Is there any way to visualize Cronbach's alpha value and test-retest reliability (intraclass correlation coefficient) analysis outcomes? Is there any way to visualize Cronbach's alpha value and test-retest reliability (intraclass correlation coefficient) analysis outcome?
I use Rstudio as my daily driver, so if there's any way to visualize the reliability test outcomes, I'd be obliged if you also could suggest to me some packages to produce that plot/figure in R?
 A: I also searched the same questions in visualizing Cronbach's alpha Value. I found research from 2019 by Niram Patel within the scope of Learning Analysis.
I honestly just stumbled upon this. I haven't learned much about it but looks really promising. I wish this might help as the foundation for creating Cronbach's Alpha Visualization.

*

*Research Publication: https://www.researchgate.net/publication/331319177_Proceedings_of_the_First_Workshop_on_Educational_Data_Visualization_EdViz-2019


*Github Repository: https://github.com/nirmalpatel/edviz-2019.git
[Edited 27 August 2022]
Disclaimer: I am not the owner or creator of the code, full documentation belongs to Nirmal Patel and the team
The main idea from Adithya Sharma and Nirmal Patel is to plot comparison between Overall Cronbach's Alpha and Cronbach's Alpha if the Item Drop. Where the data have to be presented in the long form with several variables as columns:

*

*Student: Unique Student ID

*Assessment: unique identifier for assessments/ Construct / Facet of measurement

*Question: Unique Identifier for question in assessment/ Unique Item in a Construct

*UserScore: The value from the answered questions

As the original code was created as a function, I edited and annotated some of the code for easier reading and understanding.
    library(tidyverse)
    library(psych)
    library(ltm)
    
    # import Example data
    df <- psych::bfi
    df <- df %>% 
      select(c(1:25)) %>% 
      rowid_to_column("Student") %>% 
      pivot_longer(c(2:26)) %>% 
      rename(Question = name) %>% 
      rename(UserScore = value) %>% 
      mutate(Assessment = substr(Question,1,1))
    
    # Data frame preparation
    cronbach_df <- data.frame(Assessment = NA, Question = NA,
                              CronbachAlpha = NA,   CronbachAlphaItemDrop = NA)
    
    assessments <- unique(df$Assessment)
    
    for(i in 1:length(assessments)){
      
      # Data manipulation for Alpha (Long to Wide) 
      xyz_df <- df %>% 
        dplyr::filter(Assessment %in% assessments[i]) %>% 
        dplyr::select(Student, Question, UserScore) %>% 
        tidyr::spread(Question, UserScore) %>% 
        dplyr::select(-Student)
      
      result = tryCatch({
        psych::alpha(xyz_df, check.keys=TRUE)
      }, error = function(e) {
        "Error generated"
      })
      
      # Input Result Alpha into Cronbach_df
      if(result != "Error generated"){
        coeff_df <- data.frame(result$alpha.drop) %>% 
          tibble::rownames_to_column("Question") %>% 
          dplyr::select(Question, std.alpha) %>% 
          dplyr::mutate(Question = str_replace(Question, '\\-$', '')) %>% 
          dplyr::rename(CronbachAlphaItemDrop = std.alpha) %>% 
          dplyr::mutate(CronbachAlpha = result$total$std.alpha,
                        Assessment = assessments[i])
        
        cronbach_df <- cronbach_df %>% 
          dplyr::bind_rows(coeff_df) 
      }
      # Data Visualization
      cronbach_df %>% 
        drop_na() %>% 
        dplyr::mutate(CronbachAlpha = round(CronbachAlpha, digits = 2),
                      CronbachAlphaItemDrop = round(CronbachAlphaItemDrop, digits = 2)) %>% 
        ggplot(aes(fct_reorder(Assessment, -CronbachAlpha, sum), CronbachAlphaItemDrop))+
        geom_point(alpha = .75, aes(color = 'black'))+
        geom_point(aes(y = CronbachAlpha, color = '#0570b0'))+
        geom_line(aes(group = Assessment), linetype = 2, alpha = .75)+
        geom_hline(yintercept = .7, color = '#b30000')+
        annotate("text", x = 1, y = .72, label = "Th = 0.70", color = '#b30000')+
        labs(x = 'Construct',
             y = "Cronbach's alpha",
             title = "Distribution of Cronbach's alpha when an item is dropped")+
        scale_colour_manual(name = 'Cronbach alpha\nwhen an item is',
                            values =c('#0570b0'='blue', 'black'='black'), labels = c('not dropped', 'dropped'))+
        theme_bw()+
        theme(axis.text.x = element_text(angle = 50, hjust = 1, size = 6),
              axis.text.y = element_text(size = 8),
              axis.title = element_text(size = 14, face = "bold"),
              plot.title = element_text(size = 16), legend.title = element_text(size = 14),
              legend.text = element_text(size = 12))
    


Personal note:
With the idea of graphing between Cronbach's & Cronbach Item Drop, I also tried to change the X-axis into the individual item. The result is a comparison on a smaller scale. I hope this is helping those who need to graph Cronbach's Alpha.
A: For test-retest, you really can't go wrong with a scatter plot with test values on the x-axis, retest values on the y.
For alpha, I like to think of it in terms of the distribution of scores, and the standard error for each (see this post for details on these relationships), using a plot like the one below (which I made earlier).
library(tidyverse)
# Extraversion scores from the BFI dataset
data = psych::bfi %>% 
  select(E1:E5) %>%
  head(200)

long_data = data %>%
  mutate(subject = 1:n()) %>%
  pivot_longer(-subject)

estimates = long_data %>%
  group_by(subject) %>%
  summarise(
    extraversion = mean(value, na.rm = T),
    sd = sd(value, na.rm = T),
    n = sum(!is.na(value)),
    sem = sd / sqrt(n),
    .groups = 'drop') %>%
  mutate(ordered_subject = factor(subject) %>% fct_reorder(extraversion))

ggplot(estimates, 
       aes(ordered_subject, extraversion,
           ymin = extraversion - sem,
           ymax = extraversion + sem)) +
  geom_point() +
  geom_linerange() +
  scale_x_discrete(breaks = NULL) +
  labs(x = 'Subject (ordered)',
       y = 'Extraversion score (±SEM)')


