How science works Investigating the difference between two conditions

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Presentation transcript:

How science works Investigating the difference between two conditions i.e., An experiment

Experimental Design In an experiment we manipulate an IV There are usually two values of the IV e.g. Doing homework with background noise or doing it with no noise Deep or shallow processing These determine the conditions of the experiment The conditions can be arranged different ways according to the design of experiment

Experimental Design 3 main types Independent measures Repeated measures Matched participants

Independent Measures Compare the results for the two groups Recruit a group of participants Divide them into two This group does the experimental task with the IV set for condition 1 This group does the experimental task with the IV set for condition 2 Measure the DV for each group Compare the results for the two groups

Repeated Measures Compare the results for the two conditions Recruit a group of participants Condition 1 Condition 2 The group does the experimental task with the IV set for condition 1 The group repeats the experimental task with the IV set for condition 2 Compare the results for the two conditions

Matched Participants Compare the results for the matched pairs Recruit a group of participants Find out what sorts of people you have in the group Recruit another group that matches them one for one Treat the experiment as independent measures Condition 1 Condition 2 Compare the results for the matched pairs

Problems with Independent Measures Design Participant Variables – chance variation between participants can affect DV E.G, in a test of a sample population’s mathematical ability, all participants in group 1 just happen to have a maths degree. In group 2, all participants happen to all be really bad at maths . Could imply an effect where none exists (false positive) Control by random assignment to groups Use repeated measures or matched PPs instead

Problems with Repeated Measures Design Carrying out a task repeatedly leads to changes in performance Deterioration as PPs become tired or bored Improvement due to practice Solutions Leave a long gap between conditions Counterbalanced design Use independent measures or matched participants

Counterbalancing Important control when using repeated measures Reduces ‘carry over’ effects Half PPs do condition A then B Other half do condition B then A Fully counterbalanced: ABBA

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