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Quantitative Research Methods I


Admission requirements

Designing Academic Inquiry is a required pre-requisite for this course. Quantitative Research Methods is a required methodology course for the Sustainability, International Development and World Politics majors.


“There’s something magical about watching patterns emerge from data” Ben Goldacre (The Guardian, 11 June 2011)

Today’s world relies much on accumulation, presentation, and interpretation of large quantities of information. Statistics is a tool that enables us to organize our data in an efficient manner, and provides us with methods that help us understand the relationships that occur in our data and our growingly complex world. In this course, we will draw examples from multiple disciplines, such as political science, economics, medical sciences, biology to demonstrate how to search for and evaluate patterns in large amounts of data, as well as to interpret what these patterns tell us about the world. The material in this course proceeds gradually from simple descriptive statistics to more complex inferential statistics, such as linear regression. The course will primarily focus on developing substantive and precise understanding of the statistical concepts and their application to real-world examples using elementary computer programming in R statistical package.

Course objectives

Upon completion the course aims to provide the students with the following skills:
 Critically analyze various types of data and learn to select most appropriate statistical technique to answer a research question;
 use statistical programming to enter data, generate descriptive statistics and graphs, perform hypothesis tests and estimate regression models;
 confidently communicate and present statistical results to a variety of audiences – academic experts and policy-makers.


Please see the LUC website:

Mode of instruction

The course will consist of a combination of interactive lectures and lab seminars. Interactive lectures will mainly focus on developing the understanding of the main statistical concepts. Lab seminars are designed for the students to apply these concepts in a series of assignments using R statistical software.

Assessment method

Assessment for this course comprises of a cumulative examination, individual assignments, final report and participation.

  1. Two cumulative exams – 15% each. Weeks 4 and 8
  2. Weekly individual assignments: students will apply their skills obtained in the course to a question of their choosing. Each week they will complete a set of problems using data for their chosen topic – 30% total. Ongoing, weeks 1-7
  3. Final research report: Based on the findings from their individual assignments, students will, in the end, compile a research report focused on their chosen question (2000 words) – 20%. Due week 8, Friday at 17:00
  4. Participation: Participation comprises of two components 1) students’ engagement with the material during interactive lecture (10%) and 2) their ability to apply the reading material in a lab seminar(10%). Ongoing weeks 1-7


This course is supported by a BlackBoard site

Reading list

Jessica M. Utts & Heckard, Robert, F.. 2007. Mind on Statistics, International Edition (with CD-ROM), 3rd Edition, Paperback. Thomson,Brooks/Cole.
Michael J. Crawley. 2005. Statistics: An Introduction using R, 1st Edition. Wiley-Blackwell, Paperback.
Freedman, D., Pisani, R., and Purves, R. 2007. Statistics. Norton: New York, and London, 4th edition.


This course is only open for LUC The Hague students.

Contact information

Dr. Lucie Spanihelova:

Weekly Overview

Week 1: What is statistics?
Lecture: Course introduction, purpose and limitations of statistics
Lab Seminar: Getting to know R – R package installation, data entry
Week 2: Analyzing Data: Summary and Comparison
Lecture: Types of quantitative data, types of variables, , descriptive statistics, comparison of means, correlation
Lab Seminar: histogram, measures of central tendency, dispersion
Week 3: Inference: The Theory
Lecture: reviewing basic rules for probability computations, linking probability to statistics – law of large numbers and sampling distribution
Lab seminar: series of simulation exercises in R
Week 4: Inference: Confidence Interval
Lecture: probability (revisited),confidence intervals
Lab seminar: hypothesis testing using R, review for exam 1
Week 5: Inference: Hypothesis Testing
Lecture: scatter diagram, correlation coefficient
No Lab seminar: held this week.
Week 6: Linear Regression
Lecture: what is regression, slope, intercept
Lab seminar: correlation exercises using R, graphs and scattergrams and regression exercises using R, comparing regression results with correlation.
Week 7: Inference from a Simple Regression
Lecture: sample, population, tests of significance
Lab seminar: test of significance, summary exercise and review for exam 2.
Week 8: Reading week