Analytics in Arts and Culture
- Develop students' holistic understanding of the methodology of scientific and analytical research.
- Develop students’ skills in the use of research tools, both for planning, preparing and conducting research projects in the framework of writing a term paper and master's thesis, and for performing and evaluating research and analytical work.
- Master students' capabilities to develop and implement various research strategies.
- Able to define the basic principles and peculiarities of the research and analytical method
- Able to develop a research design and research strategy
- Able to organize, conduct and analyze observation for research
- Able to create a questionnaire for a survey based on a theoretical concept, organize a representative sample, conduct a survey, encode and analyze the received data
- Able to conduct descriptive statistics on quantitative data, apply basic statistical methods and interpret results of analysis
- Able to formulate questions to identify the network structure, to apply methods of social network analysis to empirical data and develop research design within social network analysis framework
- Able to perform data collection and preparation for the analysis using text mining methods
- Able to create interview guides, conduct and analyze interviews, transcribe audio recordings, interpret results
- Able to apply and interpret advanced statistical methods to quantitative empirical data
- Introduction: Research Methods & MethodologyDifferences in methods & methodology. Basic research methods in management. Data collection and analysis methods. Research gap. Purpose and hypothesis. Ethical standards in research. Organization of scientific and analytical research. Discussion of cases. Investment fund model.
- Observation method in researchesOrganization of observation in field research. Geertz's Thick Description. Types of observation. Participant observation. Observational diaries. Data decision.
- Questionnaires and SurveysDesign and structure of questionnaires and question types. Sampling. Data encoding and data cleaning.
- Descriptive statisticsBasic methods of statistics in R and SPSS. Correlation. Regression analysis. Comparison of means (t-test, anova). Chi-squared test. Clustering methods.
- Social network analysisBasic concepts of social network analysis. Nodes, edges. Matrix and graph. Observable and perceptive networks. Centrality and centralization. In-degree and out-degree, closeness, betweenness, eigenvector centrality. Density and geodesic distance. Diameter. Reciprocity and transitivity. Node’s attributes, groups and homophily.
- Text MiningDigital Footprint Data. Data Collection: structural data and SNS data (Data Miner, Popsters). Features of text data.: Lexicon. Frequency analysis of texts. Zipf's law. Data preparation. Morphological analysis. Stamping and Lemmatization. Feature engineering: Regular expressions & Text classification.: Feature engineering. Prediction of attributes by words and features. Algorithms of classification. Naive Bayes. Regression Models. Identifying Characteristic Words: Log-likelihood.: Compares the appearance of a word indifferent collections. Collocations & PMI: Co-occurrence. N-grams. Methods for detecting collocations. “bag of words” model. Collocation measure. logDice. Semantic Networks. Topic Modeling (LDA): The operationalization of the "topic" concept as a probability distribution vocabulary. Latent Dirichlet allocation (LDA).
- Qualitative data collection methodsTypes of interviews. Formulation of research objectives and drawing up a guide. Preparing for data collection: gaining access to respondents, preparing materials for interviews. Drawing up a guide. Recording quality data. Analysis and coding of qualitative data: method, technique, software.
- Advanced statisticsLinear regression. Factor analysis. Bivariate pierced the model. Multivariate model. Structural models.
- In-class participationParticipation in seminar workshops and contribution to seminar discussions, based on the mandatory readings. In-class participation is evaluated throughout the whole course.
- Midterm tests
- Practical tasksConducting observations, conducting a survey of organizations visitors, conducting a survey of employees, interviewing managers of organizations, collecting text data.
- HomeworksReading articles, preparing guides and questionnaires, searching for materials, laboratory work.
- Interim assessment (4 module)0.2 * Examination + 0.15 * Homeworks + 0.2 * In-class participation + 0.2 * Midterm tests + 0.25 * Practical tasks
- Bamman, D., Eisenstein, J., & Schnoebelen, T. (2014). Gender identity and lexical variation in social media[The resear]. Journal of Sociolinguistics, 18(2), 135–160. https://doi.org/10.1111/josl.12080
- Introducing research methodology: A beginner's guide to doing a research project, Flick, U., 2015
- Kothari, C. R. (2004). Research Methodology : Methods & Techniques (Vol. 2nd rev. ed). New Delhi: New Age International. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277465
- Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
- Nguyen, D., Gravel, R., Trieschnigg, D., & Meder, T. (2013). “How old do you think I am?” A study of language and age in Twitter. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E50BF78
- Seetaram, N., Gill, A., & Dwyer, L. (2012). Handbook of Research Methods in Tourism : Quantitative and Qualitative Approaches. Cheltenham, UK: Edward Elgar Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=477866
- Ted Dunning. (1993). Accurate methods for the statistics of surprise and coincidence. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.421C83DD
- Gorgadze Aleksey, & Kolycheva Alina. (n.d.). Mapping Ideas: Semantic Analysis of “Postnauka” Materials. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsclk&AN=edsclk.https%3a%2f%2fcyberleninka.ru%2farticle%2fn%2fmapping-ideas-semantic-analysis-of-postnauka-materials
- Munzert, S. (2014). Automated Data Collection with R : A Practical Guide to Web Scraping and Text Mining. HobokenChichester, West Sussex, United Kingdom: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=878670
- R in action: Data analysis and graphics with R, Kabacoff, R.I., 2015