Topic outline

  • This page contains additional materials that can be useful when preparing for the course.

    Series of MOOCs that will cover basics of statistics

    While the course covers some basics of statistical inference, we focus mostly on how these tools are used in management research. It may therefore be helpful to review some of the basics of statistics before the class. The best way to do this is to watch some of the lectures in the "Data analysis and statistical inference" online course on Coursera:

    https://www.coursera.org/course/statistics

    The following courses are relevant:

    1. Introduction to Probability and Data
    2. Inferential Statistics
    3. Linear Regression and Modeling

    Getting familiar with a statistical software

    Because we have limited time to work with computers in the class, it is highly recommended that you familiarise yourself with the statistical software that you plan to use before the start of the first class.

    Stata

    Check out the getting started manual and work through the sample session (Chapter 1)

    Stata also has a Youtube channel with tutorial videos. The following video is a good starting point:

    Tour of the Stata 14 interface

    For more, see http://www.stata.com/links/video-tutorials/

    R and RStudio

    If you plan to use R for the data-analysis exercises, it is recommended that you do an online tutorial before starting the class. R has a learning curve, and without learning the basics before the course, we will end up spending too much time in learning R instead of learning how to use R for data analysis.

    DataCamp provides good, free online tutorials

    https://www.datacamp.com/

    Particularly, this course is likely to be useful

    https://www.datacamp.com/courses/introduction-to-data

    The MOOCs listed above also use R and provide tutorial on its use.

    You should also probably take a look at these books:

    Kabacoff, R. (2011). R in action data analysis and graphics with R. Shelter Island, NY; London: Manning ; Pearson Education [distributor].

    Wickham, H., & Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.

    Wickham's book presents a more modern take on R. The book is available here http://r4ds.had.co.nz

    SPSS

    While it is possible to complete the course using SPSS, this is not a good idea because SPSS is not a good choice for serious data analysis. However, if you just want to do the first assignment, which is mandatory for passing the course, that is doable with SPSS.

    For Finnish students

    Mikko Ketokivi's book is an excellent resource for basics of quantitative research:

    Ketokivi, M. (2015). Tilastollinen päättely ja tieteellinen argumentointi (2nd ed.). Helsinki: Gaudeamus.