Data Types Overview

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.

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.

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.

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.

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.

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.

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.

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.