CORRELATIONAL-RESEARCH.pptx

CORRELATIONAL RESEARCH
JUDY V. CALLANTA

Correlational research is a type of non-
experimental research method in which
a researcher measures two variables
and understands and assesses the
statistical relationship between them
with no influence from any extraneous
variable.

DESIGN
A correlational research design investigates
relationships between variables without the researcher
controlling or manipulating any of them.
A correlation reflects the strength and/or direction of
the relationship between two (or more) variables. The
direction of a correlation can be either positive or negative.
(Bhandari,2022)

EXAMPLE
The correlation coefficient shows the correlation
between two variables (A correlation coefficient is a
statistical measure that calculates the strength of the
relationship between two variables), a value measured
between -1 and +1. When the correlation coefficient is
close to +1, there is a positive correlation between the
two variables. If the value is relative to -1, there is a
negative correlation between the two variables. When
the value is close to zero, then there is no relationship
between the two variables.

Types of Correlational
Research

Types of correlational
Research
A positive relationship between two variables is when
an increase in one variable leads to a rise in the other
variable. A decrease in one variable will see a reduction
in the other variable. For example, the amount of
money a person has might positively correlate with the
number of cars the person owns.
Both variables change in the same direction
example: As height increases, weight also increases
1. POSITIVE
CORRELATION

Research
A negative correlation is quite literally the opposite of
a positive relationship. If there is an increase in one
variable, the second variable will show a decrease and
vice versa.
The variables change in opposite directions
2. NEGATIVE
CORRELATION

2. Negative Correlation
For example, being educated might negatively
correlate with the crime rate when an increase in one
variable leads to a decrease in another and vice versa.
If a country’s education level is improved, it can lower
crime rates. Please note that this doesn’t mean that
lack of education leads to crimes. It only means that a
lack of education and crime is believed to have a
common reason – poverty.

Research
There is no correlation between the two variables in
this third type. A change in one variable may not
necessarily see a difference in the other variable. For
example, being a millionaire and happiness are not
correlated. An increase in money doesn’t lead to
happiness.
There is no relationship between the variables
Example: Coffee consumption is not correlated
with height
3. NO CORRELATION

Variables: Variables
are only observed
with no manipulation
or intervention by
researchers
Purpose :
Used to test
strength of
association
between variables
Control:
Limited control is
used, so other
variables may play a
role in the
relationship
Validity:
High external validity:
you can confidently
generalize your
conclusions to other
populations or settings

Characteristics of Correlational
Research

Characteristics of Correlational Research
• Correlational study is non-experimental. It means
that researchers need not manipulate variables with
a scientific methodology to either agree or disagree
with a hypothesis. The researcher only measures and
observes the relationship between the variables
without altering them or subjecting them to external
conditioning.
1. Non-Experimental

• Correlational research only looks back at historical
data and observes events in the past. Researchers
use it to measure and spot historical patterns
between two variables. A correlational study may
show a positive relationship between two variables,
but this can change in the future.
2. Backward-looking

• The patterns between two variables from
correlational research are never constant and are
always changing. Two variables having
negative correlation research in the past can have a
positive correlation relationship in the future due to
various factors.
3. Dynamic

When to use correlational Research?
Correlational research is ideal for gathering data
quickly from natural settings. That helps
you generalize your findings to real-life situations in an
externally valid way.
There are a few situations where correlational
research is an appropriate choice.

1. To Investigate non-casual
relationship
You want to find out if there is an association between
two variables, but you don’t expect to find a causal
relationship between them.
Correlational research can provide insights into
complex real-world relationships, helping researchers
develop theories and make predictions.
When to use correlational research?

For example
You want to know if there is any correlation between the
number of children people have and which political party
they vote for. You don’t think having more children causes
people to vote differently—it’s more likely that both are
influenced by other variables such as age, religion, ideology
and socioeconomic status. But a strong correlation could be
useful for making predictions about voting patterns.

2. To explore casual relationship between
variables
You think there is a causal relationship between two
variables, but it is impractical, unethical, or too costly to
conduct experimental research that manipulates one of the
variables.
Correlational research can provide initial indications or
additional support for theories about causal relationships.

For example
You want to investigate whether greenhouse gas emissions
cause global warming. It is not practically possible to do an
experiment that controls global emissions over time, but
through observation and analysis you can show a strong
correlation that supports the theory.

3. To test new measurement tools
You have developed a new instrument for measuring
your variable, and you need to test its reliability or validity.
Correlational research can be used to assess whether
a tool consistently or accurately captures the concept it
aims to measure.

For example
You develop a new scale to measure loneliness in young
children based on anecdotal evidence during lockdowns. To
validate this scale, you need to test whether it’s actually
measuring loneliness. You collect data on loneliness using
three different measures, including the new scale, and test
the degrees of correlations between the different
measurements. Finding high correlations means that your
scale is valid.

How to collect correlational
data?
There are many different methods you can use in correlational
research. In the social and behavioral sciences, the most
common data collection methods for this type of research include
surveys, observations, and secondary data.
It’s important to carefully choose and plan your methods to ensure
the reliability and validity of your results. You should carefully
select a representative sample so that your data reflects the
population you’re interested in without research bias.

1. Survey
• In survey research, you can use questionnaires to
measure your variables of interest. You can conduct
surveys online, by mail, by phone, or in person.
• Surveys are a quick, flexible way to collect standardized
data from many participants, but it’s important to ensure
that your questions are worded in an unbiased way and
capture relevant insights.
How to collect correlational data?

For example
To find out if there is a relationship between vegetarianism
and income, you send out a questionnaire about diet to
a sample of people from different income brackets. You
statistically analyze the responses to determine whether
vegetarians generally have higher incomes.

2. Naturalistic Observation
• Naturalistic observation is a type of field research where
you gather data about a behavior or phenomenon in its
natural environment.
• This method often involves recording, counting,
describing, and categorizing actions and events.
Naturalistic observation can include both qualitative and
quantitative elements, but to assess correlation, you
collect data that can be analyzed quantitatively (e.g.,
frequencies, durations, scales, and amounts).

2. Naturalistic Observation
Naturalistic observation lets you easily generalize your
results to real world contexts, and you can study
experiences that aren’t replicable in lab settings. But data
analysis can be time-consuming and unpredictable, and
researcher bias may skew the interpretations.

For example
To find out if there is a correlation between gender and
class participation, you observe college seminars, note the
frequency and duration of students’ contributions, and
categorize them based on gender. You statistically analyze
the data to determine whether men are more likely to speak
up in class than women.

3. Secondary Data
• Instead of collecting original data, you can also use data
that has already been collected for a different purpose,
such as official records, polls, or previous studies.
• Using secondary data is inexpensive and fast,
because data collection is complete. However, the data
may be unreliable, incomplete or not entirely relevant,
and you have no control over the reliability or validity of
the data collection procedures.

For example
To find out if working hours are related to mental health,
you use official national statistics and scientific studies from
several different countries to combine data on average
working hours and rates of mental illness. You statistically
analyze the data to see if countries that work fewer hours
have better mental health outcomes.

How to analze correlational
data?
After collecting data, you can statistically analyze the
relationship between variables using correlation
or regression analyses, or both. You can also visualize
the relationships between variables with a scatterplot.
Different types of correlation coefficients and regression
analyses are appropriate for your data based on
their levels of measurement and distributions.

1. Correlation Analysis
• Using a correlation analysis, you can
summarize the relationship between variables
into a correlation coefficient: a single
number that describes the strength and
direction of the relationship between
variables. With this number, you’ll quantify
the degree of the relationship between
variables.
How to analyze correlational data?

1. Correlation Analysis
• The Pearson product-moment correlation
coefficient, also known as Pearson’s r, is
commonly used for assessing a linear relationship
between two quantitative variables.
• Correlation coefficients are usually found for two
variables at a time, but you can use a multiple
correlation coefficient for three or more variables.

2. Regression Analysis
• With a regression analysis, you can predict how much a
change in one variable will be associated with a change
in the other variable. The result is a regression
equation that describes the line on a graph of your
variables.
• You can use this equation to predict the value of one
variable based on the given value(s) of the other
variable(s). It’s best to perform a regression analysis after
testing for a correlation between your variables.

Correlation and causation
• It’s important to remember
that correlation does not imply
causation. Just because you find a
correlation between two things
doesn’t mean you can conclude one
of them causes the other for a few

Directionality Problem
• If two variables are correlated, it could be
because one of them is a cause and the
other is an effect. But the correlational
research design doesn’t allow you
to infer which is which. To err on the side of
caution, researchers don’t conclude
causality from correlational studies.

For example
You find a positive correlation between vitamin D levels and
depression: people with low vitamin D levels are more likely
to have depression. But you can’t be certain about whether
having low vitamin D levels causes depression, or whether
having depression causes reduced intakes of vitamin D
through lifestyle or appetite changes. Therefore, you can
only conclude that there is a relationship between these two
variables.

Third Variable Problem
• A confounding variable is a third variable that
influences other variables to make them seem
causally related even though they are not. Instead,
there are separate causal links between the
confounder and each variable.
• In correlational research, there’s limited or no
researcher control over extraneous variables. Even if
you statistically control for some potential
confounders, there may still be other hidden variables
that disguise the relationship between your study

For example
• You find a strong positive correlation
between working hours and work-
related stress: people with lower
working hours report lower levels of
work-related stress. However, this
doesn’t prove that lower working hours
causes a reduction in stress

For example
• There are many other variables that may
influence both variables, such as average
income, working conditions, and job
insecurity. You might statistically control for
these variables, but you can’t say for certain
that lower working hours reduce stress
because other variables may complicate the
relationship.

References
Bhandari, P. (2022). Operationalization. A Guide with
Examples, Pros & Cons.
QuestionPro Survey Software. (2022). Correlational
Research: What it is with Examples. Retrieved from
QuestionPro:
https://www.questionpro.com/blog/correlational-research/

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