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Purpose of Correlational Research

. One purpose for doing correlational research is to determine the degree to which a

relationship exists between two or more variables. Notice that I did NOT say cause-and-effect

relationship. Correlational research designs are incapable of establishing cause-and-effect.

What's the difference? Well, it's really quite simple. Variables can relate to one another

without one causing the other to occur.

The second purpose for correlational research is to develop prediction models to be able to

predict the future value of a variable from the current value of one or more other variables. A

common prediction model used in education is the use of college entrance exam scores to help

predict a prospective student's success in college. Colleges and universities work hard to

develop the best prediction models they can to ensure that the most potentially successful

students are admitted. To increase the predictive power of their models, they use correlational

research methods, some of which we'll discuss a little later in this lesson.

Correlational studies are used to look for relationships between variables. There are three possible

results of a correlational study: a positive correlation, a negative correlation, and no correlation. The

correlation coefficient is a measure of correlation strength and can range from 1.00 to +1.00.

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.

Limitations of Correlational Studies:

While correlational studies can suggest that there is a relationship between two variables,

they cannot prove that one variable causes a change in another variable. In other words,

correlation does not equal causation. For example, a correlational study might suggest that

there is a relationship between academic success and self-esteem, but it cannot show if

academic success increases or decreases self-esteem. Other variables might play a role

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including social relationships, cognitive abilities, personality, socio-economic status, and

myriad other factors.

Correlation is a statistical technique that can show whether and how strongly pairs of variables are

related. For example, height and weight are related; taller people tend to be heavier than shorter people.The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two

people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of

people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of

people 5'7'', etc. Correlation can tell you just how much of the variation in peoples' weights is related to

their heights.

A. Purposes of Correlational ResearchA. Correlational studies are carried out either to help explain important human behaviors or to

predict likely outcomes.

B. If a relationship of sufficient magnitude exists between two variables, it becomes possible to

predict a score on either variable if a score on the other variable is known.

C. The variable that is used to make the prediction is called the predictor variable.

D. The variable about which the prediction is made is called the criterion variable.

E. Both scatterplots and regression lines are used in correlational studies to predict a score on

a criterion variable.

F. A predicted score is never exact. As a result, researchers calculate an index of predictionerror which is known as the standard error or estimate.

G. Basic Steps in Correlational ResearchH.Problem Selection

I. Is variable X related to variable Y?

J. How well does variable P predict variable C?

K. What are the relationships among a large number of variables, and what predictions can be

made that are based on them?

L. Sample

M. Identify an appropriate population, one that is meaningful and from which data on each of

the variables of interest can be collected.

N. The minimum acceptable sample size for a correlational study is considered by most

researchers to be no less than 30.

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O.Instruments

P. The instruments must yield quantitative data in a correlational study.

Q. Most correlational studies involve the administration of some type of instrument such as

tests, questionnaires, and sometimes observation.R. Instruments must show evidence of validity and reliability.

S. Design and Procedures

T. The basic design used in a correlational study is quite straightforward.

U. Two or more scores are obtained from each individual in the sample, one score for each

variable of interest.

V. The pairs of scores are then correlated, and the resulting correlation coefficient indicates

the degree of relationship between the variables.

W. Data Collection

X. In an explanatory study, all the data on both variables will usually be collected within a fairly

short time.

Y. In a prediction study, the measurement of the criterion variables often takes place

sometime after the measurement of the predictor variables.

Z.Data Analysis and Interpretation

AA.When variables are correlated, a correlation coefficient is produced.

BB.The closer the coefficient is to +1.00 or -1.00, the stronger the relationship.

CC.Coefficients that are at or near .00 indicate that no relationship exists between the variables

involved.