in Description - will provide workhorse work to create the analyst paper (read the analyst role) will provide analyst example

Group Work Week 6

Group 2

“ANOVAS”

Analysis of Variance or ANOVA is a hypothesis testing procedure similar to a T-test in that it compares the mean differences between samples. Like a T-test an ANOVA is used to determine whether observed differences between samples are significant enough to conclude that the differences are the result of systematic differences between the means and not sampling error. The major advantage that a ANOVA has over a T-test is that an ANOVA can evaluate the mean differences between two or more treatments. This allows researchers much greater flexibility in experiment design, and is why we use ANOVAs and not just T-tests.

Correlation is used to measure and describe the relationship between two variables. Correlation is used with studies that have two scores per individual. Correlations are usually used to measure linear relationships (positive or negative) between two related variables. The significance of a correlation is measured by the strength or consistency of the relationship. Correlations are helpful to social scientists because we often want to know how one metric describing an individual is related to another. As an example, a question a social scientist might want to ask how does income affect levels of democracy. They could attempt to answer this question with a correlation. Through this they would gain a greater understanding of how the variables are related.

In the SDA book, they used an ANOVA to study voting tendencies of different social groups in the 2008 presidential election. An ANOVA was used because there were more than two sets of data. The election featured members of groups that were not previously part of presidential elections (African Americans and Women). This made these groups much more salient in voting tendencies. To find whether these differences where the result of a systematic difference or sampling error an ANOVA was used.