Читать книгу Practical Field Ecology - C. Philip Wheater - Страница 66

Where the question to be asked is a complicated one involving a number of dependent and/or independent variables, then multivariate analyses may be appropriate. The choice of analysis depends on whether the dependent variable is a category, or a ranked or measured variable, and on whether the independent variables are categories, ranked, or measured (or even a mixture). Whilst most such analyses only have one dependent variable, there may be multiple independent variables. For example, we may want to know whether the number of birds differs in different types of woodland when we take into account the woodland size (measured variable), woodland type (nominal variable), distance to the nearest neighbouring woodland (measured variable), age of woodland (measured variable), and the land use type surrounding the woodland (nominal variable). Here, we could enter all of the data into one analysis that would take into account the interrelationships between each variable and produce a model describing the relative importance of each variable on the number of birds (this particular example could be analysed using a generalized linear model – p. 319). Such techniques are powerful but require a full understanding of the data or data set and its attributes and may be quite complex to interpret.