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Statistical Modelling

This pathway consists of:

  • Three workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic/binary and count (Poisson) regression. Each one builds on the preceding workshop and together they show how all these analyses can be performed using the same easy to understand Generalised Linear Mixed Model (GLMM) framework and workflow. Additionally, how they can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCTs), Before After Control Impact (BACI) analysis, repeated measures, plus many more.
  • A fourth complementary workshop called Statistical Model Building which we recommend for those experienced with Linear Models or for those who have done at least the first two of our Linear Models workshops.
  • The material is organised around Statistical Workflows, applicable in any software, giving practical step-by-step instructions on how to do the analysis, including assumption testing, model interpretation, and presentation of results.

Linear Models 1: Linear Regression, ANOVA, ANCOVA and Repeated Measures (a Simple Mixed Model)

In this workshop we focus on practical data analysis by presenting statistical workflows applicable in any software for four of the most common univariate analyses: linear regression, ANOVA, ANCOVA, and repeated measures (a simple mixed model) – all assuming a normal (gaussian) residual. These workflows can be easily extended to more complex models. The R code used to create output is also included.

Linear Models 2: Logistic and Poisson/Count Regression - An Introduction to Generalised Linear Models

​In this workshop we focus on practical data analysis applicable in any software for two of the more common GLMMs: Logistic regression for binary data (using a Binomial distribution); and Poisson/count regression for count data (using a Poisson distribution). The GLM framework is also described in detail. The R code used to create output is also included. ​

Linear Models 3: Building Interpretable Models that Enable Knowledge Creation, and Other Tips and Tricks

Statistical analysis is more than just building the best predictive model, it should also enable you to make the discoveries required to build new knowledge. Constructing engaging narratives about your research is also invaluable as you look to connect with your field, the community and funding bodies. To do this you need to test hypotheses, uncover unknown patterns, and present results in insightful, intuitive and memorable ways. In short, you need to build interpretable models. In this workshop we explore tips and tricks to make your statistical analyses do just that. Topics covered will be (1) reporting tricks that aid interpretation - estimated marginal means, confidence vs prediction intervals, applying and correcting for multiple comparisons, reporting variable ‘importance’, plus other reporting and interpretation tricks; (2) model parameterisation using the design matrix - interpreting categorical predictor parameters, dummy and effects coding; (3) more on mixed models - introducing the random slope.

Statistical Model Building

In this workshop we will introduce you to the key aspects and strategies of statistical model building to help you answer your research question, and avoid common pitfalls, erroneous models and incorrect conclusions. Appropriate statistical model building will help you to gain knowledge, as opposed to simply getting the best prediction (although that can be a goal as well). We will focus on concepts such as variable selection, multi-collinearity, interactions, selecting a model building strategy, comparing models and evaluating models. In general, these concepts are useful for any statistical model building. This workshop will provide generalised linear regression model examples. The focus will be on practical application of concepts, so mathematical descriptions will be kept to a minimum.

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