Experiment and design Flashcards
IV is what
the variable that the researchers choose to manipulate
The conditions of the IV usually form which two groups
experimental condition and the control condition
DV is what
variable that researchers choose to measure
Lab experiment feature
artificial environment
controls
Field experiment feature
participant’s own natural environment
some controls
situational variables
variables from the setting that might affect the DV
Natural experiment feature (2)
- naturally occurring IV
- take place in a real-life situation
Strength of lab experiment (3)
- high standardisation meaning can replicate to test for reliability
- high control
- IV directly affecting DV- cause and effect
Weaknesses of lab experiment (3)
- artificial setting, lacks EV
- lack mundane realism
- increased demand characteristics
Strengths of a field experiment (3)
- take place in realistic environment so increased EV
- limited demand characteristics
- natural behaviour
Weaknesses of a field experiment (3)
- situational can’t be controlled
- might break ecological validity
- ethical issues
Strengths of natural experiment(2)
- high in EV
- Behaviour will be natural so it will be a valid representation of each person’s behavioural response
Weaknesses of natural experiment (2)
- don’t know for sure if the IV has caused an effect on the DV
- difficult to replicate due to reduced standardised procedures
Examples of lab experiment
Yamamoto et al. investigated the helping behaviour in chimps in controlled lab conditions using three IV conditions
Example of field experiment
Piliavin et al. was a field. There were IVs, DVs and many controls. Conducted in a New York Subway where the natural behaviour can be observed.
Example of natural environment
Study by Baron-Cohen et al. is an example of a natural experiment because participants could not be randomly allocated to either autistic or non-autistic. Eyes test was same for all four groups
Valid, lab experiment?
2
High internal validity because of controls set in place allowing the researcher to be sure that changes in the IV are causing changes in the DV
Low external validity as it may be difficult to apply findings to real life situation
Valid, field
2
Low internal validity as researchers can control some variables but not all
High internal validity as the setting is not artificial
Valid, natural
2
low internal validity as there is no control over extraneous variables
high external validity as it takes place in ppt’s natural environment
Reliable, lab?
High reliability due to many controls and standardised procedures allowing for replication to test for reliability
Reliable, field
medium levels of reliability as some elements of the study are standardised. However, full replication may be difficult
Reliable, natural
Low as there are hardly any levels of control over extraneous variables
Ethics, lab? (3)
- informed consent is easy to acquire
- deception can be dealt with if there is a full debrief
- participants know they can withdraw at any point
Ethics, field (3)
- informed consent is not always possible to gain
- participants don’t know they’re in a study so they can’t be debriefed
- if ppts don’t know they’re in a study, they can’t withdraw
Ethics, natural (3)
- informed consent can be difficult to gain for some studies
- participants don’t know they’re in a study so they can’t be debriefed
- if ppts don’t know they’re in a study, they can’t withdraw
Experimental design
Participants are allocated to conditions of the IV
Repeated measures design
Where each participant takes part in all conditions of the IV
Example of repeated measures design
In the study by Canli et al. all ten ppts saw all 96 pics, which was essential for each ppt to give consistent intensity ratings to each pic. This could not have been achieved with any other design
Strengths of repeated measures design (2)
- control of ppt variables
- only have ppts are needed
Weaknesses of repeated measures design (3)
- order effects and demand
- in some instances is impossible e.g. cannot be both left-handed and right-handed
- not necessary to duplicate apparatus
Independant measures design
Each ppt is in just one condition of the IV
Example of independant measures design
- Andrade et al, independant measures design. Ppts were randomly allocated to either doodle or not doodle. This was important because a ppt could not be in both conditions
Strengths of independant measures (2)
- no order effects
- reduced demand characteristics
Weaknesses of independant measure (2)
- participant variables
- need more participants
How to eliminate participant variables
Random allocation
Matched pairs design
Match any particular aspect of two or more groups of participants
What is the aim of the matched pairs design
Control participant variables
Example of matched pairs design
Baron- Cohen et al matched the IQ of ppts in group 4 and group 1.
Strengths of matched pairs design (2)
- No ppt variables
- No order effects
Weaknesses of matched pairs design (2)
- can’t be sure if all relevant variables have been matched
- time consuming and difficult to match participants
Single blind technique
Ppt is unaware of the behaviour that is expected of them
Demand characterisitcs
ppts responds to the experiment in a certain way in order to please the experimenter
Experimenter bias
experimenter who wants to achieve a particular outcome gives different ‘signals’ to participants
Double blind technique
Ppt and experimenter are unaware of the aims of the study
