How to Use Exploratory Data Analysis to Study the Relationship Between Mental Health and Social Media Usage

Exploratory Data Analysis (EDA) is a crucial step in understanding the relationship between variables in a dataset before diving into advanced statistical analysis. When it comes to studying the relationship between mental health and social media usage, EDA allows researchers to explore and visualize patterns, distributions, and correlations in data. By applying EDA techniques, you can uncover insights, identify potential issues with the data, and form hypotheses for more detailed analyses. Below is a step-by-step guide on how to use EDA to study the relationship between mental health and social media usage.

1. Define the Problem and Objectives

Before starting the analysis, it’s important to clearly define the objectives of your study. In this case, your primary goal is to explore the relationship between mental health and social media usage. Are you looking to assess the impact of social media on mental health, or are you comparing social media usage patterns across different groups (e.g., age, gender)? Defining these objectives will guide your data collection and analysis.

2. Collect Data

To perform EDA, you need data. The sources could be:

• Surveys or Questionnaires: You can design surveys asking respondents about their social media usage (e.g., hours spent per day, platforms used) and mental health indicators (e.g., stress levels, anxiety, depression).

• Public Datasets: If you’re working with existing datasets, some public sources include government health agencies, social media platforms, and academic research repositories.

• Behavioral Data: This includes usage patterns, frequency of posts, or interactions on social media platforms, which can also be used to assess the mental health link.

Once data is collected, ensure it’s cleaned and organized into a suitable format for analysis.

3. Clean and Preprocess the Data

Data cleaning is the first critical step in any analysis. Common tasks include:

• Handling Missing Values: Decide whether to fill missing data or drop incomplete records, depending on the nature of the data and your analysis objectives.

• Outliers: Identify and deal with outliers that might distort your findings. For instance, extreme values in hours spent on social media might skew results.

• Data Transformation: If some variables are in categorical form (e.g., social media platforms or mental health diagnoses), consider encoding them into numerical form for easier analysis.

• Normalization: For variables like hours spent on social media or mental health scale scores, normalization can help ensure that comparisons are meaningful.

4. Perform Descriptive Statistics

Descriptive statistics give you a basic understanding of your data. You should start by calculating:

• Mean, Median, Mode: For continuous variables like hours spent on social media, or scores on mental health scales, calculate these measures to understand central tendencies.

• Standard Deviation and Variance: These metrics will help you understand how spread out the values are in both social media usage and mental health scores.

• Counts and Percentages: For categorical variables such as age group or social media platform, compute the frequency distribution and proportions.

Example:

• Mental Health Score: Mean mental health score = 65, Standard Deviation = 12.

• Social Media Usage: Mean daily hours on social media = 3.5 hours, Standard Deviation = 1.2 hours.

5. Visualize the Data

Visualization is one of the most powerful tools in EDA. It helps you identify trends, patterns, and relationships between social media usage and mental health more easily. Use different types of plots and graphs to examine your data:

• Histograms: To visualize the distribution of social media usage and mental health scores. For example, you might see that most respondents spend between 1-3 hours on social media daily, while mental health scores are spread across a normal distribution.

• Box Plots: To check for outliers in both social media usage and mental health scores.

• Scatter Plots: To visually inspect the correlation between social media usage and mental health scores. For instance, you may notice a downward trend, indicating that increased social media usage correlates with lower mental health scores.

• Bar Charts: If you have categorical variables (e.g., age group or gender), use bar charts to compare social media usage and mental health outcomes across these categories.

  Example:

  • A scatter plot could show that higher hours spent on social media daily correlates with lower mental health scores, with some potential outliers.

6. Explore Correlation and Relationships

One of the core tasks of EDA is exploring correlations. You can compute the correlation coefficient (e.g., Pearson’s r) between social media usage and mental health scores to determine the strength and direction of their relationship:

• Positive correlation: Indicates that as one variable increases, so does the other.

• Negative correlation: Indicates that as one variable increases, the other decreases.

Additionally, you can perform a cross-tabulation to examine relationships between categorical variables. For example, you might cross-tabulate the time spent on social media with mental health categories (e.g., low, moderate, high stress levels).

7. Group-Based Analysis

To further explore the relationship, segment the data based on relevant groupings, such as:

• Age: Older individuals might use social media less frequently than younger individuals, which could impact the mental health relationship.

• Gender: Gender differences could play a role in social media consumption patterns and how they relate to mental health outcomes.

• Social Media Platform: Different platforms may have varying impacts. For example, Instagram’s focus on image-based content might have a different effect on mental health than Twitter, which is text-based.

By grouping your data, you can get deeper insights into how different subgroups are impacted by social media use.

8. Hypothesis Testing (Advanced EDA)

If you have formulated specific hypotheses, such as “Increased social media usage is associated with higher levels of anxiety,” you can perform statistical tests such as:

• T-tests or ANOVA: These can help compare the means of mental health scores across different levels of social media usage.

• Chi-square test: For categorical variables, this test can help determine if there is an association between social media usage (e.g., high, medium, low) and mental health categories (e.g., anxiety, depression).

9. Detecting Patterns Over Time

If your data includes time-based variables, such as the number of social media posts over time or mental health scores tracked over a period, you can explore:

• Time Series Analysis: This will help detect any trends or seasonality in the relationship between social media usage and mental health.

• Rolling Averages: To smooth out fluctuations and better understand longer-term trends.

10. Summarize Key Findings

Once you have performed your exploratory analysis, summarize the key findings:

• Visual Insights: Highlight any important visualizations (e.g., trends, correlations, or outliers).

• Key Relationships: Summarize the strength and direction of any relationships you’ve discovered (e.g., moderate negative correlation between social media usage and mental health scores).

• Potential Issues: Mention any data quality issues or limitations, such as missing values or small sample sizes that might affect the robustness of the results.

Conclusion

Exploratory Data Analysis provides valuable tools for understanding the relationship between mental health and social media usage. By following the steps above, researchers can uncover initial patterns and form hypotheses for more advanced statistical analyses. However, it is important to remember that EDA is not the end of the analysis process—it’s the foundation for deeper investigation into causality and more precise conclusions.