The document discusses inferential statistics, specifically correlation analysis and its various forms, including Pearson's r and partial correlation. It explains the significance of correlation coefficients in determining the strength and direction of relationships between variables, along with the requirements for conducting such analyses. Additionally, it outlines preliminary steps for analysis and examples of research questions related to job employment and student performance.

1. CORRELATION
INFERENTIAL STATISTICS
(CORRELATION, REGRESSION, CHI-
SQUARE & T-TEST)

2. INFERENTIAL STATISTICS
THESE ARE STATISTICS, SUCH AS CORRELATION, PARTIAL CORRELATION,
MULTIPLE REGRESSION ANALYSIS AND T-TEST THAT ALLOWS THE RESEARCHERS
TO MAKE CONCLUSIONS ABOUT THE POPULATION BEYOND OUR DATA.

3. DEFINITI
ON
• CORRELATION ANALYSIS IS USED TO DESCRIBE THE
STRENGTH AND DIRECTION OF THE LINEAR
RELATIONSHIP BETWEEN TWO VARIABLES.
• USED TO TEST THE PRESENCE, STRENGTH AND
DIRECTION OF A LINEAR RELATIONSHIP AMONG
VARIABLES.
• CORRELATION IS A NUMERICAL EXPRESSION THAT
SIGNIFIES THE RELATIONSHIP BETWEEN TWO
VARIABLES. CORRELATION ALLOWS YOU TO EXPLORE
THIS RELATIONSHIP BY 'MEASURING THE
ASSOCIATION' BETWEEN THE VARIABLES.
• CORRELATION IS A 'MEASURE OF ASSOCIATION'
BECAUSE THE CORRELATION COEFFICIENT PROVIDES
THE DEGREE OF THE RELATIONSHIP BETWEEN THE
VARIABLES.

4. CORRELATI
ON (CONT.)
THE CORRELATION COEFFICIENT : PEARSON'S R, THE
CORRELATION COEFFICIENT, IS THE NUMERIC VALUE
OF THE RELATIONSHIP BETWEEN VARIABLES. THE
CORRELATION COEFFICIENT IS A PERCENTAGE AND
CAN VARY BETWEEN -1 AND +1. IF NO
RELATIONSHIP EXISTS, THEN THE CORRELATION
COEFFICIENT WOULD EQUAL 0. PEARSON'S R
PROVIDES AN (1) ESTIMATE OF THE STRENGTH OF
THE RELATIONSHIP AND (2) AN ESTIMATE OF THE
DIRECTION OF THE RELATIONSHIP.

5. CORRELATI
ON (CONT.)
IF THE CORRELATION COEFFICIENT LIES BETWEEN -1
AND 0, IT IS A NEGATIVE (INVERSE) RELATIONSHIP, 0
AND +1, IT IS A POSITIVE RELATIONSHIP AND IS 0,
THERE IS NO RELATIONSHIP THE CLOSER THE
COEFFICIENT LIES TO -1 OR +1, THE STRONGER
THE RELATIONSHIP.

6. CORRELATI
ON (CONT.)
COEFFICIENT OF DETERMINATION PROVIDES THE
PERCENTAGE OF THE VARIANCE ACCOUNTED FOR
BOTH VARIABLES (X & Y).
EXAMPLE OF RESEARCH QUESTION
WHAT IS THE RELATIONSHIP BETWEEN PART TIME
JOB EMPLOYMENT AND STUDENT PERFORMANCE?
IS THERE RELATIONSHIP BETWEEN FACULTY
PROGRAM AND STUDENT PERFORMANCE?
NULL HYPOTHESES
THERE IS NO RELATIONSHIP BETWEEN PART TIME
JOB EMPLOYMENT AND STUDENT PERFORMANCE.
THERE IS NO RELATIONSHIP BETWEEN FACULTY
PROGRAM AND STUDENT PERFORMANCE.

7. REQUIREMENT: TWO VARIABLES: BOTH
CONTINUOUS, OR ONE CONTINUOUS AND THE
OTHER DICHOTOMOUS. (MALE OR FEMALE)

8. PRELIMINAR
Y ANALYSIS
FOR
CORRELATI
ON
BEFORE PERFORMING A CORRELATION ANALYSIS, IT
IS A GOOD IDEA TO GENERATE A SCATTERPLOT.
THIS WILL ENABLE YOU TO CHECK FOR VIOLATION
OF THE ASSUMPTION OF LINEARITY AND
HOMOSCEDASTICITY. THIS WILL GIVE A BETTER IDEA
OF THE NATURE OF THE RELATIONSHIP BETWEEN
VARIABLES.
STEP 1: CHECKING FOR OUTLIERS
STEP 2: INSPECTING THE DISTRIBUTION OF DATA
POINTS
STEP 3: DETERMINING THE DIRECTION OF THE
RELATIONSHIP
BETWEEN THE VARIABLES.

9. OUTLIERS
An outlier is a data point
that is significantly different
from the remaining data.
Anggarwal (2017).
“Observation which deviates
so much from other
observations as to arouse
suspicion it was generated
by a different mechanism”
Hawkins(1980).

10. TYPES OF
CORRELATI
ON
Pearson product moment coefficient (r) is design to
examine the relationship between interval (continuous)
variables or between categorical and continuous
variables
Spearman rank order correlation (rho) is designed to
examine the relationship between categorical variables
Partial correlation is used to explore the relationship
between two variables while statistically controlling for
a third variable

11. THE STRENGTH OF THE RELATIONSHIP
SMALL = R = 0.10 –
0.29
MEDIUM = R = 0.30 -
0.49
LARGE = R = 0.50 –
1.00

16. PROCEDURE
FOR
REQUESTING
‘R’ AND ‘RHO’

17. INTERPRETATION OF OUTPUT
 Explain information about the sample (N)
 Determining the direction of the relationship
 Determining the strength of the relationship
 Explain the variance (r x r) x 100
 Assessing the significant level

18. EXAMPLE OF WRITING THE RESULT

19. PARTIAL
CORRELATI
ON
ANALYSIS
IT IS USEFUL FOR DESCRIBING THE RELATIONSHIP BETWEEN
TWO VARIABLES, WHILE STATISTICALLY CONTROLLING FOR
ANOTHER VARIABLE (S).
RESEARCH QUESTION
IS THERE A SIGNIFICANT RELATIONSHIP BETWEEN PART TIME
JOB EMPLOYMENT AND PART TIME JOB SATISFACTION AFTER
CONTROLLING FOR STUDENT PERFORMANCE?
NULL HYPOTHESIS
THERE IS NO SIGNIFICANT RELATIONSHIP BETWEEN PART
TIME JOB EMPLOYMENT AND PART TIME JOB SATISFACTION,
AFTER CONTROLLING STUDENT PERFORMANCE.

20. VARIABLE
S
VARIABLE OF INTEREST:
• PART TIME JOB EMPLOYMENT
• PART TIME JOB SATISFACTION
CONTROL VARIABLE (COVARIATE):
• STUDENT PERFORMANCE

22. OUTPUT FOR PARTIAL CORRELATION

23. PRESENTING PARTIAL CORRELATION RESULT

24. THANK YOU