Estimates of cross-validity for stepwise regression and with predictor selection
成果类型：
Article
署名作者：
Schmitt, N; Ployhart, RE
署名单位：
Michigan State University
刊物名称：
JOURNAL OF APPLIED PSYCHOLOGY
ISSN/ISSBN：
0021-9010
DOI：
10.1037/0021-9010.84.1.50
发表日期：
1999
页码：
50-57
关键词：
摘要：
The effects of preselection of predictors (e.g., stepwise regression) on formula estimates of cross-validity were examined. Three actual data sets were used to generate populations of varying sample size, population validity, and number of predictors. No formula estimate provided an unbiased estimate of the population cross-validity, although some formula estimates were less biased than others. More important, having an adequate sample size (relative to number of predictors) was the issue most affecting the utility of the formula estimates. Another conclusion was that adjusted R-2 provided by at least some popular software programs can provide gross overestimates of cross-validity and should not be used as such.
来源URL：