Which Of The Following Is A Disadvantage Of Correlational Research

Which Of The Following Is A Disadvantage Of Correlational Research: Correlation is a statistical technique that can be used to measure and describe the strength and direction of the relationship between two variables. The variables can be anything that can be measured, such as height, weight, IQ, income, and others. There are many advantages of using correlation in research, such as its ability to help researchers identify relationships between variables and its usefulness in prediction (Mukaka, 2018). However, there are also some disadvantages of correlation that should be considered before using this technique in research.

One of the most important limitations of correlation is that it cannot tell us definitively whether one variable causes another. For example, let’s say you found that there is a strong positive correlation between the amount of time people spend watching television and the amount of time they spend eating junk food. Does this mean that watching television causes people to eat more junk food? Not necessarily. It could be that people who tend to watch a lot of television also happen to be the kinds of people who are more likely to eat junk food. Or, it could be that eating junk food causes people to watch more television (perhaps because they are trying to avoid thinking about how unhealthy their eating habits are). The only way to know for sure whether one variable is causing another is to conduct an experiment in which the researcher manipulates the independent variable and measures the dependent variable.

There is also another disadvantage which is the fact that correlation can be affected by many different factors. Several factors can influence the strength and direction of the relationship between two variables. For example, the relationship may be different in different groups of people (for instance, men and women, young and old.), at different times (for example, during a recession vs. during a boom), or in different cultures. Correlation does not also tell us about the magnitude of the relationship. When we say that two variables are positively correlated, it just means that as one variable increases, the other variable also increases. It doesn’t tell us anything about how big the effect is. For example, the correlation between height and weight is positive, but the relationship between these two variables is not very strong because there is a lot of variation in height and weight (some people are very tall and very thin, while others are short and overweight). Furthermore, how data are presented can influence our interpretation of the results. For example, we can consider the following two scatterplots. The first scatterplot shows a positive correlation between variables X and Y, while the second scatterplot shows a negative correlation between variables X and Y. However, if we look at the raw data, we can see that the relationship between the variables is the same in both scatterplots! This just goes to show that correlation can be easily misinterpreted, and it’s important to look at the data carefully before drawing any conclusions.

Just because two variables are correlated does not mean that one variable can be used to predict the other. For example, let’s say you want to use people’s heights to predict their weights.

The scatterplot below shows a strong positive correlation between height and weight. This means that, in general, taller people tend to be heavier than shorter people. However, if we try to use this relationship to predict individual weights, we will find that our predictions are often inaccurate. For example, a man who is 6 feet tall (72 inches) could weigh anywhere from 140 pounds to 240 pounds, and a woman who is 5 feet tall (60 inches) could weigh anywhere from 100 pounds to 200 pounds. Just because two variables are correlated does not mean that one variable is causing the other (Mukaka, 2018). For example, let’s say you found a positive correlation between the amount of time people spend on social media and the number of friends they have. Does this mean that spending more time on social media causes people to make more friends? Not necessarily. It could be that people who tend to spend a lot of time on social media are the kinds of people who are more likely to make friends easily. Or, it could be that people who have a lot of friends tend to spend more time on social media (perhaps because they enjoy staying connected with their friends). The only way to know for sure whether one variable is causing another is to conduct an experiment in which the researcher manipulates the independent variable and measures the dependent variable.

Even when two variables are correlated, it can be difficult to determine the nature of the relationship. For example, let’s say you found a positive correlation between the amount of time people spend on social media and the number of friends they have. Does this mean that spending more time on social media causes people to make more friends? Or does it mean that people who have a lot of friends tend to spend more time on social media? The answer is not always clear.

In general, correlation can be a helpful tool for understanding the relationships between variables, but it has its limitations. These limitations should be kept in mind when interpreting the results of any study that relies on correlational data.

Reference

Mukaka M. M. (2018). Chapter 12 Methods for Correlational Studies. Handbook of eHealth Evaluation, 24(3), 69–71.

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