Univariate and multivariate data analysis methods

Here we provide an overview of the potential applications of multivariate statistical and data mining techniques, such as principal component and factor analysis, cluster analysis, multiple linear regression, random forest regression, linear discriminant analysis, support vector machines, random forest classification, Bayesian classification, and orthogonal partial least squares discriminant analysis, to the study of auditory and vestibular dysfunction, with an emphasis on classification analytic methods that may be used in the search for biomarkers of disease.Д®xperimental phenomena in neuroscience often involve the complex, sometimes non-linear interaction, of multiple variables. The use of multivariate statistical and data mining methods provides the opportunity to analyse many variables together, in order to appreciate how they may function as a system of interacting variables, and how this system or network may change as a result of sensory disorders such as sensorineural hearing loss, tinnitus or different types of vestibular dysfunction.

The exclusive use of univariate statistical procedures, analyzing one variable at a time, may limit the potential of studies to determine how interactions between variables may, as a network, determine a particular result. 3The Eisdell Moore Centre for Hearing and Balance Research, University of Auckland, Auckland, New ZealandДisorders of sensory systems, as with most disorders of the nervous system, usually involve the interaction of multiple variables to cause some change, and yet often basic sensory neuroscience data are analyzed using univariate statistical analyses only.

2Brain Research New Zealand Centre of Research Excellence, University of Auckland, Auckland, New Zealand.

1Department of Pharmacology and Toxicology, Brain Health Research Centre, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand.

Univariate and multivariate data analysis methods