Data Types Overview

Data Types

Quantitative Data

• Definition: Numerical data that can be measured and expressed statistically.
• Types:
  • Discrete: Countable values (e.g., number of participants).
  • Continuous: Measurable values within a range (e.g., height, weight).
• Characteristics:
  • Can be subjected to statistical analysis.
  • Enables calculations such as mean, median, and mode.

Qualitative Data

• Definition: Non-numerical data that describes characteristics or qualities.
• Types:
  • Nominal: Categories without a specific order (e.g., gender, color).
  • Ordinal: Categories with a specific order (e.g., rankings).
• Characteristics:
  • Provides insights into motivations, opinions, and experiences.
  • Analysis often involves thematic coding or content analysis.

Secondary Data Sources

• Definition: Data collected by someone other than the user for a different purpose.
• Types:
  • Publications: Academic journals, government reports, and organizational records.
  • Databases: Online repositories containing previously collected data.
• Advantages:
  • Cost-effective and time-efficient.
  • Can provide valuable context and background information.
• Limitations:
  • May not perfectly align with current research needs.
  • Potential issues with data accuracy and reliability.

Survey Design Techniques

• Question Types:
  • Closed-Ended: Respondents choose from predefined options (e.g., multiple choice).
  • Open-Ended: Respondents provide their own answers, allowing for richer data.
• Scaling Techniques:
  • Likert Scale: Measures attitudes or opinions on a scale (e.g., 1 to 5).
  • Semantic Differential: Rates attributes on a bipolar scale (e.g., good-bad).
• Sampling Methods:
  • Random Sampling: Every individual has an equal chance of selection, ensuring representation.
  • Stratified Sampling: Population divided into subgroups, and samples are drawn proportionally.
• Pretesting: Testing the survey on a small group to identify problems and ensure clarity.
• Administration Methods:
  • Online Surveys: Cost-effective and can reach a broader audience.
  • Face-to-Face Interviews: More personal, allowing for deeper insights but more resource-intensive.

Data Types

Quantitative Data

• Numerical data measurable and expressible statistically.
• Discrete Data: Involves countable values, like the number of participants in a study.
• Continuous Data: Involves values that can be measured across a continuum, such as height and weight.
• Allows for statistical analysis including mean, median, and mode calculations.

Qualitative Data

• Non-numerical data that describes characteristics or qualities.
• Nominal Data: Includes categories with no inherent order, such as gender or color.
• Ordinal Data: Involves categories with a specific order, such as rankings or rating scales.
• Helps uncover insights related to motivations, opinions, and experiences.
• Analysis methods include thematic coding and content analysis to interpret non-numeric information.

Secondary Data Sources

• Collected by individuals other than the user for different purposes.
• Publications: Include academic journals, government reports, and various organizational records.
• Databases: Online repositories hold previously collected data for reference.
• Advantages: Cost and time efficiency, providing valuable context for research.
• Limitations: Data may not fully align with current research needs and could have issues regarding accuracy and reliability.

Survey Design Techniques

• Question Types:

  • Closed-Ended Questions: Respondents select from predefined options, facilitating easy analysis.
  • Open-Ended Questions: Allow respondents to provide detailed, qualitative responses for richer data.

• Scaling Techniques:

  • Likert Scale: Assesses attitudes or opinions on a scale, typically ranging from 1 to 5.
  • Semantic Differential Scale: Rates attributes along a bipolar scale, such as good vs. bad.

• Sampling Methods:

  • Random Sampling: Ensures every individual has an equal chance of selection, aiding representation.
  • Stratified Sampling: Divides population into subgroups, and samples are drawn from each proportionally.

• Pretesting: Conducts surveys on a small group to identify potential problems and ensure question clarity.

• Administration Methods:

  • Online Surveys: Cost-effective and capable of reaching broader audiences quickly.
  • Face-to-Face Interviews: More personal interactions that can yield deeper insights but require more resources.

This quiz covers the definitions and types of quantitative and qualitative data, including characteristics and examples of each. Additionally, it explores secondary data sources and their significance. Test your understanding of these fundamental concepts in data analysis.