Experimental designs | AQA A-Level Psychology Revision
- Revision Notes
- Aug 4
- 16 min read
Updated: 6 days ago
For 7182 specification, first teach in September 2025
AQA A-Level Psychology | Free Revision Notes
Estimated study time: 50 minutes
Experimental designs determine how participants are organised across the conditions of an investigation. This Experimental designs A-Level Psychology revision page covers repeated measures, independent groups and matched pairs designs, including their strengths, limitations and appropriate uses. Choosing the right design matters because it can affect control, participant differences and the validity of conclusions. By learning to recognise each design and justify a suitable choice, you will be ready to apply your knowledge to unfamiliar AQA research scenarios.
Learning Objectives 🎯
By the end of this revision page, you should be able to:
Define repeated measures, independent groups and matched pairs designs.
Explain how participants are allocated to conditions in each design.
Compare the strengths and limitations of the three experimental designs.
Explain how order effects and participant variables may affect research.
Apply knowledge of experimental designs to unfamiliar investigations.
Select and justify an appropriate design for a research scenario.
Revision Notes 📚
What is an experimental design?
An experimental design describes how participants are organised across the different conditions of an experiment.
Most experiments involve at least two conditions. For example, a psychologist investigating the effect of background noise on concentration might compare:
A condition in which participants complete a task in silence.
A condition in which participants complete the same task while background noise is played.
The researcher must decide whether the same participants will complete both conditions or whether different participants will complete each condition.
The three experimental designs required for AQA A-Level Psychology are:
Repeated measures design
Independent groups design
Matched pairs design
Experimental design is not the same as experimental method. The experimental method concerns the setting or circumstances in which the research is conducted, such as a laboratory or field experiment. The experimental design concerns how participants are allocated to conditions.
The choice of design should be made after the researcher has identified and operationalised the variables. This links to defining exactly what will be manipulated and measured.
Understanding experimental conditions
A condition is one version of the experimental situation.
In a simple experiment, the researcher manipulates the independent variable by creating different conditions. The effect of these conditions is then measured using the dependent variable.
For example:
Independent variable: whether revision takes place in silence or with music.
Condition one: revision in silence.
Condition two: revision with music.
Dependent variable: the number of correct answers on a memory test.
The design determines which participants complete condition one and which participants complete condition two.
Repeated measures design
In a repeated measures design, the same participants take part in every condition of the experiment.
For example, a researcher investigates whether background music affects reading comprehension:
Each participant reads one passage in silence and completes a comprehension test.
The same participant reads another passage while music is playing and completes a second test.
The researcher compares each participant’s scores across the two conditions.
Because the same people complete both conditions, the results can be compared within the same group of participants.
Strengths of repeated measures designs
Participant variables are controlled
Participant variables are individual differences between participants that could influence the dependent variable. These may include differences in ability, motivation or experience.
In a repeated measures design, the same people take part in each condition. Differences between participants are therefore less likely to explain differences between the conditions.
For example, if a participant has particularly strong reading skills, those skills affect their performance in both conditions rather than only one condition.
Fewer participants are required
Each participant supplies data for every condition. A researcher may therefore need fewer participants than would be required for an independent groups design.
This can make the investigation more practical when access to participants is limited.
Direct comparisons can be made
The researcher can compare each participant’s performance in one condition with the same participant’s performance in another condition.
This may make differences caused by the independent variable easier to detect.
Limitations of repeated measures designs
Order effects may occur
Order effects happen when completing one condition affects performance in a later condition.
There are two main ways this may happen:
Practice effects: participants perform better because they have already completed a similar task.
Fatigue effects: participants perform worse because they are tired, bored or less motivated.
For example, participants might score more highly in the second condition because they have become familiar with the test format. Alternatively, they might score less highly because they have become tired.
This creates an alternative explanation for differences between the conditions.
Participants may guess the aim
Completing more than one condition may make the purpose of the investigation more obvious.
Participants might recognise what has changed between conditions and alter their behaviour. This is an example of demand characteristics.
You can develop this distinction further when revising participant and researcher influences on a study.
Materials may need to be different
If exactly the same task is used twice, participants may remember their earlier responses. Researchers may therefore need to create different but equivalent versions of a task.
For example, two different word lists might be required for a memory experiment. However, the researcher must ensure that the lists are similar in difficulty.
Controlling order effects
Researchers may use counterbalancing to reduce order effects in a repeated measures design.
In a two-condition experiment:
Half of the participants complete condition A followed by condition B.
The other half complete condition B followed by condition A.
This can be represented as:
Participant group | First condition | Second condition |
Group 1 | A | B |
Group 2 | B | A |
If practice or fatigue affects the results, it should not consistently favour one condition because the order has been varied.
Counterbalancing may reduce the systematic effect of order, but it does not prevent individual participants from becoming tired or improving through practice.
Researchers could also use randomisation to determine the order in which participants complete conditions. These procedures are covered more fully when studying random allocation, randomisation and counterbalancing.
Independent groups design
In an independent groups design, different participants take part in each condition of the experiment.
Each participant completes only one condition.
For example, a psychologist investigates whether background music affects reading comprehension:
Group one reads a passage in silence.
Group two reads a passage while music is playing.
The scores of the two separate groups are compared.
The participants in the silent condition do not also take part in the music condition.
Strengths of independent groups designs
Order effects are avoided
Participants complete only one condition, so their performance cannot be affected by practising a task in an earlier condition or becoming tired after completing another condition.
This is particularly useful when taking part in one condition would permanently affect performance in another.
The aim may be less obvious
Participants do not experience the other condition, so they may be less likely to identify what the researcher is comparing.
This may reduce some demand characteristics, although participants could still guess the aim from the instructions or task.
The same materials may be used
Because participants complete only one condition, the researcher may be able to use the same task or materials with both groups.
For example, both groups could complete the same memory test under different environmental conditions.
Limitations of independent groups designs
Participant variables may affect the results
The people in one condition may differ from the people in another condition.
For example, one group may contain participants with better memories, stronger reading skills or greater motivation. These differences might affect the dependent variable and provide an alternative explanation for the results.
Participant variables are particularly problematic if the groups differ systematically before the independent variable is introduced.
More participants are required
Separate participants are needed for each condition.
If an experiment requires 20 participants in each of two conditions, the researcher needs 40 participants in total. A repeated measures design would require only 20 participants to obtain 20 scores in each condition.
This may make independent groups research less practical when participants are difficult to recruit.
Differences may be harder to interpret
If the conditions produce different results, the researcher must consider whether the independent variable or pre-existing differences between the groups caused the difference.
Random allocation in an independent groups design
Researchers may use random allocation to assign participants to conditions.
Random allocation gives each participant an equal chance of being placed in any condition. For example, a researcher could:
Give each participant a number.
Use a random method to allocate numbers to condition A or condition B.
Place each participant in the condition selected.
Random allocation may help distribute participant variables more evenly between groups. However, it does not guarantee that the groups will be identical.
This is different from random sampling:
Random sampling concerns how participants are selected from a population.
Random allocation concerns how participants who have already been selected are placed into experimental conditions.
Matched pairs design
In a matched pairs design, different participants take part in each condition, but participants are matched into pairs based on characteristics that are relevant to the investigation.
One participant from each pair is placed in one condition, while the other participant is placed in the other condition.
For example, a researcher studying memory could match participants according to their scores on an initial memory test.
Matched pair | Condition A | Condition B |
Pair 1 | Participant with memory score of 18 | Participant with memory score of 18 |
Pair 2 | Participant with memory score of 15 | Participant with memory score of 15 |
Pair 3 | Participant with memory score of 12 | Participant with memory score of 12 |
The researcher might then randomly allocate one member of each pair to each condition.
The matching characteristics should be relevant to the dependent variable. Matching participants on an unrelated characteristic would not necessarily control important participant variables.
Strengths of matched pairs designs
Relevant participant variables may be reduced
Matching participants can make the groups more similar on characteristics that are likely to affect the dependent variable.
For example, matching participants on memory ability may reduce the chance that one condition contains participants with much stronger memories than the other.
This can provide greater control over participant variables than an independent groups design.
Order effects are avoided
Each participant completes only one condition, so practice and fatigue effects do not occur across the conditions.
The aim may be less obvious
As in an independent groups design, participants do not experience every condition. They may therefore be less likely to notice what is being compared.
Limitations of matched pairs designs
Matching can be difficult and time-consuming
The researcher must decide which participant characteristics are relevant and measure those characteristics before the experiment begins.
Finding participants who match closely may require considerable time and resources.
Matches may be imperfect
Two participants cannot be made identical. Even if they are matched on one characteristic, they may differ in other important ways.
For example, two participants may have the same score on a memory test but differ in motivation, tiredness or previous experience.
Matched pairs designs reduce some participant differences, but they do not remove all participant variables.
Participants may be difficult to replace
If one participant withdraws, the data from the matched participant may become less useful because the pair is no longer complete.
The researcher might need to find and assess a suitable replacement.
More participants are required than in repeated measures
Different people are needed for each condition. This means that a matched pairs design generally requires more participants than a repeated measures design.
Comparing the three experimental designs
Feature | Repeated measures | Independent groups | Matched pairs |
Participants in each condition | The same participants complete every condition | Different participants complete each condition | Different but matched participants complete each condition |
Participant variables | Reduced because the same people complete each condition | May differ between groups | Reduced through matching relevant characteristics |
Order effects | Possible | Avoided | Avoided |
Number of participants needed | Usually fewer | Usually more | Usually more |
Demand characteristics | May be greater because participants experience all conditions | May be reduced because participants experience only one condition | May be reduced because participants experience only one condition |
Practical demands | May require equivalent tasks and control of order | Relatively straightforward to organise | Matching can be difficult and time-consuming |
Useful control procedure | Counterbalancing or randomising condition order | Random allocation to conditions | Matching followed by random allocation within pairs |
Participant variables and experimental design
Participant variables are differences between individuals that may affect the dependent variable.
Examples could include:
Ability relevant to the task.
Previous experience.
Motivation.
Age, where relevant to the investigation.
Familiarity with the materials.
In independent groups and matched pairs designs, different participants complete the conditions. The researcher must therefore consider whether participant variables could produce differences between the groups.
In repeated measures designs, the same participants complete every condition. This controls participant variables because each person is compared with themselves. However, repeated participation creates the possibility of order effects.
This produces an important trade-off:
Repeated measures controls participant variables but risks order effects.
Independent groups avoids order effects but risks participant variables.
Matched pairs attempts to reduce participant variables without creating order effects, but matching is difficult and may be incomplete.
Experimental designs A-Level Psychology revision: selecting a suitable design
There is no single experimental design that is always best. The most appropriate choice depends on the investigation.
A researcher should consider:
Whether taking part in one condition could affect performance in another.
Whether participant variables are likely to influence the dependent variable.
Whether suitable participants are easy to recruit.
Whether participants can be matched on a relevant characteristic.
Whether equivalent versions of the task can be produced.
Whether completing multiple conditions may reveal the aim.
How much time and resources are available.
A strong exam answer should not simply name a design. It should explain why the design is appropriate for the particular scenario.
When repeated measures may be appropriate
Repeated measures may be suitable when:
Participant differences are likely to affect the dependent variable.
The same participants can complete every condition without major order effects.
Counterbalancing can be used.
Only a limited number of participants are available.
Equivalent materials can be created where necessary.
Example
A psychologist investigates whether people identify emotions more accurately from photographs presented in colour or in black and white.
The same participants could complete both conditions using different photographs. A repeated measures design would control individual differences in emotion-recognition ability.
The researcher should counterbalance the order of the two conditions and ensure the sets of photographs are equivalent.
When independent groups may be appropriate
Independent groups may be suitable when:
Taking part in one condition would affect participation in another.
Order effects would be difficult to control.
The same materials need to be used in each condition.
Enough participants are available.
Participants can be randomly allocated to conditions.
Example
A researcher investigates whether receiving positive feedback affects persistence on a difficult puzzle.
Participants in one condition receive positive feedback, while participants in the other condition receive neutral feedback.
If the same participants completed both conditions, they might recognise the purpose of the feedback or change their behaviour after completing the first puzzle. An independent groups design could therefore be appropriate.
The researcher should randomly allocate participants to reduce systematic differences between the groups.
When matched pairs may be appropriate
Matched pairs may be suitable when:
Participant variables are likely to have an important effect.
Order effects would make repeated measures unsuitable.
A relevant matching characteristic can be measured.
The researcher has enough time and participants to create suitable pairs.
Example
A psychologist investigates whether a new revision activity affects performance on a mathematics test.
Previous mathematical ability is likely to affect the dependent variable. The researcher could assess participants’ existing ability and match people with similar scores.
One member of each pair could use the new revision activity, while the other uses the standard activity.
Selecting a design for a scenario
Consider the following investigation:
A psychologist wants to compare how accurately participants recall a list of words immediately after learning it and again seven days later.
A repeated measures design would be appropriate because the researcher is examining how the same participants’ recall changes over time. Different participants in each condition would make it difficult to determine whether score differences were caused by the delay or by differences in memory ability.
Now consider a second investigation:
A psychologist wants to compare the effect of two explanations given before participants watch the same unexpected film clip.
An independent groups design may be more suitable because watching the clip once would influence how participants respond when seeing it again. Repeated measures would create serious order and familiarity effects.
Finally:
A researcher compares two methods of teaching a problem-solving skill. Existing problem-solving ability is expected to influence performance.
A matched pairs design could be suitable. Participants could be matched on an initial problem-solving assessment, with one member of each pair placed in each teaching condition.
Justifying a choice of experimental design
A complete justification should include:
The design selected.
How participants would be organised.
Why the design suits the investigation.
A relevant strength.
A relevant limitation or control procedure where required.
For example:
The researcher should use an independent groups design, with different participants completing the two conditions. This would avoid order effects because experiencing the first condition could influence participants’ responses in the second. Participants should be randomly allocated to the conditions to reduce the likelihood of systematic participant differences between the groups.
This is stronger than simply stating:
Use independent groups because it is better.
The second answer does not explain how the design works or why it suits the particular investigation.
Improving repeated measures designs
A repeated measures design may be improved by:
Counterbalancing the order of conditions.
Randomising the order where appropriate.
Using equivalent versions of a task.
Giving standardised instructions.
Allowing appropriate breaks between conditions.
Avoiding unnecessary clues about the aim.
A trial run may help researchers identify whether order effects or unsuitable materials are likely to occur. This links to testing a proposed procedure before the main investigation.
Improving independent groups designs
An independent groups design may be improved by:
Randomly allocating participants to conditions.
Using groups of an appropriate size.
Standardising the procedure across conditions.
Keeping all variables except the independent variable constant.
Checking that one group has not been tested under different circumstances from the other.
Random allocation reduces the risk of systematic differences, but researchers should not claim that it removes all participant variables.
Improving matched pairs designs
A matched pairs design may be improved by:
Selecting matching characteristics that are relevant to the dependent variable.
Measuring the matching characteristic consistently.
Matching participants as closely as practical.
Randomly allocating one member of each pair to each condition.
Using the same standardised procedure for both conditions.
Matching participants on many characteristics may increase control, but it also makes finding suitable pairs more difficult.
Experimental design within a complete investigation
Selecting a design is only one part of planning an experiment. The researcher must also decide:
The aim and hypothesis.
The experimental method.
How the variables will be operationalised.
How participants will be sampled.
Which controls will be used.
How ethical issues will be addressed.
How data will be collected and analysed.
These decisions come together when selecting a method, sample, design and variables.
Once the design has been selected, the researcher must ensure that it is followed consistently when collecting data ethically and accurately.
Key Words 🔑
Key word | Student-friendly definition | How it may be used in an exam |
Experimental design | The way participants are organised across the conditions of an experiment. | Identify, explain or recommend a design for a research scenario. |
Condition | One version of the experimental situation created by manipulating the independent variable. | Explain which participants complete each version of the study. |
Repeated measures design | A design in which the same participants complete every condition. | Identify the design or explain its strengths and limitations. |
Independent groups design | A design in which different participants complete each condition. | Select the design when order effects would be a major concern. |
Matched pairs design | A design in which different participants are matched on relevant characteristics before being placed in separate conditions. | Explain how matching could control important participant variables. |
Participant variables | Individual differences between participants that may affect the dependent variable. | Explain a limitation of independent groups or a reason for matching participants. |
Order effects | Changes in performance caused by the order in which conditions are completed. | Explain a limitation of repeated measures designs. |
Practice effect | Improved performance caused by previous experience of a similar task. | Identify a possible order effect in a research scenario. |
Fatigue effect | Reduced performance caused by tiredness, boredom or loss of motivation. | Explain why performance may decline in a later condition. |
Counterbalancing | Varying the order of conditions so that different participants complete them in different sequences. | Suggest a way of reducing order effects. |
Random allocation | Using a random procedure to place participants into experimental conditions. | Suggest a way of reducing systematic participant differences. |
Standardisation | Keeping instructions, materials and procedures consistent across participants or conditions. | Explain how a fair comparison between conditions can be maintained. |
Common Mistakes ⚠️
Mistake: Confusing experimental design with experimental method.
Why this is incorrect:Experimental design concerns how participants are organised across conditions. Experimental method concerns the type or setting of the experiment.
How to improve:Ask whether the question is about where the study takes place or about which participants complete each condition.
Mistake: Saying that repeated measures uses different participants in each condition.
Why this is incorrect:A repeated measures design uses the same participants in every condition.
How to improve:Remember that participants are repeatedly measured across the conditions.
Mistake: Saying that independent groups means participants work together.
Why this is incorrect:The term refers to separate groups of participants completing separate conditions. It does not mean that participants complete the task as a group.
How to improve:Describe independent groups as a design in which each participant completes only one condition.
Mistake: Claiming that random allocation removes all participant variables.
Why this is incorrect:Random allocation may distribute individual differences more evenly, but the groups may still differ.
How to improve:State that random allocation reduces the likelihood of systematic participant differences rather than eliminating them.
Mistake: Suggesting counterbalancing for an independent groups design.
Why this is incorrect:Counterbalancing controls the order of conditions. Participants in an independent groups design complete only one condition, so there is no order to counterbalance.
How to improve:Use counterbalancing for repeated measures and random allocation for independent groups.
Mistake: Saying that matched pairs eliminates all individual differences.
Why this is incorrect:Participants can be matched only on selected characteristics. They may still differ in other ways.
How to improve:Explain that matched pairs reduces relevant participant variables but cannot create identical participants.
Mistake: Matching participants on an irrelevant characteristic.
Why this is incorrect:Matching should focus on characteristics likely to affect the dependent variable.
How to improve:Link the matching characteristic directly to the task. For a memory study, previous memory ability may be more useful than an unrelated characteristic.
Mistake: Naming a design without applying it to the scenario.
Why this is incorrect:Application questions require an explanation of why the design suits the particular investigation.
How to improve:Refer directly to order effects, participant variables, the task or the availability of participants described in the scenario.
Exam-Style Questions ✍️
Question 1
Define a repeated measures design. (1 mark)
Question 2
A psychologist uses different participants in each of the two conditions of an experiment.
Name the experimental design used. (1 mark)
Question 3
Explain one limitation of using an independent groups design. (2 marks)
Question 4
A researcher wants to investigate whether people complete a visual search task more quickly in silence or while listening to music. Each participant completes both conditions.
Explain one problem that could arise from this experimental design. (2 marks)
Question 5
A psychologist investigates whether the amount of sleep people receive affects their performance on an attention task.
The psychologist is considering using a repeated measures design.
Explain one strength and one limitation of using a repeated measures design in this investigation. (4 marks)
Question 6
A researcher wants to compare two methods of teaching vocabulary. Previous language ability is expected to have a substantial effect on the participants’ final test scores.
The researcher has enough time to assess participants’ existing language ability before the investigation.
Suggest an appropriate experimental design. Explain how the researcher should organise the participants and justify the choice of design. (4 marks)
Question 7
A psychologist investigates whether written or spoken instructions are remembered more accurately.
The psychologist uses an independent groups design. Twenty participants receive written instructions and a different group of 20 participants receives spoken instructions.
Explain how participant variables might affect the results and suggest one way the psychologist could reduce this problem. (4 marks)
Question 8
A researcher investigates whether people make more accurate decisions in the morning or in the afternoon. The same participants complete the decision-making task at both times.
All participants complete the morning condition first.
Explain why this procedure may produce order effects. Suggest how the researcher could use counterbalancing to improve the investigation. (4 marks)
Question 9
Compare repeated measures and matched pairs designs. Your answer should include at least one strength and one limitation of each design. (6 marks)
Question 10
A psychologist wants to investigate whether receiving encouraging feedback affects persistence on a difficult puzzle.
The psychologist is considering the following designs:
Repeated measures
Independent groups
Matched pairs
Select the design that you consider most appropriate. Explain how it would be carried out and justify your choice. (8 marks)
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