Concept of Correlation and it’s Uses in Educational Research
A correlation coefficient is a statistical measure
of the degree to which changes to the value of one variable predict change to
the value of another. When the
fluctuation of one variable reliably predicts a similar fluctuation in another
variable, there’s often a tendency to think that means that the change in one
causes the change in the other. However,
correlation does not imply causation.
There may be, for example, an unknown
factor that influences both variables similarly. Here’s one example: A number
of studies report a positive correlation between the amount of television
children watch and the likelihood that they will become bullies. Media coverage often cites such studies to
suggest that watching a lot of television causes children to become bullies.
However, the studies only report a correlation, not causation. It is likely
that some other factor – such as a lack of parental supervision – may be the
influential factor.
A correlation
between variables indicates that as one variable changes in value, the other
variable tends to change in a specific direction. We can understand that
relationship is useful because we can use the value of one variable to predict
the value of the other variable. For example, height and weight are
correlated—as height increases, weight also tends to increase. Consequently, if
we observe an individual who is unusually tall, we can predict that his weight
is also above the average.
In statistics, correlation is a quantitative
assessment that measures both the direction and the strength of this tendency
to vary together. There are different types of correlation that you can use for
different kinds of data. In this post, I
cover the most common type of correlation—Pearson’s correlation coefficient.
Before we get into
the numbers, let’s graph some data first so we can understand the concept
behind what we are measuring.
Graph Your Data to Find Correlations
Scatter plots are
a great way to check quickly for relationships between pairs of continuous data.
The scatter plot below displays the height and weight of pre-teenage girls.
Each dot on the graph represents an individual girl and her combination of
height and weight. These data are actual data that I collected during an
experiment.
At a glance, you
can see that there is a relationship between height and weight. As height
increases, weight also tends to increase. However, it’s not a perfect
relationship. If you look at a specific height, say 1.5 meters, you can see
that there is a range of weights associated with it. You can also find short
people who weigh more than taller people. However, the general tendency that
height and weight increase together is unquestionably present.
Examples of Positive and negative
correlations. An example of a positive correlation is the
relationship between the speed of a wind turbine and the amount of energy it
produces. As the turbine speed increases, electricity production also
increases.
An example of a
negative correlation is the relationship between outdoor temperature and
heating costs. As the temperature increases, heating costs decrease.
Graphs for different correlations. Graphs always help bring concepts
to life. The scatter plots below represent a spectrum of different
relationships. I’ve held the horizontal and vertical scales of the scatter plots
constant to allow for valid comparisons between them.
Correlation = +1: A perfect positive relationship.
Figure 1 .Positive Correlation
Correlation
= 0: No relationship. As one value
increases, there is no tendency for the other value to change in a specific
direction.
Figure 2. Zero Correlation
Correlation
= -1: A perfect negative
relationship.
Figure 3. Negative Correlation
If you are involved in educational
statistics, you must be interested to know about the Correlation Research. The present
article will help you understand the following: What is Correlation Research?
Where is the Correlation Research applied? What are the Advantages of Correlation
Research? What are the Limitations of Correlation Research? What is Coefficient
Correlation? What are the Types of Coefficient Correlation ("Positive
Correlation", "Negative Correlation", "No
Correlation", and "Perfect Correlation")?
Figure
5. Relation between Variables
Correlation
Research is a non-experimental research method. In this research method, there is no
manipulation of an independent variable.
In correlation research, the researcher studies the
relationship between one or more
quantitative independent variables and one or more quantitative dependent variables. In other
words, it can be said that in correlational research, the independent and dependent variables are quantitative. It is important to stress that correlations refer
to measures of association and do not necessarily indicate causal relationships
between variables
Correlation
research is appropriate in the following two instances:
First, it is appropriate when there is need to discover
or clarify relationships and where correlation coefficients will achieve these
ends. It is especially useful in this connection in the initial stages of a
project where a certain amount of basic groundwork has to be covered to get
some idea of the structure of relationships. In this way, it gets at degrees of
relationships which may become a source of hypotheses and further research.
The correlation approach is also valuable when
variables are complex and do not lend themselves therefore to the experimental
method and controlled manipulation. It also permits the measurement of several
variables and their relationships simultaneously in realistic settings.
Second, correlation research is appropriate where
objective, or one of a set of objectives, is to achieve some degree of
prediction. (Prediction studies are appropriate where a firm basis of previous
knowledge is present, the assumption being that at least some of the factors
will relate to the behaviour to be predicted).
Advantages of Correlation Research
Correlation research is particularly
useful in tackling the problems of education and social sciences because it
allows for the measurement of a number of variables and their relationships
simultaneously.
The experimental
approach, by contrast, is characterized by the manipulation of a single
variable and is thus appropriate for dealing with problems where simple causal
relationship exist.
In educational and behavioural
research, it is invariably the case that a number of variables contribute to a
particular outcome. Experimental research thus introduces a note of unreality
into research, whereas correlation approaches, while less rigorous, and allow
for the study of behaviour in more realistic settings.
Correlation research
yields information concerning the degree of relationship between the variables
being studied. It thus provides the researcher with insights into the way
variables operate that cannot be gained by other means
Limitations of Correlation Research
Correlation research only
identifies what goes with what—it only implies concomitance and therefore does
not necessarily establish cause-and-effect relationships.
It is less rigorous than
the experimental approach because it exercises less control over the
independent variables. It is prone to identify spurious relation patterns. It
adopts an atomistic approach.
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Correlation Coefficient
Four uses of Correlation
