This research methods in psychology video will cover experimental design.
This is about exactly how we use the participant sample in combination with the different levels
of independent variable.
The researcher has choices in how they set up the experiment, but of course there are
positives and negatives to each design.
INTRO I'm going to cover two experimental designs
first, and they are fairly simple to understand.
We can either get all participants to complete both conditions of the experiment and that
is a repeated measures design or we can randomly split the sample into two
groups, and get one group to complete condition A then condition B, and that's an independent
groups design.
Independent groups design Independent groups design, as the name suggests
the group's are independent of each other, different sets of people in each condition.
All the data collected from the group completing condition 1 is compared with the data from
the group completing condition 2.
The data we collect is called unrelated data.
Now a problem you may be able to spot with this experimental design is, because the group's
are not the same people, any change we measure may not be due to the change in independent
variable, but be due to participant variables, characteristics of the people taking part.
For example if your study was on temperature and reaction times, if in the random allocation
to groups more young people were randomly assigned to one of the conditions, this could
influence the results, maybe showing a difference between conditions that isn't really there.
Repeated measures design So an alternative is to get all the participants
to complete both conditions.
Easy to remember it's name as each participant has to repeat the experiment under each condition.
So the researcher is comparing each participant's score in condition A with their score in condition
B. Because of this data the researcher has collected is called related data, each data
point in one condition has a related data point in the other condition.
That is going to be important to remember later on when we get to the statistical testing
video.
Well that's great, now we are comparing a participant's score to their own score in
the other condition, there is no possibility that participant variables are responsible
for measuring a difference between conditions.
But now we have a new problem.
If the participants complete both conditions there may be order effects.
People may improve on the second condition because of practice, or they my get worse
on the second condition because they are bored or tired.
We can try to control order effects by counterbalancing, that is getting half the participants to do
condition A then B and the other half B then A. this will then balance the influence of
order effects across the two conditions, so if tiredness or practice does affect performance
it should be the same across the two conditions.
Notice counterbalancing controls order effects, it doesn't eliminate them.
So looking at the evaluations for both designs, there are other issues, a problem for independent
groups is this design needs more participants than repeated measures to produce the same
amount of data.
Another problem in repeated measures is because participants complete two conditions they
are more likely to work out the aims of the experiment and change their behaviour to please
the researcher, this is known as demand characteristics.
I think you have probably noticed that for these two experimental designs the strengths
are the weaknesses of the other.
But there is another design, one that attempts to fix the problem with both participant variables
in independent groups design and of order effects in repeated measures design.
Matched pairs design In a matched pairs design two separate groups
of participants are used, one for each condition, but the researcher decides on a characteristic
that may influence the result.
Depending on the study it may be IQ, age or something like aggression levels.
This is measured before the study begins.
Then we can rank our participants, we would then randomly assign the highest scoring two
participants to separate conditions of the experiment.
Then do the same for the next two, and continue until all the participants are assigned.
Then we will have two groups balanced for that characteristic.
The results of each pair in each condition are compared, in the same way as repeated
measures design, so we treat the data again as related data.
As the participants are similar to each other we have reduced the effect of participant
variables, and as each participant only takes part in one condition we also have removed
order effects.
Disadvantages are it takes longer to set up matched pairs, we need more participants for
the same amount of data compared to repeated measures and participants are similar in each
condition, but they are not identical, so there may still be some participant variables.
Outro So that was experimental design, I have 6
tutorial videos covering the 2017,18 and 19 AS and A-level research methods sections,
these videos have worked examples to every question and are full of exam tips.
Patrons at the neuron level and above can access these, and many, many more hours of
exam tutorial videos, as well as over a hundred printable resources from across the A-level
over on psych boost .com I do want to thank all the students and teachers
who have supported psych boost over on patreon during the development of the research methods
unit.
It's their support that allows me to teach part time so I can make psych boost on youtube
for everyone.
So thanks to them, and I will see you all in the next research methods video,
, variables.