IB Psychology – Key Concepts, Research Methods & Exam Framework

Conceptual Learning & Hierarchy of Concepts

  • Modern IB pedagogy stresses “conceptual learning / concept-based teaching” → students must recognise broad ideas that organise specific facts.
  • Definition of a concept
    • A general idea applicable to many specific instances.
    • Example: chair, sofa, bed = instances; “furniture” = overarching concept.
    • Knowing the higher-level concept accelerates understanding of new instances.
  • Concept hierarchy in every discipline
    • Bottom: very specific terms (e.g., Multi-Store Model of Memory, serotonin, visual cortex).
    • Middle: more generic, explanatory terms (e.g., chemical messengers, localization of function, cognitive processes).
    • Top: key concepts that capture the discipline’s essence → in IB Psychology there are six.

Six Key Concepts in IB Psychology

  • Bias
  • Causality
  • Perspective
  • Measurement
  • Change
  • Responsibility
  • Interconnectedness
    • Any topic can be viewed through any concept; concepts overlap (e.g., explaining behaviour = causality; enables prediction/control = change; ethical use = responsibility).

Bias

  • General definition: systematic deviation from truth (≠ random error).
  • Requires a source; identifying sources helps eliminate/adjust for bias.
  • Three broad manifestations
    1. Bias in research procedures
    • Researcher bias, participant bias, sampling bias, publication bias, confirmation bias, gender/cultural bias, dominant respondent bias.
    • Additional sources even when the word “bias” is absent: order effects, demand characteristics, experimental mortality, social desirability.
    • Link: credibility (trustworthiness, validity, reliability) ↔ absence of bias.
    1. Bias in interpretation of findings (theoretical orientations)
    • Biological or environmental reductionism → inflate some variables, downplay others.
    1. Bias in behaviour itself
    • Cognitive biases, stereotypes, discrimination, diagnostic bias in mental disorders.
  • Acceptable exam links: any justified connection between bias & studied content.

Causality

  • Relationship where one variable causes another.
  • Four scientific functions: Describe → Explain → Predict → Control.
    • Example: astronomy moved from description of orbits to causal laws (gravity).
  • Psychology’s challenge: human behaviour is complex & multi-determined.
    • IB organises factors into biological, cognitive, sociocultural.
    • Experiments try to isolate factors; only method enabling cause–effect inferences.
  • Evaluative questions
    • Bidirectional ambiguity?
    • Direct vs mediated causation?
    • Short- vs long-term?
    • Side effects?
    • Domino (chain) causality?
  • Strong causal claims demand high internal validity & bias control.

Perspective

  • A “way of looking” at phenomena.
  • IB tri-level framework: Biological, Cognitive, Sociocultural perspectives.
  • Additional levels
    • Competing psychological theories/models as perspectives.
    • Alternative interpretations of the same data.
  • Holistic understanding usually needs multiple perspectives.

Measurement

  • Broadly: using research to obtain data about behaviour.
    • Includes quantitative and qualitative data (not only numbers).
  • Quality of measurement = depth & objectivity of knowledge.
  • Key considerations & examples
    • Choosing an appropriate research method/tool.
    • Impact of instruments on credibility.
    • Brain-imaging techniques (strengths, limits).
    • Effect size, statistical significance.
    • In qualitative work: reflexivity, trustworthiness.

Change

  • Generic meaning: process through which something becomes different.
  • Two high-frequency IB examples
    1. Natural development (e.g., cognitive maturation, symptom progression, social learning, enculturation/acculturation).
    2. Purposeful intervention (e.g., therapy, technological aids, compliance strategies, operant conditioning, prevention programmes).
  • Less-frequent example: changing prevalence rates across time/populations.

Responsibility

  • Psychologists wield power → responsible for process & consequences of research.
  • Predominantly ethical considerations
    • During research (deception, consent, protection from harm, right to withdraw, debrief).
    • Animal research specifics.
    • Data handling: anonymisation, data sharing.
    • Reporting & dissemination: avoid stigma, oversimplification; advocate policy responsibly.
    • Ethics committees’ decision-making.
  • Responsibility considerations permeate every topic because all knowledge stems from research.

Psychology as a Scientific Study

  • Working definition: “Psychology is the scientific study of behaviour and mental processes.”
  • Demarcation from non-science (TOK crossover)
    • Based on empirical evidence.
    • Falsifiability: theories must be refutable.
    • Replication: history of independent tests.
  • Clever Hans illustration
    • Initial claim: horse performed arithmetic.
    • Pfungst’s systematic falsification revealed experimenter cues.
    • Demonstrated empirical testing & guarding against bias.

Behaviour vs Mental Processes

  • Behaviour = directly observable (actions, facial expressions, physiological responses).
  • Mental processes = internal (attention, perception, memory, thinking) → inferred through behaviour.
  • Researchers use behavioural indicators (e.g., eye gaze for attention, cortisol for anxiety).

Overview of Research Methods

Quantitative vs Qualitative

AspectQuantitativeQualitative
Aim\text{Nomothetic} – derive universal laws\text{Idiographic} – in-depth understanding
DataNumbersTexts (transcripts, notes)
Researcher roleDetached/ObjectiveInvolved/Reflexive

Quantitative Core Concepts

  • Variable: measurable characteristic.
  • Construct: theoretically defined variable (e.g., anxiety, love).
  • Operationalisation: turning constructs into observable measures (e.g., “number of swear words” for verbal aggression).

Three Quantitative Methods

  1. Experiment
    • Manipulate IV → observe DV; controls → causal inference.
  2. Correlational study
    • Measure variables, compute relationship; no causality.
  3. Quantitative descriptive (survey)
    • Describe distribution of a variable; no relation tested.

Qualitative Methods

  • Observation (naturalistic / controlled; overt / covert; participant / non-participant).
  • Interview / Focus group.
  • Case study.

Analysing Research Quality

Generalizability (External Validity)

  1. Sample → population
    • Population validity (quantitative) ↔ Sample-to-population / inferential generalizability (qualitative).
  2. Setting → real life
    • Ecological validity (quantitative) ↔ Transferability / case-to-case generalizability (qualitative).
  3. Data → construct/theory
    • Construct validity (quantitative) ↔ Theoretical generalizability (qualitative).

Credibility / Internal Validity & Bias

  • Quantitative term: internal validity (was DV change due solely to IV?).
  • Qualitative: credibility / trustworthiness (do findings reflect participants’ reality?).
  • Sampling techniques & biases differ by paradigm.

Experimentation in Detail

Confounding Variables

  • Extra variables that distort IV → DV relationship (e.g., sleep environment in sleep-memory study). Must be controlled.

Sampling Techniques

  • Random sampling: every population member equal chance.
  • Stratified sampling: mirror key population strata (age × GPA example).
  • Convenience (opportunity): easily available; limits representativeness.
  • Self-selected (volunteer): wide reach but motivation bias.

Experimental Designs

  1. Independent measures
    • Random allocation into distinct groups.
  2. Matched pairs
    • Groups matched on a critical variable (matching variable) before random assignment.
  3. Repeated measures
    • Same participants in all conditions; vulnerable to order effects → controlled by counter-balancing.

Validity Trio

  • Construct validity: adequacy of operationalisations.
  • Internal validity: freedom from confounds/bias.
  • External validity: population + ecological.
    • Typically, \text{Internal}\;\uparrow \Rightarrow \text{Ecological}\;\downarrow (inverse relation).

Common Threats to Internal Validity (Biases)

  • Selection bias – non-equivalent groups.
  • Maturation – natural developmental change over time.
  • Testing effect – earlier measurement influences later ones.
  • Instrumentation – changes in measurement tools.
  • History – external events.
  • Regression to the mean – extreme scores drift.
  • Experimental mortality – differential drop-outs.
  • Demand characteristics – participants guess aim.
  • Experimenter bias – researchers unintentionally influence outcomes (Rosenthal’s maze-bright vs maze-dull rats).
    • Solution: double-blind design.

True, Quasi & Non-Experiments

  • True experiment: random group allocation, controlled IV.
  • Quasi-experiment: groups formed by natural allocation; some control (e.g., Sharot 9/11 proximity, Maguire taxi-drivers).
  • Non-experiment: comparison of pre-existing groups without treatment.
  • Continuum: more control → stronger causal inference.

Natural, Laboratory & Field Experiments

  • Natural experiment: naturally occurring IV (e.g., government subsidy, Charlton TV on St. Helena).
  • Laboratory: high control, low ecological validity.
  • Field: real-life setting (e.g., Piliavin subway), higher ecological, lower control.

Research Methods ↔ Key Concepts

  • Causality: experiments needed to make cause–effect claims.
  • Measurement: method choice + data interpretation = essence of measurement.
  • Bias: many biases stem from sampling & procedure.
  • Perspective: qualitative vs quantitative can be seen as differing perspectives.
  • Change: longitudinal designs capture behavioural change.
  • Responsibility: ethics underpin all methods.

IB Psychology Assessment Overview (External & Internal)

  • External = Papers 1–3; Internal = IA.
  • Paper 1
    • Section A: 2\times 4-mark SAQs on content list (biological, cognitive, sociocultural).
    • Section B: 2\times 6-mark SAQs applying content to unseen scenarios.
    • Section C: 1\times 15-mark ERQ – concept-based; combines a Key Concept + Content Unit + Context (Learning & Cognition, Development, Health, Relationships).
  • Paper 2
    • Section A: Four Qs (4+4+6+6 marks) based on your four class practicals; assesses method application, concept link, comparison & study design.
    • Section B: 15-mark ERQ – discuss unseen study through ≥2 concepts.
  • Paper 3 (HL only)
    • Resource booklet with 5 sources.
    • Q1 graph interpretation (3 m).
    • Q2 data→conclusion analysis (6 m).
    • Q3 qualitative credibility/bias/transferability (6 m).
    • Q4 synthesis ERQ (15 m) using ≥3 sources + own knowledge, linked to HL extensions (Culture, Motivation, Technology).
  • IA: Proposal & report of own investigation; demonstrates methodological competence.

Exam Skills Tips (selected)

  • SAQs: brief, focused; ~10 min each.
  • Transfer skills vital for Section B – practise applying theories to novel scenarios.
  • ERQs: argument-driven, balanced evaluation; allocate ~40 min.
  • Paper 2A Q1 sets context for examiner – include aim, method, sample, procedure.
  • Paper 3 Q2/Q4: scrutinise wording of claims; adjust for causality vs association.

Mathematical & Statistical References (examples)

  • Correlation coefficient notation: r(48)=0.30,\;p=0.034.
  • Confidence interval on bar graph: \pm1.96\times SD (95% CI).
  • Science’s four functions often expressed sequentially → \text{Describe}\rightarrow \text{Explain}\rightarrow \text{Predict}\rightarrow \text{Control}.
  • Demarcation of science (empirical evidence, falsifiability, replication).
  • Methodological analysis across Areas of Knowledge: procedures to obtain knowledge determine strength of claims (TOK reflection).
  • Responsibility concept overlaps with real-world policy decisions (e.g., sensitive stereotype research; drug studies with side effects).

Study & Revision Recommendations

  • Regularly revisit definitions & examples of six concepts; practise mapping them onto new studies.
  • After each research study read, explicitly label: method, sampling, biases, validity types, ethical issues.
  • For operationalisation practice: select abstract constructs (e.g., "wisdom") and design multiple observable measures.
  • Use generative AI tactically: formulate precise prompts to test understanding, generate follow-up inquiry questions, or simulate exam scenarios.