PCHN63112 15 Semester 2 Postgraduate Taught Level 7 Garreth Prendergast Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 <p>The module will consist of ten workshops on: advanced analysis techniques in R (four workshops), Bayesian statistics (two workshops), mixed models (two workshops) and computing in Matlab (2 workshops).</p><p>The workshops on R will further develop students' data wrangling, data visualisation, and statistical modelling skills in the R programming environment. They will also provide the opportunity to use a range of advanced R packages including lme4 for (generalised) linear mixed models.</p><p>The workshop on Bayesian approaches to statistics will cover the application of Bayesian models.</p> <p>The module will consist of ten workshops on: advanced analysis techniques in R (four workshops), Bayesian statistics (two workshops), mixed models (two workshops) and computing in Matlab (2 workshops).</p><p>The workshops on R will further develop students' data wrangling, data visualisation, and statistical modelling skills in the R programming environment. They will also provide the opportunity to use a range of advanced R packages including lme4 for (generalised) linear mixed models.</p><p>The workshop on Bayesian approaches to statistics will cover the application of Bayesian models.</p> <p>To develop students' abilities in data wrangling, programming (incl. data simulation) and analysis in R, mixed models and Bayesian approaches to statistics. To provide students working in small groups with the experience of advanced decision making in the application of psychological statistics.</p><p>To provide students working in small groups with the experience of using software appropriate for each method and with the interpretation of output files.</p> <p>Having attended the course unit, students will be able to:</p><ul><li>demonstrate an understanding of the logic underlying the use of advanced linear models and (generalised) linear mixed models in R, programming in R and Bayesian approaches, and the range of circumstances appropriate for their use,</li><li>conduct analysis involving regression, hierarchical linear models, generalised linear mixed models, taking appropriate decisions and approaches and applying specialist software</li></ul> <p>In Semester 2 each workshop will consist of a four-hour session , most of which use the “flipped classroom” format. In this learning style, students work through recorded content online in their own time before the scheduled class. During the face-to-face sessions, there are group discussions and practical considerations of how various approaches can be best implemented.</p> 0 Other 100% <p>Assessment methods Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth 50% of the marks for this course unit.</p><p>Linear Mixed Modelling in R: the form of assessment varies from year to year. Each student will be required, for example, to carry out analysis based on provided data and write a short report of the analysis using R Markdown (1000 words equivalent).</p><p>Hackathon: the assessment will require each student to carry out data wrangling, data visualization, and statistical modelling on a large dataset of their choosing (e.g., downloaded from one of the 'big data' repositories such as Kaggle, Gapminder, or Google Dataset Search) or a large open dataset from a Psychological research area. The data wrangling, visualization, and modelling will be written up using R Markdown. (1000 words equivalent).</p> MRes Psychology MRes Exper Psyc with Data Sci PGDT Taught Component Mandatory MRes Psychology MRes Cog Neuro and Neuropsyc PGDT Taught Component Mandatory N <p>Recommended reading will be provided in each workshop.</p> Lectures 24 0 126