Dates (Wednesdays): October 9, 23, 30 | November 6, 13, 27 | December 4

• Last update: December 19, 2024, 19:00 (credits/grades updated)
• For grading scheme, current credits and final grades (at the end of the semester) see Grading
• Current credits

• Homepage of this course: http://statmath.wu-wien.ac.at/courses/m1bw/m1bw_en.html
• Canvas page of this course (not yet edited)
• This course in the WU course catalog
• The directory with the pdf Files is: http://statmath.wu.ac.at/~salzberger/quantmeth/pdfdownloaddirectory/.
  Please download pdf files from there. All files may only be used for educational purposes during this course.

• 15 min per topic: Written quiz (mostly multiple choice)
• 60-75 min per topic: Presentation (including practical topic and contents as given below, 30 slides approx); some exceptions apply
• 15-30 min per topic: Discussion
• Each class consists of two consecutive units (topics); some exceptions apply (measurement, design)

• William M.K.Trochim / James P.Donelly: The Research methods Knowledge Base (3rd edition) Atomic Dog.
  at URL: http://www.socialresearchmethods.net/kb/
  
• Topics for the units: http://www.socialresearchmethods.net/kb/contents.php (online version)
• The directory with the pdf Files is: http://statmath.wu.ac.at/~salzberger/quantmeth/pdfdownloaddirectory/. Please download pdf files from there. All files may only be used for educational purposes during this course.
• The textbook is primarily used as a basis for discussion. The consideration of additional literature is welcome and will be rewarded (see presentations).

Based on selected chapters, the participants prepare presentations that introduce the other participants to the topics. The objective is to trigger a lively discussion. You may devlop a short excercise, discuss a paper, include different views (other literature), etc. Your creativity is welcome. The allocation of the topics will be done in the introductory class.

• [1] The first topic deals with fundamental underpinnings of quantitative research. What is the language of research, what is validity, why is it important and what types of validity are distinguished. The topic also addresses ethical issues and principles in research.

• [2] By definition, empirical research is based on data. Unless a census is feasible and appropriate (collecting information on all subjects of the total population), sampling becomes a very important aspect. Beware, the most creative statistical analyses will not make up for flawed sampling. Therefore, better think twice about your sampling strategy. The topic also introduces us to the issue of external validity and key terms of sampling such as probability and nonprobability sampling.

• [3] As a rule quantitative research requires measurement as a type of quantification. Generally, there is no way around measurement in quantitative research. According to the main stream concept of measurement in the social sciences, measurement can take place a different levels. These will discussed as well as the consequences for data analysis. Finally, quality criteria of measurement (specifically reliability and validity) will be addressed. Whether you will develop your own measurement instruments or use existing ones, you should know what to look for at the end of this course. If you realize, measurement is fun to you, consider doing a specialized research seminar in a later semester.

• [4] While experiments are becoming more and more popular among social scientists, a lot of data is collected (and can only be collected) through surveys. Thus, we will discuss principles of good survey research. This includes types of surveys, how to select a survey method, how to construct a survey, what kind of questions is appropriate, how should they be phrased and ordered, how should a response scale look like, what are the pros and cons of survey research. Of course, one could spend the whole semester on this issues, may be even one's whole scientific career.

• [5] Now that you have an idea what measurement means and what its goals are, we look at selected methods of scaling and index construction. Specifically, we will learn about Thurstone scaling, Likert scaling (very widespread) and Guttman scaling. If there is some time left, we might briefly look at other approaches as well.

• [6] Regardless of the type of research you plan to do, design is always fundamental. At first, we discuss internal validity and take about fundamentals of establishing cause and effect.
  Then we talk about various threats that occur in single or multiple group designs.

• [7] Experimental research is becoming more and more popular. It is argued that experiments are the best way, some say the only way, to investigate causal claims.
  Thus, the experimental design is of utmost importance. Even if you do not intend to run your own experiments, you may refer to published work using experiments.
  This unit deals with two-group experimental designs, probabilistic equivalence and random selection and assignment - basics of experimental research.
  We will also address factorial designs, the randomized block designs, covariance designs and hybrid experimental designs.

• [8] True experiments are not always doable. Then quasi-experimental designs are an option. We will learn about the nonequivalent groups design, the regression-discontinuity design
  and other quasi-experimental designs

• [9] & [10] Once you collected the data, you will be ready for analysis. We will be introduced to data preparation, data description, and elementary statistics such as correlation coefficients.
  Furthermore, we will deal with fundamental inferential statistics such as the t-test. The concept of dummy variables will also be explained.
  Going full circle, we will come back to conclusion validity, threats to conclusion validity and ways to improve it.

  Note: Topics [4] and [5] will be considered in reversed order.

• Test(s): 10 mini quizzes (1 quiz will be replaced by a task in the current semester), 8 credits for each quiz, thus 10x8=80 credits; each mini quiz captures one topic
  
• Presentation(s) (number of presentations varies depending on the total number of participants, to be determined in introductory unit; up to 20 credits per presentation;
  (exception: topic 3 earns up to 40 credits (counts as two presentations)

• Attendance and active participation is required and expected
• Current score (usually updated after quizzes)
  
• Grading mode 1 (one presentation): 0-60: insufficient; 61-70: 4; 71-79: 3; 80-87: 2; 88+: 1
• Grading mode 2 (two presentations): 0-70: insufficient; 71-80: 4; 81-90: 3; 91-100: 2; 101+: 1
• Grading mode 3 (three presentations): 0-80: insufficient; 81-90: 4; 91-100: 3; 101-110: 2; 111+: 1

• Presentations should be lively and involve the audience, have a look at the key terms on the first page of each chapter to figure out what is important
• Prepare something extra where all can participate or which stimulates a discussion - e.g., a game, an exercise, a practical problem, an illustrations to be discussed (e.g. journal article, research project), an alternative view or alternative terminology (additional literature)
• Last slide should contain the key messages (what is the most important lesson?)
• Presentations should be sent to thomas.salzberger@wu.ac.at the day before the presentation
• Prepare copies of slides for all participants
• For all: read text in advance, you should be able to briefly explain the keywords,
  you are expected to (and may be asked to) contribute to the discussion