ECI 586 Final Project
Munirah Almutairi
2024-12-05
1. PREPARE
I am interested in The Usage of Social Media Among Students dataset because it offers a comprehensive view of how students interact with social media and how it impacts their academic engagement and productivity. Social media plays a central role in students’ daily lives, shaping their communication, learning habits, and even emotional well-being. Understanding these patterns is critical for developing strategies that maximize the benefits of social media while mitigating potential drawbacks.
The analysis of The Usage of Social Media Among Students dataset would primarily benefit students, educators, academic institutions, policymakers, and social media platforms. Students can gain insights into how their social media habits impact academic productivity and well-being, helping them make more informed decisions about their usage. Educators and institutions can use the findings to design strategies and workshops that promote effective social media use for academic engagement. Policymakers can leverage the results to create digital literacy campaigns and guidelines that emphasize balanced and responsible social media usage. Social media platforms, on the other hand, could adapt features to better cater to educational needs, such as creating tools for collaborative learning and minimizing distractions. Ultimately, this analysis could foster a healthier digital environment, aligning social media use with academic and personal growth.
Data Sources & Analysis
The dataset used for this analysis is titled “Actual Student Behavior Towards Social Media Usage” and was obtained from Kaggle. It was created and shared by NUR SYAFIQAH ABDULLAH and is publicly available for research and analysis. The dataset aims to explore student behavior and attitudes toward social media usage, helping to understand how social media affects their lives, particularly in educational contexts. The data includes a variety of variables related to demographics, time spent on social media, and the purposes of social media usage, such as education, entertainment, and social interaction.
Context of Data Collection
The data was collected through surveys or observations (based on the provided dataset context) and reflects real student behaviors and preferences regarding social media. The structured format of the dataset facilitates analyzing patterns in usage and correlations with factors like productivity, emotional well-being, and engagement.
Variables and Features
The dataset includes variables related to:
Demographics: Age, gender, and educational background.
Social Media Usage: Frequency, duration, and purposes of use (e.g., academic, social, entertainment).
Behavioral Metrics: The influence of social media on academic focus, emotional well-being, and daily routines.
Relevance to Research
This dataset is particularly relevant for exploring the impact of social media on student learning, engagement, and productivity. By analyzing this data, the study aims to uncover actionable insights for educators, policymakers, and students themselves, providing guidance on balancing social media use for optimal outcomes.
Research Questions
How do students’ demographic characteristics (e.g., age, gender, faculty) influence their social media usage and perceptions?
What is the relationship between the amount of time spent on social media and students’ academic productivity?
How do students use social media for academic versus non-academic purposes?
Does Fear of Missing Out (FOMO) significantly influence students’ social media behaviors, and how does it impact their academic outcomes?
Load Libraries
# Install missing libraries
install.packages(c("tidyverse", "janitor", "skimr", "summarytools", "plotly", "RColorBrewer"))## Installing packages into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Load libraries
library(tidyverse) # Data wrangling and visualization## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(janitor) # Data cleaning##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(skimr) # Summarizing data
library(summarytools) # Detailed data summaries## Warning in fun(libname, pkgname): couldn't connect to display ":0"## system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)
##
## Attaching package: 'summarytools'
##
## The following object is masked from 'package:tibble':
##
## viewlibrary(plotly) # Interactive plots (optional)##
## Attaching package: 'plotly'
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## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layoutlibrary(RColorBrewer) # Color palettes for plots2. WRANGLE
a. Import Data
data <- read_csv("The Usage of Social Media Among Students (Responses) - Form Responses 1.csv")## New names:
## Rows: 100 Columns: 26
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (23): Age, Gender, Faculty, Perceived ease of use [My interaction with s... dbl
## (1): Semester lgl (2): ...1, ...2
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
## • `` -> `...2`str(data)## spc_tbl_ [100 × 26] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ...1 : logi [1:100] NA NA NA NA NA NA ...
## $ ...2 : logi [1:100] NA NA NA NA NA NA ...
## $ Age : chr [1:100] "19-23" "19-23" "19-23" "24-26" ...
## $ Gender : chr [1:100] "Female" "Female" "Female" "Female" ...
## $ Faculty : chr [1:100] "Business & Management" "Business & Management" "Business & Management" "Business & Management" ...
## $ Semester : num [1:100] 4 4 4 4 2 4 2 4 4 2 ...
## $ Perceived ease of use [My interaction with social media would be clear and understandable.] : chr [1:100] "agree" "Agree" "Agree" "Agree" ...
## $ Perceived ease of use [I would find using social media to be flexible to interact with.] : chr [1:100] "Agree" "Agree" "Strongly agree" "Agree" ...
## $ Perceived ease of use [It would be easy for me to become skilled at using social media.] : chr [1:100] "Agree" "Neutral" "Strongly agree" "Agree" ...
## $ Perceived ease of use [I would find social media is easy to use.] : chr [1:100] "Agree" "Neutral" "Strongly agree" "Agree" ...
## $ Perceived ease of use [I use social media sites to seek help from my teachers.] : chr [1:100] "Agree" "Neutral" "Strongly agree" "Agree" ...
## $ Perceived usefulness [Using the social media helps me understand others better] : chr [1:100] "Agree" "Agree" "Strongly agree" "Agree" ...
## $ Perceived usefulness [Social media helps me in terms of social skills, study, and entertainment] : chr [1:100] "Agree" "Agree" "Agree" "Agree" ...
## $ Perceived usefulness [Using social media can improve my productivity] : chr [1:100] "Agree" "Agree" "Neutral" "Neutral" ...
## $ Perceived usefulness [I find social media useful for me as a student] : chr [1:100] "Agree" "Agree" "Agree" "Neutral" ...
## $ Perceived usefulness [Using social media can enhances my effectiveness in my assessment] : chr [1:100] "Agree" "Agree" "Agree" "Neutral" ...
## $ Attitudes towards use [I feel the need to use any social media on daily basis/] : chr [1:100] "Agree" "Agree" "Strongly agree" "Agree" ...
## $ Attitudes towards use [It is difficult for me to not use social media on daily basis.] : chr [1:100] "Agree" "Strongly agree" "Strongly agree" "Agree" ...
## $ Attitudes towards use [I am able to adapt to new features from the existing social media platforms.] : chr [1:100] "Agree" "Neutral" "Strongly agree" "Agree" ...
## $ Attitudes towards use [I am willing to invest my time and money for social media features.] : chr [1:100] "Agree" "Neutral" "Agree" "Agree" ...
## $ Attitudes towards use [I am always updated with the new features because I am scared of having "FOMO" (Fear of Missing Out).]: chr [1:100] "Neutral" "Neutral" "Agree" "Agree" ...
## $ Actual behaviors [I use social media solely for entertainment] : chr [1:100] "Neutral" "Disagree" "Agree" "Agree" ...
## $ Actual behaviors [Social media helps in increasing my self confidence] : chr [1:100] "Disagree" "Agree" "Agree" "Agree" ...
## $ Actual behaviors [Social media influences my mood throughout the day] : chr [1:100] "Agree" "Neutral" "Strongly agree" "Agree" ...
## $ Actual behaviors [I use social media to kill the boredom especially during classes] : chr [1:100] "Strongly agree" "Disagree" "Agree" "Agree" ...
## $ Actual behaviors [I am obsessed using social media and feel incomplete without it] : chr [1:100] "Agree" "Disagree" "Agree" "Agree" ...
## - attr(*, "spec")=
## .. cols(
## .. ...1 = col_logical(),
## .. ...2 = col_logical(),
## .. Age = col_character(),
## .. Gender = col_character(),
## .. Faculty = col_character(),
## .. Semester = col_double(),
## .. `Perceived ease of use [My interaction with social media would be clear and understandable.]` = col_character(),
## .. `Perceived ease of use [I would find using social media to be flexible to interact with.]` = col_character(),
## .. `Perceived ease of use [It would be easy for me to become skilled at using social media.]` = col_character(),
## .. `Perceived ease of use [I would find social media is easy to use.]` = col_character(),
## .. `Perceived ease of use [I use social media sites to seek help from my teachers.]` = col_character(),
## .. `Perceived usefulness [Using the social media helps me understand others better]` = col_character(),
## .. `Perceived usefulness [Social media helps me in terms of social skills, study, and entertainment]` = col_character(),
## .. `Perceived usefulness [Using social media can improve my productivity]` = col_character(),
## .. `Perceived usefulness [I find social media useful for me as a student]` = col_character(),
## .. `Perceived usefulness [Using social media can enhances my effectiveness in my assessment]` = col_character(),
## .. `Attitudes towards use [I feel the need to use any social media on daily basis/]` = col_character(),
## .. `Attitudes towards use [It is difficult for me to not use social media on daily basis.]` = col_character(),
## .. `Attitudes towards use [I am able to adapt to new features from the existing social media platforms.]` = col_character(),
## .. `Attitudes towards use [I am willing to invest my time and money for social media features.]` = col_character(),
## .. `Attitudes towards use [I am always updated with the new features because I am scared of having "FOMO" (Fear of Missing Out).]` = col_character(),
## .. `Actual behaviors [I use social media solely for entertainment]` = col_character(),
## .. `Actual behaviors [Social media helps in increasing my self confidence]` = col_character(),
## .. `Actual behaviors [Social media influences my mood throughout the day]` = col_character(),
## .. `Actual behaviors [I use social media to kill the boredom especially during classes]` = col_character(),
## .. `Actual behaviors [I am obsessed using social media and feel incomplete without it]` = col_character()
## .. )
## - attr(*, "problems")=<externalptr>summary(data)## ...1 ...2 Age Gender
## Mode:logical Mode:logical Length:100 Length:100
## NA's:100 NA's:100 Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Faculty Semester
## Length:100 Min. :1.000
## Class :character 1st Qu.:3.000
## Mode :character Median :4.000
## Mean :3.556
## 3rd Qu.:4.000
## Max. :8.000
## NA's :1
## Perceived ease of use [My interaction with social media would be clear and understandable.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived ease of use [I would find using social media to be flexible to interact with.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived ease of use [It would be easy for me to become skilled at using social media.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived ease of use [I would find social media is easy to use.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived ease of use [I use social media sites to seek help from my teachers.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived usefulness [Using the social media helps me understand others better]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived usefulness [Social media helps me in terms of social skills, study, and entertainment]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived usefulness [Using social media can improve my productivity]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived usefulness [I find social media useful for me as a student]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Perceived usefulness [Using social media can enhances my effectiveness in my assessment]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Attitudes towards use [I feel the need to use any social media on daily basis/]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Attitudes towards use [It is difficult for me to not use social media on daily basis.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Attitudes towards use [I am able to adapt to new features from the existing social media platforms.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Attitudes towards use [I am willing to invest my time and money for social media features.]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Attitudes towards use [I am always updated with the new features because I am scared of having "FOMO" (Fear of Missing Out).]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Actual behaviors [I use social media solely for entertainment]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Actual behaviors [Social media helps in increasing my self confidence]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Actual behaviors [Social media influences my mood throughout the day]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Actual behaviors [I use social media to kill the boredom especially during classes]
## Length:100
## Class :character
## Mode :character
##
##
##
##
## Actual behaviors [I am obsessed using social media and feel incomplete without it]
## Length:100
## Class :character
## Mode :character
##
##
##
## head(data)## # A tibble: 6 × 26
## ...1 ...2 Age Gender Faculty Semester Perceived ease of us…¹
## <lgl> <lgl> <chr> <chr> <chr> <dbl> <chr>
## 1 NA NA 19-23 Female Business & Management 4 agree
## 2 NA NA 19-23 Female Business & Management 4 Agree
## 3 NA NA 19-23 Female Business & Management 4 Agree
## 4 NA NA 24-26 Female Business & Management 4 Agree
## 5 NA NA 19-23 Female Business & Management 2 Agree
## 6 NA NA 19-23 Female Business & Management 4 Neutral
## # ℹ abbreviated name:
## # ¹`Perceived ease of use [My interaction with social media would be clear and understandable.]`
## # ℹ 19 more variables:
## # `Perceived ease of use [I would find using social media to be flexible to interact with.]` <chr>,
## # `Perceived ease of use [It would be easy for me to become skilled at using social media.]` <chr>,
## # `Perceived ease of use [I would find social media is easy to use.]` <chr>,
## # `Perceived ease of use [I use social media sites to seek help from my teachers.]` <chr>, …tail(data)## # A tibble: 6 × 26
## ...1 ...2 Age Gender Faculty Semester Perceived ease of us…¹
## <lgl> <lgl> <chr> <chr> <chr> <dbl> <chr>
## 1 NA NA 24-26 Male Hotel & Touri… 4 Neutral
## 2 NA NA 24-26 Male Medicine 1 Agree
## 3 NA NA 24-26 Male Education 4 Disagree
## 4 NA NA 27 and above Male Pharmacy 4 Disagree
## 5 NA NA 24-26 Male Hotel & Touri… 4 Disagree
## 6 NA NA 24-26 Male Hotel & Touri… 4 Disagree
## # ℹ abbreviated name:
## # ¹`Perceived ease of use [My interaction with social media would be clear and understandable.]`
## # ℹ 19 more variables:
## # `Perceived ease of use [I would find using social media to be flexible to interact with.]` <chr>,
## # `Perceived ease of use [It would be easy for me to become skilled at using social media.]` <chr>,
## # `Perceived ease of use [I would find social media is easy to use.]` <chr>,
## # `Perceived ease of use [I use social media sites to seek help from my teachers.]` <chr>, …Sone column names are complex or contain spaces, I will clean and standardize them for easier use
library(janitor)
data <- clean_names(data)I will verify that the column names are now consistent and easy to work with:
names(data)## [1] "x1"
## [2] "x2"
## [3] "age"
## [4] "gender"
## [5] "faculty"
## [6] "semester"
## [7] "perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable"
## [8] "perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with"
## [9] "perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media"
## [10] "perceived_ease_of_use_i_would_find_social_media_is_easy_to_use"
## [11] "perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers"
## [12] "perceived_usefulness_using_the_social_media_helps_me_understand_others_better"
## [13] "perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment"
## [14] "perceived_usefulness_using_social_media_can_improve_my_productivity"
## [15] "perceived_usefulness_i_find_social_media_useful_for_me_as_a_student"
## [16] "perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment"
## [17] "attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis"
## [18] "attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis"
## [19] "attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms"
## [20] "attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features"
## [21] "attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out"
## [22] "actual_behaviors_i_use_social_media_solely_for_entertainment"
## [23] "actual_behaviors_social_media_helps_in_increasing_my_self_confidence"
## [24] "actual_behaviors_social_media_influences_my_mood_throughout_the_day"
## [25] "actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes"
## [26] "actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it"I will dentify columns with missing data:
colSums(is.na(data))## x1
## 100
## x2
## 100
## age
## 0
## gender
## 0
## faculty
## 1
## semester
## 1
## perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable
## 0
## perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with
## 0
## perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media
## 0
## perceived_ease_of_use_i_would_find_social_media_is_easy_to_use
## 0
## perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers
## 0
## perceived_usefulness_using_the_social_media_helps_me_understand_others_better
## 0
## perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment
## 0
## perceived_usefulness_using_social_media_can_improve_my_productivity
## 0
## perceived_usefulness_i_find_social_media_useful_for_me_as_a_student
## 0
## perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment
## 0
## attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis
## 0
## attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis
## 0
## attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms
## 0
## attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features
## 0
## attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out
## 0
## actual_behaviors_i_use_social_media_solely_for_entertainment
## 0
## actual_behaviors_social_media_helps_in_increasing_my_self_confidence
## 0
## actual_behaviors_social_media_influences_my_mood_throughout_the_day
## 0
## actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes
## 0
## actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it
## 0colnames(data)## [1] "x1"
## [2] "x2"
## [3] "age"
## [4] "gender"
## [5] "faculty"
## [6] "semester"
## [7] "perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable"
## [8] "perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with"
## [9] "perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media"
## [10] "perceived_ease_of_use_i_would_find_social_media_is_easy_to_use"
## [11] "perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers"
## [12] "perceived_usefulness_using_the_social_media_helps_me_understand_others_better"
## [13] "perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment"
## [14] "perceived_usefulness_using_social_media_can_improve_my_productivity"
## [15] "perceived_usefulness_i_find_social_media_useful_for_me_as_a_student"
## [16] "perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment"
## [17] "attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis"
## [18] "attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis"
## [19] "attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms"
## [20] "attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features"
## [21] "attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out"
## [22] "actual_behaviors_i_use_social_media_solely_for_entertainment"
## [23] "actual_behaviors_social_media_helps_in_increasing_my_self_confidence"
## [24] "actual_behaviors_social_media_influences_my_mood_throughout_the_day"
## [25] "actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes"
## [26] "actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it"The dataset contains textual responses like “Agree,” “Strongly Agree,” and “Neutral.” These can be standardized or converted to numerical values for analysis.
data <- data %>%
mutate(across(where(is.character), ~ str_to_lower(.))) # Convert all text to lowercaseChecking for any missing values in dataset and see how to handle them:
summary(data)## x1 x2 age gender
## Mode:logical Mode:logical Length:100 Length:100
## NA's:100 NA's:100 Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## faculty semester
## Length:100 Min. :1.000
## Class :character 1st Qu.:3.000
## Mode :character Median :4.000
## Mean :3.556
## 3rd Qu.:4.000
## Max. :8.000
## NA's :1
## perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_ease_of_use_i_would_find_social_media_is_easy_to_use
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_usefulness_using_the_social_media_helps_me_understand_others_better
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_usefulness_using_social_media_can_improve_my_productivity
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_usefulness_i_find_social_media_useful_for_me_as_a_student
## Length:100
## Class :character
## Mode :character
##
##
##
##
## perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment
## Length:100
## Class :character
## Mode :character
##
##
##
##
## attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis
## Length:100
## Class :character
## Mode :character
##
##
##
##
## attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis
## Length:100
## Class :character
## Mode :character
##
##
##
##
## attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms
## Length:100
## Class :character
## Mode :character
##
##
##
##
## attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features
## Length:100
## Class :character
## Mode :character
##
##
##
##
## attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out
## Length:100
## Class :character
## Mode :character
##
##
##
##
## actual_behaviors_i_use_social_media_solely_for_entertainment
## Length:100
## Class :character
## Mode :character
##
##
##
##
## actual_behaviors_social_media_helps_in_increasing_my_self_confidence
## Length:100
## Class :character
## Mode :character
##
##
##
##
## actual_behaviors_social_media_influences_my_mood_throughout_the_day
## Length:100
## Class :character
## Mode :character
##
##
##
##
## actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes
## Length:100
## Class :character
## Mode :character
##
##
##
##
## actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it
## Length:100
## Class :character
## Mode :character
##
##
##
## colSums(is.na(data))## x1
## 100
## x2
## 100
## age
## 0
## gender
## 0
## faculty
## 1
## semester
## 1
## perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable
## 0
## perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with
## 0
## perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media
## 0
## perceived_ease_of_use_i_would_find_social_media_is_easy_to_use
## 0
## perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers
## 0
## perceived_usefulness_using_the_social_media_helps_me_understand_others_better
## 0
## perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment
## 0
## perceived_usefulness_using_social_media_can_improve_my_productivity
## 0
## perceived_usefulness_i_find_social_media_useful_for_me_as_a_student
## 0
## perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment
## 0
## attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis
## 0
## attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis
## 0
## attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms
## 0
## attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features
## 0
## attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out
## 0
## actual_behaviors_i_use_social_media_solely_for_entertainment
## 0
## actual_behaviors_social_media_helps_in_increasing_my_self_confidence
## 0
## actual_behaviors_social_media_influences_my_mood_throughout_the_day
## 0
## actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes
## 0
## actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it
## 0Based on the output, the dataset has missing values in two columns:
faculty: 1 missing value.semester: 1 missing value.
Filling the missing data:
# Fill missing faculty with "Unknown"
data$faculty[is.na(data$faculty)] <- "Unknown"
# Fill missing semester with the median
data$semester[is.na(data$semester)] <- median(data$semester, na.rm = TRUE)Recheck missing values:
colSums(is.na(data))## x1
## 100
## x2
## 100
## age
## 0
## gender
## 0
## faculty
## 0
## semester
## 0
## perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable
## 0
## perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with
## 0
## perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media
## 0
## perceived_ease_of_use_i_would_find_social_media_is_easy_to_use
## 0
## perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers
## 0
## perceived_usefulness_using_the_social_media_helps_me_understand_others_better
## 0
## perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment
## 0
## perceived_usefulness_using_social_media_can_improve_my_productivity
## 0
## perceived_usefulness_i_find_social_media_useful_for_me_as_a_student
## 0
## perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment
## 0
## attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis
## 0
## attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis
## 0
## attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms
## 0
## attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features
## 0
## attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out
## 0
## actual_behaviors_i_use_social_media_solely_for_entertainment
## 0
## actual_behaviors_social_media_helps_in_increasing_my_self_confidence
## 0
## actual_behaviors_social_media_influences_my_mood_throughout_the_day
## 0
## actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes
## 0
## actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it
## 0The recheck confirms that there are no remaining missing values in
the dataset. This indicates that the missing data in
faculty and semester columns has been
successfully handled.
str(data)## tibble [100 × 26] (S3: tbl_df/tbl/data.frame)
## $ x1 : logi [1:100] NA NA NA NA NA NA ...
## $ x2 : logi [1:100] NA NA NA NA NA NA ...
## $ age : chr [1:100] "19-23" "19-23" "19-23" "24-26" ...
## $ gender : chr [1:100] "female" "female" "female" "female" ...
## $ faculty : chr [1:100] "business & management" "business & management" "business & management" "business & management" ...
## $ semester : num [1:100] 4 4 4 4 2 4 2 4 4 2 ...
## $ perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable : chr [1:100] "agree" "agree" "agree" "agree" ...
## $ perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with : chr [1:100] "agree" "agree" "strongly agree" "agree" ...
## $ perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ perceived_ease_of_use_i_would_find_social_media_is_easy_to_use : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ perceived_usefulness_using_the_social_media_helps_me_understand_others_better : chr [1:100] "agree" "agree" "strongly agree" "agree" ...
## $ perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment : chr [1:100] "agree" "agree" "agree" "agree" ...
## $ perceived_usefulness_using_social_media_can_improve_my_productivity : chr [1:100] "agree" "agree" "neutral" "neutral" ...
## $ perceived_usefulness_i_find_social_media_useful_for_me_as_a_student : chr [1:100] "agree" "agree" "agree" "neutral" ...
## $ perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment : chr [1:100] "agree" "agree" "agree" "neutral" ...
## $ attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis : chr [1:100] "agree" "agree" "strongly agree" "agree" ...
## $ attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis : chr [1:100] "agree" "strongly agree" "strongly agree" "agree" ...
## $ attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features : chr [1:100] "agree" "neutral" "agree" "agree" ...
## $ attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out: chr [1:100] "neutral" "neutral" "agree" "agree" ...
## $ actual_behaviors_i_use_social_media_solely_for_entertainment : chr [1:100] "neutral" "disagree" "agree" "agree" ...
## $ actual_behaviors_social_media_helps_in_increasing_my_self_confidence : chr [1:100] "disagree" "agree" "agree" "agree" ...
## $ actual_behaviors_social_media_influences_my_mood_throughout_the_day : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes : chr [1:100] "strongly agree" "disagree" "agree" "agree" ...
## $ actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it : chr [1:100] "agree" "disagree" "agree" "agree" ...Drop Redundant Columns
data <- select(data, -c(x1, x2))str(data)## tibble [100 × 24] (S3: tbl_df/tbl/data.frame)
## $ age : chr [1:100] "19-23" "19-23" "19-23" "24-26" ...
## $ gender : chr [1:100] "female" "female" "female" "female" ...
## $ faculty : chr [1:100] "business & management" "business & management" "business & management" "business & management" ...
## $ semester : num [1:100] 4 4 4 4 2 4 2 4 4 2 ...
## $ perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable : chr [1:100] "agree" "agree" "agree" "agree" ...
## $ perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with : chr [1:100] "agree" "agree" "strongly agree" "agree" ...
## $ perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ perceived_ease_of_use_i_would_find_social_media_is_easy_to_use : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ perceived_usefulness_using_the_social_media_helps_me_understand_others_better : chr [1:100] "agree" "agree" "strongly agree" "agree" ...
## $ perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment : chr [1:100] "agree" "agree" "agree" "agree" ...
## $ perceived_usefulness_using_social_media_can_improve_my_productivity : chr [1:100] "agree" "agree" "neutral" "neutral" ...
## $ perceived_usefulness_i_find_social_media_useful_for_me_as_a_student : chr [1:100] "agree" "agree" "agree" "neutral" ...
## $ perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment : chr [1:100] "agree" "agree" "agree" "neutral" ...
## $ attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis : chr [1:100] "agree" "agree" "strongly agree" "agree" ...
## $ attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis : chr [1:100] "agree" "strongly agree" "strongly agree" "agree" ...
## $ attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features : chr [1:100] "agree" "neutral" "agree" "agree" ...
## $ attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out: chr [1:100] "neutral" "neutral" "agree" "agree" ...
## $ actual_behaviors_i_use_social_media_solely_for_entertainment : chr [1:100] "neutral" "disagree" "agree" "agree" ...
## $ actual_behaviors_social_media_helps_in_increasing_my_self_confidence : chr [1:100] "disagree" "agree" "agree" "agree" ...
## $ actual_behaviors_social_media_influences_my_mood_throughout_the_day : chr [1:100] "agree" "neutral" "strongly agree" "agree" ...
## $ actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes : chr [1:100] "strongly agree" "disagree" "agree" "agree" ...
## $ actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it : chr [1:100] "agree" "disagree" "agree" "agree" ...Categorical variables such as age, gender,
faculty, and Likert-scale columns can be converted to
factors:
data <- data %>%
mutate(
age = as.factor(age),
gender = as.factor(gender),
faculty = as.factor(faculty),
across(starts_with("perceived"), ~ as.factor(.)),
across(starts_with("attitudes"), ~ as.factor(.)),
across(starts_with("actual"), ~ as.factor(.))
)Checking the levels of these factors to ensure they are in the correct order:
# Check levels for one of the Likert-scale columns
levels(data$perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable)## [1] "agree" "disagree" "neutral"
## [4] "strongly agree" "strongly disagree"from the output Likert-scale responses are not ordered correctly, So
I will reorder the levels using the factor() function to
reorder the levels of all Likert-scale columns:
# Define the correct Likert scale order
likert_levels <- c("strongly disagree", "disagree", "neutral", "agree", "strongly agree")
# Apply the ordered factor levels to all Likert-scale columns
data <- data %>%
mutate(
across(starts_with("perceived"), ~ factor(., levels = likert_levels, ordered = TRUE)),
across(starts_with("attitudes"), ~ factor(., levels = likert_levels, ordered = TRUE)),
across(starts_with("actual"), ~ factor(., levels = likert_levels, ordered = TRUE))
)Rechecking the levels:
# Recheck the levels
levels(data$perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable)## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"Ensureing that all Likert-scale columns (starting with
perceived, attitudes, and actual)
are similarly reordered. To confirm, I will check the levels for any
other Likert-scale column:
levels(data$attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis)## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"levels(data$attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis)## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"levels(data$actual_behaviors_social_media_influences_my_mood_throughout_the_day)## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"summary(data)## age gender faculty semester
## 19-23 :70 female:57 business & management :54 Min. :1.00
## 24-26 :22 male :43 accountancy :13 1st Qu.:3.00
## 27 and above: 8 education :11 Median :4.00
## hotel & tourism management: 5 Mean :3.56
## health science : 4 3rd Qu.:4.00
## pharmacy : 4 Max. :8.00
## (Other) : 9
## perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable
## strongly disagree: 2
## disagree : 5
## neutral :21
## agree :54
## strongly agree :18
##
##
## perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with
## strongly disagree: 2
## disagree : 6
## neutral :12
## agree :54
## strongly agree :26
##
##
## perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media
## strongly disagree: 2
## disagree : 6
## neutral :19
## agree :56
## strongly agree :17
##
##
## perceived_ease_of_use_i_would_find_social_media_is_easy_to_use
## strongly disagree: 2
## disagree : 7
## neutral :10
## agree :49
## strongly agree :32
##
##
## perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers
## strongly disagree: 4
## disagree :10
## neutral :16
## agree :50
## strongly agree :20
##
##
## perceived_usefulness_using_the_social_media_helps_me_understand_others_better
## strongly disagree: 2
## disagree : 6
## neutral :17
## agree :63
## strongly agree :12
##
##
## perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment
## strongly disagree: 2
## disagree : 6
## neutral :17
## agree :59
## strongly agree :16
##
##
## perceived_usefulness_using_social_media_can_improve_my_productivity
## strongly disagree: 4
## disagree :16
## neutral :20
## agree :48
## strongly agree :12
##
##
## perceived_usefulness_i_find_social_media_useful_for_me_as_a_student
## strongly disagree: 2
## disagree : 6
## neutral :14
## agree :58
## strongly agree :20
##
##
## perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment
## strongly disagree: 4
## disagree : 7
## neutral :16
## agree :51
## strongly agree :22
##
##
## attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis
## strongly disagree: 2
## disagree : 8
## neutral :15
## agree :54
## strongly agree :21
##
##
## attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis
## strongly disagree: 3
## disagree : 9
## neutral :19
## agree :53
## strongly agree :16
##
##
## attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms
## strongly disagree: 2
## disagree : 7
## neutral :16
## agree :58
## strongly agree :17
##
##
## attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features
## strongly disagree: 5
## disagree :13
## neutral :36
## agree :40
## strongly agree : 6
##
##
## attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out
## strongly disagree: 8
## disagree :17
## neutral :35
## agree :33
## strongly agree : 7
##
##
## actual_behaviors_i_use_social_media_solely_for_entertainment
## strongly disagree: 2
## disagree :10
## neutral :19
## agree :48
## strongly agree :21
##
##
## actual_behaviors_social_media_helps_in_increasing_my_self_confidence
## strongly disagree: 2
## disagree :14
## neutral :33
## agree :43
## strongly agree : 8
##
##
## actual_behaviors_social_media_influences_my_mood_throughout_the_day
## strongly disagree: 4
## disagree : 8
## neutral :18
## agree :55
## strongly agree :15
##
##
## actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes
## strongly disagree: 3
## disagree : 6
## neutral :22
## agree :48
## strongly agree :21
##
##
## actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it
## strongly disagree:10
## disagree :14
## neutral :25
## agree :40
## strongly agree :11
##
## table(data$perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable)##
## strongly disagree disagree neutral agree
## 2 5 21 54
## strongly agree
## 18b. Tidy Data
Renaming columns for making data easier to work with and interpret:
data <- data %>%
rename(
interaction_clear = perceived_ease_of_use_my_interaction_with_social_media_would_be_clear_and_understandable,
interaction_flexible = perceived_ease_of_use_i_would_find_using_social_media_to_be_flexible_to_interact_with,
ease_skill = perceived_ease_of_use_it_would_be_easy_for_me_to_become_skilled_at_using_social_media,
ease_use = perceived_ease_of_use_i_would_find_social_media_is_easy_to_use,
seek_teacher_help = perceived_ease_of_use_i_use_social_media_sites_to_seek_help_from_my_teachers,
usefulness_understanding = perceived_usefulness_using_the_social_media_helps_me_understand_others_better,
usefulness_social_skills = perceived_usefulness_social_media_helps_me_in_terms_of_social_skills_study_and_entertainment,
productivity = perceived_usefulness_using_social_media_can_improve_my_productivity,
effectiveness = perceived_usefulness_using_social_media_can_enhances_my_effectiveness_in_my_assessment,
daily_need = attitudes_towards_use_i_feel_the_need_to_use_any_social_media_on_daily_basis,
difficult_without = attitudes_towards_use_it_is_difficult_for_me_to_not_use_social_media_on_daily_basis,
adapt_features = attitudes_towards_use_i_am_able_to_adapt_to_new_features_from_the_existing_social_media_platforms,
fomo = attitudes_towards_use_i_am_always_updated_with_the_new_features_because_i_am_scared_of_having_fomo_fear_of_missing_out,
entertainment = actual_behaviors_i_use_social_media_solely_for_entertainment,
self_confidence = actual_behaviors_social_media_helps_in_increasing_my_self_confidence,
mood_influence = actual_behaviors_social_media_influences_my_mood_throughout_the_day,
boredom_killer = actual_behaviors_i_use_social_media_to_kill_the_boredom_especially_during_classes,
obsession = actual_behaviors_i_am_obsessed_using_social_media_and_feel_incomplete_without_it
)Rechecking:
colnames(data)## [1] "age"
## [2] "gender"
## [3] "faculty"
## [4] "semester"
## [5] "interaction_clear"
## [6] "interaction_flexible"
## [7] "ease_skill"
## [8] "ease_use"
## [9] "seek_teacher_help"
## [10] "usefulness_understanding"
## [11] "usefulness_social_skills"
## [12] "productivity"
## [13] "perceived_usefulness_i_find_social_media_useful_for_me_as_a_student"
## [14] "effectiveness"
## [15] "daily_need"
## [16] "difficult_without"
## [17] "adapt_features"
## [18] "attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features"
## [19] "fomo"
## [20] "entertainment"
## [21] "self_confidence"
## [22] "mood_influence"
## [23] "boredom_killer"
## [24] "obsession"I had 2 colnames that does not change:
data <- data %>%
rename(
usefulness_student = perceived_usefulness_i_find_social_media_useful_for_me_as_a_student,
invest_social_media = attitudes_towards_use_i_am_willing_to_invest_my_time_and_money_for_social_media_features
)Rechecking again:
colnames(data)## [1] "age" "gender"
## [3] "faculty" "semester"
## [5] "interaction_clear" "interaction_flexible"
## [7] "ease_skill" "ease_use"
## [9] "seek_teacher_help" "usefulness_understanding"
## [11] "usefulness_social_skills" "productivity"
## [13] "usefulness_student" "effectiveness"
## [15] "daily_need" "difficult_without"
## [17] "adapt_features" "invest_social_media"
## [19] "fomo" "entertainment"
## [21] "self_confidence" "mood_influence"
## [23] "boredom_killer" "obsession"Ensuring that the columns are assigned the correct data types:
# Check the structure of the data
str(data)## tibble [100 × 24] (S3: tbl_df/tbl/data.frame)
## $ age : Factor w/ 3 levels "19-23","24-26",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ gender : Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 1 1 ...
## $ faculty : Factor w/ 12 levels "accountancy",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ semester : num [1:100] 4 4 4 4 2 4 2 4 4 2 ...
## $ interaction_clear : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 4 4 3 3 4 4 5 ...
## $ interaction_flexible : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 5 2 4 4 4 4 ...
## $ ease_skill : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 3 ...
## $ ease_use : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 5 ...
## $ seek_teacher_help : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 3 2 4 4 4 4 ...
## $ usefulness_understanding: Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 2 3 3 4 4 4 ...
## $ usefulness_social_skills: Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 4 3 2 3 4 4 4 ...
## $ productivity : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 3 3 2 2 2 4 4 2 ...
## $ usefulness_student : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 3 2 5 4 4 4 5 ...
## $ effectiveness : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 3 2 4 3 4 4 3 ...
## $ daily_need : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 4 2 2 4 4 5 ...
## $ difficult_without : Ord.factor w/ 5 levels "strongly disagree"<..: 4 5 5 4 4 4 2 4 4 5 ...
## $ adapt_features : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 4 ...
## $ invest_social_media : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 4 4 1 1 2 4 3 2 ...
## $ fomo : Ord.factor w/ 5 levels "strongly disagree"<..: 3 3 4 4 1 2 2 4 3 3 ...
## $ entertainment : Ord.factor w/ 5 levels "strongly disagree"<..: 3 2 4 4 4 4 4 4 3 5 ...
## $ self_confidence : Ord.factor w/ 5 levels "strongly disagree"<..: 2 4 4 4 3 2 4 4 3 3 ...
## $ mood_influence : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 1 2 3 4 3 5 ...
## $ boredom_killer : Ord.factor w/ 5 levels "strongly disagree"<..: 5 2 4 4 5 4 4 4 3 5 ...
## $ obsession : Ord.factor w/ 5 levels "strongly disagree"<..: 4 2 4 4 3 3 2 4 2 5 ...# Convert categorical variables to factors and numeric variables to numeric
data <- data %>%
mutate(
age = as.factor(age),
gender = as.factor(gender),
faculty = as.factor(faculty),
semester = as.numeric(semester), # Ensure it's numeric
across(starts_with("interaction"), as.factor), # Likert scale as factors
across(starts_with("usefulness"), as.factor),
across(starts_with("daily"), as.factor),
across(starts_with("fomo"), as.factor),
across(starts_with("entertainment"), as.factor),
across(starts_with("boredom"), as.factor)
)
# Recheck structure
str(data)## tibble [100 × 24] (S3: tbl_df/tbl/data.frame)
## $ age : Factor w/ 3 levels "19-23","24-26",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ gender : Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 1 1 ...
## $ faculty : Factor w/ 12 levels "accountancy",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ semester : num [1:100] 4 4 4 4 2 4 2 4 4 2 ...
## $ interaction_clear : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 4 4 3 3 4 4 5 ...
## $ interaction_flexible : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 5 2 4 4 4 4 ...
## $ ease_skill : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 3 ...
## $ ease_use : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 5 ...
## $ seek_teacher_help : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 3 2 4 4 4 4 ...
## $ usefulness_understanding: Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 2 3 3 4 4 4 ...
## $ usefulness_social_skills: Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 4 3 2 3 4 4 4 ...
## $ productivity : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 3 3 2 2 2 4 4 2 ...
## $ usefulness_student : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 3 2 5 4 4 4 5 ...
## $ effectiveness : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 3 2 4 3 4 4 3 ...
## $ daily_need : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 4 2 2 4 4 5 ...
## $ difficult_without : Ord.factor w/ 5 levels "strongly disagree"<..: 4 5 5 4 4 4 2 4 4 5 ...
## $ adapt_features : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 4 ...
## $ invest_social_media : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 4 4 1 1 2 4 3 2 ...
## $ fomo : Ord.factor w/ 5 levels "strongly disagree"<..: 3 3 4 4 1 2 2 4 3 3 ...
## $ entertainment : Ord.factor w/ 5 levels "strongly disagree"<..: 3 2 4 4 4 4 4 4 3 5 ...
## $ self_confidence : Ord.factor w/ 5 levels "strongly disagree"<..: 2 4 4 4 3 2 4 4 3 3 ...
## $ mood_influence : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 1 2 3 4 3 5 ...
## $ boredom_killer : Ord.factor w/ 5 levels "strongly disagree"<..: 5 2 4 4 5 4 4 4 3 5 ...
## $ obsession : Ord.factor w/ 5 levels "strongly disagree"<..: 4 2 4 4 3 3 2 4 2 5 ...Checking Numerical Data:
# Summary statistics for numerical columns
summary(data$semester)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 3.00 4.00 3.56 4.00 8.00# Identify unusual values
data %>% filter(semester < 1 | semester > 8) # Example for semester## # A tibble: 0 × 24
## # ℹ 24 variables: age <fct>, gender <fct>, faculty <fct>, semester <dbl>,
## # interaction_clear <ord>, interaction_flexible <ord>, ease_skill <ord>,
## # ease_use <ord>, seek_teacher_help <ord>, usefulness_understanding <ord>,
## # usefulness_social_skills <ord>, productivity <ord>,
## # usefulness_student <ord>, effectiveness <ord>, daily_need <ord>,
## # difficult_without <ord>, adapt_features <ord>, invest_social_media <ord>,
## # fomo <ord>, entertainment <ord>, self_confidence <ord>, …Checking categorical data:
# Check levels for each factor column
lapply(data %>% select(where(is.factor)), levels)## $age
## [1] "19-23" "24-26" "27 and above"
##
## $gender
## [1] "female" "male"
##
## $faculty
## [1] "accountancy"
## [2] "business & management"
## [3] "college of built environment"
## [4] "college of computing, informatics and media"
## [5] "college of creative art"
## [6] "dentistry"
## [7] "education"
## [8] "health science"
## [9] "hotel & tourism management"
## [10] "medicine"
## [11] "pharmacy"
## [12] "Unknown"
##
## $interaction_clear
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $interaction_flexible
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $ease_skill
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $ease_use
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $seek_teacher_help
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $usefulness_understanding
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $usefulness_social_skills
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $productivity
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $usefulness_student
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $effectiveness
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $daily_need
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $difficult_without
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $adapt_features
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $invest_social_media
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $fomo
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $entertainment
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $self_confidence
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $mood_influence
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $boredom_killer
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"
##
## $obsession
## [1] "strongly disagree" "disagree" "neutral"
## [4] "agree" "strongly agree"# Example: Ensure Likert responses are valid for "interaction_clear"
table(data$interaction_clear)##
## strongly disagree disagree neutral agree
## 2 5 21 54
## strongly agree
## 18# Correct invalid entries
data <- data %>%
filter(!(interaction_clear %in% c("unexpected_response"))) # Replace with real checkHandling "Unknown" in faculty:
data <- data %>% filter(faculty != "Unknown")Checking:
levels(data$faculty)## [1] "accountancy"
## [2] "business & management"
## [3] "college of built environment"
## [4] "college of computing, informatics and media"
## [5] "college of creative art"
## [6] "dentistry"
## [7] "education"
## [8] "health science"
## [9] "hotel & tourism management"
## [10] "medicine"
## [11] "pharmacy"
## [12] "Unknown"Still there, I will drop it:
data$faculty <- droplevels(data$faculty)Checking:
levels(data$faculty)## [1] "accountancy"
## [2] "business & management"
## [3] "college of built environment"
## [4] "college of computing, informatics and media"
## [5] "college of creative art"
## [6] "dentistry"
## [7] "education"
## [8] "health science"
## [9] "hotel & tourism management"
## [10] "medicine"
## [11] "pharmacy"Final recheck:
summary(data)## age gender faculty semester
## 19-23 :69 female:57 business & management :54 Min. :1.000
## 24-26 :22 male :42 accountancy :13 1st Qu.:3.000
## 27 and above: 8 education :11 Median :4.000
## hotel & tourism management: 5 Mean :3.566
## health science : 4 3rd Qu.:4.000
## pharmacy : 4 Max. :8.000
## (Other) : 8
## interaction_clear interaction_flexible ease_skill
## strongly disagree: 2 strongly disagree: 2 strongly disagree: 2
## disagree : 5 disagree : 6 disagree : 6
## neutral :21 neutral :12 neutral :19
## agree :53 agree :54 agree :55
## strongly agree :18 strongly agree :25 strongly agree :17
##
##
## ease_use seek_teacher_help usefulness_understanding
## strongly disagree: 2 strongly disagree: 4 strongly disagree: 2
## disagree : 7 disagree :10 disagree : 6
## neutral :10 neutral :16 neutral :17
## agree :48 agree :49 agree :62
## strongly agree :32 strongly agree :20 strongly agree :12
##
##
## usefulness_social_skills productivity
## strongly disagree: 2 strongly disagree: 4
## disagree : 6 disagree :16
## neutral :17 neutral :20
## agree :59 agree :47
## strongly agree :15 strongly agree :12
##
##
## usefulness_student effectiveness daily_need
## strongly disagree: 2 strongly disagree: 4 strongly disagree: 2
## disagree : 6 disagree : 7 disagree : 8
## neutral :14 neutral :16 neutral :15
## agree :58 agree :50 agree :54
## strongly agree :19 strongly agree :22 strongly agree :20
##
##
## difficult_without adapt_features invest_social_media
## strongly disagree: 3 strongly disagree: 2 strongly disagree: 5
## disagree : 9 disagree : 7 disagree :13
## neutral :19 neutral :16 neutral :36
## agree :52 agree :57 agree :40
## strongly agree :16 strongly agree :17 strongly agree : 5
##
##
## fomo entertainment self_confidence
## strongly disagree: 8 strongly disagree: 2 strongly disagree: 2
## disagree :17 disagree : 9 disagree :14
## neutral :35 neutral :19 neutral :32
## agree :32 agree :48 agree :43
## strongly agree : 7 strongly agree :21 strongly agree : 8
##
##
## mood_influence boredom_killer obsession
## strongly disagree: 4 strongly disagree: 3 strongly disagree:10
## disagree : 8 disagree : 6 disagree :13
## neutral :18 neutral :21 neutral :25
## agree :54 agree :48 agree :40
## strongly agree :15 strongly agree :21 strongly agree :11
##
## During the WRANGLE phase, the dataset was thoroughly cleaned and
prepared for analysis to ensure accuracy and reliability. Missing values
were addressed by removing incomplete entries in the
faculty and semester columns, resulting in a
dataset free of null values. Redundant columns were dropped, and overly
long column names were simplified for ease of use. Categorical variables
such as age, gender, faculty, and
Likert-scale responses were appropriately converted to factors and
ordered to reflect the scale’s hierarchy. Numerical data, such as
semester, was validated for outliers and erroneous values,
confirming data consistency. Lastly, an overall review ensured that the
dataset was tidy, with all variables having the correct format, logical
ordering, and meaningful labels. This cleaned dataset is now
well-structured and ready for exploratory and statistical analysis in
the next phase.
3. EXPLORE
1. Exploring Question 1
How do students’ demographic characteristics (e.g., age, gender, faculty) influence their social media usage and perceptions?
a. Visualizeing Demographic Distributions
# Bar charts for demographics
library(ggplot2)
# Age distribution
ggplot(data, aes(x = age)) +
geom_bar() +
labs(title = "Age Distribution", x = "Age Group", y = "Count")
# Gender distribution
ggplot(data, aes(x = gender)) +
geom_bar() +
labs(title = "Gender Distribution", x = "Gender", y = "Count")
# Faculty distribution
ggplot(data, aes(x = faculty)) +
geom_bar() +
coord_flip() + # Use horizontal bars for better readability
labs(title = "Faculty Distribution", x = "Faculty", y = "Count")
Age Distribution
The bar chart shows the distribution of age groups in the dataset.
Observation: The 19–23 age group dominates the dataset, constituting a majority of the sample. The 24–26 group is a distant second, and the 27 and above group has the fewest participants.
Gender Distribution
The bar chart highlights the gender composition of the dataset.
Observation: Female students are slightly more represented than male students in the sample.
Faculty Distribution
The horizontal bar chart visualizes the number of students from each faculty.
Observation: The “Business & Management” faculty has the highest representation in the dataset by a large margin, followed by “Accountancy” and “Education.” Faculties like “Pharmacy,” “Medicine,” and “Creative Art” have minimal representation.
Group Comparisons for Likert-Scale Responses
# Boxplot of interaction_clear by age
ggplot(data, aes(x = age, y = as.numeric(interaction_clear), fill = age)) +
geom_boxplot() +
labs(title = "Interaction Clear by Age", x = "Age Group", y = "Perceived Interaction (Numeric Scale)") +
scale_y_continuous(breaks = 1:5, labels = levels(data$interaction_clear)) +
theme_minimal()
# Boxplot for gender and productivity
ggplot(data, aes(x = gender, y = as.numeric(productivity), fill = gender)) +
geom_boxplot() +
labs(title = "Productivity by Gender", x = "Gender", y = "Productivity (Numeric Scale)") +
scale_y_continuous(breaks = 1:5, labels = levels(data$productivity)) +
theme_minimal()
Interaction Clear by Age
The boxplot illustrates the perceived clarity of interaction with social media across age groups.
Observation: The 19–23 age group shows the widest variability, with responses ranging from “strongly disagree” to “strongly agree.” The median is around “agree.”
The 24–26 age group exhibits a more consistent pattern around “neutral” and “agree,” with fewer outliers.
The 27 and above age group appears to have higher perceived clarity, with most responses concentrated around “agree” and “strongly agree.”
Productivity by Gender
The boxplot compares the perceived impact of social media on productivity between male and female students.
Observation: Both male and female students’ medians are centered around “agree,” with similar interquartile ranges. However, there is slightly more variability in responses from male students, as seen in the wider spread of their boxplot.
The Question Answer
The First Question answer based on these information:
Based on the visuals, students’ demographic characteristics such as age, gender, and faculty influence their social media usage and perceptions. Younger students (19–23) exhibit the most varied perceptions of interaction clarity, ranging from “strongly disagree” to “strongly agree,” whereas older students (27 and above) report consistently higher clarity, concentrated around “agree” and “strongly agree.” Gender differences in productivity perceptions are minimal, with both male and female students centering around “agree.” Faculty representation reveals that “Business & Management” students dominate the sample, potentially biasing findings toward their experiences. Overall, demographic factors, especially age, show meaningful differences in social media perceptions, while gender exhibits minimal impact.
2. Exploring Question 2
What is the relationship between the amount of time spent on social media and students’ academic productivity?
I am exploring the relationship between:
Time Spent on Social Media: Proxy variables could include:
daily_need(e.g., “I feel the need to use social media daily”).fomo(Fear of Missing Out, which might influence usage intensity).
Academic Productivity: Proxy variables could include:
productivity(e.g., “Using social media improves my productivity”).effectiveness(e.g., “Social media enhances my effectiveness in assessments”).a. Visualization
Box Plot for daily_need
vs. productivity
library(ggplot2)
ggplot(data, aes(x = daily_need, y = productivity, fill = daily_need)) +
geom_boxplot() +
labs(
title = "Academic Productivity by Time Spent on Social Media",
x = "Daily Need for Social Media",
y = "Productivity (Likert Scale)"
) +
theme_minimal()
Descriptive Statistics for daily_need and
productivity
data %>%
group_by(daily_need) %>%
summarize(
mean_productivity = mean(as.numeric(productivity)),
median_productivity = median(as.numeric(productivity)),
count = n()
)## # A tibble: 5 × 4
## daily_need mean_productivity median_productivity count
## <ord> <dbl> <dbl> <int>
## 1 strongly disagree 1 1 2
## 2 disagree 2.5 2 8
## 3 neutral 3.33 3 15
## 4 agree 3.72 4 54
## 5 strongly agree 3.55 4 20The scatter plot suggests a generally positive relationship between the daily need for social media and academic productivity, although some variability exists. Students who “agree” or “strongly agree” with the statement about needing social media daily tend to report higher productivity levels on average.
While higher engagement with social media (indicated by agreeing with daily need) appears to correlate with increased productivity, the relationship is not strictly linear. The slight dip in mean productivity for “strongly agree” might suggest a threshold where excessive reliance on social media could diminish productivity.
This trend might indicate that moderate and purposeful use of social media aids in productivity (e.g., collaborative learning or accessing academic resources). However, over-dependence might begin to introduce distractions, reducing productivity.
The Question Answer
The Second Question answer based on these information:
Based on the visualizations, there appears to be a moderate positive relationship between the amount of time spent on social media (as indicated by the daily need for social media) and students’ academic productivity. Students who “agree” or “strongly agree” with having a daily need for social media tend to report higher levels of productivity, with average productivity ratings around 3.5 to 3.7 on a 5-point Likert scale. Conversely, those who “disagree” or “strongly disagree” with a daily need for social media report lower productivity levels, averaging around 2.5 or less. While the data suggests that moderate use or perceived necessity of social media may be associated with higher academic productivity, further analysis would be needed to confirm whether this relationship is causal or influenced by other factors.
3. Exploring Question 2
How do students use social media for academic versus non-academic purposes?
I am exploring the columns that represent academic and non-academic uses could include:
Academic Purpose:
seek_teacher_helpusefulness_social_skillsproductivityusefulness_studenteffectiveness
Non-Academic Purpose:
entertainmentself_confidencemood_influenceboredom_killerobsession
a. Visualization
library(ggplot2)
# Academic Purposes
academic_columns <- c("seek_teacher_help", "usefulness_social_skills", "productivity",
"usefulness_student", "effectiveness")
academic_data <- data[academic_columns] # Filter for academic purpose columns
academic_long <- tidyr::pivot_longer(academic_data, everything(), names_to = "purpose", values_to = "response")
ggplot(academic_long, aes(x = purpose, fill = response)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Academic Purposes for Social Media Use", x = "Purpose", y = "Count")
# Non-Academic Purposes
non_academic_columns <- c("entertainment", "self_confidence", "mood_influence",
"boredom_killer", "obsession")
non_academic_data <- data[non_academic_columns]
non_academic_long <- tidyr::pivot_longer(non_academic_data, everything(), names_to = "purpose", values_to = "response")
ggplot(non_academic_long, aes(x = purpose, fill = response)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Non-Academic Purposes for Social Media Use", x = "Purpose", y = "Count")
Academic Purposes
The visualization shows that students generally agree on the usefulness of social media for academic purposes, particularly for “usefulness as a student” and “social skills.” Neutral responses are noticeable for “productivity” and “seeking teacher help,” indicating mixed views, while disagreement levels are minimal.
Non-Academic Purposes
For non-academic uses, students strongly agree on “entertainment” and “mood influence,” highlighting social media’s role in leisure and emotional engagement. Neutral responses are common for “obsession” and “boredom killer,” with disagreement levels slightly higher than academic uses.
Comparison
Students find social media beneficial for both academic and non-academic purposes, with agreement levels dominating in both contexts. However, non-academic uses, particularly “entertainment” and “mood influence,” are perceived as more impactful than academic purposes.
The Question Answer
The Third Question answer based on these information:
Based on the provided visuals, students use social media for both academic and non-academic purposes, but with varying levels of agreement. For academic purposes, most students agree that social media enhances their effectiveness as students, aids in social skills, and supports productivity, although some remain neutral or disagree regarding its use for seeking help from teachers. In contrast, for non-academic purposes, students strongly agree on its role in entertainment and influencing their mood. While social media serves as a tool for both domains, its non-academic applications, particularly for leisure and emotional engagement, appear more impactful and widely acknowledged than its academic benefits.
4. Exploring Question 4
Does Fear of Missing Out (FOMO) significantly influence students’ social media behaviors, and how does it impact their academic outcomes?
Step 1: FOMO and Social Media Behaviors
I will create a visualization comparing FOMO levels with:
Entertainment
Obsession
Mood Influence
Boredom Killer
Visualization
# Load necessary libraries library(ggplot2) library(tidyr) # Select relevant columns for social media behaviors and FOMO fomo_behavior_data <- data %>% select(fomo, entertainment, obsession, mood_influence, boredom_killer) %>% pivot_longer(cols = entertainment:boredom_killer, names_to = "behavior", values_to = "response") # Create the visualization ggplot(fomo_behavior_data, aes(x = fomo, fill = response)) + geom_bar(position = "fill") + facet_wrap(~behavior, scales = "free_y", ncol = 2) + labs( title = "FOMO and Social Media Behaviors", x = "FOMO Levels", y = "Proportion", fill = "Response" ) + theme_minimal()
1. Boredom Killer
Higher FOMO levels (“strongly agree” and “agree”) are associated with a higher proportion of students agreeing or strongly agreeing that they use social media to kill boredom.
Students with lower FOMO levels (“strongly disagree” or “disagree”) show relatively higher proportions of neutral or negative responses.
2. Entertainment
A similar pattern emerges: students with higher FOMO levels (“agree” or “strongly agree”) are more likely to use social media primarily for entertainment, as seen by the increased proportions of “agree” and “strongly agree”.
Students with lower FOMO levels exhibit more neutral or disagree responses.
3. Mood Influence
Social media’s influence on mood appears to increase with FOMO levels. Students who “agree” or “strongly agree” to experiencing FOMO tend to also “agree” or “strongly agree” that social media affects their mood throughout the day.
Students with low FOMO levels (“strongly disagree” or “disagree”) tend to report less mood influence from social media.
4. Obsession
High FOMO levels are strongly tied to obsessive use of social media, with a noticeable increase in “agree” and “strongly agree” responses as FOMO levels increase.
Students with low FOMO levels are less likely to feel obsessed with using social media, as seen in the higher proportion of “neutral” or “disagree” responses.
Step 2: FOMO and Academic Outcomes
I will create a similar visualization for academic-related variables:
Productivity
Effectiveness
Usefulness as a Student
Visualization
# Filter relevant columns academic_outcomes <- data %>% select(fomo, productivity, effectiveness, usefulness_student) %>% pivot_longer(cols = c(productivity, effectiveness, usefulness_student), names_to = "Outcome", values_to = "Response") # Visualization academic_outcomes %>% group_by(fomo, Outcome, Response) %>% summarize(Count = n(), .groups = "drop") %>% mutate(Proportion = Count / sum(Count)) %>% ggplot(aes(x = fomo, y = Proportion, fill = Response)) + geom_col(position = "fill") + facet_wrap(~Outcome, scales = "free") + labs( title = "FOMO and Academic Outcomes", x = "FOMO Levels", y = "Proportion", fill = "Response" ) + theme_minimal() + scale_fill_brewer(palette = "Spectral")
Productivity:
Students with lower FOMO levels (e.g., “strongly disagree” or “disagree”) show a higher proportion of responses in the “neutral” or “agree” categories for productivity.
As FOMO levels increase (e.g., “agree” or “strongly agree”), the proportion of “neutral” responses decreases, and more responses shift toward “agree” or “strongly agree.”
This suggests that students with higher FOMO tend to perceive themselves as more productive academically, possibly because they use social media actively to stay informed or engaged.
Effectiveness:
Similar to productivity, the proportion of “agree” and “strongly agree” responses increases with rising FOMO levels.
Lower FOMO levels (e.g., “strongly disagree”) are associated with more “neutral” or “disagree” responses for effectiveness.
This indicates that students experiencing higher FOMO perceive themselves as more academically effective, potentially due to their frequent use of social media for academic purposes.
Usefulness as a Student:
Higher FOMO levels are strongly correlated with increased proportions of “agree” and “strongly agree” responses for the usefulness of social media as a student.
Lower FOMO levels are associated with a significant proportion of “neutral” or “disagree” responses, indicating that students with lower FOMO may not view social media as particularly useful in their academic roles.
The Question Answer
The Fourth Question answer based on these information:
Fear of Missing Out (FOMO) significantly influences students’ social media behaviors. Students with higher FOMO levels (“agree” or “strongly agree”) are more likely to use social media for entertainment, mood regulation, and coping with boredom. For example, high FOMO individuals frequently agree with statements regarding social media’s role in mood influence and obsession, reflecting a deeper attachment to these platforms. This trend highlights that FOMO drives students to heavily rely on social media as a mechanism for emotional and psychological engagement.
FOMO also impacts academic outcomes by shaping students’ perceptions of productivity, effectiveness, and the usefulness of social media for their studies. Students with higher FOMO levels are more likely to believe that social media contributes positively to their academic productivity and effectiveness, viewing it as an essential tool for academic purposes. While these patterns suggest a positive academic association, the reliance on social media fueled by FOMO may also raise questions about potential distraction or over-dependence on digital platforms.
Summary of the Explore Phase
In the Explore phase, I analyzed the dataset to uncover patterns and insights related to the research questions. Through visualizations and descriptive analysis, I examined the relationships between demographic characteristics (age, gender, faculty) and students’ perceptions of social media, identified links between time spent on social media and academic productivity, and explored how students utilize social media for academic versus non-academic purposes. Additionally, I investigated the influence of Fear of Missing Out (FOMO) on students’ social media behaviors and academic outcomes.
4. MODEL
In the Model phase, I will try to predict academic productivity based on:
FOMO (Fear of Missing Out)
Daily need for social media
Age
Gender
Social media usage behaviors (e.g., entertainment, mood influence, obsession, etc.)
Checking for missing values:
colSums(is.na(data)) # Shows the number of missing values per column## age gender faculty
## 0 0 0
## semester interaction_clear interaction_flexible
## 0 0 0
## ease_skill ease_use seek_teacher_help
## 0 0 0
## usefulness_understanding usefulness_social_skills productivity
## 0 0 0
## usefulness_student effectiveness daily_need
## 0 0 0
## difficult_without adapt_features invest_social_media
## 0 0 0
## fomo entertainment self_confidence
## 0 0 0
## mood_influence boredom_killer obsession
## 0 0 0Drop the missing values: “I initially dropped the variable, and later encountered issues with the code during the publishing phase because the variable had already been removed. As a result, some sections of the code referencing the dropped variable could not run, and those parts of the code had to be deleted to proceed”
Rechecking the whole data:
summary(data) # Check if variables are clean## age gender faculty semester
## 19-23 :69 female:57 business & management :54 Min. :1.000
## 24-26 :22 male :42 accountancy :13 1st Qu.:3.000
## 27 and above: 8 education :11 Median :4.000
## hotel & tourism management: 5 Mean :3.566
## health science : 4 3rd Qu.:4.000
## pharmacy : 4 Max. :8.000
## (Other) : 8
## interaction_clear interaction_flexible ease_skill
## strongly disagree: 2 strongly disagree: 2 strongly disagree: 2
## disagree : 5 disagree : 6 disagree : 6
## neutral :21 neutral :12 neutral :19
## agree :53 agree :54 agree :55
## strongly agree :18 strongly agree :25 strongly agree :17
##
##
## ease_use seek_teacher_help usefulness_understanding
## strongly disagree: 2 strongly disagree: 4 strongly disagree: 2
## disagree : 7 disagree :10 disagree : 6
## neutral :10 neutral :16 neutral :17
## agree :48 agree :49 agree :62
## strongly agree :32 strongly agree :20 strongly agree :12
##
##
## usefulness_social_skills productivity
## strongly disagree: 2 strongly disagree: 4
## disagree : 6 disagree :16
## neutral :17 neutral :20
## agree :59 agree :47
## strongly agree :15 strongly agree :12
##
##
## usefulness_student effectiveness daily_need
## strongly disagree: 2 strongly disagree: 4 strongly disagree: 2
## disagree : 6 disagree : 7 disagree : 8
## neutral :14 neutral :16 neutral :15
## agree :58 agree :50 agree :54
## strongly agree :19 strongly agree :22 strongly agree :20
##
##
## difficult_without adapt_features invest_social_media
## strongly disagree: 3 strongly disagree: 2 strongly disagree: 5
## disagree : 9 disagree : 7 disagree :13
## neutral :19 neutral :16 neutral :36
## agree :52 agree :57 agree :40
## strongly agree :16 strongly agree :17 strongly agree : 5
##
##
## fomo entertainment self_confidence
## strongly disagree: 8 strongly disagree: 2 strongly disagree: 2
## disagree :17 disagree : 9 disagree :14
## neutral :35 neutral :19 neutral :32
## agree :32 agree :48 agree :43
## strongly agree : 7 strongly agree :21 strongly agree : 8
##
##
## mood_influence boredom_killer obsession
## strongly disagree: 4 strongly disagree: 3 strongly disagree:10
## disagree : 8 disagree : 6 disagree :13
## neutral :18 neutral :21 neutral :25
## agree :54 agree :48 agree :40
## strongly agree :15 strongly agree :21 strongly agree :11
##
## str(data) # Verify data types (numeric, factors, etc.)## tibble [99 × 24] (S3: tbl_df/tbl/data.frame)
## $ age : Factor w/ 3 levels "19-23","24-26",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ gender : Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 1 1 ...
## $ faculty : Factor w/ 11 levels "accountancy",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ semester : num [1:99] 4 4 4 4 2 4 2 4 4 2 ...
## $ interaction_clear : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 4 4 3 3 4 4 5 ...
## $ interaction_flexible : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 5 2 4 4 4 4 ...
## $ ease_skill : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 3 ...
## $ ease_use : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 5 ...
## $ seek_teacher_help : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 3 2 4 4 4 4 ...
## $ usefulness_understanding: Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 2 3 3 4 4 4 ...
## $ usefulness_social_skills: Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 4 3 2 3 4 4 4 ...
## $ productivity : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 3 3 2 2 2 4 4 2 ...
## $ usefulness_student : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 3 2 5 4 4 4 5 ...
## $ effectiveness : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 4 3 2 4 3 4 4 3 ...
## $ daily_need : Ord.factor w/ 5 levels "strongly disagree"<..: 4 4 5 4 4 2 2 4 4 5 ...
## $ difficult_without : Ord.factor w/ 5 levels "strongly disagree"<..: 4 5 5 4 4 4 2 4 4 5 ...
## $ adapt_features : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 5 2 4 4 4 4 ...
## $ invest_social_media : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 4 4 1 1 2 4 3 2 ...
## $ fomo : Ord.factor w/ 5 levels "strongly disagree"<..: 3 3 4 4 1 2 2 4 3 3 ...
## $ entertainment : Ord.factor w/ 5 levels "strongly disagree"<..: 3 2 4 4 4 4 4 4 3 5 ...
## $ self_confidence : Ord.factor w/ 5 levels "strongly disagree"<..: 2 4 4 4 3 2 4 4 3 3 ...
## $ mood_influence : Ord.factor w/ 5 levels "strongly disagree"<..: 4 3 5 4 1 2 3 4 3 5 ...
## $ boredom_killer : Ord.factor w/ 5 levels "strongly disagree"<..: 5 2 4 4 5 4 4 4 3 5 ...
## $ obsession : Ord.factor w/ 5 levels "strongly disagree"<..: 4 2 4 4 3 3 2 4 2 5 ...Convert Ordinal Factors to Numeric Scores:
ordinal_columns <- c("daily_need", "fomo", "interaction_clear",
"interaction_flexible", "ease_skill",
"ease_use", "seek_teacher_help",
"usefulness_understanding", "usefulness_social_skills",
"productivity", "usefulness_student", "effectiveness",
"difficult_without", "adapt_features",
"invest_social_media", "entertainment",
"self_confidence", "mood_influence",
"boredom_killer", "obsession")
data <- data %>%
mutate(across(all_of(ordinal_columns), as.numeric))Check if predictors are highly correlated, as this could affect the model’s performance:
install.packages("corrplot")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(corrplot)## corrplot 0.95 loaded# Adjusting label size and rotation
corrplot(cor(data %>% select_if(is.numeric)),
method = "circle",
tl.cex = 0.7, # Reduce text label size
tl.srt = 45) # Rotate text labels for better readability
This correlation matrix highlights the relationships between variables:
Strong Positive Correlations: Variables like
usefulness_social_skillsandusefulness_studentshow strong correlations, indicating that students who value social media for one academic purpose often value it for others as well.Weak/No Correlations: Some variables, such as
semesterandboredom_killer, show weak or negligible relationships, suggesting they may not significantly influence one another.Negative Correlations: Few red circles suggest weak negative relationships, which may indicate potential areas to explore further, such as
daily_needandproductivity.
Visualizations
to explore the relationships between fomo,
daily_need, and productivity:
ggplot(data, aes(x = fomo, y = productivity)) +
geom_jitter(aes(color = daily_need), width = 0.2) +
labs(title = "FOMO, Daily Need, and Productivity", x = "FOMO Levels", y = "Productivity") +
theme_minimal()
The visualization shows the relationship between FOMO, Daily Need, and Productivity. Higher FOMO levels (4 and 5) show diverse productivity outcomes, indicating no clear linear relationship. Daily need levels, represented by the color gradient, are distributed across all FOMO levels and productivity scores, suggesting no strong interaction. This highlights individual variability and the need for further analysis to quantify relationships between these factors.
Check Multicollinearity
I will use a Variance Inflation Factor (VIF) analysis to ensure
predictors like fomo and daily_need are not
highly collinear:
install.packages("car")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some# Example: Fit a regression model
model <- lm(productivity ~ fomo + daily_need + gender + age, data = data)
# Check VIF for multicollinearity
vif(model)## GVIF Df GVIF^(1/(2*Df))
## fomo 1.239786 1 1.113457
## daily_need 1.230140 1 1.109117
## gender 1.218793 1 1.103989
## age 1.296625 2 1.067096The Variance Inflation Factor (VIF) results indicate that there is no significant multicollinearity among the predictors in the regression model.
Build the Predictive Model
I am building a predictive model using the predictors (fomo, daily_need, gender, and age) to predict academic productivity.
# Build a linear regression model
model <- lm(productivity ~ fomo + daily_need + gender + age, data = data)
# Summary of the model
summary(model)##
## Call:
## lm(formula = productivity ~ fomo + daily_need + gender + age,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68702 -0.51629 0.05041 0.52471 2.36789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.20552 0.41664 2.893 0.00475 **
## fomo 0.47429 0.09240 5.133 1.55e-06 ***
## daily_need 0.21172 0.10522 2.012 0.04710 *
## gendermale -0.12624 0.19297 -0.654 0.51458
## age24-26 0.05456 0.23365 0.234 0.81588
## age27 and above 0.18517 0.33742 0.549 0.58446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8595 on 93 degrees of freedom
## Multiple R-squared: 0.3437, Adjusted R-squared: 0.3084
## F-statistic: 9.74 on 5 and 93 DF, p-value: 1.662e-07Evaluate the Model
install.packages("Metrics")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Evaluate model performance
library(Metrics)
# Predicted values
predictions <- predict(model, data)
# Calculate evaluation metrics
mae <- mae(data$productivity, predictions)
rmse <- rmse(data$productivity, predictions)
r_squared <- summary(model)$r.squared
list(MAE = mae, RMSE = rmse, R_squared = r_squared)## $MAE
## [1] 0.6330257
##
## $RMSE
## [1] 0.8330769
##
## $R_squared
## [1] 0.3436837- The evaluation of the predictive model indicates moderate performance. The Mean Absolute Error (MAE) of 0.633 suggests that, on average, the predictions deviate from actual academic productivity levels by about 0.63 units on the Likert scale. The Root Mean Squared Error (RMSE) of 0.833 reflects a slightly higher error magnitude, emphasizing that larger prediction errors occur but are not extreme. The R-Squared (R²) value of 0.343 indicates that the model explains approximately 34.3% of the variability in academic productivity, showing that the predictors (e.g., FOMO, daily need, gender, and age) have some influence but leave significant variance unexplained. While the model is a reasonable starting point, it suggests room for improvement, possibly through additional predictors or more complex modeling approaches.
In the Model phase, I built a predictive model to understand how various factors, such as FOMO, daily need for social media, gender, and age, influence academic productivity. After ensuring the data was properly prepared and multicollinearity was checked, I constructed a linear regression model. The model evaluation revealed moderate predictive performance, with an R-squared of 0.34, indicating that 34% of the variability in academic productivity is explained by the predictors. Metrics like MAE (0.63) and RMSE (0.83) showed the accuracy and error magnitude of the model, respectively. These results highlighted the impact of key variables on productivity, with FOMO and daily need showing notable contributions, while also suggesting that other unexplored factors might further improve the model’s performance. The findings provide a data-driven basis for understanding the complex dynamics between social media behavior and academic outcomes.
5. COMMUNICATE
Limitations and Ethical Considerations
While this study offers valuable insights into how social media influences students’ behaviors, perceptions, and academic outcomes, several limitations must be acknowledged. First, the dataset primarily relies on self-reported responses, which can introduce biases such as social desirability or inaccurate recall. The sample composition, heavily dominated by students from certain faculties (e.g., “Business & Management”), may limit the generalizability of findings to the broader student population. Additionally, the cross-sectional nature of the data restricts the ability to draw causal inferences, leaving room for ambiguity in understanding whether social media usage directly impacts academic productivity or vice versa.
From an ethical standpoint, it is essential to highlight the importance of protecting participants’ anonymity and confidentiality, as the data includes sensitive information about students’ behaviors and perceptions. Researchers should ensure that all findings are presented in an aggregated form to prevent the identification of individuals. Moreover, the study must avoid stigmatizing behaviors such as high social media use or Fear of Missing Out (FOMO) as inherently negative, as these could reflect complex socio-emotional needs. Transparency in reporting and acknowledging the limitations of the data is crucial to uphold the ethical integrity of the analysis and ensure that the findings are used constructively to support students and educational stakeholders.
Summary of Findings and Implications
This analysis explored the usage of social media among students and its impact on their academic productivity and behaviors. Key insights revealed that younger students (aged 19–23) were the most active social media users, while gender differences were minimal in their productivity-related responses. A moderate relationship was observed between the time spent on social media (daily need) and academic productivity, where excessive usage slightly reduced productivity. Students leveraged social media for academic purposes, such as seeking help from teachers and improving productivity, but also used it extensively for non-academic purposes, including entertainment and mood regulation. Notably, FOMO (Fear of Missing Out) emerged as a critical factor influencing compulsive social media behaviors and potentially hindering academic focus. Students with higher FOMO levels reported greater mood dependency and obsession with social media, indicating a need for balance and self-regulation.
A predictive model using linear regression highlighted FOMO and daily need as the most significant predictors of academic productivity, explaining 34% of the variance in productivity. This suggests that while social media can be a valuable tool for collaboration and learning, excessive or emotionally driven usage patterns could diminish its benefits. These findings emphasize the importance of managing social media habits to enhance academic outcomes.
The results have actionable implications for various stakeholders. Students should recognize how FOMO and over-reliance on social media can detract from academic focus and adopt strategies such as time-blocking and digital detoxing. Educators can integrate digital literacy into curricula and provide workshops to help students balance academic and non-academic usage effectively. Institutions can launch campaigns promoting intentional and productive social media use, while policymakers could support programs advocating responsible digital engagement. These efforts can foster a healthier digital environment, ensuring that social media use aligns with students’ academic and personal growth goals. Further research could expand on these findings by exploring additional factors like stress and motivation in the context of social media use.