Delivered at:: International laboratory for Applied Network Research

Course type:: Compulsory course

When:: 1 year, 1 module

Instructors

Oganyan, Artem

Pavlova, Irina

Full Syllabus

Abstract

This course is an introductory course in network analysis, designed to familiarize graduate students with the general concepts and basic techniques of network analysis in sociological re-search, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis, and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software. In addition, this course will provide ample opportu-nities to include network concepts in students’ master theses work.

Learning Objectives

to give students the opportunity to get acquainted with the basic concepts of statistics
to teach how to use statistical terms correctly and work with basic statistical concepts.

Expected Learning Outcomes

be able to estimate the mean and variance of a sample
be able to explain the rejection of statistical hypotheses
be able to explain the use of different methods in relation to a certain measurement scale
be able to formulate null and alternative statistical hypotheses
know the difference between different measurement scales
know which charts are suitable for which type of data

Course Contents

The Where, Why, and How of Data Collection
Graphs, Charts, and Tables—Describing Your Data
Describing Data Using Numerical Measures
Introduction to Probability
Discrete Probability Distributions
Introduction to Continuous Probability Distributions
Introduction to Sampling Distributions
Estimating Single Population Parameter
Introduction to Hypothesis Testing
Estimation and Hypothesis Testing for Two Population Parameters
Hypothesis Tests and Estimation for Population Variances
Analysis of Variance

Assessment Elements

Tests
Final test

Interim Assessment

2023/2024 1st module
0.4 * Final test + 0.6 * Tests

Bibliography

Recommended Core Bibliography

Agresti, A. (2017). Statistics: The Art and Science of Learning From Data, Global Edition. Pearson.

Recommended Additional Bibliography

James T. McClave, & Terry Sincich. (2013). Statistics: Pearson New International Edition. Pearson.

Master’s Programme 'Data Analytics and Social Statistics'

Contacts

Introduction to Statistics