Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.
In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive sel...
| Publicado en: | Psychology & Aging Vol. 30; no. 4; pp. 911 - 930 |
|---|---|
| Autores principales: | , , , , |
| Formato: | journal article |
| Publicado: |
American Psychological Association
Dec2015
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=111813123&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 111813123 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08827974 PYG jtl: Psychology & Aging issn: 08827974 maglogo: N pubinfo: dt: Dec2015 vid: 30 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 111813123 10.1037/pag0000046 ppf: 911 ppct: 19 formats: tig: atl: Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations. aug: au: Hayes, Timothy Satoshi Usami Jacobucci, Ross McArdle, John J. Usami, Satoshi affil: University of Southern California University of Tsukuba Department of Psychology, University of Tsukuba su: Psychological research Regression analysis Random forest algorithms Simulation methods & models Comparative studies sug: subj: Psychological research Research and Development in the Social Sciences and Humanities Regression analysis Random forest algorithms Simulation methods & models Comparative studies keyword: attrition classification and regression trees (CART) longitudinal data analysis machine learning missing data analysis attrition classification and regression trees (CART) longitudinal data analysis machine learning missing data analysis ab: In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers. pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|