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Бакалаврская программа «Социология и социальная информатика»

Data Analysis in Sociology

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 3, 4 модуль

Course Syllabus

Abstract

This course lasts for three years. The 1st year aims at beginners. This year starts from introductory topics (variable types, hypothesis testing, descriptive statistics) to working with some methods (chi-square, t-test, nonparametric statistics, one-way ANOVA, and linear regression). The course covers the building blocks of quantitative data analysis with the aim to train students to be informed producers and consumers of quantitative research. The applied part introduces working in R (RStudio) for calculations and reporting. Prerequisites: understanding key ideas in sampling theory and probabilities. This course is the starting point for social science and humanities students interested in pursuing training in advanced methods of data analysis or planning to use quantitative methods in their own research.
Learning Objectives

Learning Objectives

  • develop skills necessary to solve typical data analysis problems on social data in the R software environment
  • develop skills necessary to solve typical data analysis problems on social data in the R software environment
Expected Learning Outcomes

Expected Learning Outcomes

  • Choose appropriate methods and techniques for certain types of variables and certain aims of the analysis
  • Choose appropriate methods and techniques for certain types of variables and certain aims of the analysis
  • Conduct statistical analyses in RStudio
  • Conduct statistical analyses in RStudio
  • Create analytical reports describing all the stages of analysis and interpreting its results
  • Create analytical reports describing all the stages of analysis and interpreting its results
  • Give meaningful interpretation of statistical results: regression coefficients, tables, plots and diagrams (produced in R)
  • Give meaningful interpretation of statistical results: regression coefficients, tables, plots and diagrams (produced in R)
  • Perform data transformations
  • Perform data transformations
  • Represent graphically the results of the statistical analyses
  • Represent graphically the results of the statistical analyses
Course Contents

Course Contents

  • Central tendency measures
  • Central tendency measures
  • Chi-square
  • Chi-square
  • Two means comparison
  • Two means comparison
  • One-way ANOVA
  • One-way ANOVA
  • Linear regression
  • Linear regression
  • Linear regression with multiple predictors
  • Linear regression with multiple predictors
Assessment Elements

Assessment Elements

  • non-blocking Projects
    Late submissions are not considered (try us). If you are ill during the project submission, present a medical certificate to get the formula adjusted for you. If you miss more than one project, there might be a makeup assignment. When you submit a project in MS Teams, you must click on the "Turn in" button to complete the submission. All projects are, first, posted to the dedicated channel where they are peer-reviewed, and submitted in the Assignments section by each contributing student. If you have any questions about the project, sign up for a consultation.
  • non-blocking Projects
    Late submissions are not considered (try us). If you are ill during the project submission, present a medical certificate to get the formula adjusted for you. If you miss more than one project, there might be a makeup assignment. When you submit a project in MS Teams, you must click on the "Turn in" button to complete the submission. All projects are, first, posted to the dedicated channel where they are peer-reviewed, and submitted in the Assignments section by each contributing student. If you have any questions about the project, sign up for a consultation.
  • non-blocking In-class activity
  • non-blocking In-class activity
  • non-blocking Exam
  • non-blocking Exam
  • non-blocking Short tests
  • non-blocking Short tests
  • non-blocking MOOC completion
  • non-blocking MOOC completion
  • non-blocking Mid-Term Test
  • non-blocking Mid-Term Test
Interim Assessment

Interim Assessment

  • 2021/2022 3rd module
  • 2021/2022 3rd module
  • 2021/2022 4th module
    0.1 * Exam + 0.1 * In-class activity + 0.15 * MOOC completion + 0.2 * Mid-Term Test + 0.4 * Projects + 0.05 * Short tests
  • 2021/2022 4th module
    0.1 * Exam + 0.1 * In-class activity + 0.15 * MOOC completion + 0.2 * Mid-Term Test + 0.4 * Projects + 0.05 * Short tests
  • 2022/2023 3rd module
  • 2022/2023 3rd module
  • 2022/2023 4th module
  • 2022/2023 4th module
  • 2023/2024 3rd module
  • 2023/2024 3rd module
Bibliography

Bibliography

Recommended Core Bibliography

  • Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
  • Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064

Recommended Additional Bibliography

  • Agresti, A., & Finlay, B. (2014). Statistical Methods for the Social Sciences: Pearson New International Edition (Vol. Pearson new international ed., 4. ed). Harlow England: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418314
  • Crawley, M. J. (2013). The R Book (Vol. Second Edition). Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=531630
  • Little, T. D. (2013). The Oxford Handbook of Quantitative Methods, Volume 1 : Foundations. Oxford University Press.
  • Little, T. D. (2013). The Oxford Handbook of Quantitative Methods. Oxford University Press.