Quantitative Analysis: This involves statistical techniques to find patterns in numerical data. Common measures include the mean (average) to find central tendencies and the range to understand the spread between the highest and lowest values.
Qualitative Analysis: This focuses on non-numerical data, such as interpreting field sketches or photographs. Researchers use annotations to identify processes like urban sprawl or environmental degradation.
Anomalies: During analysis, researchers must identify data points that do not fit the general trend. These anomalies may be caused by human error, equipment failure, or unique local conditions.
Conclusion: The final analytical step is to state whether the evidence supports or rejects the initial hypothesis, providing reasons based on the data trends identified.
| Feature | Primary Data | Secondary Data |
|---|---|---|
| Source | Collected by the researcher | Existing records (Census, Maps) |
| Advantage | Specific to the study area | Provides historical context |
| Disadvantage | Time-consuming and costly | May be outdated or biased |
| Feature | Quantitative | Qualitative |
|---|---|---|
| Data Type | Numbers and statistics | Descriptions and images |
| Analysis | Mean, Range, Graphs | Annotation, Interpretation |
| Goal | Objective measurement | Subjective understanding |
Reliability refers to the consistency of the results. An enquiry is reliable if another researcher could repeat the same methods and achieve similar findings.
Validity concerns the accuracy of the data. It asks whether the methods used actually measured what they intended to measure (e.g., did a 5-minute traffic count truly represent daily flow?).
Subjectivity is a common issue in rural enquiries, especially in Environmental Quality Surveys where different people might give different scores to the same site. Using group averages or the mode can reduce this bias.
Improvements: Evaluation should always suggest ways to improve the study, such as increasing sample sizes, using more precise equipment, or collecting data at different times of the year.
Graph Completion: Exams often require you to complete a partially drawn graph. Always use a ruler, ensure bar widths are consistent with existing ones, and double-check the scale before plotting.
Identifying Patterns: When asked to describe data, look for the highest and lowest values, the general trend (increasing/decreasing), and any obvious anomalies.
Evaluation Questions: For longer 8-mark questions, structure your answer by discussing what went well (accuracy), what limited the study (reliability), and specific, realistic improvements.
Calculation Accuracy: When calculating the range, always show your working () to secure method marks even if the final subtraction is wrong.
