Correlational research


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Correlational research

  1. 1. Correlational Research Research Writing Aiden Yeh, PhD Wenzao Ursuline College of Languages
  2. 2.  In general, correlational research examines the covariation of two or more variables. For example, the early research on cigarette smoking examine the covariation of cigarette smoking and a variety of lung diseases. These two variable, smoking and lung disease were found to covary together.
  3. 3.  Correlational research can be accomplished by a variety of techniques which include the collection of empirical data. Often times, correlational research is considered type of observational research as nothing is manipulated by the experimenter or individual conducting the research. For example, the early studies on cigarette smoking did not manipulate how many cigarettes were smoked. The researcher only collected the data on the two variables. Nothing was controlled by the researchers.
  4. 4.  It is important to not that correlational research is not causal research. In other words, we can not make statements concerning cause and effect on the basis of this type of research.
  5. 5.  Correlational research is often conducted as exploratory or beginning research. Once variables have been identified and defined, experiments are conductable.
  6. 6. 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).
  7. 7. Research Design   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.
  8. 8. When two things are correlated  Positive correlation means that high scores on one are associated with high scores on the other, and that low scores on one are associated with low scores on the other.  Negative correlation, on the other hand, means that high scores on the first thing are associated with low scores on the second. Negative correlation also means that low scores on the first are associated with high scores on the second.
  9. 9. Another Example of data  An example is the correlation between body weight and the time spent on a weight-loss program. If the program is effective, the higher the amount of time spent on the program, the lower the body weight. Also, the lower the amount of time spent on the program, the higher the body weight.
  10. 10.  Try to have 30 or more participants; this is important to increase the validity of the research.
  11. 11.  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 cant prove a causal relationship, it can be used for prediction, to support a theory, to measure test-retest reliability, etc.
  12. 12. 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.
  13. 13. Data Analysis of Correlational Research  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).
  14. 14. Pearson r  Pearson r is a statistic that is commonly used to calculate bivariate correlations.  For an Example Pearson r = -0.80, p < .01. What does this mean?
  15. 15. Interpreting Correlation  1.   The numerical value of the correlation coefficient.  2.   The sign of the correlation coefficient.  3.   The statistical significance of the correlation.  4.   The effect size of the correlation.
  16. 16.  1.   The numerical value of the correlation coefficient.  Correlation coefficients can vary numerically between 0.0 and 1.0. The closer the correlation is to 1.0, the stronger the relationship between the two variables. A correlation of 0.0 indicates the absence of a relationship. If the correlation coefficient is –0.80, which indicates the presence of a strong relationship.
  17. 17.  2.   The sign of the correlation coefficient.  A positive correlation coefficient means that as variable 1 increases, variable 2 increases, and conversely, as variable 1 decreases, variable 2 decreases. In other words, the variables move in the same direction when there is a positive correlation.  A negative correlation means that as variable 1 increases, variable 2 decreases and vice versa. In other words, the variables move in opposite directions when there is a negative correlation. The negative sign indicates that as class size increases, mean reading scores decrease.
  18. 18.  3.   The statistical significance of the correlation.  A statistically significant correlation is indicated by a probability value of less than 0.05. This means that the probability of obtaining such a correlation coefficient by chance is less than five times out of 100, so the result indicates the presence of a relationship.  For -0.80 there is a statistically significant negative relationship between class size and reading score (p < . 001), such that the probability of this correlation occurring by chance is less than one time out of 1000.
  19. 19.  4.   The effect size of the correlation.  For correlations, the effect size is called the coefficient of determination and is defined as r2. The coefficient of determination can vary from 0 to 1.00 and indicates that the proportion of variation in the scores can be predicted from the relationship between the two variables. For r  = -0.80 the coefficient of determination is 0.65, which means that 65% of the variation in mean reading scores among the different classes can be predicted from the relationship between class size and reading scores. (Conversely, 35% of the variation in mean reading scores cannot be explained.)
  20. 20. Presentation of Results   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.).
  21. 21.  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.
  22. 22.  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.
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  24. 24.  A correlation can only indicate the presence or absence of a relationship, not the nature of the relationship. Correlation is not causation. There is always the possibility that a third variable influenced the results. For example, perhaps the students in the small classes were higher in verbal ability than the students in the large classes or were from higher income families or had higher quality teachers.
  25. 25.  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.
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