THE STRUCTURE OF CORRELATIONAL DESIGNS

A correlational research design is the measurement of two or more factors to

determine or estimate the extent to which the values for the factors are related or

change in an identifiable pattern

The correlation coefficient is a statistic used to measure the strength and direction of

linear relationship, or correlation, between two factors. The value of r can range from

-1.0 to +1.0.

r=-1.0 (the values for two factors change in opposite directions)

r=+1.0(the values for two factors change in same directions)

Describing the Relationship between Variables

The strength and direction of correlation can be readily identifies in a graph called

scatterplot

Scatterplot also called a scatter diagram or scatter gram is a graphical display of

discrete data points (x,) used to summarize the relationship between two factors.

Data points are the x-and y- coordinates for each plot in a scatterplot.

The regression line is the best-fitting straight line to a set of data points. A best-fitting

line is the line that minimizes the distance that all data points fall from it.

The Direction of the Relationship

A positive correlation is a positive value of that indicates that r the values of two

factors change in the same direction: As the values of one factor increase, values of

the second factor also increase: as the values of one factor decrease, values of the

second factor also decrease.

A negative correlation is a negative value of r that indicates that the values of two

factors change in different directions, meaning that as the values of one factor

increase, the values of the second factor decrease.

Step to Conduct Correlation Study

Select the problem

Select participants and instrument

Design and procedure

Data analysis and interpretation

CORRELATIONAL STUDY

 

• A correlational study determines whether or not two variables are correlated.

• This means to study whether an increase or decrease in one variable corresponds to

an increase or decrease in the other variable

• It is very important to note that correlation doesn't imply causation. We'll come back

to this later.

Types

There are three types of correlations that are identified:

1. Positive correlation:

• Positive correlation between two variables is when an increase in one variable

leads to an increase in the other and a decrease in one leads to a decrease in the

other.

• For example, the amount of money that a person possesses might correlate

positively with the number of cars he owns.

2. Negative correlation:

• Negative correlation is when an increase in one variable leads to a decrease in

another and vice versa.

• For example, the level of education might correlate negatively with crime. This

means if by some way the education level is improved in a country, it can lead to

lower crime. Note that this doesn't mean that a lack of education causes crime.

• It could be, for example, that both lack of education and crime have a common

reason: poverty.

3. No correlation:

• Two variables are uncorrelated when a change in one doesn't lead to a change in

the other and vice versa.

• For example, among millionaires, happiness is found to be uncorrelated to money.

This means an increase in money doesn't lead to happiness.

4. A correlation coefficient is usually used during a correlational study. It varies between +1

and -1. A value close to +1 indicates a strong positive correlation while a value close to -1

indicates strong negative correlation. A value near zero shows that the variables are

uncorrelated.

Limitations

• It is very important to remember that correlation doesn't imply causation and there is

no way to determine or prove causation from a correlational study.

• This is a common mistake made by people in almost all spheres of life.

For example, a US politician speaking out against free lunches to poor kids at school argues

-“You show me the school that has the highest free and reduced lunch, and I'll show you the

worst test scores, folks” (nymag.com). This is a correlation he is speaking about - one

cannot imply causation. The obvious explanation for this is a common cause of poverty:

people who are too poor to feed their children will not have the best test scores.