Author: Lebov, J.; Grieger, K.; Womack, D.; Zaccaro, D.; Whitehead, N.; Kowalcyk, B.; MacDonald, P.D.M.
Title: A framework for One Health research Document date: 2017_3_24
ID: k60qh7lh_22
Snippet: Using dozens (or even hundreds) of outcome variables (or many explanatory variables) is sometimes intractable, and a more parsimonious method is desired. Principal component (PC) analysis allows researchers to take advantage of correlations that exist among the variables through the construction of linear combinations of variables. In this way, many variables that are potentially correlated (e.g., poverty, household sanitation, household structur.....
Document: Using dozens (or even hundreds) of outcome variables (or many explanatory variables) is sometimes intractable, and a more parsimonious method is desired. Principal component (PC) analysis allows researchers to take advantage of correlations that exist among the variables through the construction of linear combinations of variables. In this way, many variables that are potentially correlated (e.g., poverty, household sanitation, household structure, access to insecticide), may be condensed into a single variable representing "generalized household risk". As a result, parsimony is more readily achieved [12] .
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