Support for Homosexuals Adoption Rights

This page guides you through the harmonisation process of the Support for Homosexuals Adoption Rights item.

The item was administered in EVS 2008 and ESS 2016, 2018, 2020 and 2023. Before we start harmonising, please download these data sets from the respective repositories. You can find links to the survey repositories on the Make It Equal! data documentation page, in the section devoted to Support for Homosexuals Adoption Rights. If you click on the single year, the link takes you to the specific data set. We are working with the .sav format. So please make sure to download the datasets in this format.

Set-up

Please download the statistical software R (R Core Team, 2024) and RStudio (RStudio Team, 2023). Then start a project in RStudio by clicking File > New Project and set up a new directory or assign your project to an existing directory (i.e., folder). Within the project directory, create a folder called “data”. Now save the downloaded ESS and EB survey data into this “data” folder. Lastly, please download the following packages using the install.packages() function if they have not been before.

install.packages()

packages <- c( "tidyverse", "readxl", "xlsx", "haven", "labelled", "tidyr", "knitr", "kableExtra", "dplyr", "openxlsx")

installed <- packages %in% rownames(installed.packages())
if (any(!installed)) install.packages(packages[!installed])

Every package installed in R must also be loaded. This is what the library-function is for.

library(tidyverse)
library(readxl)
library(openxlsx) # If you have problems installing or loading the xlsx-package, use the openxlsx. 
library(haven)
library(labelled)
library(tidyr)
library(knitr)
library(kableExtra)
library(dplyr)

An important step during the harmonisation process is to prepare the source data sets in a consistent and systematic way. For that, the functions “codebook” (create_codebook_short()) and ”crosswalk table” (create_cwt()) by Kołczyńska (2022) are used. They show the correspondence between source data sets and harmonised data regarding variable names and values. These functions are adopted into our needs, and the R file containing them can be downloaded here . Please save this file in the R project folder and import it into the R project via source("kolczynska2022_i4ng.R").

How-to

If you can not download the R-file and if it is opened in a new tab, please open a new file in your R-Studio project under File -> New File -> R Script and copy the text from the kolczynska2022-file into this new file. Make sure that you save this new file in your project folder under the name “kolczynska2022_i4ng.R”.

source("kolczynska2022_i4ng.R")

Now we can name our target variable as “e_pc_adop”. The name is a combination of “e” representing the main topic “equal”, the concept “Parenthood, Children and Marriage” and the variable label “Support for Homosexuals Adoption Rights”.

target_var_hrms <- "e_pc_adop"

Before we can harmonise any data, we need to prepare the source data sets:

1. Prepare Data: EVS 2008

Six steps must be carried out in the following coding section:

1.1 Import data

evs <- haven::read_sav("data/ZA4800_v5-0-0.sav", user_na=TRUE, encoding = "ISO-8859-1")

Check the source item

Use this output to compare it with your output. Check especially if the labels match.

evs %>% count(v154)

# A tibble: 8 × 2
  v154                        n
  <dbl+lbl>               <int>
1 -5 (NA) [other missing]     1
2 -2 (NA) [no answer]       581
3 -1 (NA) [don't know]     3522
4  1 [agree strongly]      4510
5  2 [agree]               9570
6  3 [agree nor disagree]  8884
7  4 [disagree]           17484
8  5 [disagree strongly]  21728

1.2. Create codebook

Not just the substantive variables but also protocol variables need to be selected from this data set. Protocol variables selected for this harmonisation are the source data’s country, weight, and ID variables. In this step, we create a codebook that provides an overview of the selected variables and their naming in the harmonised data.

codebook_evs <- create_codebook_short(evs) %>%
  mutate(survey = "EVS",
         target_var = ifelse(var_source == "c_abrv", "country_source", NA),
         target_var = ifelse(var_source == "weight_g", "w2_poststr", target_var),
         target_var = ifelse(grepl("unified respondent number", var_label_source, perl = TRUE), "caseID_source", target_var),
         target_var = ifelse(grepl("homosexual couples - adopt children", var_label_source), 
                             target_var_hrms, # NAME FOR THE TARGET VARIABLE to be harmonised
                             target_var),
         target_var = gsub(" ", "_", target_var),
         target_var = tolower(target_var))

codebook_evs includes the columns called: var_source, var_label_source, survey, target_var. The variable names which will be used in the harmonised data are listed under target_var.

var_source	var_label_source	survey	target_var
id_cocas	unified respondent number	EVS	caseid_source
c_abrv	country abbreviation	EVS	country_source
weight_g	weight	EVS	w2_poststr
v154	homosexual couples - adopt children	EVS	e_pc_adop

1.3. Retrieve names of the chosen source variables

variables <- codebook_evs %>% drop_na(target_var) %>% pull(var_source)

The retrieved variables are: id_cocas, c_abrv, weight_g, v154.

1.4. Select a subset of the survey data containing only the chosen variables

evs_small <- evs %>% select(all_of(variables)) %>% mutate(caseid = row_number())

You will see that the following data set is a subset of the EVS 2008 with the chosen variables for the harmonised data. The first six rows of the dataset are as follows:

id_cocas	c_abrv	weight_g	v154	caseid
200800800001	AL	0.9623360	-1	1
200800800002	AL	0.7617063	-1	2
200800800003	AL	0.7617063	5	3
200800800004	AL	1.0642812	5	4
200800800005	AL	0.9859014	3	5
200800800006	AL	0.7617063	4	6

1.5. Create a crosswalk table for the chosen variables

A crosswalk table provides a connection between the source variable values and the target variable values. First, it provides the variables, their labels, and the possible values. Then, the information is linked with the codebook information of the newly specified target variables. Lastly, target values are defined in a variable called target_value.

Here, weight and ID variables are excluded from the crosswalk table. Original codes for the country variable are preserved. Lastly, NAs are assigned to missing values.

evs_cwt <- create_cwt(evs_small %>% select(c_abrv, v154)) %>% 
  left_join(codebook_evs, by=join_by(var_source, var_label_source)) %>% 
  mutate(target_value = NA,
         target_value = ifelse(!grepl("not asked|know|efusal|answer|NA|applicable|Can't choose|]DK|] DK|Na| other missing", value_label_source), value_code, target_value),
         value_code = as.character(value_code)
         )

Important

There are decisions about the substantive variable made by taking into account all datasets. The utility of creating a crosswalk table is that the results of these decisions can be seen explicitly by comparing the value_code and target_value columns: Definitions of missing values differ across EVS and ESS. Missing values are not harmonised but recoded into the single missing value “NA”.

Let’s look at the correspondence between e_pc_adop and v154:

evs_cwt %>% filter(var_source == "v154") %>% 
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

var_source	var_label_source	npos	value_n	value_label_source	value_code	survey	target_var	target_value
v154	homosexual couples - adopt children	1	[-5] other missing: 1	[-5] other missing	-5	EVS	e_pc_adop	NA
v154	homosexual couples - adopt children	2	[-4] question not asked: 0	[-4] question not asked	-4	EVS	e_pc_adop	NA
v154	homosexual couples - adopt children	3	[-3] not applicable: 0	[-3] not applicable	-3	EVS	e_pc_adop	NA
v154	homosexual couples - adopt children	4	[-2] no answer: 581	[-2] no answer	-2	EVS	e_pc_adop	NA
v154	homosexual couples - adopt children	5	[-1] don't know: 3522	[-1] don't know	-1	EVS	e_pc_adop	NA
v154	homosexual couples - adopt children	6	[1] agree strongly: 4510	[1] agree strongly	1	EVS	e_pc_adop	1
v154	homosexual couples - adopt children	7	[2] agree: 9570	[2] agree	2	EVS	e_pc_adop	2
v154	homosexual couples - adopt children	8	[3] agree nor disagree: 8884	[3] agree nor disagree	3	EVS	e_pc_adop	3
v154	homosexual couples - adopt children	9	[4] disagree: 17484	[4] disagree	4	EVS	e_pc_adop	4
v154	homosexual couples - adopt children	10	[5] disagree strongly: 21728	[5] disagree strongly	5	EVS	e_pc_adop	5

Optional

It might be useful to export the crosswalk table as a spreadsheet since it serves as a reference document that visualises the connection between the source and harmonised data set.

write.xlsx(evs_cwt, paste("data/",  target_var_hrms, "_cwt_evs.xlsx", sep = ''))

1.6. Recode survey data using the crosswalk table

To be able to merge survey data with the crosswalk table information, we first need to convert the wide data format to a long data format.

evs_small_long <- evs_small %>% 
  mutate(across(everything(), as.character)) %>% 
  pivot_longer(cols = c(v154, c_abrv),
               names_to = "var_source", values_to = "value_source")

This is how the first six rows of the evs_small_long should look like:

id_cocas	weight_g	caseid	var_source	value_source
200800800001	0.962336045594876	1	v154	-1
200800800001	0.962336045594876	1	c_abrv	AL
200800800002	0.761706287022894	2	v154	-1
200800800002	0.761706287022894	2	c_abrv	AL
200800800003	0.761706287022894	3	v154	5
200800800003	0.761706287022894	3	c_abrv	AL

Now we use left_join() to recode source values according to the crosswalk table, evs_cwt.

evs_hrmsd <- evs_small_long %>%
  left_join(evs_cwt, by = c("value_source" = "value_code", "var_source")) %>% 
  select(-c(npos, value_n)) %>% 
  mutate(target_value = as.character(target_value)) %>% 
  pivot_wider(
    id_cols = c(id_cocas, weight_g, caseid, survey),    # Columns to keep as unique identifiers
    names_from = target_var,      # Column that will become the new column names
    values_from = target_value         # Column containing the values for those new columns
  ) %>% pivot_longer(
    cols = all_of(target_var_hrms), 
    names_to = "target_var", values_to = "target_value") %>% 
  rename(caseID_source = id_cocas)

Now, EVS 2008 data is ready for harmonisation. This is how the first six rows of the evs_hrmsd should look like:

caseID_source	weight_g	caseid	survey	country_source	target_var	target_value
200800800001	0.962336045594876	1	EVS	AL	e_pc_adop	NA
200800800002	0.761706287022894	2	EVS	AL	e_pc_adop	NA
200800800003	0.761706287022894	3	EVS	AL	e_pc_adop	5
200800800004	1.06428124468104	4	EVS	AL	e_pc_adop	5
200800800005	0.985901445634828	5	EVS	AL	e_pc_adop	3
200800800006	0.761706287022894	6	EVS	AL	e_pc_adop	4

Optional

At this step, the prepared data can be exported as an Rdata file and saved for later re-use.

saveRDS(evs_hrmsd, paste("data/",  target_var_hrms, "_evs.rds", sep = ''))

2. Prepare Data: ESS 2016

We will follow the same steps used for preparing the EVS 2008 item. As expected, EVS and ESS data have differences, and they are taken into account in implementing these steps.

2.1. Import data

ess16 <- haven::read_sav("data/ESS8e02_3.sav", user_na=TRUE)

Check the source item

Use this output to compare it with your output. Check especially if the labels match.

ess16 %>% count(hmsacld)

# A tibble: 8 × 2
  hmsacld                            n
  <dbl+lbl>                      <int>
1 1 [Agree strongly]              8879
2 2 [Agree]                      10197
3 3 [Neither agree nor disagree]  6959
4 4 [Disagree]                    8079
5 5 [Disagree strongly]           8823
6 7 (NA) [Refusal]                 322
7 8 (NA) [Don't know]             1123
8 9 (NA) [No answer]                 5

2.2. Create codebook

Protocol variables selected for this harmonisation are the source data’s country, weight, and ID variables. Note that there are two weight variables: design and post-stratification weights. Both of them are included.

codebook_ess16 <- create_codebook_short(ess16) %>%
  mutate(survey = "ESS",
         target_var = ifelse(var_source == "cntry", "country_source", NA),
         target_var = ifelse(grepl("Design weight", var_label_source), "w1_design", target_var),
         target_var = ifelse(grepl("Post-stratification weight", var_label_source), "w2_poststr", target_var),
         target_var = ifelse(grepl("unified respondent number|Respondent's identification number|ID SERIAL NUMBER|RESPONDENT ID", var_label_source, perl = TRUE), "caseID_source", target_var),
         target_var = ifelse(grepl("Gay and lesbian couples right to adopt children", var_label_source), 
                             target_var_hrms, # Find NAME FOR THE TARGET VARIABLE to be harmonised
                             target_var),
         target_var = gsub(" ", "_", target_var),
         target_var = tolower(target_var))

The variable names which will be used in the harmonised data are listed under target_var. The same names are used except for the weight variables because there are two weight variables.

var_source	var_label_source	survey	target_var
idno	Respondent's identification number	ESS	caseid_source
cntry	Country	ESS	country_source
dweight	Design weight	ESS	w1_design
pspwght	Post-stratification weight including design weight	ESS	w2_poststr
hmsacld	Gay and lesbian couples right to adopt children	ESS	e_pc_adop

2.3. Retrieve names of the chosen source variables

variables <- codebook_ess16 %>% drop_na(target_var) %>% pull(var_source)

The retrieved variables are: idno, cntry, dweight, pspwght, hmsacld.

2.4. Select a subset of the survey data containing only the chosen variables

ess16_small <- ess16 %>% select(all_of(variables)) %>% mutate(caseid = row_number())

You will see that the following data set is a subset of the ESS 2016 with the chosen variables for the harmonised data. The first six rows of the dataset are as follows:

idno	cntry	dweight	pspwght	hmsacld	caseid
1	AT	0.6116771	1.1784955	1	1
2	AT	1.2233541	0.8994715	1	2
4	AT	0.3890577	0.3157533	3	3
6	AT	0.6425940	0.4724674	2	4
10	AT	3.4324021	2.2467055	1	5
11	AT	1.1805793	1.0113790	4	6

2.5. Create a crosswalk table for the chosen variables

Similar to what was implemented in step 1.5, weight and ID variables are excluded from the crosswalk table. Only country and the substantive variable are used for creating the crosswalk table. Original codes for the country variable are preserved. For the substantive variable, “NA” is assigned to missing values; the other values are kept identical.

ess16_cwt <- create_cwt(ess16_small %>% select(cntry, hmsacld)) %>%
  left_join(codebook_ess16, by = join_by(var_source, var_label_source)) %>%
  mutate(target_value = NA,
         target_value = ifelse(!grepl("know|efusal|answer|NA|applicable|Can't choose|]DK|] DK|Na", value_label_source),
                               value_code, 
                               target_value),
         value_code = as.character(value_code)
  )

Important

The survey-specific missing values are recoded into the single missing value “NA”.

Let’s look at the correspondence between e_pc_adop and hmsacld:

ess16_cwt %>% filter(var_source == "hmsacld") %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

var_source	var_label_source	npos	value_n	value_label_source	value_code	survey	target_var	target_value
hmsacld	Gay and lesbian couples right to adopt children	1	[1] Agree strongly: 8879	[1] Agree strongly	1	ESS	e_pc_adop	1
hmsacld	Gay and lesbian couples right to adopt children	2	[2] Agree: 10197	[2] Agree	2	ESS	e_pc_adop	2
hmsacld	Gay and lesbian couples right to adopt children	3	[3] Neither agree nor disagree: 6959	[3] Neither agree nor disagree	3	ESS	e_pc_adop	3
hmsacld	Gay and lesbian couples right to adopt children	4	[4] Disagree: 8079	[4] Disagree	4	ESS	e_pc_adop	4
hmsacld	Gay and lesbian couples right to adopt children	5	[5] Disagree strongly: 8823	[5] Disagree strongly	5	ESS	e_pc_adop	5
hmsacld	Gay and lesbian couples right to adopt children	6	[7] Refusal: 322	[7] Refusal	7	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	7	[8] Don't know: 1123	[8] Don't know	8	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	8	[9] No answer: 5	[9] No answer	9	ESS	e_pc_adop	NA

Optional

It might be useful to export the crosswalk table as a spreadsheet since it serves as a reference document that visualises the connection between the source and harmonised data set.

write.xlsx(ess16_cwt, paste("data/",  target_var_hrms, "_cwt_ess16.xlsx", sep = ''))

2.6. Recode survey data using the crosswalk table

We first convert the wide data format to a long data format, and then merge survey data with the crosswalk table information.

ess16_small_long <- ess16_small %>%
  mutate(across(everything(), as.character)) %>%
  pivot_longer(cols = c(hmsacld, cntry),
               names_to = "var_source", values_to = "value_source")

This is how the first six rows of the ess16_small_long should look like:

idno	dweight	pspwght	caseid	var_source	value_source
1	0.611677050590515	1.17849552631378	1	hmsacld	1
1	0.611677050590515	1.17849552631378	1	cntry	AT
2	1.22335410118103	0.899471521377563	2	hmsacld	1
2	1.22335410118103	0.899471521377563	2	cntry	AT
4	0.389057725667953	0.31575334072113	3	hmsacld	3
4	0.389057725667953	0.31575334072113	3	cntry	AT

Now we use left_join() to recode source values according to the crosswalk table, ess16_cwt.

ess16_hrmsd <- ess16_small_long %>%
  left_join(ess16_cwt, by = c("value_source" = "value_code", "var_source")) %>% 
  select(-c(npos, value_n)) %>% 
  mutate(target_value = as.character(target_value)) %>% 
  pivot_wider(
    id_cols = c(dweight, pspwght, idno, caseid, survey),    # Columns to keep as unique identifiers
    names_from = target_var,      # Column that will become the new column names
    values_from = target_value         # Column containing the values for those new columns
  ) %>% pivot_longer(
    cols = all_of(target_var_hrms), 
    names_to = "target_var", values_to = "target_value") %>% 
  rename(caseID_source = idno)

ESS 2016 data is ready for harmonisation. This is how the first six rows of the ess16_hrmsd should look like:

head(ess16_hrmsd) %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

dweight	pspwght	caseID_source	caseid	survey	country_source	target_var	target_value
0.611677050590515	1.17849552631378	1	1	ESS	AT	e_pc_adop	1
1.22335410118103	0.899471521377563	2	2	ESS	AT	e_pc_adop	1
0.389057725667953	0.31575334072113	4	3	ESS	AT	e_pc_adop	3
0.64259397983551	0.472467392683029	6	4	ESS	AT	e_pc_adop	2
3.43240213394165	2.24670553207397	10	5	ESS	AT	e_pc_adop	1
1.18057930469513	1.01137900352478	11	6	ESS	AT	e_pc_adop	4

Optional

At this step, the prepared data can be exported as an Rdata file and saved for later re-use.

saveRDS(ess16_hrmsd, paste("data/",  target_var_hrms, "_ess16.rds", sep = ''))

3. Prepare Data: ESS 2018

We will follow the same steps used before.

3.1. Import data

ess18 <- haven::read_sav("data/ESS9e03_2.sav", user_na=TRUE)

Check the source item

Use this output to compare it with your output. Check especially if the labels match.

ess18 %>% count(hmsacld)

# A tibble: 8 × 2
  hmsacld                            n
  <dbl+lbl>                      <int>
1 1 [Agree strongly]              9884
2 2 [Agree]                      10804
3 3 [Neither agree nor disagree]  7837
4 4 [Disagree]                    8845
5 5 [Disagree strongly]          10136
6 7 (NA) [Refusal]                 421
7 8 (NA) [Don't know]             1589
8 9 (NA) [No answer]                 3

3.2. Create codebook

codebook_ess18 <- create_codebook_short(ess18) %>%
  mutate(survey = "ESS",
         target_var = ifelse(var_source == "cntry", "country_source", NA),
         target_var = ifelse(grepl("Design weight", var_label_source), "w1_design", target_var),
         target_var = ifelse(grepl("Post-stratification weight", var_label_source), "w2_poststr", target_var),
         target_var = ifelse(grepl("unified respondent number|Respondent's identification number|ID SERIAL NUMBER|RESPONDENT ID", var_label_source, perl = TRUE), "caseID_source", target_var),
         target_var = ifelse(grepl("Gay and lesbian couples right to adopt children", var_label_source), 
                             target_var_hrms, # Find NAME FOR THE TARGET VARIABLE to be harmonised
                             target_var),
         target_var = gsub(" ", "_", target_var),
         target_var = tolower(target_var))

The variable names which will be used in the harmonised data are listed under the target_var. The same names are used except for the weight variables because there are two weight variables.

var_source	var_label_source	survey	target_var
idno	Respondent's identification number	ESS	caseid_source
cntry	Country	ESS	country_source
dweight	Design weight	ESS	w1_design
pspwght	Post-stratification weight including design weight	ESS	w2_poststr
hmsacld	Gay and lesbian couples right to adopt children	ESS	e_pc_adop

3.3. Retrieve names of the chosen source variables

variables <- codebook_ess18 %>% drop_na(target_var) %>% pull(var_source)

The retrieved variables are: idno, cntry, dweight, pspwght, hmsacld.

3.4. Select a subset of the survey data containing only the chosen variables

ess18_small <- ess18 %>% select(all_of(variables)) %>% mutate(caseid = row_number())

You will see that the following data set is a subset of the ESS 2018 with the chosen variables for the harmonised data. The first six rows of the dataset are as follows:

idno	cntry	dweight	pspwght	hmsacld	caseid
27	AT	0.5811743	0.2181114	1	1
137	AT	1.0627724	0.4134733	4	2
194	AT	1.3765086	2.2702928	3	3
208	AT	0.9933993	0.3864834	1	4
220	AT	0.3773534	1.0321022	4	5
254	AT	1.4793528	0.5755447	3	6

3.5. Create a crosswalk table for the chosen variables

pattern_na <- "know|efusal|answer|NA|applicable|not asked| missing|]DK|] DK"

ess18_cwt <- create_cwt(ess18_small %>% select(cntry, hmsacld)) %>% 
  left_join(codebook_ess18, by = join_by(var_source, var_label_source)) %>% 
  mutate(target_value = NA,
        target_value = ifelse(!grepl(pattern_na, value_label_source), value_code, target_value),
         value_code = as.character(value_code))

Important

Missing values are recoded into the single missing value “NA”.

Let’s look at the correspondence between e_pc_adop and hmsacld:

ess18_cwt %>% filter(var_source == "hmsacld") %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

var_source	var_label_source	npos	value_n	value_label_source	value_code	survey	target_var	target_value
hmsacld	Gay and lesbian couples right to adopt children	1	[1] Agree strongly: 9884	[1] Agree strongly	1	ESS	e_pc_adop	1
hmsacld	Gay and lesbian couples right to adopt children	2	[2] Agree: 10804	[2] Agree	2	ESS	e_pc_adop	2
hmsacld	Gay and lesbian couples right to adopt children	3	[3] Neither agree nor disagree: 7837	[3] Neither agree nor disagree	3	ESS	e_pc_adop	3
hmsacld	Gay and lesbian couples right to adopt children	4	[4] Disagree: 8845	[4] Disagree	4	ESS	e_pc_adop	4
hmsacld	Gay and lesbian couples right to adopt children	5	[5] Disagree strongly: 10136	[5] Disagree strongly	5	ESS	e_pc_adop	5
hmsacld	Gay and lesbian couples right to adopt children	6	[7] Refusal: 421	[7] Refusal	7	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	7	[8] Don't know: 1589	[8] Don't know	8	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	8	[9] No answer: 3	[9] No answer	9	ESS	e_pc_adop	NA

Optional

It might be useful to export the crosswalk table as a spreadsheet since it serves as a reference document that visualises the connection between the source and harmonised data set.

write.xlsx(ess18_cwt, paste("data/",  target_var_hrms, "_cwt_ess18.xlsx", sep = ''))

3.6. Recode survey data using the crosswalk table

We first convert the wide data format to a long data format, and then merge survey data with the crosswalk table information.

ess18_small_long <- ess18_small %>%
  mutate(across(everything(), as.character)) %>%
  pivot_longer(cols = c(hmsacld, cntry),
               names_to = "var_source", values_to = "value_source")

This is how the first six rows of the ess18_small_long should look like:

idno	dweight	pspwght	caseid	var_source	value_source
27	0.581174314022064	0.218111366033554	1	hmsacld	1
27	0.581174314022064	0.218111366033554	1	cntry	AT
137	1.06277239322662	0.413473278284073	2	hmsacld	4
137	1.06277239322662	0.413473278284073	2	cntry	AT
194	1.37650859355927	2.27029275894165	3	hmsacld	3
194	1.37650859355927	2.27029275894165	3	cntry	AT

Now we use left_join() to recode source values according to the crosswalk table, ess18_cwt.

ess18_hrmsd <- ess18_small_long %>% 
  left_join(ess18_cwt, by = c("value_source" = "value_code", "var_source")) %>% 
  select(-npos, -value_n) %>% 
  mutate(target_value = as.character(target_value)) %>% 
  pivot_wider(id_cols = c(idno, ends_with("weight"), pspwght, caseid, survey),
              names_from = target_var,
              values_from = target_value) %>% 
  pivot_longer(cols = all_of(target_var_hrms),
               names_to = "target_var", values_to = "target_value") %>% 
  rename(caseID_source = idno)

ESS 2018 data is ready for harmonisation. This is how the first six rows of the ess18_hrmsd should look like:

head(ess18_hrmsd) %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

caseID_source	dweight	pspwght	caseid	survey	country_source	target_var	target_value
27	0.581174314022064	0.218111366033554	1	ESS	AT	e_pc_adop	1
137	1.06277239322662	0.413473278284073	2	ESS	AT	e_pc_adop	4
194	1.37650859355927	2.27029275894165	3	ESS	AT	e_pc_adop	3
208	0.993399322032928	0.386483430862427	4	ESS	AT	e_pc_adop	1
220	0.377353370189667	1.032102227211	5	ESS	AT	e_pc_adop	4
254	1.47935283184052	0.575544655323029	6	ESS	AT	e_pc_adop	3

Optional

At this step, the prepared data can be exported as an Rdata file and saved for later re-use.

saveRDS(ess18_hrmsd, paste("data/",  target_var_hrms, "_ess18.rds", sep = ''))

4. Prepare Data: ESS 2020

We will follow the same steps used before.

4.1. Import data

ess20 <- haven::read_sav("data/ESS10.sav", user_na=TRUE)

Check the source item

Use this output to compare it with your output. Check especially if the labels match.

ess20 %>% count(hmsacld)

# A tibble: 8 × 2
  hmsacld                            n
  <dbl+lbl>                      <int>
1 1 [Agree strongly]              7740
2 2 [Agree]                       7727
3 3 [Neither agree nor disagree]  6584
4 4 [Disagree]                    6514
5 5 [Disagree strongly]           7766
6 7 (NA) [Refusal]                 270
7 8 (NA) [Don't know]              894
8 9 (NA) [No answer]               116

4.2. Create codebook

codebook_ess20 <- create_codebook_short(ess20) %>%
  mutate(survey = "ESS",
         target_var = ifelse(var_source == "cntry", "country_source", NA),
         target_var = ifelse(grepl("Design weight", var_label_source), "w1_design", target_var),
         target_var = ifelse(grepl("Post-stratification weight", var_label_source), "w2_poststr", target_var),
         target_var = ifelse(grepl("unified respondent number|Respondent's identification number|ID SERIAL NUMBER|RESPONDENT ID", var_label_source, perl = TRUE), "caseID_source", target_var),
         target_var = ifelse(grepl("Gay and lesbian couples right to adopt children", var_label_source), 
                             target_var_hrms, # Find NAME FOR THE TARGET VARIABLE to be harmonised
                             target_var),
         target_var = gsub(" ", "_", target_var),
         target_var = tolower(target_var))

The variable names which will be used in the harmonised data are listed under target_var. The same names are used except for the weight variables because there are two weight variables.

var_source	var_label_source	survey	target_var
idno	Respondent's identification number	ESS	caseid_source
cntry	Country	ESS	country_source
dweight	Design weight	ESS	w1_design
pspwght	Post-stratification weight including design weight	ESS	w2_poststr
hmsacld	Gay and lesbian couples right to adopt children	ESS	e_pc_adop

4.3. Retrieve names of the chosen source variables

variables <- codebook_ess20 %>% drop_na(target_var) %>% pull(var_source)

The retrieved variables are: idno, cntry, dweight, pspwght, hmsacld.

4.4. Select a subset of the survey data containing only the chosen variables

ess20_small <- ess20 %>% select(all_of(variables)) %>% mutate(caseid = row_number())

You will see that the following data set is a subset of the ESS 2020 with the chosen variables for the harmonised data. The first six rows of the dataset are as follows:

idno	cntry	dweight	pspwght	hmsacld	caseid
10038	BE	0.8822203	0.9722756	1	1
10053	BE	1.0476425	0.8886350	3	2
10055	BE	1.0877413	0.7228110	2	3
10062	BE	0.9099100	1.0055653	3	4
10064	BE	0.9189489	0.6387054	1	5
10077	BE	0.9137402	0.7061659	2	6

4.5. Create a crosswalk table for the chosen variables

  ess20_cwt <- create_cwt(ess20_small %>% select(cntry,  hmsacld)) %>%
  left_join(codebook_ess20, by = join_by(var_source, var_label_source)) %>%
  mutate(target_value = NA,
         target_value = ifelse(!grepl("know|efusal|answer|NA|applicable|Can't choose|]DK|] DK|Na", value_label_source), 
                               value_code, 
                               target_value),
         value_code = as.character(value_code)
  )

Important

The survey-specific missing values are recoded into the single missing value “NA”.

Let’s look at the correspondence between e_pc_adop and hmsacld:

ess20_cwt %>% filter(var_source == "hmsacld") %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

var_source	var_label_source	npos	value_n	value_label_source	value_code	survey	target_var	target_value
hmsacld	Gay and lesbian couples right to adopt children	1	[1] Agree strongly: 7740	[1] Agree strongly	1	ESS	e_pc_adop	1
hmsacld	Gay and lesbian couples right to adopt children	2	[2] Agree: 7727	[2] Agree	2	ESS	e_pc_adop	2
hmsacld	Gay and lesbian couples right to adopt children	3	[3] Neither agree nor disagree: 6584	[3] Neither agree nor disagree	3	ESS	e_pc_adop	3
hmsacld	Gay and lesbian couples right to adopt children	4	[4] Disagree: 6514	[4] Disagree	4	ESS	e_pc_adop	4
hmsacld	Gay and lesbian couples right to adopt children	5	[5] Disagree strongly: 7766	[5] Disagree strongly	5	ESS	e_pc_adop	5
hmsacld	Gay and lesbian couples right to adopt children	6	[7] Refusal: 270	[7] Refusal	7	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	7	[8] Don't know: 894	[8] Don't know	8	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	8	[9] No answer: 116	[9] No answer	9	ESS	e_pc_adop	NA

Optional

It might be useful to export the crosswalk table as a spreadsheet since it serves as a reference document that visualises the connection between the source and harmonised data set.

write.xlsx(ess20_cwt, paste("data/",  target_var_hrms, "_cwt_ess20.xlsx", sep = ''))

4.6. Recode survey data using the crosswalk table

We first convert the wide data format to a long data format, and then merge survey data with the crosswalk table information.

ess20_small_long <- ess20_small %>%
  mutate(across(everything(), as.character)) %>%
  pivot_longer(cols = c(hmsacld, cntry),
               names_to = "var_source", values_to = "value_source")

This is how the first six rows of the ess20_small_long should look like:

idno	dweight	pspwght	caseid	var_source	value_source
10038	0.882220268249512	0.97227555513382	1	hmsacld	1
10038	0.882220268249512	0.97227555513382	1	cntry	BE
10053	1.04764246940613	0.888634979724884	2	hmsacld	3
10053	1.04764246940613	0.888634979724884	2	cntry	BE
10055	1.08774125576019	0.722811043262482	3	hmsacld	2
10055	1.08774125576019	0.722811043262482	3	cntry	BE

Now we use left_join() to recode source values according to the crosswalk table, ess20_cwt.

ess20_hrmsd <- ess20_small_long %>%
  left_join(ess20_cwt, by = c("value_source" = "value_code", "var_source")) %>% 
  select(-c(npos, value_n)) %>% 
  mutate(target_value = as.character(target_value)) %>% 
  pivot_wider(
    id_cols = c(dweight, pspwght, idno, caseid, survey),    # Columns to keep as unique identifiers
    names_from = target_var,      # Column that will become the new column names
    values_from = target_value         # Column containing the values for those new columns
  ) %>% pivot_longer(
    cols = all_of(target_var_hrms), 
    names_to = "target_var", values_to = "target_value") %>% 
  rename(caseID_source = idno)

ESS 2020 data is ready for harmonisation. This is how the first six rows of the ess20_hrmsd should look like:

head(ess20_hrmsd) %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

dweight	pspwght	caseID_source	caseid	survey	country_source	target_var	target_value
0.882220268249512	0.97227555513382	10038	1	ESS	BE	e_pc_adop	1
1.04764246940613	0.888634979724884	10053	2	ESS	BE	e_pc_adop	3
1.08774125576019	0.722811043262482	10055	3	ESS	BE	e_pc_adop	2
0.909909963607788	1.00556528568268	10062	4	ESS	BE	e_pc_adop	3
0.918948888778687	0.638705432415009	10064	5	ESS	BE	e_pc_adop	1
0.913740217685699	0.706165909767151	10077	6	ESS	BE	e_pc_adop	2

Optional

At this step, the prepared data can be exported as an Rdata file and saved for later re-use.

saveRDS(ess20_hrmsd, paste("data/",  target_var_hrms, "_ess20.rds", sep = ''))

5. Prepare Data: ESS 2023

Again, we will follow the same steps used before.

5.1. Import data

ess23 <- haven::read_sav("data/ESS11.sav", user_na=TRUE)

Check the source item

Use this output to compare it with your output. Check especially if the labels match.

ess23 %>% count(hmsacld)

# A tibble: 8 × 2
  hmsacld                            n
  <dbl+lbl>                      <int>
1 1 [Agree strongly]              5738
2 2 [Agree]                       5868
3 3 [Neither agree nor disagree]  3441
4 4 [Disagree]                    3412
5 5 [Disagree strongly]           3061
6 7 (NA) [Refusal]                 142
7 8 (NA) [Don't know]              526
8 9 (NA) [No answer]                 2

5.2. Create codebook

codebook_ess23 <- create_codebook_short(ess23) %>%
  mutate(survey = "ESS",
         target_var = ifelse(var_source == "cntry", "country_source", NA),
         target_var = ifelse(grepl("Design weight", var_label_source), "w1_design", target_var),
         target_var = ifelse(var_source == "pweight", "w2_poststr", target_var),
         target_var = ifelse(grepl("unified respondent number|Respondent's identification number|ID SERIAL NUMBER|RESPONDENT ID", var_label_source, perl = TRUE), "caseID_source", target_var),
         target_var = ifelse(grepl("Gay and lesbian couples right to adopt children", var_label_source), 
                             target_var_hrms, # Find NAME FOR THE TARGET VARIABLE to be harmonised
                             target_var),
         target_var = gsub(" ", "_", target_var),
         target_var = tolower(target_var))

The variable names which will be used in the harmonised data are listed under target_var. The same names are used except for the weight variables because there are two weight variables.

var_source	var_label_source	survey	target_var
idno	Respondent's identification number	ESS	caseid_source
cntry	Country	ESS	country_source
dweight	Design weight	ESS	w1_design
pweight	Population size weight (must be combined with dweight or pspwght)	ESS	w2_poststr
hmsacld	Gay and lesbian couples right to adopt children	ESS	e_pc_adop

5.3. Retrieve names of the chosen source variables

variables <- codebook_ess23 %>% drop_na(target_var) %>% pull(var_source)

The retrieved variables are: idno, cntry, dweight, pweight, hmsacld.

5.4. Select a subset of the survey data containing only the chosen variables

ess23_small <- ess23 %>% select(all_of(variables)) %>% mutate(caseid = row_number())

You will see that the following data set is a subset of the ESS 2023 with the chosen variables for the harmonised data. The first six rows of the dataset are as follows:

idno	cntry	dweight	pweight	hmsacld	caseid
50014	AT	1.1851145	0.3309145	3	1
50030	AT	0.6098981	0.3309145	1	2
50057	AT	1.3923296	0.3309145	1	3
50106	AT	0.5560615	0.3309145	3	4
50145	AT	0.7227953	0.3309145	2	5
50158	AT	0.9926053	0.3309145	2	6

5.5. Create a crosswalk table for the chosen variables

ess23_cwt <- create_cwt(ess23_small %>% select(cntry, hmsacld)) %>%
  left_join(codebook_ess23, by = join_by(var_source, var_label_source)) %>%
  mutate(target_value = NA,
         target_value = ifelse(!grepl("know|efusal|answer|NA|applicable|Can't choose|]DK|] DK|Na", value_label_source), 
                               value_code, 
                               target_value),
         value_code = as.character(value_code)
  )

Important

The survey-specific missing values are recoded into the single missing value “NA”.

Let’s look at the correspondence between e_pc_adop and hmsacld:

ess23_cwt %>% filter(var_source == "hmsacld") %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

var_source	var_label_source	npos	value_n	value_label_source	value_code	survey	target_var	target_value
hmsacld	Gay and lesbian couples right to adopt children	1	[1] Agree strongly: 5738	[1] Agree strongly	1	ESS	e_pc_adop	1
hmsacld	Gay and lesbian couples right to adopt children	2	[2] Agree: 5868	[2] Agree	2	ESS	e_pc_adop	2
hmsacld	Gay and lesbian couples right to adopt children	3	[3] Neither agree nor disagree: 3441	[3] Neither agree nor disagree	3	ESS	e_pc_adop	3
hmsacld	Gay and lesbian couples right to adopt children	4	[4] Disagree: 3412	[4] Disagree	4	ESS	e_pc_adop	4
hmsacld	Gay and lesbian couples right to adopt children	5	[5] Disagree strongly: 3061	[5] Disagree strongly	5	ESS	e_pc_adop	5
hmsacld	Gay and lesbian couples right to adopt children	6	[7] Refusal: 142	[7] Refusal	7	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	7	[8] Don't know: 526	[8] Don't know	8	ESS	e_pc_adop	NA
hmsacld	Gay and lesbian couples right to adopt children	8	[9] No answer: 2	[9] No answer	9	ESS	e_pc_adop	NA

Optional

It might be useful to export the crosswalk table as a spreadsheet since it serves as a reference document that visualises the connection between the source and harmonised data set.

write.xlsx(ess23_cwt, paste("data/",  target_var_hrms, "_cwt_ess23.xlsx", sep = ''))

5.6. Recode survey data using the crosswalk table

We first convert the wide data format to a long data format, and then merge survey data with the crosswalk table information.

ess23_small_long <- ess23_small %>%
  mutate(across(everything(), as.character)) %>%
  pivot_longer(cols = c(hmsacld, cntry),
               names_to = "var_source", values_to = "value_source")

This is how the first six rows of the ess23_small_long should look like:

idno	dweight	pweight	caseid	var_source	value_source
50014	1.1851145029068	0.330914527177811	1	hmsacld	3
50014	1.1851145029068	0.330914527177811	1	cntry	AT
50030	0.609898090362549	0.330914527177811	2	hmsacld	1
50030	0.609898090362549	0.330914527177811	2	cntry	AT
50057	1.39232957363129	0.330914527177811	3	hmsacld	1
50057	1.39232957363129	0.330914527177811	3	cntry	AT

Now we use left_join() to recode source values according to the crosswalk table, ess23_cwt.

ess23_hrmsd <- ess23_small_long %>%
  left_join(ess23_cwt, by = c("value_source" = "value_code", "var_source")) %>% 
  select(-c(npos, value_n)) %>% 
  mutate(target_value = as.character(target_value)) %>% 
  pivot_wider(
    id_cols = c(dweight, pweight, idno, caseid, survey),    # Columns to keep as unique identifiers
    names_from = target_var,      # Column that will become the new column names
    values_from = target_value         # Column containing the values for those new columns
  ) %>% pivot_longer(
    cols = all_of(target_var_hrms), 
    names_to = "target_var", values_to = "target_value") %>% 
  rename(caseID_source = idno)

ESS 2023 data is ready for harmonisation. This is how the first six rows of the ess23_hrmsd should look like:

head(ess23_hrmsd) %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

dweight	pweight	caseID_source	caseid	survey	country_source	target_var	target_value
1.1851145029068	0.330914527177811	50014	1	ESS	AT	e_pc_adop	3
0.609898090362549	0.330914527177811	50030	2	ESS	AT	e_pc_adop	1
1.39232957363129	0.330914527177811	50057	3	ESS	AT	e_pc_adop	1
0.556061506271362	0.330914527177811	50106	4	ESS	AT	e_pc_adop	3
0.722795307636261	0.330914527177811	50145	5	ESS	AT	e_pc_adop	2
0.992605328559875	0.330914527177811	50158	6	ESS	AT	e_pc_adop	2

Optional

At this step, the prepared data can be exported as an Rdata file and saved for later re-use.

saveRDS(ess23_hrmsd, paste("data/",  target_var_hrms, "_ess23.rds", sep = ''))

6. Harmonisation

With the following steps we will create the harmonised Support for Homosexuals Adoption Rights data.

6.1. Standardise country names & apply labels

There are functions specifically written to standardise country names and apply labels. Please download the R file containing these functions here . Then save this file in the R project folder and import it into the R project via source("func_recoded_country.R").

How-to

If you cannot download the R-file, please open a new file in your R-Studio project under File -> New File -> R Script and copy the text from the func_recoded_country-file into this new file. Make sure that you save this new file in your project folder under the name “func_recoded_country.R”.

source("func_recoded_country.R")

evs_1 <- evs_hrmsd %>% 
  recoded_country("country_source") %>%
  country_new_labels("country_new") %>%
  func_country_sample("country_source") %>%
  country_source_recoded_labels("country_source_recoded") 

ess16_1 <- ess16_hrmsd %>% 
  recoded_country("country_source") %>%
  country_new_labels("country_new") %>%
  func_country_sample("country_source") %>%
  country_source_recoded_labels("country_source_recoded")

ess18_1 <- ess18_hrmsd %>% 
  recoded_country("country_source") %>%
  country_new_labels("country_new") %>%
  func_country_sample("country_source") %>%
  country_source_recoded_labels("country_source_recoded")

ess20_1 <- ess20_hrmsd %>% 
  recoded_country("country_source") %>%
  country_new_labels("country_new") %>%
  func_country_sample("country_source") %>%
  country_source_recoded_labels("country_source_recoded")

ess23_1 <- ess23_hrmsd %>% 
  recoded_country("country_source") %>%
  country_new_labels("country_new") %>%
  func_country_sample("country_source") %>%
  country_source_recoded_labels("country_source_recoded")

Check

You can run the following code to check the country variable:

table(evs_hrmsd$country_source, evs_1$country_new)

6.2. Weights

For details on decisions and procedures for including weight variables, please see the Make It Equal! data documentation page, section Weight Variables.

evs_2 <- evs_1 %>% mutate(weight_g = ifelse(weight_g == 0,1,weight_g))
evs_fnl <- evs_2  %>% mutate(w1_design = weight_g,
                             w2_poststr = weight_g, 
                             w3_combined = as.numeric(w2_poststr))

ess16_fnl <- ess16_1 %>% mutate(w1_design = dweight,
                            w2_poststr = pspwght, 
                            w3_combined = as.numeric(w2_poststr))

ess18_fnl <- ess18_1 %>% mutate(w1_design = dweight,
                            w2_poststr = pspwght, 
                            w3_combined = as.numeric(w2_poststr))

ess20_fnl <- ess20_1 %>% mutate(w1_design = dweight,
                            w2_poststr = pspwght, 
                            w3_combined = as.numeric(w2_poststr))

ess23_fnl <- ess23_1 %>% mutate(w1_design = dweight,
                            w2_poststr = pweight, 
                            w3_combined = as.numeric(w2_poststr))

6.3. Import additional information about the source data sets

This file, including additional information, can be downloaded here . Please save it into the “data” folder you generated previously.

add_data <- read_excel("data/from_doi_links_equal.xlsx") %>% 
  select(-studyno_source, -wave)

6.4. Combine all data

commons <- c("caseID_source", "caseid", "survey",
             "target_var","target_value","year", "country_new", "country_source_recoded",
             "w1_design", "w2_poststr", "w3_combined")

evs_fnl$year <- "2008"
ess16_fnl$year <- "2016"
ess18_fnl$year <- "2018"
ess20_fnl$year <- "2020"
ess23_fnl$year <- "2023"

all_comb <- bind_rows(ess16_fnl[, commons], 
                      ess18_fnl[, commons],
                      ess20_fnl[, commons],
                      ess23_fnl[, commons],
                      evs_fnl[, commons]
) %>% 
  left_join(add_data, by = c("target_var", "survey", "year")) %>%
  mutate(
    ## variables To Be Added
    studyno_I4NG = NA,
    version_I4NG = NA,
    doi_I4NG = NA,
    caseID_I4NG = paste(survey, year, country_new, caseid, sep = ''),
    #country_year
    country_year = paste(country_new, year, sep = '_'),
    # caseID = NA,
    caseID = row_number(),
    
    # code the lengths of response scales in each project
    npoints = 5 
  ) %>%
  # Rename to avoid SPSS duplicate name error
  rename(caseid_sw = caseid, country_I4NG = country_new)

Check

Let’s check the data set:

head(all_comb) %>%
  kable(row.names = FALSE) %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))

caseID_source	caseid_sw	survey	target_var	target_value	year	country_I4NG	country_source_recoded	w1_design	w2_poststr	w3_combined	title_source	doi_source	studyno_I4NG	version_I4NG	doi_I4NG	caseID_I4NG	country_year	caseID	npoints
1	1	ESS	e_pc_adop	1	2016	AT	AT	0.611677050590515	1.17849552631378	1.1784955	Welfare attitudes, Attitudes to climate change	https://doi.org/10.21338/ess8e02_3	NA	NA	NA	ESS2016AT1	AT_2016	1	5
2	2	ESS	e_pc_adop	1	2016	AT	AT	1.22335410118103	0.899471521377563	0.8994715	Welfare attitudes, Attitudes to climate change	https://doi.org/10.21338/ess8e02_3	NA	NA	NA	ESS2016AT2	AT_2016	2	5
4	3	ESS	e_pc_adop	3	2016	AT	AT	0.389057725667953	0.31575334072113	0.3157533	Welfare attitudes, Attitudes to climate change	https://doi.org/10.21338/ess8e02_3	NA	NA	NA	ESS2016AT3	AT_2016	3	5
6	4	ESS	e_pc_adop	2	2016	AT	AT	0.64259397983551	0.472467392683029	0.4724674	Welfare attitudes, Attitudes to climate change	https://doi.org/10.21338/ess8e02_3	NA	NA	NA	ESS2016AT4	AT_2016	4	5
10	5	ESS	e_pc_adop	1	2016	AT	AT	3.43240213394165	2.24670553207397	2.2467055	Welfare attitudes, Attitudes to climate change	https://doi.org/10.21338/ess8e02_3	NA	NA	NA	ESS2016AT5	AT_2016	5	5
11	6	ESS	e_pc_adop	4	2016	AT	AT	1.18057930469513	1.01137900352478	1.0113790	Welfare attitudes, Attitudes to climate change	https://doi.org/10.21338/ess8e02_3	NA	NA	NA	ESS2016AT6	AT_2016	6	5

count(all_comb, target_var, survey, year)

# A tibble: 5 × 4
  target_var survey year      n
  <chr>      <chr>  <chr> <int>
1 e_pc_adop  ESS    2016  44387
2 e_pc_adop  ESS    2018  49519
3 e_pc_adop  ESS    2020  37611
4 e_pc_adop  ESS    2023  22190
5 e_pc_adop  EVS    2008  66280

6.5. Find equivalents of the response categories

Response categories are the same across source data. Therefore, no transformation is applied.

all_hrmsd <- all_comb %>%
  mutate(value_hrmsd = target_value) %>%
  rename(var_hrmsd = target_var) %>%
  select(-var_hrmsd, -value_hrmsd, var_hrmsd, value_hrmsd, -caseID, caseID, 
         -ends_with("_source"), ends_with("_source"), -target_value, -npoints)%>%
  pivot_wider(
    id_cols = c(doi_source, title_source, caseID_source, caseid_sw, survey, country_I4NG, country_source_recoded, year, country_year, 
                w1_design, w2_poststr, w3_combined, caseID_I4NG),    
    names_from = var_hrmsd,      # Column that will become the new column names
    values_from = value_hrmsd # Column containing the values for those new columns
    )

Now the harmonised Support for Homosexuals Adoption Rights data are ready. This is how the first six rows of the all_hrmsd should look like:

doi_source	title_source	caseID_source	caseid_sw	survey	country_I4NG	country_source_recoded	year	country_year	w1_design	w2_poststr	w3_combined	caseID_I4NG	e_pc_adop
https://doi.org/10.21338/ess8e02_3	Welfare attitudes, Attitudes to climate change	1	1	ESS	AT	AT	2016	AT_2016	0.611677050590515	1.17849552631378	1.1784955	ESS2016AT1	1
https://doi.org/10.21338/ess8e02_3	Welfare attitudes, Attitudes to climate change	2	2	ESS	AT	AT	2016	AT_2016	1.22335410118103	0.899471521377563	0.8994715	ESS2016AT2	1
https://doi.org/10.21338/ess8e02_3	Welfare attitudes, Attitudes to climate change	4	3	ESS	AT	AT	2016	AT_2016	0.389057725667953	0.31575334072113	0.3157533	ESS2016AT3	3
https://doi.org/10.21338/ess8e02_3	Welfare attitudes, Attitudes to climate change	6	4	ESS	AT	AT	2016	AT_2016	0.64259397983551	0.472467392683029	0.4724674	ESS2016AT4	2
https://doi.org/10.21338/ess8e02_3	Welfare attitudes, Attitudes to climate change	10	5	ESS	AT	AT	2016	AT_2016	3.43240213394165	2.24670553207397	2.2467055	ESS2016AT5	1
https://doi.org/10.21338/ess8e02_3	Welfare attitudes, Attitudes to climate change	11	6	ESS	AT	AT	2016	AT_2016	1.18057930469513	1.01137900352478	1.0113790	ESS2016AT6	4

6.6. Export data

# Rdata
saveRDS(all_hrmsd, paste("data/",  target_var_hrms, "_hrmsd.rds", sep = ''))

# SPSS file
write_sav(all_hrmsd, paste("data/",  target_var_hrms, "_hrmsd.sav", sep = ''))

# STATA file
write_dta(all_hrmsd, paste("data/",  target_var_hrms, "_hrmsd.dta", sep = ''))

# CSV file
write.csv(all_hrmsd, paste("data/",  target_var_hrms, "_hrmsd.csv", sep = ''), row.names = F, quote = T)

Summary

Support for Homosexuals Adoption Rights was administered in EVS 2008 and ESS 2016, 2018, 2020 and 2023. Our script, first, prepares these five data sets for harmonisation so that they are consistent in format via codebooks and crosswalk tables. Then, the response categories were aligned. After this step, the item is ready for subsequent analysis!

To go back to the Make it Equal! Toolbox page, please click here!

References

Kołczyńska, M. (2022). Combining multiple survey sources: A reproducible workflow and toolbox for survey data harmonization. Methodological Innovations, 15(1), 62–72. https://doi.org/10.1177/20597991221077923

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

RStudio Team. (2023). RStudio: Integrated development environment for R. https://www.rstudio.com/