ANOVA) Is a Test That Is Used

¶ … ANOVA) is a test that is used to help interpret the results of a research study. It is not a way of gathering data, but a way of interpreting data that has already been gathering. In fact, ANOVA is a way of trying to find out how much variability there is in a group of data. It is an important test because there will be random variations within groups that could have an impact on research results, while not actually having an important scientific impact. However, there can also be variations in a group that do have an important scientific impact. ANOVA is one way to determine the strength of the impact of variations on whatever variables are being tested.

ANOVA is "a statistical analysis tool that separates the total variability found within a data set into two components: random and systematic factors. The random factors do not have any statistical influence on the given data set, while the systematic factors do. The ANOVA test is used to determine the impact independent variables have on the dependent variable in a regression analysis" (Investopedia, 2013). What is important to keep in mind is that an ANOVA is not the end result of testing, but reflects the beginning of testing. It looks for factors that might have an impact on test results, which is the first step in determining the extent of the impact of the various factors (Investopedia, 2013). Basically, an ANOVA allows one to see if subgroups in a population are impacted the same way by a variable. "One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences -- and possible interactions -- when variables are presented from the perspective of two or more categories. When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions" (MacFarland, 2012).

Interaction

Interaction refers to the idea that rarely do single variables have a consistent impact across all subjects. Instead, the unique characteristics of each subject may interact with the variables, producing heightened or lessened effects. This is referred to as interaction. An interaction effect refers to the role of a variable in an estimated model, and its effect on the dependent variable. A variable that has an interaction effect will have a different effect on the dependent variable, depending on the level of some third variable (Statistics.com, 2013).

In order to understand interaction, it is important to understand effects. The "statistical relationships between independent and dependent variables are often referred to as effects" (Price & Oswald, 2006). Studies are generally interested in finding out the impact of the independent variable on the dependent variable. "In a factorial design, a main effect is the overall effect of one independent variable" (Price & Oswald, 2006). For example, given a population of people who are moderately overweight, it may be found that a weight loss of 10 pounds decreases the person's risk of dying of heart disease by a certain percentage. This would be the overall effect or main effect.