1. 1.2 Planning and Conducting Research
Experimental Designs
Experiments

2. Imagine…
You want to conduct an experiment looking at the influence of listening to music on students memory How will you do this?

3. A researcher must make an important decision when planning their research study They must select one of the following experimental designs…

4. Experimental design Independent measures design
Repeated measures design
Matched participants design

5. Experimental design: Independent measures design
Condition 2:
Not listening to music
Condition 1:
Listening to music
Two separate groups of participants
Each group = one condition only
‘independent’ = unrelated

6. In pairs, identify what is good about this and what is bad
 Individual differences between participants which may influence findings (one ppt may have great memory and be in ‘music’ group. Other ppt may have poor memory and be in ‘no music’ group. This can give researcher false results)
Participants take part only once
Less likely to notice cues that may tell them aim of experiment (demand characteristics)
To avoid guessing further, the researcher may deceive the ppt by not telling them exactly what the aim of the experiment is (single blind)
Double blind is where researcher and ppt don’t know which ppt is in which condition

7. Experimental design: Repeated measures design
Condition 2:
Not listening to music
Condition 1:
Listening to music
Same group of participants take part in each condition
Repeating performance under different conditions
How could they do this with this example?
Different lists of words

8. In pairs, identify what is good about this and what is bad
Individual differences between participants which may influence findings (one ppt may have great memory and be in ‘music’ group. Other ppt may have poor memory and be in ‘no music’ group. This can give researcher false results)
Repitition = order effects where ppts performance can improve through practice effects, or worsen through tiredness/boredom = fatigue effects
Each participant acts as their own baseline in which to be compared against
Eliminates participant variables (individual differences between participants such as age, gender, intelligence etc)
Any extraneous variable which will influence ppts performance, it is likely to do so over both conditions
To avoid guessing further, the researcher may deceive the ppt by not telling them exactly what the aim of the experiment is (single blind)
Double blind is where researcher and ppt don’t know which ppt is in which condition

9. One way to overcome this is counterbalancing
Condition A (no music)
Condition B (music)
Let’s say there are two conditions: condition A (no music) and condition B (music)
Half of the participant sample can be given condition A then condition B
The other half can be given condition B then condition A
This eliminates the likelihood of all participants performing better on condition A than condition B

10. One way to overcome this is counterbalancing
Condition A (no music)
Condition B (music)
Let’s say there are two conditions: condition A (no music) and condition B (music)
Half of the participant sample can be given condition A then condition B
The other half can be given condition B then condition A
This eliminates the likelihood of all participants performing better on condition A than condition B
Complete the first two rows on the evaluation grid!
Why do this?

11. Experimental design: Matched pairs design
One way to overcome the limitations of both IM and RM is through MPD
Each participant in matched with another participant on relevant variables
For example, if we were to look at memory and music
One 18 year old participant would be matched alongside another 18 year old participant
The participants are matched only on important characteristics

12. In pairs, identify what is good about this and what is bad
 eliminates personal characteristics which may influence the results
 very time consuming
 difficult to match participants on specific variables
Complete the evaluation grid!

13. Experimental design Independent measures design
Repeated measures design
Matched participants design