This research methods in psychology video covers variables. The world is chaotic,
lots of things change, or vary. Some things may directly cause other changes, but it's had to see
in all the chaos. In an experiment researchers attempt to hold most variables the same,
all while manipulating and measuring other variables. So let's talk about independent
variables, dependent variables, co-variables, extraneous and confounding variables.
INTRO In correlational studies,
researchers measure co-variables, they take two measurements and then compare the measurements
looking for relationships. These can be positive relationships, as one co-variable increases,
so does the other, or as one co-variable goes up the other goes down. In correlational research we
can say two co-variables are associated, but we can't say with co-variables that a change in one
caused a change in the other, it may be the other way around, or that a third variable
is responsible for the change in both. In an experimental set up a researcher
will manipulate, or change one variable, this manipulated variable is called the independent
variable, so for example the researcher may alter if the participant is given a drug or placebo,
is given rewards for behaviour or in a control group without rewards, or is in green light
or blue light. These levels of the independent variable are the conditions of the experiment.
The researcher will measure any change in the dependent variable, that could be the reduction
in symptoms, production of behaviour, or recall. An important concept is operationalisation,
when stating the dependant variable I need to specify how exactly it is being measured,
so if I wanted to know how effective a course of anger management therapy was, instead of saying,
reduction in anger, I may measure it by a reduction in the score on a questionnaire that
measures hostility, or, a reduction in the number of times a student has a verbally aggressive
outburst to teaching staff over the next month. So that's the ideal. In an experiment
researchers can claim to have shown a causal relationship between the independent variable
and the dependent variable. But of course that's not all that's going on. There are other variables
that can influence the dependent variable, and if these are not controlled for there won't have
been an accurate measurement of the independent variables' influence on the dependent variable.
We could then suggest the study's findings are not true because the researcher didn't set the study
up in a way that controls for other explanations for the findings, or in more scientific language,
the study lacks internal validity. # Extraneous variables
Extraneous variables are the name we give to any other variables
other than the independent variable that could influence the dependent variable.
There are lots of potential extraneous variables. Demand characteristics, these are cues that the
participant picks up on that suggests how they should behave. This could be verbal or non verbal
communication from the researcher, and we would call this experimenter effects, or maybe it's
the way the experiment is set up that gives away the aim. If the participant works out the aim,
it's very tempting for them to act in a way that they think is helpful, or even unhelpful for the
researcher. The problem is then the researcher is measuring the effect of demand characteristics
on the dependent variable not the effect of the change in IV.
Participant variables, characteristics like age, gender and cultural background can
influence behaviour, as well as any prior knowledge or skills related to the task.
Situational variables are environmental factors that could influence performance,
like any noise in the lab, maybe the temperature or visual displays.
Order effects, so in repeated measures design practice or
fatigue could be an additional variable. So you can imagine how these factors, especially
if they vary significantly between conditions can influence the performance of participants.
There is also a type of extraneous variable called a confounding variable.
We would say that a confounding variable changes systematically with the levels of IV.
This is a little tricker to explain, but imagine if you were doing a study researching if
vigorous body movement in some way influences making and recalling memories.
You are hoping to see that when you move your body your recall decreases compared to being still.
If you get half your participants to learn a list of words for five minutes,
then recall while doing star jumps. And another group to do the same task while still,
well if you don't have very fit participants you are introduced an extra variable that
changes systematically between levels of IV. That confounding variable would be exhaustion.
It's not possible to separate the body movement from exhaustion, but the exhaustion is only
in one conditions so it confounds your measurement of the dependant variable.
The extraneous variables above, if not controlled before the experiment, also become confounding,
because they now change systematically between the levels of the independent variable.
Control of Extraneous variables. Researchers who want to claim
their results are valid are going to take actions to deal with extraneous variables.
To control for participant variables we can use random allocation and matched pairs
The reason independent groups design should always use random allocation between groups if possible
is to control for participant variables,
for example pre-existing knowledge, skills or characteristics like age and gender.
If you use randomisation you are likely to spread these variables across the condition,
but you still may get unbalanced groups by chance. A better way to control participant variables is
to run a matched pairs design, this is carefully measuring a participant variable before the study
and then matching the highest score in one group with the next highest in the other group.
This will result in the participant variable being balanced between the groups.
To control for order effects we would use Counterbalancing
As completing one condition first can change participants performance in the second condition
by giving them practice or making them tired, Counterbalancing, splitting the sample of
participants in half and getting each half to do one of the conditions first means any effect due
to practice is or fatigue is cancelled out because it's the same in both conditions. But importantly
while we can say we have controlled for order effects we cant say they have been eliminated.
To control for situational variables we use standardised procedures
Environmental effects such as the temperature, lighting or sound levels in the room can form
part of a list called standardised procedures, this basically is a recipe the researcher needs
to follow to give each participant the same experience, aside from the variation in the
independent variable. The researcher would then make sure the participants in each condition
had the same experience. To control for demand
characteristics and investigator effects we use single and double blind trials
To counter the participants changing their behaviour because they have figured out the aim,
well the first thing you can do is not tell them the aim. That's a single blind trial,
the participant is unaware of the true nature of the experiment.
In order to stop the researcher influencing the results by giving hints with their body language,
so limiting investigator effects, is to use a double blind trial, having the researcher who
carries out the experiment be unaware of the true aims of the experiment.
Carefully created standardised procedures with a script for the researcher to follow
are useful in both cases, reducing the chance of giving away the aim
or the researcher using different language in each condition.
We can use pilot studies and peer review to identify extraneous variables
Pilot studies and the aims of piloting is actually a separate bullet point in the AQA specification,
there isnt enough to it to justify a separate video, but I will go into the depth you need here.
A pilot study is simply a smaller version of the research study
the researcher has planned. As it's smaller it's going to include fewer participants,
but we can make improvements to the main study based on their experience in the pilot study.
The aims of a pilot study, meaning the reason a researcher would use one, is it's a good
way to spot extraneous variables and correct them before running the full scale version.
And you can probably guess the things a well designed pilot study would catch.
Are the instructions to the participants understandable,
or do the instructions give away the aim? The same with questions that are used as part of the study.
Are the standardised procedures working well, so do the participants in both conditions have the
same experience or was something missed? If the study is an observation, are the
behavioural categories clearly operationalised? Running a pilot study may also show other
problems, like with the cost or issues raised by the participants such as not understanding
the task or running out of time. Peer review is when you show your
written up study to other scientists who are experts in your field and get feedback.
This can be useful in identifying extraneous variables you have missed. But at that stage
the researcher may need to re-do the study after dealing with the extraneous variable before they
can publish their study. I do have an entire video on peer review later in the video series and I
will go into this in a lot more depth. So that was variables,
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