From Data to Evidence

Course manual 2022/2023

Course content

In this class, we will learn how to use statistical models to learn from data. Rather than
memorizing many different types of tests and formulas, you will learn the fundamentals of
building statistical models and using models to understand data. Importantly, we will show
that dozens of different tests you may have heard of in statistics are just special cases of
general linear models. We will teach you to use this flexible framework to learn from
the many types of data you may come across in your research.

Above all else, the course is based on a philosophy to promote:

•  critical thinking about data and models
• a down-to-earth attitude to data analysis (as opposed to cookbook statistics)
• a comfortable attitude towards models, math, and statistics (should not be scary or
  intimidating)

Study materials

Syllabus

• https://bookdown.org/connect/#/apps/1c17bcd1-d444-46fd-aaed-7d00c47d2aa1/access

Software

• R and RStudio

Objectives

• Understand the role of statistical questions, analysis techniques and models in the empirical research cycle.
• Formulate appropriate statistical models based on a description of an experiment or observational study.
• Interpret and use probability density functions and cumulative distribution functions.
• Interpret the results of statistical models without relying on statistical jargon.
• Formulate, implement, and interpret statistical models with multiple independent variable.
• Recognize some of the underlying causes of the replication crisis and limitations of null hypothesis significance testing.
• Recognize common pitfalls in the application and interpretation of statistical models.

Teaching methods

• Computer lab session/practical training
• Self-study
• Lecture

Lectures: We will have one lecture per week (2 hrs) for 7 weeks. During lectures, you will be encouraged to actively participate in discussions, ask questions, and participate in live polls. Lectures will be in-person only. Recordings of lectures will be posted at the end of the day and only available on canvas to watch for one week following each lecture. 

Lab Practicals: We will have 4 computer practicals where you will work in self-selected groups of two. If you prefer to work alone that is also fine. Each pair will work on a problem set to get practical experience analyzing data in R. The goal of the lab practicals is for you to learn how to apply the theory covered in the lectures to analyze data in R. Course instructors will be present to assist groups with their work and answer questions.

Assignment: In week 6, groups of two will analyze a data set and write up a short report based on their analysis. This assignment will put into practice the theory covered in the class. There will be no opportunity for a resit on the assignment.

Self-study: It is expected you spend six hours per week on self-study. This involves reading and watching the assigned materials, reviewing course notes and lab practicals, attending question hours, and taking the practice exam.

Learning activities

Attendance

Programme's requirements concerning attendance (OER-B):

• Participation in fieldwork is compulsory and cannot be replaced by assignments or other courses.
• In case of practical sessions, the student is obliged to attend at least of 90% of the sessions and to prepare himself adequately, unless indicated otherwise in the course manual. In case the student attends less than 90%, the practical sessions should be redone entirely.
• In case of tutorials/seminars with assignments, the student is obliged to attend at least 7 out of 8 seminars and to prepare thoroughly for these meetings, unless indicated otherwise in the course manual. If the course has more than 8 seminars, the student can miss up to 1 extra meeting for every (part of) 8 tutorials/seminars. If the students attends less than the mandatory tutorials/seminars, the course cannot be completed.

Additional requirements for this course:

The student may be absent in 1 out of 4  practicals, but the absence needs to be communicated to your group lecturer.

Assessment

10% of your grade will be based on completing the 4 lab practicals. Your grade will be based on whether you followed instructions and thoughtfully attempted to answer every question on the practical.

20 % of your grade will be based on the quality of the assignment. The assignment will be a more in-depth analysis of a complex real-world data set which you will have one week to work on as a group. A grading rubric will be provided.

70% of your grade will be determined by your score on the week 8 exam.

Students need grade > 5.5 on the exam and a cumulative grade (exam, labs, report) > 5.5 to pass the course.

Assessment diagram

Every course goal will be assessed with an equal number of questions in the digital exam.

Students that were enrolled in the course in previous years

Students that have take 'From Analyisis to Evidence' last year can apply their grades for the labs and statistics report from the previous year. However, I highly recommend all students who are retaking the course to do all the labs and report again in order to master the material and pass the course.

Inspection of assessed work

Students will have the opportunity to inspect their assessed work at a date set after the work has been graded.

Assignments

In week 6, groups of two will analyze a data set and write up a short report based on their analysis. This assignment will put into practice the theory covered in the class. The assignment will be graded.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Additional information

We vinden het belangrijk dat je je op de UvA en bij Future Planet Studies veilig voelt. Krijg je onverhoopt te maken met ongewenst gedrag of voel je je onveilig, dan kun je terecht bij verschillende personen. Je melding wordt altijd vertrouwelijk behandeld. Kijk op onze website voor meer informatie over waar en bij wie je terecht kunt.

It is important that everyone feels safe at the UvA and Future Planet Studies. We are committed to provide social safety and we offer various forms of support for people experiencing inappropriate or unsafe situations. Consult the UvA website or Future Planet Studies Canvas page for more information and contact info.

Contact information

Coordinator

• dr. B.T. Martin

Staff

• Anne Uilhoorn
• Jacob Vermeule