Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


Upon completion of the course, students should be conversant with standard statistical methodologies that underlay much of the work undertaken for research in management and entrepreneurship. Being conversant means being able to understand and critically evaluate the data collection and analytic methods used in papers published in leading journals in entrepreneurship and management. Being conversant also means that students will be able to apply this same understanding to the development of their own research from survey design to data analysis and reporting of the results

Credits: 6

Schedule: 21.04.2021 - 19.05.2021

Teacher in charge (valid 01.08.2020-31.07.2022): Teemu Kautonen

Teacher in charge (applies in this implementation): Teemu Kautonen

Contact information for the course (valid 25.03.2021-21.12.2112):

Teemu Kautonen:

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):



  • Valid 01.08.2020-31.07.2022:

    The course covers the whole process of designing, implementing and reporting a quantitative research project in the field of management studies in a practice-oriented manner.

  • Applies in this implementation:


    Many doctoral students and young scholars in management studies struggle with quantitative research methods. However, the ubiquity of numerical data and statistical analyses in management studies makes obtaining a solid understanding of the process of collecting and analysing quantitative data highly useful. Even if you do not plan to use quantitative methods in your own research, you will inevitably use quantitative papers in literature reviews and, perhaps, also review such papers for journals.

    This course is designed to provide the skills for understanding quantitative research in management studies and the foundations for designing and implementing quantitative research projects. The teaching approach in the course is practical and hands-on, meaning that the primary content of the course is not learning statistical theory, but becoming familiar with the application of different techniques in common management research situations.


    The course is designed to cover the whole process of conducting a quantitative research project. To set the context, we will start by examining the typical structure of a quantitative article in management journals. Next, we will discuss the process of data collection. While some studies in management use existing datasets, collecting bespoke survey data is by far the most typical approach. Doing this right is absolutely crucial: serious mistakes done at this stage of the project cannot be corrected in later stages.


    Then we move to the stage where the data needs to be prepared for analysis. We will get to know some good practice techniques for managing your data, inspecting variables for errors, dealing with missing values, and making variable transformations. Once the data is ‘clean’, we move to basic descriptive analyses to understand the data better. After that, we review linear regression analysis in detail because it is the fundamental statistical technique used in management studies.


    Once we have a good grasp of linear regression, we move to more advanced techniques. The idea is not to provide a thorough understanding of each one of them, but rather a convenient overview of many common techniques to such level that you can run basic analyses and then read more about the techniques yourself. The reason for adopting this approach is that while there is a myriad of hands-on ‘self-help’ books for the basics, many common research designs require more advanced statistical knowledge. Learning such advanced methods requires reading specialist literature where a particular technique, such as logistic regression, is treated in one book and another technique, such as multilevel modelling, in another. Thus, it is difficult to obtain an overview of different techniques, which is necessary for choosing the best available method for each analysis. Further, such specialist books are often difficult to read for scholars with limited statistical knowledge. This course provides you both an overview of many common techniques and a foundation for further self-study.


    Prior knowledge requirements

    Even though we cover all the basics in class, we will do so in a relatively quick fashion. Hence, a basic understanding of quantitative data and statistics is a pre-requisite. The pre-assignment set for the course covers these basics.


    What you should know after completing the course

    After completing the course, you will be able to critically review quantitative academic papers. You will also have a good understanding of how to design and implement quantitative research projects. In particular, you are well aware of the types of analyses you need for writing a paper and you have a good overview of different analytic techniques, from which you can make an informed choice for your own project. You can also run many types of analyses yourself. Finally, you will have developed such knowledge of quantitative research that you can read specialist statistical literature, which is still required for successful analyses for theses and journal publications.


    Statistical software packages

    All examples in class will use the software package Stata 15. Aalto has a license for Stata, so if you want to use that software package, please download it to your laptop. However, you can use any other software package if you prefer, such as SPSS, R, or SAS. All of them should be capable of performing most of the analyses we go through in class. Just note that I cannot help you with software-specific questions with any other package than Stata but there are plenty of web resources for each statistical software package. 

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    Assignments (see syllabus for further details)

    Class attendance (at least 80% attendance required)

  • Applies in this implementation:

    The course comprises of two types of contents:

    Contact teaching: We will have five full days of contact teaching on Zoom. These will be a combination of lectures and hands-on analyses done on Stata 15. Each session comprises two learning modules, one in the morning and the other in the afternoon. Attendance on at least four of the five days is required for a pass.

    Assignments: There is an assignment for each session that consists of practical research design or statistics exercises. In addition, you will have some readings and small assignments related to those. It is highly recommended you complete the respective assignment after each session prior to the next one. However, there is only one hand-in deadline to accommodate to those who prefer to complete the assignments in a larger batch. Assignment details are in MyCourses. You need to receive a pass mark on each assignment to pass the course.

  • Applies in this implementation:

    Contact teaching: 30 h

    Self-study and assignments: 130 h 


Study Material
  • Valid 01.08.2020-31.07.2022:

    Handouts and journal articles

  • Applies in this implementation:

    All course materials will be posted on MyCourses.

Registration for Courses
  • Valid 01.08.2020-31.07.2022:

    Registration via WebOodi ends 7 days before the period starts

  • Applies in this implementation:

    The course will be taught on Zoom in spring 2021.


Details on the schedule
  • Applies in this implementation:

    Date & time



    19 Apr

    Online (MyCourses)

    Pre-assignment. This will not be marked but it is highly recommended that you complete it prior to the first class.

    21 Apr



    Module 1: Crafting a quantitative paper

    Module 2: Designing and implementing survey studies

    28 Apr



    Module 3: Data preparation and introduction to Stata

    Module 4: Descriptive analysis

    5 May



    Module 5: Factor analysis and examination of biases

    Module 6: Linear regression and diagnostics

    12 May



    Module 7: Testing moderation effects with interactions

    Module 8: Regression analysis with limited dependent variables (binary, ordinal, nominal, count)

    19 May



    Module 9: Multilevel and panel regression models

    Module 10: Snapshots of further advanced techniques

    30 May



    Deadline for all assignments