General Information
| Course Code | S_DIS |
|---|---|
| Credits | 6 EC |
| Period | P4 |
| Course Level | 100 |
| Language of Tuition | English |
| Faculty | Fac. of Social Sciences and Humanities |
| Course Coordinator | |
| Examiner | |
| Teaching Staff |
dr. M. Garnier Villarreal dr. J.C. Muis |
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Part of programme(s)
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Computer lab, Written partial exam, Lecture |
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Course Objective
Course Objectives
Knowledge and understanding - The student has acquired knowledge and understanding of:
- (1) elementary methods and techniques of descriptive and inferential statistics;
- (2) principles of open science and ethical implications of statistical analyses.
Application - The student has acquired the competences to:
- (3) answer relevant societal and social-scientific research questions based on data analyses;
- (4) use the appropriate operationalisations and descriptive statistics given the available data;
- (5) use statistical techniques for inferential statistics;
- (6) use statistical software (RStudio) for data preparation, visualisation, and analyses.
Course Content
This course provides an introduction to the statistical methods of social science research. It teaches necessary basics of quantitative data analysis to 1st year undergraduate students of various programs of the Faculty of Social Sciences and Humanities. Students develop skills to process and manipulate collected quantitative data to prepare for statistical analyses. They learn to describe the data in terms of central tendency and distribution of variables. In addition to summarizing data using numbers, students gain skills and insights into how to visualize numerical information and make graphs. Students also learn to describe, explain, and visualize relationships between variables.
Next, students develop knowledge and skills to formulate and test hypotheses for statistical generalizability (inferential statistics), and interpret, evaluate, and present the findings. In social science, it is common to draw a random sample of individuals from a population to gather data on those individuals in order to derive insights about the population as a whole. The statistical inference component of this course will discuss the logic behind such inferences and the statistical tools available to do so.
Throughout the course, students learn to appreciate and apply open science principles, such as transparency, replicability and responsible data use. Common mistakes in quantitative research will also be discussed, such as omitted variable bias and the file-drawer problem (publication bias). We will not only show examples from scientific journals, but also discuss the misuse of statistics outside academia and how statistics are presented (and often mispresented) in the news media and in political debates.
Additional Information Teaching Methods
Lectures and practical tutorialsMethod of Assessment
Midterms and final exam
Literature
To be announced (on Canvas)Additional Information Target Audience
1st year bachelorstudents in Cultural Anthropology and Development Sociology, Political Science (English track) and Communication Science (English Track).