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Title: Multiple imputation and selection of ordinal level-2 predictors in multilevel models Authors:  Carla Rampichini - University of Florence (Italy)
Omar Paccagnella - University of Padua (Italy)
Maria Francesca Marino - University of Florence (Italy)
Leonardo Grilli - University of Florence (Italy) [presenting]
Abstract: A strategy is devised to handle ordinal level-2 predictors of a two-level random effect model in a setting characterized by two nontrivial issues: (i) level-2 predictors are severely affected by missingness; (ii) there is redundancy in both the number of predictors and the number of categories of their measurement scale. We tackle the first issue by considering a multiple imputation strategy based on information at both level-1 and level-2. We tackle the second issue by means of regularization techniques for ordinal predictors, also accounting for the multilevel data structure. The motivation arises from a case study at the University of Padua about the relationship between student ratings of a course and several characteristics of the course, including teacher feelings (ordinal predictors) and practices (binary predictors) collected by a specific survey with nearly half missing respondents.