Course content

The course will prepare students for a job where they need to work with complex data and issues, while also communicating effectively with stakeholders in an often interdisciplinary context. We work with relevant tasks and tools from the fields of health, social, and environmental psychology.

In an increasingly data-driven world, the ability to find the essence in large amounts of messy data and communicate it to various stakeholders is crucial. In the course, we focus on techniques to pivot, reduce, and focus datasets to make them analysable with analytical techniques learned from courses like PSY3100 or PSY3101, or with effective descriptive analyses. Additionally, we provide students with brief introductions to types of data commonly used in the fields they will work in, such as environmental impact assessments, life cycle assessments (LCA), geographic information systems (GIS), or similar.

Data communication is also central to addressing issues. We focus on descriptive techniques and graphical presentation tools to effectively explain complex topics in a way that is understandable to people without the same expertise as ourselves. The work is carried out in project groups, where the groups work on submissions for a portfolio assignment that is assessed at the end of the semester. Project work is common in the modern workplace, and students will thus gain important experience in project management and leadership.

In the course, students will practice applying theoretical knowledge about data in a practical context. The topics encompass data material and quantitative methods typically used in societal challenges within the fields of health, social, and environmental psychology.

Learning outcome

Knowledge

The student has knowledge of:

• Effective ways to structure datasets, such as tidy data
• Data communication tools through graphical tools, such as ggplot2, GIS tools
• Project management and leadership
• Decision-making tools used in environmental psychology, such as GIS, life cycle assessments, or climate budgeting

Skills

• The student can make sense of messy and confusing datasets by pivoting, filtering out relevant information, and reducing data points to their essential components
• The students can handle suboptimal data storage formats and set up more appropriate and future-proof data management structures
• The students can understand and communicate the content of decision-making tools
• The student can convey complex relationships and points in an understandable way to stakeholders without the same background as themselves
• The student can program data analysis projects in R
• The student can lead and participate in project work

General competence

• The students can use their professional knowledge to shed light on complex, interdisciplinary questions in various contexts
• The students can synthesise advanced knowledge from various statistical sources to illuminate a specific issue
• The students can work together in projects towards concrete, often interdisciplinary, issues from relevant employers

Learning methods and activities

The course will use traditional lectures, as well as flipped classroom techniques. Students work in groups on project assignments throughout the semester. The assignments constitute the portfolio assessment.

Compulsory assignments

• 80 % mandatory attendance
• Completion of oral presentation

Specific conditions

Recommended previous knowledge

PSY3100 - Research Methodology - Quantitative, PSY3101 - Research Methodology - Qualitative

Required previous knowledge

Admission to a programme of study is required: Psychology (MPSY) specialisation in Community, Health, and Environmental Psychology