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  "Title": "Hierarchical Ordered Probit Models with Application to Reporting\nHeterogeneity",
  "Version": "0.11.5",
  "Authors@R": "person(\"Maciej J.\", \"Danko\", role = c(\"aut\", \"cre\"), \nemail = \"Maciej.Danko@gmail.com\", comment = c(ORCID = \"0000-0002-7924-9022\"))",
  "Description": "Self-reported health, happiness, attitudes, and other\nstatuses or perceptions are often the subject of biases that\nmay come from different sources. For example, the evaluation of\nan individual’s own health may depend on previous medical\ndiagnoses, functional status, and symptoms and signs of\nillness; as on well as life-style behaviors, including\ncontextual social, gender, age-specific, linguistic and other\ncultural factors (Jylha 2009\n<doi:10.1016/j.socscimed.2009.05.013>; Oksuzyan et al. 2019\n<doi:10.1016/j.socscimed.2019.03.002>). The hopit package\noffers versatile functions for analyzing different\nself-reported ordinal variables, and for helping to estimate\ntheir biases. Specifically, the package provides the function\nto fit a generalized ordered probit model that regresses\noriginal self-reported status measures on two sets of\nindependent variables (King et al. 2004\n<doi:10.1017/S0003055403000881>; Jurges 2007\n<doi:10.1002/hec.1134>; Oksuzyan et al. 2019\n<doi:10.1016/j.socscimed.2019.03.002>). The first set of\nvariables (e.g., health variables) included in the regression\nare individual statuses and characteristics that are directly\nrelated to the self-reported variable. In the case of\nself-reported health, these could be chronic conditions,\nmobility level, difficulties with daily activities, performance\non grip strength tests, anthropometric measures, and lifestyle\nbehaviors. The second set of independent variables (threshold\nvariables) is used to model cut-points between adjacent\nself-reported response categories as functions of individual\ncharacteristics, such as gender, age group, education, and\ncountry (Oksuzyan et al. 2019\n<doi:10.1016/j.socscimed.2019.03.002>). The model helps to\nadjust for specific socio-demographic and cultural differences\nin how the continuous latent health is projected onto the\nordinal self-rated measure. The fitted model can be used to\ncalculate an individual predicted latent status variable, a\nlatent index, and standardized latent coefficients; and makes\nit possible to reclassify a categorical status measure that has\nbeen adjusted for inter-individual differences in reporting\nbehavior.",
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  "Repository": "https://maciejdanko.r-universe.dev",
  "Date/Publication": "2022-10-01 12:09:14 UTC",
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  "Author": "Maciej J. Danko [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-7924-9022>)",
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