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Category: Analysis of variance. Analysis of variance (ANOVA) is an approach that allows you to compare a continuous outcome variable across a factor representing three or more groups and to examine interactions among factors. You can find the theme and closely related categories and other resources at the bottom of this page. Articles are arranged by date with the most recent entries at the top.
Stats: When does heterogeneity become a concern? (June 5, 2008). Dear Professor Mean, I have an ANOVA model and I am worried about heterogeneity--unequal standard deviations in each group. How should I check for this?
Stats: What statistic should I use when? (January 4, 2008). Someone was asking about a multiple choice question on a test that reads something like this: A group of researchers investigating in patients with diabetes on the basis of demographic characteristics and the level of diabetic control. Select the most appropriate statistical method to use in analyzing the data: a t-test, ANOVA, multiple linear regression, or a chi-square test. This is one of the more vexing things that people face--what statistic should I use when.
Stats: Analyzing data from a simple crossover design (August 22, 2007). A doctor brought me some data from a crossover design and asked me to help analyze it. The analysis was a bit trickier than I had expected, so I reviewed some of the material in Stephen Senn's book.
Stats: (Seminar notes) Confidence intervals for a variance ratio (July 17, 2006). One of the talks at the 18th Annual Applied Statistics in Agriculture Conference, sponsored by Kansas State University was "Selecting the Best Confidence Interval for a Variance Ratio (or Heritability)" by Brent Burch, Northern Arizona University. Here are my notes from that talk.
Stats: (Seminar notes) Adjustments for multiple comparisons (July 17, 2006). One of the talks at the 18th Annual Applied Statistics in Agriculture Conference, sponsored by Kansas State University was "A Comparison of Multiple Tests Procedures: Spinosad as a Treatment for Lice on Cattle" by Zhanglin Cui, Eli Lilly and Company. Daniel H. Mowrey, Alan G. Zimmermann, and Douglas E. Hutchens, also of Eli Lilly and Company were co-authors.
Stats: Post hoc comparisons (March 15, 2006). Dear Professor Mean, I need to run multiple comparisons among all possible pairs of means following an analysis of variance test. What is the best approach? Tukey? Scheffe? Bonferroni?
Stats: When the F test is significant, but Tukey is not (September 9, 2005). Someone asked me how to interpret a one factor analysis of variance where the overall F test was significant, but the Tukey folloup test comparing all four group means was not significant for any pair of means.
Stats: Multiple degree of freedom tests (September 22, 2004). Someone sent me an email describing a situation where an interaction effect in SPSS had a large p-value, but one of the individual components of that interaction had a small and statistically significant p-value. This can occur in many statistical models where you are testing a factor or interaction that involves multiple degrees of freedom.
Stats: Guidelines for ANOVA models (June 20, 2003). Dear Professor Mean, I wanted to compare two groups in my research, those who completed every test battery, and those who completed only some of them. I ran ANOVAs on age, iq, adhd score, and so forth. My professor says that I should have used a t-test instead. Why can't I use ANOVA. Isn't ANOVA better than a t-test? --Angry Anastasia
Stats: Unequal group sizes (November 2, 2001). Dear Professor Mean: I am comparing several groups of subjects, but the number of subjects in each group differ quite a bit. How does this affect the assumptions in analysis of variance?
Theme and closely related categories:
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This webpage was written by Steve Simon on 2007-06-20, edited by Steve Simon, and was last modified on 2008-07-08. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.