How do social researchers analyse and present data, and how do you read statistics critically?
Analysing and presenting data: quantitative analysis (averages, percentages, correlation) and qualitative analysis (coding, themes), tables, charts and graphs, and reading statistical evidence critically.
How data analysis and presentation work in SQA Advanced Higher Modern Studies. Covers quantitative analysis (averages, percentages, correlation versus causation), qualitative analysis (coding and themes), presenting data in tables, charts and graphs, and reading statistics critically.
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What this key area is asking
Once data is gathered it must be analysed and presented. This dot point covers how quantitative data is summarised (averages, percentages, correlation) and how qualitative data is analysed (coding into themes), how findings are displayed in tables, charts and graphs, and, crucially, how to read statistical evidence critically. The question paper often gives you sources and statistics to interpret, so analysis is an examined skill, not just background knowledge.
Analysing quantitative data
Choosing the right summary matters: a mean is distorted by extreme values, so the median is often fairer for income or wealth data. Percentages enable comparison across unequal groups, but a percentage with no base number can mislead (a 100 percent rise from two cases to four is trivial). These judgements are exactly what the question paper tests.
Analysing qualitative data
The strength of thematic analysis is depth and authenticity, letting respondents' own words drive the findings; the limitation is that interpretation introduces some subjectivity, which is why a transparent, documented coding process matters for credibility.
Presenting data
Findings are displayed to communicate them clearly:
- Tables. Preserve exact figures and detail, good for reference and checking, but slower to read for patterns.
- Bar and column charts. Compare categories at a glance.
- Line graphs. Show change and trends over time.
- Pie charts. Show parts of a whole, but become unreadable with many slices.
Presentation is also where data can mislead, through truncated or stretched axes, selective time ranges or chart types that exaggerate small differences, so honest design and critical reading go together.
Reading statistics critically
The defining skill is scepticism. For any statistic, ask: what is the source and its purpose; what is the sample behind it and is it representative; is a percentage given without its base; is a correlation being passed off as causation; and what does the figure omit? Distinguishing correlation from causation is the single most tested point: two variables can move together because a third factor drives both, or by chance, so a relationship in the data is a prompt for further investigation, not proof of cause.
Worked example
Try this
Q1. Why is the median often a fairer average than the mean for income data? [2 marks]
- Cue. The mean is distorted by a few very high earners, while the median (the middle value) is not affected by extreme values.
Q2. Give one way a chart can mislead a reader. [2 marks]
- Cue. A truncated or stretched axis, a selective time range, or a chart type that exaggerates small differences (any one).
Exam-style practice questions
Practice questions written in the style of SQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
SQA AH (research methods)12 marksEvaluate the strengths and limitations of presenting research findings using charts and graphs.Show worked answer →
A strong answer weighs how visual presentation helps and how it can mislead, reaching a judgement.
Strengths: charts and graphs make patterns, trends and comparisons immediately visible, condense large datasets, and communicate findings to a wide audience faster than tables of raw numbers. Limitations: they can oversimplify, hiding the detail and exceptions a table preserves; they can mislead through manipulated axes, selective ranges or chart types that exaggerate small differences; and they show patterns without explaining causes. The judgement should conclude that charts and graphs are valuable for communicating quantitative findings clearly, but must be designed honestly and read critically, and are best paired with the underlying figures so claims can be checked.
SQA AH (research methods)8 marksExplain the difference between correlation and causation and why it matters when analysing data.Show worked answer →
The marks reward a clear distinction and an example showing why the difference matters.
Correlation means two variables change together; causation means one variable actually brings about the change in the other. Two things can correlate without either causing the other, often because a third factor drives both, or by coincidence. It matters because treating correlation as causation produces false conclusions and, in policy, wrong decisions: for example, a correlation between an area's wealth and its exam results does not prove wealth causes attainment, since other factors are involved. A full answer defines both, explains that correlation alone cannot establish cause, and notes that controlled comparison or further evidence is needed to claim causation.
Related dot points
- The social research process: framing a research question and aim, forming a hypothesis, choosing a method, gathering and analysing data, and reporting conclusions as a repeatable cycle.
How the social research process works in SQA Advanced Higher Modern Studies. Covers framing an aim and research question, hypotheses, choosing methods, gathering and analysing data, drawing conclusions, and why research is a structured, repeatable cycle that underpins both the question paper and the dissertation.
- Primary research methods: questionnaires and surveys, interviews (structured, semi-structured, unstructured), focus groups, observation and field research, with their strengths, limitations and the quantitative-qualitative distinction.
How primary research methods work in SQA Advanced Higher Modern Studies. Covers questionnaires and surveys, structured to unstructured interviews, focus groups, observation and field research, the quantitative-qualitative distinction, and how to justify a method against a research aim.
- Secondary research methods: official statistics, academic literature, media and online sources, content analysis, and critically evaluating secondary data for bias, accuracy and currency.
How secondary research works in SQA Advanced Higher Modern Studies. Covers official statistics, academic literature, media and online sources, content analysis, and how to evaluate secondary data critically for bias, accuracy, currency and the purpose behind it.
- Evaluating research quality: reliability and replicability, validity, objectivity versus bias, representativeness and generalisability, and research ethics (informed consent, confidentiality, harm).
How research quality is judged in SQA Advanced Higher Modern Studies. Covers reliability and replicability, validity, objectivity versus bias, representativeness and generalisability, and the ethics of social research including informed consent, confidentiality and avoiding harm.
- Drawing conclusions: synthesising evidence to answer the research question, judging the hypothesis, supporting conclusions with data, acknowledging limitations, and the source-based conclusions question in the exam.
How to draw sound conclusions in SQA Advanced Higher Modern Studies. Covers synthesising evidence to answer the research question, judging the hypothesis, supporting each conclusion with data, acknowledging limitations, and the source-based draw-conclusions question in the exam.