Data Analysis in R

With the increasing use of alternative software packages like R in data analysis, now is the time to learn their ins and outs. The large number of active programmers creating R packages makes this an up-to-date programme providing a huge range of statistical analyses. Researchers also use R to write functions for analysing data, or to create professional plots.

WHO SHOULD JOIN?
Students or professionals with an interest in quantitative data analysis using R. We will use examples from Economics, Social Sciences and Biostatistics. No programming experience is required. PhD students wishing to refresh their knowledge are also welcome. If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to students with a wide variety of backgrounds.

ADDITIONAL ENTRY REQUIREMENTS
A completed undergraduate course in statistics and an acquaintance with basic linear algebra, the fundamentals of hypothesis testing, linear regression analysis and statistical tests such as the t-test.

COURSE CONTENT
This course focuses upon understanding statistical models and analysing the results whilst learning to work with R. As well as introducing the software to newcomers, it presents basic and more advanced statistics. 

We start with descriptive statistics and visual representation of data, which is the first step for most statistical analyses. We then introduce the linear regression model, a widely used model with two main purposes: modeling relationships among the data and predicting future observations. After that we will extend the linear model to the generalized linear framework, in order to analyse non-normally distributed variables. In the second week we focus on a common problem in statistics: classification. We explore the two main areas of classification (supervised learning and unsupervised learning) with theory and examples.

Every day consists of short lectures with examples, and exercises in which you apply what you have learned right away. Each week you are supposed to make an assignment which is graded. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models. By the end of the two weeks you are acquainted with various popular R packages, can write your own functions and can use attractive plots to present your data. 

LEARNING OBJECTIVES
At the end of this course you can:

• Evaluate the quality of quantitative data sources.
• Choose the appropriate method for an analysis, depending upon the data source.
• Conduct various statistical tests.
• Analyse data using generalized linear framework.
• Decide when and how to use dimension reduction.
• Conduct and interpret factor analysis.
• Enjoy your developed programming skills.

EXCURSIONS
Optional tour of “new” Amsterdam, rounded off with a drink.

ABOUT THE PROFESSOR
Meike Morren has been an Assistant Professor of Marketing at VU University Amsterdam since 2012. She was trained as a sociologist and researcher at University of Amsterdam (UvA) and obtained her master degree by completing the research master in Social Sciences in 2006. In 2011, She defended her PhD in Methods and Statistics at Tilburg University treating a mixed methods study on the quality of survey questions. Since graduation, she focuses on green behavior and data quality in surveys in general. She has been published in Journal of Environmental Psychology, Sociological Methodology, Methodology, Cross Cultural Research and Field Methods. She teachs courses at bachelor and master level in statistics and methodology. The main topics are inferential statistics, survey methods, sampling methods, cluster analysis, factor analysis and regression analysis. While in most courses she uses SPSS syntax, in some courses she uses R.

"Students should apply for Data analysis in R to discover the enormous potential of the open-source programming language R and for acquiring a series of skills and tools to analyze statistical problems of diverse nature."

COURSE READINGS
Readings to be provided at the start of the course. For those want to make a start on R: http://tryr.codeschool.com/.

ADDITIONAL ENTRY REQUIREMENTS
A completed undergraduate course in statistics and an acquaintance with basic linear algebra, the fundamentals of hypothesis testing, linear regression analysis and statistical tests such as the t-test.