Want to ace your psychology research methods? This guide breaks...
Psychology1,413 views·Updated Jun 19, 2026·9 pagesComprehensive AQA Research Methods Study Guide
1 / 9
1
of 9
Controlling Variables and Experimental Design
Variable control is absolutely crucial for getting reliable results in psychology experiments. There are several types of extraneous variables that can mess up your findings if you're not careful.
Confounding variables are outside factors that accidentally influence your results. You can reduce these by using controlled laboratory settings, though this might make your experiment feel a bit artificial. Participant variables relate to individual differences between people - things like age, personality, or background that could skew results.
The three main experimental designs each have their trade-offs. Repeated measures uses the same participants in all conditions, which is efficient but can create order effects (people getting tired or better with practice). Independent groups uses different people for each condition, avoiding order effects but introducing participant variables. Matched pairs tries to get the best of both worlds by carefully pairing similar participants.
Quick Tip: Remember that counterbalancing (splitting participants and reversing the order of tasks) is your best friend for dealing with order effects in repeated measures designs.
2
of 9
Types of Experiments and Sampling Methods
Laboratory experiments give you maximum control over variables and are easy to replicate, but they often lack ecological validity - basically, they don't reflect real life very well. Field experiments happen in natural settings, making results more realistic but harder to control.
Natural experiments study situations that occur naturally (like comparing children before and after a school policy change), whilst quasi experiments focus on characteristics that can't be randomly assigned, like personality traits or gender.
Your sampling technique massively affects whether you can generalise your findings. Random sampling gives everyone an equal chance of being selected, making your results more representative. Systematic sampling follows a pattern (like every 10th person), whilst stratified sampling ensures you get proportional representation from different subgroups.
Reality Check: Opportunity sampling (grabbing whoever's available) is the most common method in student research, but remember it can be quite biased towards certain types of people.
3
of 9
Ethics and Observational Methods
Ethical guidelines aren't just bureaucracy - they protect both participants and researchers. Informed consent means people must know what they're agreeing to, whilst deception should only be used when absolutely necessary (and participants must be debriefed afterwards).
Confidentiality protects participants' identities, and the right to withdraw ensures people never feel trapped in a study. Protection from harm means the risks shouldn't exceed what someone faces in daily life.
Naturalistic observation studies behaviour in real settings without interference, giving you authentic data but little control over other variables. Controlled observation happens in artificial settings where you can focus on specific behaviours. Covert observation (where people don't know they're being watched) avoids demand characteristics but raises serious ethical concerns.
Ethical Alert: Always consider whether covert observation is truly necessary - observing people without consent can feel invasive and may breach privacy laws.
4
of 9
Self-Report Methods and Data Collection
Questionnaires are brilliant for collecting large amounts of data cheaply, but the quality depends heavily on your question design. Closed questions give you neat, quantifiable data that's easy to analyse statistically. Open questions provide rich, detailed insights but take ages to analyse properly.
Structured interviews use standardised questions, making them reliable and easy to replicate. Unstructured interviews allow for deeper exploration of topics and can uncover unexpected insights, but they're time-consuming and require skilled interviewers.
Participant observation puts the researcher inside the group being studied, providing insider insights but potentially creating bias. Non-participant observation maintains objectivity but might miss subtle social dynamics that only insiders would understand.
Pro Tip: Group interviews can reveal how people's opinions change in social settings, but watch out for participants saying what they think others want to hear rather than their honest views.
5
of 9
Advanced Research Methods and Analysis
Meta-analysis combines results from multiple studies to identify overall patterns and effects. This approach provides more reliable conclusions than single studies but can't eliminate bias if the original research was flawed.
Understanding the difference between aims and hypotheses is fundamental. Your aim states what you want to investigate, whilst your hypothesis makes a specific, testable prediction. Directional hypotheses predict the direction of an effect, whilst non-directional hypotheses simply predict that there will be some effect.
Volunteer bias can seriously skew your results because people who volunteer for studies tend to be more motivated or have different characteristics than the general population. Random allocation helps ensure fair distribution of participants across conditions.
Research Reality: Systematic distortion (bias) can creep into any study, so always consider what factors might have influenced your participants' behaviour beyond what you intended to measure.
6
of 9
Observational Design and Correlations
Behavioural categories help you operationalise what you're observing by breaking complex behaviours (like aggression) into measurable components (like hitting or shouting). This makes your observations more reliable and systematic.
Event sampling counts how often specific behaviours occur, whilst time sampling records what happens at regular intervals. Both methods help manage large amounts of observational data but can miss important details.
Correlations show relationships between variables without proving causation. Positive correlations mean both variables increase together, negative correlations mean one increases as the other decreases, and no correlation means there's no clear relationship.
Critical Thinking: Remember that correlation doesn't equal causation - just because two things are related doesn't mean one causes the other. There might be a third factor influencing both variables.
7
of 9
Research Design and Control Measures
Open questions in questionnaires let participants express themselves fully, providing rich qualitative data that can reveal unexpected insights. However, this data is harder to analyse and compare across participants. Closed questions make analysis much simpler but might force people into categories that don't really fit their views.
Demand characteristics occur when participants figure out what the study is about and change their behaviour accordingly. You can reduce these through single-blind studies (participants don't know the aim) or double-blind studies (neither participants nor researchers know the conditions).
Investigator effects happen when researchers unconsciously influence participants through their behaviour or expectations. Standardised procedures and double-blind designs help minimise these effects.
Design Tip: Experimental realism (making the study feel natural and engaging) often works better than trying to hide everything from participants - when people feel comfortable, they're more likely to behave naturally.
8
of 9
Statistical Analysis and Data Types
Central tendency measures help you summarise your data effectively. Each type tells you something different about your results and has specific strengths and limitations depending on your data type and research question.
Understanding data types is crucial for choosing the right statistical tests. Nominal data comes in categories (like eye colour), ordinal data can be ranked (like satisfaction ratings), interval data has equal spacing between points (like temperature), and ratio data has a true zero point.
Primary data is information you collect yourself, giving you control over quality but requiring more time and resources. Secondary data uses existing information, which is cheaper and faster but might not perfectly fit your research needs.
Stats Success: The sign test is a simple non-parametric test that's perfect for repeated measures designs with nominal data - it's often the first statistical test psychology students learn to calculate.
9
of 9
Data Types and Statistical Testing
Primary data gives you complete control over data quality and collection methods, but it's expensive and time-consuming to gather. You know exactly how it was collected and can ensure it fits your research needs perfectly.
Secondary data includes existing research, government statistics, or archive material. It's much cheaper and faster to access, but might not exactly match what you need for your specific research question.
The sign test is your go-to statistical test for simple experiments using repeated measures design with nominal data. It tells you whether any differences you've found are statistically significant or just due to chance.
Data Wisdom: Always consider whether secondary data was collected in a similar context to your research - cultural differences, time periods, or different methodologies might affect how applicable the findings are to your study.
We thought you’d never ask...
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Psychology1,413 views·Updated Jun 19, 2026·9 pagesComprehensive AQA Research Methods Study Guide
Want to ace your psychology research methods? This guide breaks down everything you need to know about experimental design, sampling techniques, observations, and statistical analysis. You'll master the concepts that regularly appear on your A-level exams and learn how real...
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Controlling Variables and Experimental Design
Variable control is absolutely crucial for getting reliable results in psychology experiments. There are several types of extraneous variables that can mess up your findings if you're not careful.
Confounding variables are outside factors that accidentally influence your results. You can reduce these by using controlled laboratory settings, though this might make your experiment feel a bit artificial. Participant variables relate to individual differences between people - things like age, personality, or background that could skew results.
The three main experimental designs each have their trade-offs. Repeated measures uses the same participants in all conditions, which is efficient but can create order effects (people getting tired or better with practice). Independent groups uses different people for each condition, avoiding order effects but introducing participant variables. Matched pairs tries to get the best of both worlds by carefully pairing similar participants.
Quick Tip: Remember that counterbalancing (splitting participants and reversing the order of tasks) is your best friend for dealing with order effects in repeated measures designs.
2
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Types of Experiments and Sampling Methods
Laboratory experiments give you maximum control over variables and are easy to replicate, but they often lack ecological validity - basically, they don't reflect real life very well. Field experiments happen in natural settings, making results more realistic but harder to control.
Natural experiments study situations that occur naturally (like comparing children before and after a school policy change), whilst quasi experiments focus on characteristics that can't be randomly assigned, like personality traits or gender.
Your sampling technique massively affects whether you can generalise your findings. Random sampling gives everyone an equal chance of being selected, making your results more representative. Systematic sampling follows a pattern (like every 10th person), whilst stratified sampling ensures you get proportional representation from different subgroups.
Reality Check: Opportunity sampling (grabbing whoever's available) is the most common method in student research, but remember it can be quite biased towards certain types of people.
3
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Ethics and Observational Methods
Ethical guidelines aren't just bureaucracy - they protect both participants and researchers. Informed consent means people must know what they're agreeing to, whilst deception should only be used when absolutely necessary (and participants must be debriefed afterwards).
Confidentiality protects participants' identities, and the right to withdraw ensures people never feel trapped in a study. Protection from harm means the risks shouldn't exceed what someone faces in daily life.
Naturalistic observation studies behaviour in real settings without interference, giving you authentic data but little control over other variables. Controlled observation happens in artificial settings where you can focus on specific behaviours. Covert observation (where people don't know they're being watched) avoids demand characteristics but raises serious ethical concerns.
Ethical Alert: Always consider whether covert observation is truly necessary - observing people without consent can feel invasive and may breach privacy laws.
4
of 9Sign up to see the content. It's free!
- Access to all documents
- Improve your grades
- Join milions of students
Self-Report Methods and Data Collection
Questionnaires are brilliant for collecting large amounts of data cheaply, but the quality depends heavily on your question design. Closed questions give you neat, quantifiable data that's easy to analyse statistically. Open questions provide rich, detailed insights but take ages to analyse properly.
Structured interviews use standardised questions, making them reliable and easy to replicate. Unstructured interviews allow for deeper exploration of topics and can uncover unexpected insights, but they're time-consuming and require skilled interviewers.
Participant observation puts the researcher inside the group being studied, providing insider insights but potentially creating bias. Non-participant observation maintains objectivity but might miss subtle social dynamics that only insiders would understand.
Pro Tip: Group interviews can reveal how people's opinions change in social settings, but watch out for participants saying what they think others want to hear rather than their honest views.
5
of 9Sign up to see the content. It's free!
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- Improve your grades
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Advanced Research Methods and Analysis
Meta-analysis combines results from multiple studies to identify overall patterns and effects. This approach provides more reliable conclusions than single studies but can't eliminate bias if the original research was flawed.
Understanding the difference between aims and hypotheses is fundamental. Your aim states what you want to investigate, whilst your hypothesis makes a specific, testable prediction. Directional hypotheses predict the direction of an effect, whilst non-directional hypotheses simply predict that there will be some effect.
Volunteer bias can seriously skew your results because people who volunteer for studies tend to be more motivated or have different characteristics than the general population. Random allocation helps ensure fair distribution of participants across conditions.
Research Reality: Systematic distortion (bias) can creep into any study, so always consider what factors might have influenced your participants' behaviour beyond what you intended to measure.
6
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Observational Design and Correlations
Behavioural categories help you operationalise what you're observing by breaking complex behaviours (like aggression) into measurable components (like hitting or shouting). This makes your observations more reliable and systematic.
Event sampling counts how often specific behaviours occur, whilst time sampling records what happens at regular intervals. Both methods help manage large amounts of observational data but can miss important details.
Correlations show relationships between variables without proving causation. Positive correlations mean both variables increase together, negative correlations mean one increases as the other decreases, and no correlation means there's no clear relationship.
Critical Thinking: Remember that correlation doesn't equal causation - just because two things are related doesn't mean one causes the other. There might be a third factor influencing both variables.
7
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Research Design and Control Measures
Open questions in questionnaires let participants express themselves fully, providing rich qualitative data that can reveal unexpected insights. However, this data is harder to analyse and compare across participants. Closed questions make analysis much simpler but might force people into categories that don't really fit their views.
Demand characteristics occur when participants figure out what the study is about and change their behaviour accordingly. You can reduce these through single-blind studies (participants don't know the aim) or double-blind studies (neither participants nor researchers know the conditions).
Investigator effects happen when researchers unconsciously influence participants through their behaviour or expectations. Standardised procedures and double-blind designs help minimise these effects.
Design Tip: Experimental realism (making the study feel natural and engaging) often works better than trying to hide everything from participants - when people feel comfortable, they're more likely to behave naturally.
8
of 9Sign up to see the content. It's free!
- Access to all documents
- Improve your grades
- Join milions of students
Statistical Analysis and Data Types
Central tendency measures help you summarise your data effectively. Each type tells you something different about your results and has specific strengths and limitations depending on your data type and research question.
Understanding data types is crucial for choosing the right statistical tests. Nominal data comes in categories (like eye colour), ordinal data can be ranked (like satisfaction ratings), interval data has equal spacing between points (like temperature), and ratio data has a true zero point.
Primary data is information you collect yourself, giving you control over quality but requiring more time and resources. Secondary data uses existing information, which is cheaper and faster but might not perfectly fit your research needs.
Stats Success: The sign test is a simple non-parametric test that's perfect for repeated measures designs with nominal data - it's often the first statistical test psychology students learn to calculate.
9
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- Improve your grades
- Join milions of students
Data Types and Statistical Testing
Primary data gives you complete control over data quality and collection methods, but it's expensive and time-consuming to gather. You know exactly how it was collected and can ensure it fits your research needs perfectly.
Secondary data includes existing research, government statistics, or archive material. It's much cheaper and faster to access, but might not exactly match what you need for your specific research question.
The sign test is your go-to statistical test for simple experiments using repeated measures design with nominal data. It tells you whether any differences you've found are statistically significant or just due to chance.
Data Wisdom: Always consider whether secondary data was collected in a similar context to your research - cultural differences, time periods, or different methodologies might affect how applicable the findings are to your study.
We thought you’d never ask...
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This app is really great. There are so many study notes and help [...]. My problem subject is French, for example, and the app has so many options for help. Thanks to this app, I have improved my French. I would recommend it to anyone.
Samantha KlichAndroid user
Wow, I am really amazed. I just tried the app because I've seen it advertised many times and was absolutely stunned. This app is THE HELP you want for school and above all, it offers so many things, such as workouts and fact sheets, which have been VERY helpful to me personally.
AnnaiOS user