1 !"#$% &’ ’ ’ ( ) * * +’ ’ % , #-./!#$ LIMITED DEPENDENT VARIABLE MODELS AND PROBABILISTIC PREDICTION IN INFORMETRICS Nick Deschacht a & Tim C.E. Engels b a Faculty of Economics and Business, KU Leuven, Campus Brussel, Warmoesberg 26, 1000 Brussel, Belgium. b Centre for Research & Development Monitoring (ECOOM), Faculty of Political and Social Sciences, University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium; Antwerp Maritime Academy, Noordkasteel3Oost 6, 2030 Antwerp, Belgium. Abstract 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 chapters also shows how these models can be estimated using the statistical software Stata. Keywords: regression; categorical; binary; logit; ordered; Poisson; negative binomial; 1. Introduction A topic search in the Social Science Citation Index on November 13 th 2013 identified over 700 journal articles in Library and Information Science (LIS) that use regression analysis. In the top 25 of source titles, we find Scientometrics (64 articles), Journal of the American Society for Information Science and Technology (46), Information Processing & Management (24), Journal of Informetrics (12) and Journal of Documentation (9). Until 2004 the annual