Regression Models for Categorical Dependent Variables Using Stata (rev.)

By Russell V. Lenth, Carolyn Pillers Dobler. 2005

904 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7516555fa2ec471880230ba674827de879a7268f

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Citations in the corpus

Modeling Ordered Choices: A Primer (2010, unknown): “, Mora (2006) for a crosscountry application in banking that includes separate country dummy variables.”
Dynamic wildlife habitat models: Seasonal foods and mortality risk predict occupancy-abundance and habitat selection in grizzly bears (2010, background): “The zero-inflated count model assumed two latent groups: group A, the inflation part that only represented excess absent locations (i.e., the absences not predicted by the Poisson part of the model); and group A, which represented the counts including zeros (Long and Freese, 2003).”
Reconceptualizing entrepreneurial exit : divergent exit routes and their drivers (2010, unknown): “A robust and easy-to-interpret way to verify the differences in relationships between a specific variable and two alternative outcomes in multinomial models is to estimate binary logit models for two different outcomes (Long and Freese, 2006), which forms the tests of our hypotheses.”
Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective (2016, methodology): “MODEL ESTIMATION 2 3 This study estimated adoption rates of SAVs under three pricing scenarios ($1, $2, and $3 per 4 mile), interest in having one’s existing vehicle become a CV (for under $100), adoption timing of 5 AVs, and future home-location shifts (after AVs and SAVs become common modes of tra”
Risks at Work: The Demand and Supply Sides of Government Redistribution (2006, unknown): “See also Long and Freese (2001).”
Discrimination of Arabic-Named Applicants in the Netherlands: An Internet-Based Field Experiment Examining Different Phases in Online Recruitment Procedures (2014, methodology): “Negative binomial regression analyses are therefore the most suitable method to analyze our data14 (Long and Freese 2006).”
Leaning Out: How Negative Recruitment Experiences Shape Women’s Decisions to Compete for Executive Roles (2017, methodology): “We tested our hypotheses using ordered logit models because the outcome variable (willingness to apply again) is categorical and ordinal (Long and Freese, 2006).”
The Impact of Agricultural Technologies on Poverty and Vulnerability of Smallholders in Ethiopia: A Panel Data Analysis (2020, methodology): “We specified the model following Long and Freese (2014) and Wooldridge (2002).”
Patent Boxes Design, Patents Location and Local R&D (2015, background): “The negative binomial model preserves the conditional mean assumption of the Poisson model but it allows for a larger variation of outcomes than a Poisson and one can specify a socalled over-dispersion parameter that represents an objective proxy of the cause of this overdispersion (Long and Freese ”