I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book). You can read it here.Filed under: serious stats, stats advice Tagged: correlation and covariance, general linear model, messy d...
I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book). You can read it here.Filed under: serious stats, stats advice Tagged: correlation and covariance, general linear model, messy d...
It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can […]
It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can […]
In Chapter 2 (Confidence Intervals) of Serious stats I consider the problem of displaying confidence intervals (CIs) of a set of means (which I illustrate with the simple case of two independent means). Later, in Chapter 16 (Repeated Measures ANOVA), I consider the trickier problem of displaying of two or more means from paired or […]
In section 10.4.4 of Serious stats (Baguley, 2012) I discuss the rank transformation and suggest that it often makes sense to rank transform data prior to application of conventional ‘parametric’ least squares procedures such as t tests or one-way ANOVA. There are several advantages to this approach over the usual approach (which involves learning and applying a new test such as Mann-Whitney U, [...]
In Chapter 6 (correlation and covariance) I consider how to construct a confidence interval (CI) for the difference between two independent correlations. The standard approach uses the Fisher z transformation to deal with boundary effects (the squashing of the distribution … Continue reading →
