REPLY 3-1 XiAv (100 words and 1 reference)
The video explained linear correlation as “one variable being related to the other variable; linear correlation occurs when the relationship that exists between the two variables is linear.” (Triola 2009). I do not think that the higher the correlation between the two variables, the more likely the relationship is casual. To determine the degree of linear correlation, Pearson’s Correlation Coefficient is applied. Witte & Witte (2017) emphasize that the “presence of a correlation, as it, does not resolve the issue of whether it reflects a simple cause-effect relationship or a more complex state of affairs,” describing that the correlation alone will not determine the type of relationship. They continue to emphasize multiple factors to prove the earlier statement incorrect, such as by having a low score of X and Y, the relationship is positive, emphasizing the same for high scores of X and Y, resulting in a positive relationship but the relationship will change to negative if one score is high and the other is low.
REPLY 3-1 ShLu (100 words and 1 reference)
Regardless of if a dependent and independent variable have a correlation does not determine if there is a causal relationship. According to Witte & Witte (2016), the correlation between two variables does not imply causation, but a causal relationship between two variables must be correlated. Just because there is a cause and effect with independent and dependent variables, along with correlation, does not mean that a casual relationship is present. For example, an occurrence of one event must cause a second event to, and then the variables are taken into consideration. In a study that observes how students perform under pressure with, we want to determine the correlation between their nervousness and how they perform. Although there is a independent and dependent variable that can present a cause and effect, we cannot say that there is actual causation because there can be several other underlying variables that can contribute to the outcome of their performance. Therefore, the statement presented is incorrect.
REPLY 3-2 KiTe (100 words and 1 reference)
Correlations are simple and quick ways to determine whether or not there is a relationship between two variables, and the correlation coefficient is also a straightforward and objective method for determining and describing the relationship between the two variables. Some weaknesses are that a lack of correlation does not always indicate a lack of a relationship; it could be nonlinear, and all correlation suggests a relationship or absence of a correlation between two variables.
Due to the relationship between two variables, correlations can be harmful when one variable decreases and the other increases, and vice versa. A positive correlation occurs when the coefficient is more significant than zero, while a negative correlation occurs when the coefficient is less than zero. When the relationship between the two variables increases or decreases simultaneously, they can also be positive. The strength of a correlation is referred to as its correlation coefficient, and its values range from -1.0 to +1.0 (Witte & Witte, 2017).
The confounding variable is an unmeasured third factor that impacts the cause and effect. The confounding variable must be correlated with the independent variable. This may be a causal relationship, but it does not have to be and be causally related to the dependent variable. Causation, also known as "cause and effect," is another name for causality. According to Witte & Witte (2017), basis occurs in an experiment when one variable's value changes are reflected in the value of another variable.