This document discusses correlational research design. Correlational research examines relationships between two or more variables by measuring them for a group of individuals and calculating a correlation coefficient. There are two main types: explanatory design which collects data at one time point to examine relationships between variables, and prediction design which collects data at two time points to predict outcomes based on predictor variables. Key aspects include using scatterplots to display variable relationships and determining the direction, form, and strength of relationships based on correlation coefficients. Common statistical analyses for correlational research include Pearson, Spearman, point-biserial, and multiple regression.

1. CORRELATIONAL
RESEARCH DESIGN
Sarah & Emeral

2. What is it?
◦ Lodico (2010) said that correlational research is used to measure two or more variables and
examine whether there are relationships among the variables.
◦ McMillan (2012) suggests that a correlation is a measure of the degree of association
(relationship) between two variables and the value of one variable can be predicted by knowing
the value of the other.
◦ Creswell (2012) mentions that Correlational Design provides investigators to explain relationship
among variables and predict outcomes.
◦ Anderson G. (2005) correlational research includes the calculation of a correlation coefficient to
which variables vary in the same way.

3. The Types
Explanatory
design
Prediction
design

4. The Types
Explanatory Design
◦ Two or more variables
◦ A single group without categories
◦ Data are collected at one point in time
◦ At least 2 scores for each individuals in a group

5. The Types
Prediction Design
◦ There is “prediction” in the title
◦ Predictor Variable: a variable that is used to make a forecast about an outcome in
the correlational study.
◦ Criterion Variable: the outcome being predicted
◦ Forecast future performance
◦ Data are collected from one group of participants to measure variables
representing two different points in time

6. The Characteristics
Scatterplot
the display of
the scores
Direction, Form, Strength
the association
between scores

7. Scatterplot
◦ A pictorial
image displayed
on a graph of
two sets of
scores from one
group of
participant

8. Direction
◦ Identify the direction of the graph, whether it moves in the same
direction or the opposite direction
◦ It is POSITIVE if they move in the same direction, when X increases, so
does Y, or, alternatively, if X decreases, so does Y
◦ It is NEGATIVE if the move in the opposite direction, when X increases,
Y decreases, and when X decreases, Y increases

9. Form
◦ Identify the form of the plotted score as linear and nonlinear
◦ If there is a correlation between variables, the form will be POSITIVE
LINEAR or NEGATIVE LINEAR
◦ If there is no correlation, the form will be nonlinear.

10. ◦ Direction
◦ Form
◦ Strength

11. Strength
◦ The degree of association between two variables or sets of scores is
shown by a correlation coefficient of –1.00 to +1.00, with 0.00
indicating no linear association at all.
◦ The numbers indicate the strength and the + - signs indicate the
direction
◦ The closer the correlation coefficient to +1, the stronger the positive
relationship

12. The Steps
Identify
variable
Select a
sample
Identify
measures
Collect dataAnalyze data
Interpret
result

13. Statistical
Analysis
Pearson Product Moment
• The r coefficient
• Two Continuous Linear data (Interval & ratio)
• Most stable test with smallest amount of error
Spearman rho
• The ρ coefficient
• Non linear
• Continuous data or rank
Point-Biserial Correlation
• The rpbis coefficient
• One variable is continuous (interval or ratio), the other is
dichotomous (categorical)Phi Coefficient
• The π coefficient
• Two dichotomous variables
Multiple Regression Analysis
• The Y coefficient

14. Example of
Statistical
Analysis
Pearson Product Moment
• Relationship between hours of internet use per week and depression
Spearman rho
• Relationship between a person’s highest level of education and their
income
Point-Biserial Correlation
• Association between males and females on Toefl scores.
Phi Coefficient
• Males and females might be correlated with drug usage (no and yes).
Multiple Regression Analysis
• Does high school class rank predict a college student’s grade point
average in the first semester of college?

15. THANK YOU