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Book Reading and Emotional State Analysis
Data Science and Analytics
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"No reviews yet"
Free
About
Exploring the relationship between individual reading routines and their subsequent psychological responses. This dataset focuses on analyzing how frequently people engage with books and the corresponding effects on their emotional state. It provides valuable input for researchers studying behavioral science and mood regulation. The dataset was initially developed as a personal exercise for data analysis work.
Columns
- User_ID: A unique numerical identifier assigned to each participant in the study. All 65 records contain valid IDs.
- Age: The age of the participants. Ages range from 18 to 50, with a mean age of 33.
- Gender: Identifies the participant's gender, represented as 'f' for female and 'm' for male. The distribution is nearly balanced, with females comprising 51% of the sample.
- Favorite_Book_Genre: The specific literary category most preferred by the participant, such as Fiction or Fantasy. There are 7 unique genres identified, with Fiction and Fantasy each accounting for 17% of responses.
- Weekly_Reading_Time(hours): The average duration, measured in hours, that a participant spends reading each week. Reading times range from 1 hour up to 10 hours, with a mean of 4.65 hours.
- Mood_Impact: The participant's self-reported effect on their mood after reading. This variable includes outcomes such as Positive, Neutral, or Negative, with Positive being the most common response (57%).
Distribution
The data is contained within a file typically delivered in CSV format, specifically named
sleep and psychological effects.csv. The structure consists of 6 distinct columns and 65 valid records or rows, indicating a small, fully populated dataset with no missing entries in the key fields. The file size is approximately 1.91 kB. The expected update frequency is 'Never'.Usage
This resource is ideally suited for projects in data analytics and data visualization that seek to establish quantitative links between lifestyle habits and emotional health. Specific applications include:
- Analysing correlations between weekly reading duration and reported mood impact.
- Creating psychological profiles based on genre preference and reading frequency.
- Serving as intermediate-level practice material for Python programming and exploratory data analysis.
Coverage
The data provides demographic insights, including age (ranging 18 to 50) and gender, along with behavioral details regarding reading genre and frequency. Information regarding specific geographic locations or the time range over which the data was collected is not available in the source material.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: For developing correlation models connecting reading habits and emotional metrics.
- Psychological Researchers: To study the therapeutic or stress-reducing potential of reading across different demographic groups.
- Students and Learners: As a clean, small dataset for practising introductory data analysis and visualization techniques.
Dataset Name Suggestions
- Book Reading and Emotional State Analysis
- Reading Habits Psychological Impact Study
- Weekly Reading Time Survey Data
Attributes
Original Data Source: Book Reading and Emotional State Analysis
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