Stage 2 Psychology – Inquiry Skills Comprehensive Notes

What is Psychology?

• Systematic scientific study of thoughts, feelings, and behaviours.
• Uses empirical methods and ethical inquiry to explain, predict, and, where appropriate, modify behaviour.

Key Terminology

• Hypothesis – a testable prediction about the relationship between two variables.
– Typically framed as: "If … (IV) then … (DV)."
– Example: If students read fiction for 20 min, then their self-reported mood scores will improve.
• Variables
– Independent Variable (IV): manipulated by the experimenter.
– Dependent Variable (DV): measured outcome; reveals the IV’s effect.
– Constant / Controlled Variables: factors intentionally kept the same.
– Extraneous Variables (EVs): uncontrolled factors that may influence the DV. When they systematically affect results they become confounding variables.
• Participant Variables – individual differences (age, IQ, mood, etc.) that can influence the DV.
– Placebo Effect: change in DV caused by participants’ expectations, not the IV.
• Situational Variables – aspects of the environment (time of day, lighting, noise, room temperature) that differ between groups.
• Experimenter Effects – unintentional influence of researchers’ characteristics or behaviour on participants.

Crafting a Hypothesis (Class Activity Example)

• Spend $3$ minutes writing a reading–mood hypothesis.
• Peer-swap to identify IV and DV.
• Prepare to share with class.

Graph Interpretation Example – Caffeine & Maze-Running Rats

• Six rat groups given $0$ – $25\,\text{mg}$ caffeine; mean maze completion time recorded.
• IV: amount of caffeine consumed (mg).
• DV: mean time (s) to finish maze.

Experimental Investigation Design

Defining Features

Manipulation of IV.
Presence of treatment (experimental) and control groups.
Random allocation of participants to groups.
• Enables cause–effect conclusions.

Group Labels

• Treatment Group: receives IV manipulation (e.g.
participants drink an energy drink).
• Control Group: does not receive treatment; provides baseline.

Allocation & Bias Reduction

• Random allocation balances participant variables across groups.
• Single-blind procedure: participants unaware of group – reduces placebo effect.
• Matched-participants design: pair participants on key traits, randomly split pairs.
• Repeated-measures design: same participants experience each condition – controls participant EVs.

Experiment Types

Design	Key Points
Independent Groups	Different participants in each condition; random allocation.
Matched Participants	Similar pairs split into different groups.
Repeated Measures	One group tested under every condition; order effects need counter-balancing.

Sample Exam Scenario – Acts of Kindness & Depression

• $30$ depressed participants → random allocation Group A vs B.
• Group A comforts a crying child; Group B only observes.
• Repeated weekly $8$ -min sessions, follow-up one month later.
• DV: self-reported depressive symptoms.
• Features making it an experiment: IV manipulation & random allocation / control group.

Advantages of Experiments

• Establishes causal relationships.
• Random allocation or matched pairs ↓ participant bias.
• Single-blind combats placebo.
• Repeated measures = identical participants across conditions.
• Standardised instructions → high reliability (replicable).

Disadvantages of Experiments

• Pre-testing & matching are time-consuming; attrition of a matched pair compromises balance.
• Artificial lab context → low ecological validity.
• Potential social-desirability bias.
• Ethical/practical limits: some variables cannot be manipulated.

Observational Research Design

• Key feature: IV is naturally varying or manipulation unethical/impossible; groups are pre-existing.
• Example: Comparing shift-workers vs day-workers on fatigue.

Advantages

• Permits study of variables impossible or unethical to manipulate (gender, trauma exposure).
• Often cheaper & easier than controlled experiments.

Disadvantages

• No cause–effect inference (lack of random assignment).
• Low replicability (unique natural settings).
• Possible demand characteristics if participants know they are observed.

Qualitative Research Design

• Addresses broad questions rather than testing specific hypotheses.
• Data are non-numerical (words, images, artefacts).

Methods

• Focus Groups (6–12 participants) – facilitated open discussion.
• Delphi Technique – successive questionnaires to experts until consensus.
• Interviews – structured, semi-structured, or unstructured.

Advantages

• Rich, detailed insight into participants’ experiences.
• Can include non-verbal materials (photos, drawings).
• Flexible, adaptable to participant literacy levels.

Disadvantages

• Findings not necessarily generalisable.
• Time-consuming analysis; researcher interpretation bias.
• Small samples due to cost/time constraints.
• Potential confidentiality issues (esp. focus groups).

Data Types

• Objective Quantitative: numerical, instrument-based; e.g.
heart-rate, EEG, behavioural counts.
• Subjective Quantitative: self-reports converted to numbers; e.g.
rating scales, Likert questionnaires.
• Qualitative: word-based, subjective descriptions; e.g.
transcripts from focus groups, Delphi responses.

Statistics

Descriptive Statistics

• Summarise patterns in data. Key indices:
– Mean: $\text{Mean}=\frac{\sum X}{N}$
– Median: middle score after ordering.
– Standard Deviation (SD): average deviation from mean; higher $SD$ = greater variability.
• Example: Class A $SD=15$ vs Class B $SD=2$ ; easier to predict any student’s IQ in Class B (scores cluster tightly).

Inferential Statistics

• Allow conclusions about relationships & generalisation from sample to population.
• Indicate probability that observed effect is real vs chance (e.g.
p<0.05 significance levels).

Sampling Concepts

• Population: entire target group.
• Sample: subset drawn for study.

Sampling Methods

• Random Sampling: each member equal chance.
• Convenience Sampling: accessible participants; risk of bias.
• Stratified Sampling: population divided into strata; random sample from each stratum proportional to population.

Representative vs Unrepresentative Samples

• Representative sample: mirrors population demographics → allows generalisation (high external validity).
• Unrepresentative sample: small or biased → can’t generalise; lowers external validity.

Sample Size

• Larger $N$ increases reliability & mirrors population.
• Small $N$ susceptible to extraneous variables & sampling error.

Reliability

• Definition: consistency & stability of measurement.
• Test–retest reliability: similar results on two administrations.
• Inter-observer reliability: agreement between observers.
• Meta-analysis: aggregates multiple studies to strengthen reliability; but differing designs may limit comparability.

Validity

• Internal Validity: only IV influences DV; threatened by EVs.
• External Validity: findings generalisable beyond sample; threatened by unrepresentative sample or artificial setting.

Biopsychosocial Model of Behaviour

• Behaviour explained via interplay of:
– Biological: genetics, neurochemistry, hormones, brain structures, age, sex.
– Psychological: cognition, emotion, learning, attitudes, coping.
– Social: family, culture, SES, education, relationships.
• Encourages holistic assessment of mental & physical health issues.

Ethics in Psychological Research

• Governed by APS Code: Respect, Propriety, Integrity.

Core Ethical Principles

Voluntary Participation & Freedom from Coercion
– Right to withdraw at any time without penalty.
Informed Consent
– Participants (or guardians) receive full study information; written consent required.
Deception (only if essential)
– Must be justified; cannot cause distress; requires thorough debriefing.
Debriefing
– Explain study, correct misconceptions, provide support resources, share results.
Confidentiality & Privacy
– Secure data storage; disclose only with permission or legal duty.
Protection from Harm
– Avoid physical or psychological harm; monitor risk.
Withdrawal Rights
– Participants may cease involvement without disadvantage.

Applied Ethics Example – Acts of Kindness Study

• Informed consent crucial because participants are vulnerable (depressive symptoms) & exposed to emotional scenarios (crying child).
• Additional considerations: debriefing, confidentiality, minimising distress, parental/guardian consent where capacity limited.

Connections & Real-World Relevance

• Insights from IV–DV manipulation inform clinical treatments, educational policy, marketing strategies, etc.
• Observational & qualitative designs guide policy when experiments unethical (e.g.
emergency-service mental-health prevention).
• Statistics underpin evidence-based practice; SD variability guides teacher decisions (predicting performance).
• Biopsychosocial framing aligns with modern integrated healthcare.
• Ethical rigour maintains public trust and participant welfare.

Formula & Numerical Highlights

• Mean formula: $\text{Mean}=\frac{\sum X}{N}$
• SD interpretation: higher $SD$ ⇒ greater dispersion; lower $SD$ ⇒ scores cluster.
• Delphi Technique iteration: typically $3$ rounds of questionnaires.
• Experimental class activity: $3$ minutes hypothesis writing.
• Rat caffeine experiment: $0,5,10,15,20,25$ mg doses – maze times $40$ → $10$ s trend.

These bullet-point notes capture foundational terminology, methodological frameworks, statistical tools, sampling logic, reliability/validity issues, biopsychosocial context, and ethical standards required for Stage 2 Psychology Inquiry Skills. Use them as a comprehensive replacement for the original transcript when revising for exams.