Coefficient
Correlation – extent of correlation is indicated numerically. The Coefficient
Correlation (rxy) also known as the Pearson Product Moment
Correlation Coefficient in honor to Karl Pearson who developed the said
formula.
2 ways in identifying correlation
between variables:
1.
Using
the formula;
2.
Using
the scatter point or scattergram.
Kinds
of Correlation:
1.
Positive
Correlation – high scores in distribution x are associated with high scores in
distribution y. This means that as the value of x increases the value of y too
increases or as the value of x decreases, the y values will also decrease.
2.
Negative
correlation –high cores in distribution x are associated with low scores in
distribution y. Low scores in high distribution x are associated with high
scores in in distribution y. This means that as the value of x increases, the
value of y decrease or when the values of x decrease, the value of y increase.
[3.
Zero
Correlation – no association between scores in distribution x and in scores in
distribution y. No single can be drawn that best fitted to all points as shown
in the scattergram of zero correlation.
Pearson
Product Moment Correlation:
Rxy= (n) (Σxy) –(Σx)(Σy)
[(n)( Σx²) - (Σx)²] [(n)(Σy²) – (Σy)²]
Graph:
Scattergram of Correlation:
Scattergram of Positive Correlation
|
Scattergramof Negative Correlation
Scattergram of Zero Correlation
Computation of CorrelationCoeffecient Using
Pearson r:
Analysis:
The value of the correlation
coefficient is rxy = 0.93, which means that there is a very high
positive correlation between the scores of 10 students in mathematics and in
science. This means that students are good in mathematics are also good in
science.
Spearman Rho Coefficient
Another
way of finding the correlation between two variables is the Spearman rho
Correlation coefficient and is denoted by a Greek letter rho (ρ). The Spearman
rho correlation coefficient (ρ) is a measure of correlation when the given sets
of data are expressed in ordinal level of measurement rather than raw scores as
in Pearson r.
Formula:
ρ =1 -6 ∑D2
N(N2-1)
Where:
ρ = Spearman rho Correlation Coefficient value
D = difference between a pair
of ranks
N = number of students/cases
Steps
in Solving Spearman rho Correlation Coefficient:
1. Rank the scores in the
distribution if raw scores are given.
2. Find the difference between each
pair.
3. Square the difference.
4. Find the summation of the squared
difference.
5. Solve the value of the Spearman
rho Correlation Coefficient using its formula.
REFLECTION
REFLECTION
Correlation addresses
the relationship between two different factors (variables). A correlation
coefficient can be calculated when there are two (or more) sets of scores
for the same individuals or matched groups. There are three kinds of correlation: Positive Correlation, Negative Correlation, and Zero Correlation.
Positive correlation occurs when an increase in one variable increases the value in another. The line corresponding to the scatter plot is an increasing line.It will determine that a two subjects are related to each other as they increase in performance.
Negative correlation occurs when an increase in one variable decreases the value of another. The line corresponding to the scatter plot is a decreasing line. In contrary to Positive correlation, it determines that a two subjects are related to each other as they decrease in performance. They are opposites.
No correlation occurs when there is no linear dependency between the variables. The first two kinds can be drawn from linear form but this wont, the form cannot identify. The scores of the two subjects are not related.
The kinds of correlation determines the relationship of two subjects in terms of score. This will help the teacher to know if the the students are performing well from both subject as it is increases as well as if they are not doing well as it is decreases.
No comments:
Post a Comment