Research methods
- Syllabus
- 9990–2028–2029
- Topic
- —
- Level
- AS
Learning objectives
—Experiments• Experiments- Candidates should be able to:- describe the main features of each type of experiment:- - laboratory- - field- evaluate each type of experiment, in terms of:- - reliability- - validity- - ethics- describe and evaluate experimental designs as used in psychological research (independent measures, matched pairs and repeated measures)- describe and evaluate concepts relating to experimental designs including counterbalancing, random allocation, order effects (fatigue and practice)- evaluate the use of experiments in psychological research, including the use of experimental and control groups/control conditions- apply knowledge of experiments to a given novel research scenario.—Self-reports• Self-reports- Candidates should be able to:- describe the main features of each type of self-report:- - questionnaire, including technique (paper and pencil/online) and question format (open and closed questions)- - interview, including format (structured/unstructured/semi-structured), technique (telephone/face-to- face) and question format (open and closed questions)- evaluate the use of self-reports in psychological research- apply knowledge of self-reports to a given novel research scenario.—Case studies• Case studies- Candidates should be able to:- describe the case study method, including the main features: a single participant/unit; studied in detail- evaluate the use of case studies in psychological research- apply knowledge of case studies to a given novel research scenario.—Observations• Observations- Candidates should be able to:- describe the main features of an observation (e.g. overt/covert, participant/non-participant, structured/unstructured, naturalistic/controlled)- evaluate the use of observations in psychological research- apply knowledge of observations to a given novel research scenario.—Correlations• Correlations- Candidates should be able to:- describe correlations, positive and negative correlations and strength of correlations- identify and give operational definitions for co-variables (measured variables)- evaluate the use of correlations in psychological research, including lack of causality- apply knowledge of correlations to a given novel research scenario.—Longitudinal studies• Longitudinal studies- Candidates should be able to:- describe longitudinal studies, including experiments with longitudinal designs- evaluate the use of longitudinal studies, including experiments with longitudinal designs- apply knowledge of longitudinal studies, including experiments with longitudinal designs, to a given novel research scenario.
Experiments test causal effects by manipulating an IV and controlling comparison
An experiment deliberately manipulates an independent variable (IV), measures an operational dependent variable (DV), and compares conditions while controlling alternatives. Setting (laboratory/field) and participant design (independent, matched or repeated) are separate choices.
| Type | Defining feature | Typical strength | Typical cost |
|---|---|---|---|
| Laboratory experiment | IV manipulated in an artificial/high-control setting | Standardisation, replication and internal validity | Lower mundane/ecological validity; demand characteristics; imposed-task ethics |
| Field experiment | IV manipulated in a natural everyday setting | Natural behaviour and lower awareness/demand | Less control, harder replication/allocation; consent/debrief/withdrawal problems |
| Not an experiment | No manipulated IV/control comparison | Can describe/associate rich real behaviour | Cannot claim the measured factor caused the outcome |
| Design | Allocation | Main advantage | Main threat and remedy |
|---|---|---|---|
| Independent measures | Different participants in each condition; random allocation if possible | No order effects | Participant differences; use random allocation, sufficient sample/control |
| Matched pairs | Different people matched on relevant variable(s), one per condition | Reduces selected participant differences without order effects | Matching is slow/imperfect; loss of one may lose pair |
| Repeated measures | Same participants complete every condition | Controls participant differences; fewer participants | Practice/fatigue/carry-over; counterbalance condition order |
Novel scenario workflow: write an operational IV with at least two levels; write a measurable DV with units/scoring window; name design and allocation; hold instructions/materials/time/setting constant; add control condition/group; counterbalance if repeated; state consent, withdrawal, harm and debrief safeguards.
Evaluate in context: reliability asks whether the standardised procedure/measure could reproduce; internal validity asks whether IV rather than confounds changed DV; ecological validity asks whether task/setting represents the target behaviour; ethics asks what manipulation, deception or allocation does to participants.
Random sampling selects people from a population; random allocation assigns recruited participants to conditions. A control group receives no/alternative manipulation; a control condition can be completed by the same repeated-measures participants. Laboratory location alone does not make a study experimental.
Self-reports turn private experience into answers through designed questions
A self-report asks participants to report their own behaviour, cognition, emotion or experience. Questionnaires and interviews can both use open and closed questions; interview structure and delivery technique must be specified separately.
| Choice | Defining feature | Gain | Cost |
|---|---|---|---|
| Paper/online questionnaire | Participant completes written items | Large/cheap/anonymous; standardised | Misunderstanding, low return, no probing |
| Structured interview | Same questions/order, usually fixed prompts | Comparable and reliable | Limited depth; interviewer presence/bias |
| Unstructured interview | Flexible conversation guided by participant | Rich, unexpected detail | Low standardisation, hard replication/analysis |
| Semi-structured interview | Core standard questions plus planned/contingent probes | Comparability plus depth | Requires interviewer skill; probing can vary |
| Telephone/face-to-face | Remote voice versus co-present delivery | Reach/lower visual pressure versus rapport/non-verbal cues | Identity/rapport versus interviewer/social desirability effects |
| Item type | Example construction | Data/evaluation |
|---|---|---|
| Closed | 'In the last 7 days, on how many days did you sleep ≥8 hours? 0-7' | Quantitative, fast/reliable comparison; options may force/omit answers |
| Likert/rating | 'I felt anxious before the test: 1 strongly disagree … 5 strongly agree' | Operational score; midpoint/response-set meaning must be clear |
| Open | 'Describe one situation in which the test changed how you felt.' | Qualitative depth/validity; coding is slower and less reliable |
Write one idea per neutral item; define time/context; make response options exhaustive and mutually exclusive; avoid leading, loaded, double-barrelled, ambiguous, jargon and double-negative wording; pilot for interpretation; standardise interviewer prompts; plan coding before collection.
| Quality question | Contextual check | Improvement |
|---|---|---|
| Reliability | Would wording/order/interviewer/coding yield similar scores? | Standard script, pilot, coding scheme, inter-rater/test-retest check |
| Validity | Does answer reflect target construct rather than memory/demand? | Anonymous/private response, multiple items, open probe/triangulation |
| Bias | Social desirability, acquiescence, recall, interviewer expectations? | Neutral wording, balanced items, confidentiality, trained interviewer |
| Ethics | Sensitive disclosure, privacy, storage and right to skip? | Informed consent, skip/withdraw option, safeguarding and secure anonymisation |
Interview does not mean qualitative: closed structured questions produce quantitative data, while questionnaire open questions produce qualitative data. An anonymous response may reduce social desirability but cannot guarantee honesty, accurate memory or construct validity.
Case studies build intensive, triangulated evidence about one bounded unit
A case study investigates one bounded participant, family, group, institution or event in depth and context. It is a research strategy, not a single data-collection technique: interviews, questionnaires, observations, tests, records, biological measures and follow-up can be combined.
| Evidence layer | Possible technique | Contribution |
|---|---|---|
| History/context | Records, informant interviews, timeline | Explains onset and environmental meaning |
| Current experience | Interview/questionnaire/diary | First-person cognition/emotion |
| Observable behaviour | Naturalistic/structured observation or task | Behavioural evidence beyond self-report |
| Objective/standard measure | Test, diagnostic scale, physiological/brain measure | Repeatable comparison or mechanism evidence |
| Change over time | Treatment phases and follow-up | Trajectory, maintenance and alternative explanations |
Novel scenario: define the bounded case and why it is theoretically/clinically informative; collect several independent evidence sources; create a dated sequence; operationalise repeated measures; record contradictory evidence; obtain consent from the participant and relevant informants; anonymise identifying detail; avoid treating intervention response as controlled causal proof.
| Strength | Limitation |
|---|---|
| Rich holistic/contextual detail gives high ecological/construct insight | Unique history and tiny unit prevent statistical population generalisation |
| Triangulation can test convergence across methods/data types | Researcher interpretation and retrospective memory may bias the narrative |
| Rare/unethical-to-create cases generate hypotheses and practical learning | No control group, random allocation or isolated IV means weak causal inference |
| Repeated follow-up captures change and unusual processes | Time/cost, attrition and changing measures reduce reliability |
| Participant voice can preserve meaning | Privacy, stigma, consent capacity and deductive identification are acute ethical risks |
Case evidence can show that a phenomenon is possible, reveal a mechanism candidate and generate/refine theory. Transfer analytically by asking whether the new case shares relevant mechanisms/context—not by claiming one case represents everyone.
One participant in a laboratory experiment is not automatically a case study; intensive contextual study is required. A case can contain quantitative data and standardised tests. Depth improves understanding, not automatic validity, causality or generalisability.
Observations turn behaviour into evidence through explicit setting, role and coding choices
Observation systematically records behaviour. Classify it on four independent axes, then specify behavioural categories, sampling and observers. Observation is a technique; it becomes part of an experiment only when an IV is manipulated.
| Axis | Option A | Option B | Main trade-off |
|---|---|---|---|
| Awareness | Overt: participants know | Covert: do not know | Consent/transparency versus reactivity/demand |
| Observer role | Participant: joins group | Non-participant: remains separate | Insider access versus detachment/role bias |
| Coding | Structured: predefined categories/checklist | Unstructured: open narrative | Reliability/comparison versus depth/unexpected behaviour |
| Setting/control | Naturalistic: normal setting | Controlled: arranged setting/task | Ecological validity versus standardisation/control |
| Design step | Required operational decision |
|---|---|
| Define behaviour | Mutually exclusive, observable categories—not inferred motives (e.g. 'raises voice above conversational level for ≥2 s') |
| Sample behaviour | Event sampling counts every target event; time sampling records at fixed intervals |
| Record | Frequency, duration, latency, sequence and/or contextual field notes |
| Train observers | Examples/non-examples, blind coding where possible, pilot ambiguous cases |
| Check reliability | Two observers code same segment; compare agreement/correlation and revise categories |
| Protect participants | Consent where feasible, public/private expectation, debrief deception, anonymise and stop if harm occurs |
| Strength | Limitation and contextual remedy |
|---|---|
| Direct behaviour avoids memory/self-report bias | Cannot directly access thought/emotion; triangulate with self-report/measure |
| Naturalistic/covert designs can capture spontaneous behaviour | Low control and ethical problems; standardise window/context and debrief where possible |
| Structured categories yield quantitative comparison and replication | Category reduction may miss meaning; pilot and retain field notes |
| Unstructured records discover unexpected patterns | Observer bias/low reliability; reflexive notes, second coding and clear later scheme |
Scenario answer sequence: state all four axes; operationalise at least two categories; choose event/time sampling; say exactly who observes, where and for how long; describe inter-observer reliability; identify reactivity/confounds; add consent/privacy/debrief safeguards.
Naturalistic does not imply covert, unstructured or participant observation. Controlled observation does not automatically manipulate an IV. High observer agreement means consistent coding, not that categories validly represent aggression, empathy or another inferred construct.
Correlations describe the direction and strength of association between measured co-variables
A correlation measures how two co-variables vary together; neither is manipulated. Each participant/unit contributes a paired score plotted as one point. Direction and strength describe association, not cause.
| Pattern | Meaning | What it does not mean |
|---|---|---|
| Positive | Higher X tends to accompany higher Y (and lower with lower) | X is beneficial or causes Y |
| Negative | Higher X tends to accompany lower Y | Weak, harmful or no relationship |
| Strong | Points cluster closely around a monotonic trend; coefficient nearer ±1 | Valid measurement or causality |
| Weak/zero | Points are dispersed/no monotonic trend; coefficient nearer 0 | No non-linear relation or no subgroup pattern |
Name both co-variables and give units/scoring window: for example, 'minutes of phone use from device log between 20:00-24:00 averaged over seven days' and 'total sleep minutes from actigraphy on the same nights'. 'Phone use and sleep' is not operational.
| Step | Decision |
|---|---|
| Sample paired scores | Same units/people measured on both variables |
| Inspect scatterplot | Direction, form, outliers and possible subgroups |
| Select coefficient | Match scale/distribution/monotonic assumptions |
| Describe | State direction and strength, preferably with coefficient |
| Evaluate | Reliability/validity of both measures, range restriction, sampling and outliers |
| Infer | Predict/identify association; do not assign causal direction |
| Causal threat | Example for phone use ↔ sleep |
|---|---|
| Directionality | Phone use may reduce sleep, or inability to sleep may increase use |
| Third variable | Stress, workload or caffeine may increase use and reduce sleep |
| Selection/measurement | A narrow student sample or inaccurate self-report can create/distort association |
Strengths: studies naturally occurring variables that cannot ethically/practically be manipulated, quantifies prediction and generates hypotheses. Limits: no causal conclusion, vulnerable to third variables/directionality, and the correlation cannot be more valid or reliable than its two operational measures.
Do not call co-variables IV and DV. A coefficient sign gives direction, while absolute size gives strength. Even a perfect association cannot by itself show which variable causes which or rule out a common cause.
Longitudinal studies follow the same units to explain change and continuity over time
A longitudinal study repeatedly measures the same participant(s), case(s) or cohort across a meaningful period to investigate change, continuity or delayed effects. It can be observational/correlational or experimental if an IV/control comparison is added.
| Form | Structure | Claim boundary |
|---|---|---|
| Descriptive longitudinal | Same measures at several time points | Describes within-unit trajectory, not its cause |
| Correlational longitudinal | Earlier co-variable predicts later outcome | Establishes time order but still has third-variable/confounding threats |
| Longitudinal experiment | Manipulated condition/control followed over time | Stronger causal and maintenance inference if allocation/control/attrition remain sound |
| Pre-post only | Same unit measured before and after | Change is visible, but history, maturation, testing and regression remain alternatives |
Specify target interval and measurement schedule; keep operational measures equivalent; record baseline and relevant confounds; preserve participant IDs securely; standardise contact; plan retention and missing-data rules; add comparison/control where causal inference is intended; predefine follow-up outcome and stopping/safeguarding procedures.
| Strength | Why it matters |
|---|---|
| Within-person change | Separates individual trajectory from one-time age-group differences |
| Temporal order | Shows predictor preceded outcome, narrowing but not eliminating causal explanations |
| Delayed/maintenance effects | Tests whether learning, treatment or brain/behaviour change persists |
| Rich repeated data | Reveals turning points and individual differences hidden by group averages |
| Threat | Consequence | Mitigation |
|---|---|---|
| Attrition | Smaller sample and systematic survivor bias | Retention plan, compare dropouts, transparent missing-data analysis |
| Practice/testing | Repetition itself changes scores | Alternate forms, spacing, appropriate control |
| Historical/maturation change | Time-related events/development mimic effect | Comparison group, repeated baseline/context measures |
| Measure drift | New tools/raters change apparent score | Calibrate/equate methods and document changes |
| Cost/privacy | Long commitment and sensitive linked records | Proportionate schedule, renewed consent, secure pseudonymous linkage |
Novel scenario answer: name the same participants, at least two dated waves, an unchanged operational outcome, expected change, retention method, attrition/practice/history control and ethical re-consent/confidentiality. If treatment is manipulated, also specify allocation and control condition.
Repeated measures compares conditions using the same participants; longitudinal follows the same units over a meaningful time course. A study can be both, but they are not synonyms. Time order improves causal reasoning yet does not remove third variables, history or maturation.