Masterclass: How to Research for Academic Writing

Explore the basics of scientific work, learn how to conduct research to generate knowledge, and adopt an open-ended approach to obtain valid and reliable results for your academic thesis.

Use the chapter worksheet as your companion to think for yourself about science, compare fields, and explore views by Dürr, Watzlawick, and Popper for your project.

Define science as the activity of scientific work: a planned procedure to gain new knowledge and solve practical problems, linked to existing knowledge and published and verifiable for others.

Science is a goal-directed activity that seeks useful new knowledge by building on existing findings to answer clearly defined questions.

Emphasize the exploratory approach in science; craft a clear problem statement, goal definition, and research question to ensure a meaningful contribution beyond formal criteria.

Explore how measurement methods shape results in scientific investigations. Understand how defined measurement criteria predetermine results and why skepticism helps avoid overgeneralization across disciplines.

Examine how scientific results should be viewed, emphasizing transferable procedures over universal truths and methods tailored to specific, concrete problems such as vegan economics.

Practice falsification as a working principle, grounded in Karl Popper's critique of induction. Learn to test claims by seeking contradictions, not verification, with everyday examples like swans and diet myths.

Explore how science and communication, mutually dependent, intertwine; emphasize understanding statements and contexts, dialogue with sources and researchers, and applying hermeneutics to interpret texts for transparent, verifiable research and development.

Explore radical constructivism, showing how we construct realities from perception, why knowledge can outdate or mislead action, and how cognition guides interpretation and inquiry.

Explore how scientific findings shape reality concepts and the role of traceability, transparency, and verifiability in sound results. Learn constructivist fitting and the evolving nature of laws toward objectivity.

Explore how to craft the base elements of a scientific paper, align key terms with the outline, formulate solvable problems and knowledge goals, and assess formulations critically.

Use the worksheet to sharpen topic formulation, define the problem statement, research objective, and research question, formulate hypotheses, and check coherence with peer feedback.

Connect the basic pillars of scientific research—problem definition, objective, and research question—to ground your study and guide coherent topic formulation and procedure.

Develop clear topic formulations for scientific papers by crafting concise titles that reflect the problem, research question, objectives, and keywords, while ensuring coherence and implementability across the outline.

Define a problem with a clear, unambiguous problem statement and a testable research question in empirical work. Draw on contradictions in articles, lectures, conferences, and practice to justify the investigation.

Formulate a central, operational research question that guides analysis, methods, and conclusions, running as a 'red thread' through your paper from introduction to final discussion.

Discover how scientific hypotheses relate measurable variables through testable, falsifiable if-then statements and how criteria, features, and characteristics function as essential tools in investigations.

Learn to craft the concluding chapter as a 'discussion of results' that repeats the research question, presents logical, objective conclusions, with no external sources or personal opinions or new information.

Discover why asking why fuels critical thinking and learn to train yourself to think and act more critically in scientific work, using practical tips and dry runs.

Adopt critical thinking as a paradigm shift for scholarly work by analyzing arguments, questioning sources, and seeking alternatives to advance scientific writing.

Identify a concrete research topic using a red thread approach and an outcomes-open, methodical, and critical mindset. Examine topic formulation with blockchain in multi-sided platforms to ensure an investigable problem.

Explore how to research for academic writing by searching diverse sources, identifying relevant studies and trends, spotting controversies, and seeking contradictions to develop critical, multi-perspective arguments.

Define a single problem definition through critical thinking, exploring contradictions and perspectives, and empirically verify how blockchain could reduce transaction costs at a mid-sized company.

Define the object of study by aligning it with the research question and objective, using empirical data and clear IV and DV relationships, as in blockchain’s transaction-cost reduction example.

Distinguish between object of investigation and object of research, showing when to use each: a localized problem needs investigation, while ongoing trends define a research object.

Define the research objective by identifying the problem, selecting a single central, attainable goal, and describing blockchain's possibilities for transaction cost reduction at a concrete medium-sized company.

Craft a smart, critical research question that links problem definition to objective by specifying criteria, characteristics, and data needs, avoiding yes-no queries and guiding empirical inquiry.

Select literature essential to the investigation, prioritizing scientific journals and blockchain-related research, and evaluate sources critically to guide your research path.

Choose methods guided by the research objective, often requiring common and sometimes interdisciplinary approaches; survey the state of research to inform method selection and justify alternatives for transaction cost reduction.

Identify analysis as a part of the investigation, not the objective of your work. Apply criteria to determine the role of results and neatly incorporate them into the research path.

Explore statements and data as products of empirical research, acknowledging they are not truth but outcomes of collection, analysis, and evaluation. Maintain critical, outcomes-open stance, valuing valid and reliable results.

Examine how results are never fixed in advance and must be critically evaluated within the research path, situating findings in the scientific community and signaling the need for further research.

Explore theory building, hypotheses, and solution approaches by integrating inductive and deductive insights, deriving multiple related solutions, and staying modest about implementation expectations.

Think critically from beginning to end by applying falsification to every hypothesis, avoiding final verification, embracing provisional conclusions, and adopting a systems theory and constructivist view of science and practice.

Explore empirical research methods in the social sciences, blending qualitative and quantitative approaches with induction and deduction to design and test hypotheses.

Apply concepts and models for empirical research to a chosen project; plan the research design and empirical research components, note gaps, seek sources, and craft hypotheses and testing procedures.

Explore the basic principles of empirical work in social research, detailing quantitative and qualitative methods and how data testing supports hypotheses, theory, models, and both inductive and deductive procedures.

Explore induction and deduction through Balzert's model, linking bottom-up observations to hypotheses and theories, and top-down testing to falsification and provisional confirmation.

Explore how knowledge rises in science through an orderly, theory driven empirical process. Learn fixed social science methods, hypothesis testing, and the need to interpret data in dialogue with theories.

Identify empirical data as selected information about observable reality, collected through observation, interviews, questionnaire, psychological test, physiological measurement, or document analysis, and distinguish standardized (quantitative) from non-standardized (qualitative) methods.

Explore empirical methods by collecting and analyzing data, including third-party data, aligned with theoretical aims. Note examples: tests, ECG, guided interviews; and methods: qualitative content analysis, variance analysis.

Explore quantitative empirical research by formulating theories, deriving hypotheses, and testing them with structured data collection and statistical analysis using larger samples.

Examine how Balzert et al.'s model frames quantitative research and how researchers select methods from a toolbox, including surveys with standardized questionnaires, experimental designs, and psychometric and physiological tests.

Explore quantitative approach to measuring and analyzing data through surveys, observations, and experiments, including correlations among course evaluations, grades, and learning outcomes with emphasis on anonymity and a standardized questionnaire.

Learn the nine phases of the quantitative research process, from defining the research topic and theoretical background to design, operationalization, sampling, data collection, preparation, analysis, and presentation of results.

Master qualitative empirical research by iteratively examining unstructured data, such as interviews, field observations, texts, pictures, and video, to interpret meanings within social contexts.

Explore ethnographic field research and narrative interviews in qualitative social research. Apply grounded theory with theoretical sampling, coding by permanent comparison, and memos to generate theory from qualitative data.

Explore the qualitative approach to research, emphasizing deep understanding expressed in words, guided interviews, expert interviews, group discussions, and qualitative observation to inform research design.

Engage in qualitative empirical research by collecting unstructured data through field observation and narrative interviews, interpreting it to reconstruct context from participants' views and derive hypotheses through iterative revision.

Define the problem, goals, and research questions to choose between inductive qualitative and deductive quantitative paths. Cycle from qualitative framework and data analysis to hypothesis testing and theory building.

Compare the quantitative and qualitative research processes as outlined by Döring and Bortz, noting sequential versus circular logic and the roles of sampling, data analysis, and hypothesis generation.

Mix qualitative and quantitative research in one study via inductive or deductive paths in Balzert's model. Add open-ended questions to quantitative surveys to capture new perspectives.

Master the scientific foundations of empirical research by exploring induction, abduction, deduction, verification and falsification, search for truth, basic set and correspondence problems, and the qualitative paradigm.

Explore induction, abduction, and deduction as distinct reasoning methods in empirical research, from gathering observations to formulating hypotheses, inferring explanations, and testing theories.

Explore critical rationalism as an epistemological approach that tests assumptions and theories, using hypothetico-deductive methods and falsifiability, recognizing we can never definitively prove but only disprove.

Explore verification and falsification in scientific testing through Popper's critical rationalism, emphasizing disproving theories with empirical data and the limits of provisional confirmation.

Practice critical rationalism by continually testing and disproving assumptions and theories to gain the best knowledge possible. Use hypothetico-deductive methods to falsify ideas and keep science testable.

Examine the basis set problem and the correspondence problem under critical rationalism, showing how data are theory-laden constructions and consensus defines evidence, illustrated by television ratings.

Explore how the provability of a theory depends on experimental or observational data, predictions, and resilience against alternatives, noting that theories are never proven, only falsified and refined over time.

Explore exhaustion as exhaustively testing theories through observation or experiments, and use depletion as a constraint with exception conditions to bolster provability and avoid falsification.

Explore the qualitative paradigm, focusing on subjective experiences and interpretations through interviews, observations, and text analysis to study phenomena in natural contexts and generate new hypotheses and theories.

Explore how hypotheses drive literature-based theory building through inductive, qualitative paths and theory testing through deductive, quantitative paths, guided by Balzert’s diagram and Riesenhuber’s model.

Explore implementation stages in the quantitative research process by concretizing hypotheses into operational forms aligned with design, sample, and qualitative results, and by formulating statistical hypotheses for upcoming tests.

Identify and differentiate difference, correlation, and change hypotheses, with examples of independent and dependent variables, predictor and criterion variables, and changes over time.

Learn to formulate null hypotheses (h0) and test them with nhst, using the tv example to assess significance and in-process quality assurance.

Explore different hypothesis forms, including undirected, directed, and unidirectional hypotheses, and identify how effect size, direction, nonzero effects, and specific versus non-specific hypotheses shape conclusions.

Explore how empiricism drives qualitative and quantitative research, formulate testable hypotheses, and apply empirical procedures to literature data, interviews, or economic reports in academic writing.

Identify and apply twelve quality criteria for scientific work, including basic criteria like purposefulness, logic, and honesty, plus empirical criteria such as validity and reliability, to guide your research project.

Define a clear research objective and a rigorous methodology to guide investigation. Science is goal-directed yet outcomes-open, seeking new knowledge rather than simply writing about a topic.

Examine the specificity of scientific papers through topic choice and method, highlighting interdisciplinary approaches like quantitative content analysis in humanities and the pursuit of unique, traceable research paths.

Explore how relevance drives scientific progress by contributing new knowledge for your discipline, oriented to your own research question, and aligned with the standards of the scientific community.

Present every chain of reasoning clearly from premises to conclusion, and distinguish inductive from deductive logic. Ensure scientific papers disclose full development, derivations, and explicit justification to prevent unsupported assertions.

The lecture explains that scientific papers must be comprehensible to readers or listeners, linking this to objectivity, verifiability, reliability, validity, relevance, and logical argumentation, plus reproducibility of steps.

Honesty establishes credibility through factual, neutral presentation in scientific papers, is an ethical norm and good scientific practice, and requires checking all data and sources before inclusion.

Ensure every formulation is testable so third parties can draw the same or comparable conclusions, and verify all sources, with unpublished materials excluded and data and methods transparently described.

Explore how outcomes-open research requires naming clients and interests to ensure scientificity and relevance, such as in marketing studies for a vegan product.

Explore validity as the accuracy of measurements in empirical research, focusing on defining the sample and research population, ensuring representativeness, and avoiding logical errors that threaten conclusions.

Ensure measurement accuracy and repeatability so results match across repeated measurements. Clarify procedures and selection criteria to prevent inconsistent outcomes in surveys or non-empirical work, upholding reliability and validity.

Learn how significance tests determine if relationships exceed chance using predefined thresholds and how sample sizes influence probability calculations in empirical, quantitative research.

Explains representativeness in quantitative research, defining samples relative to the population and requiring larger case numbers; shows how representative surveys work, cautions against universal generalizations, and notes potential transferability.

The upcoming research writing practice course covers university criteria for theses, practical research issues, proper literature use, outlining a scientific paper, and practical writing tips.