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Experimental Methods

Course Dates and Times

Monday 29 February to Friday 4 March 2016
15 hours over 5 days

Stefan Palan

University of Graz

The course offers an introduction to the methods and techniques of experimental empirical research. It pursues the agenda of providing the theoretical knowledge to find a research question, deduce hypotheses and design an experiment. Hence, the focus will be on general methodological and design issues, rather than surveying the existing experimental evidence. We will follow a learning-by-doing approach, in which students will frequently participate in experiments that demonstrate abstraction, formation of hypothesis and approaches to test them along with interactive lectures. We will have a short introduction to data evaluation using non-parametric statistics as the last point of the curriculum. At the end of this course you will have gained not only the know-how needed to develop and implement an experimental research design in the laboratory but you have also gained the basic statistical skills required to gather, analyse and interpret experimental data obtained in laboratory and the field.

Instructor Bio

In this course we will cover the methodological questions, basic concepts and conventions of experimental research in the social sciences. These lectures will be accompanied by group work and pen-and-paper classroom experiments to give a first look-and-feel of participating in experiments and interpreting the collected data.

The lectures will by and large cover the theoretical foundations of experiments as well as step-by-step introduction to design and implementation. We conclude the course with a one-day workshop in non-parametric statistics.

On Day 1, the course will start with a general introduction. We will start to discuss the location of experimental methods within the empirical research process and introduce the three pillars Theory, Reduction, and Controll. We will continue discussing the reasons for experimental research and overview of a typology of experimental research in the social sciences and conclude with the connection between theoretical Models and experimental designs.

We will start Day 2 with the requirement of abstraction and the resulting concept of reduction and continue with the major advantage of experimental research: control. We will talk about randomisation and direct control and define random-block as well as factorial designs. One of the most important parts of experimental control in social sciences is the preference structure. We will therefore discuss the induced valuation at length and consider possible confounding factors. This ends the theoretical block and we will continue with the more practical section on design on the next day.

On Day 3 we will first discuss some basic definitions and consider the Environment, Institutions, and the emerging expected behaviour that constitute the framework of every design. We will discuss repeated and one-shot designs and within and between-subjects settings. Today starts with testable hypothesis and how to adapt a research question for an empirical test in the laboratory.

Day 4 After discussing the advantages and limitations of experimental research, we will move from design to the actual implementation of an experiment. We will try to cover all organisational and practical questions starting from recruitment to payoff (and where to get the money from). This will be the end of the lecture.

Throughout the lecture, we will use classroom experiments to illustrate several concepts. There will be daily assignments that have to be completed and handed in the next day.

Day 5 will be entirely devoted to an introduction into non-parametric statistical methods. Other than parametric approaches (e.g. t-test, OLS regressions), non-parametric testing makes only very limited assumptions about the structure of the analysed data. This is particularly helpful if the per-cell sample size is rather small as is mostly the case with experimental data.

Basic empirical methods, deductive reasoning, and strategic thinking. Knowledge of standard game-theoretic concepts is helpful but no precondition. Some acquaintance with statistics (and possibly STATA) will increase the benefit from the non-parametric statistics part.

Day Topic Details
1 Introduction, Organisational matters / Typology/Theory 11:00 -15:30: Classroom only
2 Reduction / Control v Randomisation / Block Design / Deception / Induced Valuation 11:00 -15:30: Classroom only
3 Design Questions / Data structure / Hypothesis/ 11:00 -15:30: Classroom only
4 Advantages and Limitations of Experiments / Implementation: Preparing and Running an Experiment 11:00 -15:30: Classroom only
5 Workshop: Non-parametric Statistics 11:00 -15:30: lab
Day Readings
1 Chapter 1: Experimental Political Science in Perspective. in Kittel et al. 2012 (see below); Chapter 1.I. in Kagel and Roth 1997 (see below);
2 Friedman, D., and S. Sunder 1994 (see below) Chapters 1-4. Morton, R.B. and K. Williams 2010 (see below) pp. 363-378
3 Schram 2005 (see below)
4 Provided Parts from Kvam and Vidakovic 2007 (see below)
5 Friedman, D., and S. Sunder 1994 (see below) Chapters 4, 6, 8.

Software Requirements

STATA (any version)

Hardware Requirements

At least one machine per two participants on Friday


Books, Overview and Background Reading

Bardsley, N., R. Cubitt, G. Loomes, P. Moffatt, C. Starmer, and R. Sugden (2010): Experimental Economics. Rethinking the Rules. Princeton: Princeton University Press.

Camerer, C.F. (2003): Behavioral Game Theory: Experiments in Strategic Interactions. Princeton: Princeton University Press.

Davis, D.D. und C.A. Holt (1993). Experimental Economics. Princeton: Princeton University Press.

Friedman, D., and S. Sunder (1994) Experimental Methods: A Primer for Economists. (Cambridge: Cambridge University Press).

Guala, F. (2005): The Methodology of Experimental Economics. Cambridge: Cambridge University Press.

Kagel, J.H. and A.E. Roth (eds.) (1997): The Handbook of Experimental Economics. Princeton: Princeton University Press.

Kittel, B., W. J. Luhan, and R. Morton (eds.) (2012): Experimental Political Science: Principles and Practices. Palgrave-Macmillan. forthcoming.

Morton, R.B. and K. Williams (2010): Experimental Political Science and the Study of Causality. From Nature to the Lab. Cambridge: Cambridge University Press.

Introduction History, Methodology

Loewenstein, G. (1999): Experimental economics from the vantage point of behavioural conomics. Economic Journal 109 (453): F25-34.

Rabin, M. (1998). Psychology and economics. Journal of Economic Literature 36 (1): 11-46.

Roth, A.E. (1988): Laboratory experimentation in economics: a methodological overview. Economic Journal 98 (393): 974-1031.

Schram, A. (2005): Artificiality: The tension between internal and external validity in economic experiments. Journal of Economic Methodology 12(2): 225-237.

Smith, V. L. (1973): Notes on some literature in Experimental Economics. Working Papers 21, California Institute of Technology.

Smith, V.L. (1976): Experimental economics: Induced value theory. American Economic Review 66 (2): 274-79.

Smith, V. L. (1994): Economics in the laboratory. Journal of Economic Perspectives 8(1): 113-131.

Non-parametric Statistics

One very popular text (yet hard to find) is:

Siegel, S. and J. Castellan (1988): Nonparametric Statistics for Behavioral Sciences. McGraw-Hill.

We will be using:

Kvam, P. H., & Vidakovic, B. (2007): Nonparametric statistics with applications to science and engineering (Vol. 653). Wiley.

Recommended Courses to Cover Before this One

Summer School:

  • Research Design Fundamentals
  • Introduction to STATA Training Course
  • Introduction to Game Theory

Winter School:

  • Research Design Fundamentals
  • Introduction to STATA

Recommended Courses to Cover After this One

Summer School:

  • Multiple Regression Analysis and Generalised Linear Modelling
  • Multiple Regression Analysis

Winter School:

  • Quantitative social research using Stata
  • Multilevel Regression Modelling