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Modern Statistical Methods for HCI

Part of the Human–Computer Interaction Series Book Series

Editors: Robertson, Judy; Kaptein, Maurits

DOI: 10.1007/978-3-319-26633-6

ISBN: 978-3-319-26631-2

Publisher: Springer International Publishing - Springer Journals

Mar 23, 2016

LitStream

Using Generalized Linear (Mixed) Models in HCI

Kaptein, Maurits

Pages: 251 –274

[In HCI we often encounter dependent variables which are not (conditionally) normally distributed: we measure response-times, mouse-clicks, or the number of dialog steps it took a user to complete a task. Furthermore, we often encounter nested or grouped data; users are grouped within companies...
LitStream

Mixture Models: Latent Profile and Latent Class Analysis

Oberski, Daniel

Pages: 275 –287

[Latent class analysis (LCA) and latent profile analysis (LPA) are techniques that aim to recover hidden groups from observed data. They are similar to clustering techniques but more flexible because they are based on an explicit model of the data, and allow you to account for the fact that the...
LitStream

Fair Statistical Communication in HCI

Dragicevic, Pierre

Pages: 291 –330

[Statistics are tools to help end users accomplish their task. In research, to be qualified as usable, statistical tools should help researchers advance scientific knowledge by supporting and promoting the effective communication of research findings. Yet areas such as human-computer interaction...
LitStream

Improving Statistical Practice in HCI

Robertson, Judy; Kaptein, Maurits

Pages: 331 –348

[So you have come to the end. You have, if all is according to plan, learned novel methods and have started to think fairly critically about the methods that are of common use in HCI. Perhaps you are convinced that we could, and should, improve our reporting practice. The previous chapter has...

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Modern Statistical Methods for HCI

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