There are three types of correlations: positive, negative, and no correlation. A positive correlation means that as one variable increases, the other also increases. A negative correlation means that as one variable increases, the other decreases. No correlation means the variables are unrelated and do not influence each other. While correlation does not prove causation, it can suggest relationships between variables and be used for prediction. Limitations of correlational research include the inability to determine cause and effect from the data.

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Types
There are three types of correlations that are identified:
1. 1. Positive correlation: Positive correlation between two variables is when an increase in
one variable leads to an increase in the other and a decrease in one leads to a decrease
in the other. For example, the amount of money that a person possesses might correlate
positively with the number of cars he owns.
2. 2. Negative correlation: Negative correlation is when an increase in one variable leads to
a decrease in another and vice versa. For example, the level of education might correlate
negatively with crime. This means if by some way the education level is improved in a
country, it can lead to lower crime. Note that this doesn't mean that a lack of education
causes crime. It could be, for example, that both lack of education and crime have a
common reason: poverty.
3. 3. No correlation: Two variables are uncorrelated when a change in one doesn't lead to a
change in the other and vice versa. For example, among millionaires, happiness is found
to be uncorrelated to money. This means an increase in money doesn't lead to
happiness.
A correlation coefficient is usually used during a correlational study. It varies between +1 and -1.
A value close to +1 indicates a strong positive correlation while a value close to -1 indicates
strong negative correlation. A value near zero shows that the variables are uncorrelated.
Limitations
It is very important to remember that correlation doesn't imply causation and there is no way to
determine or prove causation from a correlational study. This is a common mistake made by
people in almost all spheres of life.
For example, a US politician speaking out against free lunches to poor kids at school argues
-“You show me the school that has the highest free and reduced lunch, and I'll show you the
worst test scores, folks” (nymag.com). This is a correlation he is speaking about - one cannot
imply causation. The obvious explanation for this is a common cause of poverty: people who are
too poor to feed their children will not have the best test scores.
Correlational Research Guidelines
• Conducting Correlational Research
by Dr. Janet Waters
Research Design
In general, a correlational study is a quantitative method of research in which you have 2 or more quantitative
variables from the same group of subjects, & you are trying to determine if there is a relationship (or covariation)
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between the 2 variables (a similarity between them, not a difference between their means). Theoretically, any 2
quantitative variables can be correlated (for example, midterm scores & number of body piercings!) as long as you
have scores on these variables from the same participants; however, it is probably a waste of time to collect &
analyze data when there is little reason to think these two variables would be related to each other.
Try to have 30 or more participants; this is important to increase the validity of the research.
Your hypothesis might be that there is a positive correlation (for example, the number of hours of study & your
midterm exam scores), or a negative correlation (for example, your levels of stress & your exam scores). A perfect
correlation would be an r = +1.0 & -1.0, while no correlation would be r = 0. Perfect correlations would almost
never occur; expect to see correlations much less than + or - 1.0. Although correlation can't prove a causal
relationship, it can be used for prediction, to support a theory, to measure test-retest reliability, etc.
Data collection:
You may collect your data through testing (e.g. scores on a knowledge test (an exam or math test, etc.), or
psychological tests, numerical responses on surveys & questionnaires, etc. Even archival data can be used (e.g.
Kindergarten grades) as long as it is in a numerical form.
Data Analysis:
With the use of the Excel program, calculating correlations is probably the easiest data to analyze. In Excel, set up
three columns: Subject #, Variable 1 (e.g. hours of study), & Variable 2 (e.g. exam scores). Then enter your data in
these columns. Select a cell for the correlation to appear in & label it. Click "fx" on the toolbar at the top, then
"statistical", then "Pearson". When asked, highlight in turn each of the two columns of data, click "Finish", & your
correlation will appear. Charts in any statistics textbook can tell you if the correlation is significant, considering the
number of participants.
You can also do graphs & scatter plots with Excel, if you would like to depict your data that way (See Chart wizard).
Presentation of your results in a Research Report:
Use the standard APA style lab report. In the Introduction, briefly review past research & theory in your topic
question (e.g. summarize current research on stress & academic achievement). Use APA referencing style to cite
your sources. Then in the Method section, present a general description of the group of participants (their number,
mean age, gender, etc.) in theParticipants section, any materials you may have used (e.g. tests, surveys, etc.) in
the Materials section, & in the Procedure section, note that your general research strategy was a correlational
study, & describe your methods of data collection (e.g. survey, test, etc.).
In the Results section of the report, present your correlation statistic in both a table & in words, & note whether or
not it is significant. If you have more than 2 variables to correlate, present a correlational matrix, showing the
correlation between each of the variables. In the following example, 4 variables were correlated in one study. The
correlation between Exam scores & hours of study, for example, is r = +.67, p
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