| Review: |
It is difficult to define a person by a few characteristics: several characteristics may be needed to begin to represent complex personalities; the kind of characteristics that we look at depends on our purposes. Similar problems arise when we are trying to understand data – which characteristics are relevant when describing data depends largely on our purposes. Here the authors show how to interpret research outcomes and how to make better decisions that result in better research. The emphasis the General Linear model concepts, which involve understanding how different statistical methods are related to each other as well as on confidence intervals. There are chapters on: Introductory terms and concepts; Location; Dispersion; Shape; Bivariate relationships; Statistical significance; practical significance; Multiple regression analysis; A GLM interpretation rubric; ANOVA; Multiway and other alternative ANOVA models; The general linear model; ; and some logic models. To accompany the book the author has posted some datasets used in the book, as well as others, on his website. |