Types of Data in Scientific Research

Data Collection

• Data is vital for scientific research and can be sourced from organizations and institutions.
• Business data examples include sales data, revenue, profit, and stock prices.
• Government data examples include crime rates, unemployment rates, and literacy rates.
• Data is analyzed through various graphical tools and images.

Types of Data

• Qualitative Data: Descriptive information that cannot be measured numerically (e.g., observations of smell, taste, and feel).
• Quantitative Data: Numerical data that can be measured and used for statistical analysis (e.g., height, weight, volume).
  • Discrete Data: Takes limited values, can be counted (e.g., outcomes of rolling dice).
  • Continuous Data: Can have any value within a range, with infinite possibilities (e.g., temperature, height).

Primary and Secondary Data

• Primary Data: Collected for the first time directly by the researcher (e.g., surveys, experiments).
• Secondary Data: Collected by others and previously processed (e.g., books, newspapers).

Methods of Collecting Data

• Observation Method:
  • Involves direct information collection without questioning.
  • Can be structured (systematic) or unstructured (free form).
  • Includes participant (observer is part of the group) and non-participant observation (observer remains separate).
  • Controlled (pre-planned) and uncontrolled (informal) observations are also distinguished.
• Interview Method:
  • Oral communication to gather information.
  • Divided into personal (face-to-face) and telephonic interviews (short, focused).
• Questionnaire Method: A structured set of questions sent to respondents for answers.

Data Visualization

• The visual representation of data aims to communicate information effectively.
• Types of Data Visualization:
  • Temporal: One-dimensional, linear displays (e.g., scatter plots, line graphs).
  • Hierarchical: Shows group relationships (e.g., tree, ring charts).
  • Network: Displays relational connections between datasets (e.g., matrix charts).
  • Multidimensional: Involves multiple variables for three-dimensional analyses.

Data Processing

• Requires organization for analysis, often using tables and spreadsheets.
• Data Cleaning: Corrects errors, eliminates duplicates, and ensures accuracy using various techniques.

Data Analysis

• Processed, organized, and cleaned data is analyzed to derive insights.
• Visualization aids in understanding data trends and messages.
• Results are communicated efficiently to support decision-making.

Importance of Data Analysis

• Real-time Analytics: Increases immediate insights, with 91% of data scientists showing interest in real-time data work.
• Improved Marketing Efficiency: Analyzes customer preferences to shape marketing strategies effectively.

R Language Introduction

• R is a programming language used for statistical analysis, graphics, and reporting.
• Developed by Ross Ihaka and Robert Gentleman; maintained by the R Development Core Team.
• Key Features of R:
  • Effective programming capabilities with loops and user-defined functions.
  • Strong data handling and storage.
  • Comprehensive suite for data analysis and visualization.

Data Types in R

• Logical Data Type: Includes boolean values TRUE and FALSE.

This quiz explores various types of data relevant to scientific research, including qualitative data, and its applications in different fields such as business and government. Understand how data is collected and analyzed through various tools and methods. Test your knowledge about the significance of data in research and decision-making.