Lecture | Seminar
Lunch Time Seminars: Mark de Rooij
- Monday 19 December 2022
Logistic Multidimensional Data Analysis
Multivariate data are collected in many scientific disciplines. Often the data can be partitioned in a set of response variables and a set of predictor variables. When the response variables are dichotomous, logistic models are a natural choice for the analyses. In this presentation, we show an analysis framework for multivariate binary response variables with or without predictor variables, where we also distinguish between two types of response processes: the dominance and the single-peaked process. Dominance response processes arise when assessing skills or abilities, whereas single-peaked response processes arise in assessing preferences or attitudes. Models are defined in low-dimensional Euclidean spaces, which allow for visualization of the results. We show that estimation of the model parameters can be performed using majorization-minimization algorithms, where the negative log-likelihood is majorized by a least squares function. We illustrate our methods with a few empirical data sets.
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