Experimental method

• Aim = A general expression of what the researcher intends to investigate.
• Operationalised hypothesis = A statement of what the researcher believes to be true. It should be operationalised.
• Operationalised = clearly defined and measurable.
• Directional hypothesis = States whether changes are greater or lesser, positive or negative (used when theory/ research suggests the direction).
• Non-directional hypothesis = Doesn’t state the direction, just that there is a difference, correlation, association (used when there is no theory/previous research or it is contradictory).
• Independent and dependent variables = a researcher causes the independent variable to vary and records the effect of the IV on the dependent variable. There are different levels of the IV.
• Research issues = extraneous and confounding variables, demand characteristics, investigator effects, randomisation, standardisation.
• Extraneous variables = ‘nuisance’ variables that ‘muddy the water’ and may make it more difficult to detect an effect. A researcher may control some of these.
• Confounding variables = change systematically with the IV so we cannot be sure if any observed change in the DV is due to the CV or IV.
• Demand characteristics = refers to any cue from the researcher or research situation that may reveal the aim of the study, and change participants‘ behaviour.
• Investigator effects = Any effect of the investigator’s behaviour on the outcome of the research (the DV) and also on design decisions.
• Randomisation = The use of chance when designing investigations to control for the effects of bias e.g. allocating participants to conditions.
• Standardisation = using exactly the same formalised procedures for all participants in a research study, otherwise differences become EVs.
• Pilot studies = small-scale trial run of an investigation to ’road-test’ procedures, so that research design can be modified.
• Control groups/ conditions = control groups (independent groups design) or control conditions (repeated measures design) are used to set comparison. They act as a ‘baseline’ and help establish causation.
• Single blind = a participant doesn’t know the aims of the study so that demand characteristics are reduced.
• Double blind = both participant and researcher don’t know the aims of the study to reduce demand characteristics and investigator effects.
• Independent groups = One group does condition A and a second group does condition B. Participants should be randomly allocated to experimental groups.
• Independent groups advantages = + no order effects. Participants are only tested once so can’t practice or become bored/tired. This controls an important CV.

  + will not guess aim. Participants only tested once so are unlikely to guess the research aims. Therefore behaviour may be more ‘natural’ (higher realism).
• Independent groups disadvantages= - Participant variables. the participants in the two groups are different, acting as EV/CV. May reduce the validity of the study.

  - Less economical. Need twice as many participants as repeated measures for same data. More time spent recruiting which is expensive.
• Repeated measures= same participants take part in all conditions of an experiment. the order of conditions should be counterbalanced to avoid order effects.
• Repeated measures advantages = + participant variables. the person in both conditions has the same characteristics. This controls an important CV.

  + Fewer participants. Half the number of participants is needed than in independent groups. Less time spent recruiting participants.
• Repeated measures disadvantages= - Order effects are a problem. participants may do better or worse when doing a similar task twice. also practice/fatigue effects. Reduces the validity of the results.

  -Participants guess aim. participants may change their behaviour. this may reduce the validity of the results.
• matched pairs = two groups of participants are used but they are also related to each other by being paired on participant variable(s) that matter for the experiment.
• matched pairs advantages = + participant variables. participants matched on a variable that is relevant to the experiment. This controls participant variables and enhances the validity of the results .

  + no order effects. Participants are only tested once so no practice or fatigue effects. This enhances the validity of the results.
• Matched pairs disadvantages = - matching is not perfect. Matching is time-consuming and can’t control all relevant variables. Cannot address all participant variables.

  - more participants. Need twice as many participants as repeated measures for same data. More time spent recruiting which is expensive.