What you'll learn
This guide covers everything you need to know about experimental research methods for OCR GCSE Psychology. You'll learn how psychologists use experiments to establish cause-and-effect relationships, the different types of experimental designs, and how to identify and control variables. These research methods form the foundation of scientific psychology and appear across all topic areas in your exam.
Key terms and definitions
Experiment — A research method where the researcher manipulates one variable to observe its effect on another variable, while controlling other factors that might influence the results.
Independent variable (IV) — The variable that the researcher deliberately changes or manipulates in an experiment to see what effect it has.
Dependent variable (DV) — The variable that is measured by the researcher; the outcome that may be affected by changes to the independent variable.
Extraneous variables — Any variables other than the IV that could affect the DV and therefore confuse the results if not controlled.
Confounding variables — Extraneous variables that have not been controlled and do systematically vary with the IV, making it impossible to determine what caused changes in the DV.
Operationalisation — Defining variables in a clear, precise, measurable way so that they can be tested objectively.
Standardisation — Keeping procedures, instructions, and conditions the same for all participants to reduce the effect of extraneous variables.
Control condition — A baseline condition in an experiment where participants are not exposed to the IV, used for comparison with the experimental condition.
Core concepts
Types of experiments
Laboratory experiments
Laboratory experiments take place in a controlled environment where the researcher can manipulate the IV and measure the DV while controlling extraneous variables.
Strengths:
- High level of control over extraneous variables, reducing their impact on results
- Easy to replicate because procedures can be standardised
- Allows establishment of cause-and-effect relationships between variables
- Precise equipment can be used for accurate measurements
Weaknesses:
- Artificial environment may produce unnatural behaviour (low ecological validity)
- Participants know they're being studied, which can lead to demand characteristics
- May not represent real-world behaviour, limiting generalisability
- Expensive and time-consuming to set up controlled conditions
Example: Baddeley's research on encoding in memory tested participants in a psychology laboratory, asking them to recall word lists that were either acoustically similar or semantically similar.
Field experiments
Field experiments take place in the participants' natural environment, but the researcher still manipulates the IV and measures the DV.
Strengths:
- Higher ecological validity than laboratory experiments because the setting is natural
- Participants may be unaware they're being studied, reducing demand characteristics
- Behaviour is more likely to reflect real-world actions
- Useful for studying behaviour that cannot be recreated in a laboratory
Weaknesses:
- Less control over extraneous variables that might affect results
- More difficult to replicate due to changing natural conditions
- Harder to use precise measuring equipment
- Ethical issues if participants don't know they're being studied (lack of informed consent)
Example: Piliavin's subway study took place on real New York subway trains, where a confederate collapsed to measure helping behaviour in a naturalistic setting.
Natural experiments
Natural experiments occur when the researcher takes advantage of a naturally occurring IV that they have not manipulated themselves. The researcher measures the DV but does not control the IV.
Strengths:
- Allows research into variables that would be unethical to manipulate artificially
- High ecological validity because the IV occurs naturally
- Useful for studying rare phenomena or situations
- Participants behave naturally because conditions aren't artificially created
Weaknesses:
- Cannot randomly allocate participants to conditions, increasing risk of confounding variables
- Very difficult to replicate as natural events are unpredictable
- Less control over extraneous variables
- Cannot establish cause-and-effect with certainty due to lack of control
Example: Charlton's research on St Helena studied the effects of television being introduced to the island for the first time, comparing children's behaviour before and after TV arrived.
Variables in experiments
Identifying and operationalising variables
For an experiment to be scientific, both the IV and DV must be operationalised — defined in measurable, testable terms.
Poorly operationalised: "Does stress affect memory?"
- What counts as "stress"?
- What type of "memory"?
- How will you measure these?
Well operationalised: "Does completing a mental arithmetic task (IV: stress condition vs. no stress condition) affect the number of words correctly recalled from a 15-word list (DV: number of words out of 15)?"
When operationalising variables, you must specify:
- Exactly what will be manipulated (for IV)
- Exactly what will be measured (for DV)
- How it will be measured (units, scales, or categories)
- Clear categories or conditions for comparison
Controlling extraneous variables
Extraneous variables must be controlled to ensure that only the IV affects the DV. If extraneous variables aren't controlled, they become confounding variables that invalidate the results.
Common extraneous variables:
Participant variables — Individual differences between participants (age, gender, intelligence, personality, mood)
- Control through: random allocation to conditions, matched pairs design, repeated measures design
Situational variables — Features of the research environment (temperature, noise, lighting, time of day)
- Control through: standardisation of environment, conducting all tests in same location
Investigator effects — Researcher's behaviour influencing participants (tone of voice, body language, expectations)
- Control through: standardised instructions, double-blind procedures, using multiple researchers
Demand characteristics — Participants guessing the research aim and changing their behaviour accordingly
- Control through: deception (ethically questionable), single-blind procedures, cover stories
Experimental designs
Experimental design refers to how participants are allocated to different conditions of the IV.
Independent groups design
Different participants are used in each condition of the experiment. Participants are randomly allocated to conditions.
Strengths:
- No order effects (practice or fatigue) because participants only do one condition
- Less chance of demand characteristics as participants don't experience both conditions
- Can be used when repeated measures is impossible
Weaknesses:
- Participant variables may confound results (individual differences between groups)
- Needs more participants than repeated measures
- Cannot be certain groups are equivalent even with random allocation
Control: Random allocation helps distribute participant variables evenly across conditions.
Repeated measures design
The same participants take part in all conditions of the experiment.
Strengths:
- Controls for participant variables because same people in each condition
- Needs fewer participants than independent groups
- Can directly compare each person's performance across conditions
Weaknesses:
- Order effects may confound results (practice, fatigue, boredom)
- Increased demand characteristics as participants experience all conditions
- Not suitable if one condition affects performance in another
Control: Counterbalancing (half do condition A then B, half do B then A) reduces order effects.
Matched pairs design
Different participants are used in each condition, but they are matched on key characteristics relevant to the study (e.g., age, IQ, gender).
Strengths:
- Reduces participant variables while avoiding order effects
- Lower demand characteristics than repeated measures
- Each pair provides a controlled comparison
Weaknesses:
- Very time-consuming and expensive to match participants
- Impossible to match on all variables, only key ones
- Need large pool of participants to find suitable matches
- If one participant drops out, their matched partner's data may be unusable
Control: Matching on variables most likely to affect the DV.
Hypotheses in experimental research
Directional (one-tailed) hypotheses
A directional hypothesis predicts the specific direction of the results.
Example: "Participants who drink caffeine will recall significantly more words than participants who do not drink caffeine."
Use when previous research suggests a particular direction of effect.
Non-directional (two-tailed) hypotheses
A non-directional hypothesis predicts that there will be a difference, but not the direction.
Example: "There will be a significant difference in the number of words recalled between participants who drink caffeine and participants who do not drink caffeine."
Use when there is no previous research, or previous research shows contradictory findings.
Null hypothesis
The null hypothesis states that there will be no significant difference or relationship between variables. Any difference is due to chance.
Example: "There will be no significant difference in the number of words recalled between participants who drink caffeine and participants who do not drink caffeine. Any difference is due to chance."
The null hypothesis is assumed true until statistical testing provides evidence against it.
Worked examples
Example 1: Identifying components of an experiment (4 marks)
Question: A psychologist wanted to investigate whether background noise affects concentration. She tested 20 students on a word search puzzle. Ten students completed the puzzle in silence (Condition A), and ten students completed the puzzle with music playing at 70 decibels (Condition B). She measured how many words each student found in 5 minutes.
(a) Identify the independent variable in this experiment. (1 mark) (b) Identify the dependent variable in this experiment. (1 mark) (c) Identify the experimental design used. (1 mark) (d) State one extraneous variable that should be controlled and explain how. (1 mark)
Model answers: (a) The presence or absence of background noise / music at 70 decibels vs. silence (1 mark)
- Award mark for correctly identifying what was manipulated (the two conditions)
(b) The number of words found in the word search in 5 minutes (1 mark)
- Award mark for correctly identifying what was measured, including units
(c) Independent groups design (1 mark)
- Award mark for correct identification; different participants in each condition
(d) Difficulty of word search (extraneous variable) — use the same word search for all participants (control method) (1 mark)
- OR Time of day — test all participants at the same time
- OR Previous experience with word searches — randomly allocate participants to conditions
- Award mark for sensible extraneous variable and appropriate control method
Example 2: Evaluating experimental methods (6 marks)
Question: Evaluate the use of laboratory experiments in psychology. Refer to at least one strength and one weakness in your answer.
Model answer:
One strength of laboratory experiments is the high level of control over extraneous variables (1 mark). This means that the researcher can be more confident that changes in the DV are caused by the IV and not by other factors (1 mark). For example, in Baddeley's research on encoding, all participants were tested in the same controlled environment with standardised word lists, ensuring that differences in recall were due to the type of similarity (acoustic vs. semantic) rather than environmental factors (1 mark).
However, one weakness of laboratory experiments is low ecological validity (1 mark). The artificial environment and contrived tasks mean that participants may not behave as they would in real life (1 mark). For example, remembering lists of words in a laboratory is very different from how memory operates in everyday situations like remembering a shopping list or a conversation, so findings may not generalise to real-world memory (1 mark).
Mark scheme guidance:
- 1 mark for identifying strength/weakness
- 1 mark for elaborating/explaining why this matters
- 1 mark for contextualising with example or psychological research
- Maximum 6 marks total (typically 3 for strength, 3 for weakness)
Example 3: Designing an experiment (5 marks)
Question: Design a laboratory experiment to investigate whether listening to classical music improves performance on a maths test compared to no music. In your answer, you should include:
- An operationalised hypothesis
- The experimental design you would use
- One extraneous variable you would control
Model answer:
Hypothesis: Students who listen to classical music while completing a maths test will score significantly higher (out of 20) than students who complete the test in silence (1 mark).
- Award mark for operationalised directional or non-directional hypothesis with measurable DV
Experimental design: I would use a repeated measures design where all participants complete two maths tests — one while listening to classical music and one in silence (1 mark). I would use counterbalancing so that half the participants complete the music condition first and half complete the silence condition first (1 mark).
- Award 1 mark for identifying suitable design
- Award 1 mark for explaining how it would be implemented/controlled
Extraneous variable: The difficulty of the maths tests (1 mark). I would control this by using two tests of equal difficulty, with the same number and type of questions, to ensure that performance differences are due to the music and not test difficulty (1 mark).
- Award 1 mark for identifying suitable extraneous variable
- Award 1 mark for explaining appropriate control method
Common mistakes and how to avoid them
Confusing IV and DV: Remember, the IV is what the researcher manipulates (the cause), and the DV is what the researcher measures (the effect). The DV "depends on" the IV. Always check: what is being changed vs. what is being measured?
Failing to operationalise variables: Vague terms like "stress," "aggression," or "memory" aren't sufficient. Always state exactly how variables will be measured (e.g., "number of aggressive acts observed in 10 minutes" or "score out of 30 on a recall test").
Mixing up extraneous and confounding variables: Extraneous variables are controlled; confounding variables are extraneous variables that weren't controlled and therefore vary systematically with the IV. In your answer, if describing a well-designed study, refer to extraneous variables that were controlled.
Confusing experimental designs: Independent groups = different people in each condition; Repeated measures = same people in all conditions; Matched pairs = different people matched on key variables. Check how participants are allocated.
Not providing enough detail in evaluation answers: When evaluating strengths/weaknesses, follow the three-step structure: identify the point, explain why it matters, provide an example or context. Generic statements without elaboration earn few marks.
Forgetting that natural experiments involve naturally occurring IVs: In natural experiments, the researcher doesn't manipulate the IV — it occurs naturally. This is the key distinction from lab and field experiments.
Exam technique for "Research Methods: Experimental Research"
Identify command words carefully: "Identify" requires a brief label (1 mark); "Outline" needs brief description (2 marks); "Describe" requires more detail (4+ marks); "Evaluate" requires judgements about strengths/weaknesses with justification (6+ marks). Adjust your answer length accordingly.
Use the three-part structure for evaluation questions: (1) Identify the strength/weakness, (2) Explain why this matters for the research, (3) Provide a contextual example, preferably from studies you've learned. This typically maps to the mark scheme's point-evidence-example structure.
Apply research methods to novel scenarios: OCR frequently presents unfamiliar study scenarios. Practice identifying IVs, DVs, designs, and controls in new contexts, not just studies you've memorised. The principles remain the same regardless of the specific topic.
Show your working in design questions: When asked to design a study, always specify exact procedures, conditions, measurements with units, and how you'd control key variables. Vague descriptions earn few marks; specific, implementable procedures earn full marks.
Quick revision summary
Experiments manipulate an independent variable to measure its effect on a dependent variable while controlling extraneous variables. Laboratory experiments offer high control but low ecological validity; field experiments are more natural but harder to control; natural experiments study naturally occurring IVs but cannot establish causation as confidently. Experimental designs include independent groups (different participants), repeated measures (same participants), and matched pairs (matched participants). Variables must be operationalised into measurable terms, and procedures standardised to reduce confounding variables. Success in exam questions requires precise identification of components and detailed evaluation using point-evidence-example structure.