Note: This is a brief introduction to observational designs. For more on this type of study, see books by Paul Rosenbaum: Observational Studies and, for a less mathematical approach Design of Observational Studies
In statistics and research design, there are two types of study: Experiments and observational studies. Some people also use the term “quasi-experiment” but I do not like it. In an experiment, the key thing is <em>randomization</em>. We assign subjects (e.g. people) to different conditions (e.g. drug and placebo) randomly. Often, though, such assignment is not possible or not ethical. In social sciences, it is rarely possible. We cannot, for example, randomly assign people to different levels of education. We can only observe relationships between (say) education and political party.
Continue reading 'Some thoughts on observational studies'»
This is a talk that I will give at NESUG in the fall.
SA04
These are the slides for a talk at New York Area SAS Users’ Group on June 2, 2011.
NYASUG2011
Here is a poster that I will present at NESUG in September. It’s in two parts
Part 1
Part 2
When you have univariate data, that is, a single measure on a variety of units, the most common statistical graphic is a pie chart. But pie charts should not be used. Ever. When there are a lot of units, pie charts are unreadable. When there are only a few units, pie charts waste space. And research shows that, even with a moderate number of units, pie charts can distort the data (for example, using different colors leads to different estimates of the size of the wedges). Fortunately, there are better methods.
Continue reading 'Graphics for univariate data – Pie is delicious but not nutritious'»
In a previous post, I dealt with some SAS code for scatterplots. Various problems can arise when using scatterplots. One of them is overplotting, where two or more data are the same point.
There are a variety of ways of dealing with this.
Continue reading 'Scatterplots – dealing with overplotting'»
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