Mixed Models, Hackathon and Bayesian Statistics Workshop

PCHN63112 Mixed Models, Hackathon and Bayesian Statistics Workshop 15 Semester 2 Postgraduate Taught Level 7 Martyn Mcfarquhar Unit coordinator Division of Psychology Communication and Human Neu 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 The unit will consist of 12 weeks of asynchronous content coupled with synchronous workshops.   The unit will consist of 12 weeks of asynchronous content coupled with synchronous workshops.   To further develop students’ understanding of statistics as applied to non-normal outcome variables and dependent data. To provide students with experience and understanding of how to implement different methods for modelling these data using R. To provide students with a clear understanding of hierarchical/multilevel/mixed-effects models. To provide students with a clear understanding of the generalised linear models framework. To provide students with understanding and experience of how these two methods can be combined to form generalised linear mixed-effects models.  Demonstrate an understanding of the theory behind the generalised linear model framework<ul><li>Apply this framework to different types of outcome variable using R</li><li>Demonstrate an understanding of the theory behind dependent data and its impact on inference</li><li>Demonstrate an understanding of different approaches to modelling dependent data, including the traditional repeated measures ANOVA, generalised least squares (GLS) and multilevel/mixed-effects models</li><li>Apply these approaches to different forms of dependent data using R</li><li>Combined these approach to analyse non-normal dependent data using generalised linear mixed-effects models in R </li></ul> This unit will be taught exclusively using a “flipped classroom” approach. Each week, students will be provided with detailed online asynchronous materials that discuss the theory behind each week’s topic, alongside snippets of R code to help aid understanding. These materials are to be studied for 1 week before the associated synchronous workshop. During these workshops, students can engage in discussions of the materials before completing workshop activities designed to consolidate and enhance the asynchronous materials.  0 Other 100% <figure class="table"><table><tbody><tr><td>Assessment task</td><td>How and when feedback is provided</td><td><span class="TextRun SCXW214352985 BCX0 NormalTextRun" style="-webkit-tap-highlight-color:transparent;-webkit-text-stroke-width:0px;-webkit-user-drag:none;font-style:normal;font-variant-caps:normal;font-variant-ligatures:none !important;font-weight:400;letter-spacing:normal;line-height:18.3458px;margin:0px;orphans:2;padding:0px;text-align:left;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;user-select:text;white-space:normal;widows:2;word-spacing:0px;" data-contrast="none" xml:lang="EN-GB" lang="EN-GB">Weighting within unit (if relevant)</td></tr><tr><td>Assessment methods Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth either 30% or 70% of the marks for this course unit.  Generalised Linear Modelling in R: Each student will be required to carry out an analysis based on a provided dataset and write a data analysis report using R Markdown. This will focus exclusively on data with non-normal outcome variables.Mixed-effects Modelling in R: Each student will be required to carry out an analysis using either mixed-effects or generalised mixed-effects models in R. As an additional challenge, students are required to use a real-world dataset from a big data repository (using guidance from the lecturer). This will test their skills in data wrangling, as well as applying a suitable analysis in a real-world setting. The analysis must then be written-up as a data analysis report using R Markdown.</td><td>As per marking policy </td><td>30%    70%</td></tr></tbody></table></figure> MRes Psychology MRes Exper Psyc with Data Sci PGDT Taught Component Mandatory MRes Psychology MRes Cog Neuro and Neuropsyc PGDT Taught Component Mandatory N Recommended reading will be provided  as part of the online asynchronous materials Lectures 24 0 126