TY - JOUR
T1 - Ordered Beta Regression
T2 - A Parsimonious, Well-Fitting Model for Continuous Data with Lower and Upper Bounds
AU - Kubinec, Robert
N1 - Publisher Copyright:
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Society for Political Methodology.
PY - 2023/10/27
Y1 - 2023/10/27
N2 - I propose a new model, ordered Beta regression, for continuous distributions with both lower and upper bounds, such as data arising from survey slider scales, visual analog scales, and dose-response relationships. This model employs the cut point technique popularized by ordered logit to fit a single linear model to both continuous (0,1) and degenerate [0,1] responses. The model can be estimated with or without observations at the bounds, and as such is a general solution for these types of data. Employing a Monte Carlo simulation, I show that the model is noticeably more efficient than ordinary least squares regression, zero-And-one-inflated Beta regression, rescaled Beta regression, and fractional logit while fully capturing nuances in the outcome. I apply the model to a replication of the Aidt and Jensen (2014, European Economic Review 72, 52-75) study of suffrage extensions in Europe. The model can be fit with the R package ordbetareg to facilitate hierarchical, dynamic, and multivariate modeling.
AB - I propose a new model, ordered Beta regression, for continuous distributions with both lower and upper bounds, such as data arising from survey slider scales, visual analog scales, and dose-response relationships. This model employs the cut point technique popularized by ordered logit to fit a single linear model to both continuous (0,1) and degenerate [0,1] responses. The model can be estimated with or without observations at the bounds, and as such is a general solution for these types of data. Employing a Monte Carlo simulation, I show that the model is noticeably more efficient than ordinary least squares regression, zero-And-one-inflated Beta regression, rescaled Beta regression, and fractional logit while fully capturing nuances in the outcome. I apply the model to a replication of the Aidt and Jensen (2014, European Economic Review 72, 52-75) study of suffrage extensions in Europe. The model can be fit with the R package ordbetareg to facilitate hierarchical, dynamic, and multivariate modeling.
KW - Bayesian statistics
KW - limited dependent variables
KW - regression modeling
UR - http://www.scopus.com/inward/record.url?scp=85171524631&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171524631&partnerID=8YFLogxK
U2 - 10.1017/pan.2022.20
DO - 10.1017/pan.2022.20
M3 - Article
AN - SCOPUS:85171524631
SN - 1047-1987
VL - 31
SP - 519
EP - 536
JO - Political Analysis
JF - Political Analysis
IS - 4
ER -