Correlational Research
The
basic question for descriptive research is  "What are the values of a
number of variables for a given sample of subjects.
The
basic research question for correlation research is  What is the relationship
between two or more variables for a given set of subjects. Notice that we said
relationship between variables and not the effect of one variable on another
variable.
In
descriptive research we are just describing our subjects in terms of one or
more variables, while in correlational research we are looking at the
relationship between the variables.
In
future lessons we will look at research in which we are looking at the effect
of one variable (the independent variable) on another variable (the dependent
variable). This will be the case for causalcomparative reseaerch (lesson 12)
and for experimental research (lesson 13).
The
important thing to remember is that for correlational research we are just
looking at the degree of relationship between the variables and not the effect
of one variable on another variable.
An
example of a correlational research study:
One
important type of correlational research are studies conducted to provide
information about the validity and reliability of tests.
Reliability
studies are conducted to demonstrate the consistency with which tests perform
their measurement function. In one type of reliability study the same group of
subjects is given a test and then at a somewhat later date are given the test
again. We thus have two scores for each subject (the test score and the retest
score) and the correlation coefficient between the two sets of scores can be
calculated.
This
kind of correlation coefficient is referred to as a reliability coefficient.
The reliability coefficient can also be calculated by using equivalent forms of
the test. Many tests used in education, for example, standardized achievement
tests, have more than one form. To determine the reliability coefficient, a
group of subjects are given both forms of a test (e.g. Form A and form B) thus
two scores are obtained for each subject and the correlation coefficient is
calculated for the two sets of scores.
The
first type of correlation study we referred to is a testretest reliability
study and the second is an equivalent forms reliability study.
To
demonstrate the validity of a test we want to show that scores on the test
correlate highly with some external measure of what the test purportedly
measures. This external measure is referred to as the criterion. To conduct a
valid correlational study. We obtain scores for students on some test and also
record their scores on the criterion measure. Thus we have two scores for each
subject and can calculate the correlation coefficient of the sets of scores.
This correlation coefficient is referred to as a validity coefficient.
In
the follow table are listed some example tests and criteria that might be used
in validity studies.
Test

Criterion

Reading
Comprehension

Teachers
Rating on Reading
Comprehension 
Jiffy
IQ Test

Wechsler
Intelligence
Test for Children 
Clinical
Depression Scale

Psychiatrist's
Rating on
Depression Checklist 
Algebra
Prediction Test

Final
Grades for
Algebra Class 
Note
that in the first three examples the test and the criteria occurred at about
the same time. This type of validity is referred to as concurrent validity. In
the last example, note that, the criteria occurred at some time after the test.
This is referred to as predictive validity. In this case we want to show the
effectiveness of scores on the test in predicting the subjects' standing on
some criteria. Some of the same consideratins for predictive validity studies
are true for prediction studies in general, which we will discuss later.
The Nature of
Correlation
Correlational
research studies almost always use the correlation coefficient to indicate the
degree of relationship between two variables. The correlation coefficient is a
number ranging from 1 (a perfect positive correlation) through 0 (no
relationship between the variables) to 1 (a perfect negative correlation).
It
is tempting to think of a correlation coefficient as indicating the proportion
of sameness between the two variables. But this is not true. A correlation of
.90 does not mean that the two variables are 90% the same. In fact, the
proportion of common variance (an indication of sameness) for a correlation of
.90 is .81 or 81%. The proportion of common variance is the square of the
correlation coefficient. (.90 x .90 = .8100).
If
we have a correlation of .50 between two variables the proportion of common
variance is only .25 or 25% (.50 x .50 = .2500). We could say that the two
variables demonstrate 25% sameness but 75% (100  25) different ness (if there
is such a word and we can use it in the present context).
The Design of
Correlational Research Studies
Prediction Studies
In
predictive correlational studies we are using the degree of relationship that
exists between two variables to predict one variable from the other. For
example if reading and spelling are correlated, then we can use the information
to predict a student's score on the spelling test if the student has only taken
the reading test. Conversely we could predict the student's score on the
reading test given the student's score on the spelling test.
Prediction
studies are widely used to predict student academic success in college based on
such measures as high school grades, teacher grades, and aptitude test scores.
In fact, many such criteria may be used in a multiple correlation prediction
study.
Read
a correlational research study, preferably one you identified in your review of
the related literature, and state the following components for the study you
read.
 Indicate the problem for the study
 Describe the subjects for the study.
 Describe the instruments used in the study.
 Describe the procedures (how was the study conducted).
 Describe how the data for the study was analyzed.
 List the major conclusions of the study.
You
should include the following items or components about the study you read:
 Complete APA style reference for the article
 The problem
 The subjects
 The instruments
 The procedures
 The method of data analysis
 The major conclusions