1. Quantitative vs. Qualitative Data
- Quantitative Data: Numerical; measurable in numbers (e.g., height, weight, scores).
- Qualitative Data: Descriptive; not numerical (e.g., colors, opinions, behavior).
2. Objective vs. Subjective Data
- Objective Data: Observable and measurable (e.g., number of items, weight).
- Subjective Data: Based on personal reports or perceptions (e.g., pain levels, emotions).
- Example: “The box weighs 2 kg” (objective) vs. “The box feels heavy” (subjective).
3. Longitudinal vs. Cross-Sectional Data
- Longitudinal Data: Collected over time from the same group, enabling analysis of changes and causation.
- Example: Tracking student test scores across grades.
- Cross-Sectional Data: Collected at a single point or different times from various groups, providing snapshots.
- Example: A one-time survey comparing different age groups' preferences.
4. Categorical, Continuous, Discrete, and Ranked Data
Categorical Data
- Divided into groups or categories.
- Examples: Gender, yes/no responses, age groups.
Continuous Data
- Infinite values within a range.
- Examples: Height, weight, percentage scores.
Discrete Data
- Limited set of values; no fractions.
- Examples: Number of customer complaints, age in whole years.
Ranked Data
- Ordered by rank, not by exact value.
- Example: Sorting values (3, 6, 4, 7) into rank order (1st, 2nd, etc.).
- Warning: Ranking loses some data precision—use only when necessary.
Key Considerations
- Data type influences research methods, statistical tests, and conclusions.
- Objective data are preferred for reliability, but subjective data provide insights into intangible aspects.
- Longitudinal studies show causation, while cross-sectional studies provide convenient overviews.
- Use proper statistical tools for categorical, continuous, discrete, or ranked data to ensure accurate analysis.
Understanding data types allows for more informed decisions and tailored analysis, making your research more impactful.