Variable Identification: Researchers must categorize factors into Independent Variables (what is changed), Dependent Variables (what is measured), and Control Variables (what is kept the same).
Sampling Strategy: Selecting a representative subset of a population is crucial; methods include Random Sampling to reduce bias and Stratified Sampling to ensure all subgroups are represented.
Pilot Studies: Conducting a small-scale preliminary trial allows researchers to identify potential flaws in the methodology, such as ambiguous questions or equipment failure, before the full enquiry begins.
Risk Assessment: Planning must include an evaluation of potential hazards to participants or the environment, establishing safety protocols and ethical guidelines.
It is essential to distinguish between different types of data and study designs to choose the most appropriate approach for the enquiry.
| Feature | Experimental Enquiry | Observational Enquiry |
|---|---|---|
| Control | High control over variables | Minimal interference with subjects |
| Purpose | To establish cause-and-effect | To describe patterns or behaviors |
| Setting | Often in a laboratory | Usually in a natural environment |
Primary vs. Secondary Data: Primary data is collected firsthand by the researcher for the specific enquiry, whereas secondary data is gathered from existing sources like journals or databases.
Qualitative vs. Quantitative: Qualitative enquiry focuses on descriptive, non-numerical insights (e.g., interviews), while quantitative enquiry focuses on numerical data and statistical analysis.
Variable Checklist: In exam scenarios, always explicitly list the independent, dependent, and at least three control variables to demonstrate a rigorous understanding of fair testing.
Justify the Sample: When asked about sampling, explain why a specific method was chosen (e.g., 'Random sampling was used to ensure every member of the population had an equal chance of selection, minimizing selection bias').
Precision and Accuracy: Use these terms correctly; precision relates to the consistency of measurements (reliability), while accuracy relates to how close a measurement is to the true value (validity).
Evaluation of Method: Be prepared to suggest improvements to a plan, such as increasing sample size to improve reliability or using more sensitive equipment to increase precision.
Confounding Variables: A common error is failing to identify variables that might influence the dependent variable other than the independent variable, leading to false conclusions about causality.
Sample Size Bias: Students often underestimate the impact of a small sample size; small samples are more susceptible to anomalies and may not represent the wider population accurately.
Correlation vs. Causation: Just because two variables change together does not mean one causes the other; planning must account for potential third-party factors.
Ignoring the Null Hypothesis: Failing to consider that there might be no relationship between variables can lead to 'confirmation bias,' where the researcher only looks for evidence that supports their prediction.
