SOCI832 Course Overview


In this section we review the structure of semester as a whole, and also each individual class.

Structure of semester

Weeks 1-6 & 9-12: Normal class with lecture, demonstration, and exercises (Participation 25%)

Week 8: Normal class but starting with 30 minute mid-term 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: In-class exam (2 hours: Online, bring laptop. Multiple choice, fill in blank, and short answer) (Exam worth 25%)

Structure of each class

4pm - 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

  • Part 1: Lecture (45 - 60 min) - powerpoint slides to be provided at beginning of class.
  • Part 2: Demonstration in R (45 - 60 min) - R script to be provided at beginning of class.
  • Part 3: Student exercises (45 - 60 min) - PDF/Word outline of exercise to be provided at beginning of class.
  • Part 4: Example exam questions (handout, no specific time allocated) - PDF/Word document with example exam questions (multiple choice, fill in blanks, short answer).


This section provides and overview of the assessment for SOCI832

Structure of assessment

  • Participation in weekly classes (25%)
  • Week 7 Presentation and report (0% - Practice)
  • Week 8 Midsemester exam (0% - Practice)
  • Week 13 Presentation and report (50%)
  • Week 14 Final exam (25%)

Details of assessment

Participation in weekly classes (25%)

Marking criteria: Participation will be assessed according to whether student participate in class through

  1. asking questions of the lecturer and other students,
  2. demonstrating that you have done readings,
  3. listening and responding to comments and questions from the lecturer and other students,
  4. undertaking in-class exercises, and
  5. doing so in a way that is respectful of other participants in class.

Example grades and students’ behaviour that meets marking criteria:

  • 55% (Pass) - attends 70%-90% of classes, but does so with little participation beyond completing exercises
  • 65% (Credit) - attends 80%-90% of classes, and participates in discussion occassionally.
  • 75% (Distinction) - attends 90%-100% of classes, and participates actively and fully
  • 85% (High Distinction) - attends 90%-100% of classes, and shows deep engagement with teaching material and with other students. Shows serious preparation before class. Completes exercises at a very high standard, perhaps extending analysis beyond those immediately taught.

Week 7 Presentation and report (0% - Practice)


  1. Replicate a published study with public dataset: Students are to:
    1. Find an article: Find a social science (or closely related discipline) study that has been published as a peer-reviewed academic article, and that uses a publically accessable dataset, and
    2. Replicate in R: Replicate the analysis presented in the paper using R.
  2. R code should not already exist: Article and dataset should NOT already have publicly available R code (this would make the exercise pointless).
  3. By Week 7: By Week 7 students should have identified the article, downloaded the dataset, and conducted preliminary analysis (i.e. univariate and bivariate analysis).
  4. Presentation: In class in Week 7 students will present for a maximum of 12 minutes, and provide:
    • a brief introduction to the article and the dataset
    • their preliminary analysis, including tables and figures.
  5. Report: In class in Week 7 students will submit their written report (printed out, and also submitted through ilearn), which shall consist of:
    • A copy of the article to be replicated
    • A link to the dataset
    • A copy of their R code (script file) with brief annotations to explain what you have done
    • A short report of approximately 600-1000 words with no more than five tables and figures, which present a preliminary analysis of the dataset.
  6. Consultation: Students are expected to consult with the lecturer (Nick) before class (4pm - 6pm), in class (6pm - 9pm), and outside class (Facebook messenger, WhatsApp) to (1) confirm their choice of article, and (2) discuss any issues and problems they are having with the analysis.

Marking criteria:

  1. Motivates interest of audience: Presentation and report should motivate the interest of the audience by identifying both what is intellectually curious about the topic, and why it is substantively important for the public, policy makers, or other non-academic audiences.
  2. Clear writing style: Straight-forward, clear, easy to read writing. This means generally using short sentences, having a single coherent and easy to understand argument, and using paragraphs with topic sentences.
  3. Professional Tables and Figures: Tables and figures should be presented like they would appear in an academic article, which means, at the least, (1) that tables are not just cut and paste from R output, (2) that tables and figures include only the necessary information, (3) that tables and figures include all appropriate information, and (4) they should be able to be interpreted on their own (without the text), and (5) all tables and figures should be referred to by number in the text.
  4. Analysis: Analysis should be a high quality replication of the analysis in the academic article which it comes from. Analysis may briefly extend on the analysis in the article, if space and time permits.
  5. Explanation: Explanation of the analysis should be simple, clear, and correctly use terminology. It should also point out the substantive significance of the results in a way that another person with a Masters Degree, but not in this area of research, could understand.
  6. R code: R code should be as simple and tidy as possible, with brief annotated comments (after # symbols), which explain the purpose of each section of code. The R code should be in a form which the lecturer can run on their computer and replicate the analysis.

Week 8 Midterm exam (0% - Practice)


  1. 30 minutes: This will be a short (30 minute) online exam, with multiple choice, fill-in the blank, and short answer questions.
  2. Laptops; Open book: Exam will be completed on student’s laptops and be open book.
  3. Concepts from Weeks 1 to 6: Exam will test material from Week 1 to 6, with a strong focus on the “Concepts” identified at the beginning of each week’s class.
  4. Example questions: Example questions will be provided in advance of the class for students to familiarise themselves with the format.

Week 13 Presentation and report (50%)

Instructions and marking criteria:

  1. Same as Week 7 Presentation & Report, but full analysis: Instructions and marking criteria are the same as for Week 7 Presentation and Report, except that
    1. Full analysis: the full analysis should be presented, and
    2. Multivariate analysis: should include some form of multivariate analysis (some form of regression, factor analysis, ANOVA, or similar).

Week 14 Final exam (25%)


  1. Same as Week 8 Midterm Exam, but 2 hours: Instructions are the same as for Week 8 Midsemseter exam, except that exam is 2 hours, and will test all material from weeks 1 through 12.


In this section I provide and overview of what you can expect of me as your teacher and what I will be expecting of you as a learner.

What you can expect of me

Before semester starts

Topics, readings, concepts: Before semester starts I will provide weekly topics, weekly readings, and key concepts to identify in your reading

Outside class

Messenger or WhatsApp: Outside class you should consult with me via Facebook messenger or WhatsApp

Before class

Consulations 4-6pm 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: R-script 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 you

Before 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 in-class exercises, and doing so in a way that is respectful of other participants in class.

By week 4

Find 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 7

Preliminary Presentation and Report Due

Week 8

Midterm Exam

Week 13

Final Presentation and Report Due

Week 14

Final Exam


Unless otherwise specified, readings are chapters from:

Field, A., Miles, J., and Field, Z. (2012). Discovering statistics using R. Sage publications.

Week 1: Introduction to R

Reading: Chapter 3
Main concepts: Install R & RStudio, open dataset, recode variables

Week 2: Introduction to Research Methods

Reading: Chapter 1
Main concepts: Theory, hypotheses, variables, measurement

Week 3: Introduction to Statistics

Reading: Chapter 2
Main concepts (statistics): Descriptive & inferential statistics, sampling, p-value

Week 4: Univariate analysis

Main concepts (univariate analysis): central tendency, variation, histogram.

Week 5: Bivariate analysis

5.1 Comparison of means

Reading: Chapter 9
Main concepts: comparison of mean, paired/independent samples

5.2 Correlation

Reading: Chapter 6
Main concepts: pearsons r, covariance, scatterplot

5.3 Chi-square

Reading: Chapter 18
Main concepts: cross-tabulation, chi-square, degrees of freedom

Week 6: Dimension reduction

6.1 Index creation and testing

Reading: Chapter 17, section 17.8
Main concepts: Cronbach alpha, reliability with item deleted

6.2 Factor analysis

Reading: Chapter 17
Main concepts: factors, factor scores, rotation

Week 7: Student Presentations + Report Due (0% - Practice)

Week 8: Midterm (30 mins - 0% - Practice) Linear Regression + Regression diagnostics

Reading: 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 regression

Reading: Chapter 8
Optional Reading (other types of regression): Chapters 18 and 19

Week 11: Other regression


Week 12: Graphs with ggplot


Week 13: Student Presentations + Report Due (50%)

Week 14: Final exam (2 hours - 25%)