Data Analysis

After collecting data, psychologists need to analyse it to identify patterns, draw conclusions, and test hypotheses. This lesson covers the key methods of data analysis required for GCSE Psychology, including measures of central tendency, measures of spread, and how to present data.

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Types of Data

Quantitative Data

Quantitative data is numerical data — data in the form of numbers that can be measured and analysed statistically.

Examples: test scores, reaction times, number of words recalled, ratings on a scale

Strength	Weakness
Easy to analyse statistically	May oversimplify complex behaviour
Can identify patterns and trends	Lacks depth and detail
Results can be compared and replicated	Does not explain why participants behaved in a certain way

Qualitative Data

Qualitative data is non-numerical data — descriptions, opinions, feelings expressed in words.

Examples: interview transcripts, open-ended questionnaire responses, diary entries

Strength	Weakness
Provides rich, detailed information	Difficult to analyse — subjective interpretation required
Captures the meaning behind behaviour	Hard to compare between participants
High ecological validity	Time-consuming to collect and analyse