In this research methods in psychology video we will cover the use of correlations.
Sometimes a researcher collects two sets of data, analysing this data for a correlation
can show potential relationships between the sets and even how strong the relationship
between the sets is.
In this video we will define a correlation, I will tell you what the word correlation
coefficient means and how we can display correlations.
And of course talk about the strengths and limitations of correlational studies.
INTRO It's useful to consider how different a correlational
study is from an experimental study.
As a reminder, in an experiment the researcher manipulates an independent variable, and then
measures how the variation of the independent variable influences the measurement of a dependent
variable.
In a correlation the researcher does not manipulate any variables, they just measure.
A correlation has two co-variables, these are simply variables that a researcher has
measured and then compares.
We could measure and then compare for example, age, IQ, reaction time, bank account balance,
number of pets, hight, hostility level.
When the data has been collected by the researcher, they can then display this data on a graph
called a scattergram.
Let’s think of an example: a researcher may be interested in the relationship between
the percentage score on a final class test against the students' attendance over the
year.
So at the end of the year they can use the data collected for each student.
Let me show you how to draw a scatter graph, let's plot an X and Y axis.
When plotting a correlation on a scattergraph either variable can be on the X and Y axis.
We will pick a reasonable scale and will add labels to each axis and give the graph a title.
Now as we enter our data each dot is a data pair for each measurement of one student.
So let me take the data points for each student and plot them from the table.
[pause] Now what we may expect to see with this data
is a positive correlation, this means when one co-variable increases the other co-variable
increases.
So as you may have guessed a negative correlation is when one co-variable increases and the
other decreases.
We can also have zero correlation, and this is when there is no relationship between the
co-variables.
Correlation Coefficient - Measuring correlation.
It is useful to state if a relationship is positive, negative or zero, but that's a little
simple, and we can be more precise.
We may want to describe correlations as weak or strong, or even perfect.
We can describe the strength of a correlation all the way from a perfect negative to a perfect
positive with a number called a correlation coefficient.
A perfect negative is -1 and a perfect positive is 1 this means the change in one co-variable
is exactly proportional to the change in the other.
No correlation or zero of course would be 0.
We can also describe both negative and positive correlations as strong, moderate or weak.
We can use statistical tests like Pearsons and Spearmans to calculate correlation coefficients,
and psychologists will often judge a coefficient of 0.8 or higher as a strong correlation.
Two observers may test their inter-rater reliability using a test of correlation.
Evaluation of the use of correlation When considering evaluations, one of the most
important critical evaluations is that a phrase you may have heard before, correlation does
not show causation.
What this means is even though there may be a relationship between two variables, we are
not sure which variable is altering the other co variable, or is there an unknown third
variable.
Even in my example, you may assume the low attendance caused poor exam performance.
But an alternate possibility is the students who struggled with the difficulty of the work
avoided class more than the more able students.
And with correlations there can be a third factor that changes both co-variables.
One famous example is the positive correlation between ice cream sales and death by drowning.
It really looks from this real set of data that the more icecream is sold the more kids
drown.
So what’s going on here?
Should we ban ice cream?
Well it's of course a third variable, the temperature.
As the weather gets warmer, more people buy ice cream, and more people go swimming at
the beach.
So linked to that point, just measuring two pre-existing co-variables gives the researcher
no control over potential extraneous variables.
However we can say a positive evaluation of using correlations in research is it can highlight
potential causal relationships that researchers can then use to make predictions, and investigate
further with experimentation.
Also correlational research often has few ethical problems when researchers are often
measuring pre-existing variables.
Also the correlation coefficient is a useful tool in describing the strength of a correlation.
So that was Correlations, 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