Sunday, September 15, 2013

Correlational Analysis

Correlational Analysis is the statistical tool that is used to describe the degree to which one variable is linearly related to another.  The degree of relationship can be measured and represented quantitatively by the coefficient of correlation.
The  Correlation Scale
1.       Low correlation – the changes in one variable cannot be expected to signify change in the other, almost negligible relationship (-0.10 – 0.20)
2.       Weak correlation – the change in one variable may not be expected from a change in the other, definite but small relationship (-0.49— -0.20, 0.21-0.49)
3.       Moderate Correlation – the change in one variable is expected from a change in the other, substantial relationship (-0.69 - -0.50, 0.50-0.79)
4.       High correlation – the change in one variable is expected with reliability from a change in the other, marked relationship (-0.70 – -0.89, 0.80 – 0.95)
5.       Very high correlation – the change in one variable is expected to happen with greater reliability from a change in the other, (-0.90- -1.00, 0.96 – 1.00)
The Pearson Product Moment Correlation Coefficient
 
r = pearson product –moment of correlation coefficient
Sum XY = the total sum of the products of x variable and y variable
X bar = mean of the independent variable
Y bar = mean of the dependent variable
Sx = Standard deviation of the independent variable
Sy = standard deviation of the independent variable
n = number of pairs
Test of Significance for r
Where
r = correlation coefficient
n = number of pairs
Degree of freedom = n -2

1.The adviser of a Grade 7 class wanted to know if the students’ achievement test scores in Mathematics and Physics are related. He took the test results of the top 10 students of her class and presented the scores in a table.  The data indicated below: (alpha 0.05)
Mathematics (X)
46
45
43
33
31
31
29
26
25
24
Physics (Y)
48
46
42
40
36
35
37
38
44
42
2. A Class adviser was interested in determining the relationship between the Mathematics grades and Chemistry grades of the top 10 junior students.  She gathered the following data: (alpha = 0.01)
Mathematics Grade(X)
84
89
84
86
84
84
87
94
88
85
Chemistry Grade(Y)
85
88
83
86
85
88
89
93
84
84
3.  How is the academic performance of senior students related to their age? Use 1 percent level of significance
Age
16
17
15
17
16
19
17
16
16
19
17
18
20
18
17
16
14
16
18
19
16
18
16
18
20
17
18
18
19
20
17
18
16
16
18
Final Grade
86
88
85
80
80
79
78
83
75
77
80
79
79
78
76
81
77
81
82
78
76
78
79
75
75
75
75
78
76
76
82
80
78







80
78

The Point Biserial Coefficient
Some situations involve variables where one is continuous while the other is dichotomous and is assumed to be discrete.  An example of this is the situation when the two variables under consideration are the mathematics achievement test scores of the students and gender. The achievement test score is continuous while gender, which is either male or female, is a nominal dichotomous variable.  The nominal dichotomous variable classifies attribute into two mutually exclusive classifications.  Other examples of nominal dichotomous variables are attitudes (positive-negative) and responses (yes-no, true-false, agree-disagree).
rpb = point biserial coefficient
X1 = mean of the continous variable of one group
X0 = mean of the continous variable of the other group
N1 = number of cases in one group
N0 = number of cases in the other group
N = total number of cases, N1 + N0
Sx = standard deviation of all measures in the continuous variable
1.       A mathematics teacher wanted to know if the students’ mathematics achievement test scores are related to their gender.  He randomly selected test results of 10 students where she came up with six females and four males. (alpha = 0.05)
Gender(male = 1, female = 0)
1
1
1
1
0
0
0
0
0
0
Math Achievement test scores
48
45
40
35
41
40
33
30
30
25

2.       The table below presents the performance ratings of teachers who have passed and have not passed the licensure examination for teachers.  Using 5 percent level of significance, test if the teachers’ ratings are related to the results of the licensure examination for teachers .  Let 1 = passed and 0 = failed.
LET Results
1
1
0
0
1
0
1
Performance Rating
87
87
85
85
90
85
90

3.       Below is another group of composite scores on a test battery and with each score the number of individuals who did (1) or did not complete(0) a program of training. 
Composite score
9
8
7
6
5
4
3
2
1
10
Completion
1
1
1
1
0
0
0
0
0
1

Kendall’s Coefficient of Concordance (W)
Kendall’s tau, is an alternative measure of relationship to the Spearman’s rho.  This test statistic, which was developed by Kendall, is used when the data of the three or more variables are given in ordinal measures.  It determines the degree of agreement in the ranks given by three or more judges.

Test of Significance

1.        What significant agreement exists between the respondents of principals, master teachers, and classroom teachers on the factors that may increase the teachers’ morale? Alpha = 0.05
Factors
Ranks by Principal
Master Teachers
Teachers
Involvement in decision-making
1
1
1
Adequate instructional material
3
3
2
Less non-teaching assignments
4
4
4
Transparency in promotion
2
2
3
Smaller class size
5
5
5

2.       Calculate the correlation of concordance of the ranks of the judges on the singing performances of 10 contestants.
Contestants
Judge1
Judge 2
Judge 3
Judge 4
1
9
10
10
9
2
8
6
7
5
3
3
1
2
4
4
10
9
8
10
5
4
5
1
3
6
2
3
4
1
7
7
8
9
6
8
5
4
6
7
9
1
2
3
2
10
6
7
5
8
Alpha = 0.05
3.       Four judges (parole board members) rank eight convicts on “parole readiness”.  By using the coefficient of concordance, indicate the degree of consistency of the judges.
Convict
Judge 1
Judge 2
Judge 3
Judge 4
1
1
1
1
1
2
2
4
3
2
3
3
3
2
4
4
4
2
4
3
5
5
6
5
5
6
6
5
6
7
7
7
7
8
6
8
8
8
7
8



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