In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables. While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect. By observing which correlations were strongest for your current students, you could use this information to predict relative success of those students who have applied for admission into the university.Ĭorrelational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. How might you decide who should be admitted? You might try to correlate your current students' college GPA with their scores on standardized tests like the SAT or ACT. You are faced with a huge number of applications, but you are able to accommodate only a small percentage of the applicant pool. Imagine that you are on the admissions committee of a major university. As mentioned earlier, correlations have predictive value. For example, we would probably find no correlation between hours of sleep and shoe size. Keep in mind that a negative correlation is not the same as no correlation. In a real-world example of negative correlation, student researchers at the University of Minnesota found a weak negative correlation (r = -0.29) between the average number of days per week that students got fewer than 5 hours of sleep and their GPA (Lowry, Dean, & Manders, 2010). One might expect a negative correlation to exist between someone's tiredness during the day and the number of hours they slept the previous night: the amount of sleep decreases as the feelings of tiredness increase. Other examples of positive correlations are the relationship between an individual's height and weight or the relationship between a person's age and number of wrinkles. The example of ice cream and crime rates is a positive correlation because both variables increase when temperatures are warmer. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa. A negative correlation means that the variables move in opposite directions. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A positive correlation means that the variables move in the same direction. The sign-positive or negative-of the correlation coefficient indicates the direction of the relationship (Figure 2.12). The example above about ice cream and crime is an example of two variables that we might expect to have no relationship to each other. If the variables are not related to one another at all, the correlation coefficient is 0. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. The closer the number is to zero, the weaker the relationship, and the less predictable the relationships between the variables becomes. The closer the number is to 1 (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes.

The number portion of the correlation coefficient indicates the strength of the relationship. The correlation coefficient is usually represented by the letter r. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between variables. We can measure correlation by calculating a statistic known as a correlation coefficient. When two variables are correlated, it simply means that as one variable changes, so does the other. A correlation coefficient of -0.8 or lower indicates a strong negative relationship, while a coefficient of -0.3 or lower indicates a very weak one.Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect.A negative correlation indicates two variables that tend to move in opposite directions.A positive correlation indicates two variables that tend to move in the same direction.The most commonly used correlation coefficient is the Pearson coefficient, which ranges from -1.0 to +1.0.A correlation coefficient measures the strength of the relationship between two variables.

In a negative correlation quizlet