Quantitative and qualitative data

AQA

GCSE

This study area demands a rigorous evaluation of the methodological divide between quantitative (numerical) and qualitative (non-numerical) data. Candidates must analyse the theoretical underpinnings of these data types, linking quantitative data to Positivism and the search for social facts, and qualitative data to Interpretivism and the quest for Verstehen. Assessment focuses on the application of the PET framework (Practical, Ethical, Theoretical) to judge the suitability of methods such as questionnaires, interviews, and observations in specific research contexts.

Objectives

Exam Tips

Pitfalls

Key Terms

Mark Points

What You Need to Demonstrate

Key skills and knowledge for this topic

Award marks for precise definitions: Quantitative data as numerical evidence allowing for the identification of trends and patterns; Qualitative data as rich, descriptive detail providing insight into meanings and motives.
Credit responses that explicitly link Quantitative data (e.g., official statistics, closed questionnaires) to high reliability and generalisability due to standardized collection and larger sample sizes.
Credit responses that explicitly link Qualitative data (e.g., unstructured interviews, participant observation) to high validity and 'Verstehen' (empathetic understanding), despite issues with reproducibility.
Candidates must evaluate the 'fitness for purpose' of data types; for example, selecting qualitative methods for sensitive topics to build rapport, versus quantitative methods for large-scale demographic analysis.

Marking Points

Key points examiners look for in your answers

Award marks for precise definitions: Quantitative data as numerical evidence allowing for the identification of trends and patterns; Qualitative data as rich, descriptive detail providing insight into meanings and motives.
Credit responses that explicitly link Quantitative data (e.g., official statistics, closed questionnaires) to high reliability and generalisability due to standardized collection and larger sample sizes.
Credit responses that explicitly link Qualitative data (e.g., unstructured interviews, participant observation) to high validity and 'Verstehen' (empathetic understanding), despite issues with reproducibility.
Candidates must evaluate the 'fitness for purpose' of data types; for example, selecting qualitative methods for sensitive topics to build rapport, versus quantitative methods for large-scale demographic analysis.

Examiner Tips

Expert advice for maximising your marks

💡When evaluating data types, use the PERVERT mnemonic (Practical, Ethical, Reliability, Validity, Evidence, Representativeness, Theory) to structure points.
💡Always link the data type to the method that produced it; do not discuss 'quantitative data' in a vacuum, but reference 'quantitative data generated by closed questionnaires'.
💡In 12-mark questions, ensure a conclusion is reached regarding which data type is superior *for the specific issue* in the question, not generally.
💡Distinguish between primary data (collected by the researcher) and secondary data (existing sources) when discussing quantitative/qualitative outputs.

Common Mistakes

Pitfalls to avoid in your exam answers

Conflating 'reliability' (replicability/consistency) with 'validity' (truthfulness/authenticity) – a critical error in methodological evaluation.
Asserting that qualitative data is 'easy to analyse' (it is time-consuming and subjective) or that quantitative data 'lacks detail' without explaining *why* (imposition problem).
Failing to contextualize the evaluation; providing generic lists of strengths/weaknesses without applying them to the specific research scenario presented in the item.

Key Terminology

Essential terms to know

Likely Command Words

How questions on this topic are typically asked

Identify

Describe

Explain

Discuss

Evaluate

To what extent

Ready to test yourself?

Practice questions tailored to this topic