | 315.111 | 0.628 |
| date | country | 0.315 | 239.613 | 0.044 |
| date | type_of_analysis | 0.220 | 26.994 | 0.305 |
| date | gender | 0.208 | 32.134 | 0.653 |
| date | sector | 0.309 | 106.904 | 0.000 |
| date | type_of_slb | 0.266 | 170.819 | 0.013 |
| date | policy_domain | 0.205 | 70.421 | 0.019 |
| date | moving_towards | 0.214 | 42.578 | 0.694 |
| date | moving_away | 0.267 | 39.688 | 0.023 |
| date | moving_against | 0.167 | 15.546 | 0.904 |
| date | year | 0.260 | 163.951 | 0.005 |
| date | no_of_countries | 0.198 | 36.278 | 0.456 |
| date | developed_developing | 0.261 | 25.420 | 0.013 |
| date | vulnerable_clients | 0.201 | 15.049 | 0.239 |
| date | social_equity | 0.252 | 23.667 | 0.023 |
| date | covid_19 | 0.118 | 5.200 | 0.951 |
| date | rule_breaking | 0.194 | 13.932 | 0.305 |
| date | instrumental_actions | 0.160 | 9.475 | 0.662 |
| date | prioritizing | 0.255 | 24.150 | 0.019 |
| date | personal_resources | 0.144 | 7.721 | 0.807 |
| date | routinizing | 0.173 | 11.115 | 0.519 |
| date | ratioining | 0.195 | 14.161 | 0.291 |
| date | rigid_rule_following | 0.118 | 5.200 | 0.951 |
| date | agression | 0.203 | 15.375 | 0.222 |
| date | new_behavioral_coping | 0.165 | 10.096 | 0.608 |
| date | cynicism | 0.194 | 14.059 | 0.297 |
| date | compassion | 0.158 | 9.297 | 0.677 |
| date | emotional_detachment | 0.158 | 9.297 | 0.677 |
| date | empathy | 0.199 | 14.683 | 0.259 |
| date | new_cognitive_coping | 0.186 | 12.926 | 0.374 |
| date | personal_factors | 0.228 | 19.254 | 0.083 |
| date | organizational_factors | 0.212 | 16.706 | 0.161 |
| date | environmental_factors | 0.213 | 16.919 | 0.153 |
| authors | coder_last_name | 1.000 | 372.000 | 0.180 |
| authors | date | 0.434 | 456.000 | 0.020 |
| authors | journal | 0.975 | 18,721.786 | 0.075 |
| authors | country | 0.911 | 6,640.000 | 0.066 |
| authors | type_of_analysis | 0.771 | 332.000 | 0.166 |
| authors | gender | 0.814 | 492.585 | 0.135 |
| authors | sector | 0.862 | 830.000 | 0.066 |
| authors | type_of_slb | 0.913 | 4,188.518 | 0.002 |
| authors | policy_domain | 0.889 | 1,324.396 | 0.034 |
| authors | moving_towards | 0.827 | 636.453 | 0.315 |
| authors | moving_away | 0.770 | 331.275 | 0.194 |
| authors | moving_against | 0.753 | 316.792 | 0.383 |
| authors | year | 0.987 | 5,070.736 | 0.023 |
| authors | no_of_countries | 0.747 | 518.904 | 0.998 |
| authors | developed_developing | 0.668 | 166.000 | 0.241 |
| authors | vulnerable_clients | 0.668 | 166.000 | 0.241 |
| authors | social_equity | 0.657 | 160.788 | 0.338 |
| authors | covid_19 | 0.638 | 151.374 | 0.545 |
| authors | rule_breaking | 0.646 | 155.216 | 0.480 |
| authors | instrumental_actions | 0.663 | 163.516 | 0.304 |
| authors | prioritizing | 0.649 | 156.713 | 0.446 |
| authors | personal_resources | 0.658 | 161.108 | 0.352 |
| authors | routinizing | 0.661 | 162.336 | 0.327 |
| authors | ratioining | 0.657 | 160.435 | 0.366 |
| authors | rigid_rule_following | 0.660 | 161.920 | 0.335 |
| authors | agression | 0.647 | 155.596 | 0.471 |
| authors | new_behavioral_coping | 0.645 | 154.866 | 0.465 |
| authors | cynicism | 0.650 | 157.128 | 0.437 |
| authors | compassion | 0.638 | 151.337 | 0.568 |
| authors | emotional_detachment | 0.660 | 161.899 | 0.315 |
| authors | empathy | 0.644 | 154.162 | 0.504 |
| authors | new_cognitive_coping | 0.670 | 167.000 | 0.241 |
| authors | personal_factors | 0.642 | 153.434 | 0.498 |
| authors | organizational_factors | 0.641 | 152.971 | 0.508 |
| authors | environmental_factors | 0.643 | 153.939 | 0.486 |
| journal | coder_last_name | 0.805 | 240.890 | 0.084 |
| journal | date | 0.361 | 315.111 | 0.628 |
| journal | authors | 0.975 | 19,716.000 | 0.000 |
| journal | country | 0.766 | 4,697.019 | 0.000 |
| journal | type_of_analysis | 0.512 | 146.068 | 0.990 |
| journal | gender | 0.605 | 272.665 | 0.644 |
| journal | sector | 0.727 | 589.161 | 0.000 |
| journal | type_of_slb | 0.721 | 2,613.319 | 0.001 |
| journal | policy_domain | 0.720 | 866.525 | 0.002 |
| journal | moving_towards | 0.642 | 383.370 | 0.385 |
| journal | moving_away | 0.594 | 196.557 | 0.320 |
| journal | moving_against | 0.604 | 203.263 | 0.212 |
| journal | year | 0.809 | 3,412.248 | 0.000 |
| journal | no_of_countries | 0.778 | 562.361 | 0.000 |
| journal | developed_developing | 0.570 | 120.630 | 0.033 |
| journal | vulnerable_clients | 0.517 | 99.366 | 0.333 |
| journal | social_equity | 0.503 | 94.124 | 0.477 |
| journal | covid_19 | 0.521 | 100.994 | 0.292 |
| journal | rule_breaking | 0.496 | 91.626 | 0.550 |
| journal | instrumental_actions | 0.548 | 111.560 | 0.104 |
| journal | prioritizing | 0.499 | 92.699 | 0.519 |
| journal | personal_resources | 0.490 | 89.443 | 0.614 |
| journal | routinizing | 0.492 | 90.230 | 0.591 |
| journal | ratioining | 0.545 | 110.515 | 0.117 |
| journal | rigid_rule_following | 0.509 | 96.501 | 0.409 |
| journal | agression | 0.534 | 106.044 | 0.168 |
| journal | new_behavioral_coping | 0.516 | 99.035 | 0.315 |
| journal | cynicism | 0.518 | 99.906 | 0.319 |
| journal | compassion | 0.544 | 110.302 | 0.120 |
| journal | emotional_detachment | 0.517 | 99.601 | 0.327 |
| journal | empathy | 0.507 | 95.669 | 0.433 |
| journal | new_cognitive_coping | 0.505 | 95.067 | 0.450 |
| journal | personal_factors | 0.519 | 100.211 | 0.311 |
| journal | organizational_factors | 0.493 | 90.476 | 0.584 |
| journal | environmental_factors | 0.513 | 97.749 | 0.375 |
| country | coder_last_name | 0.512 | 97.698 | 0.146 |
| country | date | 0.315 | 239.613 | 0.044 |
| country | journal | 0.911 | 4,697.019 | 0.000 |
| country | authors | 0.766 | 6,640.000 | 0.066 |
| country | type_of_analysis | 0.354 | 70.130 | 0.861 |
| country | gender | 0.456 | 154.528 | 0.043 |
| country | sector | 0.545 | 330.996 | 0.000 |
| country | type_of_slb | 0.525 | 1,382.158 | 0.000 |
| country | policy_domain | 0.628 | 660.103 | 0.000 |
| country | moving_towards | 0.421 | 164.510 | 0.562 |
| country | moving_away | 0.456 | 116.157 | 0.012 |
| country | moving_against | 0.364 | 73.995 | 0.774 |
| country | year | 0.383 | 762.344 | 1.000 |
| country | no_of_countries | 0.835 | 647.707 | 0.000 |
| country | developed_developing | 0.649 | 156.567 | 0.000 |
| country | vulnerable_clients | 0.371 | 51.238 | 0.155 |
| country | social_equity | 0.301 | 33.706 | 0.815 |
| country | covid_19 | 0.439 | 71.649 | 0.003 |
| country | rule_breaking | 0.364 | 49.187 | 0.207 |
| country | instrumental_actions | 0.321 | 38.201 | 0.638 |
| country | prioritizing | 0.355 | 46.809 | 0.282 |
| country | personal_resources | 0.346 | 44.647 | 0.361 |
| country | routinizing | 0.378 | 53.052 | 0.118 |
| country | ratioining | 0.398 | 58.853 | 0.044 |
| country | rigid_rule_following | 0.348 | 45.100 | 0.344 |
| country | agression | 0.358 | 47.817 | 0.216 |
| country | new_behavioral_coping | 0.330 | 40.415 | 0.541 |
| country | cynicism | 0.369 | 50.685 | 0.168 |
| country | compassion | 0.345 | 44.295 | 0.375 |
| country | emotional_detachment | 0.336 | 41.983 | 0.472 |
| country | empathy | 0.350 | 45.570 | 0.326 |
| country | new_cognitive_coping | 0.311 | 36.093 | 0.727 |
| country | personal_factors | 0.333 | 41.308 | 0.501 |
| country | organizational_factors | 0.327 | 39.673 | 0.574 |
| country | environmental_factors | 0.380 | 53.786 | 0.105 |
| type_of_analysis | coder_last_name | 0.101 | 3.756 | 0.440 |
| type_of_analysis | date | 0.220 | 26.994 | 0.305 |
| type_of_analysis | journal | 0.771 | 146.068 | 0.990 |
| type_of_analysis | authors | 0.512 | 332.000 | 0.166 |
| type_of_analysis | country | 0.354 | 70.130 | 0.861 |
| type_of_analysis | gender | 0.103 | 5.884 | 0.436 |
| type_of_analysis | sector | 0.114 | 7.303 | 0.697 |
| type_of_analysis | type_of_slb | 0.310 | 53.616 | 0.412 |
| type_of_analysis | policy_domain | 0.187 | 19.479 | 0.245 |
| type_of_analysis | moving_towards | 0.171 | 16.295 | 0.038 |
| type_of_analysis | moving_away | 0.092 | 4.733 | 0.316 |
| type_of_analysis | moving_against | 0.087 | 4.210 | 0.378 |
| type_of_analysis | year | 0.285 | 45.335 | 0.732 |
| type_of_analysis | no_of_countries | 0.102 | 5.808 | 0.669 |
| type_of_analysis | developed_developing | 0.025 | 0.231 | 0.891 |
| type_of_analysis | vulnerable_clients | 0.133 | 6.558 | 0.038 |
| type_of_analysis | social_equity | 0.154 | 8.824 | 0.012 |
| type_of_analysis | covid_19 | 0.054 | 1.098 | 0.578 |
| type_of_analysis | rule_breaking | 0.077 | 2.182 | 0.336 |
| type_of_analysis | instrumental_actions | 0.152 | 8.586 | 0.014 |
| type_of_analysis | prioritizing | 0.140 | 7.244 | 0.027 |
| type_of_analysis | personal_resources | 0.099 | 3.627 | 0.163 |
| type_of_analysis | routinizing | 0.088 | 2.856 | 0.240 |
| type_of_analysis | ratioining | 0.070 | 1.847 | 0.397 |
| type_of_analysis | rigid_rule_following | 0.029 | 0.314 | 0.855 |
| type_of_analysis | agression | 0.045 | 0.756 | 0.685 |
| type_of_analysis | new_behavioral_coping | 0.123 | 5.645 | 0.059 |
| type_of_analysis | cynicism | 0.093 | 3.210 | 0.201 |
| type_of_analysis | compassion | 0.105 | 4.085 | 0.130 |
| type_of_analysis | emotional_detachment | 0.105 | 4.089 | 0.129 |
| type_of_analysis | empathy | 0.096 | 3.428 | 0.180 |
| type_of_analysis | new_cognitive_coping | 0.030 | 0.332 | 0.847 |
| type_of_analysis | personal_factors | 0.073 | 1.997 | 0.368 |
| type_of_analysis | organizational_factors | 0.097 | 3.498 | 0.174 |
| type_of_analysis | environmental_factors | 0.120 | 5.360 | 0.069 |
| gender | coder_last_name | 0.378 | 53.210 | 0.000 |
| gender | date | 0.208 | 32.134 | 0.653 |
| gender | journal | 0.814 | 272.665 | 0.644 |
| gender | authors | 0.605 | 492.585 | 0.135 |
| gender | country | 0.456 | 154.528 | 0.043 |
| gender | type_of_analysis | 0.103 | 5.884 | 0.436 |
| gender | sector | 0.139 | 14.302 | 0.503 |
| gender | type_of_slb | 0.258 | 49.679 | 0.995 |
| gender | policy_domain | 0.218 | 35.265 | 0.065 |
| gender | moving_towards | 0.128 | 12.236 | 0.427 |
| gender | moving_away | 0.103 | 5.909 | 0.433 |
| gender | moving_against | 0.070 | 2.747 | 0.840 |
| gender | year | 0.252 | 47.050 | 0.998 |
| gender | no_of_countries | 0.106 | 8.296 | 0.762 |
| gender | developed_developing | 0.104 | 4.046 | 0.257 |
| gender | vulnerable_clients | 0.082 | 2.505 | 0.474 |
| gender | social_equity | 0.067 | 1.652 | 0.648 |
| gender | covid_19 | 0.079 | 2.301 | 0.512 |
| gender | rule_breaking | 0.155 | 8.991 | 0.029 |
| gender | instrumental_actions | 0.099 | 3.652 | 0.302 |
| gender | prioritizing | 0.087 | 2.800 | 0.423 |
| gender | personal_resources | 0.131 | 6.355 | 0.096 |
| gender | routinizing | 0.075 | 2.089 | 0.554 |
| gender | ratioining | 0.122 | 5.542 | 0.136 |
| gender | rigid_rule_following | 0.029 | 0.310 | 0.958 |
| gender | agression | 0.044 | 0.731 | 0.866 |
| gender | new_behavioral_coping | 0.082 | 2.485 | 0.478 |
| gender | cynicism | 0.074 | 2.017 | 0.569 |
| gender | compassion | 0.071 | 1.897 | 0.594 |
| gender | emotional_detachment | 0.052 | 1.009 | 0.799 |
| gender | empathy | 0.103 | 3.963 | 0.265 |
| gender | new_cognitive_coping | 0.110 | 4.523 | 0.210 |
| gender | personal_factors | 0.117 | 5.071 | 0.167 |
| gender | organizational_factors | 0.075 | 2.084 | 0.555 |
| gender | environmental_factors | 0.048 | 0.873 | 0.832 |
| sector | coder_last_name | 0.165 | 10.079 | 0.434 |
| sector | date | 0.309 | 106.904 | 0.000 |
| sector | journal | 0.862 | 589.161 | 0.000 |
| sector | authors | 0.727 | 830.000 | 0.066 |
| sector | country | 0.545 | 330.996 | 0.000 |
| sector | type_of_analysis | 0.114 | 7.303 | 0.697 |
| sector | gender | 0.139 | 14.302 | 0.503 |
| sector | type_of_slb | 0.348 | 134.808 | 0.368 |
| sector | policy_domain | 0.145 | 23.632 | 0.982 |
| sector | moving_towards | 0.136 | 17.193 | 0.640 |
| sector | moving_away | 0.116 | 7.462 | 0.681 |
| sector | moving_against | 0.170 | 16.124 | 0.096 |
| sector | year | 0.342 | 130.411 | 0.473 |
| sector | no_of_countries | 0.189 | 33.090 | 0.033 |
| sector | developed_developing | 0.173 | 11.128 | 0.049 |
| sector | vulnerable_clients | 0.126 | 5.953 | 0.311 |
| sector | social_equity | 0.076 | 2.162 | 0.826 |
| sector | covid_19 | 0.126 | 5.923 | 0.314 |
| sector | rule_breaking | 0.076 | 2.169 | 0.825 |
| sector | instrumental_actions | 0.152 | 8.605 | 0.126 |
| sector | prioritizing | 0.107 | 4.247 | 0.514 |
| sector | personal_resources | 0.227 | 19.200 | 0.002 |
| sector | routinizing | 0.109 | 4.417 | 0.491 |
| sector | ratioining | 0.116 | 4.969 | 0.420 |
| sector | rigid_rule_following | 0.140 | 7.265 | 0.202 |
| sector | agression | 0.156 | 9.035 | 0.108 |
| sector | new_behavioral_coping | 0.095 | 3.366 | 0.644 |
| sector | cynicism | 0.067 | 1.670 | 0.893 |
| sector | compassion | 0.101 | 3.818 | 0.576 |
| sector | emotional_detachment | 0.062 | 1.432 | 0.921 |
| sector | empathy | 0.128 | 6.106 | 0.296 |
| sector | new_cognitive_coping | 0.103 | 3.985 | 0.552 |
| sector | personal_factors | 0.119 | 5.238 | 0.387 |
| sector | organizational_factors | 0.101 | 3.804 | 0.578 |
| sector | environmental_factors | 0.135 | 6.730 | 0.241 |
| type_of_slb | coder_last_name | 0.469 | 81.853 | 0.005 |
| type_of_slb | date | 0.266 | 170.819 | 0.013 |
| type_of_slb | journal | 0.913 | 2,613.319 | 0.001 |
| type_of_slb | authors | 0.721 | 4,188.518 | 0.002 |
| type_of_slb | country | 0.525 | 1,382.158 | 0.000 |
| type_of_slb | type_of_analysis | 0.310 | 53.616 | 0.412 |
| type_of_slb | gender | 0.258 | 49.679 | 0.995 |
| type_of_slb | sector | 0.348 | 134.808 | 0.368 |
| type_of_slb | policy_domain | 0.512 | 439.745 | 0.000 |
| type_of_slb | moving_towards | 0.378 | 133.136 | 0.029 |
| type_of_slb | moving_away | 0.353 | 69.504 | 0.053 |
| type_of_slb | moving_against | 0.349 | 67.829 | 0.069 |
| type_of_slb | year | 0.375 | 707.419 | 0.195 |
| type_of_slb | no_of_countries | 0.282 | 73.997 | 0.989 |
| type_of_slb | developed_developing | 0.411 | 62.937 | 0.000 |
| type_of_slb | vulnerable_clients | 0.337 | 42.338 | 0.023 |
| type_of_slb | social_equity | 0.267 | 26.561 | 0.433 |
| type_of_slb | covid_19 | 0.311 | 35.979 | 0.092 |
| type_of_slb | rule_breaking | 0.212 | 16.659 | 0.919 |
| type_of_slb | instrumental_actions | 0.253 | 23.890 | 0.582 |
| type_of_slb | prioritizing | 0.302 | 33.841 | 0.139 |
| type_of_slb | personal_resources | 0.262 | 25.476 | 0.492 |
| type_of_slb | routinizing | 0.311 | 35.968 | 0.092 |
| type_of_slb | ratioining | 0.326 | 39.451 | 0.044 |
| type_of_slb | rigid_rule_following | 0.317 | 37.323 | 0.070 |
| type_of_slb | agression | 0.323 | 38.759 | 0.051 |
| type_of_slb | new_behavioral_coping | 0.281 | 29.444 | 0.291 |
| type_of_slb | cynicism | 0.374 | 52.119 | 0.002 |
| type_of_slb | compassion | 0.237 | 20.952 | 0.744 |
| type_of_slb | emotional_detachment | 0.268 | 26.695 | 0.425 |
| type_of_slb | empathy | 0.342 | 43.477 | 0.017 |
| type_of_slb | new_cognitive_coping | 0.405 | 61.064 | 0.000 |
| type_of_slb | personal_factors | 0.264 | 25.881 | 0.470 |
| type_of_slb | organizational_factors | 0.281 | 29.351 | 0.295 |
| type_of_slb | environmental_factors | 0.234 | 20.371 | 0.774 |
| policy_domain | coder_last_name | 0.392 | 57.273 | 0.000 |
| policy_domain | date | 0.205 | 70.421 | 0.019 |
| policy_domain | journal | 0.889 | 866.525 | 0.002 |
| policy_domain | authors | 0.720 | 1,324.396 | 0.034 |
| policy_domain | country | 0.628 | 660.103 | 0.000 |
| policy_domain | type_of_analysis | 0.187 | 19.479 | 0.245 |
| policy_domain | gender | 0.218 | 35.265 | 0.065 |
| policy_domain | sector | 0.145 | 23.632 | 0.982 |
| policy_domain | type_of_slb | 0.512 | 439.745 | 0.000 |
| policy_domain | moving_towards | 0.170 | 26.854 | 0.725 |
| policy_domain | moving_away | 0.225 | 28.303 | 0.029 |
| policy_domain | moving_against | 0.136 | 10.299 | 0.851 |
| policy_domain | year | 0.340 | 193.622 | 0.755 |
| policy_domain | no_of_countries | 0.273 | 69.418 | 0.000 |
| policy_domain | developed_developing | 0.326 | 39.608 | 0.000 |
| policy_domain | vulnerable_clients | 0.336 | 42.086 | 0.000 |
| policy_domain | social_equity | 0.164 | 10.030 | 0.263 |
| policy_domain | covid_19 | 0.141 | 7.413 | 0.493 |
| policy_domain | rule_breaking | 0.103 | 3.967 | 0.860 |
| policy_domain | instrumental_actions | 0.173 | 11.175 | 0.192 |
| policy_domain | prioritizing | 0.130 | 6.303 | 0.613 |
| policy_domain | personal_resources | 0.132 | 6.451 | 0.597 |
| policy_domain | routinizing | 0.180 | 12.005 | 0.151 |
| policy_domain | ratioining | 0.249 | 23.134 | 0.003 |
| policy_domain | rigid_rule_following | 0.157 | 9.207 | 0.325 |
| policy_domain | agression | 0.153 | 8.749 | 0.364 |
| policy_domain | new_behavioral_coping | 0.148 | 8.185 | 0.416 |
| policy_domain | cynicism | 0.199 | 14.658 | 0.066 |
| policy_domain | compassion | 0.130 | 6.287 | 0.615 |
| policy_domain | emotional_detachment | 0.209 | 16.241 | 0.039 |
| policy_domain | empathy | 0.155 | 8.957 | 0.346 |
| policy_domain | new_cognitive_coping | 0.180 | 12.021 | 0.150 |
| policy_domain | personal_factors | 0.153 | 8.661 | 0.372 |
| policy_domain | organizational_factors | 0.144 | 7.749 | 0.458 |
| policy_domain | environmental_factors | 0.136 | 6.913 | 0.546 |
2. Coding by Country
Frequency Table for The Variable “Country” (Numbers and Percentages)
Code
countries_data %>%
dplyr::select(country_name) %>%
gtsummary::tbl_summary(by = NULL,
statistic = all_categorical() ~ "{n} ({p}%)") %>%
bold_labels()| Characteristic | N = 1931 |
|---|---|
| country_name | |
| Australia | 4 (2.1%) |
| Belgium | 5 (2.6%) |
| Brazil | 4 (2.1%) |
| Burkina Faso | 1 (0.5%) |
| Cameroon | 1 (0.5%) |
| Canada | 1 (0.5%) |
| China | 4 (2.1%) |
| Denmark | 25 (13%) |
| Egypt | 1 (0.5%) |
| Estonia | 1 (0.5%) |
| Finland | 1 (0.5%) |
| France | 3 (1.6%) |
| Germany | 10 (5.2%) |
| Ghana | 5 (2.6%) |
| India | 2 (1.0%) |
| Ireland | 1 (0.5%) |
| Israel | 18 (9.4%) |
| Italy | 2 (1.0%) |
| Kurdistan Region of Iraq | 1 (0.5%) |
| Latvia | 2 (1.0%) |
| Lithuania | 2 (1.0%) |
| Mexico | 3 (1.6%) |
| Netherlands | 17 (8.9%) |
| Norway | 5 (2.6%) |
| Portugal | 1 (0.5%) |
| Scotland | 1 (0.5%) |
| South Africa | 2 (1.0%) |
| South Korea | 1 (0.5%) |
| Sweden | 14 (7.3%) |
| Switzerland | 6 (3.1%) |
| Thailand | 1 (0.5%) |
| Tunisia | 2 (1.0%) |
| Uganda | 3 (1.6%) |
| United Kingdom | 7 (3.6%) |
| United States | 35 (18%) |
| Unknown | 1 |
| 1 n (%) | |
Correlations of The Variable “Country” with the Other Variables (Cramer’s V)
- Correlations cannot be calculated for the following variables —
journal_article,language,use,empirical_study,theoretical_study— either because they consist of only one category or one category is overwhelmingly dominant.
Code
tb1 <- tibble(Cramer_V = c(cramerV(countries_data $ country_name, countries_data $ coder_last_name),
cramerV(countries_data $ country_name, countries_data $ date),
cramerV(countries_data $ country_name, countries_data $ authors),
cramerV(countries_data $ country_name, countries_data $ year),
cramerV(countries_data $ country_name, countries_data $ journal),
cramerV(countries_data $ country_name, countries_data $ policy_domain),
cramerV(countries_data $ country_name, countries_data $ no_of_countries),
cramerV(countries_data $ country_name, countries_data $ developed_developing),
cramerV(countries_data $ country_name, countries_data $ vulnerable_clients),
cramerV(countries_data $ country_name, countries_data $ social_equity),
cramerV(countries_data $ country_name, countries_data $ covid_19),
cramerV(countries_data $ country_name, countries_data $ type_of_analysis),
cramerV(countries_data $ country_name, countries_data $ gender),
cramerV(countries_data $ country_name, countries_data $ sector),
cramerV(countries_data $ country_name, countries_data $ type_of_slb),
cramerV(countries_data $ country_name, countries_data $ rule_bending),
cramerV(countries_data $ country_name, countries_data $ rule_breaking),
cramerV(countries_data $ country_name, countries_data $ instrumental_actions),
cramerV(countries_data $ country_name, countries_data $ prioritizing),
cramerV(countries_data $ country_name, countries_data $ personal_resources),
cramerV(countries_data $ country_name, countries_data $ routinizing),
cramerV(countries_data $ country_name, countries_data $ ratioining),
cramerV(countries_data $ country_name, countries_data $ rigid_rule_following),
cramerV(countries_data $ country_name, countries_data $ agression),
cramerV(countries_data $ country_name, countries_data $ new_behavioral_coping),
cramerV(countries_data $ country_name, countries_data $ cynicism),
cramerV(countries_data $ country_name, countries_data $ compassion),
cramerV(countries_data $ country_name, countries_data $ emotional_detachment),
cramerV(countries_data $ country_name, countries_data $ empathy),
cramerV(countries_data $ country_name, countries_data $ new_cognitive_coping),
cramerV(countries_data $ country_name, countries_data $ personal_factors),
cramerV(countries_data $ country_name, countries_data $ organizational_factors),
cramerV(countries_data $ country_name, countries_data $ environmental_factors),
cramerV(countries_data $ country_name, countries_data $ moving_towards),
cramerV(countries_data $ country_name, countries_data $ moving_away),
cramerV(countries_data $ country_name, countries_data $ moving_against)),
Compared_To = c("coder_last_name", "date", "authors", "year", "journal", "policy_domain", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "type_of_analysis", "gender", "sector", "type_of_slb", "rule_bending", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors", "moving_towards", "moving_away", "moving_against")) %>%
mutate(key_variable = "country_name",
row_id = paste0("country_name_", row_number()))
rbind(chisq.test(countries_data $ country_name, countries_data $ coder_last_name) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ date) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ authors) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ year) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ journal) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ policy_domain) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ no_of_countries) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ developed_developing) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ vulnerable_clients) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ social_equity) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ covid_19) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ type_of_analysis) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ gender) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ sector) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ type_of_slb) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ rule_bending) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ rule_breaking) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ instrumental_actions) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ prioritizing) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ personal_resources) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ routinizing) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ ratioining) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ rigid_rule_following) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ agression) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ cynicism) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ compassion) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ emotional_detachment) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ empathy) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ personal_factors) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ organizational_factors) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ environmental_factors) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ moving_towards) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ moving_away) %>% broom::tidy(),
chisq.test(countries_data $ country_name, countries_data $ moving_against) %>% broom::tidy()) %>%
mutate(row_id = paste0("country_name_", row_number())) %>%
left_join(tb1, by = "row_id") %>%
mutate(p.value = round((p.value), 3),
statistic = round((statistic), 3)) %>%
rename(chi_square = statistic) %>%
dplyr::select(key_variable, Compared_To, Cramer_V, chi_square, p.value) %>%
gt() %>%
tab_header(title = "Chi-Square and Cramer's V Correlations",
subtitle = "Key Variable and Its Comparisons") %>%
fmt_number(columns = vars(chi_square, Cramer_V, p.value),
decimals = 3) %>%
cols_label(key_variable = "Key Variable",
Compared_To = "Compared To",
Cramer_V = "Cramer's V",
chi_square = "Chi-Square",
p.value = "P-Value") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Chi-Square and Cramer's V Correlations | ||||
| Key Variable and Its Comparisons | ||||
| Key Variable | Compared To | Cramer's V | Chi-Square | P-Value |
|---|---|---|---|---|
| country_name | coder_last_name | 0.477 | 87.697 | 0.054 |
| country_name | date | 0.314 | 247.711 | 0.001 |
| country_name | authors | 0.875 | 5,172.616 | 0.611 |
| country_name | year | 0.351 | 618.269 | 1.000 |
| country_name | journal | 0.713 | 3,428.831 | 0.002 |
| country_name | policy_domain | 0.588 | 601.400 | 0.000 |
| country_name | no_of_countries | 0.493 | 235.002 | 0.000 |
| country_name | developed_developing | 0.682 | 179.491 | 0.000 |
| country_name | vulnerable_clients | 0.378 | 55.271 | 0.012 |
| country_name | social_equity | 0.283 | 30.855 | 0.623 |
| country_name | covid_19 | 0.410 | 64.926 | 0.001 |
| country_name | type_of_analysis | 0.364 | 76.564 | 0.223 |
| country_name | gender | 0.464 | 165.927 | 0.000 |
| country_name | sector | 0.407 | 191.727 | 0.122 |
| country_name | type_of_slb | 0.442 | 1,053.449 | 0.001 |
| country_name | rule_bending | 0.473 | 43.213 | 0.134 |
| country_name | rule_breaking | 0.469 | 42.372 | 0.153 |
| country_name | instrumental_actions | 0.399 | 30.717 | 0.629 |
| country_name | prioritizing | 0.446 | 38.297 | 0.281 |
| country_name | personal_resources | 0.450 | 39.052 | 0.253 |
| country_name | routinizing | 0.461 | 40.932 | 0.192 |
| country_name | ratioining | 0.550 | 58.291 | 0.006 |
| country_name | rigid_rule_following | 0.468 | 42.205 | 0.158 |
| country_name | agression | 0.336 | 43.527 | 0.127 |
| country_name | new_behavioral_coping | 0.293 | 33.078 | 0.513 |
| country_name | cynicism | 0.451 | 39.329 | 0.243 |
| country_name | compassion | 0.397 | 30.400 | 0.645 |
| country_name | emotional_detachment | 0.307 | 36.317 | 0.361 |
| country_name | empathy | 0.349 | 23.474 | 0.912 |
| country_name | new_cognitive_coping | 0.430 | 35.619 | 0.392 |
| country_name | personal_factors | 0.291 | 32.734 | 0.530 |
| country_name | organizational_factors | 0.261 | 26.194 | 0.828 |
| country_name | environmental_factors | 0.360 | 50.142 | 0.037 |
| country_name | moving_towards | 0.392 | 118.776 | 0.853 |
| country_name | moving_away | 0.515 | 102.316 | 0.005 |
| country_name | moving_against | 0.430 | 71.435 | 0.364 |
3. Coding by SLB Type
Frequency Table for the Variable “SLB Type” (Numbers and Percentages)
Code
slb_type_data %>%
dplyr::select(type_of_slb) %>%
gtsummary::tbl_summary(by = NULL,
statistic = all_categorical() ~ "{n} ({p}%)") %>%
bold_labels()| Characteristic | N = 2081 |
|---|---|
| type_of_slb | |
| 1 | 17 (8.3%) |
| 2 | 52 (25%) |
| 3 | 25 (12%) |
| 4 | 20 (9.8%) |
| 5 | 13 (6.4%) |
| 6 | 0 (0%) |
| 7 | 7 (3.4%) |
| 8 | 45 (22%) |
| 9 | 20 (9.8%) |
| 10 | 5 (2.5%) |
| Unknown | 4 |
| 1 n (%) | |
Frequency (in percentages) of Study Variables by “SLB Type” Categories
NA values were omitted
No articles were found where
type_of_slb = 6.All variables in the table, except
type_of_slb, are binary. The percentages represent the frequency of each binary variable within the categories of thetype_of_slbvariable. For instance, approximately 30% of articles withtype_of_slb = 1exhibitRule Bending.
Code
slb_type_data %>%
dplyr::select(type_of_slb, rule_bending, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors, moving_towards_bi, moving_away_bi, moving_against_bi) %>%
drop_na() %>%
group_by(type_of_slb) %>%
dplyr::summarize(rule_bending_p = mean(rule_bending) * 100,
rule_bending_n = sum(rule_bending),
rule_bending = paste0(rule_bending_n, " (", round(rule_bending_p, 1), "%)"),
rule_breaking_p = mean(rule_breaking) * 100,
rule_breaking_n = sum(rule_breaking),
rule_breaking = paste0(rule_breaking_n, " (", round(rule_breaking_p, 1), "%)"),
instrumental_actions_p = mean(instrumental_actions) * 100,
instrumental_actions_n = sum(instrumental_actions),
instrumental_actions = paste0(instrumental_actions_n, " (", round(instrumental_actions_p, 1), "%)"),
prioritizing_p = mean(prioritizing) * 100,
prioritizing_n = sum(prioritizing),
prioritizing = paste0(prioritizing_n, " (", round(prioritizing_p, 1), "%)"),
personal_resources_p = mean(personal_resources) * 100,
personal_resources_n = sum(personal_resources),
personal_resources = paste0(personal_resources_n, " (", round(personal_resources_p, 1), "%)"),
routinizing_p = mean(routinizing) * 100,
routinizing_n = sum(routinizing),
routinizing = paste0(routinizing_n, " (", round(routinizing_p, 1), "%)"),
ratioining_p = mean(ratioining) * 100,
ratioining_n = sum(ratioining),
ratioining = paste0(ratioining_n, " (", round(ratioining_p, 1), "%)"),
rigid_rule_following_p = mean(rigid_rule_following) * 100,
rigid_rule_following_n = sum(rigid_rule_following),
rigid_rule_following = paste0(rigid_rule_following_n, " (", round(rigid_rule_following_p, 1), "%)"),
agression_p = mean(agression) * 100,
agression_n = sum(agression),
agression = paste0(agression_n, " (", round(agression_p, 1), "%)"),
new_behavioral_coping_p = mean(new_behavioral_coping) * 100,
new_behavioral_coping_n = sum(new_behavioral_coping),
new_behavioral_coping = paste0(new_behavioral_coping_n, " (", round(new_behavioral_coping_p, 1), "%)"),
cynicism_p = mean(cynicism) * 100,
cynicism_n = sum(cynicism),
cynicism = paste0(cynicism_n, " (", round(cynicism_p, 1), "%)"),
compassion_p = mean(compassion) * 100,
compassion_n = sum(compassion),
compassion = paste0(compassion_n, " (", round(compassion_p, 1), "%)"),
emotional_detachment_p = mean(emotional_detachment) * 100,
emotional_detachment_n = sum(emotional_detachment),
emotional_detachment = paste0(emotional_detachment_n, " (", round(emotional_detachment_p, 1), "%)"),
empathy_p = mean(empathy) * 100,
empathy_n = sum(empathy),
empathy = paste0(empathy_n, " (", round(empathy_p, 1), "%)"),
new_cognitive_coping_p = mean(new_cognitive_coping) * 100,
new_cognitive_coping_n = sum(new_cognitive_coping),
new_cognitive_coping = paste0(new_cognitive_coping_n, " (", round(new_cognitive_coping_p, 1), "%)"),
personal_factors_p = mean(personal_factors) * 100,
personal_factors_n = sum(personal_factors),
personal_factors = paste0(personal_factors_n, " (", round(personal_factors_p, 1), "%)"),
organizational_factors_p = mean(organizational_factors) * 100,
organizational_factors_n = sum(organizational_factors),
organizational_factors = paste0(organizational_factors_n, " (", round(organizational_factors_p, 1), "%)"),
environmental_factors_p = mean(environmental_factors) * 100,
environmental_factors_n = sum(environmental_factors),
environmental_factors = paste0(environmental_factors_n, " (", round(environmental_factors_p, 1), "%)"),
moving_towards_bi_p = mean(moving_towards_bi) * 100,
moving_towards_bi_n = sum(moving_towards_bi),
moving_towards_bi = paste0(moving_towards_bi_n, " (", round(moving_towards_bi_p, 1), "%)"),
moving_away_bi_p = mean(moving_away_bi) * 100,
moving_away_bi_n = sum(moving_away_bi),
moving_away_bi = paste0(moving_away_bi_n, " (", round(moving_away_bi_p, 1), "%)"),
moving_against_bi_p = mean(moving_against_bi) * 100,
moving_against_bi_n = sum(moving_against_bi),
moving_against_bi = paste0(moving_against_bi_n, " (", round(moving_against_bi_p, 1), "%)")) %>%
dplyr::select(type_of_slb, rule_bending, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors, moving_towards_bi, moving_away_bi, moving_against_bi) %>%
gt() %>%
tab_header(title = "Frequency Table by SLB Type") %>%
fmt_number(columns = -type_of_slb,
decimals = 2) %>%
cols_label(type_of_slb = "Type of SLB",
rule_bending = "Rule Bending",
rule_breaking = "Rule Breaking",
instrumental_actions = "Instrumental Actions",
prioritizing = "Prioritizing",
personal_resources = "Personal Resources",
routinizing = "Routinizing",
ratioining = "Rationing",
rigid_rule_following = "Rigid Rule Following",
agression = "Aggression",
new_behavioral_coping = "New Behavioral Coping",
cynicism = "Cynicism",
compassion = "Compassion",
emotional_detachment = "Emotional Detachment",
empathy = "Empathy",
new_cognitive_coping = "New Cognitive Coping",
personal_factors = "Personal Factors",
organizational_factors = "Organizational Factors",
environmental_factors = "Environmental Factors",
moving_towards_bi = "Moving Towards",
moving_away_bi = "Moving Away",
moving_against_bi = "Moving Against") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Frequency Table by SLB Type | |||||||||||||||||||||
| Type of SLB | Rule Bending | Rule Breaking | Instrumental Actions | Prioritizing | Personal Resources | Routinizing | Rationing | Rigid Rule Following | Aggression | New Behavioral Coping | Cynicism | Compassion | Emotional Detachment | Empathy | New Cognitive Coping | Personal Factors | Organizational Factors | Environmental Factors | Moving Towards | Moving Away | Moving Against |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 5 (31.2%) | 3 (18.8%) | 2 (12.5%) | 5 (31.2%) | 0 (0%) | 4 (25%) | 3 (18.8%) | 4 (25%) | 6 (37.5%) | 3 (18.8%) | 6 (37.5%) | 4 (25%) | 6 (37.5%) | 9 (56.2%) | 2 (12.5%) | 15 (93.8%) | 13 (81.2%) | 9 (56.2%) | 11 (68.8%) | 5 (31.2%) | 8 (50%) |
| 2 | 22 (42.3%) | 10 (19.2%) | 7 (13.5%) | 24 (46.2%) | 6 (11.5%) | 14 (26.9%) | 16 (30.8%) | 12 (23.1%) | 11 (21.2%) | 18 (34.6%) | 9 (17.3%) | 19 (36.5%) | 18 (34.6%) | 25 (48.1%) | 11 (21.2%) | 37 (71.2%) | 43 (82.7%) | 30 (57.7%) | 36 (69.2%) | 23 (44.2%) | 17 (32.7%) |
| 3 | 12 (48%) | 6 (24%) | 4 (16%) | 13 (52%) | 5 (20%) | 6 (24%) | 7 (28%) | 6 (24%) | 3 (12%) | 8 (32%) | 5 (20%) | 5 (20%) | 5 (20%) | 3 (12%) | 7 (28%) | 17 (68%) | 20 (80%) | 17 (68%) | 21 (84%) | 10 (40%) | 8 (32%) |
| 4 | 6 (31.6%) | 1 (5.3%) | 4 (21.1%) | 6 (31.6%) | 3 (15.8%) | 6 (31.6%) | 9 (47.4%) | 1 (5.3%) | 3 (15.8%) | 8 (42.1%) | 1 (5.3%) | 3 (15.8%) | 3 (15.8%) | 2 (10.5%) | 3 (15.8%) | 10 (52.6%) | 16 (84.2%) | 13 (68.4%) | 11 (57.9%) | 10 (52.6%) | 4 (21.1%) |
| 5 | 4 (30.8%) | 1 (7.7%) | 3 (23.1%) | 7 (53.8%) | 5 (38.5%) | 6 (46.2%) | 9 (69.2%) | 3 (23.1%) | 2 (15.4%) | 9 (69.2%) | 3 (23.1%) | 1 (7.7%) | 3 (23.1%) | 1 (7.7%) | 3 (23.1%) | 6 (46.2%) | 13 (100%) | 9 (69.2%) | 9 (69.2%) | 9 (69.2%) | 5 (38.5%) |
| 7 | 3 (42.9%) | 0 (0%) | 0 (0%) | 1 (14.3%) | 0 (0%) | 2 (28.6%) | 0 (0%) | 4 (57.1%) | 4 (57.1%) | 5 (71.4%) | 1 (14.3%) | 0 (0%) | 1 (14.3%) | 0 (0%) | 2 (28.6%) | 5 (71.4%) | 4 (57.1%) | 3 (42.9%) | 4 (57.1%) | 2 (28.6%) | 6 (85.7%) |
| 8 | 21 (47.7%) | 6 (13.6%) | 8 (18.2%) | 15 (34.1%) | 9 (20.5%) | 19 (43.2%) | 19 (43.2%) | 16 (36.4%) | 4 (9.1%) | 15 (34.1%) | 8 (18.2%) | 14 (31.8%) | 13 (29.5%) | 10 (22.7%) | 6 (13.6%) | 29 (65.9%) | 42 (95.5%) | 28 (63.6%) | 31 (70.5%) | 27 (61.4%) | 18 (40.9%) |
| 9 | 6 (30%) | 4 (20%) | 7 (35%) | 5 (25%) | 3 (15%) | 5 (25%) | 7 (35%) | 7 (35%) | 5 (25%) | 13 (65%) | 2 (10%) | 4 (20%) | 2 (10%) | 3 (15%) | 2 (10%) | 9 (45%) | 17 (85%) | 12 (60%) | 13 (65%) | 9 (45%) | 9 (45%) |
| 10 | 3 (60%) | 0 (0%) | 1 (20%) | 2 (40%) | 0 (0%) | 2 (40%) | 2 (40%) | 2 (40%) | 0 (0%) | 2 (40%) | 1 (20%) | 1 (20%) | 0 (0%) | 1 (20%) | 1 (20%) | 4 (80%) | 4 (80%) | 3 (60%) | 5 (100%) | 2 (40%) | 2 (40%) |
Correlations of the Variable “SLB Type” with Other Variables (Cramer’s V)
- Correlations cannot be calculated for the following variables —
journal_article,language,use,empirical_study,theoretical_study— either because they consist of only one category or one category is overwhelmingly dominant.
Code
tb1 <- tibble(Cramer_V = c(cramerV(slb_type_data $ type_of_slb, slb_type_data $ coder_last_name),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ date),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ authors),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ year),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ journal),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ policy_domain),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ no_of_countries),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ developed_developing),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ vulnerable_clients),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ social_equity),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ covid_19),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ type_of_analysis),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ gender),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ sector),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ country_name),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ rule_bending),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ rule_breaking),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ instrumental_actions),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ prioritizing),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ personal_resources),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ routinizing),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ ratioining),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ rigid_rule_following),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ agression),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ new_behavioral_coping),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ cynicism),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ compassion),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ emotional_detachment),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ empathy),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ new_cognitive_coping),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ personal_factors),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ organizational_factors),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ environmental_factors),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ moving_towards),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ moving_away),
cramerV(slb_type_data $ type_of_slb, slb_type_data $ moving_against)),
Compared_To = c("coder_last_name", "date", "authors", "year", "journal", "policy_domain", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "type_of_analysis", "gender", "sector", "country_name", "rule_bending", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors", "moving_towards", "moving_away", "moving_against")) %>%
mutate(key_variable = "type_of_slb",
row_id = paste0("type_of_slb_", row_number()))
rbind(chisq.test(slb_type_data $ type_of_slb, slb_type_data $ coder_last_name) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ date) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ authors) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ year) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ journal) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ policy_domain) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ no_of_countries) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ developed_developing) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ vulnerable_clients) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ social_equity) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ covid_19) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ type_of_analysis) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ gender) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ sector) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ country_name) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ rule_bending) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ rule_breaking) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ instrumental_actions) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ prioritizing) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ personal_resources) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ routinizing) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ ratioining) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ rigid_rule_following) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ agression) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ cynicism) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ compassion) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ emotional_detachment) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ empathy) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ personal_factors) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ organizational_factors) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ environmental_factors) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ moving_towards) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ moving_away) %>% broom::tidy(),
chisq.test(slb_type_data $ type_of_slb, slb_type_data $ moving_against) %>% broom::tidy()) %>%
mutate(row_id = paste0("type_of_slb_", row_number())) %>%
left_join(tb1, by = "row_id") %>%
mutate(p.value = round((p.value), 3),
statistic = round((statistic), 3)) %>%
rename(chi_square = statistic) %>%
dplyr::select(key_variable, Compared_To, Cramer_V, chi_square, p.value) %>%
gt() %>%
tab_header(title = "Chi-Square and Cramer's V Correlations",
subtitle = "Key Variable and Its Comparisons") %>%
fmt_number(columns = vars(chi_square, Cramer_V, p.value),
decimals = 3) %>%
cols_label(key_variable = "Key Variable",
Compared_To = "Compared To",
Cramer_V = "Cramer's V",
chi_square = "Chi-Square",
p.value = "P-Value") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Chi-Square and Cramer's V Correlations | ||||
| Key Variable and Its Comparisons | ||||
| Key Variable | Compared To | Cramer's V | Chi-Square | P-Value |
|---|---|---|---|---|
| type_of_slb | coder_last_name | 0.216 | 19.440 | 0.246 |
| type_of_slb | date | 0.227 | 96.535 | 0.165 |
| type_of_slb | authors | 0.803 | 1,207.688 | 0.432 |
| type_of_slb | year | 0.349 | 227.859 | 0.164 |
| type_of_slb | journal | 0.600 | 674.851 | 0.948 |
| type_of_slb | policy_domain | 0.415 | 322.547 | 0.000 |
| type_of_slb | no_of_countries | 0.180 | 33.614 | 0.389 |
| type_of_slb | developed_developing | 0.347 | 50.159 | 0.000 |
| type_of_slb | vulnerable_clients | 0.252 | 26.523 | 0.001 |
| type_of_slb | social_equity | 0.142 | 8.393 | 0.396 |
| type_of_slb | covid_19 | 0.155 | 10.064 | 0.261 |
| type_of_slb | type_of_analysis | 0.210 | 27.625 | 0.035 |
| type_of_slb | gender | 0.137 | 15.540 | 0.904 |
| type_of_slb | sector | 0.168 | 35.115 | 0.690 |
| type_of_slb | country_name | 0.463 | 400.656 | 0.004 |
| type_of_slb | rule_bending | 0.150 | 4.692 | 0.790 |
| type_of_slb | rule_breaking | 0.184 | 7.051 | 0.531 |
| type_of_slb | instrumental_actions | 0.184 | 7.082 | 0.528 |
| type_of_slb | prioritizing | 0.208 | 8.962 | 0.346 |
| type_of_slb | personal_resources | 0.245 | 12.451 | 0.132 |
| type_of_slb | routinizing | 0.169 | 5.968 | 0.651 |
| type_of_slb | ratioining | 0.274 | 15.561 | 0.049 |
| type_of_slb | rigid_rule_following | 0.245 | 12.443 | 0.133 |
| type_of_slb | agression | 0.198 | 16.251 | 0.039 |
| type_of_slb | new_behavioral_coping | 0.201 | 16.735 | 0.033 |
| type_of_slb | cynicism | 0.216 | 9.676 | 0.289 |
| type_of_slb | compassion | 0.227 | 10.752 | 0.216 |
| type_of_slb | emotional_detachment | 0.154 | 9.897 | 0.272 |
| type_of_slb | empathy | 0.373 | 29.020 | 0.000 |
| type_of_slb | new_cognitive_coping | 0.153 | 4.884 | 0.770 |
| type_of_slb | personal_factors | 0.171 | 12.171 | 0.144 |
| type_of_slb | organizational_factors | 0.155 | 10.054 | 0.261 |
| type_of_slb | environmental_factors | 0.074 | 2.299 | 0.970 |
| type_of_slb | moving_towards | 0.153 | 19.476 | 0.960 |
| type_of_slb | moving_away | 0.211 | 18.448 | 0.298 |
| type_of_slb | moving_against | 0.221 | 20.262 | 0.209 |
“SLB Type” by Mechanisms/Factors
Code
slb_type_data %>%
group_by(type_of_slb) %>%
dplyr::summarize(behavioral_mechanisms = sum(behavioral_mechanisms, na.rm = T),
cognitive_mechanisms = sum(cognitive_mechanisms, na.rm = T),
explanatory_factors = sum(explanatory_factors, na.rm = T),
super_mechanisms = sum(super_mechanisms, na.rm = T)) %>%
drop_na() %>%
mutate(total = rowSums(dplyr::select(., behavioral_mechanisms, cognitive_mechanisms, explanatory_factors, super_mechanisms)),
behavioral_mechanisms = (behavioral_mechanisms/total) * 100,
behavioral_mechanisms = round((behavioral_mechanisms), 1),
cognitive_mechanisms = (cognitive_mechanisms/total) * 100,
cognitive_mechanisms = round((cognitive_mechanisms), 1),
explanatory_factors = (explanatory_factors/total) * 100,
explanatory_factors = round((explanatory_factors), 1),
super_mechanisms = (super_mechanisms/total) * 100,
super_mechanisms = round((super_mechanisms), 1)) %>%
dplyr::select(-total) %>%
gt() %>%
tab_header(title = "Type of SLB by Mechanisms/Factors") %>%
cols_label(type_of_slb = "Type of SLB",
behavioral_mechanisms = "Behavioral Mechanisms",
cognitive_mechanisms = "Cognitive Mechanisms",
explanatory_factors = "Explanatory Factors",
super_mechanisms = "Super Mechanisms") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Type of SLB by Mechanisms/Factors | ||||
| Type of SLB | Behavioral Mechanisms | Cognitive Mechanisms | Explanatory Factors | Super Mechanisms |
|---|---|---|---|---|
| 1 | 27.6 | 22.0 | 30.7 | 19.7 |
| 2 | 34.3 | 20.1 | 27.0 | 18.6 |
| 3 | 37.2 | 13.3 | 28.7 | 20.7 |
| 4 | 37.0 | 10.2 | 32.3 | 20.5 |
| 5 | 44.1 | 9.9 | 25.2 | 20.7 |
| 7 | 40.4 | 8.5 | 25.5 | 25.5 |
| 8 | 36.8 | 14.1 | 27.7 | 21.3 |
| 9 | 43.1 | 9.0 | 26.4 | 21.5 |
| 10 | 36.8 | 10.5 | 28.9 | 23.7 |
“SLB Type” by Behavioral Mechanisms
Code
slb_type_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("rule_bending", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping"))) %>%
group_by(type_of_slb) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Type of SLB by Behavioral Mechanisms") %>%
cols_label(type_of_slb = "Type of SLB",
rule_bending_behavior = "Rule Bending",
rule_breaking_behavior = "Rule Breaking",
instrumental_actions_behavior = "Instrumental Actions",
prioritizing_behavior = "Prioritizing",
personal_resources_behavior = "Personal Resources",
routinizing_behavior = "Routinizing",
ratioining_behavior = "Ratioining",
rigid_rule_following_behavior = "Rigid Rule Following",
agression_behavior = "Agression",
new_behavioral_coping_behavior = "New Behavioral Coping") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Type of SLB by Behavioral Mechanisms | ||||||||||
| Type of SLB | Rule Bending | Rule Breaking | Instrumental Actions | Prioritizing | Personal Resources | Routinizing | Ratioining | Rigid Rule Following | Agression | New Behavioral Coping |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 16.2 | 8.1 | 5.4 | 13.5 | 0.0 | 10.8 | 8.1 | 10.8 | 16.2 | 10.8 |
| 2 | 15.7 | 7.1 | 5.0 | 17.1 | 4.3 | 10.0 | 11.4 | 8.6 | 7.9 | 12.9 |
| 3 | 17.1 | 8.6 | 5.7 | 18.6 | 7.1 | 8.6 | 10.0 | 8.6 | 4.3 | 11.4 |
| 4 | 12.5 | 2.1 | 8.3 | 14.6 | 6.2 | 12.5 | 18.8 | 2.1 | 6.2 | 16.7 |
| 5 | 8.2 | 2.0 | 6.1 | 14.3 | 10.2 | 12.2 | 18.4 | 6.1 | 4.1 | 18.4 |
| 7 | 15.8 | 0.0 | 0.0 | 5.3 | 0.0 | 10.5 | 0.0 | 21.1 | 21.1 | 26.3 |
| 8 | 15.8 | 4.5 | 6.0 | 11.3 | 6.8 | 14.3 | 14.3 | 12.8 | 3.0 | 11.3 |
| 9 | 9.7 | 6.5 | 11.3 | 8.1 | 4.8 | 8.1 | 11.3 | 11.3 | 8.1 | 21.0 |
| 10 | 21.4 | 0.0 | 7.1 | 14.3 | 0.0 | 14.3 | 14.3 | 14.3 | 0.0 | 14.3 |
“SLB Type” by Cognitive Mechanisms
Code
slb_type_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping"))) %>%
group_by(type_of_slb) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Type of SLB by Cognitive Mechanisms") %>%
cols_label(type_of_slb = "Type of SLB",
compassion_behavior = "Compassion",
emotional_detachment_behavior = "Emotional Detachment",
empathy_behavior = "Empathy",
new_cognitive_coping_behavior = "New Cognitive Coping") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Type of SLB by Cognitive Mechanisms | |||||
| Type of SLB | cynicism_behavior | Compassion | Emotional Detachment | Empathy | New Cognitive Coping |
|---|---|---|---|---|---|
| 1 | 25.0 | 14.3 | 21.4 | 32.1 | 7.1 |
| 2 | 11.0 | 23.2 | 22.0 | 30.5 | 13.4 |
| 3 | 20.0 | 20.0 | 20.0 | 12.0 | 28.0 |
| 4 | 7.7 | 23.1 | 23.1 | 23.1 | 23.1 |
| 5 | 27.3 | 9.1 | 27.3 | 9.1 | 27.3 |
| 7 | 25.0 | 0.0 | 25.0 | 0.0 | 50.0 |
| 8 | 15.7 | 27.5 | 25.5 | 19.6 | 11.8 |
| 9 | 15.4 | 30.8 | 15.4 | 23.1 | 15.4 |
| 10 | 25.0 | 25.0 | 0.0 | 25.0 | 25.0 |
“SLB Type” by Explanatory Factors
Code
slb_type_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("personal_factors", "organizational_factors", "environmental_factors"))) %>%
group_by(type_of_slb) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Type of SLB by Explanatory Factors") %>%
cols_label(type_of_slb = "Type of SLB",
personal_factors_behavior = "Personal Factors",
organizational_factors_behavior = "Organizational Factors",
environmental_factors_behavior = "Environmental Factors") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Type of SLB by Explanatory Factors | |||
| Type of SLB | Personal Factors | Organizational Factors | Environmental Factors |
|---|---|---|---|
| 1 | 38.5 | 35.9 | 25.6 |
| 2 | 33.6 | 39.1 | 27.3 |
| 3 | 31.5 | 37.0 | 31.5 |
| 4 | 26.8 | 41.5 | 31.7 |
| 5 | 21.4 | 46.4 | 32.1 |
| 7 | 41.7 | 33.3 | 25.0 |
| 8 | 30.0 | 42.0 | 28.0 |
| 9 | 23.7 | 44.7 | 31.6 |
| 10 | 36.4 | 36.4 | 27.3 |
“SLB Type” by Super Mechanisms
Code
slb_type_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("moving_towards_bi", "moving_away_bi", "moving_against_bi"))) %>%
group_by(type_of_slb) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Type of SLB by Super Mechanisms") %>%
cols_label(type_of_slb = "Type of SLB",
moving_towards_bi_behavior = "Moving Towards",
moving_away_bi_behavior = "Moving Away",
moving_against_bi_behavior = "Moving Against") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Type of SLB by Super Mechanisms | |||
| Type of SLB | Moving Towards | Moving Away | Moving Against |
|---|---|---|---|
| 1 | 48.0 | 20.0 | 32.0 |
| 2 | 47.4 | 30.3 | 22.4 |
| 3 | 53.8 | 25.6 | 20.5 |
| 4 | 46.2 | 38.5 | 15.4 |
| 5 | 39.1 | 39.1 | 21.7 |
| 7 | 33.3 | 16.7 | 50.0 |
| 8 | 40.3 | 35.1 | 24.7 |
| 9 | 41.9 | 29.0 | 29.0 |
| 10 | 55.6 | 22.2 | 22.2 |
4. Coding by Gender
Frequency Table for the Variable “Gender” (Numbers and Percentages)
Code
gender_data %>%
dplyr::select(gender) %>%
gtsummary::tbl_summary(by = NULL,
statistic = all_categorical() ~ "{n} ({p}%)") %>%
bold_labels()| Characteristic | N = 2201 |
|---|---|
| gender | |
| 0 | 57 (26%) |
| 1 | 69 (32%) |
| 2 | 92 (42%) |
| Unknown | 2 |
| 1 n (%) | |
Frequency (in percentages) of Study Variables by “Gender” Categories
NA values were omitted
All variables in the table, except
gender, are binary. The percentages represent the frequency of each binary variable within the categories of thegendervariable. For instance, approximately 37% of articles withgender = 0exhibitRule Bending.
Table for three categories for gender - 0, 1, 2 (i.e., full data)
Code
gender_data %>%
dplyr::select(gender, rule_bending, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors, moving_towards_bi, moving_away_bi, moving_against_bi) %>%
drop_na() %>%
group_by(gender) %>%
dplyr::summarize(rule_bending_p = mean(rule_bending) * 100,
rule_bending_n = sum(rule_bending),
rule_bending = paste0(rule_bending_n, " (", round(rule_bending_p, 1), "%)"),
rule_breaking_p = mean(rule_breaking) * 100,
rule_breaking_n = sum(rule_breaking),
rule_breaking = paste0(rule_breaking_n, " (", round(rule_breaking_p, 1), "%)"),
instrumental_actions_p = mean(instrumental_actions) * 100,
instrumental_actions_n = sum(instrumental_actions),
instrumental_actions = paste0(instrumental_actions_n, " (", round(instrumental_actions_p, 1), "%)"),
prioritizing_p = mean(prioritizing) * 100,
prioritizing_n = sum(prioritizing),
prioritizing = paste0(prioritizing_n, " (", round(prioritizing_p, 1), "%)"),
personal_resources_p = mean(personal_resources) * 100,
personal_resources_n = sum(personal_resources),
personal_resources = paste0(personal_resources_n, " (", round(personal_resources_p, 1), "%)"),
routinizing_p = mean(routinizing) * 100,
routinizing_n = sum(routinizing),
routinizing = paste0(routinizing_n, " (", round(routinizing_p, 1), "%)"),
ratioining_p = mean(ratioining) * 100,
ratioining_n = sum(ratioining),
ratioining = paste0(ratioining_n, " (", round(ratioining_p, 1), "%)"),
rigid_rule_following_p = mean(rigid_rule_following) * 100,
rigid_rule_following_n = sum(rigid_rule_following),
rigid_rule_following = paste0(rigid_rule_following_n, " (", round(rigid_rule_following_p, 1), "%)"),
agression_p = mean(agression) * 100,
agression_n = sum(agression),
agression = paste0(agression_n, " (", round(agression_p, 1), "%)"),
new_behavioral_coping_p = mean(new_behavioral_coping) * 100,
new_behavioral_coping_n = sum(new_behavioral_coping),
new_behavioral_coping = paste0(new_behavioral_coping_n, " (", round(new_behavioral_coping_p, 1), "%)"),
cynicism_p = mean(cynicism) * 100,
cynicism_n = sum(cynicism),
cynicism = paste0(cynicism_n, " (", round(cynicism_p, 1), "%)"),
compassion_p = mean(compassion) * 100,
compassion_n = sum(compassion),
compassion = paste0(compassion_n, " (", round(compassion_p, 1), "%)"),
emotional_detachment_p = mean(emotional_detachment) * 100,
emotional_detachment_n = sum(emotional_detachment),
emotional_detachment = paste0(emotional_detachment_n, " (", round(emotional_detachment_p, 1), "%)"),
empathy_p = mean(empathy) * 100,
empathy_n = sum(empathy),
empathy = paste0(empathy_n, " (", round(empathy_p, 1), "%)"),
new_cognitive_coping_p = mean(new_cognitive_coping) * 100,
new_cognitive_coping_n = sum(new_cognitive_coping),
new_cognitive_coping = paste0(new_cognitive_coping_n, " (", round(new_cognitive_coping_p, 1), "%)"),
personal_factors_p = mean(personal_factors) * 100,
personal_factors_n = sum(personal_factors),
personal_factors = paste0(personal_factors_n, " (", round(personal_factors_p, 1), "%)"),
organizational_factors_p = mean(organizational_factors) * 100,
organizational_factors_n = sum(organizational_factors),
organizational_factors = paste0(organizational_factors_n, " (", round(organizational_factors_p, 1), "%)"),
environmental_factors_p = mean(environmental_factors) * 100,
environmental_factors_n = sum(environmental_factors),
environmental_factors = paste0(environmental_factors_n, " (", round(environmental_factors_p, 1), "%)"),
moving_towards_bi_p = mean(moving_towards_bi) * 100,
moving_towards_bi_n = sum(moving_towards_bi),
moving_towards_bi = paste0(moving_towards_bi_n, " (", round(moving_towards_bi_p, 1), "%)"),
moving_away_bi_p = mean(moving_away_bi) * 100,
moving_away_bi_n = sum(moving_away_bi),
moving_away_bi = paste0(moving_away_bi_n, " (", round(moving_away_bi_p, 1), "%)"),
moving_against_bi_p = mean(moving_against_bi) * 100,
moving_against_bi_n = sum(moving_against_bi),
moving_against_bi = paste0(moving_against_bi_n, " (", round(moving_against_bi_p, 1), "%)")) %>%
dplyr::select(gender, rule_bending, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors, moving_towards_bi, moving_away_bi, moving_against_bi) %>%
gt() %>%
tab_header(title = "Frequency Table by Gender") %>%
fmt_number(columns = -gender,
decimals = 2) %>%
cols_label(gender = "Gender",
rule_bending = "Rule Bending",
rule_breaking = "Rule Breaking",
instrumental_actions = "Instrumental Actions",
prioritizing = "Prioritizing",
personal_resources = "Personal Resources",
routinizing = "Routinizing",
ratioining = "Rationing",
rigid_rule_following = "Rigid Rule Following",
agression = "Aggression",
new_behavioral_coping = "New Behavioral Coping",
cynicism = "Cynicism",
compassion = "Compassion",
emotional_detachment = "Emotional Detachment",
empathy = "Empathy",
new_cognitive_coping = "New Cognitive Coping",
personal_factors = "Personal Factors",
organizational_factors = "Organizational Factors",
environmental_factors = "Environmental Factors",
moving_towards_bi = "Moving Towards",
moving_away_bi = "Moving Away",
moving_against_bi = "Moving Against") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Frequency Table by Gender | |||||||||||||||||||||
| Gender | Rule Bending | Rule Breaking | Instrumental Actions | Prioritizing | Personal Resources | Routinizing | Rationing | Rigid Rule Following | Aggression | New Behavioral Coping | Cynicism | Compassion | Emotional Detachment | Empathy | New Cognitive Coping | Personal Factors | Organizational Factors | Environmental Factors | Moving Towards | Moving Away | Moving Against |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21 (36.8%) | 9 (15.8%) | 10 (17.5%) | 21 (36.8%) | 14 (24.6%) | 17 (29.8%) | 14 (24.6%) | 17 (29.8%) | 11 (19.3%) | 23 (40.4%) | 8 (14%) | 16 (28.1%) | 14 (24.6%) | 13 (22.8%) | 13 (22.8%) | 36 (63.2%) | 46 (80.7%) | 33 (57.9%) | 37 (64.9%) | 22 (38.6%) | 23 (40.4%) |
| 1 | 25 (36.8%) | 11 (16.2%) | 15 (22.1%) | 25 (36.8%) | 14 (20.6%) | 20 (29.4%) | 19 (27.9%) | 19 (27.9%) | 12 (17.6%) | 28 (41.2%) | 9 (13.2%) | 21 (30.9%) | 15 (22.1%) | 14 (20.6%) | 15 (22.1%) | 45 (66.2%) | 56 (82.4%) | 41 (60.3%) | 45 (66.2%) | 27 (39.7%) | 25 (36.8%) |
| 2 | 39 (43.3%) | 11 (12.2%) | 14 (15.6%) | 36 (40%) | 10 (11.1%) | 29 (32.2%) | 39 (43.3%) | 24 (26.7%) | 16 (17.8%) | 37 (41.1%) | 18 (20%) | 22 (24.4%) | 24 (26.7%) | 25 (27.8%) | 11 (12.2%) | 58 (64.4%) | 78 (86.7%) | 54 (60%) | 63 (70%) | 49 (54.4%) | 32 (35.6%) |
Table for two categories for gender - 0, 1 (i.e., omitting gender = 2)
Code
gender_data %>%
dplyr::select(gender, rule_bending, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors, moving_towards_bi, moving_away_bi, moving_against_bi) %>%
filter(gender != 2) %>%
drop_na() %>%
group_by(gender) %>%
dplyr::summarize(rule_bending_p = mean(rule_bending) * 100,
rule_bending_n = sum(rule_bending),
rule_bending = paste0(rule_bending_n, " (", round(rule_bending_p, 1), "%)"),
rule_breaking_p = mean(rule_breaking) * 100,
rule_breaking_n = sum(rule_breaking),
rule_breaking = paste0(rule_breaking_n, " (", round(rule_breaking_p, 1), "%)"),
instrumental_actions_p = mean(instrumental_actions) * 100,
instrumental_actions_n = sum(instrumental_actions),
instrumental_actions = paste0(instrumental_actions_n, " (", round(instrumental_actions_p, 1), "%)"),
prioritizing_p = mean(prioritizing) * 100,
prioritizing_n = sum(prioritizing),
prioritizing = paste0(prioritizing_n, " (", round(prioritizing_p, 1), "%)"),
personal_resources_p = mean(personal_resources) * 100,
personal_resources_n = sum(personal_resources),
personal_resources = paste0(personal_resources_n, " (", round(personal_resources_p, 1), "%)"),
routinizing_p = mean(routinizing) * 100,
routinizing_n = sum(routinizing),
routinizing = paste0(routinizing_n, " (", round(routinizing_p, 1), "%)"),
ratioining_p = mean(ratioining) * 100,
ratioining_n = sum(ratioining),
ratioining = paste0(ratioining_n, " (", round(ratioining_p, 1), "%)"),
rigid_rule_following_p = mean(rigid_rule_following) * 100,
rigid_rule_following_n = sum(rigid_rule_following),
rigid_rule_following = paste0(rigid_rule_following_n, " (", round(rigid_rule_following_p, 1), "%)"),
agression_p = mean(agression) * 100,
agression_n = sum(agression),
agression = paste0(agression_n, " (", round(agression_p, 1), "%)"),
new_behavioral_coping_p = mean(new_behavioral_coping) * 100,
new_behavioral_coping_n = sum(new_behavioral_coping),
new_behavioral_coping = paste0(new_behavioral_coping_n, " (", round(new_behavioral_coping_p, 1), "%)"),
cynicism_p = mean(cynicism) * 100,
cynicism_n = sum(cynicism),
cynicism = paste0(cynicism_n, " (", round(cynicism_p, 1), "%)"),
compassion_p = mean(compassion) * 100,
compassion_n = sum(compassion),
compassion = paste0(compassion_n, " (", round(compassion_p, 1), "%)"),
emotional_detachment_p = mean(emotional_detachment) * 100,
emotional_detachment_n = sum(emotional_detachment),
emotional_detachment = paste0(emotional_detachment_n, " (", round(emotional_detachment_p, 1), "%)"),
empathy_p = mean(empathy) * 100,
empathy_n = sum(empathy),
empathy = paste0(empathy_n, " (", round(empathy_p, 1), "%)"),
new_cognitive_coping_p = mean(new_cognitive_coping) * 100,
new_cognitive_coping_n = sum(new_cognitive_coping),
new_cognitive_coping = paste0(new_cognitive_coping_n, " (", round(new_cognitive_coping_p, 1), "%)"),
personal_factors_p = mean(personal_factors) * 100,
personal_factors_n = sum(personal_factors),
personal_factors = paste0(personal_factors_n, " (", round(personal_factors_p, 1), "%)"),
organizational_factors_p = mean(organizational_factors) * 100,
organizational_factors_n = sum(organizational_factors),
organizational_factors = paste0(organizational_factors_n, " (", round(organizational_factors_p, 1), "%)"),
environmental_factors_p = mean(environmental_factors) * 100,
environmental_factors_n = sum(environmental_factors),
environmental_factors = paste0(environmental_factors_n, " (", round(environmental_factors_p, 1), "%)"),
moving_towards_bi_p = mean(moving_towards_bi) * 100,
moving_towards_bi_n = sum(moving_towards_bi),
moving_towards_bi = paste0(moving_towards_bi_n, " (", round(moving_towards_bi_p, 1), "%)"),
moving_away_bi_p = mean(moving_away_bi) * 100,
moving_away_bi_n = sum(moving_away_bi),
moving_away_bi = paste0(moving_away_bi_n, " (", round(moving_away_bi_p, 1), "%)"),
moving_against_bi_p = mean(moving_against_bi) * 100,
moving_against_bi_n = sum(moving_against_bi),
moving_against_bi = paste0(moving_against_bi_n, " (", round(moving_against_bi_p, 1), "%)")) %>%
dplyr::select(gender, rule_bending, rule_breaking, instrumental_actions, prioritizing, personal_resources, routinizing, ratioining, rigid_rule_following, agression, new_behavioral_coping, cynicism, compassion, emotional_detachment, empathy, new_cognitive_coping, personal_factors, organizational_factors, environmental_factors, moving_towards_bi, moving_away_bi, moving_against_bi) %>%
gt() %>%
tab_header(title = "Frequency Table by Gender") %>%
fmt_number(columns = -gender,
decimals = 2) %>%
cols_label(gender = "Gender",
rule_bending = "Rule Bending",
rule_breaking = "Rule Breaking",
instrumental_actions = "Instrumental Actions",
prioritizing = "Prioritizing",
personal_resources = "Personal Resources",
routinizing = "Routinizing",
ratioining = "Rationing",
rigid_rule_following = "Rigid Rule Following",
agression = "Aggression",
new_behavioral_coping = "New Behavioral Coping",
cynicism = "Cynicism",
compassion = "Compassion",
emotional_detachment = "Emotional Detachment",
empathy = "Empathy",
new_cognitive_coping = "New Cognitive Coping",
personal_factors = "Personal Factors",
organizational_factors = "Organizational Factors",
environmental_factors = "Environmental Factors",
moving_towards_bi = "Moving Towards",
moving_away_bi = "Moving Away",
moving_against_bi = "Moving Against") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Frequency Table by Gender | |||||||||||||||||||||
| Gender | Rule Bending | Rule Breaking | Instrumental Actions | Prioritizing | Personal Resources | Routinizing | Rationing | Rigid Rule Following | Aggression | New Behavioral Coping | Cynicism | Compassion | Emotional Detachment | Empathy | New Cognitive Coping | Personal Factors | Organizational Factors | Environmental Factors | Moving Towards | Moving Away | Moving Against |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21 (36.8%) | 9 (15.8%) | 10 (17.5%) | 21 (36.8%) | 14 (24.6%) | 17 (29.8%) | 14 (24.6%) | 17 (29.8%) | 11 (19.3%) | 23 (40.4%) | 8 (14%) | 16 (28.1%) | 14 (24.6%) | 13 (22.8%) | 13 (22.8%) | 36 (63.2%) | 46 (80.7%) | 33 (57.9%) | 37 (64.9%) | 22 (38.6%) | 23 (40.4%) |
| 1 | 25 (36.8%) | 11 (16.2%) | 15 (22.1%) | 25 (36.8%) | 14 (20.6%) | 20 (29.4%) | 19 (27.9%) | 19 (27.9%) | 12 (17.6%) | 28 (41.2%) | 9 (13.2%) | 21 (30.9%) | 15 (22.1%) | 14 (20.6%) | 15 (22.1%) | 45 (66.2%) | 56 (82.4%) | 41 (60.3%) | 45 (66.2%) | 27 (39.7%) | 25 (36.8%) |
Correlations of the Variable “Gender” with Other Variables (Cramer’s V)
- Correlations cannot be calculated for the following variables —
journal_article,language,use,empirical_study,theoretical_study— either because they consist of only one category or one category is overwhelmingly dominant.
Code
tb1 <- tibble(Cramer_V = c(cramerV(gender_data $ gender, gender_data $ coder_last_name),
cramerV(gender_data $ gender, gender_data $ date),
cramerV(gender_data $ gender, gender_data $ authors),
cramerV(gender_data $ gender, gender_data $ year),
cramerV(gender_data $ gender, gender_data $ journal),
cramerV(gender_data $ gender, gender_data $ policy_domain),
cramerV(gender_data $ gender, gender_data $ no_of_countries),
cramerV(gender_data $ gender, gender_data $ developed_developing),
cramerV(gender_data $ gender, gender_data $ vulnerable_clients),
cramerV(gender_data $ gender, gender_data $ social_equity),
cramerV(gender_data $ gender, gender_data $ covid_19),
cramerV(gender_data $ gender, gender_data $ type_of_analysis),
cramerV(gender_data $ gender, gender_data $ type_of_slb),
cramerV(gender_data $ gender, gender_data $ sector),
cramerV(gender_data $ gender, gender_data $ country_name),
cramerV(gender_data $ gender, gender_data $ rule_bending),
cramerV(gender_data $ gender, gender_data $ rule_breaking),
cramerV(gender_data $ gender, gender_data $ instrumental_actions),
cramerV(gender_data $ gender, gender_data $ prioritizing),
cramerV(gender_data $ gender, gender_data $ personal_resources),
cramerV(gender_data $ gender, gender_data $ routinizing),
cramerV(gender_data $ gender, gender_data $ ratioining),
cramerV(gender_data $ gender, gender_data $ rigid_rule_following),
cramerV(gender_data $ gender, gender_data $ agression),
cramerV(gender_data $ gender, gender_data $ new_behavioral_coping),
cramerV(gender_data $ gender, gender_data $ cynicism),
cramerV(gender_data $ gender, gender_data $ compassion),
cramerV(gender_data $ gender, gender_data $ emotional_detachment),
cramerV(gender_data $ gender, gender_data $ empathy),
cramerV(gender_data $ gender, gender_data $ new_cognitive_coping),
cramerV(gender_data $ gender, gender_data $ personal_factors),
cramerV(gender_data $ gender, gender_data $ organizational_factors),
cramerV(gender_data $ gender, gender_data $ environmental_factors),
cramerV(gender_data $ gender, gender_data $ moving_towards),
cramerV(gender_data $ gender, gender_data $ moving_away),
cramerV(gender_data $ gender, gender_data $ moving_against)),
Compared_To = c("coder_last_name", "date", "authors", "year", "journal", "policy_domain", "no_of_countries", "developed_developing", "vulnerable_clients", "social_equity", "covid_19", "type_of_analysis", "type_of_slb", "sector", "country_name", "rule_bending", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping", "cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping", "personal_factors", "organizational_factors", "environmental_factors", "moving_towards", "moving_away", "moving_against")) %>%
mutate(key_variable = "gender",
row_id = paste0("gender_", row_number()))
rbind(chisq.test(gender_data $ gender, gender_data $ coder_last_name) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ date) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ authors) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ year) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ journal) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ policy_domain) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ no_of_countries) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ developed_developing) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ vulnerable_clients) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ social_equity) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ covid_19) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ type_of_analysis) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ type_of_slb) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ sector) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ country_name) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ rule_bending) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ rule_breaking) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ instrumental_actions) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ prioritizing) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ personal_resources) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ routinizing) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ ratioining) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ rigid_rule_following) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ agression) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ new_behavioral_coping) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ cynicism) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ compassion) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ emotional_detachment) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ empathy) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ new_cognitive_coping) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ personal_factors) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ organizational_factors) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ environmental_factors) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ moving_towards) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ moving_away) %>% broom::tidy(),
chisq.test(gender_data $ gender, gender_data $ moving_against) %>% broom::tidy()) %>%
mutate(row_id = paste0("gender_", row_number())) %>%
left_join(tb1, by = "row_id") %>%
mutate(p.value = round((p.value), 3),
statistic = round((statistic), 3)) %>%
rename(chi_square = statistic) %>%
dplyr::select(key_variable, Compared_To, Cramer_V, chi_square, p.value) %>%
gt() %>%
tab_header(title = "Chi-Square and Cramer's V Correlations",
subtitle = "Key Variable and Its Comparisons") %>%
fmt_number(columns = vars(chi_square, Cramer_V, p.value),
decimals = 3) %>%
cols_label(key_variable = "Key Variable",
Compared_To = "Compared To",
Cramer_V = "Cramer's V",
chi_square = "Chi-Square",
p.value = "P-Value") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Chi-Square and Cramer's V Correlations | ||||
| Key Variable and Its Comparisons | ||||
| Key Variable | Compared To | Cramer's V | Chi-Square | P-Value |
|---|---|---|---|---|
| gender | coder_last_name | 0.298 | 39.015 | 0.000 |
| gender | date | 0.205 | 27.882 | 0.265 |
| gender | authors | 0.609 | 244.689 | 0.995 |
| gender | year | 0.264 | 46.099 | 0.704 |
| gender | journal | 0.478 | 151.091 | 0.978 |
| gender | policy_domain | 0.184 | 22.374 | 0.132 |
| gender | no_of_countries | 0.123 | 9.965 | 0.267 |
| gender | developed_developing | 0.081 | 2.880 | 0.237 |
| gender | vulnerable_clients | 0.020 | 0.172 | 0.918 |
| gender | social_equity | 0.061 | 1.642 | 0.440 |
| gender | covid_19 | 0.030 | 0.391 | 0.822 |
| gender | type_of_analysis | 0.081 | 4.316 | 0.365 |
| gender | type_of_slb | 0.247 | 40.243 | 0.918 |
| gender | sector | 0.125 | 10.385 | 0.407 |
| gender | country_name | 0.321 | 67.833 | 0.901 |
| gender | rule_bending | 0.070 | 1.086 | 0.581 |
| gender | rule_breaking | 0.055 | 0.669 | 0.716 |
| gender | instrumental_actions | 0.072 | 1.148 | 0.563 |
| gender | prioritizing | 0.038 | 0.315 | 0.854 |
| gender | personal_resources | 0.153 | 5.157 | 0.076 |
| gender | routinizing | 0.024 | 0.128 | 0.938 |
| gender | ratioining | 0.171 | 6.435 | 0.040 |
| gender | rigid_rule_following | 0.037 | 0.294 | 0.863 |
| gender | agression | 0.015 | 0.093 | 0.954 |
| gender | new_behavioral_coping | 0.009 | 0.037 | 0.982 |
| gender | cynicism | 0.095 | 2.000 | 0.368 |
| gender | compassion | 0.064 | 0.889 | 0.641 |
| gender | emotional_detachment | 0.028 | 0.344 | 0.842 |
| gender | empathy | 0.081 | 1.456 | 0.483 |
| gender | new_cognitive_coping | 0.132 | 3.839 | 0.147 |
| gender | personal_factors | 0.021 | 0.189 | 0.910 |
| gender | organizational_factors | 0.056 | 1.395 | 0.498 |
| gender | environmental_factors | 0.011 | 0.054 | 0.973 |
| gender | moving_towards | 0.140 | 12.942 | 0.114 |
| gender | moving_away | 0.103 | 4.670 | 0.323 |
| gender | moving_against | 0.036 | 0.562 | 0.967 |
“Gender” by Mechanisms/Factors
Code
gender_data %>%
group_by(gender) %>%
dplyr::summarize(behavioral_mechanisms = sum(behavioral_mechanisms, na.rm = T),
cognitive_mechanisms = sum(cognitive_mechanisms, na.rm = T),
explanatory_factors = sum(explanatory_factors, na.rm = T),
super_mechanisms = sum(super_mechanisms, na.rm = T)) %>%
drop_na() %>%
mutate(total = rowSums(dplyr::select(., behavioral_mechanisms, cognitive_mechanisms, explanatory_factors, super_mechanisms)),
behavioral_mechanisms = (behavioral_mechanisms/total) * 100,
behavioral_mechanisms = round((behavioral_mechanisms), 1),
cognitive_mechanisms = (cognitive_mechanisms/total) * 100,
cognitive_mechanisms = round((cognitive_mechanisms), 1),
explanatory_factors = (explanatory_factors/total) * 100,
explanatory_factors = round((explanatory_factors), 1),
super_mechanisms = (super_mechanisms/total) * 100,
super_mechanisms = round((super_mechanisms), 1)) %>%
dplyr::select(-total) %>%
gt() %>%
tab_header(title = "Gender by Mechanisms/Factors") %>%
cols_label(gender = "Gender",
behavioral_mechanisms = "Behavioral Mechanisms",
cognitive_mechanisms = "Cognitive Mechanisms",
explanatory_factors = "Explanatory Factors",
super_mechanisms = "Super Mechanisms") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Gender by Mechanisms/Factors | ||||
| Gender | Behavioral Mechanisms | Cognitive Mechanisms | Explanatory Factors | Super Mechanisms |
|---|---|---|---|---|
| 0 | 37.6 | 15.3 | 27.5 | 19.6 |
| 1 | 37.5 | 14.7 | 28.4 | 19.4 |
| 2 | 36.6 | 14.6 | 27.8 | 20.9 |
“Gender” by Behavioral Mechanisms
Code
gender_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("rule_bending", "rule_breaking", "instrumental_actions", "prioritizing", "personal_resources", "routinizing", "ratioining", "rigid_rule_following", "agression", "new_behavioral_coping"))) %>%
group_by(gender) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Gender by Behavioral Mechanisms") %>%
cols_label(gender = "Gender",
rule_bending_behavior = "Rule Bending",
rule_breaking_behavior = "Rule Breaking",
instrumental_actions_behavior = "Instrumental Actions",
prioritizing_behavior = "Prioritizing",
personal_resources_behavior = "Personal Resources",
routinizing_behavior = "Routinizing",
ratioining_behavior = "Ratioining",
rigid_rule_following_behavior = "Rigid Rule Following",
agression_behavior = "Agression",
new_behavioral_coping_behavior = "New Behavioral Coping") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Gender by Behavioral Mechanisms | ||||||||||
| Gender | Rule Bending | Rule Breaking | Instrumental Actions | Prioritizing | Personal Resources | Routinizing | Ratioining | Rigid Rule Following | Agression | New Behavioral Coping |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13.4 | 5.7 | 6.4 | 13.4 | 8.9 | 10.8 | 8.9 | 10.8 | 7.0 | 14.6 |
| 1 | 13.2 | 5.8 | 7.9 | 13.2 | 7.4 | 10.6 | 10.1 | 10.6 | 6.3 | 14.8 |
| 2 | 15.5 | 4.3 | 5.4 | 14.3 | 3.9 | 11.2 | 15.1 | 9.3 | 6.2 | 14.7 |
“Gender” by Cognitive Mechanisms
Code
gender_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("cynicism", "compassion", "emotional_detachment", "empathy", "new_cognitive_coping"))) %>%
group_by(gender) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Gender by Cognitive Mechanisms") %>%
cols_label(gender = "Gender",
compassion_behavior = "Compassion",
emotional_detachment_behavior = "Emotional Detachment",
empathy_behavior = "Empathy",
new_cognitive_coping_behavior = "New Cognitive Coping") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Gender by Cognitive Mechanisms | |||||
| Gender | cynicism_behavior | Compassion | Emotional Detachment | Empathy | New Cognitive Coping |
|---|---|---|---|---|---|
| 0 | 12.5 | 25.0 | 21.9 | 20.3 | 20.3 |
| 1 | 12.2 | 28.4 | 20.3 | 18.9 | 20.3 |
| 2 | 18.6 | 21.6 | 23.5 | 25.5 | 10.8 |
“Gender” by Explanatory Factors
Code
gender_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("personal_factors", "organizational_factors", "environmental_factors"))) %>%
group_by(gender) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Gender by Explanatory Factors") %>%
cols_label(gender = "Gender",
personal_factors_behavior = "Personal Factors",
organizational_factors_behavior = "Organizational Factors",
environmental_factors_behavior = "Environmental Factors") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Gender by Explanatory Factors | |||
| Gender | Personal Factors | Organizational Factors | Environmental Factors |
|---|---|---|---|
| 0 | 31.3 | 40.0 | 28.7 |
| 1 | 32.2 | 39.2 | 28.7 |
| 2 | 30.4 | 41.2 | 28.4 |
“Gender” by Super Mechanisms
Code
gender_data %>%
rename_with( ~ paste0(., "_behavior"),
all_of(c("moving_towards_bi", "moving_away_bi", "moving_against_bi"))) %>%
group_by(gender) %>%
dplyr::summarize(across(ends_with("_behavior"), sum, na.rm = T)) %>%
mutate(total = rowSums(dplyr::select(., ends_with("_behavior"))),
across(ends_with("_behavior"), ~ . / total * 100),
across(where(is.numeric), ~ round(., 1))) %>%
dplyr::select(-total) %>%
drop_na() %>%
gt() %>%
tab_header(title = "Gender by Super Mechanisms") %>%
cols_label(gender = "Gender",
moving_towards_bi_behavior = "Moving Towards",
moving_away_bi_behavior = "Moving Away",
moving_against_bi_behavior = "Moving Against") %>%
tab_style(style = cell_text(weight = "bold"),
locations = cells_column_labels(everything()))| Gender by Super Mechanisms | |||
| Gender | Moving Towards | Moving Away | Moving Against |
|---|---|---|---|
| 0 | 45.1 | 26.8 | 28.0 |
| 1 | 45.9 | 27.6 | 26.5 |
| 2 | 44.5 | 33.6 | 21.9 |