Experimental design Flashcards
What is an experimental design?
Refers to how participants are allocated to the different conditions in an experiment
What are the 3 types of experimental design?
- Independent groups
- Repeated measures
- Matched pairs design
What is an independent groups design?
Different participants are used in each condition of the independent variable
What is a repeated measures design?
The same participants take part in each condition of the independent variable
What is a matched pairs design?
Participants are paired on a variable and then one of each pair is allocated to each experimental condition
What are order effects?
An extraneous variable arising in a repeated measures design from the order in which conditions are presented. E.g. practice effect or fatigue effect
What is counterbalancing?
An experimental technique used to overcome order effects in a repeated measures design: half the participants experience the conditions in one order and the other half in the opposite order.
What is random allocation?
Allocating participants to experimental groups or conditions using random techniques.
What is the advantage of a repeated measures?
Participant variables are controlled for
What is the disadvantage of repeated measures?
Order effects- repeating 2 tasks could create boredom or fatigue leading to deterioration in performance on 2nd task. Or could lead to practice effects where participants’ performance improves
How can we overcome order effects in a repeated measures study?
Counterbalancing
What are the two advantages Independent groups?
-No order effects
-Participants less likely to guess the aim of the experiment
What are the two disadvantages of independent groups Independent groups?
-Participant variables are not controlled for
- Less economical- need more participants
How can you control participant variables in an Independent groups study?
Random allocation
What are the two advantages of Matched pairs design?
- Less likely to have demand characteristics and order effects
- Reduces influence of participant variables