Descriptive and Inferential Statistics

URL study guide

https://studiegids.vu.nl/en/courses/2024-2025/S_DIS

Course Objective

s 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 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 (FSS). 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 by using numbers, students gain insights and skills in how to visualise numerical information and make graphs. Students also learn to describe, explain and visualise 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. Also common mistakes in quantitative research will 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.

Teaching Methods

Lectures and practical tutorials

Method of Assessment

Midterms and final exam.

Literature

To be announced (on Canvas)

Target Audience

1st year bachelorstudents in Cultural Anthropology and DevelopmentSociology, Political Science (English track) and Communication Science(English Track).