Data analysis — Edexcel GCSE Psychology

In psychology, handling data often requires arithmetic and numerical computation. You must recognise and use expressions in decimal and standard form. Standard form (A × 10ⁿ, where 1 ≤ A < 10) is useful for very large or small numbers, such as writing 0.0034 as 3.4 × 10⁻³. Estimating results is a crucial skill to check if your final calculation is reasonable; for example, estimating 48 × 11 as 50 × 10 = 500. Finally, you must use an appropriate number of significant figures. When calculating, your final answer should typically match the lowest number of significant figures given in the raw data, ensuring you do not overstate the precision of your psychological measurements.

Psychological data analysis requires calculating and interpreting descriptive statistics. Measures of central tendency include the mean (sum of all values divided by the number of values), median (the middle value when data is ordered), and mode (the most frequent value). You must also calculate ratios, fractions, and percentages to compare groups or express proportions. The range (highest value minus lowest value) is used as a measure of dispersion to show data spread. Furthermore, you must know the characteristics of a normal distribution: it forms a symmetrical, bell-shaped curve where the mean, median, and mode all fall at the exact centre, representing how many psychological traits are naturally distributed.

Psychologists use graphical representations to analyse data. Frequency tables organise raw data by showing how often values occur, which can be plotted as frequency diagrams. Bar charts display categorical data (e.g., attachment types) with gaps between bars. Histograms represent continuous data (e.g., reaction times) with no gaps, where area represents frequency. Scatter diagrams plot two continuous variables (e.g., sleep hours and test scores) to identify positive, negative, or zero correlations. You must construct these accurately from experimental data, ensuring axes are labelled and scaled. Additionally, you must translate numerical data from tables into graphical forms and interpret graphs to extract values or draw conclusions.

In psychological research, data is categorised by its source. Primary data is gathered directly by the researcher specifically for their current investigation. For example, if a psychologist conducts a new memory experiment and records participants' scores, this is primary data. It is highly relevant but time-consuming to collect. Secondary data is information that already exists, collected by someone else for a different purpose, such as government statistics or previous journal articles. While secondary data is quicker and cheaper to access, it may not perfectly fit the researcher's specific aims or might be outdated. Understanding this distinction is crucial for evaluating the validity and practical constraints of a study's methodology.

Qualitative data is non-numerical, descriptive information that captures thoughts, feelings, and meanings. For example, an interview transcript where a participant describes their experience of stress. Quantitative data is numerical information that can be counted, measured, and statistically analysed. For example, the number of words recalled in a memory test or a score on a rating scale from 1 to 10. The key difference lies in their form and analysis: qualitative data provides rich, detailed insights but is harder to analyse objectively, whereas quantitative data allows for easy comparison and statistical testing but may lack depth and context.