Skill-Building Exercise 2: Presenting Patterns Task 1: Abortion Attitudes in American Society A) Data Preparation (Preparing and cleaning the data while creating the new catagories for abortion, politcal views, education and gender)
library(gt)
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## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
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library(fst)
library(scales)##
## Attaching package: 'scales'
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##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factorgss <- read_fst ("gss2022.fst")gss_cleaned <- gss %>%
mutate(
abortion_support = case_when(
abany == "yes" ~ "Support",
abany == "no" ~ "Oppose",
TRUE ~ NA_character_
),
abortion_support = factor(abortion_support,levels = c("Support", "Oppose"))
) %>%
filter(!is.na(abortion_support))
table(gss_cleaned$abortion_support)##
## Support Oppose
## 16626 22628gss_cleaned <- gss_cleaned %>%
mutate(
polviews_triad = case_when(
polviews %in% c("liberal", "extremely liberal", "slightly liberal") ~ "Liberal",
polviews == "moderate, middle of the road" ~ "Moderate",
polviews %in% c("slightly conservative", "conservative", "extremely conservative") ~ "Conservative",
TRUE ~ NA_character_
),
polviews_triad = factor(polviews_triad,levels = c("Liberal", "Moderate", "Conservative"))
) %>%
filter(!is.na(polviews_triad))
table(gss_cleaned$polviews_triad)##
## Liberal Moderate Conservative
## 10395 14040 12450gss_cleaned <- gss_cleaned %>%
mutate(
education_level = case_when(
degree %in% c("less than high school", "high school") ~ "High School or Less",
degree == "associate/junior college" ~ "Some College",
degree %in% c("bachelor's", "graduate") ~ "Bachelor's or Higher",
TRUE ~ NA_character_
),
education_level = factor(education_level, levels = c("High School or Less", "Some College", "Bachelor's or Higher"))
) %>%
filter(!is.na(education_level))
table(gss_cleaned$education_level)##
## High School or Less Some College Bachelor's or Higher
## 25568 2292 8959gss_cleaned <- gss_cleaned %>%
mutate(
gender_binary = case_when(
sex == "male" ~ "Male",
sex == "female" ~ "Female",
TRUE ~ NA_character_
),
gender_binary = factor(gender_binary, levels = c("Male", "Female"))
) %>%
filter(!is.na(gender_binary))
table(gss_cleaned$gender_binary)##
## Male Female
## 16440 20366- Table Creation (Created a table comparing abortion support to each social characteristic)
table_polviews <- gss_cleaned %>%
group_by(polviews_triad) %>%
summarise(
sample_size = n(),
support_count = sum(abortion_support == "Support", na.rm = TRUE)
) %>%
mutate(percentage_support = (support_count / sample_size) * 100)
table_polviews## # A tibble: 3 × 4
## polviews_triad sample_size support_count percentage_support
## <fct> <int> <int> <dbl>
## 1 Liberal 10375 6177 59.5
## 2 Moderate 14010 5868 41.9
## 3 Conservative 12421 3847 31.0polviews_table <- table_polviews %>%
gt() %>%
tab_header(title = md("**Abortion Rights Support by Political Views**")) %>%
fmt_number(
columns = c(percentage_support),
decimals = 1
) %>%
cols_label(
polviews_triad = "Political Views",
sample_size = "Sample Size",
support_count = "Number Supporting",
percentage_support = "Percentage Supporting (%)"
)
polviews_table| Abortion Rights Support by Political Views | |||
| Political Views | Sample Size | Number Supporting | Percentage Supporting (%) |
|---|---|---|---|
| Liberal | 10375 | 6177 | 59.5 |
| Moderate | 14010 | 5868 | 41.9 |
| Conservative | 12421 | 3847 | 31.0 |
table_education <- gss_cleaned %>%
group_by(education_level) %>%
summarise(
sample_size = n(),
support_count = sum(abortion_support == "Support", na.rm = TRUE)
) %>%
mutate(percentage_support = (support_count / sample_size) * 100)
education_table <- table_education %>%
gt() %>%
tab_header(title = md("**Abortion Rights Support by Education Level**")) %>%
fmt_number(
columns = c(percentage_support),
decimals = 1
) %>%
cols_label(
education_level = "Education Level",
sample_size = "Sample Size",
support_count = "Number Supporting",
percentage_support = "Percentage Supporting (%)"
)
education_table| Abortion Rights Support by Education Level | |||
| Education Level | Sample Size | Number Supporting | Percentage Supporting (%) |
|---|---|---|---|
| High School or Less | 25558 | 9635 | 37.7 |
| Some College | 2291 | 1048 | 45.7 |
| Bachelor's or Higher | 8957 | 5209 | 58.2 |
table_gender <- gss_cleaned %>%
group_by(gender_binary) %>%
summarise(
sample_size = n(),
support_count = sum(abortion_support == "Support", na.rm = TRUE)
) %>%
mutate(percentage_support = (support_count / sample_size) * 100)
gender_table <- table_gender %>%
gt() %>%
tab_header(title = md("**Abortion Rights Support by Gender**")) %>%
fmt_number(
columns = c(percentage_support),
decimals = 1
) %>%
cols_label(
gender_binary = "Gender",
sample_size = "Sample Size",
support_count = "Number Supporting",
percentage_support = "Percentage Supporting (%)"
)
gender_table| Abortion Rights Support by Gender | |||
| Gender | Sample Size | Number Supporting | Percentage Supporting (%) |
|---|---|---|---|
| Male | 16440 | 7215 | 43.9 |
| Female | 20366 | 8677 | 42.6 |
- Visualization (Creating three seperate tables to plot abortion opposition to each social characteristic)
plot_polviews <- gss_cleaned %>%
group_by(year, polviews_triad) %>%
summarise(
total = n(),
oppose_count = sum(abortion_support == "Oppose", na.rm = TRUE)
) %>%
mutate(oppose_pct = (oppose_count / total) * 100) %>%
ggplot(aes(x = year, y = oppose_pct, color = polviews_triad)) +
geom_line(size = 1) +
geom_point() +
scale_y_continuous(labels = function(x) sprintf("%.1f%%", x)) +
scale_color_brewer(palette = "Set1") +
labs(
title = "Opposition to Abortion Rights over Time by Political Views",
x = "Year",
y = "Percentage Opposing Abortion Rights",
color = "Political Views"
) +
theme_minimal() +
theme(
panel.grid.minor = element_blank(),
text = element_text(size = 12)
)## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.plot_polviews
plot_education <- gss_cleaned %>%
group_by(year, education_level) %>%
summarise(
total = n(),
oppose_count = sum(abortion_support == "Oppose", na.rm = TRUE)
) %>%
mutate(oppose_pct = (oppose_count / total) * 100) %>%
ggplot(aes(x = year, y = oppose_pct, color = education_level)) +
geom_line(size = 1) +
geom_point() +
scale_y_continuous(labels = function(x) sprintf("%.1f%%", x)) +
scale_color_brewer(palette = "Set2") +
labs(
title = "Opposition to Abortion Rights over Time by Education Level",
x = "Year",
y = "Percentage Opposing Abortion Rights",
color = "Education Level"
) +
theme_minimal() +
theme(
panel.grid.minor = element_blank(),
text = element_text(size = 12)
)## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.plot_education
plot_gender <- gss_cleaned %>%
group_by(year, gender_binary) %>%
summarise(
total = n(),
oppose_count = sum(abortion_support == "Oppose", na.rm = TRUE)
) %>%
mutate(oppose_pct = (oppose_count / total) * 100) %>%
ggplot(aes(x = year, y = oppose_pct, color = gender_binary)) +
geom_line(size = 1) +
geom_point() +
scale_y_continuous(labels = function(x) sprintf("%.1f%%", x)) +
scale_color_brewer(palette = "Dark2") +
labs(
title = "Opposition to Abortion Rights over Time by Gender",
x = "Year",
y = "Percentage Opposing Abortion Rights",
color = "Gender"
) +
theme_minimal() +
theme(
panel.grid.minor = element_blank(),
text = element_text(size = 12)
)## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.plot_gender
D) I chose to examine each explanatory variable according to abortion
support separately because I found it much easier to create clear
visualizations and interpret the results in this section. Across all
three explanatory variables—gender, education, and political
views—opposition to abortion rights has declined over the past several
decades, even with variations per grouping. For gender (sex), women
actually showed higher opposition rates than men before the 1980’s, with
roughly 70% of women and 60% of men opposing abortion in the late 1970s.
By the mid-2010s, however, women’s opposition fell to around 30%,
dipping below men’s rate of approximately 40%. This shift indicates that
female opposition dropped more sharply over time.
Turning to education (degree), those with a “Bachelor’s or Higher” consistently exhibited the lowest opposition levels—around 40–50% in the 1970s, declining to around 30% by 2020. Individuals with “Some College” started near 60% opposition in the late 1970s, fluctuating around 50% over time, while those with “High School or Less” stayed near 70%, dropping to around 50% in the last decade. These patterns suggest a negative relationship between educational attainment and opposition to abortion.
Finally, “political views” (polviews) reveals the starkest contrast. “Conservatives” consistently registered the highest opposition (near 70% in early years, remaining around 60% by 2020), compared to “Liberals” who showed a sharp decline—from about 50% opposition in the 1970s to below 20% in most recent years. “Moderates” fell in between the two extremes, with opposition declining from around 60% to roughly 40%. Overall, the data indicates that while abortion opposition has generally decreased, it remains most prevalent among conservatives, individuals with less formal education, and, to a lesser extent, males. TASK 2: A) (Cleaning and preparing the varaibles with new catagories for each country)
france <- read_fst("france_data.fst")
Hungary <- read_fst("hungary_data.fst")france_cleaned <- france %>%
filter(!freehms %in% c(7, 8, 9)) %>%
mutate(
freehms_cat = case_when(
freehms %in% c(1, 2) ~ "Support",
freehms == 3 ~ "Neutral",
freehms %in% c(4, 5) ~ "Oppose",
TRUE ~ NA_character_
),
freehms_cat = factor(freehms_cat, levels = c("Support", "Neutral", "Oppose"))
) %>%
filter(!is.na(freehms_cat))
table(france_cleaned$freehms_cat)##
## Support Neutral Oppose
## 15748 1537 1600hungary_cleaned <- Hungary %>%
filter(!freehms %in% c(7, 8, 9)) %>%
mutate(
freehms_cat = case_when(
freehms %in% c(1, 2) ~ "Support",
freehms == 3 ~ "Neutral",
freehms %in% c(4, 5) ~ "Oppose",
TRUE ~ NA_character_
),
freehms_cat = factor(freehms_cat, levels = c("Support", "Neutral", "Oppose"))
) %>%
filter(!is.na(freehms_cat))
table(hungary_cleaned$freehms_cat)##
## Support Neutral Oppose
## 6756 3806 4557france_cleaned <- france_cleaned %>%
mutate(
education = case_when(
eisced %in% c(1, 2) ~ "Lower Secondary or Less",
eisced %in% c(3, 4) ~ "Upper Secondary",
eisced %in% c(5, 6, 7) ~ "Tertiary",
TRUE ~ NA_character_
),
education = factor(education, levels = c("Lower Secondary or Less", "Upper Secondary", "Tertiary"))
) %>%
filter(!is.na(education))
table(france_cleaned$education)##
## Lower Secondary or Less Upper Secondary Tertiary
## 4168 6490 4928hungary_cleaned <- hungary_cleaned %>%
mutate(
education = case_when(
eisced %in% c(1, 2) ~ "Lower Secondary or Less",
eisced %in% c(3, 4) ~ "Upper Secondary",
eisced %in% c(5, 6, 7) ~ "Tertiary",
TRUE ~ NA_character_
),
education = factor(education, levels = c("Lower Secondary or Less", "Upper Secondary", "Tertiary"))
) %>%
filter(!is.na(education))
table(hungary_cleaned$education)##
## Lower Secondary or Less Upper Secondary Tertiary
## 3476 8434 3126france_cleaned <- france_cleaned %>%
mutate(
residence = case_when(
domicil == 1 ~ "Major city",
domicil %in% c(2, 3) ~ "Urban periphery",
domicil %in% c(4, 5) ~ "Rural",
TRUE ~ NA_character_
),
residence = factor(residence, levels = c("Major city", "Urban periphery", "Rural"))
) %>%
filter(!is.na(residence))
table(france_cleaned$residence)##
## Major city Urban periphery Rural
## 2891 7044 5647hungary_cleaned <- hungary_cleaned %>%
mutate(
residence = case_when(
domicil == 1 ~ "Major city",
domicil %in% c(2, 3) ~ "Urban periphery",
domicil %in% c(4, 5) ~ "Rural",
TRUE ~ NA_character_
),
residence = factor(residence, levels = c("Major city", "Urban periphery", "Rural"))
) %>%
filter(!is.na(residence))
table(hungary_cleaned$residence)##
## Major city Urban periphery Rural
## 3847 5889 5294france_cleaned <- france_cleaned %>%
mutate(country = "France") %>%
filter(!is.na(freehms_cat) &
!is.na(education) &
!is.na(residence)) hungary_cleaned <- hungary_cleaned %>%
mutate(country = "Hungary") %>%
filter(!is.na(freehms_cat) &
!is.na(education) &
!is.na(residence)) - TABLE 1:(Creating four tables comparing LGBTQ support to each the social characteristics)
table_france_edu <- france_cleaned %>%
group_by(education) %>%
summarise(
group_size = n(),
percent_sample = round(100 * n() / nrow(france_cleaned), 1),
percent_opposing = round(100 * sum(freehms_cat == "Oppose") / n(), 1)
) %>%
ungroup()
table_france_edu <- table_france_edu %>%
gt() %>%
tab_header(
title = "Opposition to LGBTQ+ Rights by Education Level in France"
) %>%
cols_label(
education = "Education Level",
group_size = "Group Size",
percent_sample = "Percentage of Sample (%)",
percent_opposing = "Percentage Opposing LGBTQ+ Rights (%)"
) %>%
tab_source_note(
source_note = "Data from the European Social Survey (ESS)."
)
table_france_edu| Opposition to LGBTQ+ Rights by Education Level in France | |||
| Education Level | Group Size | Percentage of Sample (%) | Percentage Opposing LGBTQ+ Rights (%) |
|---|---|---|---|
| Lower Secondary or Less | 4166 | 26.7 | 13.9 |
| Upper Secondary | 6489 | 41.6 | 6.6 |
| Tertiary | 4927 | 31.6 | 3.9 |
| Data from the European Social Survey (ESS). | |||
TABLE 2
table_hungary_edu <- hungary_cleaned %>%
group_by(education) %>%
summarise(
group_size = n(),
percent_sample = round(100 * n() / nrow(hungary_cleaned), 1),
percent_opposing = round(100 * sum(freehms_cat == "Oppose") / n(), 1)
) %>%
ungroup()
table_hungary_edu <- table_hungary_edu %>%
gt() %>%
tab_header(
title = "Opposition to LGBTQ+ Rights by Education Level in Hungary"
) %>%
cols_label(
education = "Education Level",
group_size = "Group Size",
percent_sample = "Percentage of Sample (%)",
percent_opposing = "Percentage Opposing LGBTQ+ Rights (%)"
) %>%
tab_source_note(
source_note = "Data from the European Social Survey (ESS)."
)
table_hungary_edu| Opposition to LGBTQ+ Rights by Education Level in Hungary | |||
| Education Level | Group Size | Percentage of Sample (%) | Percentage Opposing LGBTQ+ Rights (%) |
|---|---|---|---|
| Lower Secondary or Less | 3476 | 23.1 | 38.7 |
| Upper Secondary | 8430 | 56.1 | 29.3 |
| Tertiary | 3124 | 20.8 | 22.8 |
| Data from the European Social Survey (ESS). | |||
TABLE 3
table_france_residence <- france_cleaned %>%
group_by(residence) %>%
summarise(
group_size = n(),
percent_sample = round(100 * n() / nrow(france_cleaned), 1),
percent_opposing = round(100 * sum(freehms_cat == "Oppose") / n(), 1)
) %>%
ungroup()
table_france_residence <- table_france_residence %>%
gt() %>%
tab_header(
title = "Opposition to LGBTQ+ Rights by Place of Residence in France"
) %>%
cols_label(
residence = "Place of Residence",
group_size = "Group Size",
percent_sample = "Percentage of Sample (%)",
percent_opposing = "Percentage Opposing LGBTQ+ Rights (%)"
) %>%
tab_source_note(
source_note = "Data from the European Social Survey (ESS)."
)
table_france_residence| Opposition to LGBTQ+ Rights by Place of Residence in France | |||
| Place of Residence | Group Size | Percentage of Sample (%) | Percentage Opposing LGBTQ+ Rights (%) |
|---|---|---|---|
| Major city | 2891 | 18.6 | 8.5 |
| Urban periphery | 7044 | 45.2 | 7.8 |
| Rural | 5647 | 36.2 | 7.2 |
| Data from the European Social Survey (ESS). | |||
TABLE 4
table_hungary_residence <- hungary_cleaned %>%
group_by(residence) %>%
summarise(
group_size = n(),
percent_sample = round(100 * n() / nrow(hungary_cleaned), 1),
percent_opposing = round(100 * sum(freehms_cat == "Oppose") / n(), 1)
) %>%
ungroup()
table_hungary_residence <- table_hungary_residence %>%
gt() %>%
tab_header(
title = "Opposition to LGBTQ+ Rights by Place of Residence in Hungary"
) %>%
cols_label(
residence = "Place of Residence",
group_size = "Group Size",
percent_sample = "Percentage of Sample (%)",
percent_opposing = "Percentage Opposing LGBTQ+ Rights (%)"
) %>%
tab_source_note(
source_note = "Data from the European Social Survey (ESS)."
)
table_hungary_residence| Opposition to LGBTQ+ Rights by Place of Residence in Hungary | |||
| Place of Residence | Group Size | Percentage of Sample (%) | Percentage Opposing LGBTQ+ Rights (%) |
|---|---|---|---|
| Major city | 3847 | 25.6 | 27.5 |
| Urban periphery | 5889 | 39.2 | 30.4 |
| Rural | 5294 | 35.2 | 31.7 |
| Data from the European Social Survey (ESS). | |||
- VISUALIZATION 1 (Visualing four plots of LGBTG rights opposition to each social characteristic)
data_france_edu <- france_cleaned %>%
group_by(education) %>%
summarise(
group_size = n(),
oppose_count = sum(freehms_cat == "Oppose"),
percent_oppose = round((oppose_count / group_size) * 100, 1)
) %>%
ungroup()
plot_france_edu <- ggplot(data_france_edu, aes(x = percent_oppose, y = education)) +
geom_col(fill = "steelblue", width = 0.6) +
geom_point(aes(x = percent_oppose, y = education), color = "black", size = 3) +
geom_text(aes(label = paste0(percent_oppose, "%")),
hjust = -0.2, color = "black", size = 3.5) +
scale_x_continuous(expand = expansion(mult = c(0, 0.05)),
limits = c(0, max(data_france_edu$percent_oppose) + 10)) +
labs(title = "Opposition to LGBTQ+ Rights by Education Level in France",
x = "Percentage Opposing LGBTQ+ Rights",
y = "Education Level") +
theme_minimal() +
theme(panel.grid.major = element_line(color = "gray80"),
panel.grid.minor = element_line(color = "gray90"),
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(face = "bold", size = 12))
plot_france_edu
VISUALIZATION 2
data_hungary_edu <- hungary_cleaned %>%
group_by(education) %>%
summarise(
group_size = n(),
oppose_count = sum(freehms_cat == "Oppose"),
percent_oppose = round((oppose_count / group_size) * 100, 1)
) %>%
ungroup()
plot_hungary_edu <- ggplot(data_hungary_edu, aes(x = percent_oppose, y = education)) +
geom_col(fill = "steelblue", width = 0.6) +
geom_point(aes(x = percent_oppose, y = education), color = "black", size = 3) +
geom_text(aes(label = paste0(percent_oppose, "%")),
hjust = -0.2, color = "black", size = 3.5) +
scale_x_continuous(expand = expansion(mult = c(0, 0.05)),
limits = c(0, max(data_hungary_edu$percent_oppose) + 10)) +
labs(title = "Opposition to LGBTQ+ Rights by Education Level in Hungary",
x = "Percentage Opposing LGBTQ+ Rights",
y = "Education Level") +
theme_minimal() +
theme(panel.grid.major = element_line(color = "gray80"),
panel.grid.minor = element_line(color = "gray90"),
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(face = "bold", size = 12))
plot_hungary_edu
VISUALIZATION 3
data_france_residence <- france_cleaned %>%
group_by(residence) %>%
summarise(
group_size = n(),
oppose_count = sum(freehms_cat == "Oppose"),
percent_oppose = round((oppose_count / group_size) * 100, 1)
) %>%
ungroup()
plot_france_residence <- ggplot(data_france_residence, aes(x = percent_oppose, y = residence)) +
geom_col(fill = "steelblue", width = 0.6) +
geom_point(aes(x = percent_oppose, y = residence), color = "black", size = 3) +
geom_text(aes(label = paste0(percent_oppose, "%")),
hjust = -0.2, color = "black", size = 3.5) +
scale_x_continuous(expand = expansion(mult = c(0, 0.05)),
limits = c(0, max(data_france_residence$percent_oppose) + 10)) +
labs(title = "Opposition to LGBTQ+ Rights by Place of Residence in France",
x = "Percentage Opposing LGBTQ+ Rights",
y = "Place of Residence") +
theme_minimal() +
theme(panel.grid.major = element_line(color = "gray80"),
panel.grid.minor = element_line(color = "gray90"),
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(face = "bold", size = 12))
plot_france_residence
VISUALIZATION 4
data_hungary_residence <- hungary_cleaned %>%
group_by(residence) %>%
summarise(
group_size = n(),
oppose_count = sum(freehms_cat == "Oppose"),
percent_oppose = round((oppose_count / group_size) * 100, 1)
) %>%
ungroup()
plot_hungary_residence <- ggplot(data_hungary_residence, aes(x = percent_oppose, y = residence)) +
geom_col(fill = "steelblue", width = 0.6) +
geom_point(aes(x = percent_oppose, y = residence), color = "black", size = 3) +
geom_text(aes(label = paste0(percent_oppose, "%")),
hjust = -0.2, color = "black", size = 3.5) +
scale_x_continuous(expand = expansion(mult = c(0, 0.05)),
limits = c(0, max(data_hungary_residence$percent_oppose) + 10)) +
labs(title = "Opposition to LGBTQ+ Rights by Place of Residence in Hungary",
x = "Percentage Opposing LGBTQ+ Rights",
y = "Place of Residence") +
theme_minimal() +
theme(panel.grid.major = element_line(color = "gray80"),
panel.grid.minor = element_line(color = "gray90"),
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(face = "bold", size = 12))
plot_hungary_residence
D)
For France, the data indicates relatively low overall opposition to LGBTQ+ rights, with differences by education and place of residence. Among those with “Lower Secondary or Less” education, who represent 26.7% of the sample, 13.9% oppose LGBTQ+ rights. This rate drops to 6.6% among individuals with “Upper Secondary” education (41.6% of the sample) and reaches its lowest level—3.9%—in the “Tertiary” educated group (31.6% of the sample). Residential patterns in France show: major city residents exhibit an 8.5% opposition rate, urban periphery dwellers 7.8%, and rural residents 7.2%. These figures suggest that in France, higher educational attainment is associated with more supportive attitudes, while place of residence has a comparatively lesser effect on opposition levels.
In Hungary, however, the data reveal higher opposition rates and more acute contrasts compared to France. Respondents with “Lower Secondary or Less” education (23.1% of the sample) show a 38.7% opposition rate, compared to 29.3% among “Upper Secondary” respondents (56.1% of the sample) and 22.8% among the“Tertiary” education group (20.8% of the sample). Geographic differences are also more pronounced than in France: major city residents register 27.5% opposition, urban periphery residents 30.4%, and rural residents 31.7%. Overall, Hungary exhibits elevated opposition across all groups. Both countries demonstrate a similar pattern for higher educational attainment correlating with reduced opposition to LGBTQ+ rights.