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    Scientific processes — AQA A-Level Psychology

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    Scientific processes explained

    An aim is a broad statement of purpose: what the researcher intends to investigate and why, usually drawn from an existing theory or from earlier findings.

    Read the full explanation

    A hypothesis is the precise, testable prediction that comes out of that purpose, written before any data is collected and phrased so that results could show it to be wrong. The purpose might be to investigate whether background noise disrupts memory; the prediction is that people who learn a list of 20 words in silence recall more of them than people who learn with music playing. Marks go missing when the purpose is written as a prediction, or when the prediction names variables without saying how they were manipulated and measured. Examiners want both conditions of the IV in the sentence and the DV in something countable.

    Hypotheses: directional and non-directional.

    A prediction is directional, or one-tailed, when it says which way the results will fall: participants recall more words in silence than with music. It is non-directional, or two-tailed, when it predicts a difference or a relationship but not which group does better. The choice is not free. Where previous research points consistently one way, a direction is justified; where findings are mixed, contradictory, or the area is new, leave the direction out, because a one-tailed prediction that goes the wrong way cannot be supported however large the effect. Alongside sits the null hypothesis, which states there is no difference beyond chance, and it is the null a statistical test allows you to reject at the 0.05 level. Either type must name both conditions and an operationalised DV.

    Sampling: the difference between population and sample; sampling techniques including: random, systematic, stratified, opportunity and volunteer; implications of sampling techniques, including bias and generalisation.

    The target population is the whole group findings are meant to describe; the sample is the handful actually tested, and each technique trades representativeness against effort. Random sampling gives everyone an equal chance, usually by numbering a complete list and drawing lots or using a random number generator, so it is unbiased, but it needs that full list and can still produce an odd sample by chance. Systematic takes every nth name from a sampling frame. Stratified identifies subgroups and samples each in proportion to the population, making it the most representative. Opportunity takes whoever happens to be available and volunteer relies on people answering an advert, as Milgram's newspaper advert did: both are quick, both attract an atypical group, and generalisation suffers.

    Pilot studies and the aims of piloting.

    A trial run is a small-scale version of the whole procedure, carried out on a few participants before the real investigation, and its point is to find problems while they are still cheap to fix. It checks that instructions are understood, that the task is neither so easy that everyone is at ceiling nor so hard that nobody scores, that the timings work, and that the manipulation of the IV actually produces a change. With a questionnaire it exposes ambiguous, leading or intrusive items; in an observation it tests whether the behavioural categories can be used consistently by two observers. It also saves money and is an ethical gain, because fewer people are put through a flawed design. The data collected are not reported as results and the hypothesis is not tested on them.

    Experimental designs: repeated measures, independent groups, matched pairs.

    With repeated measures the same people take part in every condition, so participant variables are held constant and half as many people are needed. The price is order effects such as practice, fatigue and boredom, which counterbalancing addresses, and a higher risk of demand characteristics. In an independent groups design a different set of people does each condition: no order effects, but individual differences now sit between the groups and could produce the result on their own, so allocation is randomised and twice as many participants are recruited. A matched pairs design tests different people who have been paired on variables relevant to the DV, such as age or IQ, splitting each pair across conditions, which takes time and is never perfect outside identical twins.

    Observational design: behavioural categories; event sampling; time sampling.

    Behavioural categories turn a vague construct into a checklist an observer can tick without interpreting anything: aggression becomes pushing, kicking, biting and shouting, with every category observable, clearly defined and not overlapping. Event sampling counts each occurrence of the target behaviour across the whole session, which suits behaviour that happens rarely but is hard to keep up with when several things happen at once. Time sampling records only what is happening at fixed intervals, for example every 15 seconds, as Ainsworth did in the Strange Situation: it makes the workload manageable, but the sampled moments may misrepresent everything in between. Two observers coding the same session allow inter-observer reliability to be calculated and agreement checked.

    Questionnaire construction, including use of open and closed questions; design of interviews.

    Closed questions fix the response options, such as yes or no, a rating or a Likert scale, and produce numbers that are quick to analyse and easy to compare, but they force people into a box and say nothing about why. Open questions invite a free answer, which is richer and less researcher-led, though the replies then need qualitative analysis or coding through content analysis. Well-built items are short, unambiguous and free of jargon, leading wording, double-barrelled questions and emotive language, and fillers can disguise the aim. Interviews sit on the same axis: a structured interview follows a fixed schedule in a fixed order, so it is replicable but cannot follow anything up; an unstructured interview is flexible and detailed but vulnerable to interviewer bias; a semi-structured one does both.

    Variables: manipulation and control of variables, including independent, dependent, extraneous, confounding; operationalisation of variables.

    The IV is what the researcher changes, the DV what is measured, and both must be operationalised, which means written as the exact procedure used: memory cannot be measured, but the number of words correctly recalled from a list of 20 within two minutes can. An extraneous variable is any other factor that could affect the DV, such as noise, time of day, or differences between participants; these are nuisances, and standardisation and randomisation keep them quiet. A variable becomes confounding only when it varies systematically with the IV, so its effect cannot be separated from the IV's: if everyone in the silent condition was tested first thing and everyone in the noisy condition after lunch, the study cannot say which produced the result.

    Control: random allocation and counterbalancing, randomisation and standardisation.

    These four control techniques are distinct. Random allocation decides by chance which condition each person enters, spreading individual differences evenly across independent groups. Counterbalancing tackles order effects in a repeated measures design: half do condition A then B, half do B then A. This shares practice or fatigue equally between conditions rather than removing them. Randomisation uses chance in designing materials, such as presenting a word list in a different order for every participant, preventing researcher bias. Standardisation makes the procedure and instructions identical for everyone, stopping the procedure acting as an extraneous variable and allowing replication.

    Demand characteristics and investigator effects.

    Participants are not passive. Cues in a study, from the consent form to the task itself, let them guess what is expected and act on the guess; these cues are demand characteristics. Some help by producing the result they think is wanted, others deliberately do the opposite. Orne showed participants working at pointless tasks simply because the setting demanded it. The researcher creates investigator effects through leading questions, tone, appearance and expectancy effects, changing the DV without the IV moving. A single blind hides the condition and aim from the participant to reduce demand characteristics, while a double blind hides them from the researcher too.

    Ethics, including the role of the British Psychological Society’s code of ethics; ethical issues in the design and conduct of psychological studies; dealing with ethical issues in research.

    The British Psychological Society's Code of Ethics rests on four principles: respect, competence, responsibility and integrity. From these stem key ethical issues in study design: informed consent, deception, protection from harm, privacy, confidentiality, and the right to withdraw. Dealing with ethical issues requires specific remedies. Consent is gained via a form detailing the study, or through presumptive consent if full disclosure would ruin it. Deception and distress are addressed by a full debrief, offering to withdraw data, and counselling. Confidentiality uses numbers instead of names. An ethics committee weighs likely benefits against costs before approving research.

    The role of peer review in the scientific process.

    Before a study appears in a journal, or a grant is awarded, other experts in the same field read it anonymously and recommend acceptance, revision or rejection. They judge originality and importance, whether the method and the statistics really support the conclusions, and whether anything has been overstated. Three functions are worth naming: allocating research funding, validating the quality of what gets published, and improving the work through suggested amendments. The evaluation is the more valuable half. Anonymity can shelter a reviewer settling a rivalry; journals prefer positive, novel results, so replications and null findings go unpublished, which is the file drawer problem; and established reviewers may suppress work that threatens the theory their own careers rest on, slowing any paradigm shift.

    The implications of psychological research for the economy.

    The economy here means the money and working time a country gains or loses, so a creditworthy answer runs from a finding, through a change in behaviour or policy, to a cost. Attachment research showing that fathers can be primary caregivers underpins shared parental leave, which keeps both parents in the workforce rather than one leaving it. Absence caused by mental illness costs employers billions a year, so evidence that CBT and drug therapy shorten episodes of depression returns people to work sooner and reduces sick pay. Work on circadian rhythms explains why errors cluster on night shifts, as at Chernobyl, and lets employers build rotas that avoid expensive accidents. Weak answers describe the psychology in detail and never arrive at the money, which is where the marks are.

    Reliability across all methods of investigation. Ways of assessing reliability: test-retest and inter-observer; improving reliability.

    Consistency is the whole idea: measure the same thing twice and the answer should come out the same. The test-retest method gives the same questionnaire, test or interview to the same people after a gap long enough that they cannot recall their previous answers but short enough that nothing has genuinely changed, then correlates the two sets of scores; a coefficient of 0.80 or above is the accepted threshold. Agreement between observers is checked by having two people watch the same behaviour using the same categories and correlating their tallies against that same figure. Improvement is practical work: operationalise categories so nothing is left to judgement, train observers together beforehand, rewrite ambiguous or complex items, and keep the interviewer and the schedule constant.

    Types of validity across all methods of investigation: face validity, concurrent validity, ecological validity and temporal validity. Assessment of validity. Improving validity.

    Validity asks whether a measure captures what it claims and whether findings are legitimate inside the study and beyond it. Face validity is the eyeball test, that a measure looks as though it taps the construct, sometimes judged by an expert. Concurrent validity is checked by giving the new measure and a well-established one to the same people and correlating the results, again looking for 0.80 or above. Ecological validity concerns generalising to other settings and everyday life, and depends on the mundane realism of the task rather than simply on whether the room was a laboratory. Temporal validity asks whether a finding still holds: Asch's conformity work is called a child of its time, whereas Burger's replication of Milgram in 2009 found obedience close to the original.

    Features of science: objectivity and the empirical method; replicability and falsifiability; theory construction and hypothesis testing; paradigms and paradigm shifts.

    Objectivity means keeping expectations out of data using standardised, blind designs. The empirical method insists claims rest on direct observation. Replicability requires methods detailed enough to repeat, establishing reliability. Popper's falsifiability means a theory earns scientific status by making predictions that could be proven false. Theory construction involves gathering empirical evidence to build a framework explaining phenomena. This enables hypothesis testing, generating specific, testable predictions to see if the theory holds or needs modification. Kuhn added the paradigm, the shared assumptions of a discipline; psychology's lack of a single one makes it a pre-science, and accumulated anomalies force a paradigm shift.

    Reporting psychological investigations. Sections of a scientific report: abstract, introduction, method, results, discussion and referencing.

    A report follows a fixed order because readers use it in a fixed way. The abstract is a short summary of the aim, hypothesis, method, results and conclusion, read first to decide whether the paper is worth reading at all. The introduction reviews relevant theory and studies, narrowing from the broad area to this particular aim and prediction. The method is written so that someone else could repeat the study exactly: design, participants and sampling, apparatus, procedure including the standardised instructions, and ethics. Results give descriptive statistics first, then the inferential test, its calculated and critical values, the significance level and the decision on the null hypothesis. The discussion interprets the findings, admits limitations and suggests further work; references credit every source.

    Read the Scientific processes study guideFull revision notes for AQA A-Level Psychology

    Your focus

    1. Distinguish an aim from a hypothesis by what each states and by which one carries operationalised variables.
    2. Write an aim and a matching hypothesis for a given study, naming both IV conditions and a countable DV.
    3. Identify what is missing from a poorly written aim or hypothesis and state the correction it needs.
    Show all 51 objectives
    1. State what a directional hypothesis adds to a non-directional one and where the null hypothesis sits alongside both.
    2. Write a directional and a non-directional version of the same prediction with both IV levels and an operationalised DV.
    3. Justify choosing a direction or leaving it out by whether previous findings on the topic are consistent or mixed.
    4. Describe how random, systematic, stratified, opportunity and volunteer samples are each actually drawn.
    5. Suggest how a named technique would be carried out for a stated target population and say what the sampling frame is.
    6. Evaluate a chosen technique for the bias it introduces and what that does to generalising back to the population.
    7. Outline what a pilot study is and state that its data are not reported as results or used to test the hypothesis.
    8. Suggest what a pilot would check in a given study, such as instructions, timings, ceiling effects or the IV manipulation.
    9. Explain the economic and ethical gain of finding a design fault before a full sample has been put through it.
    10. Outline who takes part in which condition under repeated measures, independent groups and matched pairs.
    11. Identify the design used in a described study and state the control it needs, counterbalancing or random allocation.
    12. Justify choosing one design over another for a given study by naming the variable each trade-off protects.
    13. Describe what makes a behavioural category usable, being observable, clearly defined and not overlapping another.
    14. Suggest behavioural categories and an event or time sampling method for a stated behaviour, giving the interval.
    15. Evaluate event against time sampling by what each one misses and how both affect inter-observer agreement.
    16. Describe what closed and open questions each yield and how structured, semi-structured and unstructured interviews differ.
    17. Write questionnaire items and an interview schedule for a stated aim, avoiding leading, jargon and double-barrelled wording.
    18. Evaluate a draft questionnaire by identifying the items that would bias the answers and saying how to rewrite them.
    19. Explain the difference between an extraneous and a confounding variable in terms of whether it varies with the IV.
    20. Identify the IV with both its levels and the operationalised DV from a described study, with units where possible.
    21. Suggest a confounding variable in a given procedure and explain why it makes the result impossible to interpret.
    22. Describe random allocation, counterbalancing, randomisation and standardisation as four separate procedures.
    23. Apply the right control to a stated problem, counterbalancing for order effects and random allocation for participant variables.
    24. Explain why counterbalancing shares order effects between the conditions rather than removing them.
    25. Explain how a cue that reveals the aim, and the researcher's own expectancy, can change the DV without the IV moving.
    26. Identify the cue in a described procedure that would reveal the aim and suggest a single or double blind remedy.
    27. Evaluate what these effects do to the internal validity of a study and how far standardised instructions repair it.
    28. Outline the BPS principles and name the issues of consent, deception, harm, privacy and the right to withdraw.
    29. Identify the ethical issue raised by a described study and state the specific remedy that it calls for.
    30. Discuss whether a study such as Milgram's would pass an ethics committee by weighing its cost against its benefit.
    31. Describe how peer review works and name its three functions of allocating funding, validating quality and improving the work.
    32. Explain what a reviewer checks, from originality to whether the statistics actually support the conclusion drawn.
    33. Evaluate peer review using anonymity, rivalry and the file drawer burial of replications and null results.
    34. Explain what economic implications mean in terms of working time, sick pay and money gained or lost.
    35. Apply a named area such as attachment, therapy for depression or circadian rhythms to a stated cost or saving.
    36. Evaluate how firm the chain is from a psychological finding to the figure that is claimed to be saved.
    37. Describe test-retest and inter-observer reliability and state the correlation of 0.80 treated as acceptable.
    38. Suggest how to raise reliability in a given study, by training observers, tightening categories or rewriting items.
    39. Evaluate a reported coefficient such as 0.62 against the accepted threshold and say what the researcher should change.
    40. Describe face, concurrent, ecological and temporal validity by what each one checks or generalises to.
    41. Explain how concurrent validity is assessed by correlating a new measure against an established one at the same time.
    42. Discuss whether a study such as Asch's still holds today, using temporal validity and Burger's replication.
    43. Outline objectivity, the empirical method, replicability and falsifiability as the demands a scientific claim must meet.
    44. Explain the cycle of theory construction and hypothesis testing in developing scientific explanations.
    45. Discuss, using Kuhn, whether psychology's lack of a shared paradigm leaves it a pre-science.
    46. Outline the six sections of a scientific report in order and state what each one contains.
    47. Identify which section a given piece of information belongs in, such as a critical value or a limitation.
    48. Explain why the method is written in subsections detailed enough for another researcher to repeat the study.

    Scientific processes exam tips

    Marking Points
    • Identifies an aim as a general statement of what is being investigated and why, developed from theory or previous research.
    • Identifies a hypothesis as a precise, testable and falsifiable prediction written before data collection.
    • States that a hypothesis contains operationalised variables and names both conditions, whereas an aim does not.
    • Gives a worked contrast, for example investigating the effect of noise on recall versus predicting that recall of a word list is higher in silence.
    • Notes that the aim comes first and the hypothesis is derived from it.
    • States that a directional hypothesis predicts the direction of the difference or correlation, using words such as more, less, faster or positive.
    • States that a non-directional hypothesis predicts a difference or relationship without specifying which way.
    • Justifies the choice by whether previous research is consistent, or mixed and contradictory.
    • Writes the hypothesis with both levels of the IV named and the DV operationalised.
    • Links the wording to the null hypothesis and to the decision made after a statistical test.
    • Distinguishes the target population from the sample drawn from it.
    • Describes random sampling as every member having an equal chance, with a named method such as lots or a random number generator.
    • Describes systematic as every nth member of a sampling frame, and stratified as proportional representation of identified subgroups.
    • Describes opportunity as using those available and volunteer as self-selection in response to an advert.
    • Evaluates each for bias and for the generalisability of findings, for example volunteer bias producing unusually helpful or curious participants.
    • Defines it as a small-scale trial of the procedure on a few participants before the main study.
    • States a purpose: checking instructions, materials, timings or the effectiveness of the IV manipulation.
    • Explains that problems are identified and the design modified before resources are committed.
    • Applies it to a named method, for example refining questionnaire items or trialling behavioural categories for observer agreement.
    • Notes the economic and ethical benefit of not exposing a full sample to a flawed design.
    • Describes each design in terms of who does which condition.
    • Gives the strength of repeated measures as control of participant variables and fewer participants needed.
    • Gives the limitation of repeated measures as order effects, with counterbalancing named as the control, and demand characteristics.
    • Gives the independent groups trade-off: no order effects, but participant variables between groups, controlled by random allocation.
    • Describes matched pairs as pairing on a relevant variable, combining the benefits, but time-consuming and imperfect.
    • Defines behavioural categories as a breakdown of the target behaviour into observable, operationalised items.
    • States that categories must be objective, mutually exclusive and cover all relevant behaviour, so no inference is needed.
    • Describes event sampling as counting every occurrence throughout the observation.
    • Describes time sampling as recording behaviour at fixed intervals, with a stated interval.
    • Evaluates the sampling methods for missed behaviour and unrepresentative intervals, and links coding to inter-observer reliability.
    • Distinguishes closed questions, with fixed options producing quantitative data, from open questions producing qualitative data.
    • Gives the trade-off: ease of analysis and comparison versus depth and detail.
    • Names design principles: clarity, avoiding leading, double-barrelled, jargon-heavy and emotive items, and using filler questions.
    • Describes structured, unstructured and semi-structured interviews by how far the schedule is fixed.
    • Covers practical design points for interviews, such as a schedule, recording, a quiet setting and reminding the interviewee of confidentiality.
    • Identifies the IV as manipulated, or naturally occurring in a quasi-experiment, and the DV as measured.
    • States both levels of the IV when identifying it from a scenario.
    • Operationalises variables in terms of how they are manipulated and measured, with units where possible.
    • Defines an extraneous variable as any variable other than the IV that may affect the DV.
    • Defines a confounding variable as one that varies with the IV, making the cause of the change impossible to establish.
    • Defines random allocation and links it to distributing participant variables across independent groups.
    • Defines counterbalancing as half the participants doing the conditions in each order, and states it balances rather than removes order effects.
    • Defines randomisation as using chance in the ordering or presentation of materials to remove researcher bias.
    • Defines standardisation as an identical procedure and instructions for all participants.
    • Links each control to what it protects: internal validity, and the replicability of the study.
    • Defines the participant effect as cues that reveal the aim, leading to a change in behaviour.
    • Distinguishes those who try to please the researcher from those who deliberately underperform.
    • Defines the researcher effect as any influence of the investigator, other than the IV, on the DV.
    • Gives examples such as leading questions, tone of voice, appearance or expectancy effects.
    • Names controls: single blind, double blind, standardised instructions and a cover story, and links them to internal validity.
    • Names the code and at least one of its principles of respect, competence, responsibility and integrity.
    • Identifies the specific ethical issue raised by the study in the question.
    • Describes the matching way of dealing with it, such as informed consent forms, presumptive consent, debriefing, or the right to withdraw.
    • Explains confidentiality and anonymity in practical terms, for example identifying participants by number.
    • Evaluates using a cost-benefit judgement, referring to ethics committees or a named study such as Milgram's.
    • Describes the process as independent assessment by experts in the same field before publication or funding.
    • States the criteria assessed: originality, significance, methodological quality and whether conclusions follow from the data.
    • Names the functions of allocating funding, validating research quality and improving the manuscript.
    • Evaluates using anonymity and the possibility of bias or rivalry.
    • Evaluates using publication bias towards positive findings and the burying of replications and null results.
    • States that economic implications concern the money and productivity gained or lost by employers and the state.
    • Identifies a relevant area of research, such as attachment, treatments for mental illness or biological rhythms.
    • Explains the change in practice or policy that the research supports.
    • Makes the financial consequence explicit, for example reduced absenteeism, lower treatment costs or fewer accidents.
    • Evaluates the link, noting that the chain from finding to saving is often indirect or hard to quantify.
    • Defines reliability as consistency of measurement across occasions or across observers.
    • Describes test-retest as repeating the same measure with the same people after a suitable interval and correlating the results.
    • Describes inter-observer reliability as two observers coding the same behaviour and correlating their records.
    • States the accepted correlation threshold of 0.80 or above.
    • Suggests improvements matched to the method, such as operationalising categories, training observers, rewriting items or standardising the interview.
    • Defines validity as whether a measure or a study measures what it claims to measure.
    • Defines face validity as a judgement, by eye or by an expert, that the measure appears appropriate.
    • Defines concurrent validity as correlating a new measure with an established one administered at the same time.
    • Defines ecological validity as generalisation to other settings and everyday life, and temporal validity as generalisation over time.
    • Suggests improvements such as control groups, standardisation, blind procedures, anonymity and lie scales.
    • Defines objectivity as freedom from personal bias, linked to standardised and blind procedures.
    • Defines the empirical method as gathering evidence through direct observation and measurement.
    • Explains replicability as a method detailed enough to be repeated, and why repetition matters for reliability.
    • Explains falsifiability, crediting Popper, with an example of a theory that cannot be falsified.
    • Explains theory construction as building explanations from empirical evidence, and hypothesis testing as generating testable predictions from theories.
    • Explains paradigms and paradigm shifts, crediting Kuhn, and applies the pre-science argument to psychology.
    • Lists the sections in order: abstract, introduction, method, results, discussion, references.
    • Describes the abstract as a concise summary of the whole study, typically a short paragraph.
    • Describes the introduction as a funnel from general theory and past research to the aim and hypothesis.
    • Describes the method in named subsections and states that it must allow replication.
    • Describes results as descriptive then inferential statistics, and the discussion as interpretation, limitations and implications.
    Examiner Tips
    • 💡Start an aim with 'to investigate' or 'to examine'; start a hypothesis with 'participants who...' so the two can never be confused on the page.
    • 💡If a scenario is given, lift its own materials and measures into your answer, because credit for operationalisation depends on using that scenario's DV.
    • 💡Check that a hypothesis contains both a clear prediction and operationalised variables that could actually be measured.
    • 💡Read the stem for the phrase 'previous research has found', which is the examiner signalling that a direction is expected.
    • 💡For correlations, write 'there will be a positive correlation between X and Y', with both co-variables measured, not manipulated.
    • 💡Keep it to one sentence in the future tense; a paragraph of reasoning earns nothing here.
    • 💡For 'how would you select the sample' questions, describe the actual procedure step by step, since credit is for the method, not the name.
    • 💡Apply the technique to the stem's population: naming the sampling frame in a school or a workplace turns a generic answer into an applied one.
    • 💡Pair every strength with the limitation that matters for generalisation, because these questions are commonly AO3-weighted.
    • 💡Always say what specifically would be trialled in that scenario and what change could follow from it, as marks come in pairs of problem and fix.
    • 💡Mention the modification explicitly: 'the researcher could then reword the item' scores where 'to check the questionnaire' does not.
    • 💡Use it as an improvement point in evaluation questions about a flawed study design.
    • 💡Justification questions want the reason tied to the stem: if the task cannot be done twice, say so and choose independent groups.
    • 💡Name the specific control for the weakness you raise, because the mark is usually for the remedy.
    • 💡In a design question, state the design, then the conditions, then how participants are allocated.
    • 💡When asked for behavioural categories, give three or four concrete actions in the scenario's own context, not a definition of the term.
    • 💡Justify the sampling method by how often the behaviour occurs: event sampling for rare behaviour, time sampling for continuous behaviour.
    • 💡Have a reliability point ready, since observation questions very often end with assessing or improving it.
    • 💡If asked to write questions, write them out in full and label them open or closed; a description of the type earns no credit.
    • 💡Justify each question against the stem's aim, because application marks depend on the question actually measuring that variable.
    • 💡For interviews, state the type first, then one procedural detail, then one strength tied to the data it yields.
    • 💡When identifying variables from a stem, quote the scenario's own wording so the operationalisation is unarguable.
    • 💡Write the IV as 'whether participants did A or B' to force both conditions into the answer.
    • 💡For a confounding variable, say explicitly that it differs between the conditions; that clause is what separates it from an extraneous variable.
    • 💡State the mechanism in the scenario, for example naming the two orders used in counterbalancing, rather than just the term.
    • 💡Always explain what each control prevents, such as stating that random allocation reduces the impact of participant variables.
    • 💡If asked how to improve a described study, choose the control matched to the specific flaw rather than listing all four.
    • 💡Pin the effect to the DV: explicitly state that scores change for a reason other than the IV, which compromises internal validity.
    • 💡Use the scenario's own cue, for example the researcher watching and taking notes, rather than an abstract definition.
    • 💡When suggesting a control, say who is kept ignorant of what, because that detail distinguishes single from double blind.
    • 💡When asked how to deal with an ethical issue, provide a concrete procedure: detail exactly what is written on the consent form or what is said in the debrief.
    • 💡Anchor the answer to the stem's participants, especially where children or vulnerable groups need parental consent.
    • 💡Keep a cost-benefit sentence in reserve, because these questions are often part of a wider evaluation.
    • 💡An evaluation question wants both the purpose and the flaws, so plan two of each before writing.
    • 💡Link it to features of science, particularly objectivity and replicability, to lift an answer into the top band.
    • 💡Use the file drawer problem by name; the specific term is what distinguishes a top-band point from 'some studies are not published'.
    • 💡Write the chain in three steps, finding, change in practice, financial consequence, and make each visible in its own clause.
    • 💡One developed example outscores three undeveloped ones in these questions.
    • 💡Have an evaluative sentence ready about how hard it is to isolate the economic impact of a single study.
    • 💡Improvement questions want the fix tied to the method named in the stem, not a list of everything you know.
    • 💡Say the word correlate and quote the threshold; both are specific credit points.
    • 💡Distinguish reliability from validity in one clause early on, because examiners penalise answers that blur them.
    • 💡Name the type of validity the question is about and keep to it, because writing about all four scatters the marks.
    • 💡Use a dated study for temporal validity, and say what changed socially to make it dated.
    • 💡For improvement questions, state the technique and the specific threat it removes.
    • 💡Questions on this topic often require you to apply the features of science to a specific psychological approach or study, so be prepared to link concepts like falsifiability to concrete examples.
    • 💡Credit Popper and Kuhn by name; attribution is frequently a discrete mark.
    • 💡Use the cognitive revolution, or behaviourism's fall, as the worked example of a paradigm shift.
    • 💡If asked what goes in a section, give two or three specific contents rather than naming the section again.
    • 💡Use replication as the reason the method is detailed; that justification is regularly worth a mark.
    • 💡Where the stem provides data, say which statistics belong in results and which interpretation belongs in the discussion.
    Common Mistakes
    • Writing the aim as a prediction, for example 'noise will reduce recall', which is a hypothesis and scores nothing as an aim.
    • Writing the hypothesis with unoperationalised terms such as 'memory' or 'intelligence' instead of the score actually recorded.
    • Naming only one condition of the IV, so there is nothing for the predicted difference to be measured against.
    • Using 'to see if' phrasing and then giving no variables at all.
    • Calling a hypothesis directional when it only says one variable 'affects' or 'influences' another, which states no direction.
    • Predicting a difference and then, in the same sentence, saying which group is higher, producing a muddled hybrid.
    • Choosing a direction on a hunch when the stem says previous findings are inconsistent.
    • Confusing a correlational hypothesis, which needs a positive or negative relationship, with an experimental one predicting a difference.
    • Describing opportunity sampling as random because the researcher 'just picked anyone', when the researcher's choice is the bias.
    • Claiming random sampling guarantees a representative sample; it removes systematic bias but not chance variation.
    • Confusing stratified with systematic, or forgetting that strata must be sampled in proportion to the population.
    • Evaluating with generic phrases such as 'it is not representative' without saying who is left out and what that does to generalisation.
    • Saying it is done 'to check the results are right', when it tests the procedure rather than the hypothesis.
    • Describing it as a small study whose data are combined with the main findings.
    • Giving only the vague purpose 'to see if it works' with no specific feature being trialled.
    • Forgetting to link it to the method in the scenario, so the answer stays generic.
    • Saying matched pairs uses the same participants twice; it uses different people who have been paired.
    • Claiming counterbalancing removes order effects rather than balancing them across conditions.
    • Claiming counterbalancing controls demand characteristics; it only addresses order effects.
    • Listing textbook strengths that do not fit the scenario, for example recommending repeated measures when the task cannot be repeated.
    • Confusing random allocation, which assigns people to conditions, with random sampling, which selects them in the first place.
    • Offering categories such as 'being aggressive' or 'seems anxious' that still require the observer to interpret.
    • Swapping the two sampling methods, or describing time sampling as recording everything over a long period.
    • Ignoring that overlapping categories make it impossible to tally a behaviour consistently.
    • Discussing covert and overt observation when the question asks about design rather than type.
    • Writing sample questions that are leading or double-barrelled while claiming to demonstrate good design.
    • Treating a Likert scale as an open question because the respondent 'chooses their own level of agreement'.
    • Evaluating with 'people might lie' and stopping there, instead of naming social desirability bias and a control such as anonymity.
    • Describing an unstructured interview as having no preparation at all, rather than no fixed set of questions.
    • Calling every uncontrolled variable confounding, when it is confounding only if it changes alongside the IV.
    • Naming the IV without both conditions, so the comparison being made is invisible.
    • Giving an unoperationalised DV such as 'how well they did' rather than a score.
    • Identifying the DV as the measuring instrument, for example 'the questionnaire', rather than the score it yields.
    • Confusing random allocation with random sampling, which is about who takes part rather than which condition they do.
    • Claiming counterbalancing eliminates order effects.
    • Using randomisation and random allocation interchangeably; randomisation concerns materials and order, not people.
    • Saying standardisation means every participant is the same, rather than that every participant's experience is the same.
    • Treating the two terms as interchangeable, when one comes from the participant and one from the researcher.
    • Saying participants 'try harder' without explaining that this changes the DV independently of the IV.
    • Describing a double blind as the participant not knowing, which is a single blind.
    • Claiming deception always removes the problem, ignoring that the setting itself still carries cues.
    • Naming an issue and then repeating it as the solution, for instance 'the issue is deception, so do not deceive them'.
    • Assuming a debrief undoes psychological harm, rather than limiting it.
    • Confusing confidentiality, protecting data, with anonymity, not collecting identities in the first place.
    • Writing about ethics in general when the question asks for the issue raised by the described procedure.
    • Describing it as checking that the results are correct, when reviewers cannot see the raw data or rerun the study.
    • Saying reviewers are chosen from other disciplines to stay neutral; they are experts in the same field.
    • Offering only the vague criticism that reviewers can be biased, without naming rivalry, publication bias or the file drawer problem.
    • Ignoring the funding role and treating peer review as only a publication gate.
    • Describing the study at length and never mentioning cost, productivity or employment.
    • Talking about the cost of doing the research rather than the economic effect of its findings.
    • Asserting a saving with no mechanism, for example 'therapy helps the economy'.
    • Using an unrelated topic because it is well known, when the question directs you to the material in the stem.
    • Treating reliability as accuracy; a consistently wrong measure is reliable but not valid.
    • Omitting the correlation, which is how both methods are actually assessed.
    • Setting a retest gap with no reasoning, when the gap is exactly what makes the check meaningful.
    • Offering 'use more participants' as an improvement, which affects generalisability rather than consistency.
    • Equating ecological validity with being outside a laboratory, ignoring whether the task itself resembles real behaviour.
    • Describing concurrent validity without the comparison measure or the correlation.
    • Confusing temporal validity with test-retest reliability; one is about whether the finding still applies, the other about consistency of a measure.
    • Improving validity with suggestions that actually improve reliability, such as training observers.
    • Defining falsifiability as a theory being false, rather than being capable of being proved false.
    • Describing a paradigm as a theory; it is the set of shared assumptions that a whole discipline works within.
    • Confusing theory construction (building the theory) with hypothesis testing (testing predictions derived from it).
    • Treating replicability and reliability as the same thing, rather than one being how the other is tested.
    • Placing conclusions in the results section rather than in the discussion.
    • Describing the abstract as an introduction to the topic instead of a summary of the whole report.
    • Omitting the subsections of the method, which is where replication marks are earned.
    • Forgetting referencing altogether, or giving an incomplete citation without journal and date.