8 Item Mean Plots

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Example: PISA Student Data

1. The first example closely follows the vignette used to demonstrate the tidyLPA package (Rosenberg, 2019).

• This model utilizes the PISA data collected in the U.S. in 2015. To learn more about this data see here.
• To access the 2015 US PISA data & documentation in R use the following code:

Variables:

broad_interest

composite measure of students’ self reported broad interest

enjoyment

composite measure of students’ self reported enjoyment

instrumental_mot

composite measure of students’ self reported instrumental motivation

self_efficacy

composite measure of students’ self reported self efficacy

#devtools::install_github("jrosen48/pisaUSA15")
#library(pisaUSA15)

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8.1 Load packages

library(naniar)
library(tidyverse)
library(haven)
library(glue)
library(MplusAutomation)
library(here)
library(janitor)
library(gt)
library(tidyLPA)
library(pisaUSA15)
library(cowplot)
library(filesstrings)
library(patchwork)
library(RcppAlgos)

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8.2 Prepare Data

pisa <- pisaUSA15[1:500,] %>%
  dplyr::select(broad_interest, enjoyment, instrumental_mot, self_efficacy)

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8.3 Descriptive Statistics

Quick Summary

summary(pisa)
#>  broad_interest    enjoyment    instrumental_mot
#>  Min.   :1.000   Min.   :1.00   Min.   :1.000   
#>  1st Qu.:2.200   1st Qu.:2.40   1st Qu.:1.750   
#>  Median :2.800   Median :3.00   Median :2.000   
#>  Mean   :2.666   Mean   :2.82   Mean   :2.129   
#>  3rd Qu.:3.200   3rd Qu.:3.00   3rd Qu.:2.500   
#>  Max.   :5.000   Max.   :4.00   Max.   :4.000   
#>  NA's   :23      NA's   :14     NA's   :21      
#>  self_efficacy  
#>  Min.   :1.000  
#>  1st Qu.:1.750  
#>  Median :2.000  
#>  Mean   :2.125  
#>  3rd Qu.:2.500  
#>  Max.   :4.000  
#>  NA's   :23

Mean Table

ds <- pisa %>% 
  pivot_longer(broad_interest:self_efficacy, names_to = "variable") %>% 
  group_by(variable) %>% 
  summarise(mean = mean(value, na.rm = TRUE),
            sd = sd(value, na.rm = TRUE)) 

ds %>% 
  gt () %>% 
  tab_header(title = md("**Descriptive Summary**")) %>%
  cols_label(
    variable = "Variable",
    mean = md("M"),
    sd = md("SD")
  ) %>%
  fmt_number(c(2:3),
             decimals = 2) %>% 
  cols_align(
    align = "center",
    columns = mean
  ) 

Descriptive Summary
Variable	M	SD
broad_interest	2.67	0.77
enjoyment	2.82	0.72
instrumental_mot	2.13	0.75
self_efficacy	2.12	0.64

Histograms

data_long <- pisa %>%
  pivot_longer(broad_interest:self_efficacy, names_to = "variable")

ggplot(data_long, aes(x = value)) +
  geom_histogram(binwidth = .3, fill = "#69b3a2", color = "black") +
  facet_wrap(~ variable, scales = "free_x") +
  labs(title = "Histograms of Variables", x = "Value", y = "Frequency") +
  theme_cowplot()

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8.4 Visualization

8.4.1 Latent Profile Plot

source(here("functions", "plot_lpa.R"))

# Read in models
output_enum <- readModels(here("lpa", "tidyLPA"), quiet = TRUE)

plot_lpa(model_name = output_enum$model_3_class_4.out)

Save figure

ggsave(here("figures", "model3_profile4.png"), dpi = "retina", bg = "white", height=5, width=8, units="in")

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8.4.2 Plots Means and Variances

plotMixtures(output_enum$model_3_class_4.out, ci = 0.95, bw = FALSE) + 
  labs(title = "Model 3: Equal Variances, Equal Covariances")

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8.4.3 Plot comparison

We can also plot the comparisons and look at the error bars.

NOTE: The plotMixtures() function is used for plotting LPA models only (i.e., means & variances)

a <- plotMixtures(output_enum$model_2_class_3.out,
  ci = 0.95, bw = FALSE) 

b <- plotMixtures(output_enum$model_4_class_3.out,
  ci = 0.95, bw = FALSE) 

a + labs(title = "Model 2") +
    theme(plot.title = element_text(size = 12)) +
b + labs(title = "Model 4") +
    theme(plot.title = element_text(size = 12))

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