Primary and secondary data

    OCR
    GCSE

    Candidates must distinguish between primary data (collected first-hand by the researcher) and secondary data (existing sources). Analysis must evaluate these data types through the 'PET' framework: Practical issues (time, cost, access), Ethical issues (consent, anonymity), and Theoretical perspectives (Positivism vs. Interpretivism). Mastery requires assessing the trade-off between reliability (replicability) and validity (authenticity) in research design.

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    Objectives
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    Exam Tips
    3
    Pitfalls
    3
    Key Terms
    4
    Mark Points

    Learning Objectives

    What you need to know and understand

    • Primary methods: Questionnaires, Interviews (structured/unstructured), Observations (participant/non-participant).
    • Secondary sources: Official Statistics (Census, crime stats), Personal Documents (diaries, letters), Media Reports.
    • Quantitative data: Numerical, statistical, associated with patterns and trends.
    • Qualitative data: Descriptive, word-based, associated with meaning and depth.
    • Key evaluative concepts: Validity, Reliability, Representativeness, Generalisability, Ethics.

    Marking Points

    Key points examiners look for in your answers

    • Award marks for precise definitions: Primary data as first-hand collection; Secondary as pre-existing information.
    • Credit application of concepts: Link quantitative data to reliability/patterns and qualitative data to validity/meaning.
    • Candidates must evaluate utility: Contrast the specific flexibility of primary methods against the scale and economy of secondary sources.
    • Reward contextualisation: Justify the suitability of data types for sensitive topics (e.g., crime statistics vs. victim surveys).

    Examiner Tips

    Expert advice for maximising your marks

    • 💡Apply the PET framework (Practical, Ethical, Theoretical) to structure evaluations of data types.
    • 💡When discussing secondary data, explicitly distinguish between official statistics and personal documents.
    • 💡For 'Evaluate' questions, ensure the conclusion directly addresses the specific research aim in the stimulus.
    • 💡Use the source material to identify potential bias or sampling errors before critiquing the data type.

    Common Mistakes

    Pitfalls to avoid in your exam answers

    • Confusing reliability (replicability) with validity (authenticity/truth).
    • Stating 'secondary data is outdated' without checking the source date provided in the exam paper.
    • Asserting 'primary data is expensive' without comparing it to the specific secondary alternative or context.

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    Comprehensive revision notes & examples

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    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

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