A note on the three layers of reality in critical realism and dissertation report writing (information from perplexity.ai)
My question 1: "Please describe the topic of the three layers of reality (in critical realism) in the context of doing MBA and Housing Studies dissertation projects.
The reply: "The three layers of reality, especially in the context of Critical Realism, are useful for understanding complex social phenomena like those studied in MBA and Housing Studies dissertation projects. This framework divides reality into three distinct but related ontological layers:
The Empirical Layer — This is the level of direct observation and experience. It includes events and phenomena that researchers can see, hear, measure, or otherwise observe during their study. For example, in housing studies, this might be observable instances of homelessness or eviction events.
The Actual Layer — At this level, the focus is on the underlying mechanisms and processes that cause the events observed in the empirical layer. Some mechanisms are visible or directly observable, while others remain hidden. For housing studies, this might include the social or economic processes causing a family to lose housing or rent arrears leading to eviction.
The Real Layer — This deepest layer consists of the underlying structures and conditions that generate the mechanisms at the actual level and events at the empirical level. These structures are generally not directly observable. For housing, the real layer could involve broader social structures like poverty, welfare systems, and labor market conditions that shape housing availability and stability.
In MBA and Housing Studies dissertation projects, applying this three-layered understanding helps in:
Recognizing that what is immediately visible (like market behavior or social outcomes) is influenced by deeper, often hidden causal processes and structures.
Going beyond surface-level data to theorize and investigate these underlying mechanisms and structural factors.
Using theory-driven analysis to connect observed data with broader societal and organizational realities.
For instance, in housing research, a researcher might observe the empirical events of eviction (empirical), explore reasons such as financial strain or policy change (actual), and link these to systemic issues like economic inequality or housing market regulation (real). In MBA studies, this framework can similarly be used to dissect organizational or market phenomena into observable actions, underlying mechanisms, and broader structural forces.
This approach aligns with the Critical Realism philosophy, which balances empirical observation with an understanding that reality includes unseen but real mechanisms and structures influencing empirical outcomes, and it encourages a layered, theory-backed analysis in dissertation projects in both fields."
My question 2: "In terms of using primary research methods (e.g. research interview, focus group, and questionnaire survey) and secondary research methods (e.g. desk research), in general, which research methods are useful to study which layers of reality (of critical realism), and how the line of reasoning and analysis should be reported in which dissertation report chapter(s) [chapter 2: literature review; chapter 3: research methodology; chapter 4: presentation of research findings; chapter 5: discussion of findings; and chapter 6: conclusions and recommendations)]?"
The reply: "Using Research Methods for the Three Layers of Reality in Critical Realism
Empirical layer (observed events): Primary research methods like interviews, focus groups, and surveys are especially useful here because they collect data on experiences, behaviors, and perceptions directly accessible to human senses. These methods help you document what actually can be observed or measured in your study.
Actual layer (events and mechanisms): While some events here are observable, others are hidden. A combination of primary methods (to capture events) and secondary research (desk research, document analysis) helps uncover patterns and processes behind observed phenomena. Analysis involves interpreting empirical data to identify causal events and regularities.
Real layer (underlying structures and causal mechanisms): This layer is not directly observable and must be inferred through theoretical reasoning and retroduction—moving beyond the data to propose explanations about the deep structures shaping the actual events. Secondary research (literature review, theory synthesis) is crucial here to locate and justify possible mechanisms and structures.
Reporting the Analysis in Dissertation Chapters
Chapter 2 (Literature Review): Present existing theories and concepts related to the 'real' layer—underlying causes and structures your study aims to investigate or explain. This chapter frames your theoretical understanding and hypothesis about causal mechanisms.
Chapter 3 (Research Methodology): Explain your choice of primary and secondary methods and how they relate to exploring the empirical and actual layers. Clarify your philosophical approach (critical realism) and reasoning strategy (e.g., retroduction).
Chapter 4 (Presentation of Findings): Report the raw data and observed events collected via your primary and secondary methods, corresponding to the empirical and actual layers.
Chapter 5 (Discussion of Findings): Engage in analytical reasoning to link empirical events to underlying actual mechanisms and infer real structures. Discuss how your findings support or challenge existing theories from Chapter 2.
Chapter 6 (Conclusions and Recommendations): Summarize insights about the real layer and implications for practice or further research, emphasizing understanding of causality and structures beyond immediate observations."
Factors and Criteria for Choosing Underlying Structures
Empirical Evidence and Theoretical Consistency: The choice of structures should be grounded in empirical data gathered (events at the empirical and actual layers), but must also be consistent with existing theories or conceptual frameworks that explain causality at a deeper level. The selection involves a balance of inductive reasoning from data and deductive reasoning guided by theory.
Demiregularities and Tendencies: Real structures are often identified through patterns or tendencies (demiregularities) that appear across different cases but are not absolute laws. Researchers look for consistent causal mechanisms that explain observed phenomena while recognizing contingency and complexity.
Inferences through Retroduction: The real layer structures cannot be observed directly; they are inferred through retroduction—where the researcher asks, "What must be true for this event or mechanism to occur?" This requires logical and reflective reasoning.
Alignment with Research Questions: Structures should be relevant to the research aims, focusing on explaining key phenomena rather than exhaustive description of all possible mechanisms.
Ethical and Practical Considerations: The chosen structures should also be feasible to study within ethical constraints and realistic access to information.
Subjectivity and Controversy in Choice
The selection of underlying structures is inevitably subjective to some extent because researchers bring their interpretations, theoretical preferences, and backgrounds to the process. Different researchers might infer different structures from the same data depending on their ontological and epistemological stance.
It is also potentially controversial, especially in social sciences like MBA and Housing Studies, where structural explanations may involve assumptions about social, economic, or political power that are open to critique or debate. Researchers should acknowledge this subjectivity and justify their reasoning transparently.
Critical realism encourages reflexivity and openness to revising chosen structures as new evidence or perspectives emerge, guarding against absolutism.
In sum, the choice of underlying real structures in dissertation projects should be carefully reasoned and justified based on empirical data, theory, and logical inference, while recognizing the inherent subjectivity and debate in this step."
A collection of blog notes on using chatgpt for research purpose.
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