Research Methods - JPM031

• Title: Research Methods
• Guaranteed by: Department of Political Science (23-KP)
• Faculty: Faculty of Social Sciences
• Actual: from 2021
• Semester: summer
• E-Credits: 10
• Examination process: summer s.:
• Hours per week, examination: summer s.:1/2, Ex [HT]
• Capacity: 50 / 50 (70)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Note: course can be enrolled in outside the study plan; enabled for web enrollment
• Guarantor: RNDr. Jan Kofroň, Ph.D.
• Teacher(s): RNDr. Jan Kofroň, Ph.D.; Mgr. Jakub Stauber, Ph.D.
• Class: Courses not for incoming students
• Is pre-requisite for: JPM682

Annotation

The course covers the most important parts of a methodological toolkit for master-level students.
The course focuses on the basics of quant (and to a lesser extent qual) analysis, a strong emphasis is put on gaining the ability to work effectively with R studio.
Given the high ECTS of the course, students should be prepared for a substantial workload.

Last update: Stauber Jakub, Mgr., Ph.D. (03.02.2025)

Literature

1) Llaudet, E. Imai, K. (2022): Data Analysis for Social Science, a friendly and practical introduction, Princeton University Press

2) Gerring, J., Christenson, D. (2017): Applied Social Science Methodology: An Introductory Guide, OUP
3) Wickham, H. (2014): Tidy Data, Journal of statistical Software. Volume 59, Issue 10.

3) http://www.cookbook-r.com/ 

Last update: Stauber Jakub, Mgr., Ph.D. (03.02.2025)

Teaching methods

Lectures with practical seminars

Last update: Stauber Jakub, Mgr., Ph.D. (03.02.2025)

Requirements to the exam

1) Do all the Homeworks 40 %

2) In class activity 10 %

3) Final task 50 %

Grading is based on A-F scale

Last update: Stauber Jakub, Mgr., Ph.D. (03.02.2025)

Syllabus

While the course provides insight into key elements of research methods, we expect you to have
a certain amount of knowledge regarding four issues mentioned below:
(i) Essentials of correct citing and quoting (academic ethics)
(ii) Essentials of academic writing (style, the logic of test structuration, etc.)
(iii) Basics of probability
(iv) Descriptive statistics (mean, median, standard deviation, etc.)
+ previous exposure to academic text is considered the sine qua non

Broadly speaking, we do expect that you have retained at least the basics of BA-level methodological
know-how (as defined by e.g. JPB 283 and 284).

Software: R (+ Excell)

Readings: Primary source is Data Analysis for Social Science, Elena Llaudet and Kosuke Imai, 2022, Princeton University Press

For the qualitative part it is the Gerring Christenson book and one Gerrings paper (link provided).

For the second class on Charts/Tables we will rely on two J.Schwabish papers (link provided)

1) Intro + arguments
a. Organization

b. Master thesis and the process of writing - a few tips and tricks

c. Types of arguments

2) Few notes on charts and tables (Schwabish 2014, 2020)

a. Charts 

b. Tables

https://www.aeaweb.org/articles?id=10.1257/jep.28.1.209

https://www.cambridge.org/core/journals/journal-of-benefit-cost-analysis/article/ten-guidelines-for-better-tables/74C6FD9FEB12038A52A95B9FBCA05A12

3) Intro to R

a) Intro to R

b) Loading the Data

c) Different types of variables/columns

d) Computing means and other summary statistics 

4) Estimating causal effects with randomized experiments

a) logic of Experiments

b) Treatment and Outcome variables

c) Individual Causal effect

d) Average Treatment Effect

5) Inferring Population Characteristics via Survey Research

a) Survey research

b) Measuring attitudes

c) Two-Way frequency and proportion tables

d) Variable relations

e) Presenting descriptive statistics

6) Predicting Outcomes Using Linear Regression

a) Independent vs. dependent variables

b) Predicted vs. observed outcomes

c) Prediction errors

d) Building an LM model

e) Understanding the LM model output

f) How well does the model fit the data?

7) Estimating Causal Effects With Observational data

a) Observational data - a few challenges

b) Difference-in-means estimator

c) Controlling for confounders - Multiple LM models

d) Internal and External validity

8) Probability

a) Events and Random variables

b) Probability distributions

c) Population Parameters vs. Sample statistics

 

9) Quantifying Uncertainty

a) Estimators and their Sampling Distributions

b) Confidence intervals

c) Hypotheses testing

d) Statistical vs. Scientific significance

10) Case study design I. (Gerring Christenson 2017)

a. Exploratory vs. diagnostic

b. Cross case

c. Within case

d. Pro and cons

11) Case study design II (Gerring 2007)

a. Selecting cases for intensive study from a regression

b. Extreme cases

b. Deviant cases

c. Pathway cases

https://journals.sagepub.com/doi/10.1177/1065912907313077

Last update: Stauber Jakub, Mgr., Ph.D. (03.02.2025)