Experimental Design

• Repeated Measures

  • Where the same participants are allocated to all groups (i.e. take part in all conditions) of an experiment.
• Strengths of repeated designs:

  • The results will not be subject to participant variables (i.e. individual differences between participants), putting more confidence in dependent variable changes being solely due to manipulated changes in the independent variable. As the same participants are used [at least] twice, extra participants do not need to be recruited.
• Weaknesses of repeated design:

  • here is risk of order effects
  • gives participants practice on the task, so their performance improves in the 2nd condition (an order effect); 
  • makes them bored or tired as they have to take part in two or more conditions, leading to deterioration in performance on the 2nd condition (an order effect); 
  • allows them to work out the aim of the study, leading to a change in behaviour (demand characteristics)
  • All of these factors may affect the DV measurement.
  • Risks can be reduced by counterbalancing
• Independent Groups Design

  • Where different participants take part in each experimental condition (they will be allocated randomly).
• Independent Groups Design strengths:

  • Order effects are not a problem, as no participants will be taking part in more than one condition.
  • Participants are less likely to work out the aim of the study as they are only taking part in one condition.
  • Data collection will be less time-consuming if all conditions of the experiment can be conducted simultaneously.
• Independent Groups Design weaknesses:

  • There is a risk of participant variables (individual differences between participants) affecting the results between conditions, rather than solely manipulation of the independent variable. Random allocation of participants to the conditions can limit the effects of participant variables.
  • Different participants need to be recruited for each condition, which can be difficult and expensive.
• Matched Pairs Design

  • Where participants take part in only one experimental condition, but they are recruited specifically to be similar in relevant characteristics (e.g. intelligence, gender, age) to ‘matched’ participants in the other condition(s).
• Strengths of matched pair design:

  • Order effects will not be a problem as participants only take part in one condition.
  • The tailored participant-matching process reduces the risk of participant variables (individual differences) from affecting results between conditions.
• Weaknesses of matched pairs design:

  • Matching participants is a very complex process, and it will always be very difficult to match participants identically.
  • Different participants need to be recruited for each condition, which is difficult and expensive.
• Counterbalancing (a way to reduce order effects when using repeated measures design)

  • Counterbalancing ensures that each condition in a repeated measures design is tested first or second in equal amounts.
  • E.g. if participants do the same memory test in the morning (A) and then in the afternoon (B), then they may do better on the second test because they’ve had practice.