Ordination techniques are valuable to the social sciences. They offer useful ways to explore and distill variation that is distributed across many variables into reduced dimensional space. They are useful for exploring relationships within large multivariate datasets as site and species relationships can be plotted in two and three dimensions.
Also, axis loadings can also be extracted for examination of variation explained along each dimension to inform the user about directions for more refined data analyses and hypothesis testing. This workshop provides simple coding frameworks for analyzing numeric, categorical, and mixed-data types via multidimensional scaling, principal component analysis, canonical variate analysis, variance components analysis, and multiple correspondence analysis. Tips for handling missing data are also discussed.
Prior Knowledge: Previous experience with (basic) R is assumed. Completion of D-Lab's R FUN!damentals workshop series should be sufficient.