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IB DP Psychology — Research Methodology | SL & HL | Paper 3 (HL) / Paper 1 (All)
🎯 Learning Intentions By the end of this note, you will be able to:
- Define true experiments, quasi-experiments, and natural experiments using IB-aligned terminology.
- Explain the role of random allocation, manipulation of the independent variable, and control conditions in establishing experimental validity.
- Distinguish between true experimental and quasi-experimental designs, identifying the key methodological differences and their implications.
- Evaluate the strengths and limitations of true and quasi-experimental designs using supporting and opposing research evidence in ACAS format.
- Apply knowledge of experimental design to a real-world case study, identifying which design is most appropriate and why.
- Analyse experimental research using relevant concept areas: Causality, Bias, Measurement, and Responsibility.
- Discuss the ethical and practical trade-offs involved in choosing between experimental designs in psychological research.
Introduction & Context
Imagine a researcher wants to know whether listening to music while studying improves memory for exam material. They could simply observe students who already listen to music and compare their grades to those who do not. But there is an immediate problem: perhaps the students who listen to music are also more motivated, wealthier, or have quieter study environments. How would the researcher ever know whether it was the music — or all those other differences — that caused any improvement? This is the fundamental challenge at the heart of psychological research: isolating cause and effect in a world full of confounding variables.
The experiment was developed precisely to meet this challenge. By deliberately manipulating one variable while holding everything else constant, researchers can make confident claims about what caused a change in behaviour. But not all experiments are created equal. Some allow researchers to control every condition with precision; others must work with the messiness of the real world, where people cannot be randomly assigned to groups and variables cannot always be manipulated. Understanding the difference between these designs — and knowing when each is appropriate — is one of the most important skills in psychological research methodology.
🔬 Analogy — The Controlled Kitchen Think of a true experiment like a professional kitchen where the chef controls every ingredient, temperature, and timing exactly. If the dish tastes different, it must be because of the one ingredient the chef deliberately changed. A quasi-experiment is more like cooking at home during a dinner party — you are working with what you have, the oven runs a little hot, and guests arrive at different times. You can still learn something about the recipe, but you can never be completely sure which variable made the difference.
Core Concepts
Before we can distinguish between types of experiments, we need to understand what makes something an experiment at all. The defining feature of any experiment is that the researcher deliberately manipulates at least one variable — called the independent variable (IV) — and measures its effect on another variable — the dependent variable (DV). This manipulation is what separates experiments from all other research methods, and it is what gives experiments their unique power to suggest causal relationships.
📖 Key Definition — Experiment An experiment is a research method in which the researcher deliberately manipulates an independent variable (IV) and measures its effect on a dependent variable (DV), while controlling for extraneous variables, in order to establish cause-and-effect relationships.
The independent variable is the variable the researcher changes or controls — it is the presumed cause. The dependent variable is the outcome that is measured — it is the presumed effect. For example, in a study on sleep deprivation and reaction time, the amount of sleep (manipulated by the researcher) is the IV, and the participant's reaction time (measured by the researcher) is the DV. Every other variable that