The document discusses correlational research, which examines the relationship between two variables but cannot prove that one causes changes in the other. It explains that correlational studies involve identifying variables, stating questions or hypotheses, collecting data from a sample, calculating correlation coefficients to measure the strength of relationships between variables, and reporting the results. Researchers must be cautious not to assume that correlation necessarily implies causation.

1. Correlational Research
Research Methodology – ENGL 6001
by Ihsan Ibadurrahman – G1025429

2. What is a correlational
research?
 It aims to look at an empirical
relationship between two variables such
that (1) changes in one are associated
with changes in the other or (2) particular
attributes of one variable are associated
with particular attributes of the other.
(Babbie, 2007)
 Not to be confused with „association‟ –
Correlation has a specific technical
meaning and needs statistical
requirements for it, whereas association
is a more general idea. (Neuman, 2011)

3. Why do we use a correlational
research?
 As a first step prior to experimentation
 As one of the criteria used to
determine Nomothetic Casuality.
 When experiments cannot be
conducted (for ethical or practical
reasons)

4. A word of caution
 Correlational studies can suggest that
there is a relationship between two
variables, but they cannot prove that
one variable causes a change in
another variable. In other words,
correlation does not equal causation.
Out-of-class language English performance in
learning class

5. How to conduct a correlational
research?
 Variables are identified
 Questions and/or hypotheses are
stated
 A sample is selected
 Data are collected
 Correlations are calculated
 Results are reported

6. Step 1: Identifying Variables
 “Predictor” variable – the variable(s)
that are believed to predict the
outcome, also called independent
variable
 “Criterion” variable – the variable to be
predicted, the outcome, also called the
dependent variable.

7. Step 2: Stating Questions
 Is level of education (independent
variable) related to family income
(dependent variable)?
 Do people who eat more eggs
(independent variable) have higher
cholesterol levels (dependent variable)?
 Do students who employ out-of-class
strategies (independent variable) more
often have higher English scores
(dependent variable)?

8. Step 3: Sampling
 Random Sampling
 Convenient / Purposeful sampling
 A minimum of 30 samples is required

9. Step 4: Gathering data
 Naturalistic Observation
Naturalistic observation involves observing and
recording the variables of interest in the natural
environment without interference or manipulation by the
experimenter.
 The Survey Method
In this method, a random sample of participants
completes a survey, test, or questionnaire that relates to
the variables of interest.
 Archival Research
Archival research is performed by analyzing studies
conducted by other researchers or by looking at
historical patient records.

10. Step 5: Calculating
correlations
 To calculate a numerical value of a
correlation we can use Pearson’s
product moment correlation
cooficcient or correlation coefficient
with the symbol of the lowercase letter
„r‟.
 A correlation coofficient ranges from -
1.0 to +1.0, with -1.0 indicating a
perfect linear negative correlation and
+1.0 a perfect linear positive
correlation.

11. Interpretation of the Strength of
Correlations
 00 - .20 – Very Weak
 .21 - .40 – Weak
 .41 - .60 – Moderate
 .61 - .80 – Strong
 .81 – 1.00 - Very Strong
Different statisticians may
have similar but slightly
different scales.

12. Step 6: Reporting results
 Positive Correlations: Both variables increase
or decrease at the same time. A correlation
coefficient close to +1.00 indicates a strong
positive correlation.
 Negative Correlations: Indicates that as the
amount of one variable increases, the other
decreases (and vice versa). A correlation
coefficient close to -1.00 indicates a strong
negative correlation.
 No Correlation: Indicates no relationship
between the two variables. A correlation
coefficient of 0 indicates no correlation.

13. Correlations
 Scatter plots are often used to
depict correlations
6000
5000
Calories per day
This chart shows
4000
a strong positive
3000 correlation
2000
1000
0
100 150 200 250 300 350 400
Weight

14. Correlations
 Scatter plots are often used to
depict correlations
160 This chart shows
Minutes of Exercise per day
140 a strong
negative
120
correlation
100
80
60
40
20
0
100 150 200 250 300 350 400
Weight

15. Correlations
 Scatter plots are often used to
depict correlations
45
Miles from Krispy Creme
40
35
This chart shows
30
virtually no
25 correlation
20
15
10
5
0
100 150 200 250 300 350 400
Weight

16. How to calculate correlations
 Excel has a statistical function. It calculates
Pearson Product Moment correlations.
 SPSS (a statistical software program for
personal computers used by graduate
students) calculates correlations.