Explore and Compare Further

🌟 Explore and Compare Further

Choice-level analysis opens the door to many new research questions that traditional profile-level analysis often overlooks. This article demonstrates how to estimate deeper quantities and compare subgroups effectively.

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📦 Setup

We use the already wrangled and cleaned data out1_arranged.

out1_arranged$labels

## # A tibble: 24 × 4
##    attribute                     level                   attribute_id level_id  
##    <chr>                         <chr>                   <chr>        <chr>     
##  1 Housing Cost (Pre-tax Income) 15%                     att1         att1:leve…
##  2 Housing Cost (Pre-tax Income) 30%                     att1         att1:leve…
##  3 Housing Cost (Pre-tax Income) 40%                     att1         att1:leve…
##  4 Presidential Vote (2020)      50% Dem, 50% Rep        att2         att2:leve…
##  5 Presidential Vote (2020)      30% Dem, 70% Rep        att2         att2:leve…
##  6 Presidential Vote (2020)      70% Dem, 30% Rep        att2         att2:leve…
##  7 Racial Composition            50% White, 50% Nonwhite att3         att3:leve…
##  8 Racial Composition            75% White, 25% Nonwhite att3         att3:leve…
##  9 Racial Composition            90% White, 10% Nonwhite att3         att3:leve…
## 10 Racial Composition            96% White, 4% Nonwhite  att3         att3:leve…
## # ℹ 14 more rows

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⚖️ Explore: Compare Trade-offs Directly

Example: Low Housing Costs vs. Low Crime Rates

qoi_1 <- set_qoi(
  .structure = "choice_level",
  .att_choose = "att1", 
  .lev_choose = "level1",
  .att_notchoose = "att6", 
  .lev_notchoose = "level2"
)

mm1 <- projoint(.data = out1_arranged,
                .qoi = qoi_1,
                .structure = "choice_level",
                .ignore_position = TRUE)

plot(mm1,
     .labels = c("Housing Cost\n(15% of pre-tax income)",
                 "Violent Crime Rate\n(20% > National Average)")) +
  labs(y = "Marginal Mean", x = NULL) +
  coord_cartesian(ylim = c(0, 1)) +
  geom_hline(yintercept = 0.5, linetype = "dashed", color = "darkgray")

## The attributes are different between the two levels. Check your setting carefully. The attribute label is not added to the figure.

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🧩 Explore: Compare Multiple Levels Simultaneously

Example: Urban vs. Suburban Preferences

qoi_2 <- set_qoi(
  .structure = "choice_level",
  .att_choose = "att7",
  .lev_choose = c("level2", "level3"),
  .att_notchoose = "att7",
  .lev_notchoose = c("level5", "level6")
)

mm2 <- projoint(.data = out1_arranged,
                .qoi = qoi_2,
                .structure = "choice_level",
                .ignore_position = TRUE)

plot(mm2,
     .labels = c("City", "Suburban")) +
  labs(y = "Marginal Mean", x = "Type of Place") +
  coord_cartesian(ylim = c(0, 1)) +
  geom_hline(yintercept = 0.5, linetype = "dashed", color = "darkgray")

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📊 Compare: Subgroup Differences

Profile-Level Subgroup Comparison: White vs. Non-White Respondents

outcomes <- c(paste0("choice", 1:8), "choice1_repeated_flipped")

df <- exampleData1 %>% mutate(white = ifelse(race == "White", 1, 0))

df_0 <- df %>% filter(white == 0) %>% reshape_projoint(outcomes)
df_1 <- df %>% filter(white == 1) %>% reshape_projoint(outcomes)
df_d <- df %>% reshape_projoint(outcomes, .covariates = "white")

data_file <- system.file("extdata", "labels_arranged.csv", package = "projoint")
if (data_file == "") stop("File not found!")

df_0 <- read_labels(df_0, data_file)
df_1 <- read_labels(df_1, data_file)
df_d <- read_labels(df_d, data_file)

out_0 <- projoint(df_0, .structure = "profile_level")
out_1 <- projoint(df_1, .structure = "profile_level")
out_d <- projoint(df_d, .structure = "profile_level", .by_var = "white")

plot_0 <- plot(out_0)
plot_1 <- plot(out_1)
plot_d <- plot(out_d, .by_var = TRUE)

plot_0 +
  coord_cartesian(xlim = c(0.2, 0.8)) +
  labs(title = "Non-white", x = "AMCE") +
  theme(plot.title = element_text(hjust = 0.5)) +

plot_1 +
  coord_cartesian(xlim = c(0.2, 0.8)) +
  labs(title = "White", x = "AMCE") +
  theme(axis.text.y = element_blank(), plot.title = element_text(hjust = 0.5)) +

plot_d +
  coord_cartesian(xlim = c(-0.4, 0.4)) +
  labs(title = "Difference", x = "Difference") +
  theme(axis.text.y = element_blank(), plot.title = element_text(hjust = 0.5))

Choice-Level Subgroup Comparison: Party Differences

df_D <- exampleData1 %>% filter(party_1 == "Democrat") %>% reshape_projoint(outcomes)
df_R <- exampleData1 %>% filter(party_1 == "Republican") %>% reshape_projoint(outcomes)
df_0 <- exampleData1 %>% filter(party_1 %in% c("Something else", "Independent")) %>% reshape_projoint(outcomes)

qoi <- set_qoi(
  .structure = "choice_level",
  .estimand = "mm",
  .att_choose = "att2",
  .lev_choose = "level3",
  .att_notchoose = "att2",
  .lev_notchoose = "level1"
)

out_D <- projoint(df_D, qoi)
out_R <- projoint(df_R, qoi)
out_0 <- projoint(df_0, qoi)

out_merged <- bind_rows(
  out_D$estimates %>% mutate(party = "Democrat"),
  out_R$estimates %>% mutate(party = "Republican"),
  out_0$estimates %>% mutate(party = "Independent")
) %>% filter(estimand == "mm_corrected")

# Plot

ggplot(out_merged, aes(y = party, x = estimate)) +
  geom_vline(xintercept = 0.5, linetype = "dashed", color = "gray") +
  geom_pointrange(aes(xmin = conf.low, xmax = conf.high)) +
  geom_text(aes(label = format(round(estimate, 2), nsmall = 2)), vjust = -1) +
  labs(y = NULL, x = "Choice-level Marginal Mean",
       title = "Preference for Democratic-majority areas") +
  theme_classic()

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🌟 What’s Next?

Congratulations! You’ve completed the step-by-step tutorial for using projoint.
You can now explore your own data, compare subgroups, and publish your findings!

Unlock richer insights using projoint! 🚀

⬅️ Back to: Visualize Your Results
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🎯 Final Takeaway

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