A-Level AQA Psychology Questions by Topic

Filter by paper, core content, 1. social influence, 3. attachment, 4 . psychopathology, 5 . approaches in psychology, 6. biopsychology, 7 . research methods, 8. issues and debates in psychology, 9. relationships, 11. cognition and development, 12. schizophrenia, 13. eating behaviour, 15. aggression, 16. forensic psychology, 17. addiction.

A-level Psychology AQA Revision Notes

Saul Mcleod, PhD

Educator, Researcher

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Revision Notes

Paper 1 : AS and A-Level

Social Influence

Paper 1 : A-Level



Research Methods

Paper 3 : Compulsory

Issues and Debates


Cognitive Development


Eating Behaviour

Forensic Psychology

There are three assessment objectives assessed in each examination: 

There may be one, two, or all (only in the extended writing 16-mark question). It is important to understand how assessment objectives are allocated to each type of question to maximize your chance of obtaining full marks.

AO1 : Demonstrate knowledge

  • Demonstrate knowledge and understanding of scientific ideas, processes, techniques, and procedures.
  • Show knowledge and understanding of psychological theories, terminology, concepts, studies, and methods.

AO 2: Application of knowledge

  • in a practical context
  • when handling qualitative data
  • when handling quantitative data
  • in a theoretical context
  • This skill area tests knowledge of research design and data analysis, and applying theoretical understanding of psychology to everyday/real-life examples.

AO3: Analyse, interpret and evaluate

Analyse, interpret, and evaluate scientific information, ideas, and evidence, including in relation to issues, to:

  • make judgements and reach conclusions
  • develop and refine practical design and procedures.

Examples of how you can score AO3 marks

  • Whether or not theories are supported or refuted by valid research evidence : After describing a theory go on to describe a piece of research evidence saying, ‘X’s study supports/refutes this theory…’ and then describe the research study.
  • Contextualising how the topic in question relates to broader debates and approaches in Psychology : For example, would they agree or disagree with a theory or the findings of the study?
  • Animal Research : This raises the issue of whether it’s morally and/or scientifically right to use animals.The main criterion is that benefits must outweigh costs. Animal research also raises the issue of extrapolation. Can we generalize from studies on animals to humans as their anatomy & physiology is different from humans?
  • General criticisms and/or strengths of theories and studies : E.g. ‘Bandura’s Bobo Doll studies are laboratory experiments and therefore criticizable on the grounds of lacking ecological validity’.To gain marks for criticising study’s methodologies the criticism must be contextualised: i.e. say why this is a problem in this particular study.‘Therefore, the violence the children witnessed was on television and was against a doll not a human’.

10% of the examination will consist of mathematical questions at the GCSE level. These questions will cover basic arithmetic, data, and graphs. There is no need to be worried if you have a GCSE pass grade of 5 or higher, as you will be familiar with these concepts.

To do well, you must get organized and plan your time logically and rationally to make sure you cover everything on the syllabus in an adequate amount of depth.

What are the most effective ways of revising subject knowledge?

  • Ask ‘How” and ‘Why’ questions  when revising and try to connect ideas (this method is called ‘elaboration’)
  • No cramming : Distribute your revision over time and use a spaced system of repetition
  • Switch topics regularly  when revising (this is called ‘ interleaving, ‘ and it will help you to identify connections between different topics)
  • Words and visuals . Combine words and visual representations to create two ways of remembering key ideas (this is called ‘dual coding’)

aqa psychology research methods past paper questions

aqa psychology research methods past paper questions

Search form

  • My Timetable
  • Revision Maths
  • Revision Science
  • Revision Videos
  • Student Jungle
  • AS & A2 LEVEL (A-Level) Revision
  • Psychology (A-Level Revision)
  • A-Level Psychology Past Papers
  • AQA A-Level Psychology Past Papers

AQA A-Level ​Psychology (7182) and AS-Level Psychology (7181) past exam papers. You can download the papers and marking schemes by clicking on the links below.

June 2022 - AQA A-Level Psychology (7182) Past Papers

November 2021 - AQA A-Level Psychology (7182) Past Papers (Labelled as June 2021)

November 2020 - AQA A-Level Psychology (7182) Past Papers (Labelled as June 2020)

November 2020 AS Psychology Paper 2: Psychology in Context (7181/2) Download Past Paper    -    Download Marking Scheme

June 2019 AS Psychology Paper 2: Psychology in Context (7181/2) Download Past Paper    -    Download Marking Scheme

For more A-Level Psychology past papers from other exam boards  click here .

aqa psychology research methods past paper questions

  • Create new account
  • Request new password
  • Cookies Policy
  • Privacy Policy

Copyright  ©  2007 - 2023 Revision World Networks Ltd.

aqa psychology research methods past paper questions

Join us after half-term for A-Level Strong Foundations workshops. Coming to Birmingham, Leeds, London and Manchester Learn more →

Reference Library


  • See what's new
  • All Resources
  • Student Resources
  • Assessment Resources
  • Teaching Resources
  • CPD Courses
  • Livestreams

Study notes, videos, interactive activities and more!

Psychology news, insights and enrichment

Currated collections of free resources

Browse resources by topic

  • All Psychology Resources

Resource Selections

Currated lists of resources

  • Quizzes & Activities

A Level Psychology Topic Quiz - Research Methods

Last updated 5 May 2017

  • Share on Facebook
  • Share on Twitter
  • Share by Email

Here is an overall topic quiz on research methods as featured in the AQA A Level Psychology specification.

Each time you take this quiz you will get 10 MCQs drawn at random from over 100 questions relevant to research methods. Try the first ten, see how you get on, and then try again with 10 different questions!

aqa psychology research methods past paper questions

Core Topics Revision Flashcards for AQA A-Level Psychology

Printed Resource

  • Research Methods
  • Laboratory Experiment
  • Correlation Coefficient
  • Coding: Content Analysis

You might also like

aqa psychology research methods past paper questions

Delaying Gratification: Could you? Can your students?

18th February 2016

Content Analysis

Study Notes

Investigator Effects

Operationalisation, case studies, laboratory experiments, natural experiments, our subjects.

  • › Criminology
  • › Economics
  • › Geography
  • › Health & Social Care
  • › Psychology
  • › Sociology
  • › Teaching & learning resources
  • › Student revision workshops
  • › Online student courses
  • › CPD for teachers
  • › Livestreams
  • › Teaching jobs

Boston House, 214 High Street, Boston Spa, West Yorkshire, LS23 6AD Tel: 01937 848885

  • › Contact us
  • › Terms of use
  • › Privacy & cookies

© 2002-2023 Tutor2u Limited. Company Reg no: 04489574. VAT reg no 816865400.


  • Written for
  • Document information
  • Connected book
  • Related courses

AQA A-Level Psychology Relationships Notes

  • Research Methods

AQA A-Level Psychology Research Methods Practice Questions

  • Institution
  • AQA Psychology for A Level Year 2

These are Practice Questions for the Research Methods Topic of AQA A-Level Psychology. I will also be uploading the other topics and creating bundles. Topics Included: - Experimental Method - Control of Variables/Research Issues - Experimental Design - Types of Experiment - Sampling - Ethi...

Preview 2 out of 5  pages


  •   Report Copyright Violation

Preview 2 out of 5 pages


Reviews received

Also available in package deal (2).

aqa psychology research methods past paper questions

AQA Psychology Research Methods Bundle

  • 1. Summary - Aqa a-level psychology research methods notes
  • 2. Other - Aqa a-level psychology research methods practice questions

AQA A Level Psychology Paper 2 Complete Bundle

  • 3. Other - Aqa a-level psychology biopsychology practice questions
  • 4. Summary - Aqa a-level psychology biopsychology notes
  • 5. Summary - Aqa a-level psychology biopsychology essay plans
  • 6. Summary - Aqa a-level psychology approaches in psychology comparison table
  • 7. Summary - Aqa a-level psychology approaches in psychology essay plans
  • 8. Summary - Aqa a-level psychology approaches in psychology notes
  • 9. Summary - Aqa a-level psychology approaches in psychology practice questions

4  reviews


By: sallypeng • 6 months ago


By: princessalagbala • 6 months ago


By: sophiajoness • 10 months ago


By: emilysarahjudge • 10 months ago

Thank you very much for your purchase and kind review, Emily x


By: arushi1 • 1 year ago

More courses for AQA > Psychology

  • Biopsychology
  • Social influence
  • Psychopathology
  • Issues and debates
  • Unit 1 psya1 - cognitive psychology, developmental psychology and research methods
  • Schizophrenia

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do i get when i buy this document.

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller emilysarahjudge. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for £0.00. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

96455 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy revision notes and other study material for 13 years now

Psychology A Level

Overview – Research Methods

Research methods are how psychologists and scientists come up with and test their theories. The A level psychology syllabus covers several different types of studies and experiments used in psychology as well as how these studies are conducted and reported:

  • Types of psychological studies (including experiments , observations , self-reporting , and case studies )
  • Scientific processes (including the features of a study , how findings are reported , and the features of science in general )
  • Data handling and analysis (including descriptive statistics and different ways of presenting data ) and inferential testing

Note: Unlike all other sections across the 3 exam papers, research methods is worth 48 marks instead of 24. Not only that, the other sections often include a few research methods questions, so this topic is the most important on the syllabus!

aqa psychology research methods past paper questions

Example question: Design a matched pairs experiment the researchers could conduct to investigate differences in toy preferences between boys and girls. [12 marks]

Types of study

There are several different ways a psychologist can research the mind, including:

  • Experiments
  • Observation
  • Self-reporting

Case studies

Each of these methods has its strengths and weaknesses. Different methods may be better suited to different research studies.

Experimental method

The experimental method looks at how variables affect outcomes. A variable is anything that changes between two situations ( see below for the different types of variables ). For example, Bandura’s Bobo the doll experiment looked at how changing the variable of the role model’s behaviour affected how the child played.

Experimental designs

Experiments can be designed in different ways, such as:

  • Independent groups: Participants are divided into two groups. One group does the experiment with variable 1, the other group does the experiment with variable 2. Results are compared.
  • Repeated measures: Participants are not divided into groups. Instead, all participants do the experiment with variable 1, then afterwards the same participants do the experiment with variable 2. Results are compared.

A matched pairs design is another form of independent groups design. Participants are selected. Then, the researchers recruit another group of participants one-by-one to match the characteristics of each member of the original group. This provides two groups that are relevantly similar and controls for differences between groups that might skew results. The experiment is then conducted as a normal independent groups design.

Types of experiment

Laboratory vs. field experiment.

Experiments are carried out in two different types of settings:

  • E.g. Bandura’s Bobo the doll experiment or Asch’s conformity experiments
  • E.g. Bickman’s study of the effects of uniforms on obedience

Strengths of laboratory experiment over field experiment:

The controlled environment of a laboratory experiment minimises the risk of other variables outside the researchers’ control skewing the results of the trial, making it more clear what (if any) the causal effects of a variable are. Because the environment is tightly controlled, any changes in outcome must be a result of a change in the variable.

Weaknesses of laboratory experiment over field experiment:

However, the controlled nature of a laboratory experiment might reduce its ecological validity . Results obtained in an artificial environment might not translate to real-life. Further, participants may be influenced by demand characteristics : They know they are taking part in a test, and so behave how they think they’re expected to behave rather than how they would naturally behave.

Natural and quasi experiment

Natural experiments are where variables vary naturally. In other words, the researcher can’t or doesn’t manipulate the variables . There are two types of natural experiment:

  • E.g. studying the effect a change in drug laws (variable) has on addiction
  • E.g. studying differences between men (variable) and women (variable)

Observational method

The observational method looks at and examines behaviour. For example, Zimbardo’s prison study observed how participants behaved when given certain social roles.

Observational design

Behavioural categories.

An observational study will use behavioural categories to prioritise which behaviours are recorded and ensure the different observers are consistent in what they are looking for.

For example, a study of the effects of age and sex on stranger anxiety in infants might use the following behavioural categories to organise observational data:

Rather than writing complete descriptions of behaviours, the behaviours can be coded into categories. For example, IS = interacted with stranger, and AS = avoided stranger. Researchers can also create numerical ratings to categorise behaviour, like the anxiety rating example above.

Inter-observer reliability : In order for observations to produce reliable findings, it is important that observers all code behaviour in the same way. For example, researchers would have to make it very clear to the observers what the difference between a ‘3’ on the anxiety scale above would be compared to a ‘7’. This inter-observer reliability avoids subjective interpretations of the different observers skewing the findings.

Event and time sampling

Because behaviour is constant and varied, it may not be possible to record every single behaviour during the observation period. So, in addition to categorising behaviour , study designers will also decide when to record a behaviour:

  • Event sampling: Counting how many times the participant behaves in a certain way.
  • Time sampling: Recording participant behaviour at regular time intervals. For example, making notes of the participant’s behaviour after every 1 minute has passed.

Note: Don’t get event and time sampling confused with participant sampling , which is how researchers select participants to study from a population.

Types of observation

Naturalistic vs. controlled.

Observations can be made in either a naturalistic or a controlled setting:

  • E.g. setting up cameras in an office or school to observe how people interact in those environments
  • E.g. Ainsworth’s strange situation or Zimbardo’s prison study

Covert vs. overt

Observations can be either covert or overt :

  • E.g. setting up hidden cameras in an office
  • E.g. Zimbardo’s prison study

Participant vs. non-participant

In observational studies, the researcher/observer may or may not participate in the situation being observed:

  • E.g. in Zimbardo’s prison study , Zimbardo played the role of prison superintendent himself
  • E.g. in Bandura’s Bobo the doll experiment and Ainsworth’s strange situation , the observers did not interact with the children being observed

Self-report method

Self-report methods get participants to provide information about themselves. Information can be obtained via questionnaires or interviews .

Types of self-report


A questionnaire is a standardised list of questions that all participants in a study answer. For example, Hazan and Shaver used questionnaires to collate self-reported data from participants in order to identify correlations between attachment as infants and romantic attachment as adults.

Questions in a questionnaire can be either open or closed :

  • >8 hours
  • E.g. “How did you feel when you thought you were administering a lethal shock?” or “What do you look for in a romantic partner and why?”

Strengths of questionnaires:

  • Quantifiable: Closed questions provide quantifiable data in a consistent format, which enables to statistically analyse information in an objective way.
  • Replicability: Because questionnaires are standardised (i.e. pre-set, all participants answer the same questions), studies involving them can be easily replicated . This means the results can be confirmed by other researchers, strengthening certainty in the findings.

Weaknesses of questionnaires:

  • Biased samples: Questionnaires handed out to people at random will select for participants who actually have the time and are willing to complete the questionnaire. As such, the responses may be biased towards those of people who e.g. have a lot of spare time.
  • Dishonest answers: Participants may lie in their responses – particularly if the true answer is something they are embarrassed or ashamed of (e.g. on controversial topics or taboo topics like sex)
  • Misunderstanding/differences in interpretation: Different participants may interpret the same question differently. For example, the “are you religious?” example above could be interpreted by one person to mean they go to church every Sunday and pray daily, whereas another person may interpret religious to mean a vague belief in the supernatural.
  • Less detail: Interviews may be better suited for detailed information – especially on sensitive topics – than questionnaires. For example, participants are unlikely to write detailed descriptions of private experiences in a questionnaire handed to them on the street.

In an interview , participants are asked questions in person. For example, Bowlby interviewed 44 children when studying the effects of maternal deprivation.

Interviews can be either structured or unstructured :

  • Structured interview: Questions are standardised and pre-set. The interviewer asks all participants the same questions in the same order.
  • Unstructured interview: The interviewer discusses a topic with the participant in a less structured and more spontaneous way, pursuing avenues of discussion as they come up.

Interviews can also be a cross between the two – these are called semi-structured interviews .

Strengths of interviews:

  • More detail: Interviews – particularly unstructured interviews conducted by a skilled interviewer – enable researchers to delve deeper into topics of interest, for example by asking follow-up questions. Further, the personal touch of an interviewer may make participants more open to discussing personal or sensitive issues.
  • Replicability: Structured interviews are easily replicated because participants are all asked the same pre-set list of questions. This replicability means the results can be confirmed by other researchers, strengthening certainty in the findings.

Weaknesses of interviews:

  • Lack of quantifiable data: Although unstructured interviews enable researchers to delve deeper into interesting topics, this lack of structure may produce difficulties in comparing data between participants. For example, one interview may go down one avenue of discussion and another interview down a different avenue. This qualitative data may make objective or statistical analysis difficult.
  • Interviewer effects : The interviewer’s appearance or character may bias the participant’s answers. For example, a female participant may be less comfortable answering questions on sex asked by a male interviewer and and thus give different answers than if she were asked by a female interviewer.

Note: This topic is A level only, you don’t need to learn about case studies if you are taking the AS exam only.

Case studies are detailed investigations into an individual, a group of people, or an event. For example, the biopsychology page describes a case study of a young boy who had the left hemisphere of his brain removed and the effects this had on his language skills.

In a case study, researchers use many of the methods described above – observation , questionnaires , interviews – to gather data on a subject. However, because case studies are studies of a single subject, the data they provide is primarily qualitative rather than quantitative . This data is then used to build a case history of the subject. Researchers then interpret this case history to draw their conclusions.

Types of case study

Typical vs. unusual cases.

Most case studies focus on unusual individuals, groups, and events.


Many case studies are longitudinal . This means they take place over an extended time period, with researchers checking in with the subject at various intervals. For example, the case study of the boy who had his left hemisphere removed collected data on the boy’s language skills at ages 2.5, 4, and 14 to see how he progressed.

Strengths of case studies:

  • Provides detailed qualitative data: Rather than focusing on one or two aspects of behaviour at a single point in time (e.g. in an experiment ), case studies produce detailed qualitative data.
  • Allows for investigation into issues that may be impractical or unethical to study otherwise. For example, it would be unethical to remove half a toddler’s brain just to experiment , but if such a procedure is medically necessary then researchers can use this opportunity to learn more about the brain.

Weaknesses of case studies:

  • Lack of scientific rigour: Because case studies are often single examples that cannot be replicated , the results may not be valid when applied to the general population.
  • Researcher bias: The small sample size of case studies also means researchers need to apply their own subjective interpretation when drawing conclusions from them. As such, these conclusions may be skewed by the researcher’s own bias and not be valid when applied more generally. This criticism is often directed at Freud’s psychoanalytic theory because it draws heavily on isolated case studies of individuals.

Scientific processes

This section looks at how science works more generally – in particular how scientific studies are organised and reported . It also covers ways of evaluating a scientific study.

Study features and design

Studies will usually have an aim . The aim of a study is a description of what the researchers are investigating and why . For example, “to investigate the effect of SSRIs on symptoms of depression” or “to understand the effect uniforms have on obedience to authority”.

Studies seek to test a hypothesis . The experimental/alternate hypothesis of a study is a testable prediction of what the researchers expect to happen.

  • E.g. “That SSRIs will reduce symptoms of depression” or “subjects are more likely to comply when orders are issued by someone wearing a uniform”.
  • E.g. “That SSRIs have no effect on symptoms on depression” or “subject conformity will be the same when orders are issued by someone wearing a uniform as when orders are issued by someone bot wearing a uniform”

Either the experimental/alternate hypothesis or the null hypothesis will be supported by the results of the experiment.

It’s often not possible or practical to conduct research on everyone your study is supposed to apply to. So, researchers use sampling to select participants for their study.

  • E.g. all humans, all women, all men, all children, etc.
  • E.g. 10,000 humans, 200 women from the USA, children at a certain school

For example, the target population (i.e. who the results apply to) of Asch’s conformity experiments is all humans – but Asch didn’t conduct the experiment on that many people! Instead, Asch recruited 123 males and generalised the findings from this sample to the rest of the population.

Researchers choose from different sampling techniques – each has strengths and weaknesses.

Sampling techniques

Random sampling.

The random sampling method involves selecting participants from a target population at random – such as by drawing names from a hat or using a computer program to select them. This method means each member of the population has an equal chance of being selected and thus is not subject to any bias.

Strengths of random sampling:

  • Unbiased: Selecting participants by random chance reduces the likelihood that researcher bias will skew the results of the study.
  • Representative: If participants are selected at random – particularly if the sample size is large – it is likely that the sample will be representative of the population as a whole. For example, if the ratio of men:women in a population is 50:50 and participants are selected at random, it is likely that the sample will also have a ratio of men to women that is 50:50.

Weaknesses of random sampling:

  • Impractical: It’s often impractical/impossible to include all members of a target population for selection. For example, it wouldn’t be feasible for a study on women to include the name of every woman on the planet for selection. But even if this was done, the randomly selected women may not agree to take part in the study anyway.

Systematic sampling

The systematic sampling method involves selecting participants from a target population by selecting them at pre-set intervals. For example, selecting every 50th person from a list, or every 7th, or whatever the interval is.

Strengths of systematic sampling:

  • Unbiased and representative: Like random sampling , selecting participants according to a numerical interval provides an objective means of selecting participants that prevents researcher bias being able to skew the sample. Further, because the sampling method is independent of any particular characteristic (besides the arbitrary characteristic of the participant’s order in the list) this sample is likely to be representative of the population as a whole.

Weaknesses of systematic sampling:

  • Unexpected bias: Some characteristics could occur more or less frequently at certain intervals, making a sample that is selected based on that interval biased. For example, houses tend to be have even numbers on one side of a road and odd numbers on the other. If one side of the road is more expensive than the other and you select every 4th house, say, then you will only select even numbers from one side of the road – and this sample may not be representative of the road as a whole.

Stratified sampling

The stratified sampling method involves dividing the population into relevant groups for study, working out what percentage of the population is in each group, and then randomly sampling the population according to these percentages.

For example, let’s say 20% of the population is aged 0-18, and 50% of the population is aged 19-65, and 30% of the population is aged >65. A stratified sample of 100 participants would randomly select 20x 0-18 year olds, 50x 19-65 year olds, and 30x people over 65.

Strengths of stratified sampling:

  • Representative: The stratification is deliberately designed to yield a sample that is representative of the population as a whole. You won’t get people with certain characteristics being over- or under-represented within the sample.
  • Unbiased: Because participants within each group are selected randomly , researcher bias is unable to skew who is included in the study.

Weaknesses of stratified sampling:

  • Requires knowledge of population breakdown: Researchers need to accurately gauge what percentage of the population falls into what group. If the researchers get these percentages wrong, the sample will be biased and some groups will be over- or under-represented.

Opportunity and volunteer sampling

The opportunity and volunteer sampling methods:

  • E.g. Approaching people in the street and asking them to complete a questionnaire.
  • E.g. Placing an advert online inviting people to complete a questionnaire.

Strengths of opportunity and volunteer sampling:

  • Quick and easy: Approaching participants ( opportunity sampling) or inviting participants ( volunteer sampling) is quick and straightforward. You don’t have to spend time compiling details of the target population (like in e.g. random or systematic sampling ), nor do you have to spend time dividing participants according to relevant categories (like in stratified sampling ).
  • May be the only option: With natural experiments – where a variable changes as a result of something outside the researchers’ control – opportunity sampling may be the only viable sampling method. For example, researchers couldn’t randomly sample 10 cities from all the cities in the world and change the drug laws in those cities to see the effects – they don’t have that kind of power. However, if a city is naturally changing its drug laws anyway, researchers could use opportunity sampling to study that city for research.

Weaknesses of opportunity and volunteer sampling:

  • Unrepresentative: The pool of participants will likely be biased towards certain kinds of people. For example, if you conduct opportunity sampling on a weekday at 10am, this sample will likely exclude people who are at work. Similarly, volunteer sampling is likely to exclude people who are too busy to take part in the study.

Independent vs. dependent variables

If the study involves an experiment , the researchers will alter an independent variable to measure its effects on a dependent variable :

  • E.g. In Bickman’s study of the effects of uniforms on obedience , the independent variable was the uniform of the person giving orders.
  • E.g. In Bickman’s study of the effects of uniforms on obedience , the dependent variable was how many people followed the orders.

Extraneous and confounding variables

In addition to the variables actually being investigated ( independent and dependent ), there may be additional (unwanted) variables in the experiment. These additional variables are called extraneous variables .

Researchers must control for extraneous variables to prevent them from skewing the results and leading to false conclusions. When extraneous variables are not properly controlled for they are known as confounding variables .

For example, if you’re studying the effect of caffeine on reaction times, it might make sense to conduct all experiments at the same time of day to prevent this extraneous variable from confounding the results. Reaction times change throughout the day and so if you test one group of subjects at 3pm and another group right before they go to bed, you may falsely conclude that the second group had slower reaction times.

Operationalisation of variables

Operationalisation of variables is where researchers clearly and measurably define the variables in their study.

For example, an experiment on the effects of sleep ( independent variable ) on anxiety ( dependent variable ) would need to clearly operationalise each variable. Sleep could be defined by number of hours spent in bed, but anxiety is a bit more abstract and so researchers would need to operationalise (i.e. define) anxiety such that it can be quantified in a measurable and objective way.

If variables are not properly operationalised, the experiment cannot be properly replicated , experimenters’ subjective interpretations may skew results, and the findings may not be valid .

Pilot studies

A pilot study is basically a practice run of the proposed research project. Researchers will use a small number of participants and run through the procedure with them. The purpose of this is to identify any problems or areas for improvement in the study design before conducting the research in full. A pilot study may also give an early indication of whether the results will be statistically significant .

For example, if a task is too easy for participants, or it’s too obvious what the real purpose of an experiment is, or questions in a questionnaire are ambiguous, then the results may not be valid . Conducting a pilot study first may save time and money as it enables researchers to identify and address such issues before conducting the full study on thousands of participants.

Study reporting

Features of a psychological report.

The report of a psychological study (research paper) typically contains the following sections in the following order:

  • Title: A short and clear description of the research.
  • Abstract: A summary of the research. This typically includes the aim and hypothesis , methods, results, and conclusion.
  • Introduction: Funnel technique: Broad overview of the context (e.g. current theories, previous studies, etc.) before focusing in on this particular study, why it was conducted, its aims and hypothesis .
  • Study design: This will explain what method was used (e.g. experiment or observation ), how the study was designed (e.g. independent groups or repeated measures ), and identification and operationalisation of variables .
  • Participants: A description of the target population to be studied, the sampling method , how many participants were included.
  • Equipment used: A description of any special equipment used in the study and how it was used.
  • Standardised procedure: A detailed step-by-step description of how the study was conducted. This allows for the study to be replicated by other researchers.
  • Controls : An explanation of how extraneous variables were controlled for so as to generate accurate results.
  • Results: A presentation of the key findings from the data collected. This is typically written summaries of the raw data ( descriptive statistics ), which may also be presented in tables , charts, graphs , etc. The raw data itself is typically included in appendices.
  • Discussion: An explanation of what the results mean and how they relate to the experimental hypothesis (supporting or contradicting it), any issues with how results were generated, how the results fit with other research, and suggestions for future research.
  • Conclusion: A short summary of the key findings from the study.
  • Book: Milgram, S., 2010. Obedience to Authority . 1st ed. Pinter & Martin.
  • Journal article: Bandura, A., Ross, D. and Ross, S., 1961. Transmission of Aggression through Imitation of Aggressive Models . The Journal of Abnormal and Social Psychology, 63(3), pp.575-582.
  • Appendices: This is where you put any supporting materials that are too detailed or long to include in the main report. For example, the raw data collected from a study, or the complete list of questions in a questionnaire .

Peer review

Peer review is a way of assessing the scientific credibility of a research paper before it is published in a scientific journal. The idea with peer review is to prevent false ideas and bad research from being accepted as fact.

It typically works as follows: The researchers submit their paper to the journal they want it to be published in, and the editor of that journal sends the paper to expert reviewers (i.e. psychologists who are experts in that area – the researchers’ ‘peers’) who evaluate the paper’s scientific validity. The reviewers may accept the paper as it is, accept it with a few changes, reject it and suggest revisions and resubmission at a later date, or reject it completely.

There are several different methods of peer review:

  • Open review: The researchers and the reviewers are known to each other.
  • Single-blind: The researchers do not know the names of the reviewers. This prevents the researchers from being able to influence the reviewer. This is the most common form of peer review.
  • Double-blind: The researchers do not know the names of the reviewers, and the reviewers do not know the names of the researchers. This additionally prevents the reviewer’s bias towards the researcher from influencing their decision whether to accept their paper or not.

Criticisms of peer review:

  • Bias: There are several ways peer review can be subject to bias. For example, academic research (particularly in niche areas) takes place among a fairly small circle of people who know each other and so these relationships may affect publication decisions. Further, many academics are funded by organisations and companies that may prefer certain ideas to be accepted as scientifically legitimate, and so this funding may produce conflicts of interest.
  • Doesn’t always prevent fraudulent/bad research from being published: There are many examples of fraudulent research passing peer review and being published (see this Wikipedia page for examples).
  • Prevents progress of new ideas: Reviewers of papers are typically older and established academics who have made their careers within the current scientific paradigm. As such, they may reject new or controversial ideas simply because they go against the current paradigm rather than because they are unscientific.
  • Plagiarism: In single-blind and double-blind peer reviews, the reviewer may use their anonymity to reject or delay a paper’s publication and steal the good ideas for themself.
  • Slow: Peer review can mean it takes months or even years between the researcher submitting a paper and its publication.

Study evaluation

In psychological studies, ethical issues are questions of what is morally right and wrong. An ethically-conducted study will protect the health and safety of the participants involved and uphold their dignity, privacy, and rights.

To provide guidance on this, the British Psychological Association has published a code of human research ethics :

  • Participants are told the project’s aims , the data being collected, and any risks associated with participation.
  • Participants have the right to withdraw or modify their consent at any time.
  • Researchers can use incentives (e.g. money) to encourage participation, but these incentives can’t be so big that they would compromise a participant’s freedom of choice.
  • Researchers must consider the participant’s ability to consent (e.g. age, mental ability, etc.)
  • Prior (general) consent: Informing participants that they will be deceived without telling them the nature of the deception. However, this may affect their behaviour as they try to guess the real nature of the study.
  • Retrospective consent: Informing participants that they were deceived after the study is completed and asking for their consent. The problem with this is that if they don’t consent then it’s too late.
  • Presumptive consent: Asking people who aren’t participating in the study if they would be willing to participate in the study. If these people would be willing to give consent, then it may be reasonable to assume that those taking part in the study would also give consent.
  • Confidentiality: Personal data obtained about participants should not be disclosed (unless the participant agreed to this in advance). Any data that is published will not be publicly identifiable as the participant’s.
  • Debriefing: Once data gathering is complete, researchers must explain all relevant details of the study to participants – especially if deception was involved. If a study might have harmed the individual (e.g. its purpose was to induce a negative mood), it is ethical for the debrief to address this harm (e.g. by inducing a happy mood) so that the participant does not leave the study in a worse state than when they entered.


Study results are reliable if the same results can be consistently replicated under the same circumstances. If results are inconsistent then the study is unreliable.

Note: Just because a study is reliable, its results are not automatically valid . A broken tape measure may reliably (i.e. consistently) record a person’s height as 200m, but that doesn’t mean this measurement is accurate.

There are several ways researchers can assess a study’s reliability:


Test-retest is when you give the same test to the same person on two different occasions. If the results are the same or similar both times, this suggests they are reliable.

For example, if your study used scales to measure participants’ weight, you would expect the scales to record the same (or a very similar) weight for the same person in the morning as in the evening. If the scales said the person weighed 100kg more later that same day, the scales (and therefore the results of the study) would be unreliable.


Inter-observer reliability is a way to test the reliability of observational studies .

For example, if your study required observers to assess participants’ anxiety levels, you would expect different observers to grade the same behaviour in the same way. If one observer rated a participant’s behaviour a 3 for anxiety, and another observer rated the exact same behaviour an 8, the results would be unreliable.

Inter-observer reliability can be assessed mathematically by looking for correlation between observers’ scores. Inter-observer reliability can be improved by setting clearly defined behavioural categories .

Study results are valid if they accurately measure what they are supposed to. There are several ways researchers can assess a study’s validity:

  • E.g. let’s say you come up with a new test to measure participants’ intelligence levels. If participants scoring highly on your test also scored highly on a standardised IQ test and vice versa, that would suggest your test has concurrent validity because participants’ scores are correlated with a known accurate test.
  • E.g. a study that measures participants’ intelligence levels by asking them when their birthday is would not have face validity. Getting participants to complete a standardised IQ test would have greater face validity.
  • E.g. let’s say your study was supposed to measure aggression levels in response to someone annoying. If the study was conducted in a lab and the participant knew they were taking part in a study, the results probably wouldn’t have much ecological validity because of the unrealistic environment.
  • E.g. a study conducted in 1920 that measured participants’ attitudes towards social issues may have low temporal validity because societal attitudes have changed since then.

Control of extraneous variables

There are several different types of extraneous variables that can reduce the validity of a study. A well-conducted psychological study will control for these extraneous variables so that they do not skew the results.

Demand characteristics

Demand characteristics are extraneous variables where the demands of a study make participants behave in ways they wouldn’t behave outside of the study. This reduces the study’s ecological validity .

For example, if a participant guesses the purpose of an experiment they are taking part in, they may try to please the researcher by behaving in the ‘right’ way rather than the way they would naturally. Alternatively, the participant might rebel against the study and deliberately try to sabotage it (e.g. by deliberately giving wrong answers).

In some study designs, researchers can control for demand characteristics using single- blind methods. For example, a drug trial could give half the participants the actual drug and the other half a placebo but not tell participants which treatment they received. This way, both groups will have equal demand characteristics and so any differences between them should be down to the drug itself.

Investigator effects

Investigator effects are another extraneous variable where the characteristics of the researcher affect the participant’s behaviour. Again, this reduces the study’s ecological validity .

Many characteristics – e.g. the researcher’s age, gender, accent, what they’re wearing – could potentially influence the participant’s responses. For example, in an interview about sex, females may feel less comfortable answering questions asked by a male interviewer and thus give different answers than if they were asked by a female. The researcher’s biases may also come across in their body language or tone of voice, affecting the participant’s responses.

In some study designs, researchers can control for demand characteristics using double- blind methods. In a double-blind drug trial, for example, neither the participants nor the researchers know which participants get the actual drug and which get the placebo. This way, the researcher is unable to give any clues (consciously or unconsciously) to participants that would affect their behaviour.

Participant variables

Participant variables are differences between participants. These can be controlled for by random allocation .

For example, in an experiment on the effect of caffeine on reaction times, participants would be randomly allocated into either the caffeine group or the non-caffeine group. A non -random allocation method, such as allocating caffeine to men and placebo to women, could mean variables in the allocation method (in this case gender) skew the results. When participants are randomly allocated, any extraneous variables (e.g. gender in this case) will be allocated evenly between each group and so not skew the results of one group more than the other.

Situational variables

Situational variables are the environment the experiment is conducted in. These can be controlled for by standardisation .

For example, all the tests of caffeine on reaction times would be conducted in the same room, at the same time of day, using the same equipment, and so on to prevent these features of the environment from skewing the results.

In a repeated measures experiment, researchers may use counterbalancing to control for the order in which tasks are completed.

For example, half of participants would do task A followed by task B, and the other half would do task B followed by task A.

Implications of psychological research for the economy

Psychological research often has practical applications in real life. The following are some examples of how psychological findings may affect the economy:

  • Attachment : Bowlby’s maternal deprivation hypothesis suggests that periods of extended separation between mother and child before age 3 are harmful to the child’s psychological development. And if mothers stay at home during this period, they can’t go out to work. However, some more recent research challenges Bowlby’s conclusions, suggesting that substitutes (e.g. the father , or nursery care) can care for the child, allowing the mother to go back to work sooner and remain economically active.
  • Depression : Psychological research has found effective therapies for treating depression, such as cognitive behavioural therapy and SSRIs. The benefits of such therapies – if they are effective – are likely to outweigh the costs because they enable the person to return to work and pay taxes, as well avoiding long-term costs to the health service.
  • OCD : Similar to above: Drug therapies (e.g. SSRIs) and behavioural approaches (e.g. CBT) may alleviate OCD symptoms, enabling OCD sufferers to return to work, pay taxes, and avoid reliance on healthcare services.
  • Memory : Public money is required to fund police investigations. Psychological tools, such as the cognitive interview , have improved the accuracy of eyewitness testimonies, which equates to more efficient use of police time and resources.

Features of science

Theory construction and hypothesis testing.

Science works by making empirical observations of the world, formulating hypotheses /theories that explain these observations, and repeatedly testing these hypotheses /theories via experimentation.

  • E.g. A tape measure provides a more objective measurement of something compared to a researcher’s guess. Similarly, a set of scales is a more objective way of determining which of two objects is heavier than a researcher lifting each up and giving their opinion.
  • E.g. Burger (2009) replicated Milgram’s experiments with similar results.
  • E.g. The hypothesis that “water boils at 100°c” could be falsified by an experiment where you heated water to 999°c and it didn’t boil. In contrast, “everything doubles in size every 10 seconds” could not be falsified by any experiment because whatever equipment you used to measure everything would also double in size.
  • Freud’s psychodynamic theories are often criticised for being unfalsifiable: There’s not really any observations that could disprove them because every possible behaviour (e.g. crying or not crying) could be explained as the result of some unconscious thought process.

Paradigm shifts

Philosopher Thomas Kuhn argues that science is not as unbiased and objective as it seems. Instead, the majority of scientists just accept the existing scientific theories (i.e. the existing paradigm) as true and then find data that supports these theories while ignoring/rejecting data that refutes them.

Rarely, though, minority voices are able to successfully challenge the existing paradigm and replace it with a new one. When this happens it is a paradigm shift . An example of a paradigm shift in science is that from Newtonian gravity to Einstein’s theory of general relativity.

Data handling and analysis

Types of data, quantitative vs. qualitative.

Data from studies can be quantitative or qualitative :

  • Quantitative: Numerical
  • Qualitative: Non-numerical

For example, some quantitative data in the Milgram experiment would be how many subjects delivered a lethal shock. In contrast, some qualitative data would be asking the subjects afterwards how they felt about delivering the lethal shock.

Strengths of quantitative data / weaknesses of qualitative data:

  • Can be compared mathematically and scientifically: Quantitative data enables researchers to mathematically and objectively analyse data. For example, mood ratings of 7 and 6 can be compared objectively, whereas qualitative assessments such as ‘sad’ and ‘unhappy’ are hard to compare scientifically.

Weaknesses of quantitative data / strengths of qualitative data:

  • Less detailed: In reducing data to numbers and narrow definitions, quantitative data may miss important details and context.

Content analysis

Although the detail of qualitative data may be valuable, this level of detail can also make it hard to objectively or mathematically analyse. Content analysis is a way of analysing qualitative data. The process is as follows:

  • E.g. A bunch of unstructured interviews on the topic of childhood
  • E.g. Discussion of traumatic events, happy memories, births, and deaths
  • E.g. Researchers listen to the unstructured interviews and count how often traumatic events are mentioned
  • Statistical analysis is carried out on this data

Primary vs. secondary

Researchers can produce primary data or use secondary data to achieve the research aims of their study:

  • Primary data: Original data collected for the study
  • Secondary data: Data from another study previously conducted


A meta-analysis is a study of studies. It involves taking several smaller studies within a certain research area and using statistics to identify similarities and trends within those studies to create a larger study.

We have looked at some examples of meta-analyses elsewhere in the course such as Van Ijzendoorn’s meta-analysis of several strange situation studies and Grootheest et al’s meta-analysis of twin studies on OCD .

A good meta-analysis is often more reliable than a regular study because it is based on a larger data set, and any issues with one single study will be balanced out by the other studies.

Descriptive statistics

Measures of central tendency: mean, median, mode.

Mean , median , and mode are measures of central tendency . In other words, they are ways of reducing large data sets into averages .

The mean is calculated by adding all the numbers in a set together and dividing the total by the number of numbers.

  • Example set: 22, 78, 3, 33, 90
  • 22+78+3+33+90=226
  • The mean is 45.2
  • Uses all data in the set.
  • Accurate: Provides a precise number based on all the data in a set.


  • E.g.: 1, 3, 2, 5, 9, 4, 913 <- the mean is 133.9, but the 913 could be a measurement error or something and thus the mean is not representative of the data set

The median is calculated by arranging all the numbers in a set from smallest to biggest and then finding the number in the middle. Note: If the total number of numbers is odd, you just pick the middle one. But if the total number of numbers is even, you take the mid-point between the two numbers in the middle.

  • Example set: 20, 66, 85, 45, 18, 13, 90, 28, 9
  • 9, 13, 18, 20, 28 , 45, 66, 85, 90
  • The median is 28
  • Won’t be skewed by freak scores (unlike the mean).
  • E.g.: 1, 1, 3 , 9865, 67914 <- 3 is not really representative of the larger numbers in the set.
  • Less accurate/sensitive than the mean.

The mode is calculated by counting which is the most commonly occurring number in a set.

  • Example set: 7, 7, 20 , 16, 1, 20 , 25, 16, 20 , 9
  • There are two 7’s, but three 20’s
  • The mode is 20
  • Makes more sense for presenting the central tendency in data sets with whole numbers. For example, the average number of limbs for a human being will have a mean of something like 3.99, but a mode of 4.
  • Does not use all the data in a set.
  • A data set may have more than one mode.

Measures of dispersion: Range and standard deviation

Range and standard deviation are measures of dispersion . In other words, they quantify how much scores in a data set vary .

The range is calculated by subtracting the smallest number in the data set from the largest number.

  • Example set: 59, 8, 7, 84, 9, 49, 14, 75, 88, 11
  • The largest number is 88
  • The smallest number is 7
  • The range is 81
  • Easy and quick to calculate: You just subtract one number from another
  • Accounts for freak scores (highest and lowest)
  • Can be skewed by freak scores: The difference between the biggest and smallest numbers can be skewed by a single anomalous result or error, which may give an exaggerated impression of the data distribution compared to standard deviation .
  • 4, 4, 5, 5, 5, 6, 6, 7, 19
  • 4, 16, 16, 17, 17, 17, 18, 19 19

Standard deviation

The standard deviation (σ) is a measure of how much numbers in a data set deviate from the mean (average). It is calculated as follows:

  • Example data set: 59, 79, 43, 42, 81, 100, 38, 54, 92, 62
  • Calculate the mean (65)
  • -6, 14, -22, -23, 16, 35, -27, -11, 27, -3
  • 36, 196, 484, 529, 256, 1225, 729, 121, 729, 9
  • 36+196+484+529+256+1225+729+121+729+9=4314
  • 4314/10=431.4
  • √431.4=20.77
  • The standard deviation is 20.77

Note: This method of standard deviation is based on the entire population. There is a slightly different method for calculating based on a sample where instead of dividing by the number of numbers in the second to last step, you divide by the number of numbers-1 (in this case 4314/9=479.333). This gives a standard deviation of 21.89.

  • Is less skewed by freak scores: Standard deviation measures the average difference from the mean and so is less likely to be skewed by a single freak score (compared to the range ).
  • Takes longer to calculate than the range .


A percentage (%) describes how much out of 100 something occurs. It is calculated as follows:

  • Example: 63 out of a total of 82 participants passed the test
  • 63/82=0.768
  • 0.768*100=76.8
  • 76.8% of participants passed the test

Percentage change

To calculate a percentage change, work out the difference between the original number and the after number, divide that difference by the original number, then multiply the result by 100:

  • Example: He got 80 marks on the test but after studying he got 88 marks on the test
  • His test score increased by 10% after studying

Normal and skewed distributions

Normal distribution.

A data set that has a normal distribution will have the majority of scores on or near the mean average. A normal distribution is also symmetrical: There are an equal number of scores above the mean as below it. In a normal distribution, scores become rarer and rarer the more they deviate from the mean.

An example of a normal distribution is IQ scores. As you can see from the histogram below, there are as many IQ scores below the mean as there are above the mean :

statistical infrequency bell curve

When plotted on a histogram , data that follows a normal distribution will form a bell-shaped curve like the one above.

Skewed distribution

positive skew and negative skew histograms

Skewed distributions are caused by outliers: Freak scores that throw off the mean . Skewed distributions can be positive or negative :

  • Mean > Median > Mode
  • Mean < Median < Mode


Correlation refers to how closely related two (or more) things are related. For example, hot weather and ice cream sales may be positively correlated: When hot weather goes up, so do ice cream sales.

Correlations are measured mathematically using correlation coefficients (r). A correlation coefficient will be anywhere between +1 and -1:

  • r=+1 means two things are perfectly positively correlated: When one goes up , so does the other by the same amount
  • r=-1 means two things perfectly negatively correlated: When one goes up , the other goes down by the same amount
  • r=0 means two things are not correlated at all: A change in one is totally independent of a change in the other

The following scattergrams illustrate various correlation coefficients:

correlation coefficient scatter graph examples

Presentation of data

table example

For example, the behavioural categories table above presents the raw data of each student in this made-up study. But in the results section, researchers might include another table that compares average anxiety rating scores for males and females.


scattergram example

For example, each dot on the correlation scattergram opposite could represent a student. The x-axis could represent the number of hours the student studied, and the y-axis could represent the student’s test score.

eyewitness testimony loftus and palmer

For example, the results of Loftus and Palmer’s study into the effects of different leading questions on memory could be presented using the bar chart above. It’s not like there are categories in-between ‘contacted’ and ‘hit’, so the bars have gaps between them (unlike a histogram ).

A histogram is a bit like a bar chart but is used to illustrate continuous or interval data (rather than discrete data or whole numbers).

histogram example

Because the data on the x axis is continuous, there are no gaps between the bars.

line graph example

For example, the line graph above illustrates 3 different people’s progression in a strength training program over time.

pie chart example

For example, the frequency with which different attachment styles occurred in Ainsworth’s strange situation could be represented by the pie chart opposite.

Inferential testing

Probability and significance.

The point of inferential testing is to see whether a study’s results are statistically significant , i.e. whether any observed effects are as a result of whatever is being studied rather than just random chance.

For example, let’s say you are studying whether flipping a coin outdoors increases the likelihood of getting heads. You flip the coin 100 times and get 52 heads and 48 tails. Assuming a baseline expectation of 50:50, you might take these results to mean that flipping the coin outdoors does increase the likelihood of getting heads. However, from 100 coin flips, a ratio of 52:48 between heads and tails is not very significant and could have occurred due to luck. So, the probability that this difference in heads and tails is because you flipped the coin outside (rather than just luck) is low.

Probability is denoted by the symbol p . The lower the p value, the more statistically significant your results are. You can never get a p value of 0, though, so researchers will set a threshold at which point the results are considered statistically significant enough to reject the null hypothesis . In psychology, this threshold is usually <0.05, which means there is a less than 5% chance the observed effect is due to luck and a >95% chance it is a real effect.

Type 1 and type 2 errors

When interpreting statistical significance, there are two types of errors:

  • E.g. The p threshold is <0.05, but the researchers’ results are among the 5% of fluke outcomes that look significant but are just due to luck
  • E.g. The p threshold is set too low (e.g. <0.01), and the data falls short (e.g. p=<0.02)

Increasing the sample size reduces the likelihood of type 1 and type 2 errors.

Key maths skills made easy!

psychology research methods maths skills revision guide

Types of statistical test

Note: The inferential tests below are needed for A level only, if you are taking the AS exam , you only need to know the sign test .

There are several different types of inferential test in addition to the sign test . Which inferential test is best for a study will depend on the following three criteria:

  • Whether you are looking for a difference or a correlation
  • E.g. at the competition there were 8 runners, 12 swimmers, and 6 long jumpers (it’s not like there are in-between measurements between ‘swimmer’ and ‘runner’)
  • E.g. First, second, and third place in a race
  • E.g. Ranking your mood on a scale of 1-10
  • E.g. Weights in kg
  • E.g. Heights in cm
  • E.g. Times in seconds
  • Whether the experimental design is related (i.e. repeated measures ) or unrelated (i.e. independent groups )

The following table shows which inferential test is appropriate according to these criteria:

Note: You won’t have to work out all these tests from scratch, but you may need to:

  • Say which of the statistical tests is appropriate (i.e. based on whether it’s a difference or correlation; whether the data is nominal, ordinal, or interval; and whether the data is related or unrelated).
  • Identify the critical value from a critical values table and use this to say whether a result (which will be given to you in the exam) is statistically significant.

The sign test

The sign test is a way to calculate the statistical significance of differences between related pairs (e.g. before and after in a repeated measures experiment ) of nominal data. If the observed value (s) is equal or less than the critical value (cv), the results are statistically significant.

Example: Let’s say we ran an experiment on 10 participants to see whether they prefer movie A or movie B .

  • n = 9 (because even though there are 10 participants, one participant had no change so we exclude them from our calculation)
  • In this case our experimental hypothesis is two-tailed: Participants may prefer movie A or movie B
  • (The null hypothesis is that participants like both movies equally)
  • In this case, let’s say it’s 0.1
  • The experimental hypothesis is two-tailed
  • So, in this example, our critical value (cv) is 1
  • In this example, there are 2 As, so our observed value (s) is 2
  • In this example, the observed value (2) is greater than the critical value (1) and so the results are not statistically significant. This means we must accept the null hypothesis and reject the experimental hypothesis .


  • Account details

AQA GCSE Psychology Research Methods

This section provides revision resources for AQA GCSE psychology and the Research Methods chapter. The revision notes cover the AQA exam board and the new specification. As part of your GCSE psychology course, you need to know the following topics below within this chapter:

  • AQA Psychology
  • Research Methods

aqa gcse psychology research methods notes

We've covered everything you need to know for this research methods chapter to smash your exams.

  • The latest AQA GCSE Psychology specification (2023 onwards) has been followed exactly so if it's not in this resource pack, you don't need to know it.
  • We've provided practice questions at the end to help you get better with this topic.
  • Completely free for schools , just get in touch using the contact form at the bottom.
  • Teachers can print and distribute this resource freely in classrooms to aid students and teaching.
  • Instant download, no waiting.

Formulation of Testable Hypotheses

For the formulation of testable hypotheses, the psychology specification states you need to know following:

  • Null hypothesis and alternative hypothesis.

A hypothesis is simply a formal and testable statement of the relationship between two variables that is to be tested through experimentation. In psychology, as well as other sciences, we use them as part of the scientific method.

The hypothesis is not strictly speaking a prediction and should not be used in the future tense i.e. “this will happen”. It is only at the end of the study that the researcher decides whether the research evidence supports the hypothesis or not.

There are different types of hypotheses used in psychology, however, the main ones that crop up frequently are:

  • Directional hypotheses
  • Non-directional hypotheses
  • Null hypotheses
  • Alternative hypotheses

For GCSE Psychology and the AQA specification, we need to know about null hypotheses and alternative hypotheses .

What is a Null Hypothesis?

A null hypothesis is a general statement that the observed variables will have no impact as there is no relationship between them. This hypothesis assumes that any difference observed is due to sampling or experimentation errors.

An example of a null hypothesis for a hypothetical scenario is “watching television before bed has no impact on how well you sleep”

What is an Alternative Hypothesis?

The alternative hypothesis would be a prediction that one variable will affect the other.

An example would be “watching scary movies before bed affects how fast you fall asleep”. The alternative hypothesis does not specify the direction of the outcome, merely that there will be an effect.

Formulating Hypotheses

Once you know enough about hypotheses, you need to consider how to apply them. When conducting research, most of the time the experiment comes from a simple or vague idea we wish to test.

Here’s an example: does music affect peoples ability to learn?

This is rather a vague question and to turn it into a testable experiment, we need to be able to operationalise the two key variables; music and learning .

These two variables are then known as the independent variable and dependent variable – often referred to as the IV and DV for short. More information is given on them below.

Hypotheses are then easier to form, a suitable one for this experiment would be an alternative hypothesis such as:

  • “ The presence or absence of music has an effect on the score in a learning test ”

A null hypothesis for this example would simply be:

  • “The presence of music has no effect on the score in a learning test”

Type of Variables

For the different types of variables, the GCSE psychology specification states you need to know the following:

  • Independent variable, dependent variable, extraneous variables.

There are 3 different types of variables we need to know about which are:

  • The independent variable (IV)
  • The dependent variable (DV)
  • Extraneous variables.

Independent Variable

An experiment will look to measure the effect of one variable on another. These two variables have special names, which are the independent variable and dependent variable.

The independent variable is what researchers manipulate in order to test its effect on the dependent variable (the outcome). Let’s use the example mentioned earlier about music and learning to illustrate this: We are conducting an experiment to see if music affects the ability of students to learn. In this case, the independent variable (IV) we will be manipulating is music.

Within the context of an experiment, we may simply have two conditions where one group is exposed to music while another group is not while engaging in some learning activity. We would then compare the findings to assess the results.

Dependent Variable

The dependent variable (DV) is the outcome or effect we are measuring within the study. So using the example above, the dependent variable would be how well the students are able to learn with or without music. This may be measured in a number of ways (taking a memory test for example or quiz).

So to clarify – the independent variable is what we change and the dependent variable is the outcome we then measure .

A good way to remember the difference is to think of it like this:

  • The dependent variable “depends” on what's being changed (the independent variable).
  • Another way would be to remember that “we measure the effect of the IV on the DV”.

If you remember that the independent variable (IV) always comes first, you should be able to recall that the dependent variable (DV) is then the outcome. These are just two simple ways of remembering the difference between the IV and DV but feel free to use what works for you.

Extraneous Variable

The extraneous variable is a third variable that may unknowingly be affecting the outcome of the study (the DV).

We conduct experiments to measure the effect of the IV on the DV but sometimes extraneous variables are actually the cause of the changes. They can be seen as “nuisance variables” that affect the study and make it difficult to know whether it is the IV that affects the DV.

Let’s use that example mentioned earlier about how music may affect a students ability to learn. We may conduct this experiment and find that music improves learning as the students who listened to the music performed better.

We may, therefore, conclude music improves students ability to learn, however, what if it was actually a third variable affecting the results which is unaccounted for? (an extraneous variable).

Perhaps we find that the students who performed the best were those with prior knowledge of the questions in the test?. The extraneous variable could then be argued to be prior knowledge participants had that we have not accounted for or could control.

Looking into the study we could perhaps argue the extraneous variable may be the intelligence of participants from one group to another that is affecting the outcome. It may be that some participants in one group were more educated and therefore better problem solvers, and this is an extraneous variable that is affecting the dependent variable (outcome).

With research studies you will be presented, you can almost always find arguments to highlight extraneous variables in some form. It is handy to get into the habit of recognising these different forms as they prove useful in critically analysing studies and topping up your points with further evaluation marks, especially if you go on to study A-level psychology.

Sampling Methods

  • Random sampling
  • Opportunity sampling
  • Systematic sampling
  • Stratified sampling
  • Strengths and weaknesses of each sampling method
  • Understand principles of sampling as applied to scientific data.

This section of AQA GCSE psychology requires you to know about 4 different sampling methods and their strengths and weaknesses.

Sampling methods are merely the different strategies researchers use to get participants for their studies. In any psychological research study, there is usually a target population, which is the group of individuals the researcher is interested in. The aim of the researcher is to try and take a representative sample from this target population using a sampling method. The goal is to gain a representative sample that then allows the researcher to make generalisations across the whole population, based on the findings of this sample.

The four sampling methods you are required to know about are:

Random Sampling

Random sampling involves the researcher identifying members of the target population, numbering them and then attempting to draw out the required number of people for their study.

The selection of participants can be done in a randomised way such as drawing out numbers from a hat if the sample size is small or having a computer randomly select the participants if the sample size is large.

Strengths and Weaknesses of Random Sampling

  • Random sampling has the benefit of being more unbiased as all members of the target population have an equal chance of being selected for the study. This would mean that the sample is likely to be more representative of the target population making more valid generalisations possible from the research findings.
  • Random sampling also means there is less chance that researchers can influence the results as they have no say as to who is picked. This reduces the impact of investigator effects which means the findings may have more validity.
  • However, even despite this, it is still possible for the researcher to end up with an unbalanced and biased sample by chance, particularly if the sample size is too small.
  • Gathering randomised samples can also be time-consuming, as attempting to gather enough willing participants from the target population takes a considerable amount of time and effort.

Opportunity Sampling

Opportunity sampling is a form of sampling method that means you ask those who are around you and most easily available , that represent the target population, to participate in the study. This may involve asking those around you in your class, school or people walking in the street for their involvement.

Strengths and Weaknesses of Opportunity Sampling

  • The main benefit of opportunity sampling is it is one of the fastest and easiest ways to gather participants for a study when compared to other sampling methods.
  • Opportunity samples have a greater chance of being biased because the sample is drawn from a very narrow part of the target population. For example, if you selected participants at school, your sample is likely to consist of mostly students and the behaviours they display in the study may not generalise to adults. Participants may also try to “help” the researcher in a way that would support the hypothesis so the results may be unreliable and invalid.
  • With opportunity sampling methods, it is possible the researcher can influence those selected as the process is not randomised. The researcher may select the people they think will support their hypothesis, so investigator effects is a potential hindrance.

Systematic Sampling

Systematic sampling involves selecting every “nth” member of the target population . An example of this would be if the researcher decided that “n” will be “5”, every 5th person in the target population is selected as a participant.

This is still unbiased as the researcher has no influence as to who is picked and it is technically not a “random sample” either as not everyone gets an equal opportunity to be selected (it is only the person 5 positions away). Be sure not to confuse this with the random sampling method due to this slight difference; just remember that there is a fixed systematic way for selection that determines this to be a systematic sample.

Strengths and weaknesses of systematic sampling

  • A strength of the systematic sampling method is that it is a simple way for researchers to gather participants and there is little risk of research bias influencing this. Therefore the participants gathered should, in theory, be representative and unbiased which should lead to more reliable results.
  • A weakness, however, is participants gathered could still be unrepresentative and biased due to chance selection. This would make the results unreliable when re-tested.
  • Another weakness of systematic sampling is you need a bigger sample size to be able to filter out participants based on the “nth” selection. If you require 100 participants for a study and picked them based on every 10 participants, you would need 1000 participants to filter through. Therefore gathering participants for a study based on systematic sampling methods can be very time-consuming.

Stratified Sampling

Stratified sampling is the most complex of the sampling methods and it is most often used in questionnaires. Sub-groups (or strata) within the population are identified (e.g. boys and girls or age groups: 10-12 years, 13-15 years etc) and then participants are gathered from each strata in proportion to their occurrence in the population . The selection of participants is generally done using a random technique.

For example, in a school, there are several subgroups such as teachers, support staff, students and other staff. If the teachers made up 10% of the whole school’s population, then 10% of the sample must be teachers. This is then repeated for each sub-group.

Strengths and Weaknesses of Stratified Sampling

  • A major strength of using stratified sampling techniques is that they are very representative of the target population. This means the findings should have high reliability and validity to make generalisations to the target population.
  • A major weakness of using stratified sampling is that it is very time-consuming to identify the subgroups, select necessary participants and attempt to get a proportionate sample involved in the study. Therefore this form of sampling method is extremely difficult to execute and can be impractical.

Volunteer Sampling

A volunteer sample consists of people that have volunteered to take part in the study . Volunteers can be gathered in a number of ways such as putting an advert out on the newspaper, internet or some media outlet to try and gather people to take part.

Volunteers may put themselves forward to be part of the study but they may not necessarily be told the aim of the study or what they are really being tested in. For example, Milgram’s shock study gathered volunteers who agreed to take part but did not necessarily know what they were being tested on (obedience).

Strengths and Weaknesses of Volunteer Sampling

  • A strength of using volunteer sampling is participants should be willing to give their informed consent to be a part of the study. The people that tend to volunteer tend to be those motivated to take part in the study.
  • Volunteer sampling can also be a fast and efficient way of gathering research participants. Instead of having to search for volunteers, an advert could be placed to gather participants based on the traits/characteristics the researcher requires.
  • A weakness of using volunteer sampling is the people that tend to volunteer may be a biased sample that are not representative of the target population. For example, volunteers are already motivated to engage in the research (volunteer bias) and more motivated than those that do not and this can influence the outcome of the study in some way.

Designing Research

This section on designing research for GCSE psychology and research methods is quite extensive and requires you to know about quite a few different aspects of designing psychological research studies.

The topics you need to know for research methods include:

Independent group design

  • Repeated measures design
  • Matched pairs design
  • Strengths and weaknesses of each design
  • Laboratory experiments
  • Field and natural experiments


  • Case studies
  • Observation studies
  • Strengths and weaknesses of each research method and types of behaviour for which they are suitable.

An independent group design is the simplest to understand and conducted with participants involved in the study usually divided into two subgroups .

One group will take part in the experimental condition (with the independent variable introduced), while the other group would not be exposed to this and form the control group for comparison.

Let’s use the example we mentioned earlier with a study that measures the effects of music on learning.

In an independent group design, one group of participants would be measured on their ability to learn with music being played while the other group would be tested on their learning ability without music.

The results (dependent variable) are then compared between the two groups to measure the effects.

If the results are significantly different then researchers may conclude that this is because of the independent variable, which in our case would be music affecting learning ability.

Strengths and Weaknesses of Independent Group Design

  • A strength of using independent group designs is there are no order effects that can invalidate the results, as participants only take part in one of the conditions. Order effects are apparent in experiments where repeated measure designs are used and this involves participants learning or improving from their experience of having to do the experiment more than once. This does not happen in independent group designs which can give more valid results.
  • Independent group designs are beneficial as the materials or apparatus can usually be used across both the experimental condition and the control group (minus the independent variable being manipulated or introduced as required). This makes setting up independent group designs far easier than other experimental conditions due to saving time.
  • Another strength of independent group designs is that participants are less likely to display demand characteristics. Demand characteristics are when participants change their own behaviour as they figure out (or think they do) the purpose of the study. The participants may then display behaviour that is different in response which can invalidate findings. Demand characteristics are less likely in independent group designs as participants are only exposed to one condition and they don’t have the opportunity to learn or adjust their behaviour in another condition (as they cannot compare).
  • A weakness of independent group designs is that differences between the experimental condition and control group may be due to participant variables, such as individual differences between the two groups, rather than the independent variable. Just by probability or chance, one group may be smarter than another or have individual characteristics that make them more able (or less able) for the condition they are exposed. This would then be a confounding variable that affects the results. Using the music example mentioned previously, the group that performs best (whether its the group exposed to music or not) may do so simply because they have more educated or intelligent people than the other condition.
  • Another criticism of using independent group designs in experiments is that you need to gather more participants. For example, you need a large enough sample to be exposed to the experimental condition to make generalisations but you then need to gather this number again for the control group condition. Using our example earlier, if we wanted to test how music affects people’s ability to learn and we gather 50 people, we need another 50 people for the control condition that is exposed to no music. Gathering too few participants increases the risks of individual differences being the difference in results while gathering a large number requires more time, effort and resources.

Repeated Measures Design

A repeated measures design sees all the gathered participants of the study being exposed to both conditions of the experiment.

Referring to our music and learning scenario (once again!), we would have a group of 50 participants that would first be exposed to the experimental condition whereby they attempt to learn with music present and then they would attempt to learn without music.

The results would then be compared between the conditions to assess what impact the IV had on the DV. In experiments where there were numerous different conditions, the same participants would be used across them while exposed to different independent variables.

Strengths and Weaknesses of Repeated Measures Design

  • A major strength of repeated measure designs is that they require less effort to gather participants as they use the same people across the different experimental conditions. Therefore setting up the experiment tends to be faster compared to group designs such as independent measures where you would require double the amount of participants to cross-compare against.
  • Another strength of using repeated measure designs is participant variables are eliminated. This is because the same people are used across the different conditions and they are comparing against themselves directly. This means there is less chance of individual differences influencing the results.
  • A weakness of using repeated measure designs is that there is a high risk of order effects affecting the validity of findings. As participants are required to do multiple tasks across different conditions, there is the risk that participants may improve as they repeat the experiments. For example, if they were tested on their learning ability while music was played in one condition, when they are tested without music, the experience and practice gained from the first condition may see them improve. Researchers may then incorrectly view this improvement as due to the independent variable (IV) rather than order effects.
  • Another criticism of using repeated measures is you need to create multiple different tasks or materials between the conditions. For example, you could not use the same content for participants to memorise from one condition to another in a memory test experiment. You would need to create content that was judged to be similar in difficulty which in itself would be a subjective measure. For example, having participants memorise 20 “easy” words with similar syllables in one condition, would require a researcher to spend significant time and effort in creating another set of similar words for another condition.
  • There is a higher risk of demand characteristics when using repeated measure designs. This is because participants may be able to guess the purpose of the study (if it is intentionally obscured to improve the validity of findings) and then adjust their behaviour accordingly. This is more likely to happen as the same participants are used across the different conditions and they may notice the different setups and the purpose of the study. This may lead to invalid findings from the behaviour that is observed.

Matched Pairs Design

A matched pairs design involves gathering participants and testing them prior to them taking part in the study on certain characteristics . The tests allow them to be matched in pairs with someone who is deemed to have similar qualities as to them which may be relevant to the study.

The pairs may be identified as Pair Aa or Pair Bb etc.

In conducting a matched pairs design research study, one pair will take part in one experimental condition while their matched partner/pair is exposed to another experimental condition.

The results are then compared by the researcher between the conditions and treated as if they were gathered from one individual despite coming from two individuals.

Within psychological research, the most ideal matched pairs participants tend to be identical twins as they account have identical biology (as they are similar) and potentially very similar personality factors too.

Strengths and Weaknesses of Matched Pairs Design

  • One strength of using matched pairs designs in research is they reduce participant variables which can affect the results. This is because the people are paired up together based on similar traits that are relevant to the study.
  • Another strength of using matched pairs is that there are no order effects, unlike repeated measure design studies. This is because everyone does the experiment once and have no opportunity to learn from their previous attempts.
  • Matched pairs designs can re-use the same materials/apparatus across the pairs as everyone will only be exposed to them once. This makes the setup of the experiment easier as researchers do not have to create unique set-ups across the two groups which can be time-consuming.
  • A weakness of using matched pairs design is matching people on key variables is time-consuming and not always successful. Attempting to find people who can be matched requires an initial large sample to filter through and this can take a very long time to do.
  • It is difficult to match people based on personality variables or filter out individual differences for certain. You can generally only match people based on fixed traits such as gender (sex), age, height etc, however, personality factors may be what determine differences in the experiments. Therefore matched pair designs can produce invalid results that are not the result of the independent variable.

Laboratory Experiments

Laboratory experiments are experiments that are conducted in a controlled setting , usually a research laboratory where participants are aware of being observed and part of a study.

Laboratory experiments tend to have high internal validity because researchers can control all the variables so the main differences between the experimental condition and control group are only the independent variable whose effect is being monitored. This allows researchers to more confidently assume that any differences between the conditions are due to the independent variable.

Strengths and Weaknesses of Laboratory Experiments

  • A major strength of laboratory experiments is they have high validity. This means that researchers can be confident to a higher degree that what they are measuring is in fact due to the effect of the independent variable because this is the only difference between the experimental condition and control group.
  • Another strength of using a laboratory setup is this limits the role of extraneous variables from influencing the results as researchers have complete control of the environment. This means unaccounted for outside influences are limited and makes drawing cause and effect between the IV and DV more reliable. Laboratory experiments can be checked for reliability as they are easier to replicate. Due to the artificial setup of the experiments (being in a laboratory setting), other researchers can recreate the experiment exactly to check the results for reliability. This can be harder to do with other setups.
  • A weakness of using laboratory experiments is they lack ecological validity. This is because the setup of the experiment is artificial and in a completely controlled environment and the results gathered in the lab, may not generalise to real-world situations due to their contrived setup. Therefore laboratory experiments tend to lack ecological validity as the setup involved to test behaviour may not occur similarly in real life e.g. testing memory ability and learning in a lab setup is unlikely to be how people learn with or without music being present – or using a film clip to test eyewitness testimony is not realistic.
  • Participants in laboratory setups may display demand characteristics and adjust their behaviour due to the contrived setup and being aware that they are being observed. Therefore the behaviour observed may lack validity as it may not be indicative of how people are likely to behave in the real world if they think they are not being observed or under supervision. Participants may, therefore, behave how they think researchers want them or what would be deemed normal with others watching, not necessarily what they would actually do.

Field Experiments

A field experiment is conducted in a more natural or everyday environment , unlike the laboratory experiment where the behaviour being measured is more likely to occur.

The field experiment can be conducted anywhere in real-world settings with researchers manipulating an independent variable to measure its impact on the dependent variable. A field experiment can include confederates that participants are unaware of also being involved to test their response in the field setting.

One key difference between a field experiment compared to a laboratory experiment, are participants may not be aware of being observed or studied. This is in an attempt to generate more realistic behaviour or responses from them that can generalise to real-world settings.

Strengths and Weaknesses of Field Experiments

  • A strength of using field experiments is they are high in ecological validity as the setup and environments are more realistic. This is thought to increase more realistic responses from participants as they are not aware always aware of being observed (unlike lab settings). The argument here is field experiments have higher internal validity and the behaviours from participants can then be generalised to the wider population.
  • A weakness of using field experiments is they are at higher risk of extraneous variables influencing the behaviour of participants. Researchers, therefore, have less control and cannot say with as much certainty that the behaviour they observed was in fact due to the independent variable or not.
  • Another criticism of field experiments is they are difficult to replicate. Participants may be members of the public with personality factors that influence the results which are unaccounted for and the environment itself may be difficult to recreate in order to test the study for reliability in its findings. Therefore replication and reliability become an issue for field experiments.
  • Another weakness of using field experiments is they raise ethical issues in regards to informed consent. This is because participants may be unaware of being observed or part of a study and this raises ethical concerns. On the other hand, this may also provide us with more realistic and valid results without demand characteristics being a potential confounding variable.

Natural Experiments

A natural experiment is conducted when ethical or practical reasons to manipulate an independent variable (IV) are not possible. It is therefore said that the IV occurs 'naturally'.

The dependent variable (DV), may however, be tested in a laboratory, for example, the effects of institutionalisation in some form, which may occur naturally due to imprisonment or disruption of attachment through the care system and how it may affect psychological development such as intellect or emotional development.

Another good example of a natural experiment is the study by Charlton et al. (2000) which measured the effects of television. Prior to 1995, the people of St. Helena, a small island in the Atlantic had no access to TV however it's arrival gave the researchers to examine how exposure to western programmes may influence their behaviour. The IV in this case was the introduction of TV which was not controlled by researchers and something they took advantage of would be practically difficult to control. The DV was measures of pro or anti-social behaviours that were assessed through the use of questionnaires, observations and psychological tests.

These types of experiments would either impractical or unethical to implement and therefore cases where this occurs naturally due to normal circumstances may be examined through natural experiments.

Strengths and Weaknesses of Natural Experiments

  • One major weakness of natural experiments is the lack of control. It is more difficult to control extraneous variables which makes it difficult to establish causality.
  • A strength of natural experiments is they are high in ecological validity. Due to the 'real world' environment, the results relate to everyday behaviour and can be generalised to other settings.
  • Another strength of natural experiments is they often produce no demand characteristics as the participants are unaware of the experiment. Therefore the behaviour observed is more likely to be realistic and indicative of behaviour that can be generalised across wider populations.
  • A weakness of natural experiments is they are difficult to replicate to double check the findings. As the conditions are never exactly the same, it becomes difficult to establish reliability in such experiments which then affects validity as causality cannot be determined. 
  • Participants are often not aware of being observed or taking part in natural experiments and this raises ethical issues, in particular, informed consent. They may not wish to take part or be monitored and this is another weakness of natural experiments, although they may be debriefed after the experiment and given the option of giving consent to use the data collected from them.

One way psychologists find out about peoples behaviour is to quite simply ask them through the form of interviews. 

Interviews involve a researcher in direct contact with the participant and this could either be face to face or via phone/video call. The vast majority of interviews involve a questionnaire that the researcher records the responses on at the time of the interview. There are different forms of interviews used which vary in structure and we will look at specifically structured and unstructured interviews for GCSE psychology.

Structured Interviews

Structured interviews involve all participants being asked the same pre-set questions in the same order . The researcher is unable to ask additional questions outside of this.

The questions are often closed questions that require a yes or no response , or they can be open questions that simply require the researcher to record the participant’s response.

Open questions can be questions that begin with who, what, where, when, why and how.

These force a participant to explain their answers beyond simply saying yes or no.

Strengths and Weaknesses of Structured Interviews

  • Structured interviews can be replicated far more easily than unstructured interviews as the questions are all pre-set. This helps in testing the reliability of research findings to check for consistency and validity in the conclusions drawn.
  • A criticism/weakness of using structured interviews is they can be incredibly time consuming and require skilled researchers. People’s responses can also be affected by social desirability bias.
  • Structured interviews gather quantitative data but lack qualitative data. When participants can only answer yes or no, this does not tell us why they think or respond this way which may be more important to understand behaviour.

Unstructured Interviews

In unstructured interviews, participants are free to discuss anything freely . The interviewer may devise new questions as the interview progresses or on the previous answers given, to explore further.

With unstructured interviews, each participant is likely to be asked different sets of questions within the interview. The questions asked in unstructured interviews may be a mix of open and closed questions.

Strengths and Weaknesses of Unstructured Interviews

  • Unstructured interviews provide rich and detailed information however they can not be replicable and people’s responses cannot be easily compared.
  • Unstructured interviews have the benefit of allowing participants to explain their responses which can help us understand why they think or behave in particular ways which may be more valuable than structured interviews telling us merely how they would behave.
  • Unstructured interviews can be more time-consuming as there is no structure or guideline to follow in regards to how many questions are being asked. They also require more trained interviewers who are able to articulate themselves and the questions they wish to ask, unlike structured interviews which can merely be read from a list and explained more easily.

Questionnaires are an example of a survey method that are used to collect large amounts of information from a target group that may be spread out across the country.

The researcher must design a set of questions for participants to answer; people taking part in a survey are referred to as “respondents” because their answers or behaviours are in response to the questions presented. Questionnaires can be conducted face to face, via phone or video call too.

Questionnaires are similar to structured interviews as respondents all answer the same questions, in the same order and they often narrow the possible responses to closed questions (yes or no answers).

Strengths and Weaknesses of Questionnaires

  • Questionnaires are practical ways for researchers to gather large amounts of information very quickly on topics where the responses are best suited for yes or no responses.
  • Another strength of using questionnaires is that they can be replicated very easily as all the questions are pre-set. Responses can be gathered again to check for reliability and validity this way far more easily.
  • Problems arise in the use of questionnaires when the questions are unclear or if they suggest or lead respondents into a desirable response. Responses can be affected by social desirability bias so participants may not necessarily answer truthfully which can invalidate findings.
  • Another criticism of using questionnaires in research is respondents can only answer yes or no. This limits the amount of information that can be gathered but also participants may not be able to answer in certain terms yes to every presented scenario (or no). It may be that their responses only represent given situations but can be different in other situations.
  • Respondents may misunderstand the meaning of questions and therefore answer incorrectly. Unlike structured interviews that allow participants to ask questions to clarify their understanding, respondents may misread or misunderstand questions and answer in a way that is not truly representative of their views.
  • The researcher needs to make sure that in writing the questions, they are clear and unambiguous. This can be a difficult task to achieve and requires a great deal of time to construct questions that do not bias or lead the respondents into responses.

Case Studies

A case study is a very detailed study of the life and background of either one person, a small group of people or an institution or an event . Case studies use information from a range of sources, such as the person concerned, related family members or even friends.

Various techniques may be used such as interviewing people or observing people as they engaged in daily life. Psychologists may also use various tests such as IQ tests, personality tests or some questionnaire to produce psychological data about the target in question.

Researchers may also refer to school or work records for an individual or carry out observations of the individual or groups in question. The case study is then written up as a description of the target individual or group and interpreted information based on psychological theories.

Case studies tend to be longitudinal and follow the target over a long period of time (often many years).

Strengths and Weaknesses of Case Studies

  • A strength of using case studies is they provide detailed information about individuals (or target group/institution) rather than collecting a score on a metric test from a person.
  • Another benefit is case studies collect information over a long period of time so changes in behaviour can be observed and comparisons are drawn over this period to understand the changes.
  • A weakness of using case studies is they target a single individual and this makes it difficult to generalise the findings to others. The situation or factors that influence this individual’s outcomes may not necessarily do the same for others due to individual differences. The data collected is also very subjective as it relies on usually peoples perceptions of things and their memories may not be so reliable over such a long period of time. There is also the risk that the researcher themselves projects their own biases onto the findings and makes their own interpretations of the content making the case study unreliable.
  • There can be ethical concerns with using case studies as the people or group being followed are usually of interest because of some psychological problem. This could make them vulnerable and raise ethical concerns about whether they can give informed consent.

Observation Studies and the Observational Method

In an observational study, the researcher watches or listens to the participants engaging in whatever behaviour is being studied and records their behaviour . In most natural observations, people are observed in their normal environments without interference from the researcher.

In some studies, a researcher may cause something to happen to gauge the responses of people and record these.

Here’s an example of one such study:

A nurse is called by a “doctor” via telephone and instructed to give medicine to a patient which is against the rules. The study was conducted in the nurses natural setting of the hospital and researchers then observe whether the nurse follows this instruction or not.

In some studies, the data may also be collected in a “laboratory setting” although this may not necessarily be a laboratory. This may be a natural setting that has been organised by the researcher to make it easier to observe the targets.

Strengths and Weaknesses of Observation Studies and the Observational Method

  • What people say is often very different from what they may do in a given situation. The observational method is high in ecological validity and its use is very suitable for social behaviours as it allows researchers to gauge peoples true responses. If participants were asked about their behaviour prior, they may give socially desirable responses which may not be what they would really do and observational studies allow us to see true behaviour without this bias.
  • The behaviours observed in observational studies have higher external validity as they can be more easily generalised. Unlike laboratory studies that test participants under contrived circumstances (e.g. memorising lists of words to test memory), observational studies and their setup are more natural providing more ecologically valid results.
  • A weakness, however, is although researchers see and record behaviour in an observational study, they do not know why the behaviour happened. This then requires the researcher to make a judgement on its cause which may be riddled with bias or may simply be incorrect.
  • Participants or subjects may become aware of being observed and thus change their behaviour leading to researchers recording incorrect responses. Also, the researcher themselves may make a mistake recording the behaviour which can invalidate findings.
  • Observational studies also raise ethical issues particularly around informed consent as participants are usually not aware of being observed or part of a study. Informing them prior may lead to their behaviour altering when they are aware of being observed however not informing them raises ethical issues of privacy and lack of consent.

Categories of behaviour

In order to make sure that accurate records of behaviour are made, researchers use categories of behaviour systems.

If researchers wanted to observe “playground behaviour”, researchers would not necessarily know what they were looking for in this definition or what may be classified as “playground behaviour”. The observers would need to know what they are looking for to make accurate recordings and therefore behavioural categories are created to make it clear what behaviours are to be recorded.

Inter-observer reliability

When an observation study is conducted, observers record the number of times certain behaviours occur (usually in the form of a tally chart).

This record of the number of incidents for the different behaviours needs to be accurate and ensure that the observer is recording the correct behaviour within the correct categories.

In observation studies, observers may miss the behaviour and so accuracy of recording the behaviour becomes an issue as it cannot be seen again in live environments. A solution to this problem is to design a record sheet with the pre-defined suitable behaviour categories and then have two observers independently observe the targets at the same time and location . Each would then record what they see in their own individual sheets independently from the other.

At the end of the study, the observers may compare their record sheets to check for consistency . If the sheets have been recorded correctly, they should have matching or very similar recordings of their observations. If this occurs, they have established inter-observer reliability. If the record sheets are considered vastly different, this would mean the study lacks inter-observer reliability and the results lack validity as they are not measuring what they are supposed to measure accurately.

What is a Correlation?

For this section of Research Methods, we need to know about the following in relation to Correlations:

  • An understanding of association between two variables and the use of scatter diagrams to show possible correlational relationships.
  • The strengths and weaknesses of correlations.

A correlation is quite simply a relationship between two variables. There are 3 types of correlations which are:

  • Positive correlation,
  • Negative correlation
  • Zero correlation

With positive and negative correlations, the relationship is seen as a “cause and effect” relationship whereby one variable has a direct impact on the other . Correlations form part of a statistical technique to analyse and display the possible relationship between the two variables.

Let’s work through a few subjective examples for each: Let’s assume there is a correlation (relationship) between the two variables age and beauty. As people get older they may be seen to be more beautiful. This would be considered a positive correlation because both the variables increase together .

If however people disagreed and thought that as people age and get older, they are less beautiful, this would be a negative correlation. This is because as one variable increases, the other one decreases which in our case would be age increasing while beauty decreases.

The third way of looking at this is thinking that age has no effect on perceived beauty. As people get older you may think this has no bearing on a person’s beauty so the two variables would be seen as having zero correlation.

Below we have some examples of scattergrams that give you an idea of how each correlation would look if presented to you. You may sometimes be asked to draw a line of central tendency too within a correlation; all this means is you draw a line down the middle of all the correlations with equal amounts on either side of it.

Positive Correlations

Negative correlations, zero correlation, strengths and weaknesses of correlations.

  • Correlational research can be very useful as they allow a researcher to see if two variables are connected in some way. Once a relationship has been established between two variables, a researcher can then use an experiment to try and find the true cause of the correlation.
  • Correlational research can be used in situations where it may be unethical or impossible to carry out an experiment. For example, if we wanted to check for the relationship between smoking and cancer, this would be unethical to test (asking people to smoke to see if they develop cancer). However, plotting the rates of cancer developing in people who already smoke can help us establish links between these two variables. This knowledge can then be helpful in influencing future research.
  • A weakness of using correlations is although this type of tool can tell us if two variables are related, it does not tell us which of the two variables caused the relationship. It is also possible that there may be third unknown variables that lay in between and influence the two we measure in research which may be the actual cause.
  • For correlational research to be helpful, we first need to gather large amounts of data to establish the pattern in the scattergraph. This means researchers are required to make lots of measurements of both variables so that the patterns in the data can be reliably established. Using correlational research for small populations is not reliable so it can be very time-consuming establishing a large data set.

Research Procedures

What the GCSE Psychology specification says you need to learn for this section on Research Procedures:

  • Standardised procedures
  • Instructions to participants
  • Randomisation,
  • Allocation to conditions


  • Extraneous variables (including explaining the effect of extraneous variables and how to control for them).

Standardised Procedures

When conducting experiments, researchers need to ensure that standardised procedures are used.

Standardised procedures are a set of sequences that apply to all the participants when necessary to ensure the experiment is unbiased . Standardised procedures allow the researcher to try and control all the variables and events so the results of the experiment can be safely attributed to the independent variable.

Instructions to Participants

When standardising procedures, another issue researchers need to be mindful of is how instructions to participants are put across to make sure they know what to do but without biasing the study in any way. This can include verbal and written instructions.

Instructions can be interpreted in a way that can influence their performance and these can become extraneous variables. For example, if instructions were worded with leading questions, this may cause participants to answer in one particular way. If instructions are ambiguous, this can also affect the results of the study.

To address this issue, the usual practice is to write as much information as possible for participants and ensure they all receive this same information. This is usually done in sections as follows:

  • Briefings: this is where participants are encouraged to participate with a log of what is discussed to gain their consent. This can include ethical information about consent, anonymity, the right to withdraw etc.
  • Standardised instructions are given: these are clear instructions given to each participant explaining their role and what they need to do.
  • Debriefing: at the end of the study, participants are given a detailed explanation about the aims of it, what their role was and why they were given their tasks or roles. Ethical issues are also raised again with participants given the opportunity to withdraw their data/contributions if they feel unhappy about their performance or participation.


Randomisation simply means to make sure there are no biases in the procedures .

Let’s use our music and learning example again for a moment to highlight how randomisation may be implemented in a psychological study.

Participants are being tested on their ability to learn through the use of 20 random words they are presented with. All the words are considered to be of equal difficulty because they are everyday nouns with only six letters. The researcher has to decide which order they should be presented to each of the participants in the study however instead of the researcher determining the order, randomisation is used.

All 20 words are written down on a piece of paper and put into a hat. They are then randomly selected one after the other with their order being written down in which they have been selected. This order is then determined to be the order to which all participants will be exposed within the experiment.

Using randomisation, all the words had an equal chance of selection and now with an order established, all participants will be exposed to them in the same way. Randomisation can be implemented in a number of ways within an experiment to filter out biases and you may be given a question on how to best implement this or its benefits.

Another major issue researchers face, is how to allocate the participants to the experimental condition or control group.

To reduce researcher bias, two methods used are random allocation and counterbalancing .

Random Allocation

When the design of the study uses an independent group design, the researcher can use random allocation to avoid any potential researcher bias.   Participants can be randomly selected in turns for either condition A or condition B by pulling their name out of a hat for example.

A similar method can be employed if the design of the experiment is a matched pairs design. Participants can be randomly allocated to their pairs by them pulling out the letters for each pair from a hat e.g. the two people who pull out A+a from a hat form a pair, the same with B+b, C+c etc and so forth.

For experimental designs such as the repeated measures design, all the participants are required to take part in the experiment for both conditions. The problem with this is that order effects can occur whereby participants learn from experience and thus do better in all the following conditions after their initial one.

Counterbalancing helps balance out order effects by splitting the group of participants into two groups. One half will then complete condition 1 while the other half complete condition 2. 

After completing this, they swap and complete the opposite condition so those who completed condition 1, then move on to complete condition 2, those that completed condition 2, go on to complete condition 1.

Using counterbalancing does not get rid of order effects but allows for the effects of it to be balanced out equally between the two conditions for participants and thus providing more valid results.

Ethical considerations

For Ethical Considerations, the specification states you need to know the following:

  • Ethical issues in psychological research as outlined in the British Psychological Society guidelines
  • Ways of dealing with each of these issues.

This next section focuses on all the ethical considerations based on the British Psychological Society guidelines and ways in which each can be dealt.

Ethical issues arise when there are two conflicting points of view;

  • One is what the researcher needs to do in order to conduct a useful and meaningful study
  • The second is the rights of the participants which need to be considered .

Ethical issues are therefore all the conflicts that arise about what is acceptable to do as part of the research.

As part of your GCSE psychology course, you need to be able to highlight ethical concerns and generate ways in which to deal with them. You may also be given a scenario where you need to highlight the relevant concerns and comment on how to deal with them.

The Code of Ethics and Conduct (2009) and Code of Human Research Ethics (2014) from the British Psychological Society underpin the activities of all practising psychologists.

What Are The British Psychological Society Guidelines?

When research is conducted by any practising psychologist, The Code of Ethics and Conduct (2009) and Code of Human Research Ethics (2014) will underpin their work. 

The British Psychological Society (BPS) guidelines explain what is required:

  • Participants should be respected as individuals and unfair or prejudiced practices are to be avoided.
  • The data collected should also be confidential and anonymised so participants cannot be identified from the research.
  • Participants should have also given informed consent and know fully what they are consenting to. They should also be told at the beginning what the study is about prior to taking part.
  • Deception must be avoided although the BPS recognises that some studies are not possible without this to gather meaningful results. Any deceptions that do take place must be explained to participants as soon as possible once the study concludes.
  • They should also be aware of their right to withdraw from the study at any time.

Psychologists should maintain high standards in their professional work which includes:

  • Being aware of the code of conduct
  • Recognising that ethical dilemmas will inevitably arise and seeking to resolve them
  • They should only give advice if they are qualified to do so and not trying to do things that are beyond their competence.
  • Staying within the law if ethical principles conflict with the law but try to maintain the ethical principles as far as possible
  • Monitoring their own health and lifestyle to recognise times when they may be unable to carry out their work competently


Responsibility within the British Psychological Society (BPS) is generally about avoiding harm to clients, avoiding misconduct that would bring psychology into disrepute and looking out for other psychologists that may be breaching these guidelines.

The BPS states researchers should:

"Consider all research from the standpoint of research participants, for the purpose of eliminating potential risks to psychological well-being, physical health, personal values, or dignity"

This can be done by:

  • Ensuring researchers protect participants from physical and psychological harm.
  • Making sure the risk of physical or psychological harm is no greater than what one would expect from everyday life and their wellbeing should not be at risk.
  • At the end of the experiment, participants should be debriefed at the end of the investigation so they fully understand the true aim of the study. This would then allow them to make an informed decision about whether they wish to withdraw their results.

Informed Consent

Informed consent means revealing to the participant the real aims of the study or telling them what will happen within the study. This becomes an ethical issue because revealing the true aims or details may lead to the participants adjusting their behaviour which could lead to invalid results.

For example, if we wanted to study whether people are more likely to obey a male or female as part of research into obedience, revealing the aims of this study will almost certainly affect their behaviour and invalidate findings.

Researchers may therefore not always give out the full details of the study however this means participants can not give their full informed consent. From a participants point of view, they should be told what they are required to do in the study so they can make an informed decision about whether they wish to take part.

This became a basic human right that was established during the Nuremberg war trials after the second world war. During the war, Nazi doctors conducted various experiments on prisoners without their consent and the war trials afterwards decided that consent should become a basic human right for participants to be involved in a study.

Epstein and Lasagna found that only a third of participants volunteering for experiments really understood what they had agreed to take part in despite giving informed consent. This demonstrates that even if researchers sought to and obtained informed consent, this does not always guarantee that participants understand what they are involved in or doing.

How to deal with ethical issues of informed consent

  • Participants could be asked to formally indicate their agreement to take part based on information concerning the nature and purpose of the study and how their role fits in. 
  • Presumptive consent may also be gained; this can be done by asking a group of people whether they feel a planned study is acceptable and assume that the participants themselves would have felt the same if given the opportunity to say so.
  • Researchers can offer the right to withdraw at any stage of the study to participants so if at any stage they feel uncomfortable or do not wish to continue, they can exit the research.

Some experiments require deception about the true aims of research otherwise participants might alter their behaviour and the study’s findings become meaningless . A distinction could be made in some cases between withholding some details about the study (reasonably acceptable) compared to deliberately providing false information (less acceptable).

From the participant’s point of view, deception would be unethical and thus they should not be misled without good reason.

An issue with deception is it prevents participants from giving informed consent . Participants may agree to take part without fully knowing what they have agreed to and become quite distressed by the experience. Baumrind (1985) argued that deception was morally wrong based on three generally accepted ethical rules within western society: the right of informed consent, the obligation of researchers to protect the welfare of participants and the responsibility of the researcher to be trustworthy.

Others have argued that deception can be harmless in some studies i.e. testing memory, and deception may be necessary to gain meaningful insights that would not be otherwise possible.

How to deal with ethical issues of deception

  • The need for deception in research could be approved by an ethics committee which weighs up the potential benefits of the research, against the costs to participants.
  • Participants should be fully debriefed after the study and given the opportunity to request that their data is withheld.

The Right to Withdraw

Participants would deem the right to withdraw from an experiment as important. If a participant begins to feel distressed or uncomfortable, they should have the right to withdraw from the study. This becomes more important particularly if they have been deceived about the nature of the study or their role.

From a researchers point of view, participants being able to withdraw midway through a study could bias the results in some way when comparing the results of those that stayed.

Within some experiments, participants are offered financial payments for completing the study and withdrawing is compromised because they may not get paid and thus feel like they can not withdraw.


A researcher may find that maintaining confidentiality can be difficult as they wish to publish the findings. They may guarantee anonymity and withhold the participants’ names, but even then it may be evident for some who the participants are.

In some locations or communities which are remote or the population is low, naming even the geographical area can identify the individual. The Data Protection Act makes confidentiality a legal right and it is only acceptable for a person’s data to be recorded if it does not make it available in a form that can make the people identifiable.

To tackle this researchers should not record any names or personal details about the participants using numbers or fake names instead.

Privacy may be difficult to accomplish from a researchers point of view, particularly when studying participants without their awareness.

Participants may feel that they should not be expected to be observed or watched by others in some situations e.g. within the privacy of their own homes although not when in public areas such as a park.

To tackle this researchers should not observe anyone without their informed consent unless it is in a public place where this may be expected to some degree. Participants could also be asked to give their retrospective consent or withhold the data entirely.

Data Handling

What the GCSE Psychology specification says you need to learn for this section:

  • Quantitive and qualitative data
  • Primary and secondary data
  • Computation
  • Descriptive statistics
  • Interpretation and display of quantitative data
  • Normal distributions

There are two types of data research studies collect which are:

  • Quantitive data
  • Qualitative data

What is Quantitive data?

  • Primary data is data that has been collected firsthand from the source (participants) directly by researchers. The majority of data collected in psychological research will be primary data.
  • Secondary data is data that has been already published and simply used by researchers in their own work.

Strengths and Weaknesses of Quantitive Data

  • Quantitive data tends to be objective and easy to measure for researchers.
  • Precise measures are used,
  • The data is high in reliability and can be checked through replication.
  • The data can be more easily examined to check for patterns through the use of correlations and presented in the form of scattergrams.
  • Weaknesses of quantitive data include the possibility that meaningful details could be lost or lacking as researchers focus on a narrow set of responses or pre-defined questions people answer.

What is Qualitative Data?

Qualitative data is descriptive data that is non-numerical. This type of data provides detailed information which can provide insights into the thoughts and behaviours of individuals because the answers are not restricted to yes or no responses. For example, in an observational study, researchers may describe what they see and this would be deemed a form of qualitative data.

Qualitative data tends to be collected through the use of open questions (questions that begin with who, what, where, when, why or how) that encourage participants to explain themselves. This is done usually through questionnaires or unstructured interviews.

Qualitative data cannot be counted or quantified as easily although it can be placed into categories to count the frequency in which it is reported to occur. For example, we may be able to count how many times participants in Milgram’s study reported being stressed or worried.

However as the responses from participants can be completely subjective to them, the data can be incredibly varied based on their responses and difficult to quantify or generalise with any meaning. 

Strengths and Weaknesses of Qualitative Data

  • A major strength of qualitative data is it tends to be rich in detail.
  • Another strength is qualitative data can help researchers understand peoples attitudes, thoughts and beliefs which may better explain their behaviour rather than them having to guess.
  • A weakness of using qualitative data is it tends to be completely subjective.
  • Qualitative data tends to also be an imprecise measure that is difficult to quantify.
  • Another criticism of qualitative data is the difficulty in checking for reliability as participants all give subjective responses. This makes it difficult to generalise to other people.

What Is Primary and Secondary Data?

What is the mean, median, mode and range.

There are three types of averages that can be calculated from the raw data obtained from studies which allow researchers to identify patterns in the behaviour.

These three are:

  • The mean average
  • The median average

You can also work out the range although this is not an average.

The Mean Average

The Mean Average is calculated by adding together all the values in a set of scores and then dividing that number by the number of values in the set .

For example, if we wanted to work out the mean average for what Brad Pitts score would be on a beauty scale from us questioning 12 people, we would take their scores, add them up and then divide them by the number of people in the study (in our case, this would be 12).

Let’s work through an example assuming that the beauty score is out of 10:

So to work out the mean average we would need to add up all the scores all 12 people have given – this would then be:

  • 7 + 8 + 7 + 8 + 9 + 7 + 10 + 8 + 7 + 6 + 9 + 8 = 94
  • 94 is the total score of all 12 participants. We then divide this by 12 (the number of participants)
  • 94 divided by 12 = 7.83

Brad Pitt’s mean average score would be 7.83/10 (out of 10)

The Median is the middle value of a set of scores.

  • To calculate the median you must arrange all the values in order from the lowest to the highest .
  • Then you must find the middle value . If there isn’t an obvious middle value due to an even number set, you work out the midpoint of the of the two middle values.

Let’s work out the median value using the example above; we must first order the numbers from lowest to highest.

6, 7, 7, 7, 7 , 8, 8, 8, 8, 9, 9, 10

Having ordered the numbers we can see that the midpoint is 8 either side. Therefore the median in the example is 8.

The Mode is the most frequently occurring value in a set of scores.

Sometimes there may be no mode (if no number occurs more frequently than another) or there can be more than one mode.

Let’s work out the mode using the example above again by first writing down all the scores; we must first order the numbers from lowest to highest.

6, 7, 7, 7, 7 , 8, 8, 8, 8 , 9, 9, 10

We can see that the mode is 7 and 8 because they both appear the most which is 4 times.

The Range is the numerical difference between the highest and lowest set of scores.

So using our example above we can see that the highest score is 10 and the lowest score is 6.

We therefore minus 6 from 10 as follows:

The range is therefore 4.

Ratios, Fractions, Percentages

This section focuses on recognising and using expressions in decimal and standard form.

These include:

  • Fractions/decimals


A ratio is a way of comparing the amounts of something between each other and this is usually expressed in its simplest form.

If we had 15 boys and 12 girls in one class and we wanted to compare this as a ratio, this would be 15:12.

When we break this down into its simplest form this would be 5:4 because we can divide both sides by 3.

Fractions and Decimals

A fraction is a way of expressing a part of a whole number.

For example, if we had a group of 20 boys and 15 of those produced the action of running which we wished to express, the fraction would be 15/20 or 3/4 in its simplest form.

As a decimal, this may be expressed as 0.75 as the total or whole amount is always represented as 1. The number of boys that did not express running would, therefore, be 0.25

Percentages are a way of expressing a fraction of a hundred which is considered the full amount.

So 50/100 would be expressed as 50% (percent). This is sometimes used in psychology to express how often something happens e.g. running occurred 75% of the time.

So using the example before, if we had a group of 20 boys and 15 of them were seen to be running and we wanted to work out the percentage of this, we could calculate it in the following way:

  • 15 x 100 divided by 20 (total no. of people) = 75%.

So to rephrase:

  • 15 (boys) x 100 (the whole amount) divide by 20 (the total number of boys) = 75%

Bar charts are used to display data that is in categories.

Each bar represents a separate category with them labelled across the x-axis which is at the bottom (horizontal). The frequency or amount for each category is labelled on the y-axis which runs along the side (vertical). The bars drawn should not touch and be separated from one another.

Here’s a picture of the one we used earlier to measure the hypothetical study of beauty:

Histograms are used to present data that are continuous measurements such as test scores or even height.

The continuous scores are on the x-axis across the bottom and the frequency of these scores are on the y-axis. Histograms have no spaces between the bars (unlike bar charts) as the data is continuous.

Here’s an example below:


We’ve already looked at scattergrams when discussing correlations earlier.

Here is an example of a Scattergram showing a positive correlation below – notice how all the recording measure along an invisible line almost going diagonally across:

Normal Distributions

The normal distribution is the predicted distribution when considering an equally likely set of results.

On a graph, this shows as a bell-shaped curve encompassing the mean, median and mode .

For example, in an IQ test, most scores for the whole population would be around the mean average with decreasing scores away from this for those with lower IQ’s as well as higher.

In a normal distribution the mean, median and mode scores tend to be of very similar value when plotted to produce a distinctive curve. The curve shape is what we call the normal distribution curve.

Here is an example of a normal distribution curve below:

Leave a Reply Cancel reply

You must be logged in to post a comment.

Get Free Resources For Your School!

Welcome Back.

Don’t have an account? Create Now

Username or Email Address

Remember Me

Create a free account.

Already have an account? Login Here

  • Centre Services
  • Associate Extranet
  • All About Maths

Request blocked

This request has been blocked as part of the aqa security policy.

Your support ID is: 14772493161167302624

If you're seeing this message in error, call us on 0800 197 7162 (or +44 161 696 5995 outside the UK) quoting the support ID above.

Return to previous page



    aqa psychology research methods past paper questions

  2. New Spec (2017) AQA, GCSE Psychology (Paper 1) Research Methods

    aqa psychology research methods past paper questions

  3. AQA A-Level Psychology

    aqa psychology research methods past paper questions

  4. AQA Psychology Paper 1 Practice exam paper plus markscheme

    aqa psychology research methods past paper questions

  5. AQA Psychology A level Research Methods

    aqa psychology research methods past paper questions

  6. AQA_A-LEVEL PSYCHOLOGY. Exampro. MARK SCHEME. Practice Paper 1

    aqa psychology research methods past paper questions


  1. the 12 mark research method question

  2. Introduction to Psychology

  3. Variables

  4. AQA A-Level Psychology

  5. Ethics

  6. Aggression


  1. A-Level AQA Psychology Questions by Topic

    Past Papers Notes Videos Core Content 1. Social Influence 2. Memory 3. Attachment 4. Psychopathology 5. Approaches in Psychology 6. Biopsychology 7. Research Methods 8. Issues and Debates in Psychology Option 1 9. Relationships 10. Gender 11. Cognition and Development Option 2 12. Schizophrenia 13. Eating Behaviour 14.

  2. AQA A-Level Psychology Past Papers With Answers

    Updated on October 14, 2023 Reviewed by Olivia Guy-Evans, MSc AQA A-Level Psychology (7182) and AS-Level Psychology (7181) past exam papers and marking schemes. The past papers are free to download for you to use as practice for your exams. Paper 1: Introductory Topics Paper 2: Psychology in Context Paper 3: Issues and Options

  3. A Level Psychology Past Papers & Questions by Topic

    Past Papers OCR A Level Psychology Past Papers Common Questions What is Psychology A Level? How to revise for A Level Psychology? What do you learn in Psychology A Level? What can you do with Psychology A Level? How hard is Psychology A Level? What GCSEs do you need for Psychology A Level? Exam paper questions organised by topic and difficulty.

  4. AQA

    Assessment resources Page 1 2 3 Mark schemes Question papers Showing 46 results for research methods. Reset search Mark scheme (A-level): Paper 3 Issues and options in psychology - November 2021 Published 29 Jul 2022 | PDF | 356 KB Question paper (AS): Paper 2 Psychology in context - November 2020 Published 18 Jan 2022 | PDF | 558 KB

  5. AQA

    1 2 3 4 Showing 86 results Exampro: searchable past paper questions, topic tests, marks and examiner comments [exampro.co.uk] Promoted Published 3 Sep 2015 Answers and commentary (A-level): Paper 3 Issues and options in psychology - Sample set 4 New Published 18 Nov 2023 | PDF | 746 KB

  6. PDF Question paper (A-level) : Paper 2 Psychology in context

    TOTAL blank pages. If you need extra space for your answer(s), use the lined pages at the end of this book. Write the question number against your answer(s). Do all rough work in this book. Cross through any work you do not want to be marked. Information The marks for questions are shown in brackets. The maximum mark for this paper is 96.

  7. PDF Question paper (AS) : Paper 2 Psychology in context

    Materials For this paper you may use: a calculator. Instructions Use black ink or black ball-point pen. Fill in the boxes at the top of this page. Answer all questions. You must answer the questions in the spaces provided. Do not write outside the box around each page or on blank pages.

  8. Example Answers for Research Methods: A Level Psychology, Paper 2, June

    Section C - Research Methods: Q12 [1 Mark] C = 27%. Section C - Research Methods: Q13 [3 Marks] Pilot studies are small-scale prototypes of a study that are carried out in advance of the full research to find out if there are any problems with the methodology. This helps to ensure that time, effort and money are not wasted on a flawed ...

  9. A-level Psychology AQA Revision Notes

    Paper 1: A-Level Psychopathology Paper 2 Approaches Biopsychology Research Methods Paper 3: Compulsory Issues and Debates Option 1 Relationships Gender Cognitive Development Option 2 Schizophrenia Eating Behaviour Stress Option 3 Aggression Forensic Psychology Addiction past paper questions Help us complete the revision notes.

  10. A Level Psychology (AQA)

    A post-research interview designed to inform participants of the true nature of the study and to restore them to the state they were in at the start of the study. It may also gain useful feedback about the procedures used in the study. It is a means with dealing with ethical issues. Concern questions of right and wrong.

  11. AQA A-Level Psychology Past Papers

    AQA A-Level Psychology (7182) and AS-Level Psychology (7181) past exam papers and marking schemes, the past papers are free to download for you to use as practice for your exams. Skip to main content. Search form ... June 2022 - AQA A-Level Psychology (7182) Past Papers . A-Level Psychology Paper 1: Introductory Topics in Psychology (7182/1)

  12. AQA

    Practice questions (1) Question papers Component Paper 1 (19) Paper 2 (18) Paper 3 (13) Exam series June 2022 (15) November 2020 (15) November 2021 (7) Sample set 1 (7) Sample set 2 (6) Qualification A-level (39) AS (12) Modified papers Modified A4 18pt (13) Standard (13) Modified A3 36pt (11) Page 1 2

  13. AQA

    Showing 32 results Answers and commentary: Paper 1 Cognition and behaviour - Sample set 2 New Published 18 Oct 2023 | PDF | 1.9 MB Answers and commentary: Paper 2 Social context and behaviour - Sample set 2 New Published 18 Oct 2023 | PDF | 580 KB Examiner report: Paper 2 Social context and behaviour - June 2022

  14. Research Methods: MCQ Revision Test 1 for AQA A Level Psychology

    AS, A-Level. Board: AQA. Last updated 12 May 2019. This 15-question revision video will help you test our knowledge and understanding of Research Methods in the AQA A Level Psychology specification. Research Methods: MCQ Revision Test 1 for AQA A Level Psychology.

  15. AQA

    Availability of past papers. Most past papers and mark schemes will be available on our website for a period of three years. This is due to copyright restrictions. Find out when we publish question papers and mark schemes on our website. Search past paper question banks and easily create custom material for teaching, homework and assessment.

  16. A Level Psychology Topic Quiz

    AQA. Last updated 5 May 2017. Here is an overall topic quiz on research methods as featured in the AQA A Level Psychology specification. Each time you take this quiz you will get 10 MCQs drawn at random from over 100 questions relevant to research methods. Try the first ten, see how you get on, and then try again with 10 different questions!

  17. AQA

    © AQA 2023 | AQA is not responsible for the content of external sites

  18. AQA A-Level Psychology Research Methods Practice Questions

    AQA Psychology for A Level Year 2. These are Practice Questions for the Research Methods Topic of AQA A-Level Psychology. I will also be uploading the other topics and creating bundles. Topics Included: - Experimental Method - Control of Variables/Research Issues - Experimental Design - Types of Experiment - Sampling - Ethi...

  19. Research Methods

    Overview - Research Methods. Research methods are how psychologists and scientists come up with and test their theories. The A level psychology syllabus covers several different types of studies and experiments used in psychology as well as how these studies are conducted and reported:. Types of psychological studies (including experiments, observations, self-reporting, and case studies)

  20. AQA GCSE Psychology Research Methods Revision

    GCSE AQA Psychology Research Methods Table of Contents Primary Item (H2) Sub Item 1 (H3) Sub Item 2 (H4) Sub Item 3 (H5) Grade 9 Resources used by hundreds of schools and thousands students. Everything You Need, Fully Covered The best investment you'll make.

  21. AQA

    Assessment resources. Showing 46 results for research methods. Reset search. Question paper (AS): Paper 2 Psychology in context - June 2022. Question paper (Modified A4 18pt) (AS): Paper 2 Psychology in context - June 2022. Question paper (Modified A3 36pt) (AS): Paper 2 Psychology in context - June 2022.

  22. AQA A Level Psychology Topic Questions 2017

    Past paper and exam-style questions organised by topic, with student-friendly answers written by teachers and examiners. View PDF List 1. Social Influence 1.1 Conformity 1.2 Obedience 1.3 Explanation of Resistance to Social Influence 1.4 Minority Influence 2. Memory 2.1 The Multi-Store Model of Memory 2.2 Working Memory Model 2.3 Forgetting

  23. Research Methods

    Model Answers 1 2 marks A psychologist was interested in finding out whether dream themes differed between males and females, particularly in terms of social interaction. She decided to conduct a pilot study. Twenty undergraduate students (8 male and 12 female) volunteered for the study.