Data Analysis in the Social Sciences

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Course info

Dear students,

Here will be published the materials of the course "Data Analysis in the Social Sciences", taught at the Master programme "Politics. Economics. Philosophy." in 2018-2019 academic year.

  • Instructor: Alla Tambovtseva
  • Modules: 2-4
  • Course syllabus: link


During this course we will use R as a programming language and RStudio as a GUI.

How to install R and RStudio?

1. Download R (you can choose another mirror here if you wish) and install it on your computer. Make sure you did it before installing RStudio.

2. Download RStudio (you need RStudio Desktop Open Source License) and install it on your computer. It is recommended to create a shortcut for RStudio during installation.

How to use RStudio?

Read the instruction here.

For successful submission of assignments you should be able to create and save R code files (.R). However, it would be helpful for your own research projects to learn how to create RMarkdown files.


Date Topic Theory R Optional
01 November Data collection-1. Population and samples lecture1 r-intro RMarkdown: official page, cheatsheet
08 November Data collection-2. Sampling. Sources of bias lecture2 r-types r-vectors
15 November Data types. Intro to exploratory analysis lecture3 r-dataload Titanic.csv csv in R files
22 November Exploratory analysis. Data visualisation lecture4 Chile.csv codebook r-explore sample quartiles
29 November Exploratory analysis R only r-tables Chile.csv r-rnorm wordcloud code
10 January Statistical estimates. Statistical laws lecture6 r-loops r-laws
17 January Confidence intervals lecture7 r-conf-ints Chile.csv visualization by K.Magnusson
24 January Hypotheses testing lecture8 t-test
31 January Data manipulation with dplyr. Correlation analysis lecture9 r-dplyr r-corr marketing.csv more on dplyr
07 February Contingency tables and chi-squared test lecture10 Lab1 L1-solutions CPDS.csv

stringi: library for text handling
14 February Visualising association between variables R only r-visualisation wgi_fh.csv
Lab2 L2-solutions

more on scatterplots
guess correlation game

21 February Visualisation with ggplot2 R only r-ggplot2 wgi_fh.csv
Lab3 L3-solutions demography.csv

types of visualisation, funny quiz on graphs
interactive bubble plot for inspiration

28 February Exporting output via stargazer R only
stargazer for non-LaTeX users
7 March Comparing multiple groups: ANOVA [lecture11]

21 March Midterm

04 April Simple linear regression. OLS lecture12 r-reg1 2011.csv
Lab 4 L4-solutions

18 April Multiple linear regression lecture13 r-reg2 flats.csv
Lab 5 L5-solutions Griliches.csv

jtools for regression
25 April Multiple linear regression. Model diagnostics
lecture14 r-reg3 wgi_fh.csv
23 May Categorical predictors. Interaction effects
lecture15 r-reg4 wgi-new.csv r-reg5 flats.csv

30 May Fixed and random effects
Princeton handbook on FE & RE models
06 June Lab on regressions. Logistic regression
Lab 6 L6-solutions logistic-reg spanish.csv
UCLA helper on logit models
13 June Principal component analysis
PCA USJudges.csv
visualisation (text in Russian)

R lectures in pdf

01 November: r-intro, 08 November: r-types, r-vectors, 15 November: r-dataload csv-add, 22 November: r-explore1, 29 November: r-tables, r-rnorm 12 January: r-loops, r-laws, 17 January: r-conf-ints, 24 January: t-test, 31 January: r-dplyr, r-corr, 14 February: r-visualisation

Home assignments


We will use two books as compulsory for this course:

  • D.Diez et al. OpenIntro Statistics. 2015. (freely&legally available online)
  • Ch.Weelan. Naked statistics. 2013.