Further Developments and Applications of Oxazoline-Containing Ligands in Asymmetric Catalysis.

The chiral oxazoline motif is present in many ligands that have been extensively applied in a series of important metal-catalyzed enantioselective reactions. This Review aims to provide a comprehensive overview of the most significant applications of oxazoline-containing ligands reported in the literature starting from 2009 until the end of 2018. The ligands are classified not by the reaction to which their metal complexes have been applied but by the nature of the denticity, chirality, and donor atoms involved. As a result, the continued development of ligand architectural design from mono(oxazolines), to bis(oxazolines), to tris(oxazolines) and tetra(oxazolines) and variations thereof can be more easily monitored by the reader. In addition, the key transition states of selected asymmetric transformations will be given to illustrate the features that give rise to high levels of asymmetric induction. As a further aid to the reader, we summarize the majority of schemes with representative examples that highlight the variation in % yields and % ees for carefully selected substrates. This Review should be of particular interest to the experts in the field but also serve as a useful starting point to new researchers in this area. It is hoped that this Review will stimulate both the development/design of new ligands and their applications in novel metal-catalyzed asymmetric transformations.

Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.

Exploratory factor analysis (EFA) is used routinely in the development and validation of assessment instruments. One of the most significant challenges when one is performing EFA is determining how many factors to retain. Parallel analysis (PA) is an effective stopping rule that compares the eigenvalues of randomly generated data with those for the actual data. PA takes into account sampling error, and at present it is widely considered the best available method. We introduce a variant of PA that goes even further by reproducing the observed correlation matrix rather than generating random data. Comparison data (CD) with known factorial structure are first generated using 1 factor, and then the number of factors is increased until the reproduction of the observed eigenvalues fails to improve significantly. We evaluated the performance of PA, CD with known factorial structure, and 7 other techniques in a simulation study spanning a wide range of challenging data conditions. In terms of accuracy and robustness across data conditions, the CD technique outperformed all other methods, including a nontrivial superiority to PA. We provide program code to implement the CD technique, which requires no more specialized knowledge or skills than performing PA.