Chapter XII: The Correlational Research Strategy
12.1 An Introduction To Correlational Research
• Five basic research strategies: experimental, non-experimental, quasi-experimental,
correlational, and descriptive.
• The goal of the correlational research strategy is to examine and describe the associations
and relationships between variables.
• More specifically, the purpose of a correlational study is to establish that a relationship
exists between variables and to describe the nature of the relationship.
• The correlational strategy does not attempt to explain the relationship and make no attempt
to manipulate, control, or interfere with the variables.
• The data for a correlational study consist of two measurements for each individual, one for
each of the two variables being examined.
• Measurements can be made in natural surrounding or the individuals can be measured in a
• The important factor is that the researcher simply measures the two variables being
• The measurements are then examined to determine whether or not they show any
consistent pattern of relationship.
• A correlational study can involve measuring more than two variables but usually involves
relationships between two variables at a time.
In the correlational research strategy, two variables are measured and recorded for each
individual. The measurements are then reviewed to identify any patterns of relationship that
exist between the two variables and to measure the strength of the relationship.
• The individual is intended to be a single source, not necessarily a single person.
• A correlational study could be described as requiring two scores from the same individual.
• The individual can be any single source, such as family, a couple, or a single person.
12.2 The Data For A Correlational Study
• A correlational research study produces two scores for each individual.
• The scores are identified as X and Y.
• The data can be presented in a list showing the two scores for each individual.
• The data can be presented in a graph known as a scatter plot.
• In the scatter plot, each individual is represented by a single point with a horizontal
coordinate determined by the individual’s X score and the vertical coordinate
corresponding to the Y value.
• The value of a scatter plot is that it allows you to see the characteristics of the relationship
between the two variables.
• Typically, researchers are interested in three aspects of the relationship:
(I) The direction of relationship.
In a positive relationship, there is a tendency for two variables to change in the
same direction; as one variable increases, the other also tends to increase.
In a negative relationship, there is a tendency for two variables to change in
opposite directions; increases in one variable tend to be accompanied by
decreases in the other.
(II) The form of the relationship.
A linear relationship is one in which the data points in the scatter plot tend to
cluster around a straight line.
A monotonic relationship means that the relationship is consistently one-
directional, either positive or consistently negative. To note, the amount of increase
needs not be constantly the same size.
(III) The consistency or strength of the relationship. In correlational studies, the consistency of a relationship is typically measured and
described by the numerical value obtained for a correlation coefficient. Different
kinds of correlations are used to measure different kinds of relationships.
A Pearson correlation measures linear relationships.
A Spearman correlation is used to measure monotonic relationships.
• For both Pearson & Spearman correlation coefficients, a value of 1.00 (or -1.00) indicates a
perfectly consistent relationship and a value of zero indicates no consistency whatsoever.
• Intermediate values indicate different degrees of consistency.
• A correlation coefficient simply describes the consistency or strength of a relationship
• Even the strongest correlation of 1.00 does not imply that there is a cause and effect
relationship between the two variables.
Comparing Correlational, Experimental, and Differential Research
• The goal of an experimental study is to demonstrate a cause and effect relationship
between two variables.
• To accomplish this goal, an experiment requires the manipulation of one variable to create
treatment conditions and the measurement of the second variable to obtain a set of scores
within each condition.
• All other variables are controlled.
• The researcher then compares the scores from each treatment with the scores from other
• If there are differences between treatments, the researcher has evidence of a relationship
• Specifically, the research can conclude that manipulating one variable causes changes in
the second variable.
• An experimental study involves measuring only one variable and looking for differences
between two or more groups of scores.
• A correlational study is intended to demonstrate the existence of a relationship between
• A correlational study is not trying to explain the relationship.
• To accomplish its goal, a correlational study typically does not involve manipulating,
controlling, or interfering with variables.
• The researcher simply measures two different variables for each individual.
• The researcher then looks for a relationship within the set of scores.
• A correlational study does not compare groups of scores but rather looks for a relationship
within a single group of participants.
• Differential research (non-experimental design) is very similar to correlational research.
• A correlational study looks directly at the two variables to determine whether they are
• Differential design established the existence of a relationship by demonstrating a difference
• A differential design uses one of the two variables to create groups of participants and then
measures the second variable to obtain scores within each group.
• The primary focus of the correlational study is on the relationship between two variables.
• The primary focus of the differential study is on the difference between groups.
12.3 Applications of the Correlational Strategy
• The use of correlational results to make predictions is not limited to predictions about future