Experimental Design

Question 1

it refers to?

Answer 1

how people are ALLOCATED to different conditions/ IV levels in an experiment

most common way to design an experiment= divide them into 2 groups: EG & CG- then introduce a change to EG; not the CG- the researcher must then decide HOW she’ll allocate her sample to the IV levels

Question 2

What is Independent measures design aka?

Answer 2

BETWEEN groups

Question 3

How does IMD work?

Answer 3

• different participants are used in EACH condition of the IV
• means that EACH condition of the experiment includes a different group of PPs
• should be done by RANDOM ALLOCATION: ensures that EACH PP has an EQUAL chance of being assigned to EITHER condition
• thus involves 2 SEPARATE groups of participants; one in EACH condition

Question 4

Strengths of an IMD

Answer 4

avoids order effects as participate in 1 condition only- ex: fatigue & practice effects

Question 5

Limitations of an IMD

Answer 5

• more people are needed than w/ RMD so more time consuming & $$
• Participant variables (type of EV)- differences between participants in the groups may affect results

Question 6

how to control PVs in an IMD

Answer 6

AFTER they’ve been recruited, they should be RANDOMLY ALLOCATED to their groups
this ensures the groups are SIMILAR, on average- thus reducing PVs

Question 7

What is a Repeated Measures Design aka?

Answer 7

• WITHIN groups

Question 8

Format of a RMD

Answer 8

the SAME participants take part in EACH condition of the IV- means EACH condition contains the SAME group of PPs

Question 9

Strengths of RMDs

Answer 9

• fewer people needed as they take part in ALL conditions- so saves time & $$
• as the SAME PPs are used in each condition, PVs= reduced

Question 10

Cons of RMDs

Answer 10

• there may be order effects

- BUT this can be controlled by COUNTERBALANCING

Question 11

counterbalancing

Answer 11

• to combat OEs the researcher counterbalances the ORDER of the conditions for the participants
• ALTERNATING the order in which PPs perform indifferent conditions of an experiment
• ABBA (EG: A, CG: B- G1 does A then B, G2 does B then A)
• although OEs occur for each participant, because they occur EQUALLY in both groups, they BALANCE each other out in the results

Question 12

What is Matched Pairs Design?

Answer 12

• each condition uses different but SIMILAR PPs
• an effort is made to MATCH the PPs in each condition in terms of wichtig individual differences which may affect performance- ex: age, IQ, etc
• 1 member of each MP must be RANDOMLY ASSIGNED to the EG & the other to the CG

Question 13

Strengths of MPDs?

Answer 13

• reduces PVs because the researcher has tried to pair up PPs so that EACH condition has people w/ similar abilities
• avoids OEs so counterbalancing NOT necessary

Question 14

Limitations of MPDs

Answer 14

• if 1 PP drops out you LOSE 2 PPs’ data
• v time consuming to find closely MPs
• impossible to match people EXACTLY- bar identical twins

Question 15

Control of MPDs

Answer 15

members of EACH pair should be RANDOMLY ASSIGNED to the conditions- however NOT a panacea