Universität Wien

390016 DK PhD-M: Experimental Methods for Behavioral Sciences (2024W)

5.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 15 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 04.11. 09:45 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 05.11. 09:45 - 18:15 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 06.11. 09:45 - 14:45 Seminarraum 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 06.11. 15:00 - 18:15 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

The objective of this course is to provide students with an understanding of the essential principles and techniques for conducting scientific experiments on human behavior. It is tailored for individuals with an interest in doing research (using experimental methods) in areas such as psychology, judgment and decision making, behavioral economics, consumer behavior, organizational behavior, and human performance. The course covers a variety of topics, including the formulation of research hypotheses, the construction of experimental designs, the development of experimental tasks and stimuli, how to avoid confounds and other threats to validity, procedural aspects of administering experiments, the analysis of experimental data, the reporting of results obtained from experiments, and ethical aspects of experimental behavioral science. Classes are conducted in an interactive seminar format, with extensive discussion of
concrete examples, challenges, and solutions.
Topics:
The topics covered in the course include:
• Basic principles of experimental research
• Formulation of research question and hypothesis development
• Experimental paradigms
• Design and manipulation
• Measurement
• Factorial designs
• Implementation of experiments
• Data analysis and reporting of results
• Advanced methods and complex experimental designs
• Ethical issues

Assessment and permitted materials

Each student is to identify 3 specific challenges related to experimental research methods that they are facing in their own (planned) research, and to submit a detailed description of each of these challenges.

The 3 days of in-person class time will be used for interactive, in-depth discussion of the student-generated challenges (and questions), complemented by mini lectures on specific topics.

Each student is to write a paper on a research question, or on a set of tightly-related research questions, that they would like to examine rigorously using (at least in part) an experimental approach, and that they believe has the potential to be the foundation for a manuscript they might ultimately develop for submission to a major journal. This course paper should include (1) a conceptual part roughly resembling the front end of a journal article and (2) a proposal for the methods to be used to empirically examine the research question(s).

Minimum requirements and assessment criteria

A student’s overall grade for the course is based on the following components:

Quality of Advance Submission 10%
Engagement and Thoughtful Participation During In-Person Sessions 30%
Quality of Course Paper 60%

Examination topics

Reading list

There is no textbook for this course.
However, here are some recommended books on the design (and analysis) of experiments:
Abdi, Edelman, Valentin, and Dowling (2009), Experimental Design and Analysis for Psychology, Oxford University Press.
Field and Hole (2003), How to Design and Report Experiments, Sage.
Keppel and Wickens (2004), Design and Analysis: A Researcher's Handbook, Pearson.
Kirk (2013), Experimental Design: Procedures for the Behavioral Sciences, Sage.
Martin (2007), Doing Psychology Experiments, Wadsworth.
Oehlert (2010), A First Course in Design and Analysis of Experiments, available online at: http://users.stat.umn.edu/~gary/book/fcdae.pdf
Online Statistics Education: A Multimedia Course of Study, Project Leader: David M. Lane,
Rice University, available online at: http://onlinestatbook.com/2/index.html
In addition, the following papers are recommended as background readings for the course:
Oppenheimer, Meyvis, and Davidenko (2009), “Instructional Manipulation Checks: Detecting Satisficing to Increase Statistical Power,” Journal of Experimental Social Psychology, 45, 867-872.
Zhao, Lynch, and Chen (2010), “Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis,” Journal of Consumer Research, 37, 197-206.
Pieters (2017), “Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication,” Journal of Consumer Research, 44, 3, 692-716.
Spiller, Fitzsimons, Lynch, and McClelland (2013), “Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression,” Journal of Marketing Research, 50, 277-288.
Cumming, Geoff (2014), “The New Statistics: Why and How,” Psychological Science, 25, 1, 7-
29.
Elrod, Häubl, and Tipps (2012), “Parsimonious Structural Equation Models for Repeated Measures Data, With Application to the Study of Consumer Preferences,” Psychometrika, 77, 2, 358-387.
Simmons, Nelson, and Simonsohn (2011), “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant,” Psychological Science, 22, 11, 1359-1366.
Simonsohn, Nelson, and Simmons (2014), “P-Curve: A Key to the File-Drawer,” Journal of Experimental Psychology: General, 143, 2, 534-547.
Goodman and Paolacci (2017), “Crowdsourcing Consumer Research,” Journal of Consumer Research, 44, 1, 196-210.
Meyvis and Van Osselaer (2018), “Increasing the Power of Your Study by Increasing the Effect Size,” Journal of Consumer Research, 44, 5, 1157-1173.
Morales, Amir, and Lee (2017), “Keeping It Real in Experimental Research—UnderstandingWhen, Where, and How to Enhance Realism and Measure Consumer Behavior,” Journal of Consumer Research, 44, 2, 465-476.
McShane and Böckenholt (2017), “Single-Paper Meta-Analysis: Benefits for Study Summary, Theory Testing, and Replicability,” Journal of Consumer Research, 43, 6, 1048-1063.
Vosgerau, Simonsohn, Nelson, and Simmons (2019), “99% Impossible: A Valid, or Falsifiable, Internal Meta-Analysis,” Journal of Experimental Psychology: General, 148, 9, 1628- 1639.

Other Resources:
Amazon Mechanical Turk (a marketplace for “hiring” study participants):
www.mturk.com
CloudResearch (tools for participant recruitment; formerly known as TurkPrime):
www.cloudresearch.com
Prolific (platform for participant recruitment):
www.prolific.co
Qualtrics (an easy-to-use web-based system for implementing experiments):
www.qualtrics.com

Association in the course directory

Last modified: Th 31.10.2024 11:46