Principal component analysis is a versatile statistical method for reducing a cases-by-variables
data table to its essential features, called principal components. Principal components …

Multidimensionalscaling (MDS) isatechniquefortheanalysisofsimilarity or dissimilarity data
on a set of objects. Such data may be intercorrelations of test items, ratings of similarity on …

In recent years, there has been a considerable amount of research on the use of regularization
methods for inference and prediction in quantitative genetics. Such research mostly …

Very few genetic variants have been associated with depression and neuroticism, likely
because of limitations on sample size in previous studies. Subjective well-being, a phenotype …

Humans vary substantially in their willingness to take risks. In a combined sample of over 1
million individuals, we conducted genome-wide association studies (GWAS) of general risk …

The authors provide a didactic treatment of nonlinear (categorical) principal components
analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the …

This book introduces MDS as a psychological model and as a data analysis technique for
the applied researcher. It also discusses, in detail, how to use two MDS programs, Proxscal (a …

The genetic architecture of human reproductive behavior—age at first birth (AFB) and number
of children ever born (NEB)—has a strong relationship with fitness, human development, …

Multidimensional scaling (MDS) is a powerful statistical method that maps proximity data on
pairs of objects (ie, data expressing the similarity or the dissimilarity of pairs of objects) into …

Drawing from visionary leadership and strategy process research, we theorize and test the
mechanism through which middle and lower-level managers’ visionary leadership affects …