The document discusses correlational research, which examines relationships between two variables without manipulating them. Correlational research can establish that variables are related but cannot prove one causes the other. Key points covered include: - Correlation coefficients measure the strength and direction of relationships between variables. - Positive correlations indicate variables increase together, while negative correlations mean one decreases as the other increases. - Correlational research has advantages like lower cost but cannot prove causation like experiments can. - Common data collection methods are naturalistic observation, surveys, archival data, and secondary data. Correlation and regression analyses are frequently used to analyze correlational data and make predictions.

3. Example
• Imagine that a scientist is looking into the
relationship between cancer and anxiety.
• Cancer and Anxiety are the two variables in this
study.
• Let’s imagine there is a positive correlation
between cancer and anxiety. Therefore, those
who have cancer experiences high levels of
anxiety.
• This does not necessarily imply that cancer
creates feelings of anxiety, though. It is not
possible to prove what causes what in
correlational research.

5. • Between -1 and +1 is the correlation
coefficient range.
• A perfect positive correlation between two
variables is shown by a correlation value of 1
• a perfect negative connection is indicated by a
correlation coefficient of -1.
• If the correlation coefficient is 0, there is no
correlation between the variables being
studied.

7. Positive Correlation
• Positive correlational research examines how
two variables are related in a linear fashion.
• A positive correlation indicates that the two
variables have a favorable association. As one
variable increases in this kind of relationship,
the other variable increases as well.

9. Example of Positive Correlation
• For instance, a person’s salary and the size of
the house they own are strongly associated.
The more money a person makes, the huge
house they own.

10. Negative Correlation
• Negative correlational research looks at the
relationship between two variables in an
inverse fashion.
• A negative correlation indicates that the two
variables do not correlate well.
• As one variable increases this type of
correlation, the other variable decreases.

12. • Example of Negative Correlation
• For instance, stress levels and life satisfaction
are negatively correlated. This suggests that
life happiness decreases as stress levels rise.

13. Zero correlation
• Zero correlation refers to the complete
absence of any relationship between two
variables.
• In statistics, this is represented by a
correlation coefficient of zero.
• A zero correlation means that there is no
linear relationship between the variables, and
they do not change together.

14. Example of Zero correlation
• The link between intelligence and height is an
illustration of zero correlation. There is no
correlation between an individual’s weight
and IQ.

15. Positive vs Negative vs Zero
Correlation

17. When to Use Correlational Research?
• 1- Test new measurement instruments
You need to test the validity or reliability of the
new instrument you built to measure your
variable.
To determine whether an instrument regularly
or accurately measures the notion it is intended
to measure, correlational research might be
utilized.

18. Example
• Based on anecdotal evidence during lockdowns,
you create a new scale to assess depression in
adults. You must determine whether this scale is
indeed assessing depression in order to validate
it.
• You gather information about depression using
three different scales, including the new one,
and test the strength of the degrees of
correlations between them.
• Finding strong correlations indicates the
reliability of your scale.

19. 2. Investigate the causal relationships between
the factors:
• You believe there is a causal relationship
between two factors, but conducting
experimental research in which one of the
variables is altered is unfeasible, immoral, or
hugely expensive.

20. • Example :
• Causal relationship between smoking and lung
cancer.

21. Example
• You want to look into whether emissions of
greenhouse gases contribute to global
warming.
• Although it is not realistic to conduct an
experiment that continuously monitors global
emissions, observation and analysis can
demonstrate a substantial correlation that
lends weight to the idea.

22. 3- To look into non-causal
relationships
• Although you don’t expect to discover a causal
connection between two variables, you want
to determine whether there is an association
between them.
• Correlational analysis can shed light on
intricate real-world linkages, assisting
researchers in the creation of theories and
forecasts.

23. Example
• You’re interested in finding out if there’s a
connection between people’s family size and the
political party they support.
• You don’t believe that having more children
affects how people vote; rather, you believe that
other factors like age, religion, ideology, and
socioeconomic status have a greater impact on
both.
• However, a high correlation could be helpful in
predicting voting trends.

24. Characteristics of Correlational
Research
Dynamic Non-experimental Backward-looking
• Correlational study
shows that the
relationships between
two variables are never
static and are constantly
shifting.
• Due to numerous
causes, two variables
with a history of
negative correlation
studies may eventually
show a positive
correlation relationship.
• Correlational research is
a non-experimental
method. It implies that
in order to support or
refute a theory,
researchers do not
necessarily need to
modify variables using a
scientific technique.
• the researcher just
measures and examines
the connection
between them.
• Correlational research
solely considers
historical information
and keeps track of past
events.
• It is measured and used
by researchers to
identify historical
patterns between two
variables.

25. Pros of Correlational Research
• The benefits of correlational research are listed below.
• You may examine the statistical link between the two
variables with the aid of this research.
• Both the cost and the amount of time are lower.
• In a natural context, you can observe the variables.
• You can conduct this research instead of carrying out
an experiment if you can’t.
• You are motivated by a variety of ideas that could aid in
your future research.
• Large amounts of data can be gathered quickly.

26. Cons of Correlational Research
• Only capable of establishing a relationship between two
variables, never more. So scope of this research is
constrained.
• You can only learn a few things from this research. Finding
the cause is not done; rather the relationships between
variables are studied.
• It does not demonstrate a causal relationship between the
variables. This means that it is unable to identify the
variable that ultimately drives the statistical pattern.
• The variables are out of your control. You can only observe
the variables and their statistical patterns with this method.

27. Data Collection method
• Naturalistic observation
• Naturalistic observation is a method of data collection that
involves observing people’s behavior where it naturally
occurs, which is where they are most often found. This
approach is a kind of field study. It could imply that a
researcher is studying individuals in public spaces like a
playground, movie theatre, or grocery store.
• Example
• As was previously indicated, let’s use the grocery store as an
example, where shoppers may be seen gathering items from
the aisle and placing them in their shopping bags. Since this is
morally permissible, the majority of researchers decide to
record their observations in open spaces.

28. • Surveys
• You can measure your key variables in survey
research using questionnaires. You can conduct
surveys in-person, over the phone, through the
mail, or online.
• Surveys are a quick and flexible way to gather
standardized data from a large number of
respondents, but it’s crucial to make sure your
questions are framed objectively and capture the
right insights.

29. • Archival data
• The usage of archival data is another method for handling
correlational data. The data that has previously been
gathered through conducting similar types of study is
known as archival information. Primary research is typically
used to make archival data accessible.
• Archived data can provide information that is more easily
understood than naturalistic observation.
• Example
• For instance, using social security information, it is easy to
estimate the approximate number of Johns residing in each
state in the United Kingdom.

30. • Secondary data
• You can use data that has already been obtained for a different
purpose, such as official records, polls, or prior research, in place of
collecting original data.
• Because secondary data collection is thorough, using it is quick and
cheap. However, you have no control over the validity or reliability
of the data gathering methods, thus the data may be inaccurate,
partial, or not totally relevant.
• Example
• You send a questionnaire on food to a sample of people from
various income categories to see if there is a correlation between
vegetarianism and income. To find out if vegetarians earn more on
average, you statistically evaluate the responses.

31. How to analyze Correlational Data?
• Correlational Analysis
A correlation coefficient, which is a single value
that expresses the degree and direction of the
association between variables, can be used to
distil the relationship between variables using
correlation analysis. You may measure how
closely two variables are related by using this
number.

32. • The Pearson product-moment correlation
coefficient, also referred to as Pearson’s r, is
frequently used to determine if two
quantitative variables have a linear
relationship.

34. Regression Analysis
• You can estimate how much a change in one
variable will affect a change in the other
variable using a regression analysis.
• A regression equation that depicts the line on
a graph of your variables is the end result.
• By using the provided value(s) of the other
variable, you can use this equation to forecast
the value of one variable (s).

35. • The line passing through
the data points is the
graph of the estimated
regression equation: ŷ =
42.3 + 0.49x.
The parameter estimates,
b0 = 42.3 and b1 = 0.49,
were obtained using the
least squares method.
• For instance, given a
patient with a stress test
score of 60, the predicted
blood pressure is 42.3 +
0.49(60) = 71.7.

36. Purpose of Correlational Research
• Finding links, describing these associations,
and then making predictions are frequently
the objectives of correlational research.
• Such studies frequently act as a springboard
for additional experimental studies

37. Conclusion
• In summary, it is possible to do correlational
research in cases where experimental research is
not feasible.
• It investigates the connection between two
variables. Since it is dynamic, changes may occur
at any time.
• You can quickly obtain data via correlational
research.
• To ensure that your findings are legitimate and
trustworthy, you can gather data using a variety
of techniques.