Friedrich Leisch
University of Natural Resources and Life Sciences, Vienna
230 Papers
917 Citations
Friedrich Leisch is an academic researcher from University of Natural Resources and Life Sciences, Vienna. The author has contributed to research in topics: Market segmentation & Computer science. The author has an hindex of 49, co-authored 219 publications. Previous affiliations of Friedrich Leisch include Vienna University of Technology & University of Erlangen-Nuremberg.
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Papers
Quick, simple and reliable: forced binary survey questions
TL;DR: The authors compare ordinal multi-category answer formats (such as Likert-type scales) with forced binary scales and show that the binary format is quicker and perceived as less complex.
A review on the temporal pattern of deer-vehicle accidents: impact of seasonal, diurnal and lunar effects in cervids
TL;DR: Seasonal, diurnal and lunar peak accident periods are identified for deer, although seasonal pattern are not consistent among and within species or regions and data on effects of lunar cycles on DVAs is almost non-existent.
128
Guided self-help versus cognitive-behavioral group therapy in the treatment of bulimia nervosa.
Ursula F. Bailer,Martina de Zwaan,Friedrich Leisch,A. Strnad,Claudia Lennkh-Wolfsberg,N. El-Giamal,Kurt Hornik,Siegfried Kasper +7 more
TL;DR: Guided self- help incorporating the use of a self-help manual offers an approach that can be effective in the short and long-term treatment of bulimia nervosa.
123
Increasing sample size compensates for data problems in segmentation studies
TL;DR: Results indicate that insufficient sample sizes lead to suboptimal segmentation solutions; biases in survey data have a strong negative effect on segment recovery; and improvement in segment recovery at high sample size levels occurs only if additional data is free of bias.
120
Evaluation of Structure and Reproducibility of Cluster Solutions Using the Bootstrap
TL;DR: A benchmarking framework based on bootstrapping techniques that accounts for sample and algorithm randomness is proposed that provides much needed guidance both to data analysts and users of clustering solutions regarding the choice of the final clusters from computations that are exploratory in nature.
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