This document discusses correlational research methodologies. It defines correlational research as investigating relationships between two variables through quantitative analysis. The main purposes are explanatory, to help explain behaviors, and predictive, to predict outcomes. Key aspects covered include using scatter plots to visualize relationships and predict scores, developing prediction equations, more complex techniques like multiple regression and factor analysis, basic research steps, and threats to internal validity like subject characteristics, location, instrumentation, and mortality.

1. CORRELATIONAL RESEARCH
QUANTITATIVE RESEARCH
METHODOLOGIES
Presented By: Manoj Patel
Asst. Professor
JHUNJHUNWALA BUSINESS SCHOOL

2. THE NATURE OF
CORRELATIONAL RESEARCH
• Sometimes called associational research
• It investigates the possibility of relationships
between only two variables
• Also sometimes referred to as a form of
descriptive research
• Describes the degree to which two or more
quantitative variables are related

3. PURPOSES OF CORRELATIONAL
RESEARCH
• Two basic purposes
1. Help explain important human behaviors
(Explanatory Studies)
2. Predict likely outcomes
(Prediction Studies)

4. EXPLANOTARY STUDIES
• Researchers often investigate a number of
variables they believe are related to a more
complex variable.
• Unrelated variables dropped from further
consideration
• Most researchers most probably trying to
gain some ideas about cause and effect
• However it does not establish cause and
effect

5. PREDICTION STUDIES
• Predict a score on one variable if a score on
the other variable is known
• Determine the predictive validity of
measuring instruments
• Predictor Variable; variable that is used to
make the prediction
• Criterion Variable; variable about which the
prediction is made

6. Using Scatter plots to Predict a Score
• We can use the scatter plots to find a
correlation between the variables
• correlational research.pptx

7. A simple Prediction Equation
• Used to express the regression line
• We gain confidence in using the
prediction equation to make future
predictions if there is a close similarity
between two results
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8. MORE COMPLEX
CORRELATIONAL TECHNIQUES
1. Multiple Regressions; technique that
enables researchers to determine a
correlation between a criterion variable
• The best combination of two or more
predictor variables

9. 2. The Coefficient of Multiple Correlation
• Symbolized by R; indicates the strength of
the correlation between the combination of
the predictor variables and the criterion
variables.
• multiple correlation.jpg
• The higher R is, the more reliable a
prediction will be

10. 3. The Coefficient of Determination
• The square of the correlation between a
predictor and a criterion variable
• Indicates the percentage of the variability
among the criterion scores that can be
attributed to differences in the scores on
the predictor variable

11. 4. Discriminant Function Analysis
• Technique used when the technique of
multiple regression cannot be used when
the criterion variable is categorical
5. Factor Analysis
• Technique that allows a researcher to
determine if many variables can be
described by a few factors.

12. BASIC STEPS IN
CORRELATIONAL RESEARCH
1. Problem Selection
• Three major types of problems;
a. is variable X related to variable Y?
b. how well does variable P predict variable C?
c. What are the relationship among a large
number of variables and what predictions can
be made?

13. 2. Sample
• Should be selected carefully, and if
possible, randomly.
• Not less than 30.
3. Instruments
• Most correlational studies involve the
administration of some types of
instruments (tests, questionnaire, and so
on).

14. 4. Design and Procedures
• Design used quite straightforward.
5. Data Collection
• Data on both variables will usually be
collected in a short time.
• Instruments used are administered in a
single session or two sessions

15. THREATS TO INTERNAL
VALIDITY
• There are some threats identified in
conducting correlational research
1. Subject Characteristics
• Individuals or groups have two or more
characteristics; might be a cause of
variation in the other two variables.

16. 2. Location
• Location is different for different subject
• One location may be more comfortable
compared to others
3. Instrumentation
• Instrument decay; care must be taken to ensure
the observers don’t become tired, bored or
inattentive
• Data collector characteristics; different gender,
age or ethnicity may affect specific response

17. 4. Testing
• Experience of responding to the first
instrument may influence subject responses
to the second instrument
5. Mortality
• Loss of subjects may make a relationship
more (or less) likely in the remaining data

18. EVALUATING THREATS TO
INTERNAL VALIDITY
• Follows a procedure similar to the
experimental research.
1. Subject Characteristics
• Four of many possible characteristics
a. Severity of disability
b. Socioeconomic level of parents
c. Physical strength and coordination
d. Physical appearance

19. 2. Mortality
• Loss of subjects can be expected to reduce
magnitude of correlation
3. Location
• Threats could be controlled by
independently assessing the job-site
environments.

20. 4. Instrumentation
• Instrument decay; observations should
scheduled
• Data collector characteristics; interaction of
data collectors and supervisors is a
necessary parts
• Data collector bias; observers should have
no knowledge of job ratings