When you read about linear mixed models, you will quickly come across the terms ‘fixed factors’ and ‘random factors’. What do these terms refer to? Why are they so important to understand when first learning about linear mixed models? Fixed factors Most students and researchers should be familiar with fixed factors. Fixed factors are those that we we traditionally would
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Linear mixed models – also called linear mixed-effects models or hierarchical models – are statistical models that extend ordinary linear regression to situations where data points are not independent of each other. What do we mean by this? Examples of non-independent data Hierarchy in data Schools, students and maths. Imagine we were trying to determine whether a student’s age in
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Introduction Everyday statistics –those I use regularly to answer research questions –have evolved since my first statistics course over 20 years ago. I learned about the t-test, ANOVA, repeated-measures ANOVA, correlation, simple and multiple regression, and non-parametric statistics like the Wilcoxon test, the Mann-Whitney U-test and the Chi-square test. While these tests are still commonplace in many disciplines, there is
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Scientifically Sound 