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 ] --- # In this intro... + The dapR2 team -- + What each week will look like. -- + Expectations --- # dapR2 Team + Monica Truelove-Hill (CO, Lectures, Labs) + Emma Waterston (CO, Labs) + Zach Horne (Lectures) + Umberto Noe + Josiah King + Aja Murray (Stats Curriculum Coordinator) + Wonderful tutors (lots) --- # What is R? + A very flexible programming language for all things data. + It does pretty much any statistical method you can think of. (Cool? I think so) + But it does a lot more. + We will also continue to teach you Rmarkdown + this is a really neat way to integrate text and analysis + and to write reproducible documents and analyses + Open source (free) software --- # What is R? + Huge community worldwide which means lots of free resources in addition to your course materials -- + Some examples: + [interactive plots](https://shiny.rstudio.com/gallery/movie-explorer.html) + [interactive dashboards](https://gallery.shinyapps.io/086-bus-dashboard/) + Documents which automatically include results from analysis + [books](https://bookdown.org/csgillespie/efficientR/) + [websites](https://rmarkdown.rstudio.com/) + Presentations (like the one you are looking at) --- # dapR2 + In dapR2, we will teach you how to... -- + Deal with data in R, tidy it, describe it and visualize 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 generalized 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 + Semester 1: + Weeks 1 to 4: Basics of the linear model + Week 5: Application of weeks 1-4 + Weeks 6 & 7: Working with categorical data + Weeks 8 & 9: Robustness checks for our models + Week 10: Application of weeks 6-9 -- + Semester 2: + Weeks 1 to 4: Interactions & Analysing Experiments + Week 5: Application of weeks 1-4 + Week 6: Power Analyses + Week 7-8: Generalised Linear Model + Week 9: Application of weeks 6-8 + Week 10: Revision --- # Basis for dapR3 (Hons Stats) + In dapR3, we will teach you how to... -- + Run, interpret and use linear mixed models to analyse repeated measures data -- + Run, interpret and use data reduction techniques -- + Run, interpret and use path models for testing more complex relations between variables --- # 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 R's last year) + ***Core purpose***: Learning new material -- + **Labs** + 1 hour per week in person labs (note these may take more than 1 hour to complete = 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 discussion boards (monitored daily) + ***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: Assessment (the facts) + **Weekly quizzes** (10%) + 20 quizzes in total + Quizzes 1 and 2 are practices. + The rest comprise your grade. + Mark is the average of your best 14/18 scores. -- + **Coursework report** (30%) + Group-work based (working in groups of approx. 5) + Early semester 2 allocated to your group + Report completed in your groups outside of teaching time (i.e., does not form lab exercises like in DAPR1) + Grades will be peer adjusted - you will rate each group member on a number of criteria + Provided a dataset and some questions + Task is to construct models to answer the questions, describe them, run them, report the results. -- + **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 --- # dapR2: Assessment (the reason) + **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: Assessment - 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 -- + What is 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 chat-gpt) for any assessment on this course + All of the above are examples (not a comprehensive list) of academic misconduct. + We will report cases of misconduct. + Find out more about academic misconduct is and the University procedures for investigating students accused of misconduct [here](https://www.ed.ac.uk/academic-services/students/conduct/academic-misconduct/what-is-academic-misconduct) and [here](https://www.ed.ac.uk/arts-humanities-soc-sci/taught-students/student-conduct/academic-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 on-line) -- + The weekly folders will become available incrementally. --- # 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. + We can help construct peer study groups to learn from one another. + Monitor your quiz performance. --- class: center, middle # And that is it for this intro!