Data Analysis in the Social Sciences

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

Software

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.

Materials

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 socling.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		firms.csv	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

• Homework 1 (deadline: 18 November, 23:59)

• Homework 2 (deadline: 20 December, 23:59)

• Homework 3 (deadline: 04 February, 23:59)

• Homework 4 (deadline: 18 February, 23:59)

• Homework 5 (deadline: 27 April, 23:59), link to submit

Readings

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.