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examBoard: AQA
examType: GCSE
lessonTitle: Independent Groups Design
Independent groups design (sometimes called "between-subjects design") is one of the main experimental designs used in psychology research. In this design, different participants are used in each condition of the experiment, meaning each person only takes part in one condition.
Key Definitions:
In an independent groups design, your participants are split into separate groups. Each group experiences only one condition of the experiment. For example, if you're testing whether a new revision technique helps students remember more information, one group would use the new technique (experimental group) while another group would use a standard technique (control group).
The most important feature of independent groups design is that each participant only experiences ONE condition. This means different people are in each group and no one takes part in multiple conditions of the study.
When designing a study using independent groups, you need to follow these steps:
Stanley Milgram's famous obedience study used an independent groups design. Different participants were placed in different variations of the experiment (e.g., different authority figure locations, different settings). Each participant only experienced one version of the study, allowing Milgram to compare obedience rates between different conditions without worrying about practice effects.
Since participants only take part in one condition, there's no risk of order effects (like practice, fatigue, or boredom) influencing the results.
Participants are less likely to guess the aim of the study as they only experience one condition, reducing demand characteristics.
Can be used in situations where it's impossible for participants to experience multiple conditions (e.g., when testing different teaching methods over a school year).
Individual differences between participants in different groups could affect results rather than the IV itself.
Requires more participants than other designs because you need a separate group for each condition.
Has less statistical power than repeated measures designs, meaning you might need larger groups to find significant effects.
The biggest challenge with independent groups designs is dealing with participant variables - the natural differences between people that might affect your results. Here are the main ways to control for these:
The most common method is to randomly assign participants to groups. This spreads individual differences evenly across groups. You can use methods like drawing names from a hat, using random number generators, or alternating assignment.
Example: If testing a new memory technique, you might assign students to either the control or experimental group by flipping a coin for each participant.
Sometimes researchers match participants across groups based on important characteristics. For each participant in one group, you find someone with similar characteristics for the other group.
Example: Matching participants by age, IQ, gender, or prior knowledge before randomly assigning one from each pair to different conditions.
Independent groups design is particularly useful in these situations:
Imagine a researcher wants to test whether a new interactive teaching method improves GCSE results compared to traditional teaching. They randomly assign 10 classes to either use the new method or continue with traditional teaching for a full term. At the end, all students take the same assessment. This is an independent groups design because different students experience different teaching methods and it would be impossible for the same students to learn the same material twice in different ways.
Let's walk through how you might design your own independent groups study:
When evaluating independent groups design in exams, remember these key points:
When asked to design a study using independent groups:
Independent groups design is a fundamental research method in psychology where different participants experience different conditions of an experiment. Its main advantage is avoiding order effects, but it requires careful control of participant variables through techniques like random allocation. It's particularly useful for studies where learning effects are likely or when testing can only happen once per participant.
Remember that no research design is perfect - each has strengths and limitations. The key is choosing the right design for your specific research question and understanding how to minimise the impact of any limitations.
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