Experimental Design- Research methods

Independent groups design (IGD)

• .Uses different participants in each of the experimental conditions.
• Strengths:
• .No order effects-The order in which conditions are done don't effect the outcome as participants only do one condition.
• .Demand characteristics-less chance can guess purpose of study.
• .Time saved- Both sets of participants can be tested at the same time.
• Weaknesses:
• .More participants needed
• .Group differences-Differences in results between the two conditions may be due to participant variables rather than manipulations of the IV. This can be minimised by random allocation of participants to each condition.

Repeated measures design (RMD)

• .Each participant is tested in all conditions of an experiment.
• Strengths:
• .Group differences- No participants variables as same people measured in all conditions.
• .More data/fewer participants- fewer participants needed to get same amount of data as IGD.
• Weaknesses:
• .Order effects- Participants may perform worse in the second condition due to fatigue or boredom (negative order effect) or perform better due to practise or learning (positive order effect). Counterbalancing can be used to control this, half of the participants do condition A followed by condition B and the other half do the conditions the opposite way round.
• .Demand characteristics-participants more likely to guess the purpose of the study and act accordingly.
• .Takes more time- A gap may be needed between conditions to counter the effects of boredom or fatigue.

Matched pairs design (MPD)

• .Different, but similar participants are used for each condition.Participants are matched on characteristics important for a particular study. Identical twins are often used as the form perfect matched pairs, sharing identical genetic characteristics.
• Strengths:
• .No order effects- different participants do each condition.
• .Demand characteristics- less chance of them guessing purpose of study as they only do one condition.
• .Group differences- As participants are matched less chance of participant variables affecting the results.
• Weaknesses:
• .More participants
• .Matching is difficult-It is impossible to match all variables between participants.
• .Time consuming-Its a lengthy process to match participants.