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Thursday, January 23, 2014

Conducting Correlational Research


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) 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 the Participants 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 <.01. This indicates a significant positive relationship between the number of hours of study & subsequent exam scores.

Number of hours of study & subsequent exam scores

Hours of study    +.67*                      -                            -
Stress level           - .45*                     -.10                       -
# of Piercings      -.15                         -.2                        +.18
                           Exam Scores      Hrs of Study        Stress level

* p < .01  

 In the Discussion section, relate your results to past or current research & theory you had cited & described in the Introduction. Do note the statistical significance of your findings, & limits to their generalizability. Remember that even if you did not obtain the significant differences you had hoped to, your results are still interesting, & must be explained, with reference to other research & theory.

This article was taken from Janet Waters at Capilano University Canada US

© Janet Waters