Gothenburg
2010-10-04
Research Design and Method (AG2340)
Part III: Quantitative Track, 11 hp
Course Plan
Instructors:
The course is a collaborative effort between instructors from Economics and Political Science. The
person below marked with (*) is responsible for the course segment.
Pelle Ahlerup* (PA)
Agnes Cornell (AC)
Stefan Dahlberg (SD)
Henrik Lindholm (HL)
Andreea Mitrut (AM)
Pelle.Ahlerup@economics.gu.se
agnes.cornell@pol.gu.se
stefan.dahlberg@pol.gu.se
Henrik.Lindholm@pol.gu.se
Andreea.Mitrut@economics.gu.se
031-786 13 70
031-786 61 45
031-786 17 81
031-786 30 86
031-786 12 56
Organization of the course:
The course comprises three blocks.
Block 1: Introduction.
In this block, key concepts in statistics are discussed. The statistical software SPSS is introduced in the
first of four computer labs of the course. In all computer labs, the students are encouraged to work in
pairs.
Block 2: OLS
This block presents ordinary least squares (OLS, a workhorse and good starting point for much
quantitative analysis. The logic and assumptions behind the technique are presented. Regression
diagnostics and the issue of correlation versus causality are discussed.
Block 3: Beyond Vanilla
In this block we go beyond correlations and linear regressions and investigate binary models and
models for panel data estimation. This block also discusses methods of policy evaluation and study
design.
Learning Outcomes:
After completing the course the students is expected to be able to:
 Evaluate the validity and reliability of secondary data sources

Evaluate and independently design data collection methods such as surveys, and field
experiments

Evaluate, specify and test hypotheses and explanatory models

Understand and apply quantitative methods such as regression analysis

Identify and assess ethical issues related to research on human behavior

Design and plan an independent research project using quantitative methods
Literature:
Bryman, Alan (2008) Social Research Methods, Oxford: Oxford University Press.
Hamilton, Lawrence (1992) Regression with Graphics: A Second Course in Applied Statistics, Belmont:
Duxbury.
Schedule:
L = lecture
S = seminar
Lab = computer lab
Block 1: Introduction
29/9
L1
AC
Data and key concepts
13-15
D137
1/10
L2
AC
Correlation
15-17
A012
4/10
6/10
Lab 1
HL
Lab 1
Lab 1 due at 12.00
9-17
308*
6/10
8/10
L3
L4
SD
SD
13-15
15-17
A012
A012
11/10
Lab 2
HL
Basics of OLS
Models, Assumptions and
diagnostics
Lab 2
9-17
D207
13/10
13/10
15/10
L5
SD
13-15
A110
15/10
L6
SD
13-15
220*
18/10
20/10
Lab 3
HL
9-17
D207
15-17
13-14
A012
C220
14-15
C220
Block 2: OLS
Lab 2 due at 12.00
More complex models
Proposal for project (course
paper) due at 12.00
Causality and linearity
Lab 3
Lab 3 due at 12.00
Block 3: Beyond vanilla
20/10
21/10
L7
S1
PA
SD
S1
SD
Logit
Group discussions of
project proposals: Group 1
Group discussions of
26/10
25/10
27/10
27/10
28/10
1/11
3/11
S1
AC
S1
AC
L8
LAB 4
PA
HL
L9
L10
AM
AM
S2
SD
S2
AC
project proposals: Group 2
Group discussions of
project proposals: Group 3
Group discussions of
project proposals: Group 4
Instruments and Panel Data
Lab 4
Lab 4 due at 12.00
Policy Evaluation
Designing Studies
Course Paper due at 9.00
Course paper discussion,
Group A
Course paper discussion,
Group B
13-14
C217
14-15
C217
13-15
9-17
403
D207
13-15
13-15
A012
A012
13-15
D140
13-15
D205
Notes: Rooms 220, 308, and 403 are in Annedalsseminariet, Seminariegatan 1A.
Lab Assignment and Course Paper:
There are four computer labs in the course segment. The students can work in pairs for the hand-in to
the labs.
In the course paper the students will design a study and conduct preliminary analyses. The Research
questions should build on available data. More information about the course paper is available in a
separate document on GUL.
The final paper and the answers to the lab assignments will be controlled in Urkund.
Course requirements and grading
The students are required to hand in answers to all lab assignments, and all lab assignments must
receive a passing grade before credits for the course are given. Active participation at the two seminars
is a requirement to get a passing grade on the course.
The course grade will be on the Swedish grading scale of Pass (G), Pass with distinction (VG), or Fail (U).
The course grade comes fully from the grade on course paper.
Detailed description of topics covered and reading for the lectures and labs
Lectures:
Lecture 1
“Data and key concepts”
Topics:
 plots – univariate and bivariate)
 Types of variables (discrete, interval, binary)
 Descriptives (crosstabs)
 Introduction of final project
Reading:
Bryman, Ch. 7 pp. 164-190 and Ch. 14, pp. 313-338
Lecture 2
“Correlation”
Topics:
Reading:
Lecture 3
“Basics of OLS”
Topics:
Reading:
 statistical significance, normal distribution
 mean, std dev
 Overview of available data sources
Hamilton, Ch. 1 pp.1-28 and Ch. 2 pp.29-32.



Bivariate regression
Interpreting coefficients
Need for control variables (eliminating plausible rival
hypotheses)
 Proxy measures
Hamilton: pp. 29-48, 65-88
Lecture 4
“Models, Assumptions and diagnostics”
Topics:
 Bivariate regression
 Interpreting coefficients
 Need for control variables (eliminating plausible rival
hypotheses)
 Proxy measures
Reading:
Hamilton: pp. 84-88, 110-124
Lecture 5
“More complex models”
Topics:



Reading:
Lecture 6
“Causality and linearity”
Topics:
Reading:
Lecture 7
“Logit”
Topics:
Reading:
Lecture 8
Country and region dummies
Outliers
Correlation of residuals (i.e. countries are affected by
neighbour countries)
Hamilton: pp. 85-92, 125-138
 Lagged dependent variables
 Linearity assumption and transformations
Hamilton: pp. 4-22, 51-58, 124
 Linear probability model
 Logit
 Interpretation of coefficients
Hamilton, Ch.7
“Instruments and Panel Data”
Topics:
 Instrumental variables techniques
Reading:

Panel Data methods

Acemoglu, Johnson, & Robinson (2001) “The Colonial
Origins of Comparative Development: An Empirical
Investigation”, The American Economic Review, Vol. 91,
No. 5 (Dec., 2001), pp. 1369-1401.
Acemoglu, Johnson, Robinson, & Yared (2008) “Income and
Democracy”, The American Economic Review, Vol. 98, No.
3 (Dec., 2001), pp. 808-842.

Lecture 9
Lecture 10
“Policy evaluation”
Topics:
Reading:
“Designing Studies”
Topics:
Reading:

TBA
Critically evaluating data



TBA
Surveys
Experiments
field experiments
Computer Labs:
Lab 1





Introduction to SPSS
Descriptive statistics
Introduction to correlation
Hamilton, Ch. 1 pp.1-28 and Ch. 2 pp.29-32.
Bryman,, Ch. 7 pp. 164-190 and Ch. 14, pp. 313-338, Ch.
15 pp. 339-362
Topics:
Reading:


TBA
TBA
Topics:
Reading:


TBA
TBA
Topics:
Reading:


TBA
TBA
Topics:
Reading:
Lab 2
Lab 3
Lab 4