# Applied multivariate statistics for the social sciences stevens pdf

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## Applied Multivariate Analysis

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Published 14.06.2019

## On the Application of Multivariate Statistical and Data Mining Analyses to Data in Neuroscience

James Stevens' best-selling text, Intermediate Statistics, is written for those who use, rather than develop, statistical techniques. Stevens focuses on a conceptual understanding of the material rather than on proving the results. I also appreciate that this new edition gives more attention to the APA-style write-ups. I look forward to using the 6th edition of this text in my class. Yet, it is written in an accessible way that students will find useful and comprehensible. The authors did a great job in revising the chapters, and the new coverage of binary logistic regression, multivariate multilevel modeling, and missing data analysis are a "must" for both applied researchers and graduate students. Introduction 2.

Research in neuroscience, whether at the level of genes, proteins, neurons or behavior, almost always involves the interaction of multiple variables, and yet many areas of neuroscience employ univariate statistical analyses almost exclusively. Since multiple variables often work together to produce a neuronal or behavioral effect, the use of univariate statistical procedures, analyzing one variable at a time, limits the ability of studies to reveal how interactions between different variables may determine a particular outcome. Multivariate statistical and data mining methods afford the opportunity to analyze many variables together, in order to understand how they function as a system, and how this system may change as a result of a disease or a drug. The aim of this review is to provide a succinct guide to methods such as linear discriminant analysis, support vector machines, principal component and factor analysis, cluster analysis, multiple linear regression, and random forest regression and classification, which have been used in circumscribed areas of neuroscience research, but which could be used more widely. Experimental phenomena in neuroscience usually involve the complex interaction of multiple variables. Nonetheless, historically, statistical analysis has been dominated by the comparison of one variable at a time between treatment groups.

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