Empirical Research Methods Version of May 18, 2014 (V2.2 Back to the original room in Session 19) Course instructors Name: Prof. Dr. Oliver Alexy Room: 2516 (Building 0505, Entrance Luisenstr.) Ext: 25741 e-mail: o.alexy@tum.de (Note: please use Moodle for all course-related communication) Course aims Understanding how research works is essential for any student and practitioner of management. All insights we draw on may they come from teaching, research, or actual business activities must meet a certain level of academic rigor to be trustworthy, and only trustworthy information should become a source of learning and a foundation of managerial decision making. This course will give you an introduction to empirical research methods, including the higher aims of empirical research, the standards it needs to meet, and a set of methods that you can actually apply. By the end of course, you will thus be able to understand the scientific process in general and in the context of management studies in particular and be able to evaluate whether a result or statement you are confronted with is indeed trustworthy. In doing so, not only will you become able to more critically evaluate everyday information (such as news items or pseudo-scientific studies) but you will also be prepared to participate in the scientific process yourself by improving your ability to read and understand academic work, and getting to know the steps you will need to take to make a contribution yourself, as you will be required to do in other parts of your study programs, such as in research seminars or your final thesis. Course objectives Knowledge Objectives After the course students will be able to: understand the nature of the scientific process, in particular in the context of management studies explore different approaches toward solving (scientific) problems use and apply selected empirical research methods understand the structure and evaluate the quality of academic papers in management studies Skills Objectives diagnostic and analytical skills to think creatively about how best to solve complex problems build up judgment and interpretation skills learn how to evaluate different strategic options work together efficiently in groups Learning Objectives At the end of this course, students will be able to demonstrate understanding, critical assessment and 1
application of the following: assess (pseudo-)scientific work in general, and in particular in the context of management studies understand and evaluate potential approaches toward answering academic questions utilize tools and techniques of empirical research for their own future studies Course Procedures Have fun Our challenge is to make sure you that you learn about the importance of empirical research methods and their relevance to and application in today s business environment. Importantly, even if you do not intend to embark on a career on an academic career, knowing about the research process and how it is executed well are essential pieces of knowledge for anyone in any industry. Thus, look at this class as an opportunity to acquire and sharpen a set of skills you will need in a couple of months/years when you might be working in a company, possible using or evaluating one of the methods explored in this very lecture! Attend and prepare for class While we understand that many of you will not be able to come to all sessions of this course, our hope and ambition is that you will try. Put differently, we promise to make the lectures interesting enough so that they are worth attending. Also, we will provide you with instructions as to how to prepare so that you can take the most out of each lecture at the very least, you should have looked at these in advance! Note how your preparation is essential for the tutorials and labs, the success for which depends on your contributions. Participate Actively Despite this being a fairly large class, we will try to conduct this course in an interactive manner. The more actively you participate during class, the better you will be prepared for the exam and the more of this course you will remember for your work life. Thus, do not try to hide in a large crowd, but summon your courage, take a chance, and rise to the challenge of participating. Outstanding participation will be rewarded with your name being entered into the prize draw (see Assessment Prize draw below). Design your own learning experience At several places throughout this course, we will give you an opportunity to participate in the design and execution of this course. For example, over the course of the term, you will have the opportunity to contribute multiple choice question for each class, which everyone in the end can use to prepare for the exam and if a question is particularly good, we might even include it in the actual exam! Also, the top five submitters of questions we approve will be entered into the prize draw (see Assessment Prize draw ). Instructions on submitting multiple choice questions are provided on Moodle. Give feedback Your feedback in class or in private on any aspect of this course is welcome at any time. It can help make this course an excellent experience for you and for us. We encourage you to comment on this course on Moodle and we will respond as soon as possible. If you wish to see one of us in person, please let us know and schedule an appointment in advance so that we can prepare. Come prepared. Assessment Exam Grading is based on a 100% multiple-choice exam with about 50-60 questions at the end of the lecture. We will announce specifics about time and location as soon as it is made known to us. 2
Extra credit Accompanying this class, you will be able to participate in two types of work to earn extra credit toward your grade. This means that completing this work is not mandatory, and full marks can be achieved without participating. Nonetheless, upon successful completion of this exercise, you may be awarded as much as 10% of points (compared to the maximum score of the exam). There will be six homework assignments, each worth 1%, and a teamwork exercise in which you will come up with a research design on your own, worth up to 4%. Information on both types of extra credit work will be provided separately on Moodle. Note that all team members that contribute towards the design of the Wiki with the highest student evaluation will further enter the course prize draw (more below). Prize draw At the end of this lecture, we will have a prize draw where up to five students may win a free lunch at Tenmaya, the Japanese restaurant at the corner Theresienstr.-/Luisenstr. We will be there as well. There are several ways to enter the prize draw: - participate in generating the highest-rated research project Wiki as chosen by your peers - be among the top five submitters of accepted multiple choice questions - make a fantastic contribution, both during the lecture as well as outside (e.g., online) Note that your name can be entered into the draw multiple times, but it can only be drawn once. Preparation, Readings, and Exam Subject Matter You will find that for each session, we will upload individual preparation sheets specifying what we expect you to have done before class. These sheets will also contain information on reading materials that elaborate on what we cover in class. Note that everything specified as mandatory by these preparation sheets is also part of the subject matter for the exam. All mandatory readings will be provided when they cannot be easily accessed through the library resources available to you. Also note how everything we do in class is relevant to the exam importantly, this includes all questions asked in class, irrespective of whether they are answered in class or not. In case you want to do preparatory or additional reading on empirical research methods, we recommend the following textbooks (on which we will also draw to some degree for the lecture): Singleton, R. A., Straits, B. C., & Straits M. M.1993 (or newer). Approaches to Social Research ( 2 nd ed.). Oxford University Press. (Abbreviated ASR in preparation sheets) In German: Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. 2010 (or newer). Multivariate Analysemethoden: Eine anwendungsorientierte Einführung ( 13th ed.). Berlin: Springer. Salkind, N.J. 2008 (or newer)). Statistics for people who think they hate statistics ( 3 rd ed.). Thousand Oaks, CA: Sage. Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. 2005 (or newer. Multivariate data analysis ( 6 th ed.). Upper Saddle River, NJ: Prentice Hall. Course outline This course consists of a combined series of lectures and tutorials while TUMonline specifies separate days for this, we will mix-and-match according to subject matter, distinguishing three types of session: - lectures will be largely taught by an instructor based on a slide deck with some interactive elements - tutorials will feature a lower number of slides and largely build on class contributions - labs will actually take place in the computer pools (CIP) where you will be doing hands-on work. 3
Session Day Date Type Topic 1 Tue Apr 08 Lecture Introduction - what is theory? Wed Apr 09 No class 2 Tue Apr 15 Lecture How does theory advance? 3 Wed Apr 16 Tutorial Research ethics Tue Apr 22 No class 4 Wed Apr 23 Tutorial Good research, good journals, why good research 5 Tue Apr 29 Lecture Research question and their implications 6 Wed Apr 30 Tutorial Correlation and causality 7 Tue May 06 Lecture Choosing a research design 8 See Moodle LAB Paper reading, positioning, and contributions 9 Wed May 07 Lecture Qualitative research 10 Tue May 13 Tutorial Interviewing practice 11 Wed May 14 Lecture Quantitative research design 12 Tue May 20 Tutorial Using existing scales and data 13 Wed May 21 Tutorial Surveying practice 14 Tue May 27 Lecture Quantitative analysis I data preparation, descriptive statistics Wed May 28 No class 15 Tue Jun 03 Lecture 16 Wed Jun 04 Lecture Quantitative analysis II basic inferential statistics (correlations and OLS regressions) Quantitative analysis III hypothesis testing using inferential statistics 17 See Moodle LAB Data preparation and descriptive statistics Tue Jun 10 No class 18 Wed Jun 11 Lecture Quantitative analysis IV factor and cluster analysis Tue Jun 17 No class Wed Jun 18 No class 19 Tue Jun 24 Lecture Quantitative analysis V interactions, mediation, and moderation 20 Wed Jun 25 Tutorial Quantitative analysis VI problems, tips, and tricks 21 See Moodle LAB Factor analysis and regressions 22 Tue Jul 01 Lecture Experimental designs 23 Wed Jul 02 Tutorial Experimental practice 24 Tue Jul 08 Tutorial Exam preparation Wed Jul 09 No class Exam Tue Jul 15 The exam is currently scheduled for this date, 8:00am You will see how the course plan follows a clear structure. In sessions 1-8, we will study the foundations of empirical research methods to establish what they are good for, and to learn more about the context of management research in which we want to apply them. Sessions 9-23, in turn, introduce a set of standard 4
empirical research methods, covering qualitative, quantitative, and experimental approaches. Content will usually be introduced in lecture format, intensified in an accompanying tutorial, and (in parts) be directly applied by students in the lab sessions. We are currently working hard to ensure that all lectures and tutorials will be recorded. All lectures and tutorials will take place in Room 2300 in Building 0503, Stammgelände (Friedrich von Thiersch Hörsaal (0503.02.300)). Tuesday sessions will last from 4:45pm (sharp!) to 6:15pm, Wednesday session from 1:15pm (sharp!) to 2:45pm. For the labs (not recorded), we will be offering several time slots for each session, for which you will be able to register in advance. The time slots will most likely last between 1.5 and 2 hours each, so you can just pick that time slot that works best for you (provided that there are still seats available). Registration for all lab sessions will open in the second week of class, and will close one week before the first time slot of each respective lab. All labs will take place in room Z510, building 0505. The dates for the labs will be: - Lab 1: May 2 and May 5 - Lab 2: June 5 and June 6 - Lab 3: June 26 and June 27 5