Revision notes for AQA GCSE Psychology Handling data. Open the guide for explanations and worked examples. Written against the AQA GCSE Psychology (8182) specification, so the content matches what's examinable rather than general Psychology background.
Handling data
What you'll learn
- How to organise psychology data using tables, bar charts, histograms and scatter diagrams.
- How to calculate and interpret mean, median, mode and range.
- How to describe correlations and normal distributions at GCSE level.
- How to tell the difference between qualitative/quantitative and primary/secondary data.
Why handling data matters in psychology
Psychologists collect evidence about behaviour and mental processes. That evidence often becomes data, meaning information gathered during research.
Handling data means turning messy results into something clear: a table, graph, percentage, average, or written interpretation. In GCSE Psychology, you are not expected to use inferential statistics such as p-values, chi-square, sign test or standard deviation.
Key Idea
GCSE data handling
At GCSE, your job is to describe, calculate, display and interpret data clearly. You do not need to prove statistical significance.
Types of data
Quantitative and qualitative data
Definition
Quantitative data
Quantitative data is numerical data. It can be counted or measured, such as a memory score out of 20 or the number of participants who conformed.
Definition
Qualitative data
Qualitative data is descriptive data, usually words. It includes interview answers, observations, explanations and personal opinions.
Quantitative data is useful because it is easier to compare, graph and summarise. Qualitative data is useful because it can give richer detail about thoughts, feelings and experiences.
For example, in a study of stress, “7 out of 10 on a stress scale” is quantitative. “I felt panicky before the test” is qualitative.
Common Mistake
Calling all questionnaire data quantitative
A questionnaire can produce quantitative data if it uses rating scales, but it can also produce qualitative data if it asks open questions such as “Explain how you felt.”
Primary and secondary data
Definition
Primary data
Primary data is data collected first-hand by the researcher for their own study.
Definition
Secondary data
Secondary data is data that already exists, such as official statistics, published research, school attendance records or NHS reports.
Primary data is often more directly relevant to the aim of the study, but it can take time to collect. Secondary data can be quicker and cheaper, but it may not match the researcher’s exact question.
Sampling and scientific data
A sample is the group of people who actually take part in a study. A target population is the wider group the researcher wants to generalise to.
For example, if you test 30 Year 11 students to learn about GCSE revision habits, the sample is those 30 students. The target population might be all Year 11 students.
Good sampling matters because biased samples can lead to misleading conclusions.
Definition
Sampling
Sampling is the process of selecting participants from a target population.
Common GCSE sampling ideas include:
- Random sampling: everyone in the target population has an equal chance of being chosen.
- Opportunity sampling: the researcher uses people who are available.
- Volunteer sampling: participants choose to take part after seeing an advert or invitation.
- Stratified sampling: the sample reflects important subgroups in the population, such as gender or age groups.
Example
Judging whether a sample is representative
A psychologist wants to study sleep habits in teenagers. They ask 20 students from one top-set psychology class.
- The target population is teenagers, but the sample only includes students from one class.
- This could be biased because psychology students may not represent teenagers in general.
- A better approach could be to sample students from different year groups, schools and backgrounds so the findings are more generalisable.
Significant figures and sensible rounding
Significant figures are the important digits in a number. They help you give answers that are precise enough without pretending your data is more accurate than it really is.
For GCSE Psychology, use an appropriate number of significant figures. If a calculation gives a long decimal, round it sensibly.
For example, 67.333333% could be written as 67.3% or 67%, depending on the context.
Tip
Rounding sanity check
If your original data is simple, your final answer usually should be simple too. A mean score of 8.666666 from a small class test is normally better reported as 8.7.
Example
Rounding a percentage
In a memory task, 13 out of 20 participants recalled the first word correctly.
- Convert the fraction to a percentage by calculating 1320×100\frac{13}{20} \times 1002013×100.
- This gives 65.
- The answer is 65%, which does not need extra decimal places.
Averages: mean, median and mode
An average is a typical value that represents a set of scores. GCSE Psychology uses three averages: mean, median and mode.
Definition
Mean
The arithmetic mean is found by adding all the scores together and dividing by the number of scores.
Definition
Median
The median is the middle score when all scores are placed in order.
Definition
Mode
The mode is the most common score.
Each average tells you something slightly different. The mean uses every score, but it can be affected by very high or very low scores. The median is useful when there are extreme scores. The mode is useful for the most frequent category or score.
Example
Finding mean, median and mode
A group of students score: 4, 7, 7, 8, 9 on a memory test.
- Find the mean by adding the scores: 4 + 7 + 7 + 8 + 9 = 35.
- Divide by the number of scores: 35 divided by 5 = 7, so the mean is 7.
- The scores are already in order, so the middle score is 7. The median is 7.
- The most common score is 7, because it appears twice. The mode is 7.
Common Mistake
Forgetting to order scores for the median
You must put scores in size order before finding the median. The “middle” score in the original list may not be the true median.
Range as a measure of dispersion
Definition
Range
The range is a measure of dispersion found by subtracting the lowest score from the highest score.
Dispersion means how spread out the scores are. A small range suggests the scores are close together. A large range suggests the scores are more varied.
Example
Calculating the range
Two groups complete a perception task.
- Group A scores: 6, 7, 7, 8, 9
- Group B scores: 2, 5, 7, 10, 13
- For Group A, compare the highest and lowest scores: 9 and 6.
- Calculate 9 − 6 = 3, so Group A’s range is 3.
- For Group B, compare the highest and lowest scores: 13 and 2.
- Calculate 13 − 2 = 11, so Group B’s range is 11.
- Group B’s scores are more spread out because its range is larger.
Common Mistake
Range is sensitive to extreme scores
One unusually high or low score can make the range look very large, even if most participants scored similarly.
Frequency tables and diagrams
A frequency is how often something happens. A frequency table organises counts into rows.
For example, if students rate a therapy as “helpful”, “unsure” or “not helpful”, you can count how many students chose each response.
| Response | Frequency |
|---|---|
| Helpful | 14 |
| Unsure | 5 |
| Not helpful | 6 |
A frequency diagram is a graph showing frequencies. At GCSE, the most important ones are bar charts and histograms.
Bar charts and histograms
A bar chart is used for categories, such as “agree”, “unsure” and “disagree”. The bars have gaps because the categories are separate.
A histogram is used for continuous or grouped numerical data, such as score bands. The bars touch because the groups form a continuous scale.
Example
Choosing the correct graph
A researcher records how many participants gave each type of answer: “yes”, “no” or “not sure”.
- The data is grouped into separate categories, not continuous numbers.
- Separate categories should be shown with gaps between bars.
- The correct graph is a bar chart, not a histogram.
Common Mistake
Using a histogram for categories
Do not use a histogram for labels like “male/female”, “yes/no” or “agree/disagree”. Use a bar chart because the categories are separate.
Scatter diagrams and correlation
A scatter diagram plots two variables for each participant. One variable goes on the x-axis and the other goes on the y-axis.
Definition
Correlation
A correlation is a relationship between two variables. It shows whether they are linked, but it does not prove that one causes the other.
At GCSE, you do not calculate a correlation coefficient. You describe the pattern from the scatter diagram:
- Positive correlation: as one variable increases, the other also tends to increase.
- Negative correlation: as one variable increases, the other tends to decrease.
- No correlation: there is no clear pattern.
Example
Interpreting a scatter diagram
A psychologist plots hours of sleep against reaction time errors.
- If the points slope downwards, more sleep is linked with fewer errors.
- This is a negative correlation because one variable increases while the other decreases.
- You cannot conclude that sleep definitely caused fewer errors, because another factor, such as caffeine or stress, may be involved.
Key Idea
Correlation is not causation
A correlation shows a relationship, but it does not prove cause and effect.
Normal distributions
A normal distribution is a bell-shaped pattern of scores. Many people score near the middle, and fewer people score at the very low and very high ends.
In a normal distribution:
- the shape is symmetrical;
- the mean, median and mode are all at the centre;
- most scores cluster around the average;
- extreme scores are rare.
This can appear in psychology when measuring traits or test scores across a large group, such as memory scores or reaction times.
Example
Recognising a normal distribution
A class takes a memory test. Most students score around 10 out of 15, a few score very low, and a few score very high.
- The scores cluster around the middle rather than being evenly spread.
- There are fewer scores at both extremes.
- This suggests the data may be approximately normally distributed.
Translating between graphs and numbers
You need to move both ways:
- from numbers to graphs;
- from graphs to numbers;
- from a graph to a written conclusion.
When reading a graph, check the axis labels, the scale, and what each bar or point represents.
Example
Reading values from a bar chart
A bar chart shows the number of participants choosing each emotion label after viewing a face: happy = 12, angry = 5, fearful = 8.
- Identify the tallest bar: happy has the highest frequency at 12.
- Compare the frequencies: happy was chosen 7 more times than angry because 12 − 5 = 7.
- Write a psychological interpretation: participants most often interpreted the face as happy.
Plotting two variables and interpreting graphs
When plotting two variables, each participant gives a pair of scores. For example:
| Participant | Revision hours | Test score |
|---|---|---|
| A | 1 | 4 |
| B | 2 | 5 |
| C | 3 | 7 |
| D | 4 | 8 |
Each pair becomes one point on a scatter diagram: revision hours on one axis and test score on the other.
Example
Plotting paired data
Participant C revised for 3 hours and scored 7.
- Find 3 on the x-axis because revision hours is the first variable.
- Find 7 on the y-axis because test score is the second variable.
- Plot the point where these two values meet.
- Repeat for all participants, then judge whether the overall pattern shows a positive, negative or no correlation.
Exam technique
In the exam
- Check what type of data you have before choosing a graph: categories usually need a bar chart, while paired variables need a scatter diagram.
- For averages, show your working clearly and choose the best measure: mean for a balanced data set, median if extreme scores may distort the mean.
- When describing a correlation, state the direction and strength from the scatter diagram, but do not claim cause and effect.
Self review
Check yourself
- What is the difference between quantitative and qualitative data?
- How do you calculate the mean and range from a small set of scores?
- How would you recognise a positive correlation on a scatter diagram?