Explaining about Correlational Research Design that a researcher can employ in his/her research

1. CORRELATIONAL
DESIGNS [CD]
RESEARCH METHODOLOGY GB6013 - UKM [TESL
GROUP]
GROUP MEMBERS
DHACHAINI A/P PRABHAKARAN (GP03743)
JASIDAH IDANG (GP03760)
KUMARESEN A/L MAHALINGAM (GP03771)
LU HUI PING (GP04061)
VIMALA A/P P. MOOKIAH (GP03810)

2. POINTS OF DISCUSSION
1. WHAT IS CD?
2. WHEN TO USE CD?
3. HOW DID CD DEVELOP?
4. TYPES OF CD
5. KEY CHARACTERISTICS OF CD
6. HOW TO CONDUCT CORRELATIONAL STUDY?
7. HOW TO EVALUATE CORRELATIONAL STUDY?
* Reference: Cresswell, J.A. (2008) Educational Research: Planning,
Conducting, and Evaluating Quantitative and Qualitative Research.
Third Edition. Pearson Prentice Hall. USA.

3. WHAT IS CORRELATIONAL
RESEARCH
 According to Creswell, correlational research
designs are used by investigators to describe and
measure the degree of relationship between two
or more variables or sets of scores.
 A procedure in which subjects’ scores on two
variables are simply measured, without
manipulation of any variables, to determine
whether there is a relationship.
 Correlational research examines the relationship
between two or more non manipulated variables.

4. WHAT IS CORRELATIONAL
RESEARCH
What is the relationship
between smoking and
the healthcare cost?
What is the link between ethnicity and certain
healthcare condition?

5. WHEN DO WE USE CD?
 when we want to see if there is a relationship
between variables or to predict an outcome.
important NOTE:
 Correlation can be positive or negative.
 There is no perfect 1:1 relationship between
items
 Correlations cannot tell us the cause of any
relationship.

6. WHEN DO WE USE CD?
simple example:
We, as teachers, practice correlation
research often in the forms of pre-tests,
quizzes, etc., where we correlate
(based on years of experience) the
outcome of these assessments with
anticipated final test results. We will
often modify our teaching in response
to the data to modify the outcome.

7. HOW DID CD DEVELOP?
 Late 19th century
 Karl Pearson (1895)
correlation formula
 Yule (1897)
theory of regression & the ability to predict scores using info based on
correlating correlation coefficients.
 Spearman (1904)
Spearman's rho
 Fisher (1935)
significant testing & ANOVA
 Campbell & Stanley (1963)
new impetus (encouraged researchers to both recognize and specify the
extensive threats to validity in this form of research)
 Advent of computers

8. HOW DID CD DEVELOP?
 Statisticians first developed the procedures for
calculating the correlation statistics in the late 19th
century
(Cowles, 1989).
 Karl Pearson presented the familiar correlation formula
we know today in a paper before the Royal Society in
England in November 1895 (Cowles, 1989).
 In 1897, Yule (Pearson’s student) developed solutions
for correlating two, three, and four variables.
 With Pearson, Yule also advanced the theory of
regression and the ability to predict scores using
information based on correlating correlation
coefficients.

9. HOW DID CD DEVELOP?
 During the 1970s and 1980s, quantitative researchers
started the correlation studies.
 Hence, with computers, they could statistically
remove the effects of a large number variables to
examine the relationship among a small set of
variables.
 For example, they could explore the combination of
variables (eg. Gender, age, and SAT scores) and an
outcome (e.g., college grade point average)

10. TYPES OF CD
The two primary correlation designs:-
1. THE EXPLANATORY/explanation DESIGN
2. THE PREDICTION DESIGN

11. TYPES OF CD
Explanation Prediction
explain the association between or
among variables
Identify variables that will predict an
outcome or criterion.
correlate two or more variables In this form of research, the
investigators identifies one or more
predictor variable and a criterion.
collect data at one point in time measure the predictor variable(s) at
one point in time and the criterion
variable at a later point in time
The researcher obtains at least two
scores for each individual in the
group.
The authors forecast performance

12. THE EXPLANATION DESIGN
 Other names of this designs:
• 'relational' research (Cohen & Manion, 1994, p.123)
• 'accounting-for-variance studies' (Punch, 1998, p.78)
• 'explanatory' research (Fraenkel & Wallen, 2000, p.
360)
 Is a correlational design in which we are interested in the
extent to which two/more variables co-vary.
 Consists of a simple association between two or more
variables.

13. THE EXPLANATION DESIGN
 Characteristics
• we correlate two/more variables
• we collect data at one point in time
• we analyze all participants as a single group
• we obtain at least 2 scores for each individual in the
group (one for each variable)
• we report the use of the correlation statistical test in
the data analysis
• we make interpretations/draw conclusions from the
statistical test results.

14. THE PREDICTION DESIGN
 Seek to anticipate outcomes by using certain variables
as predictors.
 Purpose = to identify predictor variables that will predict
an outcome or criterion.
 Will report correlations using the correlation statistical
test; may include advanced statistical procedures.
 Characteristics:
• typically include the word 'prediction' in the title (might
also be in the purpose statement/research questions).
• typically measure the predictor variable(s) at one point
in time and the criterion variable at a later point in
time.
• forecast future performance.

15. KEY CHARACTERISTICS OF
CD
Correlation research includes specific characteristics:-
 Displays of scores
scatterplots
matrices
 Associations between scores
direction
form
strength
 Multipe variable analysis
partial correlations
multiple designs

17. Scatterplot are
vitally important to
correlational
research as they
allow researchers
to determine:
The degree of
the
association
The form of
the
association
The type of
association
The existence
of extreme
scores
The direction
of the
association

18. The form of
the
relationship
Linear Non-Linear
Positive Negative

19. Positive Non-Linear Negative

21. Degree of association
-is the association between two variables or set
-scores is a correlation coefficient of -1.00 to +1.00
-with 0.00 indicating no linear association at all
-reflects consistent and predictable association between the scores
-square the correlation and use the r value to measure the strength
Coefficient of determination
-assesses the proportion of variability in one variable that can be determined or
explained by a second variable

22. Standards for interpreting the strength of the
association.
.20 - .35 - there is only a slight relationship
.35 –.65 - useful for limited prediction.
- used to identify variable membership in the statistical procedure of
factor analysis
- many correlation coefficients for bivariate relationships fall into this
area.
.66 –.85 - good prediction can result from one variable to the other.
- considered very good.
.86 and above - typically achieved for studies of construct validity or test– retest
reliability.
- when two or more variables are related, correlations this high are
seldom achieved.

23. - Significant testing - determine whether the value is
meaningful
- The null hypothesis would be no relationship or
association among the scores in the population.
Testing these hypothesis involves:
- setting a level of significance
- calculating the test statistic
- examining whether the correlation coefficient value falls
into the region of
rejection rejecting or failing to reject the null hypothesis.
r squared
- expresses the magnitude of two variables or sets of scores.
- represents the effect size

24. Multiple Variable Analysis
-Partial Correlations
-Multiple Regression
Partial Correlation
-Determine the amount of variance that an intervening
variable explains in both the independent and dependent
variables.
-Used because of various number of variables as
predictors of the outcome.
-These variables are called as the mediating or
intervening variable.
-The variables ‘stands between’ the independent and
dependent variables and influences both of them.

26. Multiple regression.
- Regression analysis used to see the impact of multiple
variables on an outcome.
- Involves a regression line and the analysis using
regression.
Regression line
- Is a line ‘best fit’ for all of the points of scores on the
graph.
- The line comes the closest to all the points on the plot.
- Calculated by drawing a line that minimizes the squared
distance of the points
from the line.

29. Multiple Regression / Multiple Correlation
- multiple independent variables combines to correlate
with a dependent variable

30. Regression Table
-calculate regression coefficients for each variable,
assess the combined influence of all variables and provide
a picture of the results
-shows the overall amount of variance explained in a
dependent variable by all independent variables, called R²
or R squared
-shows the regression weight (beta)

32. Beta Weight
-beta weight indicates the magnitude of prediction for
a variable after removing the effects of all other
predictors.
-identifies the strength of the relationship of a
predictor variable of the outcomes.
-reported in a standardised form, a z score with a
value from +1.00 to -1.00.

33. Meta analysis
•Authors integrate the findings of many research studies in meta
analysis.
•Meta-analysis conducting process follows systematic steps.
1.locate the studies on a single topic and notes the results for all
the studies.
2.Calculates an overall result for all of the studies and reports this
information.
•By conducting this process, the investigator synthesizes the
literature, providing a secondary source of primary research report.

34. HOW TO CONDUCT CD?
1.
Identify two
variables that
maybe related
rather indicates
an association
between two or more variables
Sample r.q. :
- Is creativity related to IQ test
scores for elementary children?
(associating two variables)
- What factors explain a student
teacher’s ethical behaviour
during student-teaching
experience? (exploring a
complex relationship)
- Does high school class rank
predict a college student’s grade
point average in the first
semester of the college? (prediction)
avoid the
“shotgun
approach”

35. HOW TO CONDUCT CD?
2.
Identify
sample to
study
at least 30
individuals;
select randomly
heterogeneous sample
produces wide ranges of scores
compared to homogenous
sample; helps to determine the
true relationship between
variables.
narrowed group of
population may
influence the
strength of the
correlation
relationships

36. HOW TO CONDUCT CD?
3.
Select a method of
measurement
complex part of a correlational
study is determining how to
effectively measure each
variable.
validity and reliability
from literature search of past
studies to obtain instruments
obtaining permissions from
publishers or authors to use
the instruments

37. HOW TO CONDUCT CD?
4.
Collect Data
and Monitor
Potential
Threats
the two sets of data should be
collected for each of the
participants
multiple independent
variables are collected to
understand complex
relationshipsprediction studies require data
collection at more than one
point in time. In such cases,
researchers often assign
numbers to participants to
ensure that data remains
confidential

38. Student
Iowa Assessment National
Standard Score
Average Time Spent on
Homework Nightly
Matthew 142 0
Jane 167 10
Daniel 130 10
Jose 180 10
Armando 150 30
Kelby 194 15
Loren 162 20
Samantha 202 15
Andrew 216 50
Britney 216 45
Kiedis 219 40
Ethan 223 60
Dakota 230 65
Mia 244 90
Damarcus 270 80
Alejandro 252 75

39. HOW TO CONDUCT CD?
5.
Analyze the Data
and Represent
the Results
look for a pattern of
responses and uses
statistical procedures to
determine the strength of
the relationship
If a statistically significant
relationship is found, it is not
the cause and effect but merely
an association between the
variables relationships
needs to determine the
appropriate statistic to use. --an
initial question is whether the
data are linearly or curvilinearly
Data from
correlational
research is
analyzed by using
statistical tests that
depend greatly on
the type of
variables being
studied

40. scatterplot

41. HOW TO CONDUCT CD?
6.
Interpret the
Results
findings of correlational
research is often presented in a
correlational matrix
Asterisks are often used to
indicate correlations that
are statistically significant.
Overall concern is whether the
data support the theory, the
hypotheses, or
questionsremains confidential

42. HOW DO YOU EVALUATE A
CORRELATIONAL STUDY?
Below are the criteria we use to evaluate and assess the quality of a
correlational study:-
 Is the size of the sample adequate for hypothesis testing?
 Does the researcher adequately display the results in matrices or graphs?
 Is there an interpretation about the direction and magnitude of the association
between two variables?
 Is there an assessment of the magnitude of the relationship based on the coefficient
of determination, p values, effect size, or the size of the coefficient?
 Is the researcher concerned about the form of the relationship so that an appropriate
statistic is chosen for analysis?
 Has the researcher identified the predictor and the criterion variables?
 If a visual model of the relationships is advanced, does the researcher indicate the
expected direction of the relationships among variables? Or the predicted direction
based on observed data?
 Are the statistical procedures clearly identified?

43. REFERENCE:
ISBN-13: 978-0133831535
ISBN-10: 0133831531