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Regular version of the site

Qualitative and Quantitative Methods of Social Research

2021/2022
Academic Year
ENG
Instruction in English
10
ECTS credits
Course type:
Compulsory course
When:
1 year, 1-3 module

Instructors

Course Syllabus

Abstract

The course aims to help students to develop a deeper understanding of both qualitative and quantitative research methods and become proficient in employing them. It will introduce students to the key principles and practice of social research. The course will discuss different qualitative and quantitative research methods and how they may be used in research.
Learning Objectives

Learning Objectives

  • To provide students with knowledge of the key methods of qualitative analysis
  • To help students to better understand how to design and execute qualitative research
  • To equip students with practical skills in collecting, analysing, and interpreting qualitative data
Expected Learning Outcomes

Expected Learning Outcomes

  • To get general understanding of qualitative research conceptualization
  • Learn new research methods and approaches on his/her own, to evolve professionally
  • Ability to analyze and improve familiar research methods and professional skills
  • Able to carry out research and other professional activities in changing environment
  • Ability to analyze political, economic and sociological data using different qualitative and quantitative methods.
  • Able to formulate their own research questions and identify methodological units.
  • Be able to work with various data structures and know the ways of collecting empirical data for quantitative analysis
  • Learn how to collect and manage survey data, extract sub-samples, split and merge datasets, and produce long and short data formats
  • Learn how to explore variables using basic statistics and apply basic commands of SPPSS software for computing and calculating basic statistics
  • Know principles of normal and non-normal distribution and obtain skills on how to handle data accordingly
  • Know how to distinguish the two basic forms of statistical relationship, correlation and causation
  • Know how to navigate amidst different types of regressions and correctly match a regression model to the data they use
  • Able to to build up bivariate and multivariate linear regressions and estimate these models using ordinary least squares (OLS) estimator
  • Able to model nonlinear relationships between variables
  • Learn how to apply parametrical analysis of data in SPSS, STATA and R (optional).
Course Contents

Course Contents

  • In-depth interviewing
    In this lecture and seminar, we examine in greater depth what is called in-depth or semi-structured interviewing. We examine how to conduct an interview – the social form of interviewing – and the aims and goals of interviewing. It also considers your role in the interview, and how to manage the flow of the interview. Finally, it considers the end of the interview, how to find interviewees and how the pandemic might affect recruitment and conducting interviews.
  • What your thesis is not, and what can it hope to reveal. Conceptualising qualitative research and conducting interviews
    In this lecture and seminar, we consider what your thesis is and what it is not. We then explore how to conceptualise qualitative research, and how the knowledge provided by it differs from that derived from quantitative research. We then introduce qualitative interviewing as a research method. Finally, we consider the kinds of changes that might be made to this kind of data collection during the coronavirus pandemic.
  • Focus Groups
    In this session we introduce and examine focus groups and the types of focus groups that are possible in qualitative research. We examine the similarities with interviewing, including the role of the interviewer and the management of dialogue and exchange among participants. Finally, we examine how interviews and focus groups can be combined and how to analyse the data.
  • Participant Observation
    In this lecture and seminar we examine participant observation. We explore how participant observation differs from other qualitative research methods, the epistemology that undergirds its use in the social sciences, and some of the contemporary uses of participant observation and its limitations.
  • Qualitative research during a pandemic
    The corona virus pandemic creates practical challenges for using qualitative methods. In these sessions we examine a range of alternative methods for collecting qualitative data during this time, ranging from digital ethnography to social media analysis, we explore more dependable ways of collecting data during a challenging time.
  • Content analysis
    Introduction to the specific set of research tools. Written and oral texts as source of information for social research. Why is it necessary to look for the implicit information? What makes the results relevant and valid? How to collect the corpus, what data can be obtained within different approaches, what research questions can be answered, what conclusions can be drawn. Limitations and techniques of content analysis. Questionable quality of automated content analysis. How could it help in your research? What questions cannot be answered by content analysis.
  • Discourse as an object of study
    Theories of discourse, which contribute to social research. What exactly might constitute the corpus, if discourse is endless by its nature? Once you have a corpus, is it necessary to start with a content analysis? How to structure your corpus in accordance with your questions? Where should you search answers? Different types of discourse analysis. Political discourse theory. Critical discourse analysis (CDA). Historical discourse analysis (HDA) in CDA. How to conduct a research within these frameworks if you suppose it to be necessary.
  • Linguistic criticism: research techniques based on linguistic approach.
    Language is not neutral, so how to figure out the implications? Possible levels of description and expected research results.
  • Research Design and Research Methodology of a Quantitative Study
    Advantages and limitations of a quantitative study. Developing a typology of research questions, examining the structure of the research problem, show how a proper problem formulation incorporates both the research purpose of a study and clear identification of the unit of analysis. Students will practice in formulating their own research questions and identifying methodological units.
  • Macro- and Micro-Data. Introduction to Opensource Datasets
    Various data structures and ways of collecting empirical data for quantitative analysis. Differences between macro- and micro-statistics. Introduction to opensource statistical databanks and examining their questionnaires. Students will be acquainted with widely used survey data, i.e. World Value Survey (WVS), European Value Survey (EVS), European Social Survey (ESS), Eurobarometer and the Russian Longitudinal Monitoring Survey (RLMS-HSE). Students will learn how the datasets are stored, distributed, and structured; and what software to use to open data. A brief introduction to interface and functionality of the IBM Statistics (SPSS), STATA and R (optional).
  • Data Management. Scaling. Indicators
    Students will learn how to collect and manage survey data, extract sub-samples, split and merge datasets, and produce long and short data formats. Students will learn typology of scales (e.g. Likert scale, binary scale, etc.) and acquire skills on how to recode variables in SPSS. We will also consider recoding and rescaling techniques and learn how to build simple and composite social indicators for various purposes of social study.
  • Descriptive Analysis
    This segment encompasses an introduction to basic statistical terms, i.e. bivariate and multivariate distribution, central tendency and dispersion. Students will learn how to explore variables using basic statistics and apply basic commands of SPPSS software for computing and calculating basic statistics. They will learn how to design and describe visual diagrams. Students will be encouraged to work with the ESS database.
  • Exploring Distributions and Statistical Inference
    Students will learn principles of normal and non-normal distribution and obtain skills on how to handle data accordingly. This session introduces the idea of the Null Hypothesis (H0) and p-value. Students will learn how to pose the Null and test H0 using T-tests and ANOVA, Mann-Whitney, Kruskal-Wallis, Wilcoxon / Friedman tests (regarding the measurement level of the data). During the lab session, students will be encouraged to work with the ESS data. The session will focus on the interpretation output and presentation of the results obtained.
  • Statistical Relationship. Correlation
    During this session, students will learn how to distinguish the two basic forms of statistical relationship, correlation and causation. We will consider canonical ways to measure statistical relationships regarding the measurement of the variables of interest. Students will learn how to explore differences between groups using Chi-square test and contingency tables in SPSS. They will also learn how to perform correlation analysis with categorical (rank correlation coefficients) and interval (Pearson’s correlation coefficient) variables using SPSS software. We will study how to construct and interpret correlation matrices and learn the geometric representation of correlations.
  • Statistical relationship. Causation
    This session focuses on the problem of causation and the concept of parametrical analysis (regression) as a dominant instrument of the multivariate analysis. We will consider different types of regression analysis. Students will learn how to navigate amidst different types of regressions and correctly match a regression model to the data they use.
  • Linear Regression Models
    During this session, we will consider the basic assumption of the linear regression, BLUE. Students will learn how to build up bivariate and multivariate linear regressions and estimate these models using ordinary least squares (OLS) estimator. The session will include practical exercises focused on visualising and interpreting linear regression output.
  • Nonlinear Regression Models
    Students will learn how to model nonlinear relationships between variables. We will consider logit- and probit models. Students will learn how to build up bivariate and multivariate nonlinear regressions and estimate these models using maximum likelihood (ML) estimator. Students will learn how to compare regression models. Students will also learn how to visualise and interpret outputs of logistic regression analysis.
  • Introduction to the Classification Analysis
    During this session, we will consider non-parametrical analysis, i.e. multidimensional scaling, trait analysis, factor and cluster analysis, tree analysis etc. Students will learn how to these types of data analysis in SPSS, STATA and R (optional).
  • Advanced Topics of Quantitative Analysis
    During this session, we will consider some advanced topics. Students will choose one of the following topics: exploring heterogeneity (multilevel analysis), text analysis (formal analysis of texts), advanced classification analysis (latent class analysis, Bayesian clustering, random forests etc.). Advanced topics will be presented in R.
Assessment Elements

Assessment Elements

  • non-blocking Reflection paper (qualitative methods)
  • non-blocking Written assignment (qualitative methods)
  • non-blocking Presentation
  • non-blocking Quiz
  • non-blocking Homework
  • non-blocking Participation
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * Homework + 0.1 * Participation + 0.13 * Presentation + 0.2 * Quiz + 0.14 * Reflection paper (qualitative methods) + 0.13 * Written assignment (qualitative methods)
Bibliography

Bibliography

Recommended Core Bibliography

  • Berg, B. L. . (DE-588)140800271, (DE-576)165756063. (2009). Qualitative research methods for the social sciences / Bruce L. Berg. Allyn and Bacon.
  • Drisko, J. W., & Maschi, T. (2016). Content Analysis. New York, NY: Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1107420
  • Handbook of interview research context & method ed. Jaber F. Gubrium; James A. Holstein. (2002).
  • McNabb, D. E. . V. (DE-588)128628677, (DE-627)376934980, (DE-576)185381413, aut. (2021). Research methods for political science quantitative, qualitative and mixed methods approaches David E. McNabb.

Recommended Additional Bibliography

  • David Kaplan. (2004). The SAGE Handbook of Quantitative Methodology for the Social Sciences. SAGE Publications, Inc.
  • JeongHoon Min. (2019). NSCALE: Stata module to scale data. Statistical Software Components.