Using Data to Think Productive, Be Social

Posted on Oct 25, 2021

Data Science Background

Using data science, we are profoundly social creatures; we long for meaningful connection with other individuals. Social connectedness positively affects our mental and physical health, while feelings of loneliness are associated with a decline in quality of life. In this project, I suggest that a sense of social connection is allied with an additional substantial benefit: increased productivity.

To investigate this hypothesis, I employed Exploratory Data Analysis (EDA) techniques using Python to draw meaningful patterns and insights on the relationship between social connectedness and productivity. Exploring the under-researched link between the two variables may bring great value to both employers and employees who seek to improve performance.

The Data

The dataset contains 15,977 survey responses with 24 attributes describing how we live our lives, including attributes that reflect on both our social connectedness and productivity levels. The dataset was obtained from Kaggle and can be accessed HERE. The original survey can be viewed at www.Authentic-Happiness.com.

Key Variables

My data preparation process included the creation of several new variables to best reflect the notions of interest, using the available data. To further enrich my analysis, I generated two versions for each new variable: categorical and continuous.

Productivity

The dependent variable of my analysis, productivity, contains two variables obtained from the row data: 'TODO_COMPLETED' and 'FLOW'. 'TODO_COMPLETED' represents how well responders complete their weekly to-do lists on a typical week. 'FLOW' reflects the number of hours participants experience 'flow' in a typical week. According to the survey, 'flow' is defined as the mental state, in which you are fully emersed in performing an activity.

Social Connection

The independent variable, social connection, includes two variables obtained from the row data: 'SOCIAL_NETWORK' and 'CORE_CIRCLE'. 'SOCIAL_NETWORK' estimates of the number of people the responder interacts with on a typical day, while 'CORE_CIRCLE' represents the number of people who have a close relationship with the participant. The created variable, social connection, serves as a proxy of social connectedness, which is often defined as the feeling that you belong to a group and generally feel close to other people.

Findings

Interestingly, both social connection and productivity fluctuate by age and gender. As can be viewed in Figure 1, on average, women are more productive than men for all age groups. Overall, both gender groups witness an increase in productivity over the years.

   Figure 1

According to Figure 2, on average, women have higher level of social connection compared to men, for all age groups. Individuals who are 20 years old or less, on average, have a higher level of social connection compared to other age groups.
Both men and women experience a sharp decrease in social connection in the transition between the youngest (20 or less) and the second youngest (21 to 35) age groups. They then experience a rise in their level of social connection in the transition to the third age group (36 to 50), and finally another drop as they enter the oldest age group (51 or more).

Figure 2

The EDA confirmed my hypothesis: social connection and productivity are positively correlated. On average, individuals with high level of social connectedness are more productive compared to persons with lower levels of social connectedness (view Figure 3).

Figure 3: Productivity by Levels of Social Connection

Social connection and productivity are positively correlated for both gender groups. The productivity gender gap discussed earlier is only present among individuals with ordinary and weak social connection. There seem to be no gender gap in productivity between men and women with strong social connection (view Figure 4).

Figure 4: Productivity by Gender and Levels of Social Connection

Social connection and productivity are positively correlated for all age groups. Individuals of age 51 or more, on average, are the most productive for all levels of social connection. Individuals of age 20 or less are, on average, the least productive age group among the two lowest levels of social connection (view Figure 5).

Figure 5: Productivity by Age and Levels of Social Connection

Conclusions and Recommendations

The EDA I conducted provides preliminary evidence of the association between social connectedness and productivity. The results suggest that social connectedness is a good predictor of level of productivity. Given the substantial amount of time an average employee could spend at work within her lifetime, the relationships we form in the workplace may play a significant role in determining our level of social connectedness. Thus, employers who encourage social connections at work support their employees in being more productive, in addition to being healthier and happier.

Limitations

Self-Report Bias - People are frequently biased when they report on their own life experiences. Thus, survey-based data introduces the risk of self-report bias.
Sampling Bias – The data collection process, online survey responses, may potentially lead to sampling bias. In other words, certain groups of the overall population have a lower or higher sampling probability than others.
Correlation != Causation - The correlation between productivity and social connection does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Further research is required in order to establish causation.

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

About Author

Ayelet Hillel

Data Science Professional with experience in research alongside program management. I am passionate about developing data-driven solutions using statistical methodologies and programming languages including Python and R.
View all posts by Ayelet Hillel >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI