Correlational Research “Correlation is a statistical technique that can show whether and how strongly pairs of variables are related” (Creative Research Systems, 2010). Correlation research method is used in scientific research to study the association and/or relationship between variables. When the association between two variables becomes correlation coefficient, it is being calculated through quantitative measure. The goal for using this method is to observe if one or more variables cause and predict other variables, without having a causal relationship between them (Creative Research Systems, 2010).
One great article I found is about money and happiness: “Can Money Buy Happiness: Are Lottery Winners any Happier in The Long Run? ” At first people see how happy and ecstatic people that win the lottery are on television, however, past that point, there are no details on how their life is from there on. The question of whether they are happier or not still remains. The researchers developed the study by asking two paralyzed accident victims, a control group and lottery winners about their level of happiness. There was no statistically significant difference between the lottery winners and the control group with respect to how happy they were at this stage of their lives” (Brikman, 1978). The control group as well as the lottery winners did not give any “evidence” of how happy they are going to be in couple of years (statistically insignificant). The lottery winners did not think, judge or be concerned about how happy they will be in few years, as the accident victims did. The results were that the relationship between money and the level of happiness is not linear.
The increase of money might or might not increase your happiness (depends on the events). “These findings may also suggest that happiness may be relative. We may not be able to reach a higher level of happiness as a result of winning the lottery. Winning the lottery may simply raise our standards” (Brikman, 1978). Researchers may use correlational method to determine variables between characteristics, attitudes, behaviors and events. As I mentioned before, the goal of this method is to find out if there is a direct relationship between the variables, as well as any commonalities in each relationship.
Even though it does not indicate the cause and effect relationship, when it’s present, one variable might reflect the change of the other variable. The only way a researcher would find out the effects a variable has on another variable is through research and experiment (Wiley, 2011). When it comes to using correlational method, to develop research, causation must not be used, because it cannot prove that one variable can change the other. The method only shows, in a systematic way that the variables are related. To be able to prove the cause and effect relationship and experimental method must be used.
In this case, being that it cannot test the cause and effect, the result can be deceiving and misinterpreted, especially when there are more than two variables involved. When cause and effect cannot be proved through this method, assumption is done, which leads to error in the outcome. Therefore, as mentioned before, correlation does not mean causation, which is a limitation when it comes to conclusions to be made (Bradley, 2000). Positive correlation occurs when the increase of one variable impacts the increase of another variable.
For example, the more money people win, the happier they are, however it does not specify long term results. Being that they are happy for the moment, the more times they win, the happier they get, which results in a positive correlation. When it comes to negative correlation, the variables work the opposite: when one increases it impacts the other one to decrease. In the example above, I would say that people that win the lottery also have a lot more responsibilities to handle, which occurs in more effort, time and energy.
Their happiness level might increase for the moment; however, it will start to decrease in the long run, due to all the extra “work” and pressure they would have. Last, but not least, there is the zero correlation that occurs when there is no relationship between the variables. The example study above wants to demonstrate if more money brings more happiness. Being that the two of the subjects did not win the lottery, it would not really prove whether or not money would bring them happiness. Asking about their current and future level of happiness has no correlation with the people that already won the lottery and whose life has hanged (McLeod, 2008). Being that the correlation coefficient does not reflect nonlinear association between two variables, “the correlation coefficient measures whether there is a trend in the data, and what fraction of the scatter in the data is accounted for by the trend”, as opposed to how nearly a scatterplot follow a straight line (Stark, 2011). Being that correlation coefficient only measures linear relationships, it is possible to see a nonlinear relationship when r is close to 0 or even 0.
In this case, the diagram indicates a slight presence of existence of nonlinear relationship between the two variables. If the “r” is not correctly interpreted, the result will make no sense and therefore a non-sense correlation would occur. One can also assume that the two variables are related, however, it cannot prove that “r” is the cause and effect of the relationship between the two (Amit, 2009). Another problem that might occur is when the function has multiple independent variables it is very hard to attribute changes to one independent variable.
That is why it is important for a researcher to make sure the research is being developed and experimented within the two variables, which means selecting the most significant and credible of the correlated variables and use in the function. When it comes to floor and ceiling effects, a researcher analyzes “data using analysis of variance, the interaction effect would very likely be statistically significant” (Zechmeister, 2001). When a minimum is scored in any condition of the experiment, a floor effect occurs. However, when a maximum performance occurs, ceiling effect happens.
In this case the researcher has the choice to select the dependent variables, to avoid the floor and ceiling and effect, and determine differences across conditions. “Thus, the danger of floor and ceiling effects is that they may lead researchers to believe an interaction is present in the data, when in fact the interaction occurs because the measurement scale does not allow the full range of responses that participants could make” (Zechmeister, 2001, p. 202). When the experiment is too easy, the experimental manipulation in the ceiling effect shows little or no effect.
It can be reduced by making the experiment harder and more challenging. When the task is too challenging or too difficult, the experimental manipulation will not be able to show effect (floor effect). It can be reduced or eliminated by making the experimental task more difficult, that way it will balance. A pilot study may also be conducted to find out if a floor effect or a ceiling effect is present (Huron, 2000). References Bradley, Megan (2000). Cyberlab for Psychology Research. Methods of Research. Retrieved November 26, 2011, from url: http://faculty. frostburg. du/mbradley/researchmethods. html#corr Brickman, P. , Coates, D. , Janoff-Bulman, R. (1978). Lottery Winners and Accident Victims: Is Happiness Relative? Journal of Personality and Social Psychology. Retrieved November 26, 2011, from url: http://www. psychologyandsociety. com/lotterystory. html Choudhury, Amit (2009). Statistical Correlation. Retrieved November 26, 2011, from url: http://www. experiment-resources. com/statistical-correlation. html Creative Research Systems (2010). The Survey System. Correlation. Retrieved November 26, 2011, from url: http://www. urveysystem. com/correlation. htm Huron, David (2000). Glossary of Research Terms in Systematic Musicology. Retrieved November 26, 2011, from url: http://musicog. ohio-state. edu/Music829C/glossary. html#floor effect McLeod, Saul (2008). Simply Psychology. Correlation. Retrieved November 26, 2011, from url: http://www. simplypsychology. org/correlation. html Stark, P. B (2001). Correlation and Association. Chapter 7. Retrieved November 26, 2011, from url: http://www. stat. berkeley. edu/~stark/SticiGui/Text/correlation. htm Wiley, John (2011). CliffNotes.
Research Designs and Methods. Retrieved November 26, 2011, from url: http://www. cliffsnotes. com/study_guide/Research-Designs-and-Methods. topicArticleId-26831,articleId-26754. html Zechmeister, J. S. , Zechmeister, E. B. , & Shaughnessy, J. J. (2001). Essentials of Research Methods in Psychology. Chapter 5. New York: McGraw Hill. Retrieved from Kaplan University DocSharing. Zechmeister, J. S. , Zechmeister, E. B. , & Shaughnessy, J. J. (2001). Essentials of Research Methods in Psychology. Chapter 7. New York: McGraw Hill. Retrieved from Kaplan University DocSharing.