Informatics, Quantitative Methods in Information Systems Research, Second Cycle, 7.5 credits

Learning outcomes

After completing the course, the student should be able to:
(1) understand the logic of quantitative research methods and their application from a social science perspective
(2) explain how research problems affect the choice of data collection schemes and statistical methods
(3) design quantitative studies in the area of information systems
(4) critically assess methods carried out in quantitative studies in information systems
(5) apply methods for quantitative studies in information systems accordingly using statistical software
(6) analyse quantitative data from real-world cases.

Content

The course focuses on quantitative methods required to conduct a research study covering the whole chain including study design, data collection, analysis, interpretation, and presentation of results.

The course consists of the following modules:

1. Concepts
During this module the students design a quantitative study focusing on the choices of appropriate data collection schemes and quantitative methods in relation to the research problems. The module also highlights the importance of data collection strategies focusing on types of data such as ordinal, nominal, qualitative, and quantitative. This also includes sampling methods including power statistics, and random sampling, among others.
Further focus is on common analysis methods used in quantitative studies including descriptive statistics, data exploration using graphical tools, hypothesis testing for means and variances, linear models (Regression analysis), ordinal models (Logistic regression), and classification (Principal Component Analysis and Factor Analysis).The final part of this module is about discussion of results focusing on assessment of methods using standard criteria and their interpretation and presentation. Cases of a quantitative nature from information security area are used.

2. Application
This module focuses on practical use of the content in module 1 where the students carry out two tasks. The first task is to examine a given research problem from a quantitative perspective, which includes a critical discussion and justification of the chosen study design, sampling methods, and quantitative methods. The second task is to apply quantitative methods to a real-world data. This includes data analysis using statistical software, interpretation and presentation of results.

Examinations and grades

Designing a Quantitative Study, 3.5 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Data Analysis, 4 credits (Code: A002)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

For an approved final grade on the course, an approved result is required for all examinations. The letter grades A-E are weighted into a final grade based on the examinations of the entire course.

Modes of assessment

Designing a Quantitative Study, 3.5 credits (Code: A001)
An individual written report and oral presentation of the design of the study. The student needs to provide arguments for the design choices made in planning and designing all aspects of the study. (Assesses goals 1-4)

Data Analysis, 4 credits (Code: A002)
An individual written report and oral presentation of data analysis based on a given case with quantitative data. The student needs to provide arguments for choices of analytical tools and methods to analyze the data and to find patterns and identify the main problem/problems. (Assesses goals 1-6)

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

Informatics, Basic Course 30 Credits, 30 Credits at intermediate course level within Informatics and successful completion of at least 15 Credits at advanced course level within Informatics. Alternatively Business Administration, Basic Course, 30 Credits, Business Administration, Intermediate Course, 30 Credits and successful completion of at least 15 Credits at advanced course level within Business Administration. Alternatively 30 Credits within G1N in Computer Science and 45 Credits within G1F in Computer Science.
The applicant must also have qualifications corresponding to the course "English B" or "English 6" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations.

Other provisions

The course is offered in English and therefore all examinations will be conducted in English.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Required Reading

De Veaux, Richard; Velleman, Paul; Bock, David (2015)
Stats: Data and Models
Pearson Education Ltd., ISBN10:1292101636

Svensson, Elisabeth (2001)
Guidelines to Statistical Evaluation of Data from Rating Scales and Questionnaires
J Rehab Med, (33), page 47-48 [Article in periodical]