Member Training: An Overview of Effect Size Statistics and Why They are So Important

Whenever we run an analysis of variance or run a regression one of the first things we do is look at the p-value of our predictor variables to determine whether

they are statistically significant. When the variable is statistically significant, did you ever stop and ask yourself how significant it is? How large of an impact does it actually have on our outcome variable?

The American Psychological Association expects all published research to answer that last question. But what statistics answer that? This webinar will cover that topic and more.

In the webinar we will travel beyond “statistical significance” to “practical significance”, “how big of a difference” rather than “is there a difference”. You probably know that the size of our sample can have a very large impact on the p-value of a predictor variable. We will discuss how using effect size statistics will help us avoid the interpretational quagmire created by sample size.

In addition we will discuss the “two families” of effect size, the d family (differences between groups) and the r family (measures of association). We will learn how to measure and interpret their results. There is much to cover!

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About the Instructor

Jeff Meyer is owner of the consulting firm Optimizing Outcomes.

As a consultant, he works with non-profits to help them determine the impact of their outcomes. By looking at outcomes on both a quantitative and monetary basis, an effective cost benefit analysis can be utilized.

Jeff has an MBA from the Thunderbird School of Global Management and an MPA with a focus on policy from NYU Wagner School of Public Service.

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