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"The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data."--Publisher's description

Data measurement, and data -- Looking at data -- Simple linear and locally weighted regression -- Multiple linear regression -- The equivalence of analysis of variance and multiple linear regression, and an introduction to the generalized linear model -- Logistic regression -- Survival analysis -- Linear mixed models for longitudinal data -- Multivariate data and multivariate analysis -- Principle components analysis -- Factor analysis -- Cluster analysis -- Grouped multivariate data

Background: This study tests whether depression, psychosocial adversity, and limited social assets offer protection or suggest vulnerability to the process of radicalisation. Methods: A population sample of 608 men and women of Pakistani or Bangladeshi origin, of Muslim heritage, and aged 18–45 were recruited by quota sampling. Radicalisation was measured by 16 questions asking about sympathies for violent protest and terrorism. Cluster analysis of the 16 items generated three groups: most sympathetic (or most vulnerable), most condemning (most resistant), and a large intermediary group that acted as a reference group. Associations were calculated with depression (PHQ9), anxiety (GAD7), poor health, and psychosocial adversity (adverse life events, perceived discrimination, unemployment). We also investigated protective factors such as the number social contacts, social capital (trust, satisfaction, feeling safe), political engagement and religiosity. Results: Those showing the most sympathy for violent protest and terrorism were more likely to report depression (PHQ9 score of 5 or more; RR = 5.43, 1.35 to 21.84) and to report religion to be important (less often said religion was fairly rather than very important; RR = 0.08, 0.01 to 0.48). Resistance to radicalisation measured by condemnation of violent protest and terrorism was associated with larger number of social contacts (per contact: RR = 1.52, 1.26 to 1.83), less social capital (RR = 0.63, 0.50 to 0.80), unavailability for work due to housekeeping or disability (RR = 8.81, 1.06 to 37.46), and not being born in the UK (RR = 0.22, 0.08 to 0.65). Conclusions: Vulnerability to radicalisation is characterised by depression but resistance to radicalisation shows a different profile of health and psychosocial variables. The paradoxical role of social capital warrants further investigation.