Digital Technologies in Social Science Research

Digitalne tehnologije v družboslovnem raziskovanju

Course credits:

6.00 ECTS / 60 (30 hours of lectures, 0 hours of exercises, 30 hours of seminars, 0 hours other forms of work)

Course holder:

izr. prof. dr. Katja Lozar Manfreda

Type:

Required

Language:

English

Semester:

First semester

Study degree

2. level

Course execution:

• Postgraduate Programme of Social Informatics

Prerequisits:

Students are allowed to participate in the course provided that they have completed the enrolment procedure. Basic knowledge on social science methodology and statistics is recommended.

Objectives and competences

The objective of the course is to present usage of digital technologies in social science research from conceptual and practical point. Here, the digital technologies are mostly referred to as a research tool, especially as a tool to collect data in social science research. In addition, the course focus also on some other aspects of the contemporary research process where digital technologies play and important role. Competences: Knowing and understanding concepts and assumptions of social science methodology using digital technologies; Ability to use digital technologies to collect data, Ability to collect, analyse and integrate (merge) data, collected by digital technologies from different sources; Ability to critically evaluate the quality of data collected by digital technologies or available in the digital environment; Solving issues with the usage of digital technologies in social science research; Ability to plan and manage research projects, assisted by digital technologies.

Content (Syllabus outline)

The course brings content which is specific for social sciences when speaking about data science and data analytics. While data science usually focuses on management, visualization and statistical data analysis, this course focuses on the usage of digital technologies for collecting data in social sciences, including classical social science methods, such as surveys and qualitative methods. In addition, it focuses also on integration of »classical« social science data with big data as prevalent type of data in modern quantified society, and on selected aspects of modern research process. Topics: 1. Type of data in the digital environment: data collected as a “side product” of web activities (e.g. online traces from log files, such as hypertext links, time stamps, type of activities and other web metrics: text and link on social media and forums; location data; mobile traffic data; web survey paradata) which are usually big data; data collected using digital technologies from research subjects actively involved in data collection (survey data, qualitative data from focus groups and in-depth interviews). 2. Digital technologies as a tool to collect primary data: online focus groups, online in-depth interviews, virtual ethnography, digital story-telling, web surveys (on desktop/laptops and mobile devices), other advanced technologies to collect data (e.g. peoplemeters, location and movement tracers, telemeters, eye-trackers). 3. Issues in secondary data on the web: searching the internet: strategies, the problem of quantity and lack of structure, validity and reliability, quality of data; web libraries, catalogues, repositories; online (social science) data archives, official statistics data repositories. 4. Integration of different approaches: integration (merging) of big data with traditional social science data (surveys, qualitative data), methodological issues in collecting and using big data (quality, sampling, evaluation), integration of (online) qualitative and quantitative methods. 5. Selected approaches to data analysis, organization, management in digital environment: computer-assisted analysis of qualitative data, analysis of web metrics, analysis and visualisation of geolocation data. 6. Selected aspects of modern research process, assisted by digital technologies: e-science concept, digital technologies and data fusion, collection, analysis, reporting and integration of data in business processes, ethical and legal issues in using digital technologies in research process.

Intended learning outcomes:

Knowledge on structure, organization and type of data on the web and broader digital environment. Knowledge of data collection methods using digital technologies. Understanding advantages and limitations of the usage of digital technologies in social science research in practice. Usage of methods to collect primary and secondary data using digital technologies in actual (social) science research projects. Critically evaluate the usage of digital technologies in collection and analysis of social science data.

Learning and teaching methods:

Lectures, seminars, project work, individual and group work. E-learning.

Assessment

30 % Writen exam 70 % shorter home assignments or project assignement

Obligatory literature

Additional literature