Analysing and presenting data: qualitative and quantitative data, organising and interpreting results, using tables, graphs and statistics, and drawing valid conclusions linked to the research question

What this dot point is asking

You need to understand the difference between qualitative and quantitative data, how each is analysed, how results are presented clearly, and how conclusions are tied back to the research question and hypothesis. This is the stage where collected data becomes findings, and it is assessed directly in the Independent Research Project.

Qualitative and quantitative data

Quantitative data is numerical, such as how many respondents chose each option, and it answers questions of how much or how many. Qualitative data is descriptive, such as interview comments or open-ended responses, and it captures meaning, opinions and reasons. Most CAFS projects collect both: a questionnaire might produce quantitative counts alongside qualitative open answers. Knowing which type you have determines how you analyse it.

Analysing quantitative data

Quantitative data is organised by counting, tallying and calculating. Researchers use simple statistics such as totals, percentages and averages to summarise responses. For example, reporting that 68 per cent of respondents agreed with a statement is more useful than listing every individual answer. Care is needed with small samples, where percentages can mislead, so the raw numbers should usually be shown alongside them.

Analysing qualitative data

Qualitative data is analysed by reading responses closely and identifying recurring themes, patterns and contradictions. The researcher groups similar comments, notices common ideas, and selects representative quotes to illustrate them. This is more interpretive than quantitative analysis, so the researcher must guard against reading their own expectations into the data. Themes should emerge from the responses, not be imposed on them.

Presenting findings

Clear presentation helps readers grasp findings quickly. Quantitative results suit tables and graphs: column or bar graphs for comparisons, pie charts for proportions, and line graphs for trends over time. Every table or graph needs a title and labelled axes. Qualitative findings suit short selected quotes and described themes. The aim is to make the data tell its story honestly, choosing the format that fits the data rather than decorating the report.

Drawing conclusions

Conclusions interpret what the findings mean in relation to the research question and hypothesis. The researcher states whether the data supported, partly supported or rejected the hypothesis, and explains what the findings suggest. Strong conclusions also acknowledge limitations, such as a small or biased sample, that affect how far the findings can be trusted or generalised. Overstating conclusions beyond what the data supports is both a research-skill error and an ethical one.

Linking back to validity

Analysis and presentation are where the earlier design choices pay off. A valid method and representative sample make the conclusions defensible; a biased sample or leading questions show up as conclusions that cannot really be supported. In the IRP and exam, the best responses connect the findings directly to the research question, present data clearly, and discuss limitations honestly rather than claiming more certainty than the data allows.