sarvanich

[sarvanich]

Remote work and well being numbers

Preliminary Statistical Analyses

1. Data Source

Our dataset comes from a national survey conducted yearly between 2020 and 2024, focusing on full-time U.S. employees across industries like tech, healthcare, education, and finance. After filtering remote workers, the final sample includes 1,250 participants.

2. Key Variables

The three main variables analyzed are:

Productivity (self-reported on a 1–10 scale)

Mental health (measured using the GHQ-12 questionnaire)

Remote work frequency (low: 0–1 days/week, moderate: 2–3 days/week, high: 4–5 days/week)

3. Statistical Approach

To uncover relationships, I conducted multiple linear regression analyses:

Predicting productivity and mental health based on remote work frequency, industry type, and demographics (age, gender, race).

Investigating interaction effects — for instance, whether remote work affects tech employees differently than healthcare employees.

For model validation:

I split the data into an 80/20 training/testing set.

Used 5-fold cross-validation to ensure the model’s stability.

Graphs/Plots

Here are the types of graphs that helped visualize these patterns:

1. Average Productivity by Remote Work Frequency

Bar chart showing that employees who work remotely 4–5 days a week report slightly higher productivity (mean = 8.1) compared to those working remotely only 0–1 day (mean = 7.2). Remote Work Frequency Mean Productivity Low (0-1 days) 7.2 Moderate (2-3 days) 7.8 High (4-5 days) 8.1

Interpretation: More remote days generally correlate with higher productivity.

2. Mental Health Scores Across Remote Work Levels

Box plot comparing mental health distress scores across low, moderate, and high remote work groups.

Employees with moderate remote schedules (2-3 days) report the lowest mental distress.

Employees who are either fully remote or mostly in-office show higher distress levels.

Interpretation: A balance between remote and in-office work might be the sweet spot for mental health.

[sarvanich]

Behind the Research: Methods for Exploring the Impact of Remote Work on Productivity and Mental Health on Employees

As I dive deeper into this project on remote work, I want to share the approach I'm using to explore how working from home has affected both productivity and mental health since 2020. Below is a breakdown of my methodology, including how I selected participants, what data I’m analyzing, and how I plan to interpret the results.

1. Sample

The data for this study is drawn from a national survey administered annually between 2020 and 2024, targeting U.S.-based employees across various industries. The population includes full-time workers aged 18–65, with at least six months of experience working remotely during the study period. To keep the sample relevant, I excluded part-time workers and freelancers.

After applying inclusion criteria, the final sample size consists of 1,250 participants. Demographically, the sample is 54% female, 45% male, and 1% non-binary/other, with a mean age of 36. Participants represent a diverse range of industries, including tech, healthcare, education, and finance. Approximately 62% identify as White, 16% as Black or African American, 13% as Hispanic/Latino, and 9% as Asian or other ethnicities.

2. Measures

The primary variables include:

Productivity (self-reported, on a 1–10 scale)

Mental health (measured using the General Health Questionnaire-12)

Remote work frequency (days per week working remotely)

Industry type (categorized into tech, healthcare, education, finance, and other)

Demographics (age, gender, race/ethnicity, education level)

For data management:

I created a composite productivity score based on three separate productivity questions, standardized and averaged.

Remote work frequency was binned into three categories: low (0–1 days), moderate (2–3 days), and high (4–5 days).

Mental health scores were grouped into low, moderate, and high distress based on standard cutoffs from the GHQ-12 scoring guidelines.

3. Analyses

To analyze the data, I’m using multiple linear regression to examine the relationship between remote work frequency and both productivity and mental health, controlling for industry and demographics. This method helps identify how each independent variable influences the outcomes while adjusting for other factors.

Additionally, I’ll be exploring interaction effects to see if the impact of remote work varies by industry or gender.

For model validation:

The data is split into 80% training and 20% testing sets to assess model performance.

I’ll use 5-fold cross-validation on the training set to ensure the regression models generalize well and to minimize overfitting.

By using this approach, I hope to uncover nuanced patterns in how remote work affects different people in different ways. These insights can ultimately inform better workplace practices that support both performance and well-being.

[sarvanich]

Is Remote Work a yay or nay? Exploring the impact on Productivity

Project Title: The Impact of Remote Work on Productivity and Mental Health of the Employees

Research Question: How has the shift to remote work affected employee productivity and mental health across different industries since 2020?

Motivation: The COVID-19 pandemic has changed the way people work, by making remote work a widespread and often a permanent solution for some companies. As someone deeply interested in workplace culture and employee well-being, I want to understand whether remote work has truly benefited workers, or if it's introduced new challenges that aren't being fully addressed. By examining trends across industries, I hope to uncover patterns that reveal how different environments shape employee experiences and outcomes.

Potential Implications: Answering this question could help employers design better hybrid or remote work policies, balancing productivity with mental wellness. It may also provide insights for HR professionals, organizational leaders, and policymakers looking to improve job satisfaction, reduce burnout, and boost efficiency. On a broader level, understanding this shift can influence how society views the future of work and its relationship to mental health.