Quantitative Research VI: Recent advances and current debates in quantitative research

Schedule and location

Thursday May 18th –  Friday May 19th  

University of Jyväskylä, Jyväskylä, Agora building (visiting address: Mattilanniemi 2)

Thursday: room AgC134 (1st floor, morning) and computer class Ag B112.1 (afternoon).

Friday:  room AgC134 (1st floor)



Registration is open April 3rd - May 11th.


Dr. Mikko Rönkkö, University of Jyväskylä, Finland.


Dr. Mikko Rönkkö, University of Jyväskylä, Finland.


Many researchers use quantitative research designs and statistical analysis tools without sufficient understanding on what these techniques are based on. This course is a part of a series of six courses where the goal is to develop an understanding of how multivariate statistical methods are used in information systems research and how results are usually presented in journal articles. The main goal of the course is to provide a foundation that enables independent self-study of quantitative research methods. Rather than addressing simply how the methods are used, we focus on why certain methods are used and how and why these methods work.

The course is designed for both those interested in just reading and understanding research done with statistical methods and for those who already use or plan to use statistical research methods in their own work.

During the course series we will go through six empirical papers published in leading IS and management journals, and analyze how these papers were done. The methods and research designs used in these papers cover a majority of methods and designs used in these journals. If you master all the methods and techniques presented during the course, you would be ideally capable to publish in these journals – if you have already a new theoretical insight and access to appropriate data.

This final course will take a broader look at where the field of quantitative IS and social science research more generally is heading. As an applied discipline, the IS field draws from other fields, such as psychology, for its methodology. We will go through a number of problems and solutions, and how they have been addressed in and outside of the IS field. The topics coved include 1) replicability crisis, p-value problem, and replications 2) endogeneity and causality, 3) making meaningful theoretical progress, 4) conceptualization and measurement issues, and 5) problems in data-analysis, interpretation, and problematic analysis techniques.

We will also address data-analysis issues that are important, but commonly ignored during basic research methods training, including choosing the correct analysis technique (a model) and handling missing data and sample selection. The course concludes by discussing how to develop yourself as a quantitative researcher and where to get help when you face problems.

You can complete the course using either Stata, R, or SPSS.

The course has a pre-class readings package and a pre-class assignment that must be returned before the course. You should reserve several days for the pre-class assignment. You can participate the course even if you did not participate on the previous courses.


Detailed Program

Day 1 – Morning

Course introduction

Review of courses 1 to 5

Missing data

  • Introduction to missing data problem
  • Missing data mechanisms
  • Simple imputation techniques
  • Multiple imputation
  • Full information maximum likelihood estimation

Day 1 - Afternoon (computer class)

Demonstrations and practice of

  • Missing data and sample selection diagnostics
  • Sample selection model
  • Multiple imputation
  • Full information maximum likelihood estimation

Day 2

  • Current issues and debates in social science research

    • P-value problems and solutions
    • Hypothesizing after data collection
    • Endogeneity
    • Neophilia, theorrea, and disjuntivis

    Problems in quantitative research in IS (and how to deal with them)

    • Research model deficiency
    • Causality confusion
    • Construct clarity problems
    • Common method bias
    • Formative construct issues
    • Self-report data validation issues
    • Sample selection limitation
    • Analytic technique problems

    Research process in practice

    • General workflow of writing a quantitative paper
    • Choosing analysis techniques
    • Building statistical models
    • Reporting practices

    Where to get help and how to develop yourself as a quantitative researcher



Credit points

Doctoral students participating in the seminar can obtain 2 credit points. This requires participating on both days and returning the pre-class assignment and post-class report.

Registration fee

This seminar is free-of-charge for Inforte.fi member organization's staff and their PhD students. For others the participation fee is 400 €. The participation fee includes access to the event and the event materials. Lunch and dinner are not included.