FinalPresentation

Author

Alexandra Adrien

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Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Rows: 6000 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): addiction_level
dbl (8): age, daily_screen_time, social_media_hours, study_hours, sleep_hour...

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.

# A tibble: 6 × 2
  usage_group avg_productivity
  <fct>                  <dbl>
1 0–2                    57.1 
2 2–4                    44.3 
3 4–6                    32.2 
4 6–8                    19.3 
5 8+                      8.86
6 <NA>                   34.5

usage_group	avg_productivity
0–2	57.073368
2–4	44.306276
4–6	32.242165
6–8	19.344239
8+	8.862466
NA	34.479231

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The Scroll vs. The Score: How Social Media Usage Impacts Productivity

Research Question

To what extent does social media usage shape patterns of productivity and focus in digitally saturated environments?

Methodology

This study uses a data-driven approach to examine how social media usage in digital life affects workforce productivity. It relies on secondary datasets and analyzes variables like usage time and productivity scores using RStudio.

The method is correlational and exploratory, focusing on identifying relationships rather than causation. Data is cleaned, standardized, and analyzed through basic statistical tests and visualizations to identify patterns between social media use and productivity.

Quantitative Methods

Qualitative Methods

The quantitative analysis measures the relationship between social media usage and productivity using numerical data.

Key methods include:

Correlation analysis to assess relationships
Linear regression to test predictive effects
Data visualizations (e.g., scatterplots) to show trends

These techniques provide measurable evidence of how social media usage relates to productivity.

A qualitative interpretive approach is used to explain the patterns found in the data.

Rather than collecting new qualitative data, this study interprets results in the context of digital work habits, such as attention fragmentation and constant connectivity. This helps explain what the numerical trends suggest about productivity in digital environments.

Introduction

This project examines how social media usage within digital life influences productivity in the modern workforce. As social platforms become embedded in daily routines, they increasingly intersect with how individuals focus, manage time, and complete work-related tasks. This study analyzes datasets that measure time spent on social media alongside productivity outcomes to identify patterns between digital engagement and work performance.

The significance of this analysis lies in its relevance to today’s digitally saturated work environments. Understanding how social media impacts productivity provides insight into broader questions about attention, efficiency, and digital behavior. These findings can help clarify whether social media acts as a distraction, a neutral tool, or a factor that reshapes how productivity is experienced in modern work settings.

Data Set

Social Media Addiction vs Productivity Dataset
- https://www.kaggle.com/datasets/asifxzaman/social-media-addiction-vs-productivity-dataset

Data Set Analysis

1. Correlation Table (Key Relationships)

Variable	Productivity Score Relationship
Social Media Hours	-0.51 (moderate negative)
Focus Score	+0.57 (strong positive)
Sleep Hours	~0.00 (no real relationship)

What this means:

As social media usage increases, productivity decreases (clear negative relationship)
Focus is a major driver of productivity
Sleep, in this dataset, is not strongly tied to productivity

2. Productivity by Social Media Usage

Daily Social Media Hours	Avg Productivity Score
0–2 hours	57.07
2–4 hours	44.31
4–6 hours	32.24
6–8 hours	19.34
8+ hours	8.86

Key Insight:

Productivity drops consistently and sharply as social media use increases
Heavy users (8+ hours) have extremely low productivity

This supports the idea that social media acts as a disruptor in digital work environments.

3. Productivity by Addiction Level

Addiction Level	Avg Productivity
Low	56.93
Medium	42.16
High	20.32

Key Insight:

Higher addiction = significantly lower productivity
This reinforces that it’s not just time, but behavioral dependence

Conclusion

This analysis shows a consistent relationship between social media usage and productivity in digital work environments. Across the datasets, higher levels of social media engagement are generally associated with lower productivity, suggesting that increased screen time may interfere with focus and task completion. While the strength of this relationship varies, the overall pattern supports the idea that social media functions as a competing demand for attention within the modern workforce. These findings highlight the importance of managing digital habits, as productivity in today’s work environment is closely tied to how individuals navigate constant connectivity and distraction.