Магистратура 2020/2021

Методология и методы исследований в социологии

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»

Лучший по критерию «Новизна полученных знаний»

Статус: Курс обязательный (Сравнительные социальные исследования / Comparative Social Research)

Направление: 39.04.01. Социология

Кто читает: Департамент социологии

Где читается: Факультет социальных наук

Когда читается: 1-й курс, 2 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Алмакаева Анна Михайловна

Прогр. обучения: Сравнительные социальные исследования

Язык: английский

Кредиты: 6

Контактные часы: 52

Course Syllabus

Abstract

The course aims to provide students with understanding of key concepts and methods of modern statistical data analysis. It gives an overview and practice of the skills necessary for conducting independent research with quantitative survey data, using R software. The course also puts these skills into the broader academic context by reviewing how statistics are used in published scientific journal articles.

Learning Objectives

• Provide students with understanding of key concepts and methods of modern statistical data analysis

Expected Learning Outcomes

• know the key concepts of statistics
• know the main methods and techniques of statistical data analysis
• know the main procedures of data transformation and data analysis using R
• to be able to choose correct statistical methods and procedures according to the research questions
• to be able to interpret and present the results of data analysis in oral and written form
• to be able to analyze secondary data at the level required for an MA thesis
• Have skills in using R Studio for statistical data analysis
• Have skills in interpreting results and writing research papers
• Have skills in the evaluation of the quality of published research papers

Course Contents

• Statistical inference in social sciences
  Descriptive and inferential statistics. Random and non-random sampling, the case of “Literary Digest”. Normal distribution and z scores. The logic of statistical inference and Central limit theorem. Confidence level and confidence intervals.
• T-tests, One-way ANOVA
  Steps of hypothesis testing, Two-tailed/one tailed assumptions, Type I/Type II errors. One sample t-test. Independent samples test, paired samples test. T-distribution for small samples. ANOVA (Analysis of variance), F-distribution, post hoc tests. Equality of variance tests and tests for normal distribution.
• Non-parametric analogues of T-tests and one-way ANOVA
  Differences between parametric and non-parametric methods. Signed-Rank Test, Mann-Whitney U-test, Wilcoxon signed rank test, Kruskal-Wallis test, post hoc tests for non-parametric methods.
• Pearson's chi-square for contingency tables
  Properties of Pearson's chi-square, independence of two variables, expected and observed frequencies. Chi-square distribution, Yates correction for 2x2 tables. Limitations of the chi-square test.
• Correlations
  Association between two variables, types of dependence. Pearson`s r for metric scales, Pearson`s R for metric scales and test of significance. Spearman`s rank correlation coefficient, Kendall`s rank correlation coefficient. Partial correlation.
• Linear regression
  Bivariate regression, quality of the bivariate model. Multiple regression and quality of multiple regression model. Interpretation of regression coefficients, the significance of regression coefficients. Standardization and interpretation of standardized coefficients.
• Regression diagnostics: heteroscedasticity, multicollinearity, non-linearity
  Testing regression assumptions: linearity, multicollinearity, heteroscedasticity, normal distribution of errors, autocorrelation, outliers.
• Categorical independent variables in linear regression and interaction effects
  Regression with dummy variables, interpretation of coefficients. Regression with categorical and nominal independent variables. Interaction effects, interpretation of interaction effects. Limitations of traditional regression tables for interpretation of interactions effects
• Binary logistic regression
  Key concept of binary logistic regression: probability, odds, odds ratio, logit. Interpretation of coefficients. Quality of a model.

Assessment Elements

• Reading published research
• Essay
• Home assignment
  Grading criteria: 1) The correct method of data analysis. 2) Correct R function. 3) Correct interpretation.
• Exam
  The task of the first reexam is similar to the first exam. Students will have 3 hours to do it. The second reexam is similar to the first one. The weight of the reexam in the final grade is 0.3.
• Reading published research
• Essay
• Home assignment
  Grading criteria: 1) The correct method of data analysis. 2) Correct R function. 3) Correct interpretation.
• Exam
  The task of the first reexam is similar to the first exam. Students will have 3 hours to do it. The second reexam is similar to the first one. The weight of the reexam in the final grade is 0.3.

Interim Assessment

• Interim assessment (2 module)
  0.3 * Essay + 0.3 * Exam + 0.3 * Home assignment + 0.1 * Reading published research

Bibliography

Recommended Core Bibliography

• Handbook of univariate and multivariate data analysis and interpretation whith SPSS, Ho, R., 2006

Recommended Additional Bibliography

• Cramer D. Advanced Quantitative Data Analysis. 2003.
• Gelman, A. B., & Hill, J. (2015). Data analysis using regression and multilevel/hierarchical models. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4E4FBAE7
• Mirkin, B. Core concepts in data analysis: summarization, correlation and visualization. – Springer Science & Business Media, 2011. – 388 pp.
• Upton, G. J. G. (2016). Categorical Data Analysis by Example. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1402878
• Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.
• Willem Mertens, Amedeo Pugliese, & Jan Recker. (2017). Quantitative Data Analysis. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.3.319.42700.3