Advanced Data Skills, Open Science and Reproducibility

PCHN63101 Advanced Data Skills, Open Science and Reproducibility 15 Semester 1 Postgraduate Taught Level 7 George Farmer 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 topics covered will include:<ul><li>Basic principles of coding in R</li><li>Data wrangling and data visualisation</li><li>Producing reproducible reports in RMarkdown</li><li>Implementing and interpreting statistical models in R under the General Linear Model framework </li></ul> The topics covered will include:<ul><li>Basic principles of coding in R</li><li>Data wrangling and data visualisation</li><li>Producing reproducible reports in RMarkdown</li><li>Implementing and interpreting statistical models in R under the General Linear Model framework </li></ul> <ul><li>To familiarise students with a range of advanced, quantitative analytical techniques at a level not normally encountered in undergraduate study.</li><li>To equip students with the confidence and skills necessary to apply the methods to datasets using R.</li><li>To provide sufficient understanding for sophisticated statistical decision-making and interpretation of results. </li></ul> Having attended the unit, students will be able to:  <ul><li>Write clear and functional code in base R to import, clean, transform, summarise, and visualise data.  </li><li>Produce reproducible analytical reports using R Markdown that integrate code, output, and narrative.  </li><li>Explain the logic of statistical modelling within the General Linear Model (GLM) framework, including assumptions, parameter estimation, and inference.  </li><li>Implement GLM-based analyses ranging from simple linear regression to multi-factor ANOVA using R. </li><li>Interpret model outputs, check assumptions and conduct follow-up tests </li></ul> In Semester 1 there will be weekly 2-hour seminars providing an introduction and explanation of each technique and practical training. Each technique will be demonstrated using R or other appropriate software.    0 Other 100% <figure class="table"><table><tbody><tr><td>Assessment task</td><td>How and when feedback is provided</td><td>Weighting within unit (if relevant)</td></tr><tr><td>Continuous assessment. Two assignments.Each topic will be formally assessed by a written assignment, worth 50% of the marks for this module.Data Wrangling and Visualisation: The form of assessment varies from year to year. You will be required to import, clean, transform, and summarise a dataset that will be provided, and to create appropriate visualisations using R. The assignment will assess good practice in writing base R code. Your report will be produced in R Markdown, integrating code, output, and interpretation.ANOVA: the form of assessment varies from year to year. You will analyse and discuss data sets provided to you. You will be asked to carry out the appropriate analyses and for each dataset, write a results section (where you only report on descriptive and inferential statistics) and a brief discussion section (where you interpret the results, based on the analyses you carried out). Your report will be written in R Markdown.   </td><td>   As per feedback policy</td><td> 50%   50%  </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 Appropriate online R-based resources will be made available alongside each lecture.  Lectures 22 0 128