1 Category “goodness” ranking task task

For each of the categories below, rank each member of the category on a scale of 1-7, where 1 means that the item is a ‘very good example’ of that category, and 7 means that the item is a ‘poor example’ of that category.

1.1 load libs

library("tidyverse")

1.2 plot settings

txt <- element_text(size = 18, family = "Avenir", color = "black")
txt.x.rot <- element_text(size = 14, family = "Avenir", color = "black", angle = 45, vjust = 0.5)
txt.y.rot <- element_text(size = 14, family = "Avenir", color = "black", angle = 0)

theme_set(
  theme_classic() +
    theme(legend.position = "none",
          text = txt,
          axis.text.x = txt.x.rot,
          axis.text.y = txt.y.rot)
)

1.3 load raw data

path.data <- file.path("~", "Downloads", "mia4_submissions.csv")

df.semantic <- path.data %>% 
  read_csv(show_col_types = FALSE) %>% 
  select(-Timestamp, -`Email Address`, -Name, -`SUNet ID`) %>% 
  pivot_longer(everything()) %>% 
  separate(name, c("type", "instance")) %>% 
  mutate(value = 8 - value) # reverse score the ratings

1.4 category rating task results (N = 77)

set.seed(100)

df.semantic %>% 
  ggplot(mapping = aes(x = reorder(instance, value),
                       y = value, 
                       color = type)) +
  geom_jitter(width = 0.2,
              height = 0.1,
              alpha = 0.3) +
  stat_summary(fun.data = mean_cl_boot,
               color = "black") +
  labs(title = "Category \"goodness\" ranking task",
       subtitle = paste0("Psych 45 - Spring 2024 (N = ", path.data %>% read_csv(show_col_types = FALSE) %>% pull(`SUNet ID`) %>% unique %>% length, ")"),
       x = "",
       y = "\"Goodness\" as example of class") +
  facet_wrap(~type, 
             scales = "free_x")