FF:RLMgB553 Contemporary Religion in Data

RLMgB553 Contemporary Religion in Data

Faculty of Arts
Autumn 2022

Extent and Intensity

1/1/0. 5 credit(s). Type of Completion: k (colloquium).

Teacher(s)

Guaranteed by

Mgr. Martin Lang, Ph.D.
Department for the Study of Religions – Faculty of Arts
Contact Person: Mgr. Matouš Vencálek
Supplier department: Department for the Study of Religions – Faculty of Arts

Timetable

each even Monday 16:00–17:40 K12 nerezervovat, each even Monday 18:00–19:40 K12 nerezervovat

Course Enrolment Limitations

The course is offered to students of any study field.

Course objectives

The course aims to provide a basic overview of how to work with quantitative data in the study of religion and how to present this data in a comprehensible way. The course first introduces general principles and good practices in working with quantitative data (data collection methods, statistical inference, visualization of results) so that students are able to critically evaluate the results of quantitative studies, whether scientific or presented in the media. Furthermore, the course combines lectures with seminar instruction. Each lecture is followed by a case study illustrating a particular type of data that can be worked with in the study of religion (e.g., survey data or social network data). This study is first discussed and its methods are further implemented in practical demonstrations together with the students in the seminar. During the course, students also work on their own research questions and process data suitable for answering them. No prior knowledge of statistics or data visualization is required - statistical inference will be discussed in the basic outlines necessary to understand publicly presented statistical inference. Similarly, knowledge of statistical data analysis and results visualization software is not required - the basics of using these software will be discussed in the seminars.

Learning outcomes

Graduates of the course will gain:

- data literacy and the ability to critically evaluate statistical/causal inference from presented data (e.g. how religious affiliation affects voting preferences in the Czech Republic);
- an overview of available data regarding current religious situations, beliefs, behaviors, and attitudes;
- the ability to find and retrieve relevant data from publicly available databases, repositories, and social networks for research on religion ("data scraping");
- basic skills in data processing ("data wrangling"), data analysis, and presentation of results ("data visualization").

Syllabus

1) Introduction, organizational notes.
2) Understanding quantitative data and statistical inference (lecture).
3) Basic data processing tools (seminar).
4) Visualizing quantitative data (lecture).
5) Visualizing quantitative data (seminar).
6) Survey methods (lecture).
7) Survey methods (seminar).
8) Working with sociological data (lecture).
9) Working with sociological data (seminar).
10) Working with databases (lecture).
11) Working with databases (seminar).
12) Working with social networks (lecture).
13) Working with social networks (seminar).
14) Discussion of preliminary proposals for student project topics (seminar).

Literature

recommended literature

(Third edition, international student edition). W. W. Norton & company. Nordmann E, McAleer P, Toivo W, Paterson H, DeBruine LM. Data Visualization Using R for Researchers Who Do Not Use R. Advances in Methods and Practices in Psychological Science. Apri
Ritter, R. S., Preston, J. L., & Hernandez, I. (2014). Happy Tweets: Christians Are Happier, More Socially Connected, and Less Analytical Than Atheists on Twitter. Social Psychological and Personality Science, 5(2), 243–249. https://doi.org/10.1177/19485
Allan Visochek. (2017). Practical Data Wrangling. Packt Publishing.
Shariff, A. F., & Rhemtulla, M. (2012). Divergent effects of beliefs in heaven and hell on national crime rates. PLoS ONE, 7(6), 1–5. https://doi.org/10.1371/journal.pone.0039048
Bentzen, J. S. (2020). In crisis, we pray: Religiosity and the COVID-19 pandemic. Economic Behavior and Organization, 192(January), 541–583.
Jackson, J. C., Caluori, N., Gray, K., & Gelfand, M. (2021). The new science of religious change. American Psychologist, 76(6), 838.
Rosling, H., Rosling, O., & Rosling Rönnlund, A. (2019). Factfulness : ten reasons we’re wrong about the world - and why things are better than you think (Paperback edition). Sceptre.
Sosis, R., Kress, H. C., & Boster, J. S. (2007). Scars for war: Evaluating alternative signaling explanations for cross-cultural variance in ritual costs. Evolution and Human Behavior, 28(4), 234–247. https://doi.org/10.1016/j.evolhumbehav.2007.02.007
BERGSTROM, Carl T. and Jevin D. WEST. Calling bullshit : the art of skepticism in a data-driven world. First edition. New York: Random House, 2020, xvi, 318. ISBN 9780525509189. info
MORLING, Beth. Research methods in psychology : evaluating a world of information. Third edition, international. New York: W. W. Norton & company, 2018, xxviii, 62. ISBN 9780393643602. info

Teaching methods

A combination of lectures with discussion and practical seminars in which active participation of students will be required.

Assessment methods

A written term paper (4-6 thousand words) that illustrates the ability to apply newly acquired skills (quantitative data processing and presentation, including tables and graphs).

FF:RLMgB553 Contemporary Religion in Data - Course Information

RLMgB553 Contemporary Religion in Data

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