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IB DP Psychology — Research Methodology | SL & HL | Paper 3
🎯 Learning Intentions By the end of this note, you will be able to:
- Identify appropriate graph types (bar charts, histograms, scatterplots, line graphs) and explain when each is used in psychological research.
- Interpret data presented in tables, graphs, and frequency distributions, drawing accurate conclusions about patterns and trends.
- Distinguish between normal and skewed distributions and explain what each reveals about a dataset.
- Define measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation) using IB-aligned terminology.
- Explain how the shape of a distribution affects which measure of central tendency is most appropriate to use.
- Evaluate the strengths and limitations of different methods of data representation using relevant concept areas: Measurement, Bias, and Causality.
- Apply knowledge of data representation to interpret research findings presented in a Paper 3 stimulus source.
- Analyse how misleading graphs or inappropriate data representations can introduce bias into psychological research.
Introduction & Context
Imagine a researcher has just finished collecting data from 200 participants about how many hours of sleep they get per night and how well they performed on a memory test the following morning. They now have 200 rows of numbers sitting in a spreadsheet. Looking at raw numbers alone tells us almost nothing — the human brain is not designed to extract meaning from a wall of figures. Something needs to transform that data into a form we can actually understand, compare, and communicate.
This is precisely the role of data representation. Before any conclusions can be drawn, before any claims about human behaviour can be made, researchers must organise, summarise, and display their data in ways that reveal patterns. The choice of how to represent data is not a neutral, cosmetic decision — it shapes what we see, what we miss, and what conclusions we are led toward. A poorly chosen graph can obscure a real effect; a misleading scale can make a trivial difference look dramatic. Understanding data representation is therefore not just a technical skill — it is a critical thinking skill.
🔍 Analogy Think of raw data as a pile of unsorted puzzle pieces. A table organises those pieces into groups so you can see what you have. A graph arranges them into a picture so you can see the pattern. A distribution tells you the shape of the completed puzzle — whether it is balanced and symmetrical, or lopsided. Without these tools, you are just staring at a pile of pieces and guessing.
Core Concepts
The most fundamental way to organise data is the table. Before any graph can be drawn, data must be structured — and a well-constructed table is often the first step. In psychological research, tables typically present raw scores, frequency counts, or summary statistics (such as means and standard deviations) for different groups or conditions. The ability to read a table accurately is the foundation of all further data interpretation.
📖 Key Definition — Data Table A data table is a structured arrangement of information into rows and columns, used to organise and display raw scores, frequencies, or summary statistics in a clear and comparable format. In psychology, tables commonly present group means, standard deviations, and sample sizes for each experimental condition.
When reading a table, the first step is always to identify what each row and column represents. The column headers tell you the variables being measured; the row labels tell you the groups or conditions. A common error is to read individual cells in isolation without comparing them across rows or columns — the meaning of a number in a table almost always comes from its relationship to other numbers in the same table.
For example, if a table shows that Group A had a mean memory score of 14.2 and Group B had a mean of 9.7, the difference (4.5 points) is only