Incremental criterion prediction of personality facets over factors: obtaining unbiased estimates and confidence intervals
Anglim,J and Grant,SL 2014, Incremental criterion prediction of personality facets over factors: obtaining unbiased estimates and confidence intervals, Journal of research in personality, vol. 53, pp. 148-157, doi: 10.1016/j.jrp.2014.10.005.
Attached Files
Name
Description
MIMEType
Size
Downloads
Title
Incremental criterion prediction of personality facets over factors: obtaining unbiased estimates and confidence intervals
Many researchers have argued that higher order models of personality such as the Five Factor Model are insufficient, and that facet-level analysis is required to better understand criteria such as well-being, job performance, and personality disorders. However, common methods in the extant literature used to estimate the incremental prediction of facets over factors have several shortcomings. This paper delineates these shortcomings by evaluating alternative methods using statistical theory, simulation, and an empirical example. We recommend using differences between Olkin-Pratt adjusted r-squared for factor versus facet regression models to estimate the incremental prediction of facets and present a method for obtaining confidence intervals for such estimates using double adjusted-. r-squared bootstrapping. We also provide an R package that implements the proposed methods.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.