Кто читает:: Департамент социологии

Статус:: Курс обязательный

Когда читается:: 2-й курс, 3, 4 модуль

Преподаватели

Волченко Олеся Викторовна

Широканова Анна Александровна

Full Syllabus

Abstract

This course lasts for three years. The 1st year aims at beginners and serves to develop skills necessary to solve typical problems in analysing social data in R software environment. The course goes from introductory topics (variable types, hypothesis testing, descriptive statistics) to some statistics and methods (chi-square, t-test, nonparametric statistics, oneway ANOVA, and linear regression). The 2nd and 3rd years provide an intermediate-advanced statistical analysis for quantitative research in sociology. In the 2nd year, the course covers two main topics - factor analysis and statistical prediction, including linear regression and structural equation modelling. We also discuss key issues in statistical analysis, such as creating indices and identifying causality based on the results of the analysis. The 3rd year focuses on multivariate analysis of categorical data. It includes special types of prediction models (logistic regression), techniques of dimension reduction (correspondence analysis and multidimensional scaling) and classification (cluster analysis). The course covers the building blocks of quantitative data analysis with the goal of training students to be informed consumers and producers of quantitative research. This course is also the starting point for students interested in pursuing advanced methods training or planning to use quantitative methods in their own research.

Learning Objectives

The 1st year aims at beginners and serves to develop skills necessary to solve typical problems in analysing social data in R software environment. The course goes from introductory topics (variable types, hypothesis testing, descriptive statistics) to some statistics and methods (chi-square, t-test, nonparametric statistics, oneway ANOVA, and linear regression).

Expected Learning Outcomes

Student can formulate research goals, objectives and methods according to academic standards, can process the data from international sources; can assess the quality and analyze given samples of international research; can replicate the procedures from international studies; can carry out research projects in international teams
Students can carry out statistical analyses of a data set, propose hypotheses and choose the methods needed to reach the goals, interpret the results and assess the quality of proposed solutions. Students provide reasons for their choice of techniques, interpret the outputs correctly, and assess the quality of their own and others’ models
Student can set research goals, propose a research plan based on the results of previous research and social theory, carry out data analysis and report the results
Students can apply a theoretical framework to define hypotheses and explain the results of a study; can apply appropriate statistical models and generalize the results

Course Contents

Topic 1. Research hypotheses vs. statistical hypotheses. Variable types
The cycle of research. Data analysis as part of the research process. Goals of data analysis. Theory of sampling recap. Survey data, behavior data. Posing and testing hypotheses. Research hypotheses vs. statistical hypotheses testing. Directed and non-directed hypotheses. Dependent and independent variables. Variable scales: nominal, ordinal, continuous (interval and ratio). Descriptive statistics of a variable depending on its type. Getting to know R and RStudio.
Topic 2. Exploratory data analysis
Central tendency measures. Mean, median, mode. Standard normal distribution and its use. Z-scores. Moments of distributions. Distribution plots and reading them. Sources of bias in data. Interpretation of z-scores. Mean as a data model. Creating objects, types of objects, basic functions in R. Descriptive statistics in R. Tidy data. Plots for univariate and bivariate distributions. Histogram, bar plot, box plot, scatterplot, stacked bar plot.
Topic 3. Chi-square and association measures
Observed and expected frequencies. Measures of association for categorical variables. Reading and interpreting chi-square tests. Assumptions of chi-square. Independence. Standardized residuals. Odds ratio. Chi-square and other association measures in R.
Topic 4. Two means comparison
Independent and paired samples. Assumptions behind the t-test. Two-sample t-tests. Testing for normality and homogeneity of variance. Nonparametric tests for two samples. Reading and interpreting means comparison. Confidence intervals. Weights in surveys. Post-stratification weights and design weights. Means comparison in R.
Topic 5. One-way ANOVA
One-way analysis of variance (ANOVA). Difference from association measures and t-test. Assumptions and usage of ANOVA. Between-group and within-group variance, their ratio. Planned and non-planned comparisons; corrections. Post hoc comparisons for equal and unequal variances. Reading and interpreting ANOVA. Plots of post hoc tests. Non-parametric tests for multiple comparisons. One-way ANOVA in R. Presenting the results of ANOVA. Getting to know RMarkdown: reports and slide shows.
Topic 6. Correlation and linear regression
Idea of correlations. Pearson’s product-moment correlation. Research problems for correlational analysis. Correlation coefficients for different types of data. Correlation matrices. ANOVA, correlation, and regression as linear models. Building a linear regression. Ordinary least squares. Fitting the regression line. Assumptions behind linear regression. Reading and interpreting regression coefficients. Goodness of fit measures. Presenting and interpreting a linear regression. Categorical predictors in a linear regression. Dummy-coding. Linear regression in R. Plotting linear regressions in R (case studies).
Topic 7. Multiple linear regression
Difference between simple and multiple linear regression. The concept of interaction effects for categorical by categorical, categorical by continuous, and continuous by continuous variables. Multicollinearity. Centering. Model selection in multiple regression. Reading and interpreting interaction models in a linear regression. Testing for interactions in R. Graphs for interaction effects. Reporting and interpreting a linear regression with interactions in R.

Assessment Elements

Project
Students create teams of 2-3 and work together on their project during the whole course, submitting and peer-reviewing them by each computer lab. Final projects are submitted in full and presented in the classroom. Each group selects one country from the European Social Survey, then picks the topic of interest within the scope of available survey questions (e.g. Health, Democracy, Religion, etc.) and performs all the tests covered in class on these data. One day before each computer lab, the due piece of work is to be submitted and blindly peerreviewed by two other groups in LMS. The instructors would assign reviewers, while students might not know who would be their reviewers next time. Final projects are presented in two steps. At the first stage, the group submits the code with interpretations. After this, they present the findings and procedures in class. Students are expected to choose and perform correctly the ways to analyse and interpret the data, as well as to demonstrate their knowledge and skills in presenting these results to the audience. Individual contribution of each student is graded. Projects themselves should be submitted as scripts or RMarkdown objects; in-class presentations should be adapted for the slide shows (e.g. Prezi, LibreOffice Impress, etc.). Project details are available in LMS
Test
All students fill in a comprehensive paper-and-pencil test covering all previous topics.
In-class activity
In-class activity during lectures and seminars. Students are expected to ask questions and participate in discussions, as well as help other students during practice sessions. Small regular tests held at seminars are also part of this grade.
Exam
The exam is aimed at checking the skills students should have obtained during the course. Its structure is close to the structure of projects but covers all the topics: standard problems including descriptive statistics, measures of association, comparing two or more means, and linear regression.

Interim Assessment

Interim assessment (4 module)
0.4 * Exam + 0.12 * In-class activity + 0.3 * Project + 0.18 * Test

Bibliography

Recommended Core Bibliography

Stowell, S. (2014). Using R for Statistics. Berkeley, CA: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174344

Recommended Additional Bibliography

Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604

Бакалаврская программа «Социология и социальная информатика»

Data Analysis in Sociology

Автор программы