Supplements to the 7th edition

• Chapter One — Basic Concepts
• No supplemental material
• Chapter Two — Describing and Exploring Data
• Kernel-Density
A short description of how to calculate kernel density plots in R and SPSS.
• Chapter Three — The Normal Distribution
• Chapter Four — Sampling Distributions and Hypothesis Testing
• Chapter Five — Basic Concepts of Probability
• Chapter Six — Categorical Data and Chi-Square
• Alternative Research Designs
This is a short entry that I wrote for the International Handbook of Statistical Sciences (in press), edited by Miodrag Lovric. It discusses the fact that we can construct contingency tables from a variety of different research designs, and these tables can be treated differently.
• Ordinal Categorical Variables
The standard chi-square test on a contingency tables is not affected by the order of the categorical variable. But when the categorical variable you are using is ordinal, you can obtain significantly greater power for your test by taking that ordering into account.
• Testing Change Over Two Measurements in Two Independent Groups
This entry addresses a question about two independent proportions of change. In other words, if Group A improved by 30% and Group B improved by 35%, is that difference significant. It started out as a simple question but soon became more interesting. I admit that I got a bit carried away.
• Chapter Seven — Hypothesis Tests Applied to Means
• Pitman Test
• Confidence Intervals on Effect Size
This entry should perhaps fit better under Chapter 18 because it deals with randomization tests. However it focuses on one- and two-sample tests, which is why it is here. It relates to a program written by Gerard E. Dallal, though I don't know recall how I came across it. I have modified the document slightly to provide a link to the test itself.
• Chapter Eight — Power
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• Chapter Nine — Correlation and Regression
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• Chapter Ten — Alternative Correlational Techniques
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• Chapter Eleven — Simple Analysis of Variance
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• Chapter Twelve — Multiple Comparisons Among Treatment Means
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• Chapter Thirteen — Factorial Analysis of Variance
• Unequal cell sizes
Unequal cell sizes can often make a significant difference in a factorial analysis of variance. There are alternative ways of treating unequal ns (see Type I II III.pdf), but the standard default, while generally appropriate, can sometimes give you misleading results.
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• SAS for Random Effects and Nested Designs
In Section 13.8 of Chapter 13 I discuss how to deal with random effects and how to deal with nested designs. The two often go together. I have compiled SAS code for those analyses, and have presented the printout. The printout can be found at SAS for Alternative Designs and contains the links to the code and the raw data. You should know that there are other ways to write some of this code, but this will give you what you need. I would like to thank Karl Wuentsch for his suggestions and for pointing out that the code that I give in the text, while it will work, is not optimal.
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• Chapter Fourteen — Repeated-Measures Designs
• Analysis of Repeated Measures as a Mixed Model--Part 1
• Analysis of Repeated Measures as a Mixed Model--Part 2
While the standard repeated measures analysis of variance is one of the easiest ways of analyzing such data, the approach using mixed models has a great deal to recommend it, especially with missing data.
• A Quickie on Trend Analysis
This is a quick document that needs a lot of work. It focuses on trend analysis as a way to draw out information on differences across a within subject variable.
• Chapter Fifteen — Multiple Regression
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Last revised: 1/11/2010