Loss of Power in Logistic, Ordinal Logistic, and Probit Regression When an Outcome Variable Is Coarsely Categorized

By Aaron B. Taylor, S. West, L. Aiken. 2006

115 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/be07b380e90ddbe401896bf8fc7f8351b0f62b63

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Ken Kelley
2. S. Maxwell
3. Joseph R. Rausch
4. Kristopher J Preacher
5. Joachim Stoeber
6. Andrew F. Hayes
7. Jochen Peter
8. Patti M. Valkenburg

In Management Science and Operations Research

1. Ken Kelley
2. S. Maxwell
3. Joseph R. Rausch
4. Kristopher J Preacher
5. Gregory Francis
6. Dong Kyu Lee
7. Joachim Stoeber
8. Andrew F. Hayes

In Statistics and Probability

1. Ken Kelley
2. S. Maxwell
3. Joseph R. Rausch
4. Daniël Lakens
5. Kristopher J Preacher
6. Gregory Francis
7. Dong Kyu Lee
8. Andrew F. Hayes

Citations in the corpus

• Sample size planning for statistical power and accuracy in parameter estimation. (2008, background): “Taylor et al. (2006) illustrate the loss of power and necessary increase in sample size to achieve the same level of power when a continuous variable is categorized, either for the purpose of simplifying the analysis or via the process of measuring a continuous variable with ordinal categories.”