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.
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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 methods including: random, systematic, stratified, opportunity and volunteer; implications of sampling methods, including bias and generalisation.
Sampling involves selecting a representative group (sample) from a larger target group (population) to conduct research. Random sampling gives everyone an equal chance of selection, while systematic sampling uses a predetermined system, like every nth person. Stratified sampling reflects the population's exact proportions by dividing it into strata. Opportunity sampling selects those readily available, and volunteer sampling relies on participants self-selecting. The chosen method heavily impacts bias and generalisation. For example, volunteer samples often suffer from volunteer bias, limiting the ability to generalise findings to the wider population, whereas stratified samples offer high representativeness.
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; operationalisation of variables.
In psychological research, the independent variable (IV) is deliberately manipulated by the researcher to observe its effect, while the dependent variable (DV) is the outcome that is measured. To ensure valid results, researchers must control extraneous variables (EVs)—any factors other than the IV that could affect the DV, such as noise levels or participant age. If left uncontrolled, EVs can become confounding variables. Operationalisation is the crucial process of defining these variables in strictly measurable and testable terms. For example, instead of a vague IV like 'caffeine' and DV like 'memory', a researcher operationalises them as 'drinking 200 milligrams of caffeine' and 'the number of words correctly recalled from a list of twenty'.
Control: random allocation and counterbalancing, randomisation, standardisation and control groups.
Researchers use specific techniques to minimise bias and extraneous variables. Random allocation ensures every participant has an equal chance of being assigned to any condition, reducing participant variables. In repeated measures designs, counterbalancing controls for order effects by having half the participants complete condition A then B, and the rest B then A. Randomisation uses chance methods to determine the order of trials, reducing investigator effects. Standardisation ensures all participants experience exactly the same environment and instructions. Finally, control groups provide a baseline measure by receiving no experimental treatment, allowing researchers to isolate the true effect of the independent variable.
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 measuring reliability: test-retest and inter-observer; improving reliability.
Reliability refers to the consistency of a measure across all methods of investigation, including experiments, observations, and questionnaires. If a tool is reliable, it produces the same results when repeated under identical conditions. Test-retest reliability measures external consistency by administering the same test to the same participants on two separate occasions and correlating the scores (a correlation coefficient of +0.80 or higher indicates good reliability). Inter-observer reliability assesses consistency between two or more independent observers coding the same behaviour; their records are correlated to ensure objectivity. Reliability can be improved by standardising procedures, operationalising variables clearly, training observers, or replacing ambiguous questionnaire items with closed questions.
Types of validity across all methods of investigation: face validity, concurrent validity, ecological validity and temporal validity. Measurement of validity. Improving validity.
Validity concerns the accuracy of a measure across all methods of investigation—whether it genuinely measures what it claims to. Face validity assesses if a test appears to measure the target variable 'on the face of it'. Concurrent validity compares results from a new test with an established test; a strong positive correlation (+0.80) indicates high validity. Ecological validity refers to whether findings generalise to real-world settings, while temporal validity assesses if findings hold true across different eras. Validity is measured via correlation or expert review. It can be improved by using control groups, standardising procedures to reduce investigator effects, ensuring anonymity to minimise social desirability, or using covert observations.
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.
Your focus
- Distinguish an aim from a hypothesis by what each states and by which one carries operationalised variables.
- Write an aim and a matching hypothesis for a given study, naming both IV conditions and a countable DV.
- Identify what is missing from a poorly written aim or hypothesis and state the correction it needs.
Show all 51 objectives
- State what a directional hypothesis adds to a non-directional one and where the null hypothesis sits alongside both.
- Write a directional and a non-directional version of the same prediction with both IV levels and an operationalised DV.
- Justify choosing a direction or leaving it out by whether previous findings on the topic are consistent or mixed.
- Distinguish between a target population and a research sample.
- Describe the procedures for random, systematic, stratified, opportunity and volunteer sampling.
- Evaluate how different sampling methods affect sample bias and the generalisability of research findings.
- Outline what a pilot study is and state that its data are not reported as results or used to test the hypothesis.
- Suggest what a pilot would check in a given study, such as instructions, timings, ceiling effects or the IV manipulation.
- Explain the economic and ethical gain of finding a design fault before a full sample has been put through it.
- Outline who takes part in which condition under repeated measures, independent groups and matched pairs.
- Identify the design used in a described study and state the control it needs, counterbalancing or random allocation.
- Justify choosing one design over another for a given study by naming the variable each trade-off protects.
- Describe what makes a behavioural category usable, being observable, clearly defined and not overlapping another.
- Suggest behavioural categories and an event or time sampling method for a stated behaviour, giving the interval.
- Evaluate event against time sampling by what each one misses and how both affect inter-observer agreement.
- Describe what closed and open questions each yield and how structured, semi-structured and unstructured interviews differ.
- Write questionnaire items and an interview schedule for a stated aim, avoiding leading, jargon and double-barrelled wording.
- Evaluate a draft questionnaire by identifying the items that would bias the answers and saying how to rewrite them.
- Distinguish between independent, dependent, and extraneous variables in a given research scenario.
- Explain the importance of controlling extraneous variables to establish cause and effect.
- Operationalise variables by defining them in strictly measurable and testable terms.
- Describe how random allocation and randomisation reduce bias in experimental designs.
- Explain the mechanism and purpose of counterbalancing in repeated measures designs.
- Evaluate the importance of standardisation and control groups in establishing internal validity.
- Explain how a cue that reveals the aim, and the researcher's own expectancy, can change the DV without the IV moving.
- Identify the cue in a described procedure that would reveal the aim and suggest a single or double blind remedy.
- Evaluate what these effects do to the internal validity of a study and how far standardised instructions repair it.
- Outline the BPS principles and name the issues of consent, deception, harm, privacy and the right to withdraw.
- Identify the ethical issue raised by a described study and state the specific remedy that it calls for.
- Discuss whether a study such as Milgram's would pass an ethics committee by weighing its cost against its benefit.
- Describe how peer review works and name its three functions of allocating funding, validating quality and improving the work.
- Explain what a reviewer checks, from originality to whether the statistics actually support the conclusion drawn.
- Evaluate peer review using anonymity, rivalry and the file drawer burial of replications and null results.
- Explain what economic implications mean in terms of working time, sick pay and money gained or lost.
- Apply a named area such as attachment, therapy for depression or circadian rhythms to a stated cost or saving.
- Evaluate how firm the chain is from a psychological finding to the figure that is claimed to be saved.
- Define reliability and distinguish it from validity across various research methods.
- Describe how to measure reliability using test-retest and inter-observer methods.
- Propose appropriate modifications to research designs to improve their reliability.
- Differentiate between face, concurrent, ecological, and temporal validity.
- Explain how validity can be measured using concurrent and face validity techniques.
- Suggest appropriate methodological changes to improve the validity of an investigation.
- Outline objectivity, the empirical method, replicability and falsifiability as the demands a scientific claim must meet.
- Explain the cycle of theory construction and hypothesis testing in developing scientific explanations.
- Discuss, using Kuhn, whether psychology's lack of a shared paradigm leaves it a pre-science.
- Outline the six sections of a scientific report in order and state what each one contains.
- Identify which section a given piece of information belongs in, such as a critical value or a limitation.
- 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.
- A population is the entire target group, whereas a sample is the specific subset selected for the study.
- Random sampling requires a complete list of the population and a lottery method to ensure equal selection probability.
- Stratified sampling involves calculating the correct proportions of subgroups (strata) to ensure the sample accurately reflects the population.
- Opportunity and volunteer sampling are highly prone to bias, which significantly reduces the ability to generalise findings.
- 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.
- Identify the independent variable as the factor manipulated by the researcher and the dependent variable as the factor measured.
- Explain that extraneous variables are nuisance factors that must be controlled to prevent them from affecting the dependent variable.
- Define operationalisation as the process of making variables physically measurable or testable.
- Apply operationalisation to a novel scenario by providing specific, quantifiable measures for both the independent and dependent variables.
- Describe random allocation as a method to evenly distribute participant differences across experimental conditions.
- Explain counterbalancing as a technique to mitigate order effects in repeated measures designs, typically using the ABBA method.
- Define standardisation as keeping all situational variables and procedures identical for every participant.
- Explain the purpose of a control group as establishing a baseline to compare against the experimental group.
- Identify randomisation as the use of chance to dictate the order of stimulus presentation or procedural steps.
- 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.
- Define reliability as the consistency of findings or measuring instruments across different research methods.
- Explain test-retest reliability as administering the same test to the same people twice, with an appropriate time gap to prevent recall but avoid genuine change.
- Describe inter-observer reliability as multiple researchers independently recording behaviour using the same behavioural categories, then correlating their data.
- Identify that a Spearman's rho or Pearson's r correlation coefficient of +0.80 or greater demonstrates strong reliability.
- Explain methods to improve reliability, such as standardising instructions, operationalising behavioural categories, or rewriting ambiguous questions.
- Define validity as the extent to which an observed effect is genuine and measures what it intends to measure.
- Distinguish between ecological validity (generalising to other settings) and temporal validity (generalising to other times).
- Explain that concurrent validity is measured by correlating scores on a new test with an existing, recognised test, requiring a coefficient of +0.80 or higher.
- Describe face validity as an intuitive check, often by an expert, to see if the instrument looks like it measures the intended construct.
- Identify ways to improve validity, such as incorporating a control group, using double-blind procedures, or ensuring participant anonymity.
- 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.
- 💡When evaluating sampling methods, always explicitly link the method to the concepts of bias and generalisability.
- 💡Be prepared to calculate the number of participants needed for a specific stratum in a stratified sample using percentages.
- 💡Use the term 'target population' rather than just 'people' when defining who the sample is drawn from.
- 💡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 asked to write a hypothesis, always ensure both the independent and dependent variables are fully operationalised.
- 💡Use the scenario provided in the stem to identify specific extraneous variables rather than giving generic examples like 'temperature'.
- 💡Clearly state how the independent variable is manipulated by describing the different conditions the participants will experience.
- 💡When designing an experiment in an exam, explicitly state how you would randomly allocate participants, such as drawing names from a hat.
- 💡Use the term 'baseline' when explaining the purpose of a control group in evaluating the independent variable's effect.
- 💡Specify exactly what is being standardised in a given scenario, such as the time of day or the exact wording of the brief.
- 💡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.
- 💡When asked how to assess reliability in a scenario, explicitly state whether test-retest or inter-observer is most appropriate for the method used.
- 💡Always mention the statistical threshold (+0.80 correlation coefficient) when explaining how to establish reliability.
- 💡If asked to improve reliability in an observation, specifically mention operationalising behavioural categories so they are mutually exclusive and observable.
- 💡When evaluating a study's ecological validity, focus on the nature of the task (mundane realism) as well as the setting.
- 💡If asked how to assess validity, explicitly describe the process of concurrent validity, including the need for a strong positive correlation.
- 💡To suggest improvements for validity in questionnaires, recommend incorporating a lie scale to control for social desirability bias.
- 💡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.
- Confusing random and opportunity sampling: Students often say picking people randomly on the street is random sampling; the correction is that this is opportunity sampling, as not everyone in the target population has an equal chance.
- Misunderstanding systematic sampling: Describing it as just picking people in order; the correction is that it requires a fixed interval (every nth person) from a sampling frame.
- Misusing the term generalisation: Stating a study is generalisable without linking it to the sample's representativeness; the correction is to explicitly link the sampling method's bias to the validity of generalising.
- 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.
- Confusing the independent and dependent variables; remember that the independent variable is manipulated (the cause) and the dependent variable is measured (the effect).
- Failing to fully operationalise variables in exam answers; stating 'memory' is incorrect, whereas 'number of words recalled' is the correct operationalised form.
- Using the terms extraneous and confounding variables interchangeably; an extraneous variable is a potential nuisance, whereas a confounding variable has actually varied systematically with the independent variable.
- Confusing random sampling with random allocation; random sampling selects the participants from a population, whereas random allocation assigns already selected participants to conditions.
- Stating that counterbalancing eliminates order effects; it does not remove them, but rather balances their impact across the different conditions.
- Failing to specify how standardisation is achieved; simply stating 'keep things the same' is insufficient compared to specifying 'using standardised written instructions'.
- 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.
- Confusing reliability with validity; correction: reliability is about consistency (getting the same result), whereas validity is about accuracy (measuring what is intended).
- Stating that inter-observer reliability involves observers discussing their findings to reach an agreement; correction: observers must record data independently before comparing and correlating their results.
- Suggesting a short time gap for test-retest reliability; correction: the gap must be long enough so participants forget their original answers, but short enough that their actual traits have not changed.
- Equating ecological validity simply with the realism of the environment; correction: ecological validity specifically refers to whether the findings can be generalised beyond the research setting to everyday life.
- Confusing concurrent validity with reliability; correction: concurrent validity compares a new test to a different, established test, whereas reliability compares the same test against itself.
- Assuming face validity is a robust statistical measure; correction: face validity is a subjective, superficial assessment and does not statistically prove validity.
- 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.