Project: Social Media Use, Self Esteem, Loneliness, and Wellbeing

Data source:

Apaolaza, V., Hartmann, P., Medina, E., Barrutia, J. M.,

and Echebarria, C. (2013).

The relationship between socializing on the Spanish online

networking site Tuenti and teenagers’ subjective wellbeing:

The roles of self esteem and loneliness.

Computers in Human Behavior, 29, 1282 to 1289.

Student: Genesis Santos

Purpose:

This script organizes and visualizes published summary data

from Apaolaza et al. (2013). The project uses reported article

values, not raw participant level survey data.

#Loading packages

# Install pacman only if needed
if (!require("pacman")) install.packages("pacman")

## Loading required package: pacman

## Warning: package 'pacman' was built under R version 4.5.3

# Load packages used for organization and visualization
pacman::p_load(
  tidyverse,
  ggplot2
)

2. Enter reported descriptive data from Table 1

tuenti_items <- tibble(
  construct = c(
    "Socializing", "Socializing", "Socializing", "Socializing",
    "Self esteem", "Self esteem", "Self esteem", "Self esteem",
    "Loneliness", "Loneliness", "Loneliness",
    "Wellbeing", "Wellbeing", "Wellbeing",
    "Tuenti use"
  ),
  item = c(
    "Contact with friends",
    "Talk to friends living far away",
    "Meet and talk to friends from the past",
    "Friends show interest",
    "Deserve to be appreciated",
    "Belief in personal virtues",
    "Capable of doing things",
    "Positive attitude toward self",
    "Feeling lonely",
    "Having no company",
    "Feeling isolated",
    "Life close to personal ideal",
    "Conditions of life",
    "Life satisfaction",
    "Time spent daily using Tuenti"
  ),
  mean = c(
    3.35, 3.66, 3.32, 3.24,
    3.37, 3.43, 3.44, 3.33,
    1.39, 1.32, 1.26,
    4.41, 4.89, 5.13,
    2.18
  ),
  sd = c(
    .76, .71, .67, .89,
    .69, .61, .64, .68,
    .65, .56, .53,
    1.17, 1.11, 1.09,
    1.30
  )
)

#Summarize item means by construct

construct_summary <- tuenti_items %>%
  group_by(construct) %>%
  summarise(
    number_of_items = n(),
    average_item_mean = mean(mean),
    average_item_sd = mean(sd),
    .groups = "drop"
  )

print(construct_summary)

## # A tibble: 5 × 4
##   construct   number_of_items average_item_mean average_item_sd
##   <chr>                 <int>             <dbl>           <dbl>
## 1 Loneliness                3              1.32           0.58 
## 2 Self esteem               4              3.39           0.655
## 3 Socializing               4              3.39           0.758
## 4 Tuenti use                1              2.18           1.3  
## 5 Wellbeing                 3              4.81           1.12

Figure 1: Reported item means

figure_1 <- ggplot(tuenti_items, aes(x = reorder(item, mean), y = mean)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Reported Item Means from the Tuenti Social Media Study",
    subtitle = "Data organized from Apaolaza et al. (2013)",
    x = "Survey item",
    y = "Reported mean"
  )

print(figure_1)

ggsave(
  filename = "figure_1_reported_item_means.png",
  plot = figure_1,
  width = 10,
  height = 7,
  dpi = 300
)

5. Figure 2: Average construct means

figure_2 <- ggplot(construct_summary, aes(x = reorder(construct, average_item_mean), y = average_item_mean)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Average Reported Item Mean by Construct",
    subtitle = "Construct summaries based on Apaolaza et al. (2013), Table 1",
    x = "Construct",
    y = "Average reported item mean"
  )

print(figure_2)

ggsave(
  filename = "figure_2_average_construct_means.png",
  plot = figure_2,
  width = 8,
  height = 5,
  dpi = 300
)

#reported standardized regression coefficients

path_coefficients <- tibble(
  relationship = c(
    "Tuenti use to socializing",
    "Socializing to self esteem",
    "Socializing to loneliness",
    "Self esteem to wellbeing",
    "Loneliness to wellbeing"
  ),
  coefficient = c(
    .30,
    .22,
    -.24,
    .47,
    -.27
  )
)

print(path_coefficients)

## # A tibble: 5 × 2
##   relationship               coefficient
##   <chr>                            <dbl>
## 1 Tuenti use to socializing         0.3 
## 2 Socializing to self esteem        0.22
## 3 Socializing to loneliness        -0.24
## 4 Self esteem to wellbeing          0.47
## 5 Loneliness to wellbeing          -0.27

#Figure 3: Corrected path coefficients

figure_3 <- ggplot(path_coefficients, aes(x = reorder(relationship, coefficient), y = coefficient)) +
  geom_col() +
  geom_hline(yintercept = 0) +
  coord_flip() +
  labs(
    title = "Reported Relationships in the Tuenti Social Media Study",
    subtitle = "Standardized regression coefficients from Apaolaza et al. (2013)",
    x = "Relationship",
    y = "Standardized regression coefficient"
  )

print(figure_3)

ggsave(
  filename = "figure_3_path_coefficients_corrected.png",
  plot = figure_3,
  width = 10,
  height = 6,
  dpi = 300
)

#Exporting cleaned summary tables

write_csv(tuenti_items, "tuenti_reported_item_data.csv")
write_csv(construct_summary, "tuenti_construct_summary.csv")
write_csv(path_coefficients, "tuenti_path_coefficients_corrected.csv")