MEASURES OF RELATIONSHIP

 

    Correlation is a statistical method that characterizes the existence of a relationship between two variables. It is also known as a “bivariate” statistic, with bi- meaning two and variate indicating variable or variance. There are 3 types of correlation. They are positive correlation, negative correlation and zero correlation.

1.      Positive Correlation

The change in one variable is followed by the change in other variable in the same direction.

Eg: radius and area of the circle, intelligence and achievement etc.

2.      Negative Correlation

The change in one variable is followed by the change in the other variable in the opposite direction. i.e. one increases and other decreases.

Eg: increase in speed and decrease in time taken.

3.      Zero Correlation

The change in one variable does not affect the other variable.

Eg: size and achievement.

 

Correlation Coefficient

Correlation coefficient is a quantitative indicator of the degree of relationship between variables. It is a simple number that tells us to what extent two variables are related and what extent the variations in one variable go with the variations in the other. Hence, it is the ratio indicating the degree of correlation between two variables.  

Correlation coefficient ranges from –1.00 to +1.00. Any values outside this range are invalid.

|-----------------------------|------------------------------|--------------------------|------------------------|

-1.00                             -.50                                       0                                 +.50                        +1.00

strong negative relationship                           weak or none strong           positive relationship

When the correlation coefficient approaches r = +1.00 (or greater than r = +.50) it means there is a strong positive relationship or high degree of relationship between the two variables. This also means that the higher the score of a participant on one variable, the higher the score will be on the other variable. Also, if a participant scores very low on one variable then their score will also be low on the other variable. When correlation coefficient is +1, then it is perfect one.

When the correlation coefficient approaches r = -1.00 (or less than r = -.50), it means that there is a strong negative relationship. This means that the higher the score of a person on one variable, the lower the score will be on the other variable (EG: Academic stress and Achievement)A correlation coefficient that is close to r = 0.00 (note that the typical correlation coefficient is reported to two decimal places) means knowing a person's score on one variable tells you nothing about their score on the other variable. For example, there might be a zero correlation between the number of letters in a person's last name and the number of miles they drive per day. If you know the number of letters in a last name, it tells you nothing about how many miles they drive per day. There is no relationship between the two variables; therefore, there is a zero correlation.

Interpretation of Correlation Coefficient

Correlation Coefficient	Interpretation
0	There exists no relationship.
0.01 – 0.20	Negligible relationship
0.21 – 0.40	There exists low correlation.
0.41 – 0.70	Moderate correlation
0.71 – 0.90	High correlation
0.91 – 0.99	Very high correlation
+1	Perfect positive correlation

 

Uses of the Co-Efficient of Correlation

The use of coefficient of correlation is very wide. In almost all social sciences this is now being increasingly used. It is useful whenever each number of a group of individuals has been measured on two or more traits. It expresses at one figure the average degree of resemblance etc., between the two traits.

Its specific uses may be given as follows:

1. Prognosis (Prediction):

The coefficient of correlation is used quite profitably in Prediction. In a number of studies it is used to predict the success one will achieve in his further educational careers.

2. Reliability:

The co-efficient of correlation has been used very often to test the reliability. Through calculation of this statistics it has been sought to be asserted whether or not a test measures on two successive occasions the same type of thing.

3. Validity:

A test’s width value can be obtained through correlation. Whenever a test is constructed the tests, not what it claims to test. This question is answered by the magnitudes of the coefficient with various criteria.

4. Test Construction:

The coefficient of correlation is also being used in the test construction. Whenever a new test is constructed, there is always the questions of whether each element of the test is related to other elements or to the test as a whole and as to whether each element is related to the criteria chosen. Those relationships are all examined through the technique of correlation.