Regression Models for Cylindrical Data in Psychology.

Cylindrical data; interpersonal behavior; regression; Algorithms; Data Interpretation, Statistical; Humans; Interpersonal Relations; Models, Psychological; Multivariate Analysis; Psychometrics/methods; Psychometrics/statistics & numerical data; School Teachers/psychology; Self Efficacy; Regression Analysis; Psychometrics; School Teachers; Statistics and Probability; Experimental and Cognitive Psychology; Arts and Humanities (miscellaneous); General Medicine

Abstract :

[en] Cylindrical data are multivariate data which consist of a directional, in this paper circular, and a linear component. Examples of cylindrical data in psychology include human navigation (direction and distance of movement), eye-tracking research (direction and length of saccades) and data from an interpersonal circumplex (location and intensity on the IPC). In this paper we adapt four models for cylindrical data to include a regression of the circular and linear component onto a set of covariates. Subsequently, we illustrate how to fit these models and interpret their results on a dataset on the interpersonal behavior of teachers.

Disciplines :

Mathematics
Social & behavioral sciences, psychology: Multidisciplinary, general & others

Author, co-author :

Cremers, Jolien ; Department of Public Health, Section of Biostatistics, Copenhagen University, Copenhagen, Denmark ; Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands

Pennings, Helena J M; TNO, Soesterberg, Netherlands

LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) ; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

External co-authors :

yes

Language :

English

Title :

Regression Models for Cylindrical Data in Psychology.

Publication date :

2020

Journal title :

Multivariate Behavioral Research

ISSN :

0027-3171

eISSN :

1532-7906

Publisher :

Routledge, United States

Volume :

Issue :

Pages :

910 - 925

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2019.1693332

Available on ORBilu :

since 25 November 2023

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