How much of A-level psychology is maths?

A-level Psychology requires a solid foundation in GCSE-level mathematics, focusing on data handling, statistical calculations, graphical representation, and understanding statistical tests, all applied within psychological research contexts.

1. Descriptive Statistics (Calculating and Interpreting)

These questions involve summarising data using measures of central tendency (mean, median, mode) and dispersion (range, standard deviation), as well as using percentages.

• Calculating the Mean: You might be asked to calculate the mean from a given set of data. For example, one question requires calculating the mean accuracy score for a cognitive interview condition. The method involves adding all scores and dividing by the number of scores.
• Calculating the Mode: Questions ask you to identify the mode (most frequently occurring value) in a data set.
• Calculating the Median: You may need to find the median (the numerical midpoint) by arranging scores in order. The mark scheme notes how this is done.
• Calculating Percentages: You could be asked to calculate percentages from data, or estimate a percentage from a graph or figure. For instance, one question asks for the percentage of students who felt anxious based on provided data.
• Calculating the Range: The calculation of the range (difference between highest and lowest score, sometimes adding 1) is also assessed. You might need to estimate the range when commenting on data spread.

2. Data Presentation and Interpretation (Graphs and Tables)

This involves selecting appropriate ways to display data and interpreting information presented graphically.

• Choosing and Justifying Appropriate Graphs: You might be asked to name a suitable graph for a given data set and explain why it is appropriate. For example, a bar chart is appropriate for displaying means of different groups, while a scattergram is suitable for showing the relationship between two variables in a correlational study.
• Constructing Graphs: You could be asked to sketch a graph based on numerical data. This includes constructing bar charts or scattergrams. When sketching graphs, you need to include appropriate titles and label axes clearly. Examiner reports note that students sometimes use inappropriate graphs for the data type, such as histograms or bar charts for correlations. For bar charts, bars should be separate for discrete data.
• Interpreting Graphs and Tables: You might need to extract information from tables or interpret trends shown in graphs, such as comparing results between different conditions. This also includes interpreting scattergrams to describe the nature of the relationship shown.
• Understanding Distributions: You need to know the characteristics of normal and skewed distributions (positive and negative skew). Questions may ask you to identify the type of distribution shown by a set of scores and justify your answer, or sketch a distribution curve, labelling the positions of the mean, median, and mode. Examiner reports indicate that identifying and justifying the type of distribution can be challenging.

3. Data Handling Concepts

Understanding different types of data is a key mathematical requirement.

• Primary vs. Secondary Data: You need to understand the difference between primary (collected directly by the researcher for the study’s purpose) and secondary data (already exists, collected by someone else). Questions may ask you to state whether data is primary or secondary or identify it from a scenario. Examiner reports note confusion regarding this distinction.
• Qualitative vs. Quantitative Data: You need to distinguish between qualitative (non-numerical, descriptive) and quantitative data (numerical). Questions ask what is meant by qualitative data , or require you to explain strengths and limitations of collecting quantitative data or handling qualitative data. You might also need to explain how qualitative data (like an interview transcript) could be converted into quantitative data.
• Levels of Measurement: Understanding nominal, ordinal, and interval data is crucial. Questions ask you to state the level of measurement used in a study and explain limitations of a particular level.

4. Statistical Testing

This is a core mathematical requirement, focusing on knowing when to use tests and interpreting their outcomes. Calculation of the sign test is explicitly required.

• Choosing a Statistical Test: You must understand factors influencing the choice of statistical test, such as the level of measurement and experimental design (test of difference vs. association, related vs. unrelated design). Questions ask you to select an appropriate test for a given study and justify your choice53 .
• The Sign Test: The specification specifically mentions the sign test. You need to know when it is appropriate to use and how to perform the calculation.
• Interpreting Significance: You need to understand probability and significance, including the conventional 0.05 level (p<0.05). Questions may ask you to explain what a result being ‘significant at p<0.05’ means in the context of a study58 . You also need to understand Type I and Type II errors.

5. Algebra and Symbols

While not heavily algebraic, there are requirements related to using mathematical symbols and simple calculations.

• Using Symbols: You must understand and use symbols like =, <, >, <<, >>, ∝, ~ . An example is expressing the outcome of an inferential test using appropriate symbols, like stating significance at the 0.05 level. Understanding the meaning of ‘<0.05’ is specifically mentioned as important.
• Solving Simple Equations: This includes tasks like calculating degrees of freedom for a Chi Square test or substituting values into formulas.