Other Paper
hopit R-package: hierarchical ordered probit models with application to reporting heterogeneity
https://cran.r-project.org/, CRAN
CRAN - the Comprehensive R Archive Network - is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. (2019)
Abstract
Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness, as well as life-style behaviors including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 <doi:10.1016/j.socscimed.2009.05.013>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). This package offers versatile functions for analyzing different self-reported ordinal variables and helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 <doi:10.1017/S0003055403000881>; Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). In contrast to standard ordered probit models, generalized ordered probit models relax the assumption that individuals use a common scale when rating their own statuses, and thus allow for distinguishing between the status (e.g., health) and reporting differences based on other individual characteristics. In other words, the model accounts for heterogeneity in reporting behaviors. The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The model helps adjust for these specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual latent status variable that serves as a proxy of the true status. In case of self-reported health, the predicted latent health variable can be standardized to a health index, which varies from 0 representing the (model-based) worst health state to 1 representing the (model-based) best health in the sample. The standardized latent coefficients (disability weights for the case of self-rated health) provide information about the individual impact of the specific latent (e.g., health) variables on the latent (e.g., health) construct. For example, they indicate the extent to which the latent health index is reduced by the presence of Parkinson’s disease, poor mobility, and other specific health measures (Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The latent index can in turn be used to reclassify the categorical status measure that has been adjusted for inter-individual differences in reporting behavior. Two methods for doing so are available, one which uses model estimated cut-points, and a second which reclassifies responses according to the percentiles of the original categorical response distribution (Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>).