Multivariate analysis of variance (MANOVA) - summary of chapter 17 of Statistics by A. Field (5th edition)

StatisticsChapter 17Multivariate analysis of variance (MANOVA) Multivariate analysis of variance (MANOVA) is used when we are interested in several outcomes.The principles of the linear model extend to MANOVA in that we can use MANOVA when there is one independent variable or several, we can look at interactions between outcome variables, and we can do contrasts to see which groups differ.Univariate: the model when we have only one outcome variable.Multivariate: the model when we include several outcome variables simultaneously.We shouldn’t fit separate linear models to each outcome variable.Separate models can tell us only whether groups differ along a single dimension, MANOVA has the power to detect whether groups differ along a combination of dimensions.Choosing outcomes It is a bad idea to lump outcome measures together in a MANOVA unless you have a good theoretical or empirical basis for doing so.Where there is a good theoretical basis for including some, but not all, of your outcome measures, then fit separate models: one for the outcomes being tested on a heuristic and one for the theoretically meaningful outcomes.The point here is not to include lots of outcome variables in a MANOVA just because you measured them. A matrix: a grid of numbers arranged in columns and rows.A matrix can have many columns and rows, and we specify its dimensions using numbers.For example: a 2 x 3 matrix is a matrix with two rows and three columns...

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# Discovering statistics using IBM SPSS statistics by A. Field (5th edition) a summary

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