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Tara has taught Psychology and has a master's degree in evolutionary psychology.
The acronym ANOVA refers to analysis of variance and is a statistical procedure used to test the degree to which two or more groups vary or differ in an experiment. In most experiments, a great deal of variance (or difference) usually indicates that there was a significant finding from the research. In this lesson, we will look at a detailed example of how an ANOVA works and how it can be applied to real life situations.
In the majority of experiments, you first need a null hypothesis and an alternative hypothesis. A null hypothesis is the assumption that there will be no differences between groups that are tested and therefore, no significant results will be revealed. The alternative hypothesis, on the other hand, is the hypothesis stating that there will be a difference between groups as indicated by the ANOVA performed on the data that is collected.
Let's use an experiment scenario to help explain things. Imagine that you are running an experiment to see if there is a relationship between people's religion and what they consider the ideal family size to be. You would likely do this by recruiting individuals from different religious groups and asking them to report what they consider the ideal number of children in a family should be. Let us further say that you ended up recruiting 10 Catholics, 10 Protestants, and 10 Jewish individuals to answer this question.
In this case, you have one independent variable, which is religion, that is thought to have an effect on the opinion of ideal family size, which is the dependent variable in this scenario. Additionally, this experiment includes three different levels of the independent variable. In this case, the three levels are the three different groups of religions.
The fact that we have differing levels of the independent variable of religion is what allows us to carry out an ANOVA. Let's say that after asking all the people in all three groups what they consider the ideal number of children in a family to be, you record each person's answer and then calculate the mean, or average, number reported by each collective group. You discover that the average number of children reported by the Catholic group is 3, for the Protestant group it is 2, and for the Jewish group it is 1.
At first glance, it may seem like there is a definite difference between these three groups in their opinion on the ideal number of children. However, we must keep in mind that this could be due to chance, and these numbers could be very different if we asked 10 different Catholics, 10 different Protestants, and 10 different Jewish individuals. Therefore, an ANOVA is a good test to use as it will control for this and determine if there really is a difference between the three groups beyond mere random chance.
In this particular example, the differences between the averages of the three groups were statistically significant (as computed by the ANOVA test) and not due to chance. This means that religious affiliation does influence opinions on the ideal number of children in a family. Therefore, we have shown that the null hypothesis is false, since there is a significant difference between the three religious groups, and that the alternative hypothesis has also been proven true.
After reading this experiment, you might be thinking about how you can use this in the real world. The ANOVA can come in handy in a large number of real life situations.
For instance, in the social sciences, there is much research devoted to figuring out what factors influence people's opinions and behaviors. The previous example involving religion and number of children fits into this category. Other examples include the effect of political party on views of same-sex marriage. You could design an experiment in which you have a group of Democrats, a group of Republicans, and a group of Independents and give them a survey that asks them about their views on same-sex marriage. You could then use an ANOVA to compare the difference in the average number of people in each group claiming to support same-sex marriage.
ANOVAs can also be used in the medical profession. When scientists want to test the effectiveness of a new drug, they can implement an ANOVA to see just how effective (or possibly ineffective) that drug is. For example, if doctors want to test the effectiveness of a new cancer treatment, they could design an experiment that involves 4 different levels of the independent variable (which is the cancer treatment). One group of participants could receive chemotherapy, another group could receive radiation treatment, still another could receive no treatment, and finally the last group would receive the new drug in question. By comparing the percentage of reduction in cancer cells in each treatment group with an ANOVA, scientists could easily tell which type of treatment would be most effective.
These are not the only situations in which an ANOVA can be useful. It can be used in any experiment that involves two (but usually three or more) levels of the independent variable. Additionally, it can work for more than one independent variable being tested simultaneously.
The analysis of variance, or ANOVA, has been recognized as the most common statistical procedure used in psychological research, and its main goal is to determine how much the groups in an experiment differ for purposes of statistical significance. More specifically, in experiments that involve ANOVAs, there is usually required at least two levels (or groups) of the independent variable. Any difference in the dependent variable of each group from the average of the other groups is also measured. When the difference is observed to be significant, this means that the variability, or difference, is greater than what was expected due to mere chance. When this proves to be the case in an ANOVA, then the hypothesis being tested, the alternative hypothesis, proves to be true, and therefore the independent variable does influence the dependent variable.
Although ANOVAs are used most commonly in psychological research, they can be used in any social science. Not only this, but they can be used in medical research, and implemented in experiments in the natural sciences. Although the minimum number of groups for the independent variable is two, ANOVA experiment designs can involve more than two, and there can even be more than one independent variable being tested.
Once you are finished, you should be able to:
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Back To CourseGRE Prep: Help and Review
22 chapters | 185 lessons