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Once data have been collected, they must be organised, summarised, and analysed in order to draw meaningful conclusions. At A-Level, you need to understand the distinction between different types of data, measures of central tendency and dispersion, methods of data presentation, distributions, and levels of measurement. These concepts are fundamental to evaluating research findings and conducting your own investigations.
Key Definition: Data analysis is the process of organising, summarising, and interpreting collected data to identify patterns, draw conclusions, and evaluate hypotheses.
| Type | Description | Example | Strengths | Limitations |
|---|---|---|---|---|
| Quantitative | Numerical data that can be measured and analysed statistically | Reaction times (ms), test scores, number of errors | Objective; easy to analyse; allows statistical testing; comparisons between groups | May lack depth; may oversimplify complex behaviours |
| Qualitative | Non-numerical data expressed in words, descriptions, or themes | Interview transcripts, diary entries, open-ended survey responses | Rich and detailed; captures the meaning and complexity of behaviour | Subjective; difficult to analyse and compare; researcher interpretation bias |
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