class: center, middle, inverse, title-slide .title[ # <b>Course Introduction </b> ] .subtitle[ ## Data Analysis for Psychology in R 2<br><br> ] .author[ ### dapR Team ] .institute[ ### Department of Psychology<br>The University of Edinburgh ] --- # dapR2 Team -- + Marju Kaps (CO, Lectures, Labs) + Emma Waterston (CO, Labs) + Zach Horne (Lectures) + Wonderful tutors (Labs) --- # What is R? -- + A very flexible programming language for all things data. + It does pretty much any statistical method you can think of + But it does a lot more + We will also continue to teach you R Markdown + this is a really neat way to integrate text and analysis + and to write reproducible documents and analyses + Open source (free) software + Huge community worldwide which means lots of free resources in addition to your course materials --- # dapR2 -- + In dapR2, we will teach you how to... -- + Deal with data in R, tidy it, describe it and visualise it -- + Run, interpret and use linear models for observational designs -- + Run, interpret and use linear models for experimental designs -- + Introduce the basic concepts of the generalised linear model -- + Talk about some common issues when applying the linear model to data. --- # dapR2 building from dapR1 -- + dapR2 builds on dapR1 -- + Core concepts: + types of data + plotting + significance testing + bootstrap + correlation + independent t-tests -- + You can access these materials via the welcome week folder in LEARN --- # dapR2 -- + The course will help develop a large number of transferable skills: -- + Working with data -- + Logical thinking and problem solving -- + Collaborative working (in labs solving the lab problems) -- + Report writing (taking something complex, and presenting it in a digestible way) --- # dapR2 Topics -- + **Semester 1:** + Weeks 1-4: Basics of the linear model + Week 5: Application of weeks 1-4 + Week 6: Catch-up week (no lectures, labs, quiz, office hours) + Weeks 7-8: Working with categorical data + Weeks 9-10: Robustness checks for our models + Week 11: Application of weeks 7-10 -- + **Semester 2:** + Weeks 1-4: Interactions & Analysing Experiments + Week 5: Application of weeks 1-4 + Flexible Learning Week (no lectures, labs, quiz, office hours) + Week 6: Power Analyses + Weeks 7-8: Generalised Linear Model + Week 9: Application of weeks 6-8 + Week 10: Revision --- # dapR2 Course Delivery -- + **Lectures** + Two 50 minute lectures per week. + These will be a mix of new content and practical demonstration/Q&A (like the Live Rs last year) + ***Core purpose***: Learning new material -- + **Labs** + Check your personal timetable for your lab allocation + 1 hour per week in-person labs (additional self-study expected) + Structured reading and exercises with R + ***Core purpose***: VERY important sessions for practical skills --- # dapR2 Course Delivery -- + **Supported by** + Office hours (see LEARN for times) + Piazza peer discussion boards + ***Core purpose***: Resolving ambiguities and questions, helping answer others' questions -- + **Reading** + All reading is from free-to-access sources + You do not need to buy a book and should be able to access everything electronically + ***Core purpose***: Scaffolding knowledge --- # dapR2: Assessments (Overview) -- + **Weekly quizzes** (10%) + 20 quizzes in total (Quizzes 1 and 2 for practice, the rest marked) + Four lowest quizzes dropped - mark is the average of your best 14/18 scores + Released Mondays at 9am, due Sunday at 5pm + Time limit 60 minutes + Make sure to complete, in order to get feedback -- + **Coursework report** (30%) + Group-work based (working in groups of approx. 5) + Groups formed early Semester 2 - if you are not present in your Lab session, you will be randomly allocated + Report completed in your groups outside of teaching time (not in the Lab sessions) + Set Thursday 13th February at 12 noon (Semester 2 Week 5), Due 12 noon Thursday 6th March 2025 (Semester 2 Week 7) + Grades will be peer-adjusted - you will rate each group member on a number of criteria + Provided a dataset and some questions, your task is to construct models to answer the questions, describe them, run them, report the results --- # dapR2: Assessments (Overview) -- + **Exam** (60%) + Summer exam block + Mix of MCQ, short answer R/calculation, and larger interpretation questions + Closed book, though you will be given an equation sheet to refer to within the exam + Sessions for example exam questions and Q&A taking place at the end of Semester 2 --- # dapR2: Assessments: Why? -- + **Weekly quizzes** (10%) + Encourage continual engagement + Act as a knowledge check for you each week to monitor your progress -- + **Coursework report** (30%) + To practice working with data, converting questions to models, and interpreting results + Continue to develop your group work skills + This is what you will be doing in your Mini-dissertation (year 3) and Dissertation (year 4) -- + **Exam** (60%) + Primary assessment of individual learning --- # dapR2: Academic Integrity -- **All assessments are individual assessments (with the exception of the group report)** -- + What is acceptable (encouraged): + Going over your quizzes together once your marks and feedback have been released + Discussing the approach you plan to take to the report + Helping one another think through and solve problems (as in the lab) + Revising with one another and helping each other prepare for the exam -- + Some examples of academic misconduct (not acceptable!): + Copying code for the report + Copying blocks of text for the report + Sharing questions and answers on quizzes + Cheating in the exam + Use of AI tools (such as GPT) for any assessment on this course -- + We will report cases of misconduct --- # Materials -- + All of your materials for each week will be within the weekly folders on LEARN -- + It will contain: + Slide decks and links to lecture recordings + Links to lab material + Links to the weekly quizzes + Any comments or specific instructions for the week + Links to discussion boards + Reading (all freely available online) -- + The weekly folders will become available incrementally. -- --- # Expectations -- **What you can expect from us** 1. We will work hard to help you learn 2. We will be open and communicate with you 3. We will be polite, respectful and treat you like adults -- **What we expect of you** 1. You work hard 2. That you talk to us 3. That you are polite, and respect the teaching team and your classmates 4. Try and have fun --- # A brief word on engagement -- + dapR courses require consistent work across the course + You can not really cram this content -- + We want to make sure everyone keeps on top of the course + To do that, we need to know when people are struggling -- + Please help us by being proactive: + Come to all sessions + Use office hours to clarify any questions you have + Talk to peers at your lab session and consider meeting for study groups to learn from one another + Monitor your quiz performance --- # Installing R and RStudio -- **Please install a version of R and RStudio on your own computer** + You will need both R and RStudio installed before your first Lab in Week 1 + Instructions for installation and updating available at <a href="https://edin.ac/3B0oi5A" target="_blank">https://edin.ac/3B0oi5A</a> + Please follow these instructions carefully -- + For those of you who have Chromebooks: + Local installation is not possible so you should (continue to) use the PPLS RStudio Server at <a href="https://rstudio.ppls.ed.ac.uk/" target="_blank">https://rstudio.ppls.ed.ac.uk/</a> + If you need to request access, fill out the form here: <a href="https://edin.ac/3Le1mEW" target="_blank">https://edin.ac/3Le1mEW</a> --- class: center, middle # See you in class!