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

flowchart TD
    A[Data Analysis] --> B[Quantitative]
    A --> C[Qualitative]
    B --> D[Descriptive statistics]
    D --> E[Central tendency]
    D --> F[Spread]
    E --> G[Mean]
    E --> H[Median]
    E --> I[Mode]
    F --> J[Range]
    B --> K[Presentation]
    K --> L[Bar chart]
    K --> M[Histogram]
    K --> N[Line graph]
    K --> O[Scatter diagram]
    C --> P[Themes / interview transcripts]

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Measures of Central Tendency

Measures of central tendency are ways of finding the "average" or typical value in a data set. There are three measures:

Mean

The mean is calculated by adding up all the values and dividing by the number of values.

Formula: Mean = Sum of all values ÷ Number of values

Example: Data = 3, 5, 7, 8, 12 Mean = (3 + 5 + 7 + 8 + 12) ÷ 5 = 35 ÷ 5 = 7

Strength	Weakness
Uses all the data in the calculation	Distorted by extreme values (outliers)
Most sensitive measure	Can produce a value that is not a whole number and may not represent any actual data point

Median

The median is the middle value when data is arranged in order.

Example: Data = 3, 5, 7, 8, 12 → Median = 7 If there is an even number of values: Data = 3, 5, 7, 8 → Median = (5 + 7) ÷ 2 = 6

Strength	Weakness
Not affected by extreme values	Does not use all the data
Easy to calculate	Less sensitive than the mean

Mode

The mode is the most frequently occurring value.

Example: Data = 3, 5, 5, 7, 8 → Mode = 5

Strength	Weakness
Easy to identify	May be unrepresentative if the most common value is not typical
Not affected by extreme values	There may be no mode or multiple modes
Can be used with non-numerical data (e.g. most common eye colour)

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Measures of Spread

Range

The range is the difference between the highest and lowest values.

Formula: Range = Highest value − Lowest value

Example: Data = 3, 5, 7, 8, 12 → Range = 12 − 3 = 9

Strength	Weakness
Easy to calculate	Affected by extreme values (outliers)
Gives a quick indication of spread	Only uses two values — ignores the rest of the data

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Presenting Data

Tables

Tables organise data clearly in rows and columns. A good table should have:

• A clear title
• Labelled columns and rows (including units of measurement)
• Relevant summary statistics (mean, median, etc.)

Bar Charts

Bar charts display categorical (discrete) data using bars of different heights. The bars do not touch because the categories are separate.

• Used for: comparing scores between different groups or conditions
• Example: mean memory test score for each condition

Histograms

Histograms display continuous data. The bars touch because the data is continuous (no gaps between categories).

• Used for: showing the distribution of scores (e.g. distribution of IQ scores)

Line Graphs

Line graphs show how data changes across a continuous variable (usually time).

• Used for: showing trends over time
• Example: change in anxiety levels over several weeks of therapy

Scatter Diagrams

Scatter diagrams show the relationship between two variables in a correlation.

• Each point represents one participant's scores on both variables
• The pattern of dots shows whether the correlation is positive, negative, or non-existent

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Key Points

• Quantitative data is numerical; qualitative data is descriptive.
• Three measures of central tendency: mean (uses all data, affected by outliers), median (middle value, not affected by outliers), mode (most frequent).
• Range measures spread but is affected by outliers.
• Data can be presented using tables, bar charts, histograms, line graphs, and scatter diagrams.

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Why Data Analysis Matters

The purpose of data analysis is to turn raw data into meaningful information. Without analysis, a long list of test scores is just a collection of numbers; with analysis, it becomes a pattern that can support or refute a hypothesis.

Good analysis is honest — it reports not only the central tendency but also the spread of the data, so readers can judge how typical the average is. It presents data in clear tables and graphs so comparisons are obvious. It uses appropriate statistics for the type of data (e.g. mode for categorical data, mean for normally distributed quantitative data).

Weak analysis, by contrast, selects only the statistics that favour the researcher's preferred conclusion, hides the variability of the data, or uses misleading graphs (e.g. axes that start at non-zero values to exaggerate small differences). Being aware of these pitfalls helps you evaluate published research and answer exam questions critically.

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When to Use Each Average

Choosing the right measure of central tendency depends on the data: