1. After carefully reviewing the readings for Module 1, I noted three critical topics/issues that I want to highlight and factor into my doctoral studies project. First is the concept of judging the quality of a research design. Relative to positivist terminologies, case studies have a unique problem demonstrating research quality, given the qualitative nature of the findings. I found the discussion by Yin (Yin, 2018) helpful with great examples. Furthermore, the related readings allowed me to compare familiar terms such as validity and reliability to similar qualitative principles such as dependability, credibility, and transferability. Furthermore, I found it interesting how each criterion requires a different lens for case study designs. A chain of evidence, meticulous design details and a case study database are unique tactics to strengthen the research design.
The second issue I found important was the concept of a case study protocol, as explained by Yin (Yin, 2018). I liked how the protocol acts as a roadmap for the research design, ensuring that the collection procedures support the mission, theoretical framework, and research questions. The connection to protocol questions is vital, given the variety of data collection techniques available in case studies.
The third important issue is the variety of data collection techniques used in case study research. Archival records, documentation, observation, interviews, and physical artifacts have essential roles in gathering evidence, and a deep understanding of their strengths and weaknesses will ensure that such research is complementary.
Regarding two topics I would like to explore in greater detail, the idea of a conceptual framework deserves additional attention, given its importance in justifying the study, the research questions, and the final conclusions. While a detailed literature review helps formulate the conceptual framework, it is unclear how to decide on the components of the actual framework. The second topic I feel deserves increased attention is the concept of triangulation. While the idea sounds logical in its approach, I am unsure how to prove triangulation after completing the readings. It's clear that each data collection technique provides complementary insight to the research question but "how" that is achieved needs additional attention.
Finally, I found the philosophical underpinnings hard to follow at times. For example, after reviewing the readings by Yin (Yin, 2018) and Farquhar (Farquhar, 2012), the references to establishing a positivist or post-positivist epistemological perspective were confusing at times. While I have been able to gather the nuances between positivist and interpretivist, the exact impact on the research design is challenging to understand. A simple table that differentiates each would be useful.
2. The three key ideas most significant from the module readings are identifying and establishing the logic of the case study, collecting case study evidence, and analyzing case study evidence. Yin (2003) states that the topics of interest need to be well explored and that the phenomenon's essence is revealed. A case study design should be considered when: (a) the focus of the study is to answer "how" and "why" questions; (b) you cannot manipulate the behavior of those involved in the study; (c) you want to cover contextual conditions because you believe they are relevant to the phenomenon under study; or (d) the boundaries are not clear between the phenomenon and context.
The research design should include five components – case study questions, propositions (relevant questions to be collected), logic linking data to the requests, and criteria for interpreting the findings. The sense of a case study needs to accommodate the research design. There needs to be ample access to the data being collected so that a conclusion can be derived from the question.
Collecting case study evidence is a strategy. It requires preparation and planning. The system should include developing a protocol, vetting participants, and conducting a preliminary case study.
Case study evidence should include any archived records, interviews, and observations. The researcher's analysis of the case study evidence should be assessed by peers to independently code a set of data (Baxter and Jack, 2008). It is wise also to involve participants in the researcher's interpretations to clarify and provide perspective.
The two ideas/topics from the readings that need to be further explored are how to compose the case study (its various formats and methods) and addressing the use of theory to generalize from case studies. Theory development in case study research takes time and may be complex, and the existing knowledge base may be of poor quality (Rule & John, 2015).
One element that is difficult to understand in case study research methods is the criteria for judging the quality of research designs – construct validity, internal validity, external validity, and reliability. In Yin's book (2017), these four tests are used to establish the quality of empirical social research. Finding the right tactics and strategy to assess these criteria isis difficult to fully comprehend. With additional research, the difficulty of understanding will decrease.
3. This writer reviewed the literature related to this module and observed that despite the significant time difference between the writings (from Stake 1995 to Yin 2017), the critical concepts expressed in the various were essentially related. The writer’s views about them were not overly divergent, although they had, in some instances, different areas of focus. Case study research is a method that involves an in-depth analysis of a specific condition or case to comprehend a complex phenomenon. It is an observational study that researches a phenomenon within its real-life context or setting (Yin 2017) and provides a holistic, in-depth perception. It is used to understand or grasp complex, distinctive, or uncommon phenomena in real-life situations. It can generate new ideas, test existing assumptions, or assess programs and policies (Yin 2017).
In this writer's view, three significant ideas from the readings are the importance of a rigorous approach to case study research, triangulation, and research ethics.
Rigor and robustness of case study research – the authors alluded to this issue differently. According to Stake (1995), Case study research does not utilize only a single method but instead deploys a “family of methods” to investigate a wide range of phenomena. The author recommended choosing the right or appropriate method(s) and following a clear and rigorous case design (Stake 1995). For Yin (2017), Case study research methodology involves an in-depth examination of a specific situation or case and is used to understand a complex phenomenon. Case study research can provide valuable, thorough explanations and insights into the research subject. The author also stresses that proper design and planning of case study research play a crucial role in improving the quality and dependability of the research, beginning with clarity of the research question, a defined study population, and a comprehensive data collection plan, among other essential features, help to ensure that the resultant research is valid and trustworthy (Yin 2017). Flyvbjerg (2006) urges that the conventional wisdom in favor of the rule-based approach, which claims that case study is not rigorous, is misconceived and should be forgotten, arguing that Case study research methodology is rigorous. Thus, there is a convergence around the robustness and significance of case study research.
Triangulation is using multiple data sources to strengthen the authenticity and reliability of research findings. It enhances the potential for generalizability of the research findings or outcomes (Yin 2017, Flyvbjerg 2006). Using various methods and data sources allows the researcher to develop a more complete and nuanced sense of the phenomenon (Ezeako 2022, Mertens & Hesse-Biber 2012, Baxter & Jack 2008, Stake 1995).
Triangulation, comparative case studies, and other methods like multiple case methods enhance the generalizability of the findings from case study research (Yin 2017, Flyvbjerg, 2006, Stake 1995). However, case study researchers must be aware of the limit of generalization and be cautious in generalizing beyond the specific case studied (Yin 2017, Flyvbjerg 2006).
Ethical consideration in research – this was addressed extensively by Stake (1995). The author emphasized the need for ethics in research. Research involving human participants requires the observance of research ethics to protect human participants. Case study research, by its nature, includes qualitative research and often involves human participants. Accordingly, ethical considerations would come up. Research has its ethics. In this regard, a critical factor in research ethics is informed consent. Informed consent relates to ensuring that the participation of the humans involved in research is voluntary and not forced; they fully understand what they are consenting to; thus, their consent is informed (Ezeako 2022, Bhattacherjee 2012, Stake 1995). Accordingly, they must be made aware of their right to take part, not to participate, and to withdraw their consent at any time before their inclusion in the report (Bhattacherjee 2012). Sometimes, they are minors, and where this happens, informed consent necessitates that their parents or guardians are present when they give their consent (Bhattacherjee 2012, Stake 1995).
Two areas from the readings that interest this writer for further exploration are Mixed Methods and Research Bias.
Mixed Methods (MM) – in this writer’s view, MM is closely related to Triangulation (Yin, 2014, Howe 2012). Flyvbjerg (2006) highlights the misconception that case-study research is only qualitative, emanating from a narrow view of it, arguing that case-study research can integrate both qualitative and quantitative data and that MM frequently delivers a fuller grasp of a phenomenon. Thus, this writer considers it worthwhile to investigate further the advantages of employing MM in case study research and the specific ways qualitative and quantitative data might be effectively blended.
Mixed methods research involves gathering, analyzing, and interpreting quantitative and qualitative data in one study. This happens in various ways, deploying qualitative data like observations, interviews, and documents which provide a rich, nuanced understanding of a phenomenon, and quantitative data like numerical measurements to validate or contradict theories and provide more generalizable results (Yin 2014), thereby enabling the triangulation of data to strengthen findings (Flyvbjerg 2006).
Thus, with MM, researchers gather and evaluate data, combine the results, and draw conclusions from working with qualitative and quantitative methods in the same research. They may begin with a qualitative study to establish hypotheses and then examine them quantitatively or gather quantitative and qualitative data and evaluate them jointly. (Ezeako, 2022, Tashakkori & Creswell 2007, Flyvbjerg 2006). However, except for triangulation, MM approaches have been criticized in the literature (Silverman 2021, Howe 2012, Mertens & Hesse-Biber 2012).
Researcher Bias – case study research, according to Yin (2017) and Flyvbjerg (2006), is prone to bias. According to the authors, writer bias impacts research design, execution, and interpretation. Researchers can influence case selection, study design, data collecting, analysis, interpretation, and reporting. They may dismiss or ignore data that contradicts their assumptions or hypotheses and skew the findings.
Thus, researcher bias must be addressed. The authors suggest several ways of bias reduction, including self-awareness about their ideas, values, opinions, attitudes, and beliefs and transparent how they influence the research. Additionally, deploying diverse data collection methods and sources, rigorous data analysis, member checking, and peer debriefing reduce researcher bias and ensure the study is credible, reliable, and valid. Thus, triangulation is a means of reducing bias in research(Flyvbjerg 2006).
One concept that could be difficult to understand in the context of case study research is the generalizability of research.
Researchers may need help to generalize or apply case study findings to other settings. Generalization means applying study results to a larger population or setting. A key feature of research is that it is expected to be generalizable. However, case study research often focuses on one case, making it difficult to generalize the findings. Thus, the method has been criticized by some writers. However, Flyvbjerg (2006) believed that this criticism of case study is based on a misconception of the nature of case study research. According to the author, a case study is not intended to be generalizable in the same way that theoretical research and is unsuitable for statistical inferences (Flyvbjerg 2006).
Accordingly, researchers must be conscious of its generalization limitations when interpreting and presenting data from case study research (Stake 1995). The use of triangulation, “member checking,” employing multiple-case design to promote and boost the generalizability of the findings by identifying common themes or patterns across examples, have been suggested (Yin 2017, Baxter & Jack 2008, Flyvbjerg 2006, Stake 1995). Theoretical sampling, which selects instances based on theoretical relevance, increases case variance and generalizability. However, multiple case design has limitations and can prove challenging for novice researchers, so it is rarely used (Stake 1995).
Still, case study research emphasizes an in-depth analysis of a case or phenomenon. Thus, generalizations should be considered while analyzing and reporting case study results. Case study research is frequently focused on a single example, and the sample size is usually small and not randomly picked, making generalization difficult (Yin 2017). Thus, when evaluating and reporting findings of case research, the researchers must be transparent and upfront about the limits of generalization and disclose how the case picked is indicative of the topic being examined (Yin 2017, Flyvbjerg 2006). Researchers can still gain valuable insights and understanding of complex phenomena by being aware of the limits of generalization.
On the one hand, Flyvbjerg (2006) describes case study research as not requiring generalizability; on the other hand, the author describes it as generalizable.
4. The three most significant ideas from the readings were the importance of planning the basic design, the data collection procedures, and the data analysis. In regards to the planning and strategizing, the rest of the case study sits on the foundation of the protocol. While outlines and other methods of planning are subject to change, having as detailed planning as possible helps solve other issues; Yin (2017) discusses the importance of remaining targeted on the topic of the case study, which is accomplished by clear planning. Yin also illustrates the importance of having a tentative report outline created for the protocol, which allows that report to remain at the forefront of the researcher’s thoughts as it is being developed.
An integral part of the protocol planning and strategy is at the core: the design of the research. As Flyvbjerg (2006) references, methodology always depends on the problem; the research methods being used should be the most applicable for the actual problem. The entire rest of the research process stems from that. Farquhar (2012) goes even further and breaks down the planning into ensuring that the proper words are used in objectives.
The next significant idea of data collection procedures illustrates the potential challenges involved. Farquhar (2012) acknowledges that qualitative data is especially helpful in case studies, while there is also room for supporting quantitative data. Collection of the data is something which needs to be well planned. In case studies, the researcher is devoid of most control provided in other research. Flexibility and planning for alternate arrangements in meeting with the participants is key; schedule changes and conflicts are an inherent part of organizations and need to be considered, as well as general access to them (Yin, 2017). Yin (2017) also references the value of a “word table” being used in a shell table to help ensure that ensure that the questions, answers, and data all align.
Another important data collection component was highlighted in Gagnon (2010); this is that data collection ideas need to be clear in the protocol, and especially descriptive on the entire process and the participants. These items referenced by Farquhar (2012), Yin (2017), and Gagnon (2010) all illustrate that there are many challenges to data collection and that the need for planning and extremely clear documentation is vital.
In the data analysis phase, avoiding bias is also an item to note. Having a clear protocol—including software usage—is The last of the top three significant items to note is the data analysis component. After the extensive data collection process, the value of that data rests on the analysis of it. Gagnon (2010) highlights the importance of reliability; this would need to be both internally, as well as externally if applicable. The internal reliability is strengthened by strong protocols, and despite the focused nature of case studies, external reliability is also aided by those and can allow broader usage (Flyvbjerg, 2006).
needed to reduce bias; while bias can be an issue during the data collection and other phases, the analysis phase is where it can have an oversized effect (Gagnon, 2010). The analysis should also be tied to the literature review as an additional point of cohesion, avoiding bias and aligning with the rest of the process (Farquhar, 2012).
Two items of particular note which would lead to benefit by being explored further would be the critical cases and alternative explanations. The critical cases were laid out well in Flyvbjerg (2006) in that they are cases in which a question can be answered and applied on a wide scope, such as if an event can occur at a certain place which would be the least likely to have it take place, it can occur anywhere. Determining when this is able to be applied is of particular interest, while the examples make perfect sense.
For alternative explanations, Gagnon (2010) references including rivaling and unknown explanations into the conclusions. That is particularly interesting, as it would involve extensive additional literature review and study of the problem and data.
Finally, one item which took some additional thoughts to understand is analysis units. It is a common item of difficulty, as it has been pointed out that even seasoned researchers can sometimes struggle with it (Baxter & Jack, 2008). It was explained well in Yin (2017), that the collective group must be considered, while the data itself is from people. However, it still took additional review to fully understand the implications on research of it.
