Analysing Gender Differences in Multitasking: A Data Simulation Approach

Published: 22 May 2024| Version 1 | DOI: 10.17632/7p9k4yzp72.1
Sunil Maria Benedict


Understanding the data model. 1. Libraries and Setup: • The script starts by importing two essential Python libraries: numpy and matplotlib.pyplot. • numpy: Used for numerical operations and data generation. • matplotlib.pyplot: Used for creating visualizations. 2. Aspects of Multitasking: • The script defines three key aspects of multitasking: • Task Switching Speed: How quickly an individual can switch between tasks. • Attention Allocation: How effectively an individual can allocate their attention to multiple tasks. • Task Completion Time: The time it takes to complete tasks. 3. Data Simulation: • The script generates simulated data for 50 individuals (both women and men) for each multitasking aspect. • For Task Switching Speed and Attention Allocation, the performance scores are simulated using a normal distribution with: • Mean of 0.8 and standard deviation of 0.1 for women. • Mean of 0.6 and standard deviation of 0.1 for men. • For Task Completion Time, the performance scores are simulated using a normal distribution with: • Mean of 10 and standard deviation of 2 for women. • Mean of 12 and standard deviation of 2 for men. • These simulated values are stored in dictionaries women_data and men_data. 4. Visualization: • The script creates histograms to compare the distribution of performance scores for women and men across each multitasking aspect. • Each histogram is plotted with: • bins=20 for better granularity. • alpha=0.5 to set the transparency of the bars. • color='red' for women and color='blue' for men. • Titles, labels, legends, and grids are added for clarity. Analytical Insights The simulated data and resulting histograms offer several insights into the comparative performance of women and men in multitasking: 1. Task Switching Speed: • Women: The histogram is centered around a higher mean (0.8), suggesting that women generally have better task switching speed. • Men: The histogram is centered around a lower mean (0.6), indicating relatively slower task switching speed compared to women. • Interpretation: The distribution shows that women, on average, switch tasks more quickly than men. 2. Attention Allocation: • Women: The data follows a similar trend with a mean of 0.8, showing effective attention allocation. • Men: With a mean of 0.6, men display less efficiency in attention allocation. • Interpretation: Women tend to allocate their attention more effectively across multiple tasks. 3. Task Completion Time: • Women: The mean task completion time is 10 units, with most data points clustered around this value. • Men: The mean is higher at 12 units, indicating longer task completion times. • Interpretation: Women, on average, complete tasks faster than men, reflecting higher efficiency in task completion.


Steps to reproduce

import numpy as np import matplotlib.pyplot as plt # Define aspects of multitasking aspects = ['Task Switching Speed', 'Attention Allocation', 'Task Completion Time'] # Simulate data for women and men across each aspect women_data = {} men_data = {} for aspect in aspects: # Simulate data for women women_data[aspect] = np.random.normal(0.8, 0.1, 50) if aspect != 'Task Completion Time' else np.random.normal(10, 2, 50) # Simulate data for men men_data[aspect] = np.random.normal(0.6, 0.1, 50) if aspect != 'Task Completion Time' else np.random.normal(12, 2, 50) # Create multiple graphs for each aspect of multitasking for aspect in aspects: plt.figure(figsize=(8, 6)) plt.hist(women_data[aspect], bins=20, alpha=0.5, color='red', label='Women') plt.hist(men_data[aspect], bins=20, alpha=0.5, color='blue', label='Men') plt.title(f'{aspect} Comparison between Women and Men') plt.xlabel('Performance') plt.ylabel('Frequency') plt.legend() plt.grid(True)




Psychology, Women's Studies, Innovation, Career Development of Women, Career Psychology