Data Analyses and Interpretation (5cr)

Evaluation methods and criteria

A passing grade requires that all sub-assignments are passed.

Evaluation scale

Hylätty/Hyväksytty

Content scheduling

The detailed schedule of the content is provided in the course's Reppu page.

Objective

Student
- is proficient in qualitative and quantitative data processing and analysis methods and is able to apply them in research and development activities
- can analyse and interpret qualitative and quantitative data
- is able to interpret and use scientific publications in research and development activities at work and in the work community
- master the basics of combining, linking and merging data and interpreting data according to mixed methods
- can critically assess the reliability and ethics of the processing, analysis and interpretation of data and the potential for their use
- master the key research methods related to research and development in their field

Accomplishment methods

Preliminary taskT
Distance days (2-3)
Partly online course
Final assignments
Independent work

Content

Requirements for qualitative research data and conditions for data analysis
Processing, analysis, interpretation and quantification of qualitative research data
Different types of content analysis
Requirements and conditions for the analysis of quantitative research data
Processing, analysis and interpretation of quantitative survey data
Combining qualitative and quantitative data (mixed methods)

Location and time

-

Materials

Detailed information about the course and materials are available in the Reppu learning environment.

Teaching methods

Lectures and independent study take place in the online learning environment.

Independent assignments.

Employer connections

-

Exam schedules

The course does not include an exam.

International connections

The course makes use of national and international scientific literature and research.

Completion alternatives

There are no optional methods of completion.

Student workload

The extent of this course is 5 ECTS credits. One ECTS credit corresponds to approximately 27 hours of student work, making a total of about 135 hours of student work.

Assessment criteria, approved/failed

A passing grade requires that all sub-assignments are passed.

Assessment criteria, approved/failed

Approved
The student masters the basic concepts related to different types of data and is able to apply them. The student masters the basics of processing, analysis and interpretation of qualitative and quantitative data. The student is able to critically analyse and interpret qualitative and quantitative data. The student is able to perform statistical runs in a planned and correct manner and to analyse and interpret the results of analyses.

Further information

Use of Artificial Intelligence:
Preliminary assignments must be completed without the use of artificial intelligence. The student must rely solely on their own knowledge, understanding, and skills. The use of artificial intelligence is prohibited for a justified reason and is considered cheating.

The qualitative research Content Analysis assignment must be completed without the use of artificial intelligence. The student must rely solely on their own knowledge, understanding, and skills. The use of artificial intelligence is prohibited for a justified reason and is considered cheating.

In the quantitative research exercise, artificial intelligence can be used for information retrieval, but the student must clearly declare its use. Artificial intelligence may not be used in the actual data analysis. Failing to report the use of artificial intelligence is considered cheating.