Pearson Edexcel · GCSE · Psychology

    Course version: 1PS0 · Version from Sept 2017

    Understand, and know the difference between: a. qualitative data b.…

    Full specification wording

    Data analysis: 11.2.5 Understand, and know the difference between: a. qualitative data b. quantitative data

    Qualitative data is non-numerical, descriptive information that captures thoughts, feelings, and meanings.

    Checked against the Pearson Edexcel 1PS0 specification (opens in a new tab) on .

    • Point 5 of 5
    • 2 key terms

    Study notes

    From the checked teaching notes for this specification point.

    The idea

    Qualitative data is non-numerical, descriptive information that captures thoughts, feelings, and meanings. For example, an interview transcript where a participant describes their experience of stress. Quantitative data is numerical information that can be counted, measured, and statistically analysed. For example, the number of words recalled in a memory test or a score on a rating scale from 1 to 10. The key difference lies in their form and analysis: qualitative data provides rich, detailed insights but is harder to analyse objectively, whereas quantitative data allows for easy comparison and statistical testing but may lack depth and context.

    What earns the marks

    • Identify qualitative data as descriptive, non-numerical information such as words or pictures.
    • Identify quantitative data as numerical information that can be measured or counted.
    • Distinguish between the two types of data based on their format and how they are analysed.
    • Evaluate the strengths and weaknesses of each data type, such as the depth of qualitative data versus the statistical comparability of quantitative data.

    Common mistakes and how to put them right

    • Mistaking a rating scale for qualitative data because it asks about feelings.

      Put it right: Rating scales produce numerical scores, making them quantitative data.

    • Assuming qualitative data is always better because it is detailed.

      Put it right: Both have strengths, but qualitative data is subjective and harder to analyse statistically.

    • Confusing the terms qualitative and quantitative.

      Put it right: 'quant' relates to quantity (numbers) and 'qual' relates to quality (descriptions).

    Exam tips

    • When asked to identify the type of data in a scenario, look for whether the result is a number (quantitative) or a description in words (qualitative).
    • Be prepared to explain one advantage of using quantitative data over qualitative data, such as ease of statistical analysis.
    • If a question asks how to convert qualitative data to quantitative data, mention categorising responses and counting their frequencies.

    Try the lesson

    The start of the Data analysis: 11.2.5 Understand, and know the difference between: a. qualitative data b. quantitative data lesson, made from checked teaching notes for this specification point.

    Have a go first

    Which of the following is the best example of quantitative data being collected in a psychological study?

    Listen

    Qualitative data is non-numerical, descriptive information that captures thoughts, feelings, and meanings. For example, an interview transcript where a participant describes their experience of stress. Quantitative data is numerical information that can be counted, measured, and statistically analysed. For example, the number of words recalled in a memory test or a score on a rating scale from 1 to 10. The key difference lies in their form and analysis: qualitative data provides rich, detailed insights but is harder to analyse objectively, whereas quantitative data allows for easy comparison and statistical testing but may lack depth and context. A strong answer earns marks for these points. Identify qualitative data as descriptive, non-numerical information such as words or pictures. Identify quantitative data as numerical information that can be measured or counted. Distinguish between the two types of data based on their format and how they are analysed. Evaluate the strengths and weaknesses of each data type, such as the depth of qualitative data versus the statistical comparability of quantitative data. A common slip: Mistaking a rating scale for qualitative data because it asks about feelings. Instead, rating scales produce numerical scores, making them quantitative data. Exam tip: When asked to identify the type of data in a scenario, look for whether the result is a number (quantitative) or a description in words (qualitative).

    See it

    A top-down flowchart starting from a single node labelled Data, which splits into two branches. The left branch is Qualitative data, leading to three nodes: Descriptive, non-numerical; Rich, detailed insights; and Harder to analyse objectively. The right branch is Quantitative data, leading to three nodes: Numerical, can be counted or measured; Easy comparison and statistical testing; and May lack depth and context.
    A classification of data into qualitative and quantitative types, showing the form, strengths and weaknesses of each.

    Key words

    Say what each word means before you turn the card.

    Qualitative data

    Try answering aloud or in your head before turning the card.

    Quantitative data

    Try answering aloud or in your head before turning the card.

    See every definition
    Qualitative data
    Descriptive, non-numerical information such as words or pictures that captures thoughts, feelings, and meanings.
    Quantitative data
    Numerical information that can be counted, measured, and statistically analysed.

    Label the diagram

    Some boxes have lost their names. Pick the right name for each number.

    The diagram again, with 3 boxes numbered instead of named. A top-down flowchart starting from a single node labelled Data, which splits into two branches. The left branch is Qualitative data, leading to three nodes: Descriptive, non-numerical; Rich, detailed insights; and Harder to analyse objectively. The right branch is Quantitative data, leading to three nodes: Numerical, can be counted or measured; Easy comparison and statistical testing; and May lack depth and context.
    • 1Box 1

    • 2Box 2

    • 3Box 3

    0 of 3 labelled

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