Understanding the distinction between methodology (the overall approach) and method (the specific procedures), and correctly identifying and controlling variables, is fundamental to designing a valid scientific investigation.
| Term | Definition | Example |
|---|---|---|
| Methodology | The general philosophical and strategic approach to the investigation | Fieldwork |
| Method | The specific, detailed, replicable procedure used within that methodology | ‘Place five 1×1 m quadrats randomly within each habitat type; count and identify all plant species within each quadrat’ |
Analogy: Methodology is the style of cooking (e.g. baking); method is the specific recipe.
Match the methodology to the research question:
| If you want to… | Use this methodology |
|---|---|
| Establish a cause–effect relationship | Controlled experiment (manipulate one variable) |
| Measure biodiversity in natural settings | Fieldwork |
| Understand a real-world management scenario in depth | Case study |
| Identify patterns in existing data | Correlational study |
| Simulate or predict outcomes | Modelling |
| Synthesise what is already known | Literature review |
Example: Distance from urban centre (0–1 km, 1–5 km, 5–10 km, >10 km from CBD)
Common error: Having multiple IVs in one investigation — this makes it impossible to attribute observed effects to a single cause. One IV per investigation.
Example: Simpson’s Index of Diversity of plant species within each distance zone
Example for a biodiversity survey across urban gradient:
- Controlled: quadrat size (1×1 m), time of year of survey (October–November), total sampling area per zone (5 quadrats × 1 m² = 5 m²), observer experience
Potential extraneous (uncontrolled) variables:
- Weather on survey day (rain vs. sun affects plant detection)
- Soil type (may vary independently of distance)
- Elevation (affects plant species)
When justifying your variable choices in a report:
| Variable | What to Justify |
|---|---|
| IV | Why this variable is the logical predictor of the DV; why this range was chosen |
| DV | Why this measure captures what you want to know; how it is operationally defined |
| CVs | Why these variables could influence the DV if not controlled |
These three elements must be internally consistent:
| Element | Example |
|---|---|
| Research question | Does habitat fragmentation reduce bird species diversity? |
| IV | Patch size (isolated vegetation patches of 0.1, 0.5, 2, 5 ha) |
| DV | Simpson’s Index of Diversity of bird species (measured via point counts) |
| Hypothesis | Larger vegetation patches will have higher SID, because they support greater habitat heterogeneity and are less affected by edge effects |
| CVs | Survey method (5-min point count at patch centre); season; time of day; observer |
EXAM TIP: VCAA investigation design questions frequently ask students to identify the IV, DV and two CVs. Responses that define each clearly in the context of the specific investigation score highest. A common error is listing ‘the experiment’ as a controlled variable — CVs are specific measurable factors, not the general procedure.
