How is data described and analysed in psychology?
Quantitative and qualitative data, primary and secondary data. Measures of central tendency and dispersion. Presentation of quantitative data, distributions, and the analysis of qualitative data.
Covers AQA 4.7 data handling: quantitative and qualitative data, primary and secondary data, measures of central tendency and dispersion, distributions and presenting data.
Reviewed by: AI editorial process; not yet individually human-reviewed
Have a quick question? Jump to the Q&A page
Jump to a section
What this dot point is asking
AQA wants you to describe types of data, measures of central tendency and dispersion, distributions and how data is presented. The exam skill is to calculate the descriptive statistics correctly and to justify which measure suits which kind of data.
Types of data and central tendency
Data first divides by form: quantitative data is numerical (for example a reaction time in milliseconds) and lends itself to statistical analysis, while qualitative data is descriptive (for example an interview transcript) and gives richer detail but is harder to analyse objectively. Data also divides by source: primary data is gathered first-hand by the researcher for the current study, whereas secondary data already exists (such as official statistics or another researcher's data) and is quicker to obtain but may not fit the aim exactly. The three measures of central tendency each describe the typical value differently. The mean is the arithmetic average, found by adding all values and dividing by the number of values; it uses every score, which is its strength, but it is distorted by extreme outliers. The median is the middle value when scores are placed in order; it is unaffected by outliers, making it ideal for skewed data, but it does not use all the values. The mode is the most frequently occurring value; it is the only measure usable with categorical data but can be unrepresentative or absent.
Dispersion and distributions
Measures of dispersion describe how spread out the data are. The range is the simplest, calculated as the highest value minus the lowest; it is quick but uses only two values, so a single extreme score can make it misleading. The standard deviation is a more sophisticated measure that reflects the average distance of all the scores from the mean, so a larger standard deviation means the scores are more spread out and a smaller one means they cluster tightly around the mean; because it uses every value it is more sensitive than the range, though it too can be distorted by extreme outliers. Distributions describe the overall shape of the data. In a normal distribution the data are symmetrical and bell-shaped, with the mean, median and mode all falling at the same central point, and most scores clustered near the middle. In a skewed distribution the data are asymmetrical: a positive skew has a long tail of high scores (with the mean pulled to the right of the median), and a negative skew has a long tail of low scores (with the mean pulled to the left). Quantitative data are presented in tables, bar charts (for categories), histograms (for continuous data) and scattergrams (for correlations), while qualitative data are analysed through content analysis (coding material into categories) and thematic analysis (identifying recurring themes).
Exam-style practice questions
Practice questions written in the style of AQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
AQA 20194 marksA researcher recorded the number of words recalled by seven participants: 8, 5, 9, 12, 5, 7, 10. Calculate the mean and the range, showing your working.Show worked answer →
A 4-mark calculation item (2 marks each). Markers reward correct method and answer, with working shown.
Mean: add the values and divide by the number of values. The sum is . There are values, so the mean is words.
Range: subtract the lowest value from the highest. The highest is and the lowest is , so the range is words. (Some boards add to include both endpoints, giving , but the standard AQA method is highest minus lowest.)
A full-mark answer shows the working for each, not just the final number, since method marks are available even if the arithmetic slips.
AQA 20213 marksExplain why the median may be a more suitable measure of central tendency than the mean for a skewed set of scores.Show worked answer →
A 3-mark item. Markers want the effect of outliers explained.
The mean is calculated using every value, so it is heavily affected by extreme scores (outliers). In a skewed distribution, a few very high or very low values pull the mean towards the tail, so it no longer represents the typical score well. The median is simply the middle value when the data are ordered, so it is not distorted by outliers and gives a better picture of the central or typical value in skewed data.
A full-mark answer states that the mean uses all values and is distorted by outliers, that skewed data contains such extreme values, and that the median (the middle value) is resistant to this distortion.
Related dot points
- Experimental method: laboratory, field, natural and quasi-experiments. Aims, hypotheses, independent and dependent variables, operationalisation, extraneous and confounding variables.
Covers AQA 4.7 experimental methods: laboratory, field, natural and quasi-experiments, aims and hypotheses, IVs and DVs, operationalisation, and extraneous and confounding variables.
- Observational techniques: naturalistic and controlled, covert and overt, participant and non-participant. Observational design: behavioural categories, event and time sampling.
Covers AQA 4.7 observational techniques: naturalistic and controlled, covert and overt, participant and non-participant observation, and observational design (behavioural categories, event and time sampling).
- Self-report techniques: questionnaires; interviews, structured and unstructured. The design of questionnaires, including the use of open and closed questions.
Covers AQA 4.7 self-report techniques: questionnaires, structured and unstructured interviews, open and closed questions, and the design of effective questionnaires.
- Correlations: analysis of the relationship between co-variables. The difference between correlations and experiments. Positive, negative and zero correlations.
Covers AQA 4.7 correlations: co-variables, positive, negative and zero correlations, scattergrams, the difference from experiments, and why correlation does not show causation.
- Experimental designs: independent groups, repeated measures and matched pairs. Design of investigations, including control of variables, randomisation and counterbalancing.
Covers AQA 4.7 experimental design: independent groups, repeated measures and matched pairs, with their strengths and limitations, and the use of randomisation and counterbalancing.
- Sampling: the difference between population and sample; sampling techniques including random, systematic, stratified, opportunity and volunteer; implications of sampling techniques, including bias and generalisation.
Covers AQA 4.7 sampling: population versus sample, random, systematic, stratified, opportunity and volunteer sampling, and the implications of bias and generalisation.
Sources & how we know this
- AQA A-level Psychology (7182) specification — AQA (2015)