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2019/2020 KAN-CIBSO1061U Applied Business Research
| English Title | |
| Applied Business Research |
Course information |
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| Language | English |
| Course ECTS | 7.5 ECTS |
| Type | Mandatory |
| Level | Full Degree Master |
| Duration | One Semester |
| Start time of the course | Autumn |
| Timetable | Course schedule will be posted at calendar.cbs.dk |
| Study board |
Study Board for MSc in Economics and Business
Administration
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| Course coordinator | |
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| Teaching methods | |
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| Last updated on 26-11-2019 | |
Relevant links |
| Learning objectives | ||||||||||||||||||||||||
| Learning objectives: At the end of the course you
should be able to:
• Calculate and interpret summary and comparative measures of the data. • Distinguish among different types of quantitative data (categorical, continuous, etc.) and recognize the types of information they provide and their limitations. • Recognize the main types of distributions, how they relate to the nature of data at hand, and how to use them in statistical analysis. • Draw inferences about population characteristics from samples. • Recognize the key features of statistical testing (significance, power, confidence intervals) and conduct statistical tests on data. • Understand the concepts of correlation, partial correlation, single- and multivariate regression, and conduct tests related to such regressions, including residual analysis. • Generate and interpret the output from pre-developed software packages. • Discuss and recommend solutions to problems encountered in the analysis of a specific phenomenon. • Perform hypotheses testing of both simple and more composite hypotheses. • Report and interpret the results of the analysis clearly and effectively to a reader who does not have a technical background in statistics and econometrics. • Use simple time series models and smoothing to construct forecasts and confidence intervals of future variables. • Make recommendations based upon the results of the analysis. |
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| Prerequisites for registering for the exam (activities during the teaching period) | ||||||||||||||||||||||||
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Number of compulsory
activities which must be approved: 2
Compulsory home
assignments
In order to qualify for the final exam, the students must get 2 out of 4 activities described below approved. All activities are individual. Further, they are independent, i.e., completion of one activity is not conditional on completion of the others. 1. Activity 1 – Problem Set 1 The students will receive a problem set consisting either of one long or two shorter problems, and the associated data set(s). They will be required to perform different types of statistical analyses and answer a set of questions based on the analyses. The deliverables include the Stata code to perform the analyses, the Stata output and a maximum 2 pages long document with answers to the questions. 2. Activity 2 – Problem Set 2 The students will receive a problem set consisting either of one long or two shorter problems, and the associated data set(s). They will be required to perform different types of statistical analyses and answer a set of questions based on the analyses. The deliverables include the Stata code to perform the analyses, the Stata output and a maximum 2 pages long document with answers to the questions. 3. Activity 3 – Paper Critique The students are asked to write a critical review of an applied research paper published in an academic peer-reviewed journal. The article must be applied, i.e., it must contain empirical (statistical) analysis. The purpose of the assignment is to enhance students critical thinking and written communication skills. The article to be critiqued must be selected by the students and submitted to the instructor for approval. 4. Activity 4 – Replication Exercise Replications require researchers to reproduce a set of results using the same sample. The assignment is to select an article that has utilized publicly-available data and attempt to replicate the results of the analysis. The instructor will provide a list of potential articles and each student will select one among those. Upon selection the student will download the data and write own program to extract the analysis samples and perform the analysis. The deliverable includes up to 3 pages report that outlines the goal of the original paper, what data set was used, a short description of the analysis sample and any sample selection restrictions employed by the author, plus a short description of your attempts to replicate the results. The programming code that produced your results should also be attached. Students will not have extra opportunities to get the required number of compulsory activities approved prior to the ordinary exam. If a student has not received approval of the required number of compulsory activities or has been ill, the student cannot participate in the ordinary exam. If a student prior to the retake is still missing approval for the required number of compulsory activities and meets the pre-conditions set out in the program regulations, an extra assignment is possible. The extra assignment is a 10 page home assignment that will cover the required number of compulsory activities. If approved, the student will be able to attend retake |
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| Examination | ||||||||||||||||||||||||
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| Course content, structure and pedagogical approach | ||||||||||||||||||||||||
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This course will introduce students to key methods of
quantitative analysis that are widely applied in business and
economic research. Topics covered include, among others,
representing quantitative data, characterizing the data using
numerical and graphic representations, performing tests and drawing
inference from them, recognizing potential weaknesses and/or
pitfalls of quantitative analysis, and using data for business
decision making such as forecasting. The purpose of the course is
to make students educated users of quantitative analysis by
introducing the main theoretical concepts and issues rather than
giving you an extensive training in the underlying mathematical and
statistical theory. The course emphasizes how to apply various
statistical techniques in the support of decisions in the various
functional areas of business and economics.
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| Description of the teaching methods | ||||||||||||||||||||||||
| Teaching methods: The course consists of 12 lectures,6 tutorials and an introductory session to STATA. A detailed plan of topics to be covered in each lecture, as well as respective readings for each topic, will be presented in the course syllabus to be made available before the start of the course. Lectures focus on presenting theory and insights, although numerical examples will be presented to help better understanding of theoretical concepts. On the other hand, tutorials focus exclusively on applying the concepts to concrete examples using real-world data. The tutorials also provide the hands-on experience to problem-solving. | ||||||||||||||||||||||||
| Feedback during the teaching period | ||||||||||||||||||||||||
| In the tutorial sessions feedback is provided by the instructor at the end of each session. At the end of the course, a special 2-hour session is arranged for the course coordinator to provide the whole class feedback related to the written exam. | ||||||||||||||||||||||||
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| Expected literature | ||||||||||||||||||||||||
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Instructor PP slides and supplementary notes, if necessary. Individual academic papers and book chapters downloadable from CBS Library.
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Last updated on
26-11-2019