Welcome/Course Intro

Multivariate Statistics and Methodology using R

Welcome to Multivariate Statistics and Methodology Using R

What We Will Learn

Multilevel Modelling (Weeks 1-5)

Dan Mirman

  • All things multilevel modeling
  • (aka mixed effects models/random effect models/hierarchical models)

Data Reduction, Path, SEM (Weeks 7-11)

Aja Murray

  • Principal Components Analysis (PCA)
  • Exploratory Factor Analysis (EFA)
  • Confirmatory Factor Analysis (CFA)
  • Path Analysis
  • Structural Equation Modelling (SEM)

Shape of the Course

Lectures
often include live coding

Readings/Walkthroughs/Papers
you’re encouraged to work along with these

Labs (Exercises)
work individually or in groups, with help on hand from a team of tutors

Discussion Forums and Support
via learn page

Assessment
4 Quizzes and an individual report (covers both blocks)

Lectures & Readings

  • broadly, about concepts

    • statistics

    • coding

Exercises

  • broadly, how to

    • coding

    • data manipulation

    • statistics

  • lots of hints, links to readings

solutions will be available at the end of each week

Labs

  • a time and place to work on the exercises

  • encouraged to work in groups

  • a team of tutors will be there to help

  • labs are the best place to get to grips with R and statistics

  • you are expected to attend

The times they are a’ changing!
Both Labs are on Fridays
10:00-12:00 and 14:10-16:00
Check your timetable!

Discussions

  • piazza discussion forums for the course on Learn

    • ask questions, share experiences, talk to the course team

    • post anonymously if preferred

    • an important way to keep in touch

Support

we are here to help you

  • lectures: feel free to ask questions

  • labs: ask the tutors (they want to help!)

  • piazza discussion forums: any time

  • office hours: see Learn page for details

Course Quizzes (20%)

  • 4 assessed quizzes (2 for each part of the course)
  • quizzes each have approximately 10 questions
  • for each quiz, one attempt which must be completed within 60 min

Note that quizzes are not weekly as they were for USMR.
They will be in Week 3, 5, 9 and 111

released Fridays at 17:00

due the following Friday at 17:00

quizzes should be taken individually

Individual Report (80%)

What you’ll have to do

  • Two sections (one for each part of the course)

  • Each section has a dataset and a series of research aims to address

  • Answers written up as a report (recommended structure: ‘methods’ and ‘results’)

  • .Rmd/.R file submitted separately

What we’re looking for

  • Selecting appropriate method(s) to address research aims

  • Explaining and justifying decisions made

  • Implementing methods in R

  • Interpreting and presenting findings

Individual Report (80%)

released Thursday 4th April

due Thursday 25th April at Midday

Unlike USMR, this is an individual project!

Making the most of the course

  • active engagement!

  • attend lectures, ask questions in lectures/labs/discussion forum/office hours

    • no such thing as a stupid question
    • learning stats is cumulative - feel free to ask questions about things we learned in USMR too!

  • keep on top of quizzes

    • useful checkpoints!

  • remember:

    • some things will feel difficult at first
    • R’s help docs contain examples!
    • google is your friend!
    • trial and error is a great way to learn!

RStudio/RStudio Server

By week 7, please switch to using a local installation of RStudio.

Why?

  • We only have a license for the server for teaching purposes - it can’t be used for dissertations.

  • Installing locally means no reliance on internet connection.

  • Good preparation for any future work you do that might need R!

How?

  • instructions at https://edin.ac/3B0oi5A

  • process is a bit more involved. Follow the instructions carefully!

  • just ask (labs/office hours) if you get stuck with it and we can try to help

End