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Correlation - Coggle Diagram
Correlation
When encountering Correlations, there are four additional considerations:
- Correlation describes a relationship between variables but does not explain why they are related
- The value of Correlation can be effected greatly by the range of scores represented in the data
- The data points, called outliers can have a dramatic effect on the value of a Correlation
- A Correlation should not be interpreted as a population
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The most common correlation is the Pearson Correlation which measures the degrees of straight-line relationship.
-Represented and identified by the letter r and represents the correlation of an entire population using the Greek letter rho
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-The direction of the relationship - The sign of the relationship (+/-) describes the direction of the relationship
-The form of the relationship - While the most common use of correlation is measuring straight-line relationships, other forms exist and are measured by special correlations.
-The strength of consistency of the relationship - Measured by the numerical value of the correlation
Positive Correlation- Two variables tend to change in the same direction. whereas when one variable increases or decreases, the other has a similar reflection
Negative Correlation - The two variables go in opposite directions. An increase relationship where one variable either increases or decreases, causing the other to do the opposite
To calculate the Pearson Correlation, it is necessary to introduce the concept of Sum of Products of deviations. The is used to measure varaibility for a single variable.
-Because the Pearson Correlation describes the pattern formed by the data points, any factor that does not change the pattern also does not change the correlation.
As the possibility increases with influence of a third variable, the statistical technique call Partial Correlation may be introduced. This allows researches to measure the relationship between two variables while eliminating or holding constant influences of a third variable.
The Point-Biserial Correlation is used to measure the relationship between two variables in situations in which one variable consists of regular, numerical scores, but the second variable only has two values. A variable with only two values is called a dichotomous variable or a binomial variable.
To complete the Point-Biserial Correlation, the dichotomous value is first converted to numerical values by assigning a value of zero to one category and a value of one to the other. The Pearson Correlation is then used with the converted data.
Correlation is a statistical technique that is used to measure and describe the relationship between two variables
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When the Pearson Correlation formula is sued with the data from an ordinal scale, the result is called the Spearman Correlation, which is used in two situations
When measuring the relationship between X and Y when both variables are measured on ordinal scales
Used as a valuable alternative to the Pearson Correlation, when the original new scores are on an interval or a ration scale