Course 2018-2019 a.y.

• Introduction to applied research, research design, formulation of research questions and hypothesis, associations and causality.
• Sampling and data sources, sample selection, finding data for research projects.
• Regression analysis: the simple one regressor case, multivariate regression, assumptions and properties, violation of assumptions and remedies
• Factor analysis: model, extraction, rotation, interpretation.
• Scale construction and evaluation: reliability analysis and composite scores.
• Cluster Analysis.
• Regression analysis revisited: regression analysis in combination with factor analysis and cluster analysis, binary response models.

• Select, clean and manage large and complex data sets to be used for applied research.
• Distinguish between different kinds of variables.
• Explain how variables can be used in standard statistical techniques.
• Explain linear regression, test for its underlying assumptions and make relevant interpretations.
• Explain the methods of cluster analysis and factor analysis.

• Distinguish between associations and causal effects in statistical analysis and assess their implications for policy.
• Apply statistical software to analyse and answer defined research questions and hypotheses.
• Formulate, analyse data, and write a structured applied research project.
• Confidently apply the methods of linear regression, cluster analysis and factor analysis.

• Face-to-face lectures
• Online lectures
• Exercises (exercises, database, software etc.)
• Group assignments

Online Lectures:

• One online lecture to outline and explain the process of implementing the research project assignment.
• One webinar to both explain and answer questions concerning the research project/assignment.

Exercises:

• There are a set of meetings in the computer rooms, where we demonstrate and explain how to use the statistical software SPSS, and where students are given tasks of answering and solving tasks related to the SPSS and the use of large and complex data sets.
• Standard exercises concerning applied statistical calculations.

Group assignments:

• The project/assignment is an applied research project which may be conducted by students working alone or in groups of up to 4. You have to choose your group - it is not assigned by the professors. 
• In terms of content, students are required to design an applied research project that can be completed using secondary data. To perform data analysis, students are expected to use one or more of the techniques introduced in this course, interpret the results and draw conclusions. For the project you have to use a data source provided by the professors. The maximum length of the project is 10 pages.

Assessment is based on:

A project (30%):

• The project can be submitted once only. The maximum grade available to students who do not submit a project is 21/30. The partial  exam (i.e. 1st partial exam) is available for third year BIEM students in the first semester. Provided you pass this exam, you can take the second partial exam in December. If you fail the partial exam or you are unable to attend (e.g. being away on exchange) – you need to take the general exam (which covers all material) being held in December and February or to be arranged on later dates (exact dates to be confirmed).

A written exam (70%):

• The written exam (i.e. 70%) consists of short questions. The questions cover theory, methods and interpretation of the results of applied research. The exam covers material covered in the lectures, in the text book and other set of readings, such as the lecture notes may be included in the exam. 

• Lecture notes online on the Bboard platform.
• P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics and Student CD, Prentice Hall (International Edition), 2012, 8th edition (Note that this book is the one you have used for your previous Statistics course. The book is used in the lectures on Linear Regression and ANOVA).
• R. WARNER,  Applied Statistics: From Bivariate through Multivariate Techniques, Los Angeles: Sage, 2008 (We only use chapter 4 from this book, which is made available online from the library).
• J.F. HAIR, R.L. TATHAM, R.E. ANDERSON, et al., Multivariate Data Analysis International Edition (Paperback), Pearson Education, International Edition, 5th or 6th edition,(ISBN-10: 0139305874) (note that this book is used for Factor Analysis and Cluster Analysis (lectures from end of October onwards) – whereby chapter 3 (FA) and chapter 8 (CA) are made available online from the library). 
• M.J. NORUSIS, IBM SPSS Statistics, 19 Statistical Procedures Companion (or any more recent version), Upper Saddle River, New Jersey, Prentice Hall (optional).

In this course we are using the software SPSS. It is more advanced than Excel but also more user-friendly given the tasks we are dealing with. We introduce SPSS through the lectures and tutorial office hours, but bear in mind that SPSS is highly user-friendly and we expect you to learn SPSS through “learning-by-doing”. There are several books available for doing statistics with SPSS (most of them available on Amazon). There is also a lot of online material on SPSS. The "Norusis" book tells you how to perform linear regression, FA and CA, scale construction, descriptive statistics, and general data analysis. Although we have not classified this book as “required” it can be a useful companion when doing your project (see below).