The following are some readings on the basics of ANOVA.
Field, A. (2018). Discovering statistics using IBM SPSS: North American
edition (5th ed.). Sage.
o Chapter 12 describes the basics of ANOVA.
The 7864 Course Study Guide [PDF] highlights content to focus on as you
read and absorb the materials. You may also find some additional articles
with direct relevance to your field here.
This week, you will study the theory and logic of ANOVA. Recall that a t test
requires a predictor variable that is dichotomous (it has only two levels or
groups). The advantage of ANOVA over a t test is that the categorical predictor
variable can have two or more groups. Just like a t test, the outcome variable in
ANOVA is continuous and requires the calculation of group means.
This week is all about analysis of variance.
Please post something to the discussion board related to the content covered
Here are some ideas for your post to get you started:
Let’s say you were working on a project examining the effects of studying
on test grades. You have three groups that you are comparing—one with
learners who didn’t study at all, one with learners who studied two hours or
less, and one that studied more than two hours. You run an ANOVA that
shows that learners who didn’t study at all had the highest scores on the
test. This leaves you somewhat puzzled. How might you explain this
result? What factors might you need to control for to make sure you got it
right? Share your thoughts and information in Yellowdig. Give your post a
hashtag in the body (e.g., #screwystats).
Which outside resource (image, video, article) is a good example of when
a researcher did not control for essential variables? Share the link within
your post. What struck you the most about this example?
What about this week’s content is relevant to your own professional or
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