Secondary data exploration 24-SODU-EDZ

- Data, information, knowledge, big data - basic concepts defining the boundaries between different types and classes of data and relations between them;
- Critical analysis of existing studies, analysis of secondary data of different types, most common pitfalls
- Search engines and other tools for obtaining data on the Internet: history, cultural context, mechanism of operation, using techniques
- Exploration of existing studies and literature: database and search engines
- Literature and bibliography management and sharing, Zotero
- Secondary data mining: data archives and repositories, statistical data banks, and other sources
- Web content mining and algorithmic retrieval
- Social media: the exploration of networks of links between participants and the messages they create
- Social media: content extraction and data mining
- Computer software to support data mining and exploration
- Data visualization techniques and tools

Module learning aims

1. to teach what the secondary data is and what are the contemporary contexts and opportunities of its collection and use 2. to develop skills to work with different types of data from different sources: search, critically evaluate, select, collect, organize and analyze 3. to teach practical skills in the field of computer programs designed for assisting the researcher in the steps described above

Information on where to find course materials

Materials are made available via Microsoft Teams to class participants at the first meeting

Major

sociology

Methods of teaching for learning outcomes achievement

- Lecture with a multimedia presentation of selected issues - Problem-based learning - Solving tasks and exercises - Individual consultations and assistance

Student workload (ECTS credits)

(in Polish) 3

Cycle of studies

2nd cycle

Module type

elective

Year of studies (where relevant)

Year 1

Pre-requisites in terms of knowledge, skills and social competences

Basic knowledge of social research methods and basic computer skills

Course coordinators

Filip Schmidt

Learning outcomes

Upon completion of coursework and confirmation of achieved effects of learning
the student:
- is able to select appropriate web tools for obtaining specific data
- knows basic types of data and techniques of its processing
- distinguishes basic mechanisms of data usage in sociology
- creates scenarios for searching and processing data
- is able to use IT and socio-cultural knowledge in a complementary way when working with data
- creatively explores the secondary data
- is aware of the usefulness and open to the necessity of using knowledge from other science disciplines
- is able to adapt the acquired knowledge in the course of empirical research
- is sensitive to a critical and reliable selection of information
- is able to act in accordance with legal requirements and ethical rules regarding work with collected data

Assessment criteria

1. Homework assignments, consisting of the submission of self-analyzed examples or the results of an independent exercise
2.
a. Completion of computer-based tasks involving the extraction, exploration, and critical analysis of data using the indicated techniques and tools learned in class, with commentary
OR
b. Preparing and carrying out their own data analysis supported by the tools and programs learned in class, then submitting the results form of a text file or multimedia presentation, along with a description of the assumptions, activities performed, and interpretation of the results and attached data files, followed by a discussion on the work done during the last class, demonstrating the ability to work independently with the file.

Bibliography

• Analiza danych zastanych: przewodnik dla studentów, red. Marta Makowska, Wydawnictwo Naukowe Scholar, Warszawa 2013.
• Halavais Alexander, Wyszukiwarki internetowe a społeczeństwo, Wydawnictwo Naukowe PWN, Warszawa 2012.
• Levene Mark, An Introduction to Search Engines and Web Navigation, Wiley, New Jersey 2010
• Fronczak Agata i Fronczak Piotr, Świat sieci złożonych: od fizyki do Internetu, Wydawnictwo Naukowe PWN, Warszawa 2009.
• Gibbs Graham, Analizowanie danych jakościowych, Wydawnictwo Naukowe PWN, Warszawa 2011.
• Niedbalski Jakub, Komputerowe wspomaganie analizy danych jakościowych (CAQDAS) w projektowaniu i prowadzeniu badań, „Nauka i szkolnictwo wyższe”, 1/2013, s.185-202.
• SAGE handbook of social media research methods, red. Luke Sloan i Anabel Quan-Haase, SAGE, Londyn i in 2017.
• Udo Kuckartz, Stefan Rädiker, Analyzing Qualitative Data with MAXQDA: Text, Audio, and Video, 2019 (ebook)
• https://help.parsehub.com/hc/en-us/categories/202632177-Getting-Started
• https://gephi.org/users/
• https://www.maxqda.com/help-mx22/welcome

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 24-SODU-EDZ in USOSweb