Experimental methods

• aim

  a general expression of what the researcher intends to investigate.
• hypothesis

  a statement of what the researcher believes to be true.

  it should be operationalised, i.e. clearly defined and measurable.
• directional hypothesis (one-tailed)

  states whether changes are greater or lesser, positive or negative, etc.
• non-directional hypothesis (two-tailed)

  doesn't state the direction, just that there is a difference, correlation, association.
• experimental method

  a researcher causes the independent variable (IV) to vary and records the effect of the IV on the dependent variable (DV).

  there are different levels of the IV.
• extraneous variables

  'nuisance' variables that do not vary systematically with the IV. a researcher may control some of these.
• confounding variables (e.g. boredom, characteristics)

  change systematically with the IV so we cannot be sure if any observed change in the DV is due to the CV or the IV. CVs must be controlled.
• demand characteristics

  refers to any cue from the researcher or research situation that may reveal the aim of the study.
• investigator effects

  any effect of the investigator's behaviour on the outcome of the research (the DV).
• what are four research issues?

  extraneous and confounding variables, demand characteristics and investigator effects.
• what are five research techniques

  randomisation, standardisation, control groups, single blind and double blind.
• randomisation

  the use of chance when designing investigations to control the effects of bias.
• standardisation

  using exactly the same formalised procedure for all participants in a research study.
• control groups

  used for the purpose of setting a 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 do condition A and a second group do condition B.

  participants should be randomly allocated to experimental groups.
• strengths of independent groups

  1. no order effects. participants are only tested once so can't practise or become bored/tired. this controls an important CV.
  2. will not guess aim. participants only tested once so are unlikely to guess the research aims. therefore behaviour may be more 'natural'.
• limitations of independent groups

  1. participant variables. the participants in the two groups are different, acting as EV/CV. may reduce the validity of the study.
  2. more participants. need twice as many participants as repeated measures for same data. more time spend 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.
• strengths of repeated measures

  1. participant variables. the person in both conditions has the same characteristics. this controls an important CV.
  2. fewer participants. half the number of participants is needed than in independent groups. less time spend recruiting participants.
• limitations of repeated measures

  1. order effects are a problem. participants may do better or worse when doing a similar task twice. reduces the validity of the results.
  2. participants may guess the aims. 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.
• strengths of matched pairs

  1. participant variables. participants matched on a variable that is relevant to the experiment. this enhances the validity of the results.
  2. no order effects. participants are only tested once so no practice or fatigue effects. this enhances the validity of the results.
• limitations of matched pairs

  1. matching is not perfect. matching is time-consuming and can't control all relevant variables. may not address participant variables.
  2. more participants. need twice as many participants as repeated measures for the same data. more time spent recruiting which is expensive.
• what are some examples of confounding variables?

  boredom and characteristics.