Гилев Алексей Владимирович
Горгадзе Алексей Алексеевич
Турченко Михаил Сергеевич
Чапковский Филипп Игоревич
- Develop students' skills in formulating and justifying their research designs
- Master students' capabilities to develop and implement various research strategies
- Acquaint students with the selected method of research and their applications
- Able to formulate and present results of the research based on the selected method
- Able to interpret the results of the research using the selected method, highlighting its strengths and weaknesses
- Able to perform data collection and preparation for the analysis using the selected research method
- Able to perform empirical data analysis using the selected method
- Able to select and overview scientific literature to characterize a research field
- Compares and characterizes the basic research strategies
- Defines the basic principles and peculiarities of the selected research method
- Develops a research design and research strategy using the selected method
- Enumerates and characterizes the basic elements of research
- Formulates and justifies the research design and its key components
- Topic 1.1. Introduction to the CourseContent and peculiarities of the course. Refreshing the knowledge from year 1 and 2
- Topic 1.2. Elaborating a Research QuestionLiterature Review. Developing a Research Question. Research Design Elements and Research Strategies
- Topic 1.3. Evaluating a Research DesignDeveloping an introductory part of the research
- Topic 2.12.1 Introduction: The logic of sets according to Ch. Ragin. Necessary and sufficient conditions. Advantages and assumptions of QCA.; 2.2 Introduction to the Social Network Analysis: Basic terms and principles of the SNA; 2.3 Introduction. Features of text data: Lexicon. Frequency analysis of texts. Zipf's law. Data preparation. Morphological analysis. Stamping and Lemmatization.
- Topic 2.22.1 Boolean Algebra in Comparative Analysis: Boolean algebra, conjunction, disjunction procedures, logical OR and AND. Truth tables. Variables’ presentation.; 2.2 Network theory and applications 1: Personal networks and social capital; elites and power structure; 2.3 Feature engineering: Regular expressions & Text classification: Feature engineering. Prediction of attributes by words and features. Algorithms of classification. Naive Bayes. Regression Models.
- Topic 2.3"2.1 The Power of QCA in Political Science (classification, hypotheses): Classifications, hypotheses. Minimization procedures. Causal mechanisms. Using software for QCA. Calibration, the necessity of exogenous criteria. Choosing variables. Working with reminders, controversies and missings; 2.2 Network theory and applications 2: Political networks, social movements and collective action; networks in research, digital networks and social media; 2.3 Identifying Characteristic Words: Loglikelihood, Collocations & PMI, Sentiment analysis: Compares the appearance of a word indifferent collections. LogLikelihood: G-squared. Effect size: Log odds ratio; Co-occurrence. N-grams. Methods for detecting collocations. ―bag of words‖ model. Collocation measure. logDice. Pointwise Mutual Information Of Pairs Of Items (PMI); Automatic text sentiment analysis. Extracting opinions and assessments. Analysis of reviews as a classification problem. Dictionaries of evaluation vocabulary."
- Topic 2.42.1 Testing Hypotheses. A Variety of QCA Techniques: MVQSA, FSQCA.: Calibrating and recalibrating data. Testing Hypotheses. The Variety of Techniques: MVQSA, FSQCA; working with non-dichotomous variables; 2.2 Network in bibliometrics: Using SNA for bibligraphic search and analysis; 2.3 Semantic Networks (LSA): Semantic Networks. Latent semantic analysis (LSA).
- Topic 2.52.1 Fuzzy Sets: Working with fuzzy sets. Measures of Consistency and Coverage. Criticism.; 2.2 Network terminology and metrics - 1: Describing, visualising and analysing networks; 2.3 Topic Modeling (LDA): The operationalization of the "topic" concept as a probability distribution vocabulary. Latent Dirichlet allocation (LDA).
- Topic 2.6Presentation of the students' projects
- Topic 3.1 Causal inference and experimental designThe problem of establishing causality in social science. Rubin causal model. External and internal validity.
- Topic 3.2. Designing online and lab experimentsExperiments on voting; Cooperation games. Measuring trust and reciprocity experimentally. Studying crime in the lab. Anti-social behavior
- Topic 3.3. Introduction to experiments in RObtaining data from R and some elementary data exploration.
- Topic 3.4. Conducting experiments online and in the lab: practical knowledgeRandomization between treatments, group composition, between- vs. within-subject designs, belief elicitation
- Topic 3.5. Experimental approaches to data analysisComparing group means. ANOVA, ANCOVA. Analysis of factorial between-subjects design. ANOVA for within-subjects design.
- In-class participationParticipation in seminar workshops and contribution to seminar discussions, based on the mandatory readings.
- ProjectA written group assignment (can be done individually upon request from a student), evaluating students’ progress during the second part of the course. The content of the assignment depends on which track from part 2 is chosen.
- Final TestFinal examination questions cover the materials of the course and include closed (multiple choice) and open questions with a short answer.
- Interim assessment (4 module)The cumulative grade is calculated as follows: 65 per cent of in-class participation and 35 per cent of project. The final mark is calculated as follows: 65 per cent of cumulative grade and 35 per cent of the final test grade
- Approaches and methodologies in the social sciences : a pluralist perspective / ed. by Donatella della Porta . (2008). Cambridge [u.a.]: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.283822104
- Chen, D. L., Schonger, M., & Wickens, C. (2016). oTree—An open-source platform for laboratory, online, and field experiments. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E2EC659C
- Collier, D., & Adcock, R. (2001). Measurement Validity: A Shared Standard for Qualitative and Quantitative Research. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4759268E
- Fu, X., Luo, J.-D., & Boos, M. (2017). Social Network Analysis : Interdisciplinary Approaches and Case Studies. Boca Raton, FL: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1499393
- Geddes, B. (DE-588)171415787, (DE-576)132211866. (2003). Paradigms and sand castles : theory building and research design in comparative politics / Barbara Geddes. Ann Arbor, Mich.: University of Michigan Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.104638176
- Jason Barabas. (2004). How Deliberation Affects Policy Opinions. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.A0C9A969
- Mahoney, J. (2003). Long-Run Development and the Legacy of Colonialism in Spanish America. American Journal of Sociology, 109(1), 50–106. https://doi.org/10.1086/378454
- 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
- Ragin, C. C. (2013). The Comparative Method : Moving Beyond Qualitative and Quantitative Strategies. Oakland, California: University of California Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=784602
- Silge, J., & Robinson, D. (2017). Text Mining with R : A Tidy Approach (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1533983
- Simon Gächter, & Elke Renner. (2010). The effects of (incentivized) belief elicitation in public goods experiments. Experimental Economics, (3), 364. https://doi.org/10.1007/s10683-010-9246-4
- 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
- Huber, L. M., & Schneider, H. L. (2008). Social Networks : Development, Evaluation and Influence. New York: Nova Science Publishers, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=311265
- 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
- William Q Judge, Stav Fainshmidt, & J Lee Brown III. (2014). Which model of capitalism best delivers both wealth and equality? Journal of International Business Studies, (4), 363. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.pal.jintbs.v45y2014i4p363.386