"Music helps you revise." How could you test that?

From a claim to a prediction

• An aim says what a study will investigate, e.g. whether music affects memory.
• A hypothesis is a precise, testable prediction.
• The alternative hypothesis predicts a difference or relationship: "Students recall more words in silence than with music."
• The null hypothesis predicts no difference or relationship: "There will be no difference in recall with music and in silence."

What you change and what you measure

• The independent variable (IV) is what the researcher changes (manipulates) to create the conditions.
• The dependent variable (DV) is what the researcher measures.
• Operationalise each variable: define it precisely so it can be measured.

What you keep the same

• An extraneous variable is any other variable that could affect the DV.
• If it isn't controlled, you can't tell whether the IV caused the change in the DV.
• Situational variables come from the setting: noise, time of day, the word list.
• Participant variables come from the people: age, memory ability, tiredness.
• Control them by keeping them the same for every participant, e.g. the same room, list and time limit.

Lab or real classroom: which experiment's result should you trust?

The laboratory experiment

• The researcher manipulates the IV and measures the DV in a controlled, artificial setting.
• Participants know they are taking part.
• Strengths: extraneous variables are tightly controlled, so cause and effect can be shown. Standardised procedures make it easy to replicate.
• Weaknesses: artificial tasks may not reflect real life (low ecological validity). Participants may guess the aim and change their behaviour (demand characteristics).

Field and natural experiments

• In a field experiment, participants often don't know they are in a study.
• In a natural experiment, the researcher compares groups that already differ, e.g. pupils at schools that already start the day at different times.

Trading control for realism

• Reliability means consistency: repeat the study and get similar results.
• Validity means the study measures what it intends and applies beyond the study.
• Field: higher ecological validity and fewer demand characteristics, but less control and harder to replicate. Participants often can't give consent.
• Natural: can study IVs that would be unethical or impossible to manipulate, but participants aren't randomly allocated, so participant variables may explain results.

Same people in both conditions, or different people: which is better?

Different people in each condition

• In an independent groups design, each participant takes part in only one condition.
• Strengths: no order effects, and participants are less likely to guess the aim.
• Weaknesses: participant variables: the groups may differ (e.g. one has better memories), and that could cause the result. More participants are needed.
• Control: random allocation to conditions (e.g. drawing names from a hat) spreads participant variables across the groups.

The same people in every condition

• In a repeated measures design, each participant takes part in every condition.
• Strengths: no participant variables between conditions, and fewer participants are needed.
• Weaknesses: order effects: a practice effect (better second time) or a fatigue effect (worse through tiredness or boredom). Participants may guess the aim (demand characteristics).
• Control: counterbalancing: half do condition A then B, half do B then A, so order effects balance out.

Matched pairs

• In a matched pairs design, participants are paired on a variable that could affect the DV, e.g. a memory test score or age.
• One member of each pair does each condition.
• Strengths: reduces participant variables, and there are no order effects.
• Weaknesses: matching takes time and is never perfect. If one person drops out, the pair is lost.

Keeping the procedure fair

• Standardised procedures: every participant gets the same tasks, materials, timings and setting, and standardised instructions (the same words).
• Randomisation: chance decides things like the order of word lists, so the researcher's choices can't bias the results.

You can't test every UK teenager. Are 20 from your class enough?

Population and sample

• The target population is the whole group a researcher wants to find out about, e.g. UK 13 to 16-year-olds.
• The sample is the group who actually take part.
• A representative sample has the same mix of characteristics as the target population.
• Then the findings can be generalised: applied to the whole target population.

Random and opportunity sampling

• Pulling names from a hat is random only if every member's name is in the hat.

Systematic and stratified sampling

• Systematic: pick every nth person from a list of the target population, e.g. every 10th name on a register. Objective once n is set, but unrepresentative if the list has a pattern.
• Stratified: identify the subgroups (strata) in the population, e.g. year groups, then randomly pick from each in the same proportions as the population.
• Highly representative of the subgroups, but slow, and it needs detailed information about the population.

How do you study a crowd, a childhood, or what people really think?

Questionnaires

• A questionnaire is a set of written questions, on paper or online.
• It can reach a large sample quickly and cheaply, and everyone gets the same questions.

Interviews

• An interview asks questions face to face, or by phone or video.
• Structured: the same questions in the same order for everyone. Easy to compare and repeat, but no follow-up.
• Unstructured: like a conversation; the interviewer follows up answers. Rich detail, but hard to compare.
• Interviewees and questionnaire respondents may give socially desirable answers: ones that make them look good.

Observations

• In an observation, researchers watch and record behaviour as it happens.
• Categories of behaviour are decided in advance, e.g. "pushes", "shares a toy", and tallied each time they occur.
• Inter-observer reliability: two observers record separately, then compare. High agreement means the records are reliable.
• Strength: shows what people actually do, not what they say. Weakness: shows what happens, not why.

Case studies

• A case study is an in-depth study of one person or a small group, often over a long time.
• It combines methods (interviews, observations, records) and mainly gives qualitative data from a small sample.
• Strengths: rich detail; can study rare cases, e.g. an unusual brain injury.
• Weaknesses: findings may not generalise; hard to replicate; relies on memory and the researcher's interpretation.

More ice creams sold, more swimmers rescued. Is ice cream to blame?

Two things that change together

• A correlation is an association between two variables.
• Both variables are measured using quantitative data. Neither is manipulated, so there is no IV or DV.
• A scatter diagram shows a correlation: one variable on each axis and one dot for each person or case.

Positive, negative or zero

• Positive correlation: as one variable increases, so does the other, e.g. if more hours of revision went with higher test scores.
• Negative correlation: as one variable increases, the other decreases, e.g. if more screen time went with less sleep.
• Zero correlation: no relationship, e.g. shoe size and test score.
• The closer the dots lie to a straight line, the stronger the correlation.

Linked, but not proved to cause

• A correlation can't show that one variable causes the other.
• A third variable may cause both, or the cause may run the other way.
• Strengths: can study links that would be unethical or impossible to manipulate; quick, often using existing data; suggests ideas to test in experiments.
• Weaknesses: no cause and effect; an unmeasured third variable may explain the link.

Psychologists have lied to participants. When is that allowed?

The rules psychologists follow

• An ethical issue arises when a study could harm participants or ignore their rights.
• UK psychologists follow the British Psychological Society (BPS) Code of Ethics and Conduct.
• Its four principles are respect, competence, responsibility and integrity.
• Researchers must weigh what a study could teach us against any cost to participants, and protect participants first.

Consent and deception

• Informed consent: participants agree to take part after being told what the study involves. Under-16s also need consent from a parent or guardian.
• Deception: misleading participants or hiding the true aim.
• Deception is acceptable only if telling the aim would spoil the results, it won't cause distress, and participants are debriefed afterwards.

Harm, withdrawal and confidentiality

• Protection from harm: participants should leave in the same state as they arrived, facing no more physical or psychological risk than in everyday life.
• Right to withdraw: participants can leave at any time and ask for their data to be removed.
• Confidentiality: personal information stays private; participants get numbers, not names, so they can't be identified.

Dealing with ethical issues

• Debriefing: at the end, explain the true aim, reveal any deception, check participants are OK and remind them they can withdraw their data.

Ethics in classic studies

• Real studies show why the guidelines matter.
• In Piliavin's subway study, passengers on a New York subway train didn't know they were in a study, so they gave no informed consent and weren't debriefed.
• In Milgram's obedience study, participants were deceived: the electric shocks were fake. Many showed stress, such as sweating and trembling.

Two classes have the same average mark. What can the average hide?

Kinds of data

• Quantitative data can be summarised with averages, measures of spread and charts.

Averages and spread

• Mean: add the scores and divide by how many there are. Uses every score, but extreme scores distort it.
• Median: the middle score when they are in order (with two middle scores, take halfway between). Not affected by extreme scores.
• Mode: the most common score.
• Range: highest minus lowest, a measure of dispersion (spread).

Charts and the normal distribution

• A normal distribution is a symmetrical, bell-shaped curve. The mean, median and mode are equal, at the centre. Most scores are near the middle; few are at the extremes.

Big ideas

• A hypothesis is a testable prediction: the alternative predicts a difference or relationship, the null predicts none.
• The IV is changed and the DV is measured; both must be operationalised. Extraneous variables, situational or participant, must be controlled.
• Lab and field experiments both manipulate an IV; a natural experiment uses an IV that varies naturally.
• Labs give control and replicability (reliability); field and natural experiments give higher ecological validity but less control.
• Choose the type that suits the question: a lab for cause and effect, the field for real behaviour, a natural experiment when the IV can't ethically be manipulated.
• Independent groups: different people in each condition, so watch for participant variables (use random allocation).
• Repeated measures: the same people in every condition, so watch for order effects and demand characteristics (use counterbalancing).
• Matched pairs reduces participant variables; standardised procedures, instructions and randomisation keep the test fair.
• A sample should represent the target population so the findings can be generalised.
• Random, opportunity, systematic and stratified sampling each trade convenience against representativeness.
• Questionnaires and interviews ask; observations watch; case studies go deep into one person or small group.
• Closed questions give quantitative data and open questions give qualitative data; answers may be socially desirable.
• Observations use categories of behaviour, checked by inter-observer reliability.
• A correlation measures two variables and shows whether they are positively, negatively or not related, usually on a scatter diagram.
• Correlations can study links that can't be manipulated, but they can't show cause and effect: a third variable may explain the link.
• Psychologists follow the BPS Code of Ethics and Conduct.
• The key issues are informed consent, deception, protection of participants, the right to withdraw and confidentiality; each has a way of dealing with it, such as debriefing.
• Data can be quantitative or qualitative, primary or secondary.
• Mean, median and mode describe the centre; the range describes the spread.
• A normal distribution is symmetrical and bell-shaped, with mean, median and mode equal at the centre.