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2023/2024  KAN-CPSYV1051U  The Craft of Data Analysis in Behavioral Research

English Title
The Craft of Data Analysis in Behavioral Research

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
Study Board for BSc/MSc in Business Administration and Psychology, MSc
Course coordinator
  • Georgios Halkias - Department of Marketing (Marketing)
Main academic disciplines
  • Methodology and philosophy of science
  • Statistics and quantitative methods
  • Business psychology
Teaching methods
  • Blended learning
Last updated on 13-02-2023

Relevant links

Learning objectives
Sound knowledge of data analysis is a requirement and a critical asset across many industries – more than one would expect! This course equips students with (a) key frameworks of analytical inference and (b) various different methods of analyzing behavioral data. The course acknowledges that people often “think” that they are not good with numbers and have a general dislike toward statistics and quantitative analysis. With that in mind, the course integrates analytical theory, practical examples, (interactive) visualizations, and other online tools in a logical and straightforward manner in order to guide students through different techniques of quantitative data analysis.

The course is especially curated to accommodate behavioral and social science research (e.g., marketing, consumer behaviour, psychology, HR, communication, advertising) and does not take the analytical perspective of disciplines such as econometrics, financial statistics, optimization or programming. Hence, focus is placed on training students in drawing on empirical data to predict individuals’ behavioral tendencies (e.g., relative product preferences, decisions and choices, willingness to pay), make specific forecasts about future outcomes (e.g., employee churn rate, likelihood of customer switching, probability of being hired/fired, and expected sales), identify group differences/similarities (e.g., across gender, nationality, social class, or other characteristics), and assess the efficacy of alternative interventions and action plans.

Overall, the course provides students with an advanced toolbox of analytical skills that are essential for their professional development and career prospects, rendering them better-informed and critically-thinking individuals.

On completion of this course, students will be able to:
  • Describe the logic and reasoning behind quantitative data analysis in behavioral research.
  • Utilize a toolbox of advanced analytical skills for data-driven inference-making.
  • Apply the appropriate technique to answer different research questions.
  • Recognize the relative strengths and weaknesses of alternative analytical approaches in behavioral research.
  • Interpret and critically assess the validity of empirically-based evidence.
  • Discern “hidden” or “not-that-obvious” information in the data.
  • Draw valid research-based conclusions to improve decision-making.
  • Utilize computer software and online tools/apps to analyze, explain and communicate empirical data.
Course prerequisites
The course does not assume that students have particular knowledge of or experience with quantitative data analysis. That said, a general introductory course in research methods and statistics facilitates effective understanding of the issues covered throughout the course.
The Craft of Data Analysis in Behavioral Research:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
Release of assignment An assigned subject is released in class
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter and Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The exam is based on a written assignment. Students are given a mini case study along with a dataset and are invited to identify, perform, and report the appropriate analytical technique to effectively address a number of different questions.


The exam essentially assesses students’ ability in understanding, conducting, and interpreting analysis of behavioral data, drawing on dimensions that involve critical and analytical reasoning as well as context understanding of social and behavioral phenomena.


Lectures are accompanied with example exercises, based on a “working” dataset made available on CANVAS in the beginning of the course. Students can voluntarily tackle these exercises at their own pace and discuss with the lecturer. These exercises serve to (a) summarize the lectures’ content and (b) prepare students for the final exam providing appropriate guidance. The solutions of the exercises can be discussed on CANVAS. In this context, students are encouraged to engage in peer-review discussion as well as in personal/group queries with the lecturer which can focus on certain idiosyncrasies and provide more personalized support.


Note. While the course utilizes (and recommends) specific commercial and open-access software packages for demonstration purposes and applications (e.g., IBM SPSS and/or JAMOVI), students are absolutely free to use any alternative software they wish. Successfully completing the course is not limited to or depends on the use of a certain software.


A detailed document including all necessary guidelines about the exam’s content, structure, and format will be uploaded on CANVAS in due time before the examination period, while additional information will be provided in class and in dedicated Q&A sessions.

Course content, structure and pedagogical approach

The course places emphasis on critically understanding the mechanics behind behavioral data analysis and illustrates that statistical reasoning is an intuitive part of our lives. As such, it utilizes (a) a clear developmental structure, (b) creative and multi-faceted learning material, and (c) continuous support, enabling students in making sense and endorsing the craft of data analysis.


Sessions combine theory, real-life examples, (interactive) visualizations and other online tools in a way that enables effective understanding of the concepts/techniques discussed. The course is structured as follows:












 1. Introduction: What is it and what does it do?



 2. Analytical inference I: Populations, samples, & parameter estimation.



 3. Analytical inference II: Testing & everything you need to know about it.



 4. Analytical inference III: …and all that jazz!



 5. Feel the data: Measuring and summarizing data & how you can get fooled by it!



 6. Analysis bias & data issues: Why should we (not) play by the rules?



 “Examine the professor” | interactive workshop






 7. Making (simple) comparisons I 



 8. Making (complex) comparisons II


 9. Investigating (simple) relationships I


 10. Investigating (complex) relationships II 


 11. Investigating (more complex) relationships III



 12. Issues & tricks of the trade| interactive workshop






Description of the teaching methods
The course follows a blended learning approach, combining online and on-site sessions with in-class discussions and workshops. The sessions integrate analytical theory with hands-on examples, (interactive) visual material, and other tools to optimize the learning experience and ensure effective understanding. The sessions explain the logic behind analytical techniques and guide student through their implementation, while the exercises offer the opportunity to apply this knowledge to real and/or simulated datasets.

Another advantage of the course is that it is, essentially, software-independent. While the course primarily utilizes IBM SPSS (and JAMOVI; a rather new, open-access program) software package for demonstration purposes, the skills and knowledge obtained goes beyond such technical specificities. This also implies that to successfully complete the course students are not necessarily forced to (or limited by the) use of a specific software.
Feedback during the teaching period
In all sessions, students are highly encouraged to actively participate, ask questions, and raise issues relevant to the course. Among other things, such interaction offers the possibility to make relevant adjustments throughout the semester making sure that the learning objectives are delivered effectively. In addition, workshops that are specifically dedicated to feedback and interaction are scheduled, so students can monitor their performance throughout the semester (see, “interactive workshop” in the syllabus). Overall, these sessions (and additional ones, if need be), intend to provide additional time and space for inquiries and problem-solving discussions, allowing students to reflect on the course’s material from different angles.
Student workload
Preparation 123 hours
Teaching 33 hours
Exam 50 hours
Expected literature

The basic literature consists of a core textbook and several other complementary books and open-access material as well as the lecture slides and lecture notes (if available). The content of the course is covered by the following material (additional open-access material will be provided in class/online):



  • Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 9781446249185] 



  • Diamantopoulos, D., Schlegelmilch, B. and Halkias, G., (2023), Taking the Fear out of Data Analysis (3rd edition), Edward Elgar: London [ISBN: 978-1-86152-430-0] 
  • Marshall, E. (2016), The Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests, University of Shefield - Statstutor Community Project – open-access book, uploaded on CANVAS.
  • Navarro DJ and Foxcroft DR (2019), Learning statistics with jamovi: a tutorial for psychology students and other beginners. (Version 0.70). DOI: 10.24384/hgc3-7p15 – open-access book, uploaded on CANVAS.
Last updated on 13-02-2023