Limited Dependent Variable Models and Probabilistic Prediction in Informetrics
This chapter explores the potential for informetric applications of limited dependent variable models, i.e., binary, ordinal, and count data regression models. In bibliometrics and scientometrics such models can be used in the analysis of all kinds of categorical and count data, such as assessments scores, career transitions, citation counts, editorial decisions, or funding decisions. The chapter reviews the use of these models in the informetrics literature and introduces the models, their underlying assumptions and their potential for predictive purposes. The main advantage of limited dependent variable models is that they allow us to identify the main explanatory variables in a multivariate framework and to estimate the size of their (marginal) effects. The models are illustrated using an example data set to analyze the determinants of citations. The chapter also shows how these models can be estimated using the statistical software Stata.
The authors thank Fereshteh Didegah, Raf Guns, Edward Omey, and Ronald Rousseau for their suggestions during the writing of this chapter. We also thank Richard Williams and Paul J Wilson for their feedback and excellent suggestions.
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