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IB DP Psychology — Concepts (Causality) | SL & HL | Paper 1
🎯 Learning Intentions By the end of this note, you will be able to:
- Define complexity, interaction, and statistical significance using IB-aligned terminology.
- Explain why human behaviour cannot typically be attributed to a single cause and how multiple factors interact to produce psychological outcomes.
- Distinguish between correlation and causation, and explain the conditions under which causal claims can and cannot be made.
- Describe what statistical significance means and why a statistically significant result does not automatically confirm a causal relationship.
- Evaluate research evidence related to complexity and interaction using supporting and opposing studies in ACAS format.
- Apply these concepts to a real-world case study, identifying how complexity and interaction shape behaviour.
- Analyse psychological research using the concept areas of Causality, Measurement, Bias, and Perspective.
Introduction & Context
Imagine reading a headline: "Scientists discover that social media use causes depression in teenagers." It sounds definitive. It sounds alarming. But pause for a moment — how confident should we actually be in that claim? Did every teenager who used social media become depressed? Did teenagers who were already feeling low perhaps turn to social media more? Could it be that a third factor, like sleep deprivation or academic stress, explains both the social media use and the depression simultaneously?
These questions sit at the heart of one of the most important ideas in psychology: the concept of causality. Psychology seeks to explain why people think, feel, and behave the way they do — but human behaviour is rarely the product of a single, clean cause. Instead, it emerges from a web of interacting biological, cognitive, and sociocultural factors. Understanding how to reason carefully about cause and effect — and how to interpret the statistical tools researchers use — is essential for evaluating any psychological claim.
This note explores three tightly connected ideas: the complexity of behaviour (the idea that behaviour has multiple causes), interaction (the idea that these causes influence each other), and statistical significance (the mathematical tool researchers use to decide whether a finding is likely to be real). Together, these concepts form a critical lens through which all psychological research should be viewed.
🔍 Analogy — The Weather System Think of human behaviour like the weather. A meteorologist cannot say "it rained because of low pressure" — low pressure interacts with temperature, humidity, wind patterns, and geography to produce rain. Remove any one factor and the outcome changes. Similarly, a psychologist cannot say "this person became anxious because of their genes" — genes interact with childhood experiences, social environment, stress levels, and cognitive patterns. The weather analogy also reminds us that even with sophisticated models and statistics, prediction is probabilistic, not certain. Complexity and interaction are not obstacles to understanding — they are the reality we must work with.
Core Concepts
When psychologists study behaviour, they are always asking some version of the same question: what caused this? But before we can answer that question responsibly, we need to understand what kind of answer is even possible. The first concept to grapple with is the sheer complexity of the causes of human behaviour.
📖 Key Definition — Complexity Complexity refers to the idea that human behaviour is the product of multiple interacting causes, rather than any single factor. These causes span biological, cognitive, and sociocultural levels of analysis, and no single cause is sufficient on its own to fully explain a behaviour.
Consider something