Many times, researchers will categorize continuous variables. For example, birth weight of human infants is often categorized as “low birth weight” vs. “normal”; sometimes it is “very low birth weight”, “low birth weight” and normal. The cutoff for low birth weight is usually 2.5 kg. IQ tests are categorized with labels such as “gifted”. Depression tests are categorized. And so on. This rarely makes sense, either statistically or substantively. Continue reading 'The perils of categorizing continuous variables'»
This is a talk that I will give at NESUG in the fall.
SA04
When dealing with ordinal data, many methods require you to assign a number or score to each level of a variable. For instance, if you ask people about their political orientation and whether it is very conservative, somewhat conservative, moderate, somewhat liberal or very liberal, you might assign these scores of 1, 2, 3, 4 and 5, respectively. But that is somewhat arbitrary.
One alternative was suggested by Bross (1958) and brought to my attention in reading Alan Agresti’s excellent book: Analysis of Ordinal Categorical Data . Continue reading 'Using ridits to assign scores to categories of ordinal scales'»
Suppose your dependent variable (DV) is a Likert scale or something similar. That is, it’s some sort of rating, from 1 to 5 or 1 to 7 or some such. And suppose you want to regress that on several independent variables. What should you do? Continue reading 'How to analyze Likert type dependent variables'»
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Themocracy