 Structure
 Assessment
 Expectations
 Lessons
 Week 1: Introduction to R
 Week 2: Introduction to Research Methods
 Week 3: Introduction to Statistics
 Week 4: Univariate analysis
 Week 5: Bivariate analysis
 Week 6: Dimension reduction
 Week 7: Student Presentations + Report Due (0%  Practice)
 Week 8: Midterm (30 mins  0%  Practice) Linear Regression + Regression diagnostics
 Week 9: Public Holiday (No classes)
 Week 10: Logistic regression + mediation, moderation, path analysis.
 Week 11: Other regression
 Week 12: Graphs with ggplot
 Week 13: Student Presentations + Report Due (50%)
 Week 14: Final exam (2 hours  25%)
Structure 
Structure of semesterWeeks 16 & 912: Normal class with lecture, demonstration, and exercises (Participation 25%) Week 8: Normal class but starting with 30 minute midterm exam. (Midterm 0%  Practice) Weeks 7 & 13: Student presentations (replication of analysis from an academic paper) + report due in class (Week 7 Presentation & Report 0%  Practice; Week 13 Presentation & Report 50%) Week 14: Inclass exam (2 hours: Online, bring laptop. Multiple choice, fill in blank, and short answer) (Exam worth 25%) Structure of each class4pm  6pm Mondays: Drop in consultations (weeks 1 to 14, not including break weeks; room TBC, but probably the same room as class, or a room near by) 6pm  9pm Mondays: Class

Assessment 
Structure of assessment
Details of assessmentParticipation in weekly classes (25%)Marking criteria: Participation will be assessed according to whether student participate in class through
Example grades and students’ behaviour that meets marking criteria:
Week 7 Presentation and report (0%  Practice)Instructions:
Marking criteria:
Week 8 Midterm exam (0%  Practice)Instructions:
Week 13 Presentation and report (50%)Instructions and marking criteria:
Week 14 Final exam (25%)Instructions:

Expectations 
What you can expect of meBefore semester startsTopics, readings, concepts: Before semester starts I will provide weekly topics, weekly readings, and key concepts to identify in your reading Outside classMessenger or WhatsApp: Outside class you should consult with me via Facebook messenger or WhatsApp Before classConsulations 46pm Monday: I will hold a consultation in the two hours before class (4pm to 6pm) In Weeks 1 to 14. In class, each week:By 6pm Monday (the beginning of class) I will upload to methods101, and iLearn: 1. Slides: powerpoint slides for that week’s lecture 1. R code: Rscript for that week’s demonstration 1. Exercises: a PDF or Word Doc with the student exercises for that week Lecture: Present a 30 to 60 minute lecture on the topic for that week Demonstration: Present a 30 to 60 minute demonstration of the concepts for that week, in the form of an analysis in R (we will review an RScript, and look at the output) Exercise: Facilitate a 30 to 60 minute student exercise  where you will be given a dataset and series of tasks to accomplish. You will do this individually, so you all develop the skills and get the practice needed, but I will be present to help with problems and answer questions. What I can expect of youBefore the first class:Install R and RStudio on your laptop before week 1’s class Each week, before class:Read required readings before class, and note any questions you have Each week, in class:Laptop: Bring your laptop to class. Make sure it is charged, or you have the charging cable. Participate: Participate in class through asking questions, demonstrating that you have done readings, listening and appropriately responding to comments and questions from me and other students, undertaking inclass exercises, and doing so in a way that is respectful of other participants in class. By week 4Find an article, a dataset, and confirm with Nick: By class in Week 4, I expect you will have indentified an article you whose analysis you wish to replicate, and have shown this article to Nick and got his approval. Week 7Preliminary Presentation and Report Due Week 8Midterm Exam Week 13Final Presentation and Report Due Week 14Final Exam 
Lessons 
Field, A., Miles, J., and Field, Z. (2012). Discovering statistics using R. Sage publications. Week 1: Introduction to RReading: Chapter 3 Week 2: Introduction to Research MethodsReading: Chapter 1 Week 3: Introduction to StatisticsReading: Chapter 2 Week 4: Univariate analysisReading: methods101.com Week 5: Bivariate analysis5.1 Comparison of meansReading: Chapter 9 5.2 CorrelationReading: Chapter 6 5.3 ChisquareReading: Chapter 18 Week 6: Dimension reduction6.1 Index creation and testingReading: Chapter 17, section 17.8 6.2 Factor analysisReading: Chapter 17 Week 7: Student Presentations + Report Due (0%  Practice)Week 8: Midterm (30 mins  0%  Practice) Linear Regression + Regression diagnosticsReading: Chapter 7 (diagnostics is section 7.7 onwards) Week 9: Public Holiday (No classes)Week 10: Logistic regression + mediation, moderation, path analysis.10.1 Logistic regressionReading: Chapter 8 Week 11: Other regressionReading: methods101.com Week 12: Graphs with ggplotReading: methods101.com Week 13: Student Presentations + Report Due (50%)Week 14: Final exam (2 hours  25%) 