Correlational designs allow researchers to examine relationships between two or more variables by collecting data on all variables at the same point in time. Key aspects of correlational designs include correlating predictor and criterion variables to predict outcomes, displaying scores in scatter plots and matrices to show associations between variables, and using techniques like partial correlations and multiple regression to analyze multiple variables. Conducting high quality correlational research requires adequately sampling participants, selecting appropriate statistical tests, and properly interpreting results to understand the direction, magnitude, and strength of relationships between variables.

1. CORRELATIONAL
DESIGNS
By: Faridah Hanim Bt Jasmon
2017998003
Research designs
Researchdesigns

2. Historical Development
1895 Karl Pearson
develops correlation
formula
1897 Yule develops
solutions for
correlating two, three
and four variables
1935 Fisher pioneers
significance testing and
analysis of variance
1963 Campbell and
Stanley write on
experimental and
quasi-experimental
designs
1970’s and 1980’s
computers give the
ability to statistically
control variables and
do multiple regression

3.  When you want to relate
two or more variables to see
if they influence each other.
 Use this design when you
know and can apply
statistical knowledge based
on calculating the correlation
statistical test.
3

4.  correlate two or more variables
 collect data at one point in time
 obtain at least two scores for each
individual in the group
 often use the phrase ‘degree of
association between two
variables’
4
Types of Correlational Designs

5.  predict likely outcomes
 identify one or more predictor
variable and a criterion
 measure the predictor variable(s)
at one point in time and the
criterion variable at a later point
in time
 forecast future performance –
mention ‘predict’ some outcome
5
Types of Correlational Designs
Predictor variable: variable that is used to make the prediction
Criterion variable: variable about which the prediction is made

6. Key Characteristics
Displays of
Scores
(scatter plots and
matrices)
- Plot scores on a graph,
present in a table
Associations
Between Scores
(direction, form and
strength)
- Linear relationship
- Uncorrelated and Non
linear relationships
Multiple Variable
Analysis
(partial correlations and
multiple regression)
- technique that enables
researchers to determine a
correlation between a
criterion variable
- best combination of two or
more predictor variables
6
+How strong the r/ship
+What type of r/ship

7. When one value
increases, the other
increases
When one value
increases, the other
decreases
No relationship
between the data
Correlation coefficient??
A measure of the strength and direction of the linear r/ship
between two variables

8. Correlation Matrix

9. Linear and Non Linear Relationships

10. Partial Correlations

11. Multiple Regression

12. Steps In Conducting A Correlational
Study
• Problem Selection
- Is variable X related to variable Y? (creativity & IQ test scores)
- What factors explain M towards N? (student teacher’s ethical
behaviour & student-teaching experience?
- Does Q predict P? (class rank & students’ grade point average
in first semester)
• Sample (randomly-if possible)-not less than 30
• Instruments (tests, questionnaire)
• Collect data and monitor potential threats to the validity of
the scores
• Analyze the data & represent the results
• Interpret the results

13. Quality Criteria of a Correlational
Study
• Adequate sample size
• Display results in a table or graph
• Select an appropriate statistical test
• Interpret about the direction and magnitude of
the association among the variables
• Provide the coefficient of determination, p
values, effect size, size of the coefficient
• Identify the predictor and criterion variables
• Present a visual model of the relationship among
the variables

14. 14
Conclusion
 Help explain important human behaviours
 Collect data as fast as possible
 Not involving lots of participants
THE END…