The document discusses correlational research methods, which examine relationships between two measured variables without manipulating variables. Correlational research can describe the direction (positive/negative), form (linear/nonlinear), and strength (magnitude) of relationships. Statistical analyses like Pearson correlation and regression can analyze correlational data. While correlational research allows observation of many variables, it cannot prove causation, as relationships may be due to other unmeasured variables.

1. Chapter NO. 7
Correlational research method
Definition
The correlational method is a type of nonexperimental method that describes the
relationship between two measured variables. In addition to describing a relationship, correlations
allow us to make predictions from one variable to another. If two variables are correlated, we can
predict from one variable to the other with a certain degree of accuracy. For example, knowing
that self-esteem and academic performance are correlated allows us to estimate, within a certain
range, an individual’s self-esteem based on knowing that person’s academic performance.
Sometimes researchers choose to conduct correlational research because they are interested in
measuring many variables and assessing the relationships between them. For example, they might
measure various aspects of personality and assess the relationship between dimensions of
personality.
Characteristics
A correlational research method describes three characteristics of a relationship.
• The direction (positive / negative) of the relationship.
• The form (linear/ nonlinear) of the relationship.
• The consistency or strength (magnitude) of the relationship.
Positive relationship
An increase in one variable is related to an increase in the other, and a decrease in one is
related to a decrease in the other is the positive relationship between the two variables. For
example, there is a positive significant relationship between self-esteem and academic
performance among college students. This shows that with the increase in the self-esteem
academic performance is also increase both variables are in same direction.
Negative relationship
This negative correlation indicates that an increase in one variable is accompanied by a
decrease in the other variable. This represents an inverse relationship. For example, there is a
negative significant relationship between social anxiety and interpersonal relationship among
university students. It indicates that with the increase in the social anxiety the interpersonal
relationship decreases both the variables move in opposite direction.
Magnitude
An indication of the strength of the relationship between two variables is called magnitude.
The magnitude or strength of a relationship is determined by the correlation coefficient describing
the relationship. A correlation coefficient (r) is a measure of the degree of relationship between

2. two variables; it can vary between +1.00 and -1.00. The weaker the relationship between the
variables, the closer the coefficient is to 0.
Correlation
coefficient (r)
Strength of
relationship
0.00_____0.19
0.20_____0.39
0.40_____0.59
0.60_____0.79
0.80_____1.00
Very weak
Weak
Moderate
Strong
Very strong
Research designs in correlational research
1. Explanatory research design
Explanatory design is a correlational design in which the researcher is interested in
the extent to which two variable or more covary, that is where changes in one variable are reflected
in changes in the other. OR the research study that describes the relationship between the two or
more variables. Title for exploratory research “relationship between self-esteem and academic
performance among college” or “self-esteem and academic performance among college students:
correlational study. Hypothesis “there is a positive significant relationship between the self- esteem
and academic performance among college students”.
2. Prediction research design
Correlational predictive design is used in those cases when there is an interest to
identify predictive relationship between the predictor and the outcome/criterion variable. The
synonym of correlation is “association”, and it is referred to the direction and magnitude of the
relationship between two variables. Title for prediction research “self-esteem as a predictor of
academic performance among college students” or “self-esteem and academic performance among
college students”. Hypothesis “self-esteem will significant positively predict academic
performance among college students” or “self-esteem will be significant predictor of predicting
academic performance among college students”.
Causality
Causality is the study of cause and effect relationship between the variables. The one
variable is a cause and other variable is an effect. Causal effect occurs when variation in one
phenomenon, an independent variable, leads to or results, on average, in variation in another
phenomenon, the dependent variable. For example, to analyze the effects of re-branding
initiatives on the levels of customer loyalty
Differentiate between correlation and causality
Correlation Causality
Correlation is the relationship between two
sets of variables used to describe or predict
information.
The relation between something that happens
and the thing that causes it. The first thing that
happens is the cause and the second thing is the
effect

3. For example, gender as a risk factor of mental
health, impact of gender on mental health or
function of gender on mental health.
For example, relationship between self-esteem
and academic performance among college
students.
Spurious relationship
A spurious relationship is a relationship between two variables in which a common-causal
variable produces and “explains away” the relationship. If effects of the common-causal variable
were taken away, or controlled for, the relationship between the predictor and outcome variables
would disappear. In the example, the relationship between aggression and television viewing might
be spurious because by controlling for the effect of the parents’ disciplining style, the relationship
between television viewing and aggressive behavior might go away.
Partial correlation
Partial correlation involves measuring all three variables and then statistically removing
the effect of the third variable from the correlation of the remaining two variables. If the third
variable (in this case, education) is responsible for the relationship between electrical appliances
and contraceptive use, then the correlation should disappear when the effect of education is
removed.
Mediator variable
In general, a given variable may be said to function as a mediator to the extent that it
accounts for the relation between the predictor and the criterion. Mediators explain how external
physical events take on internal psychological significance. Whereas moderator variables specify
when certain effects will hold, mediators speak to how or why such effects occur. Title “personality
traits as mediator between self-esteem and academic performance among college students”.
Hypothesis for mediator “the personality traits are significantly mediate the relationship between
self-esteem and academic performance among college students”.
Moderator variable
In general terms, a moderator is a qualitative (e.g., sex, race, class) or quantitative (e.g.,
level of reward) variable that affects the direction and/or strength of the relation between an
independent or predictor variable and a dependent or criterion variable. Specifically within a
correlational analysis framework, a moderator is a third variable that affects the zero-order
correlation between two other variables. Title for moderator “the role of personality traits as
moderator between the self-esteem and academic performance among college students”.
Hypothesis for moderator “self-esteem and academic performance among college students:
moderating role of personality traits”.
Statistical analysis
Following statistical analysis:
 Pearson correlation (use in explanatory research design)
 Spearman rank correlation (use to find out the strength and direction of the two variables)
 Regression correlation (use in prediction research design)
Advantages
Correlational research allows researchers to collect much more data than experiments.
Another benefit of correlational research is that it opens up a great deal of further research to other

4. scholars. Third advantage of correlational research it allows researchers to determine the strength
and direction of a relationship so that later studies can narrow the findings down and, if possible,
determine causation experimentally.
Disadvantages
Correlation is not and cannot be taken to imply causation. Even if there is a very strong
association between two variable we cannot assume that one causes the other. For example,
suppose we found a positive correlation between watching violence on T.V. and violent behavior
in adolescence. It could be that the cause of both these is a third (extraneous) variable - say for
example, growing up in a violent home - and that both the watching of T.V. and the violent
behavior are the outcome of this. Correlation does not allow us to go beyond the data that is given.
For example, suppose it was found that there was an association between time spent on homework
(1/2 hour to 3 hours) and number of G.C.S.E. passes (1 to 6). It would not be legitimate to infer
from this that spending 6 hours on homework would be likely to generate 12 G.C.S.E. passes.