This Workgroup Meeting

  • Introduction
  • Course structure
  • Work group structure
  • Assignments of the course


    • Break


  • Starting first assignment

    • Introduction

    Introduce yourself briefly:

  • What is your background (what study)?
  • Why chose this course/minor?

    • Course Structure

    Monday 17:15:

    Lecture by Kyle Lang
    On location


    Thursday morning:

    Work group meeting:

    • Work on (group) assignments

    Tuesday 13:15:

    Remainder of lectures, Q&A,
    and discussion of practicals

    Online, by Kyle Lang

    By yourself:

    • Do the required reading
    • Work on practicals (deadline is next Monday 17:00, before the next lecture)
    • Continue to work on group assignment

    Workgroup Meetings

    Working on assignments

    Applying the lecture topics to own (real) data sets.

    Assignments - Overview

    Assignment 1

    • Group assignment
    • Groups with max. 4 members
    • Linear regression
    • Deadline: Monday 16th of December, 17:00

    Assignment 2

    • Group assignment
    • Same groups as Assignment 1
    • Logistic regression
    • Deadline: Thursday 16th of January, 17:00

    Assignments - General Comments

    Grading:

    • Both group assignments contribute 25% to your final grade (50% together).
    • All group members receive same grade for group assignments
    • If names are omitted from groups/assignments, we expect everyone to be aware of that.

    Hand-in:

    • Hand in assignments via SurfFileDrop
    • Hand in .zip folder with R project and all files (HTML, Rmarkdown, data sets)
    • Make sure all code and text are visible in the HTML files
    • HTML files are graded; supplementary files are needed when HTML seems incomplete
    • Find all relevant information for the assignments on the course website.

    Assignment preparations

    Week 1:

    • Make groups of (maximum) 4 students.
    • Look for data set online.
    • Inspect variables
    • Come up with research questions for linear regression and logistic regression.
      > The chosen data set should be able to answer RQs.

    Week 2:

    • Pre-processing of data set.

    Data set - Requirements

    The data set for the group assignments has a number of requirements. The data set should at least contain:

    • A continuous outcome variable
    • At least two continuous predictor variables
    • A categorical predictor variable
    • A dichotomous outcome variable

    Data set - Tips

    There are a lot of online places that offer good data sets for these assignments. Below, some examples are listed:

    • Kaggle
    • Github
    • CBS / Eurostat
    • RIVM
    • Via scientific papers
    • Via large panel studies (e.g. PISA, LISS, EVS, ESS, TIMSS)
    • Previous courses?


    Datasets from lectures and practicals are not allowed for these assignments

    Example Research Questions:

    Linear Regression:

    • Can body weight predict the level of cholesterol in Dutch adults?
    • What variables are related to income in the Netherlands?

    Logistic Regression:

    • Do body weight, calorie intake, fat intake, and age have an influence on the occurrence of a heart attack?
    • What variables can be used to predict whether students pass a driving test?

    Remainder of the meeting

    1. Make groups of (max.) 4 students


    - How do you work together as a group? See this example.

    2. Start looking for a data set:

    • Look for something you find interesting
    • Make sure the data set meets the requirements
    • Check if you can actually obtain the data!

    3. Start formulating research questions:

    • Based on the variables in your data set/the topic of your data set, come up with questions that you want to investigate.
    • Research questions need to be about relations between the variables

    4. Start inspecting your data set:

    • Is recoding of variables needed?
    • Is any processing needed? (If you want to use factor analysis or PCA, you are free to do so)

    Use of generative AI

    You are allowed to

    • gain inspiration
    • ask for help on how to tackle an assignment

    You are not allowed to

    • submit generated text or code as if it was written by you
    • have text or code revised

    Before the end of the meeting:

    As group, send email to WG instructor:

    • Names of group members;
    • How you agreed to collaborate;
    • Selected data set (topic and link);
    • Research questions you want to answer in your assignments.


    Send this email before the end of the meeting!