Notes

Need for Interdisciplinary Collaboration:

Complex global challenges (e.g., climate change, urbanization, health, food security) require integrated efforts from diverse scientific disciplines.
Traditional mono-disciplinary approaches are insufficient for studying interactions between humans and the environment.
Challenges in Interdisciplinary Research:

Differences in training, paradigms, and scientific cultures across disciplines.
Barriers such as



Research Process Approach:

The Methodology for Interdisciplinary Research (MIR) adopts a process-oriented approach, emphasizing that research questions and hypotheses guide all decisions during research design and execution.
Focuses on aligning researchers' diverse backgrounds towards a shared goal.
Three Key Phases in MIR:

Conceptual Design:
Focuses on the "why" and "what" of the research.
Establishes common goals and a conceptual framework outlining research objectives, central theories, research questions, and operationalized constructs.
Bridges gaps between disciplines (e.g., natural sciences focus on observable models, social sciences on theoretical constructs).
Encourages discussions on weighting multi-dimensional interdisciplinary concepts (e.g., sustainability as a composite of social, economic, and ecological dimensions).
Technical Design:
Deals with "how, where, and when" research is conducted.
Covers study design, data collection methods, sampling, instrument selection, and data analysis plans.
Highlights pragmatic design decisions tailored to research questions rather than disciplinary preferences.
Discusses diverse methodologies, such as longitudinal studies for cause-effect relationships or case studies for little-known phenomena.
Execution and Synthesis:
Teams may work modularly in their disciplines during data collection and analysis, synthesizing findings in the final phase.
Emphasizes interdisciplinary integration to produce holistic conclusions.
Interdisciplinary Collaboration and Challenges:

Teams often consist of researchers trained in different disciplines, creating opportunities for creativity but also challenges in communication and decision-making.
Requires explicit discussions about measurement methods, particularly the observer's role, which differs significantly across disciplines (e.g., neutral in natural sciences vs. participatory in social sciences).
Utility and Flexibility of MIR:

Adaptable to various scales and types of research, from small mixed-method projects to large multi-national studies.
Facilitates integration of qualitative and quantitative approaches and modular research setups.
Real-Life Application and Broader Contexts:

Illustrated with practical examples in teaching and research.
Discussed relevance to inclusive research and mixed methods research, highlighting its potential to address diverse, complex global challenges.
The MIR framework represents a structured yet flexible methodology for designing and executing interdisciplinary research, fostering collaboration across diverse fields to achieve comprehensive and impactful results.


Role of Interviewer and Instrument:

The interviewer and interviewee are integral to the measurement process in qualitative methods (e.g., interviews), but standardized tools (e.g., online or mail questionnaires) eliminate the interviewer to ensure consistency.
Secondary Data Usage:

Researchers may use existing data sources, which vary by discipline (e.g., European Social Survey or PANGAEA).
Secondary analysis involves extracting data relevant to interdisciplinary operationalizations from bibliographic databases or repositories.
Instrument Design:

Instruments may include devices (e.g., pyranometers), questionnaires, interview guides, or data extraction forms.
Collaborations across disciplines can yield innovative measurement approaches (e.g., measuring chemical traces in sewage vs. survey responses).
Sampling Design:

Research questions dictate the sampling strategy:
Random sampling is ideal for inventories (e.g., biodiversity or lodging).
Purposive sampling suits studies exploring specific phenomena in defined contexts (e.g., comparing villages impacted by landslides).
Data Analysis Plan:

Should detail:
Univariate, bivariate, and multivariate methods (e.g., Chi-square, factor analysis).
Techniques specific to modules and their synthesis for answering overarching research questions.
In mixed methods research, the plan must specify how and when results from different methodologies will be integrated.
Interdisciplinary Sensitivity:

Researchers are encouraged to identify commonalities across disciplines, enhancing collaboration and methodological alignment (e.g., shared principles in biodiversity and housing sampling).

Role of Interviewer and Instrument:

The interviewer and interviewee are integral to the measurement process in qualitative methods (e.g., interviews), but standardized tools (e.g., online or mail questionnaires) eliminate the interviewer to ensure consistency.
Secondary Data Usage:

Researchers may use existing data sources, which vary by discipline (e.g., European Social Survey or PANGAEA).
Secondary analysis involves extracting data relevant to interdisciplinary operationalizations from bibliographic databases or repositories.
Instrument Design:

Instruments may include devices (e.g., pyranometers), questionnaires, interview guides, or data extraction forms.
Collaborations across disciplines can yield innovative measurement approaches (e.g., measuring chemical traces in sewage vs. survey responses).
Sampling Design:

Research questions dictate the sampling strategy:
Random sampling is ideal for inventories (e.g., biodiversity or lodging).
Purposive sampling suits studies exploring specific phenomena in defined contexts (e.g., comparing villages impacted by landslides).
Data Analysis Plan:

Should detail:
Univariate, bivariate, and multivariate methods (e.g., Chi-square, factor analysis).
Techniques specific to modules and their synthesis for answering overarching research questions.
In mixed methods research, the plan must specify how and when results from different methodologies will be integrated.
Interdisciplinary Sensitivity:

Researchers are encouraged to identify commonalities across disciplines, enhancing collaboration and methodological alignment (e.g., shared principles in biodiversity and housing sampling).

argon and Shared Understanding
Key quality assessment terms include:
Triangulation, validity, reliability, saturation, etc.
Terms may have different meanings across paradigms; team members should:
Explain their use and significance within their discipline.
Share ownership and respect for varying interpretations.
Discussions foster mutual appreciation and highlight common quality concerns.
Example: Calibration of a quantitative instrument parallels ensuring anonymity in qualitative interviews.
Ethics in Interdisciplinary Research
Ethics must be explicitly addressed throughout the research process, involving all stakeholders.
Ethical considerations span:
Human ethics: Adhering to principles like beneficence, justice, and respect (e.g., Belmont Report).
Animal welfare and ecological impacts.
Legislation, funding implications, policy outcomes, and open-access data sharing.
Avoiding sloppy research practices and considering long-term consequences.
Ethics must be viewed as a team-wide responsibility rather than a module-specific concern.