Methodological concepts
- Syllabus
- 9990–2028–2029
- Topic
- —
- Level
- AS
Learning objectives
—Aims and hypotheses• Aims and hypotheses- Candidates should be able to:- describe and write aims- describe and recognise null hypotheses and alternative hypotheses including directional (one-tailed) and non-directional (two-tailed) hypotheses.—Variables• Variables- Candidates should be able to:- describe what is meant by an independent variable and a dependent variable- describe how dependent variables can be measured- identify independent variables and dependent variables in studies- understand what is meant by an 'operational definition'- operationalise:- - an independent variable- - a dependent variable- apply knowledge of variables to a novel research situation.—Controlling of variables• Controlling of variables- Candidates should be able to:- describe how psychologists can control variables (use 'controls') in a study- understand control of variables/standardisation of a procedure- understand uncontrolled, participant and situational variables- apply knowledge of control of variables ('controls') to a novel research situation.—Types of data• Types of data- Candidates should be able to:- describe what is meant by quantitative and qualitative data and subjective and objective data- evaluate the use of types of data as collected in psychological research- apply knowledge of types of data to a novel research situation.—Sampling of participants• Sampling of participants- Candidates should be able to:- describe what is meant by the sample and population, and the sampling techniques of opportunity sampling, random sampling and volunteer (self-selecting) sampling- evaluate different sampling techniques as used in psychological research, including generalisations- apply knowledge of sampling techniques to a novel research situation.—Ethics• Ethics- Candidates should be able to:- describe ethical guidelines as used in psychological research, in relation to human participants:- - minimising harm (and maximising benefit)- - valid consent including informed consent- - right to withdraw- - lack of deception- - confidentiality- - privacy- - debriefing- describe ethical guidelines as used in psychological research, in relation to animals:- - minimising harm (and maximising benefit)- - replacement- - species- - numbers- - procedures ○ pain, suffering and distress ○ housing ○ reward, deprivation and aversive stimuli- evaluate studies based on ethical guidelines- apply knowledge of ethical guidelines to a novel research situation.—Validity• Validity- Candidates should be able to:- describe validity, including ecological validity- evaluate studies based on their validity:- - subjectivity/objectivity- - demand characteristics- - generalisability- apply knowledge of validity to a novel research situation.—Reliability and replicability• Reliability and replicability- Candidates should be able to:- describe different types of reliability, including inter-rater and inter-observer reliability, test-retest reliability- evaluate studies based on their reliability- apply knowledge of reliability to a given novel research situation- understand replicability- apply understanding of replicability to the planning of studies.—Data analysis• Data analysis- Candidates should be able to:- present and interpret data in tables- understand the meaning of 'measure of central tendency'- name, recognise and know how to find measures of central tendency:- - mode- - median (no calculation necessary)- - mean (no calculation necessary)- understand the meaning of 'measure of spread'- - name, recognise and know how to find range- - recognise, interpret and understand standard deviation- name, recognise, draw, change and interpret data from a:- - bar chart- - histogram- - scatter graph.- Note: Candidates will not be required to carry out calculations.- Note: Candidates will not be required to understand or interpret statistical tests or findings other than as specified.
Aims ask the question; hypotheses state the testable outcome pattern
An aim states what a study intends to investigate. An alternative hypothesis predicts a difference/relationship; a null predicts no difference/relationship beyond chance. Every hypothesis must identify the population and operational variables.
| Statement | Experiment form | Correlation form |
|---|---|---|
| Aim | Investigate whether condition X affects measured Y in population P | Investigate the relationship between measured X and Y in P |
| Directional alternative | P in X1 will score higher/lower on Y than P in X2 | As X increases, Y will increase/decrease in P |
| Non-directional alternative | There will be a difference in Y between X1 and X2 for P | There will be a relationship between X and Y in P |
| Null | There will be no difference in Y between X1 and X2 for P | There will be no relationship between X and Y in P |
Use a directional hypothesis only when prior theory/evidence justifies the direction before data collection. Use non-directional when an effect is expected but its direction is uncertain. Write scores/behaviour, not vague 'better' or causal wording for a correlation.
Audit: population named; IV levels or both co-variables operationalised; DV measure named; comparison/relationship word present; direction only when justified; null is exact logical counterpart.
One-tailed means one predicted direction, not one condition. The null is not 'nothing happened'; it is a population statement of no difference/association. Results support or fail to support a hypothesis—they do not rewrite it after seeing data.
Operational variables make manipulation and measurement exact and replicable
The IV is deliberately changed in an experiment; the DV is the measured outcome. An operational definition states exactly how each variable is created or scored so another researcher could reproduce it.
| Concept | Weak label | Operational version |
|---|---|---|
| IV: background sound | Music vs silence | 70 dB instrumental track through headphones versus identical headphones with no audio during a 10-minute task |
| DV: memory | Memory score | Number of 20 nouns correctly freely recalled in 2 minutes, duplicates/intrusions excluded |
| Correlational co-variable | Stress | Total score on named 10-item scale completed after school |
A DV can be frequency, duration, latency, accuracy/error, test/scale score, choice, physiological value or coded category. State unit, observation window, scoring rules and direction. Pilot floor/ceiling effects and ambiguity.
| Operational gain | Possible cost |
|---|---|
| Precision and replicability | Narrow measure may omit the construct |
| Quantitative comparison | Score may reward speed/strategy rather than target ability |
| Standardisation | Artificial task may reduce ecological validity |
Predictors/co-variables are not IVs unless manipulated. 'Aggression', 'happiness' and 'learning' are constructs, not complete DVs. Operational clarity improves replicability but does not automatically establish validity.
Controls reduce alternative explanations by standardising or balancing relevant variation
A control holds, removes, measures or balances a variable so conditions differ mainly in the IV. Standardisation gives every participant the same procedure. An uncontrolled variable becomes a confound when it systematically co-varies with the IV and could change the DV.
| Source | Example | Feasible control |
|---|---|---|
| Participant | Baseline memory, age, sleep | Repeated measures, matching, random allocation, baseline measurement |
| Situational | Room noise, time, device, experimenter tone | Same setting/time/material/script; randomise/balance sessions |
| Order | Practice, fatigue, carry-over | Counterbalance, rest/washout, alternate forms |
| Demand/experimenter | Guessing aim, cueing responses | Blind/double-blind, cover story where ethical, standard script |
For each threat: name it; explain how it differs between IV levels; explain how it could alter the operational DV; specify an exact control. A generic 'keep everything the same' earns less than a mechanism-linked control.
Controls strengthen internal validity/reliability but may make tasks artificial, restrict natural variation, increase cost or create ethical issues. Measure rather than eliminate important real-world factors when ecological validity is central.
Not every uncontrolled variable is a confound; it must vary systematically with the IV and explain the DV. Standardisation cannot remove participant differences by itself. Random allocation balances conditions; random sampling addresses population recruitment.
Data form and data source are separate: quantitative/qualitative and objective/subjective
Quantitative versus qualitative describes data form; objective versus subjective describes dependence on personal judgement/experience. The two axes can combine in four ways.
| Objective/externally verifiable | Subjective/judgement-based | |
|---|---|---|
| Quantitative | Reaction time, heart rate, correct-answer count | Participant's 0-8 distress rating; observer category score requiring judgement |
| Qualitative | Verbatim audio transcript/recorded words as raw event | Participant's interpretation or researcher's thematic account |
| Type | Main value | Main limit |
|---|---|---|
| Quantitative | Compact comparison, graphs, replicable scoring | Reduction may omit meaning/context |
| Qualitative | Rich explanations and unexpected themes | Slow coding, interpretation and lower inter-rater reliability |
| Objective | Less affected by self-presentation/interpretation | May measure proxy rather than lived construct |
| Subjective | Direct access to private experience | Social desirability, memory and perspective bias |
Choose data to fit the question, then triangulate: e.g. combine sleep minutes from actigraphy (quantitative/objective), rating of sleep quality (quantitative/subjective) and diary account (qualitative/subjective). Agreement strengthens confidence; disagreement is evidence to explain, not delete.
Numbers are not automatically objective: a Likert score quantifies a subjective judgement. Words are not automatically subjective: an exact recorded utterance is an observable event, though coding its meaning may be subjective. No data type is universally best.
Sampling links a target population to the people who actually provide data
The population is the full group to which a researcher wants to generalise; the sample is the participating subset. Sampling technique controls who gets an invitation, but non-response and eligibility still shape the final sample.
| Technique | Exact procedure | Strength | Bias/limit |
|---|---|---|---|
| Opportunity | Recruit available eligible people at chosen place/time | Fast, cheap, practical | Place/time/researcher-access bias; often unrepresentative |
| Volunteer/self-selecting | Advertise and eligible people opt in | Consent/interest, reaches dispersed people | Volunteer traits: motivation, time, topic interest, payment response |
| Random | Build complete sampling frame; use random generator/lottery so each has equal selection chance | Reduces researcher selection bias; potentially representative | Frame may omit people; selected non-response; costly |
Novel scenario: define target population with inclusion/exclusion; identify/construct sampling frame if random; state recruitment location/channel/time; describe exact selection—not just name; anticipate who is missed/refuses; compare achieved sample demographics with population; bound generalisation accordingly.
| Evidence | Generalisation consequence |
|---|---|
| Narrow age/sex/culture/occupation | Findings may not transfer where relevant mechanism differs |
| Large but biased online volunteer sample | Precision can increase while representativeness remains poor |
| Small random sample | Less selection bias but greater sampling fluctuation |
| Attrition/non-response | Final sample may differ from those initially selected |
Random sampling recruits from a population; random allocation assigns a recruited sample to conditions. Large does not equal representative. Opportunity sampling is based on availability, not deliberate quota matching. Generalisability also depends on task/setting/time, not sample alone.
Ethical research converts risk-benefit principles into human and animal safeguards
| Human guideline | Requirement and safeguard |
|---|---|
| Minimise harm/maximise benefit | Risk assess, monitor distress, stop/refer/support; use least harmful effective procedure |
| Valid informed consent | Capacity, understandable purpose/procedure/risks/data use; guardian consent plus assent where relevant |
| Right to withdraw | Leave and remove data without penalty; make route/reminder practical |
| Lack of deception | Disclose truth unless justified/minimal and impossible otherwise; never deceive about material risk |
| Confidentiality | Limit access, pseudonymise, secure storage/reporting |
| Privacy | Observe/collect only where people reasonably expect and consent; minimise intrusion |
| Debriefing | Reveal aim/deception, restore understanding, answer questions, offer data withdrawal/support |
| Animal guideline | Applied question |
|---|---|
| Minimise harm/maximise benefit | Is scientific/welfare value proportionate to pain, distress and lasting effects? |
| Replacement | Can non-animal, simulation, existing data or less sentient model answer it? |
| Species | Is species scientifically appropriate and welfare expertise available? |
| Numbers | Use minimum needed for valid evidence—too few also wastes animals |
| Procedures | Refine handling/anaesthesia/endpoints; suitable housing/social needs; justified reward/deprivation; avoid aversive stimuli |
Exam chain: name guideline → cite exact procedure/sample/data feature → explain likely harm/autonomy/welfare or benefit → judge severity/probability/reversibility → propose feasible safeguard and its methodological trade-off. For animals, use the syllabus animal guideline rather than importing human consent/right-to-withdraw labels.
| Method need | Ethical tension | Design response |
|---|---|---|
| Avoid demand characteristics | Deception/incomplete disclosure | Minimal deception, no risk deception, prior/retrospective consent, prompt debrief |
| Natural public behaviour | Consent/privacy | Public-expectation audit, anonymised low-risk recording, gatekeeper/debrief where possible |
| Stress/emergency simulation | Harm and withdrawal | Lower intensity, screening, stop rule, support and alternative task |
| Animal reinforcement | Deprivation/aversive welfare | Preferred reward, minimal restriction, voluntary participation and welfare endpoints |
Signed consent is not automatically valid if information, capacity or freedom is missing. Debriefing mitigates deception but cannot undo severe harm. Confidentiality concerns data identity; privacy concerns access to the person/behaviour. Ethical acceptability is a reasoned balance, not a checklist score.
Validity asks whether the evidence supports the intended measure, cause and transfer
Validity is the extent to which a measure/study supports the intended interpretation. Ask separately whether the construct was measured, the IV caused the DV, the behaviour represents real life and the finding transfers to the target population/context.
| Validity question | Main threats | Contextual improvement |
|---|---|---|
| Construct/measurement | Proxy score, subjective coding, social desirability | Operational/pilot/validated measure, blind coding, triangulation |
| Internal/causal | Confounds, allocation/order/experimenter effects | Control/standardise, random allocation, counterbalance, blind |
| Ecological | Artificial task/setting, low mundane realism | More representative task/context, field evidence—while retaining controls |
| Population/generalisation | Narrow/biased sample and cultural/time differences | Broader/stratified/random recruitment, replication across groups |
Demand characteristics arise when participants infer the aim and alter behaviour; reduce through credible neutral instructions, unobtrusive/indirect measures, blinding or justified deception/debrief. Subjectivity can add insight but requires transparent coding/checks. Objectivity reduces judgement, not necessarily proxy invalidity.
Write: identify exact evidence feature → name the interpretation threatened/supported → explain mechanism → judge consequence for this conclusion → propose improvement and trade-off. 'Laboratory means low validity' is incomplete without showing why this task differs from the target behaviour.
A field setting can contain an invalid measure; a laboratory task can validly test a narrow mechanism. Reliability is necessary for many valid measurements but consistent bias remains invalid. Generalisability extends beyond sample to setting, task, culture and time.
Reliability checks consistency; replicability makes an independent repetition possible
Reliability is consistency of measurement/procedure. Replicability is whether documentation/materials are sufficient for another researcher to repeat the study and test whether the pattern recurs.
| Check | Same/different element | Use | Improve when low |
|---|---|---|---|
| Inter-rater | Two raters score same response/product | Interviews, tests, qualitative coding | Rubric, examples, training, blind double-code |
| Inter-observer | Two observers code same behaviour/time | Structured observation | Operational categories, observer training, video recode |
| Test-retest | Same measure to same people at two suitable times | Stable traits/questionnaires/tests | Clarify items, standardise conditions; avoid interval/practice extremes |
| Procedural replication | Independent study repeats documented method | Tests result robustness | Full script/materials/operational rules, sample and analysis transparency |
Plan: standardise instructions, timing, apparatus and scoring; define categories/units; pilot ambiguity; preserve versioned materials; report recruitment/allocation/exclusions; have independent coders; choose a reliability check that targets the likely source of inconsistency.
High reliability narrows random/observer inconsistency and makes differences interpretable. Low reliability can hide or create effects. Yet a consistently wrong scale, biased item or invalid category can be highly reliable.
Two researchers repeating a whole study is replication, not inter-rater reliability. Test-retest uses the same measure/construct at two times, not two experimental conditions. Agreement does not prove validity, objectivity or truth.
Descriptive analysis matches centre, spread and display to the structure of data
Descriptive analysis organises and summarises observed data. Central tendency gives a typical/central value; spread shows variability. Cambridge requires recognition/finding/interpretation, not calculations or unspecified statistical tests.
| Measure | How found | Best feature | Main weakness |
|---|---|---|---|
| Mode | Most frequent value/category | Works with nominal categories; shows common response | May be multiple/none; ignores rest |
| Median | Middle ordered score (average two middles if even) | Resistant to extreme/skew; ordinal suitable | Ignores distances and much data |
| Mean | Sum divided by number | Uses every interval/ratio score; sensitive comparison | Distorted by extremes/skew; unsuitable for categories |
| Measure | Meaning | Interpretation |
|---|---|---|
| Range | Highest minus lowest (state convention if endpoints included) | Overall span; one extreme can dominate |
| Standard deviation | Typical dispersion around mean | Low SD = scores clustered/consistent; high SD = dispersed—not a high/low mean |
| Display | Data structure | Construction/reading rule |
|---|---|---|
| Table | Any organised categories/conditions | Clear title, labelled rows/columns, units, no ambiguous totals |
| Bar chart | Separate categories/conditions | Equal-width separated bars; axes/units; height = frequency/summary |
| Histogram | Continuous scores grouped into adjacent intervals | Bars touch; numerical ordered x-axis; frequency on y |
| Scatter graph | Paired co-variable scores | One dot per pair; both axes operational variables; inspect direction/strength/outliers |
Compare centre and spread together: equal means can hide different consistency; lower mean may accompany wider overlap. Describe exact pattern with units, largest/smallest, difference and variability. Do not infer cause or significance from descriptive displays.
Bar charts display discrete categories with gaps; histograms display continuous intervals with touching bars; scatter graphs do not display group frequencies. Low SD means low variation, not low scores. A mean difference alone does not prove a reliable population effect.