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Correlational Research Types, Examples & Methods

Correlational research

Correlational research is a quantitative research method that tries to determine if there is a relationship or covariation between two or more quantitative variables which are collected from the same subjects or a group of subjects. The subjects must be from the same participants if any correlation is to be assumed to exist. The more the number of subjects available, the higher is the probability of having valid data (Sonderegger, 1998). This method of study was developed by Francis Galton and later perfected by Karl Pearson who formulated the mathematical concept. The correlation may be positive, negative or may indicate that there is no relationship between the variables. Correlation shows the strength of a relationship between variables but it does not prove a theory or indicate the cause of the relationship (Waters N.d).

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Correlation is used when the researcher has an interest in knowing the nature of the relationship between variables to be able to predict the future. This is because once the nature and the strength of the relationship are determined, the nature of the future reactions to a change in either or both of the variables to each other can be foreseen and corrective measures are taken.

It can also be used to help understand and construe meanings from a primary study. This is where it is used in a larger study as a means of providing data in that after the research is thorough, a researcher may need to know the extent of the relationship between the variables so that he/she may be able to draw the right conclusions (Raulin, 1999).

An example of a correlation study is whether people with higher IQ make wiser decisions. This was a study published in the science daily and was done by researchers from Minnesota university and was conducted on truck drivers at national Schneider (University of Minnesota, 2009). Measurement of cognitive skills and economic experimentation was done and it was discovered that people with better cognitive skills were making more consistent choices, saved money, and took some calculated risks (University of Minnesota, 2009).

In this case, correlation is used because valid data would be obtained from it. This is because the researcher has no control over the subject’s behavior hence there is a very high likelihood that the data collected would be valid and reliable; this acts to remove the researcher’s bias.

The use of this method enables the researchers to apply large amounts of data and since the population, in this case, was 20,000 and the sample 1000, this enables the analysis of large amounts of data due to the large samples hence better understanding of the relationships and this gives rise to valid conclusions (University of Minnesota 2009, April 28)

The correlation method is much cheaper than the other methods of the research process and due to the large sample, in this case, this method is more economical because the data of the sample can be accessed from the employer or other free databases. This makes the time spent on the process lesser and cheaper.

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It is also used in this study because it provides the researcher with the degree of the relationship of the variables and can be used to render support to the theory that people with high IQ make better decisions

One of the limitations of using correlation is that it does not tell the researcher the causes and the effects of a relationship. This is because it only shows if there is a relationship between or among variables. Small correlations may be construed as important yet the case is that there might be no relationship.

Due to the limitation of the researcher to influence and control the research, intervening or confounding variables is a major issue. These are the outside variables that may influence the research but are not under study. Indeed, their effect on the variables may be assumed to be coming from the variables under study hence wrong conclusions (Raulin, 1999).

A positive correlation is a relationship where the two variables change in the same direction at the same time. An increase or decrease in one variable leads to an increase or decrease in the other variable respectively. A correlation coefficient is used to measure the strength of the relationship and it ranges from -1 to +1. A co-efficient close to +1 shows that the relationship is strong and positive. For example, a high possession of cognitive skills leads to increased job retention or/and social awareness.

A negative correlation is a condition whereas a change in one of the variables results in a negative change in the other variable. An increase in one variable leads to a decrease in the other variable and vice versa. A correlation coefficient of close to -1 shows a strong negative relationship (Raulin, 1999). For example, lasting marriages are fewer among those people who are highly educated or highly successful people are less likely to indulge in social vices. Lastly, zero correlation is a situation where there is no relationship whatsoever between the variables and is denoted by the correlation coefficient zero. In this case, a change in the independent variable does not enhance the prediction of the dependent variable, for example, the frequency of absenteeism from work and the price of a football shoe.

The correlation coefficient is calculated by observing the activities of all the responses hence making it an average measure. It measures the degree of linear dependence between two variables. Non-linear relationship among two variables influences the correlation coefficient; the correlation coefficient cannot take the place of the individual variables. This is because not all variables react the same way to the changes in the other variables and this leads to a scattered spread. The points out of the line are called outliers and may be due to wrong information or the right but different information. The wrong information can be eliminated but this must be justified while the rest should be used to determine the coefficient of correlation. Moreover, outliers can be identified through the use of a scatter plot.

Floors and ceilings effects occur because there are problems with the process and means used to measure the dependent variables. This is where there is not enough room to allow for variability. A barrier may be put in place to limit the extremes hence the emergence of the effects. These effects affect the representation of the subjects outside the barrier which may result in misrepresentation of the group. This leads to changes in the variance and this results in inaccurate data for comparison with other groups. The ceiling and floor effects can be minimized by choosing the independent variables that will allow for ample room to measure the variations within the given conditions (Jeanne et al, 2000, p. 201-202).

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Correlation is used to gauge the relationship between variables. Although it does not indicate the cause it plays a major role in the understanding of the results of social research for it tells the researcher if there is a relationship between the variables and what kind of relationship it is. It can also be used to support a theory put forward. A wide range of the sample is recommended in this kind of research to be able to get the correct data about the subject under question. This form of research is efficient and can get results for a wide sample in a single study hence more economical interns of time and money.

References

Jeanne, S. et al. (2000). Essentials of research methods in psychology. McGraw-Hill Higher Education.

Raulin, M. (1999). Correlational and Differential Research Methods. Web.

Waters, J. (N.d). Correlational Research. 2010. Web.

Sonderegger, T. (1998). Psychology. New York, Wiley publishing Inc. Web.

University of Minnesota. (2009). People with Higher IQs Make Wiser Economic Choices, Study Finds. ScienceDaily. Web.

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