Data Handling and Analysis

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.

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Types of Data

Quantitative vs Qualitative Data

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