analysis of alternatives in case study

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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Masterful Decision-Making: Identifying the Right Alternatives

by Logapps LLC June 17, 2020

Proper planning and research are the foundation of alternatives analysis. The first step is to establish the problem and define the decisions that will be made. The client may come to you with an initial problem or a problem with a solution that has already been implemented. Apart from the system itself, stakeholders and decision makers may have disagreements amongst themselves. Planning involves comprehensive research and meetings with team members and stakeholders in the process to outline objectives, designate efforts, and estimate costs. Evaluating the time and resources available is important to ensure that the project itself can be carried out. These initial steps give the project direction and set goals for completion. 

Many decision makers do not know where to start when gathering information about the market. One approach is to study the competition: research if competitors have encountered a comparable problem in the past and learn from their successes and failures. Case studies are excellent sources of information, because they are real-world examples of implemented solutions.  Alternatively, reaching out to the consumers may be equally as effective. Surveys and focus groups are a direct method of obtaining feedback on past projects or proposals for new technologies. An external perspective may be the key to a solution.

Deciding on alternatives should be a team effort to incorporate a variety of perspectives. Having an open discussion or multiple “brainstorming sessions” with team members may highlight options not considered before. This allows for input from legal, economic, and technological perspectives as well as various levels of experience. The aim of identifying alternatives could be to resolve a problem with an initially proposed decision or simply have a backup plan in case a decision goes awry. These may include modification, elimination, developing a new system, delaying a decision, or maintaining the “status quo”- keeping the current system in place. Depending on the given situation, some alternatives may be more beneficial than others. While adjusting the system, removing a part of the system, or creating a new system altogether may seem more progressive, sometimes delaying the decision or keeping the system in place may be the “right” alternative at the moment. Decision delayal, if implemented with set goals, allows for more time to research and identify the correct approach to an issue. Keeping the status quo is a method that allows for continued experimentation with the current system, considering modifications later on.

There may be many more alternatives available than the ones detailed above. It is important to evaluate multiple alternatives, because the decision-making process may require more than one. There must be set criteria to evaluate each alternative and compare them to one another. This will provide a method of elimination for ineffective alternatives. This process can be time-consuming but is worthwhile in the end. According to a report on the Analysis of Alternatives in Defense Acquisitions from the Government Accountability Office (GAO), “programs that considered a broad range of alternatives tended to have better cost and schedule outcomes than the programs that looked at a narrow scope of alternatives” [1]. A robust assessment of alternatives will help to develop working solutions better fit to the client’s needs. 

The presentation of results to stakeholders and decision-makers is an opportunity to showcase the information generated so far. Whether the presentation is a PowerPoint, a report, an infographic, or an open discussion, presenting the results of the alternatives analysis as clearly, consistently, and accurately as possible is the priority. This will let major stakeholders know how the project is going and if more resources are needed. The team must also provide a detailed analysis of the systematic and engineering risks. This will help plan for future implementation, make accurate cost estimates, and ensure that the project is completed on schedule. Direct and honest communication with stakeholders throughout the process will result in a better outcome.

Meticulously researching the contexts of the problem, analyzing and comparing various alternatives, and consulting the findings will help foster successful solutions. It may take time to identify the correct alternatives and it may take multiple trials to implement them. However, the key to establishing successful alternatives is organization and a continued effort toward a specific goal. If the time frame, team, and costs are planned in detail, the decision-making process will become much more manageable.

[1]  “Defense Acquisitions:  Many Analyses of Alternatives Have Not Provided a Robust Assessment of Weapon System Options,” United States Government Accountability Office, Report to the Chairman, Subcommittee on National Security and Foreign Affairs, Committee on Oversight and Government Reform, House of Representatives, Sep. 2009.

Additional Resources

“Analyses of Alternatives,”  The MITRE Corporation , Aug. 2013.

S. Bauer, “The Art of Decision Making – Part 4: Identifying Alternatives,”  Product Anonymous , 15-Sep-2013.

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Do Your Students Know How to Analyze a Case—Really?

Explore more.

• Case Teaching
• Student Engagement

J ust as actors, athletes, and musicians spend thousands of hours practicing their craft, business students benefit from practicing their critical-thinking and decision-making skills. Students, however, often have limited exposure to real-world problem-solving scenarios; they need more opportunities to practice tackling tough business problems and deciding on—and executing—the best solutions.

To ensure students have ample opportunity to develop these critical-thinking and decision-making skills, we believe business faculty should shift from teaching mostly principles and ideas to mostly applications and practices. And in doing so, they should emphasize the case method, which simulates real-world management challenges and opportunities for students.

To help educators facilitate this shift and help students get the most out of case-based learning, we have developed a framework for analyzing cases. We call it PACADI (Problem, Alternatives, Criteria, Analysis, Decision, Implementation); it can improve learning outcomes by helping students better solve and analyze business problems, make decisions, and develop and implement strategy. Here, we’ll explain why we developed this framework, how it works, and what makes it an effective learning tool.

The Case for Cases: Helping Students Think Critically

Business students must develop critical-thinking and analytical skills, which are essential to their ability to make good decisions in functional areas such as marketing, finance, operations, and information technology, as well as to understand the relationships among these functions. For example, the decisions a marketing manager must make include strategic planning (segments, products, and channels); execution (digital messaging, media, branding, budgets, and pricing); and operations (integrated communications and technologies), as well as how to implement decisions across functional areas.

Faculty can use many types of cases to help students develop these skills. These include the prototypical “paper cases”; live cases , which feature guest lecturers such as entrepreneurs or corporate leaders and on-site visits; and multimedia cases , which immerse students into real situations. Most cases feature an explicit or implicit decision that a protagonist—whether it is an individual, a group, or an organization—must make.

For students new to learning by the case method—and even for those with case experience—some common issues can emerge; these issues can sometimes be a barrier for educators looking to ensure the best possible outcomes in their case classrooms. Unsure of how to dig into case analysis on their own, students may turn to the internet or rely on former students for “answers” to assigned cases. Or, when assigned to provide answers to assignment questions in teams, students might take a divide-and-conquer approach but not take the time to regroup and provide answers that are consistent with one other.

To help address these issues, which we commonly experienced in our classes, we wanted to provide our students with a more structured approach for how they analyze cases—and to really think about making decisions from the protagonists’ point of view. We developed the PACADI framework to address this need.

PACADI: A Six-Step Decision-Making Approach

The PACADI framework is a six-step decision-making approach that can be used in lieu of traditional end-of-case questions. It offers a structured, integrated, and iterative process that requires students to analyze case information, apply business concepts to derive valuable insights, and develop recommendations based on these insights.

Prior to beginning a PACADI assessment, which we’ll outline here, students should first prepare a two-paragraph summary—a situation analysis—that highlights the key case facts. Then, we task students with providing a five-page PACADI case analysis (excluding appendices) based on the following six steps.

Step 1: Problem definition. What is the major challenge, problem, opportunity, or decision that has to be made? If there is more than one problem, choose the most important one. Often when solving the key problem, other issues will surface and be addressed. The problem statement may be framed as a question; for example, How can brand X improve market share among millennials in Canada? Usually the problem statement has to be re-written several times during the analysis of a case as students peel back the layers of symptoms or causation.

Step 2: Alternatives. Identify in detail the strategic alternatives to address the problem; three to five options generally work best. Alternatives should be mutually exclusive, realistic, creative, and feasible given the constraints of the situation. Doing nothing or delaying the decision to a later date are not considered acceptable alternatives.

Step 3: Criteria. What are the key decision criteria that will guide decision-making? In a marketing course, for example, these may include relevant marketing criteria such as segmentation, positioning, advertising and sales, distribution, and pricing. Financial criteria useful in evaluating the alternatives should be included—for example, income statement variables, customer lifetime value, payback, etc. Students must discuss their rationale for selecting the decision criteria and the weights and importance for each factor.

Step 4: Analysis. Provide an in-depth analysis of each alternative based on the criteria chosen in step three. Decision tables using criteria as columns and alternatives as rows can be helpful. The pros and cons of the various choices as well as the short- and long-term implications of each may be evaluated. Best, worst, and most likely scenarios can also be insightful.

Step 5: Decision. Students propose their solution to the problem. This decision is justified based on an in-depth analysis. Explain why the recommendation made is the best fit for the criteria.

Step 6: Implementation plan. Sound business decisions may fail due to poor execution. To enhance the likeliness of a successful project outcome, students describe the key steps (activities) to implement the recommendation, timetable, projected costs, expected competitive reaction, success metrics, and risks in the plan.

“Students note that using the PACADI framework yields ‘aha moments’—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.”

PACADI’s Benefits: Meaningfully and Thoughtfully Applying Business Concepts

The PACADI framework covers all of the major elements of business decision-making, including implementation, which is often overlooked. By stepping through the whole framework, students apply relevant business concepts and solve management problems via a systematic, comprehensive approach; they’re far less likely to surface piecemeal responses.

As students explore each part of the framework, they may realize that they need to make changes to a previous step. For instance, when working on implementation, students may realize that the alternative they selected cannot be executed or will not be profitable, and thus need to rethink their decision. Or, they may discover that the criteria need to be revised since the list of decision factors they identified is incomplete (for example, the factors may explain key marketing concerns but fail to address relevant financial considerations) or is unrealistic (for example, they suggest a 25 percent increase in revenues without proposing an increased promotional budget).

In addition, the PACADI framework can be used alongside quantitative assignments, in-class exercises, and business and management simulations. The structured, multi-step decision framework encourages careful and sequential analysis to solve business problems. Incorporating PACADI as an overarching decision-making method across different projects will ultimately help students achieve desired learning outcomes. As a practical “beyond-the-classroom” tool, the PACADI framework is not a contrived course assignment; it reflects the decision-making approach that managers, executives, and entrepreneurs exercise daily. Case analysis introduces students to the real-world process of making business decisions quickly and correctly, often with limited information. This framework supplies an organized and disciplined process that students can readily defend in writing and in class discussions.

PACADI in Action: An Example

Here’s an example of how students used the PACADI framework for a recent case analysis on CVS, a large North American drugstore chain.

The CVS Prescription for Customer Value*

PACADI Stage

Summary Response

How should CVS Health evolve from the “drugstore of your neighborhood” to the “drugstore of your future”?

Alternatives

A1. Kaizen (continuous improvement)

A2. Product development

A3. Market development

A4. Personalization (micro-targeting)

Criteria (include weights)

C1. Customer value: service, quality, image, and price (40%)

C2. Customer obsession (20%)

C3. Growth through related businesses (20%)

C4. Customer retention and customer lifetime value (20%)

Each alternative was analyzed by each criterion using a Customer Value Assessment Tool

Alternative 4 (A4): Personalization was selected. This is operationalized via: segmentation—move toward segment-of-1 marketing; geodemographics and lifestyle emphasis; predictive data analysis; relationship marketing; people, principles, and supply chain management; and exceptional customer service.

Implementation

Partner with leading medical school

Curbside pick-up

Pet pharmacy

E-newsletter for customers and employees

Employee incentive program

CVS beauty days

Expand to Latin America and Caribbean

Healthier/happier corner

Holiday toy drives/community outreach

*Source: A. Weinstein, Y. Rodriguez, K. Sims, R. Vergara, “The CVS Prescription for Superior Customer Value—A Case Study,” Back to the Future: Revisiting the Foundations of Marketing from Society for Marketing Advances, West Palm Beach, FL (November 2, 2018).

Results of Using the PACADI Framework

When faculty members at our respective institutions at Nova Southeastern University (NSU) and the University of North Carolina Wilmington have used the PACADI framework, our classes have been more structured and engaging. Students vigorously debate each element of their decision and note that this framework yields an “aha moment”—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.

These lively discussions enhance individual and collective learning. As one external metric of this improvement, we have observed a 2.5 percent increase in student case grade performance at NSU since this framework was introduced.

Tips to Get Started

The PACADI approach works well in in-person, online, and hybrid courses. This is particularly important as more universities have moved to remote learning options. Because students have varied educational and cultural backgrounds, work experience, and familiarity with case analysis, we recommend that faculty members have students work on their first case using this new framework in small teams (two or three students). Additional analyses should then be solo efforts.

To use PACADI effectively in your classroom, we suggest the following:

Advise your students that your course will stress critical thinking and decision-making skills, not just course concepts and theory.

Use a varied mix of case studies. As marketing professors, we often address consumer and business markets; goods, services, and digital commerce; domestic and global business; and small and large companies in a single MBA course.

As a starting point, provide a short explanation (about 20 to 30 minutes) of the PACADI framework with a focus on the conceptual elements. You can deliver this face to face or through videoconferencing.

Give students an opportunity to practice the case analysis methodology via an ungraded sample case study. Designate groups of five to seven students to discuss the case and the six steps in breakout sessions (in class or via Zoom).

Ensure case analyses are weighted heavily as a grading component. We suggest 30–50 percent of the overall course grade.

Once cases are graded, debrief with the class on what they did right and areas needing improvement (30- to 40-minute in-person or Zoom session).

Encourage faculty teams that teach common courses to build appropriate instructional materials, grading rubrics, videos, sample cases, and teaching notes.

When selecting case studies, we have found that the best ones for PACADI analyses are about 15 pages long and revolve around a focal management decision. This length provides adequate depth yet is not protracted. Some of our tested and favorite marketing cases include Brand W , Hubspot , Kraft Foods Canada , TRSB(A) , and Whiskey & Cheddar .

Art Weinstein , Ph.D., is a professor of marketing at Nova Southeastern University, Fort Lauderdale, Florida. He has published more than 80 scholarly articles and papers and eight books on customer-focused marketing strategy. His latest book is Superior Customer Value—Finding and Keeping Customers in the Now Economy . Dr. Weinstein has consulted for many leading technology and service companies.

Herbert V. Brotspies , D.B.A., is an adjunct professor of marketing at Nova Southeastern University. He has over 30 years’ experience as a vice president in marketing, strategic planning, and acquisitions for Fortune 50 consumer products companies working in the United States and internationally. His research interests include return on marketing investment, consumer behavior, business-to-business strategy, and strategic planning.

John T. Gironda , Ph.D., is an assistant professor of marketing at the University of North Carolina Wilmington. His research has been published in Industrial Marketing Management, Psychology & Marketing , and Journal of Marketing Management . He has also presented at major marketing conferences including the American Marketing Association, Academy of Marketing Science, and Society for Marketing Advances.

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

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The Defense Acquisition Encyclopedia

Acquisition Process

An Analysis of Alternatives (AoA) is an analytical comparison of the operational effectiveness, suitability, and life-cycle cost of alternative material solutions that satisfy an established capability need to be identified in an Initial Capabilities Document (ICD) . It focuses on the identification and analysis of alternatives, Measures of Effectiveness (MOE) , schedule, Concepts of Operations (CONOPS) , and overall risk.  An AoA also assesses Critical Technology Elements (CTEs) associated with each proposed material solution, including; technology maturity, integration risk, manufacturing feasibility, and technology maturation and demonstration needs.

Definition: The Analysis of Alternatives (AoA) is a documented evaluation of the performance, operational effectiveness, operational suitability, and estimated costs of alternative systems to meet a capability need that has been identified through the Joint Capabilities Integration and Development Systems (JCIDS) process.

When is the Analysis of Alternatives (AoA) Conducted

The AoA is conducted during the Materiel Solution Analysis (MSA) Phase before Milestone A . The final AoA supporting a Milestone A decision is provided to the Director of Cost Assessment and Program Evaluation (DCAPE) not later than 60 days prior to the milestone decision review meeting. The DCAPE normally develops and approves AoA Study Guidance . [1]

How the Analysis of Alternative (AoA) Supports Materiel Solution Analysis (MSA) Phase

The AoA process greatly aids the Materiel Solution Analysis (MSA) Phase. The AoA process is required to define better the trade space between cost, schedule, and performance after a program has an approved Materiel Development Decision. This will help the Defense Acquisition Executive (DAE) and Service Sponsor choose a preferred materiel solution addressing the capability gaps in the approved Initial Capabilities Document (ICD) .

Analysis of Alternative (AoA) Study Guidance

The AoA Study Guidance is developed to guide an Analysis of Alternatives. The guidance is approved by the Director of Cost Assessment and Program Evaluation (DCAPE) with the input of other DoD officials. Prior to the Materiel Development Decision (MDD) review, the DCAPE provides the AoA study guide to the DoD Component designated by the Milestone Decision Authority (MDA) . Following receipt of the AoA study guidance, the DoD Component prepares an AoA study plan that describes the intended methodology for the management and execution of the AoA. The AoA study plan is coordinated with the MDA and approved by DCAPE prior to the MDD review.

Steps to Conducting an Analysis of Alternatives (AoA)

DoD Components should make sure that the AoA is conducted with transparency. More openness makes it easier to understand the analysis and helps leaders focus on what the analysis means because they know where it came from. It also lets Components deal with any problems that might arise before they waste time and effort and do more work. The typical steps in conducting an AoA include:

• Step 1 Plan: Determine the goals, schedule, stakeholders, funding, team, and deliverables.
• Step 2 Establish analysis foundation: Determine the problem and scope being addressed and the ground rules and assumptions.
• Step 3 Identify and Define Alternatives: Identify the alternatives to the problem set.
• Step 4 Assess Alternatives: Assess each of the alternatives identified
• Step 5 Compare Alternatives: Determine the pros and cons of alternative solutions
• Step 6 Report results: Document results for decision-makers
• Step 7 Follow-up: Conduct a follow-up analysis on any chosen alternative

Notional Analysis of Alternative (AoA) Outline

An AoA will normally include the following sections, although it can (and should) be tailored or streamlined to support the given situation. An outline is provided in the AoA Main References Section below. The normal AoA sections are:

• Capability Need, Deficiencies, and Opportunities
• Program Description
• Operational Environments
• Operational Concept
• Operational Requirements
• Status Quo (Baseline) and Alternatives
• System Design, Performance, and Measures of Effectiveness
• Life-Cycle Costs of Baseline and each alternative
• Life Cycle Cost per unit system
• Life Cycle Cost per specified quantity of systems
• Analysis of Alternatives
• Trade-off Analysis
• Sensitivity Analysis
• Recommendations and Conclusions

Analysis of Alternatives (AoA) Main References

Below are the main references that address conducting an AoA within the defense acquisition system.

Instruction: DoD Instruction 5000.84 “Analysis of Alternatives”

Handbook: analysis of alternative (aoa) handbook – aug 2017, outline: recommended outline for the aoa plan.

AcqNotes Tutorials

Analysis of Alternatives (AoA) Reporting and Certification

For Major Defense Acquisition Programs (MDAP) at Milestone A, the Milestone Decision Authority (MDA) must certify in writing to Congress that the Department has completed an AoA consistent with study guidance developed by the Director of Cost Assessment and Program Evaluation (DCAPE).  For MDAPs at Milestone B , the MDA must certify in writing to Congress that the Department has completed an AoA with respect to the program. [1]

When is the Analysis of Alternatives (AoA) Updated

An AoA should be updated and performed in each acquisition phase and throughout the lifecycle of a program to guarantee that the correct material solution is being developed.  The update should be used to refine the proposed material solution and reaffirm the rationale in terms of cost-effectiveness.

Analysis of Alternatives (AoA) Input into the Technology Development Strategy (TDS)

The Technology Development Strategy (TDS) should highlight how the risks identified in the AoA areas are going to be addressed and minimized in the Technology Maturation & Risk Reduction (TMRR) Phase and on the path to full manufacturing capability in the Production and Deployment (PD) Phase .

Analysis of Alternatives (AoA) Input into an Alternative Systems Review (ASR)

Completion of the Alternative Systems Review (ASR) should provide a comprehensive rationale for the proposed material solution(s), based upon the AoA that evaluated relative cost, schedule, performance (hardware, human, software), and technology risks.

Analysis of Alternatives (AoA) Statutory Requirements

For MDAP, Major Automated Information System (MAIS) programs, and all Automated Information Systems (AIS) programs, including National Security Systems (NSSs), at Milestone A. Updates are required through Milestone C (or Milestone B if there is no Milestone C) for MAIS programs, and all AIS programs.

Analysis of Alternatives (AoA) Regulatory Requirements

For all other specified Program Type/Event combinations.

• The Milestone Decision Authority (MDA) will determine if a new or updated AoA is needed after Milestone A. Make sure you know the AoA requirements during each phase.

AcqLinks and References:

• [1] Defense Acquisition Guidebook
• DoD Instruction 5000.84 “Analysis of Alternatives”
• Analysis of Alternatives (AoA) Handbook – 4 Aug 2017
• Analysis of Alternatives (AoA) Study Guidance
• (Old Version) Analysis of Alternatives (AoA) Handbook – July 2016
• (Old Version)  Analysis of Alternative (AoA) Handbook – July 2008
• Decision-Based upon AoA by Dr. David G. Ullman – January 2009
• Recommended Outline for the AoA Study Plan

Updated: 2/13/24

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What Is Alternative Analysis in Project Management?

There’s more than one way to skin a cat. That is to say—you can reach the same outcome through different means. To discover those means, you can use an alternative analysis. An alternative analysis is how project managers choose how they’re going to approach a project.

There are always options, and knowing which is the best one for your project takes time, research and an understanding of possible scenarios. An analysis of alternatives is required, and today’s blog will guide you through the tools and methods you should use to get the data you need.

What is Alternative Analysis?

An alternative analysis is the evaluation of the various routes you can pursue to achieve the goal of a project or a particular project management objective. It looks beyond the status quo to compare different ways of getting work done.

These factors can be operational, such as cost, risk and effectiveness, as well as the potential shortfalls of those operational factors. To perform an alternative analysis, you use tools such as life-cycle costing, sensitivity analysis, cash flow analysis and cost-benefit analysis .

Analysis of alternatives, also called AOA, is part of the decision-making process when looking at existing portfolios, programs and projects or while initiating a new project. This decision-making process helps you find cost-effective actions and avoid unnecessary effort duplication. Using AOA will help reduce the risk of project failure.

When Should Alternative Analysis Be Used in Project Management?

Alternative analysis is used whenever a solution is needed. When decision-makers are in the decision-making process, they want to know the best course for moving forward. An alternative analysis will show them the way.

An AOA is typically done at the initiation of a project  but is also used throughout the life cycle of the project. It’s one way to determine if the decision-making process is sound. Making updates throughout the process will refine the solution and reaffirm the assessment criteria.

However, an AOA is not something that’s simple to do. Analysis is often time-consuming. Because of the time and labor involved in an AOA, it’s more likely that you will apply it to portfolios or programs. These portfolios or programs are more of an ongoing concern than a single project, and managers are constantly on the lookout for ways to import the cost-effectiveness of the work.

Whatever way you use alternative analysis, you’ll need project management software to evaluate and choose the best course of action from a variety of options. ProjectManager delivers insightful real-time data to help in the analytic comparison. Real-time dashboards capture data for projects and automatically calculate that information into easy-to-read graphs and charts. Try ProjectManager today for free!

Benefits of Analysis of alternatives (AOA)

Using an analysis of alternatives (AOA) will help the project, portfolio and program managers identify, understand and evaluate the alternatives open to them when managing a project. It will also help them to select the best course of action as it concerns project costs and risks.

By looking at alternatives, decision-makers have more data to make an informed choice with. Being able to identify, define and understand a problem is the best way to solve it, and is essential to any worthwhile analysis.

When decision-makers have many alternatives to choose from, they have more power to control the outcome. But the data must be good. Avoiding prejudices, such as paying more attention to information that is easy to access, is important as well.

How to Execute an Alternative Analysis

An alternative analysis provides a framework to look at your process and seek out improvements that are of the greatest advantage to you. Follow these steps to help you determine which of these routes would be most advantageous.

1. Make a Plan

The first thing to do is make a plan . This means defining the various decisions that you can make to meet your objective and achieve operational effectiveness. You’ll want to include stakeholders, but also define the timing, effort or costs involved. There will need to be a study team assembled and a study plan to direct their activities.

2. Organize the Analytic Framework

The next step is to define the analysis problem statement, the context of the problem, scope and a framework for alternative comparisons. This includes the comparison criteria you’ll use. Frame the analysis with the ground rules you’ll use, including any assumptions you might have. Before and during the study, you’ll want to access the data needs, collection and sources used.

3. Identify and Define Alternatives

Now you identify the various alternative routes you can take from the data sources. There will be many, but be sure to keep one as the status quo. These alternatives will address the problem you stated in your plan within the context and scope you’ve already defined. The alternatives you evaluate must come out of thorough research, vetting and filtering before you can begin the decision-making process.

4. Assess the Alternatives

Look over each of the alternatives you’ve come up with. Evaluate them against your established criteria, such as cost, risk, life cycle cost-effectiveness, benefits and likelihood analysis. You’ll also need to conduct a sensitivity analysis, which is a financial model that looks at a target variable and how it is impacted by changes from input variables.

5. Compare the Alternatives

Here, you’ll weigh the pros and cons of the various alternatives you’ve identified. Determine what the merits of each are, as shown by the analysis you’ve made.

6. Report the Results

Finally, you’ll want to document the results of the AOA to show the life cycle cost that supports the alternative or status quo you chose, and how that will support the project decision-makers and/or stakeholder needs.

Alternative Analysis Tips

When managing a project, the alternative analysis should be part of your decision-making. It lets you to find the best way forward. To do this, you’ll need to create a study plan. It will act as a roadmap for how your analysis should proceed, and explain who is responsible for what.

You can’t do an alternative analysis without first devoting the time and resources needed. You need to give yourself enough time to look at the alternatives and determine the risks involved with each.

Be sure to always have a baseline of your status quo in order to have something to compare your alternatives against. It might also prove that the status quo is the best approach. Often collecting baseline data is time-consuming, so make sure you have scheduled enough time and resources to do it.

Manage Stakeholders

It’s also important to understand your stakeholders and their needs and manage their expectations. This will inform the scope and execution of your analysis.

Stakeholders will inform your comparison criteria, too. Just as knowing your stakeholders is important, so is assembling an appropriate team with the right skills and experience.

Be open to many perspectives and use the appropriate methods, tools and data to support the decision-making process. The data must work with the methodology used, or you’ll have bad data in and bad data out.

How ProjectManager Helps with Alternative Analysis & Project Evaluation

ProjectManager is a cloud-based project and work management software that helps you better organize your alternative analysis, whether for a single project, program or portfolio of projects. You get real-time data that leads to better decision-making and makes it easy to connect hybrid teams and share results with stakeholders.

Plan Alternative Analyses on a Gantt Chart

Plan your alternative analysis with ProjectManager’s Gantt chart view. You can organize all your tasks, then filter a baseline to capture the status quo. You can make assignments and share the plan with stakeholders. If you’re working on more than one project, the portfolio roadmap is a Gantt chart that puts all your projects on a shared timeline.

Different Work Views for Hybrid Teams

Once your team goes to work on determining alternatives, they can work how they want to with ProjectManager’s multiple project views. Whether they prefer a task list, calendar or kanban board, all the data is shared across the multiple project views. Each view shows the status of the work. You can set priority, add tags and comment to foster collaboration no matter where or when your team is working.

Generate Frequent Status Reports to Track Progress

Once you have all the data collected from your team, you can generate one-click reports for status reports, portfolio status reports and much more. All the data can be filtered to show just the information you want to see. Then, share the report as a PDF, Excel or print it out to present to stakeholders.

ProjectManager is award-winning software that organizes work for hybrid teams. You can plan, monitor and report on projects and generate alternative analyses so decision-makers have more insightful data to work with. Join the tens of thousands of users worldwide, from NASA to Siemens and Nestle, who use our product to better manage their projects. Try ProjectManager today for free!

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• Environ Health Perspect
• v.125(6); 2017 Jun

Advancing Alternative Analysis: Integration of Decision Science

Timothy f. malloy.

1 UCLA School of Law, University of California, Los Angeles (UCLA), Los Angeles, California, USA

2 UCLA Fielding School of Public Health, UCLA, Los Angeles, California, USA

3 University of California Center for the Environmental Implications of Nanotechnology, UCLA, Los Angeles, California, USA

Virginia M. Zaunbrecher

Christina m. batteate.

4 Environmental and Public Health Consulting, Alameda, California, USA

William F. Carroll, Jr.

5 Department of Chemistry, Indiana University Bloomington, Bloomington, Indiana, USA

Charles J. Corbett

6 UCLA Anderson School of Management, UCLA, Los Angeles, California, USA

7 UCLA Institute of the Environment and Sustainability, UCLA, Los Angeles, California, USA

Steffen Foss Hansen

8 Department of Environmental Engineering, Technical University of Denmark, Copenhagen, Denmark

Robert J. Lempert

9 RAND Corporation, Santa Monica, California, USA

Igor Linkov

10 U.S. Army Engineer Research and Development Center, Concord, Massachusetts, USA

Roger McFadden

11 McFadden and Associates, LLC, Oregon, USA

Kelly D. Moran

12 TDC Environmental, LLC, San Mateo, California, USA

Elsa Olivetti

13 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Nancy K. Ostrom

14 Safer Products and Workplaces Program, Department of Toxic Substances Control, Sacramento, California, USA

Michelle Romero

Julie m. schoenung.

15 Henry Samueli School of Engineering, University of California, Irvine, Irvine, California, USA

Thomas P. Seager

16 School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA

Peter Sinsheimer

Kristina a. thayer.

17 Office of Health Assessment and Translation, National Toxicology Program, National Institute of Environmental Health Sciences, Morrisville, North Carolina, USA

Supplemental Material is available online ( https://doi.org/10.1289/EHP483 ).

The authors declare they have no actual or potential competing financial interests.

Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact vog.hin.shein@enilnophe . Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Associated Data

Background:.

Decision analysis—a systematic approach to solving complex problems—offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.

Objectives:

We assessed whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.

A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and were prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups’ findings.

We concluded that the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients and would also advance the science of decision analysis.

Conclusions:

We advance four recommendations: a ) engaging the systematic development and evaluation of decision approaches and tools; b ) using case studies to advance the integration of decision analysis into alternatives analysis; c ) supporting transdisciplinary research; and d ) supporting education and outreach efforts. https://doi.org/10.1289/EHP483

Introduction

Policy makers are faced with choices among alternative courses of action on a regular basis. This is particularly true in the environmental arena. For example, air quality regulators must identify the best available control technologies from a suite of options. In the federal program for remediation of contaminated sites, government project managers must propose a clean-up method from a set of feasible alternatives based on nine selection criteria ( U.S. EPA 1990 ). Rule makers in the Occupational Safety and Health Administration (OSHA) compare a variety of engineering controls and work practices in light of technical feasibility, economic impact, and risk reduction to establish permissible exposure limits ( Malloy 2014 ). At present, as we describe below, some agencies must identify safer, viable alternatives to chemicals for consumer and industrial applications. Such evaluation, known as alternatives analysis, requires balancing numerous, often incommensurable, decision criteria and evaluating the trade-offs among those criteria presented by multiple alternatives.

The University of California Sustainable Technology and Policy Program, in partnership with the University of California Center for Environmental Implications of Nanotechnology (CEIN), hosted a workshop on integrating decision analysis and predictive toxicology into alternatives analysis ( CEIN 2015 ). The workshop brought together approximately 40 leading decision analysts, toxicologists, law and policy experts, and engineers who work in national and state government, academia, the private sector, and civil society for two days of intensive discussions. To provide context for the discussions, the workshop organizers developed a case study regarding the search for alternatives to copper-based marine antifouling paint, which is used to protect the hulls of recreational boats from barnacles, algae, and other marine organisms. Participants received data regarding the health, environmental, technical, and economic performance of a set of alternative paints (see Supplemental Material, “Anti-Fouling Paint Case Study Performance Matrix”). Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups’ findings. This article focuses on the workshop discussion and on conclusions regarding decision making.

We first review regulatory decision making in general, and we provide background on selection of safer alternatives to hazardous chemicals using alternatives analysis (AA), also called alternatives assessment. We then summarize relevant decision-making approaches and associated methods and tools that could be applied to AA. The next section outlines some of the challenges associated with decision making in AA and the role that various decision approaches could play in resolving them. After setting out four principles for integrating decision analysis into AA, we advance four recommendations for driving integration forward.

Regulatory Decision Making and Selection of Safer Alternatives

The consequences of regulatory decisions can have broad implications in areas such as human health and the environment. Yet within the regulatory context, these complex decision tasks are traditionally performed using an ad hoc approach, that is to say, without the aid of formal decision analysis methods or tools ( Eason et al. 2011 ). As we discuss later, such ad hoc approaches raise serious concerns regarding the consistency of outcomes across different cases; the transparency, predictability, and objectivity of the decision-making process; and human cognitive capacity in managing and synthesizing diverse, rich streams of information. Identifying a systematic framework for making effective, transparent, and objective decisions within the dynamic and complex regulatory milieu can significantly mitigate those concerns ( NAS 2005 ). In its 2005 report, the NAS called for a program of research in environmental decision making focused on:

[I]mproving the analytical tools and analytic-deliberative processes necessary for good environmental decision making. It would include three components: developing criteria of decision quality; developing and testing formal tools for structuring decision processes; and creating effective processes, often termed analytic-deliberative, in which a broad range of participants take important roles in environmental decisions, including framing and interpreting scientific analyses. ( NAS 2005 )

Since that call, significant research has been performed regarding decision making related to environmental issues, particularly in the context of natural resource management, optimization of water and coastal resources, and remediation of contaminated sites ( Gregory et al. 2012 ; Huang et al. 2011 ; Yatsalo et al. 2007 ). This work has begun the process of evaluating the application of formal decision approaches to environmental decision making, but numerous challenges remain, particularly with respect to the regulatory context. In fact, very few studies have focused on the application of decision-making tools and processes in the context of formal regulatory programs, taking into account the legal, practical, and resource constraints present in such settings ( Malloy et al. 2013 ; Parnell et al. 2001 ). We focus upon the use of decision analysis in the context of environmental chemicals.

The challenge of making choices among alternatives is central in an emerging approach to chemical policy, which turns from conventional risk management to embrace prevention-based approaches to regulating chemicals. Conventional risk management essentially focuses upon limiting exposure to a hazardous chemical to an acceptable level through engineering and administrative controls. In contrast, a prevention-based approach seeks to minimize the use of toxic chemicals by mandating, directly incentivizing, or encouraging the adoption of viable safer alternative chemicals or processes ( Malloy 2014 ). Thus, under a prevention-based approach, the regulatory agency would encourage or even mandate use of what it views as an inherently safer process using a viable alternative plating technique. Adopting a prevention-based approach, however, presents its own challenging choice—identifying a safer, viable alternative. Effective prevention-based regulation requires a regulatory AA methodology for comparing and evaluating the regulated chemical or process and its alternatives across a range of relevant criteria.

AA is a scientific method for identifying, comparing, and evaluating competing courses of action. In the case of chemical regulation, it is used to determine the relative safety and viability of potential substitutes for existing products or processes that use hazardous chemicals ( NAS 2014 ; Malloy et al. 2013 ). For example, a business manufacturing nail polish containing a resin made using formaldehyde would compare its product with alternative formulations using other resins. Alternatives may include drop-in chemical substitutes, material substitutes, changes to manufacturing operations, and changes to component/product design ( Sinsheimer et al. 2007 ). The methodology compares the alternatives with the regulated product and with one another across a variety of attributes, typically including public health impacts, environmental effects, and technical performance, as well as economic impacts on the manufacturer and on the consumer. The methodology identifies trade-offs between the alternatives and evaluates the relative overall performance of the original product and its alternatives.

In the regulatory setting, multiple parties may be involved to varying degrees in the generation of an AA. Typically, the regulated firm is required to perform the AA in the first instance, as in the California Safer Consumer Products program and the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) authorization process ( DTSC 2013 ; European Parliament and Council 2006 ). The AA, which may be done within the firm or by an outside consultant retained by the firm, is generally performed by an interdisciplinary team of experts (hereafter collectively referred to as the “analyst”) ( DTSC 2013 ). The firm submits the AA to the regulatory agency for review. The regulatory agency will often propose a final decision regarding whether a viable, safer alternative exists and the appropriate regulatory action to take. ( DTSC 2013 ; European Parliament and Council 2006 ). Possible regulatory actions include a ban on the existing product, adoption of an alternative, product labeling, use restrictions, or end-of-life management. Stakeholders such as other government agencies, environmental groups, trade associations, and the general public may provide comments on the AA and regulatory response. Ultimately, the agency retains the authority to require revisions to the analysis and has the final say over the regulatory response ( Malloy 2014 ).

Development of effective regulatory AA methods is a pressing and timely public policy issue. Regulators in California, Maine, and Washington are implementing new programs that call for manufacturers to identify and evaluate potential safer alternatives to toxic chemicals in products ( DTSC 2013 ; MDEP 2012 ; Department of Ecology, Washington State 2015 ). At the federal level, in the last few years, the U.S. Environmental Health Protection Agency ( EPA ) began to use AA as part of “chemical action plans” in its chemical management program ( Lavoie et al. 2010 ). In the European Union, the REACH program imposes AA obligations upon manufacturers seeking authorization for the continued use of certain substances of very high concern ( European Parliament and Council 2006 ). The stakes in developing effective approaches to regulatory AA are high. A flawed AA methodology can inhibit the identification and adoption of safer alternatives or support the selection of an undesirable alternative (often termed “regrettable substitution”). An example of the former is the U.S. EPA’s attempt in the late 1980s to ban asbestos, which was rejected by a federal court that concluded, among other things, that the AA method used by the agency did not adequately evaluate the feasibility and safety of the alternatives ( Corrosion ProoFittings v. EPA 1991 ). Regrettable substitution is illustrated by the case of antifouling paints used to combat the buildup of bacteria, algae, and invertebrates such as barnacles on the hulls of recreational boats. As countries throughout the world banned highly toxic tributyltin in antifouling paints in the late 1980s, manufacturers turned to copper as an active ingredient ( Dafforn et al. 2011 ). The cycle is now being repeated as regulatory agencies began efforts to phase out copper-based antifouling paint because of its adverse impacts on the marine environment ( Carson et al. 2009 ).

AA frameworks and methods abound, yet few directly address how decision makers should select or rank the alternatives. As the 2014 NAS report on AA observed, “[m]any frameworks … do not consider the decision-making process or decision rules used for resolving trade-offs among different categories of toxicity and other factors (e.g., social impact), or the values that underlie such trade-offs” ( NAS 2014 ). Similarly, a recent review of 20 AA frameworks and guides identified methodological gaps regarding the use of explicit decision frameworks and the incorporation of decision-maker values ( Jacobs et al. 2016 ). The lack of attention to the decision-making process is particularly problematic in regulatory AA, in which the regulated entity, the government agency, and the stakeholders face significant challenges related to the complexity of the decisions, uncertainty of data, difficulty in identifying alternatives, and incorporation of decision-maker values. We discuss these challenges in detail below.

A variety of decision analysis tools and approaches can assist policy makers, product and process designers, and other stakeholders who face the challenging decision environment presented by AA. For these purposes, decision analysis is “a systematic approach to evaluating complex problems and enhancing the quality of decisions.” ( Eason et al. 2011 ). Although formal decision analysis methods and tools suitable for such situations are well developed ( Linkov and Moberg 2012 ), for the reasons discussed below, they are rarely applied in existing AA practice. The range of decision analysis methods and tools is quite broad, requiring development of principles for selecting and implementing the most appropriate ones for varied regulatory and private settings. Following an overview of the architecture of decision making in AA, we examine how various formal and informal decision approaches can assist decision makers in meeting the four challenges identified above. We conclude by offering a set of principles for developing effective AA decision-making approaches and steps for advancing the integration of decision analysis into AA practice.

Overview of Decision Making in Alternatives Analysis

In the case of regulatory AA, the particular decision or decisions to be made will depend significantly upon the requirements and resources of the regulatory program in question. For example, the goal may be to identify a single optimal alternative, to rank the entire set of alternatives, or to simply differentiate between acceptable and unacceptable alternatives ( Linkov et al. 2006 ). As a general matter, however, the architecture of decision making is shaped by two factors: the decision framework adopted and the decision tools or methods used. For our purposes, the term “decision framework” means the overall structure or order of the decision making, consisting of particular steps in a certain order. Decision tools and methods are defined below.

Decision Frameworks

Existing AA approaches that explicitly address decision making use any of three general decision frameworks: sequential, simultaneous, and mixed ( Figure 1 ). The sequential framework includes a set of attributes, such as human health, environmental impacts, economic feasibility, and technical feasibility, which are addressed in succession. The first attribute addressed is often human health or technical feasibility because it is assumed that any alternative that does not meet minimum performance requirements should not proceed with further evaluation. Only the most favorable alternatives proceed to the next step for evaluation, which continues until one or more acceptable alternatives are identified ( IC2 2013 ; Malloy et al. 2013 ).

Decision frameworks. Compares the process for decision making under sequential, simultaneous, and mixed frameworks.

The simultaneous framework considers all or a set of the attributes at once, allowing good performance on one attribute to offset less favorable performance on another for a given alternative. Thus, one alternative’s lackluster performance in terms of cost might be offset by its superior technical performance, a concept known as compensation ( Giove et al. 2009 ). This type of trade-off is not generally available in the sequential framework across major decision criteria. Nevertheless, it is important to note that even within a sequential framework, the simultaneous framework may be lurking where a major decision criterion consists of sub-criteria. For example, in most AA approaches, the human health criterion has numerous sub-criteria reflecting various forms of toxicity such as carcinogenicity, acute toxicity, and neurotoxicity. Even within a sequential framework, the decision maker may consider all of those subcriteria simultaneously when comparing the alternatives with respect to human health ( NAS 2014 ; IC2 2013 ).

The mixed or hybrid framework, as one might expect, is a combination of the sequential and simultaneous approaches ( NAS 2014 ; IC2 2013 ; Malloy et al. 2013 ). For example, if technical feasibility is of particular importance to an analyst, she may screen out certain alternatives on that basis, and subsequently apply a simultaneous framework to the remaining alternatives regarding the other decision criteria. A recent study of 20 existing AA approaches observed substantial variance in the framework adopted: no framework (7 approaches), mixed (6 approaches), simultaneous (4 approaches), menu of all three frameworks (2 approaches), and sequential (1 approach) ( Jacobs et al. 2016 ).

Decision Methods and Tools

There are a wide range of decision tools and methods, that is to say, formal and informal aids, rules, and techniques that guide particular steps within a decision framework ( NAS 2014 ; Malloy et al. 2013 ). These methods and tools range from informal rules of thumb to highly complex, statistically based methodologies. The various methods and tools have diverse approaches and distinctive theoretical bases, and they address data uncertainty, the relative importance of decision criteria, and other issues differently. For example, some methods quantitatively incorporate the decision maker’s relative preferences regarding the importance of decision criteria (a process sometimes called “weighting”), whereas others make no provision for explicit weighting. For our purposes, they can be broken into four general types: a ) narrative, b ) elementary, c ) multicriteria decision analysis (MCDA), and d ) robust scenario analysis. Each type can be used for various decisions in an AA, such as winnowing down the initial set of potential alternatives or for ranking the alternatives. As Figure 2 illustrates, in the context of a mixed decision framework, two different decision tools and methods could even be used at different decision points within a single AA.

Multiple decision tool use in mixed decision framework. Demonstrates one potential scenario for using multiple decision tools in one chemical selection process. (Derived from Jacobs et al. 2016 ).

Narrative Approaches

In the narrative approach, also known as the ad hoc approach, the decision maker engages in a holistic, qualitative balancing of the data and associated trade-offs to arrive at a selection ( Eason et al. 2011 ; Linkov et al. 2006 ). In some cases, the analyst may rely on explicitly stated informal decision principles or on expert judgment to guide the process. No quantitative scores are assigned to alternatives for the purposes of the comparison. Similarly, no explicit quantitative weighting is used to reflect the relative importance of the decision criteria, although in some instances, qualitative weighting may be provided for the analyst by the firm charged with performing the AA. The AA methodology developed by the European Chemicals Agency (ECHA) for substances that are subject to authorization under REACH is illustrative ( ECHA 2011 ). Similarly, the AA requirements set out in the regulations for the California Safer Consumer Products program, which mandates that manufacturers complete AAs for certain priority products, adopt the ad hoc approach, setting out broad, narrative decision rules without explicit weighting ( DTSC 2013 ). This approach could be particularly subject to various biases in decision making, which we will address later.

Elementary Approaches

Elementary approaches apply a more systematic overlay to the narrative approach, providing the analyst with specific guidance about how to make a decision. Such approaches provide an observable path for the decision process but typically do not require sophisticated software or specialized expertise. For example, Hansen and colleagues developed the NanoRiskCat tool for prioritization of nanomaterials in consumer products ( Hansen et al. 2014 ). The structure may take the form of a decision tree that takes the analyst through an ordered series of questions. Alternatively, it may offer a set of checklists, specific decision rules, or simple algorithms to assist the analyst in framing the issues and guiding the evaluation. Elementary approaches can make use of both quantitative and qualitative data and may incorporate implicit or explicit weighting of the decision criteria ( Linkov et al. 2004 ).

MCDA Approaches

The MCDA approach couples a narrative evaluation with mathematically based formal decision analysis tools, such as multi-attribute utility theory (MAUT) and outranking. The output of the selected MCDA analysis is intended as a guide for the decision maker and as a reference for stakeholders affected by or otherwise interested in the decision. MCDA itself consists of a range of different methods and tools, reflecting various theoretical bases and methodological perspectives. Accordingly, those methods and tools tend to assess the data and generate rankings in different ways ( Huang et al. 2011 ). However, they generally share certain common features, setting them apart from the type of informal decision making present in the narrative approach. Each MCDA approach provides a systematic, observable process for evaluating alternatives in which an alternative’s performance across the decision criteria is aggregated to generate a score. Each alternative is then ranked relative to the other alternatives based on its aggregate score. Figure 3 provides an example of the type of ranking generated from an MAUT tool. In most of these types of ranking approaches, the individual criteria scores are weighted to reflect the relative importance of the decision criteria and sub-criteria ( Kiker et al. 2005 ; Belton and Stewart 2002 ).

Sample output from MAUT decision tool comparing alternatives to lead solder. SnPb is a solder alloy composed of 63% Sn/37% Pb; SAC (Water) is a solder alloy composed of 95.5% Sn/3.9% Ag/0.6% Cu; water quenching is used to cool and harden solder; SAC (air) is a solder alloy composed of 95.5% Sn/3.9% Ag/0.6% Cu; air is used to cool and harden solder; SnCu (water) is a solder alloy composed of 99.2% Sn/0.8% Cu; water quenching is used to cool and harden solder; SnCu (air) solder alloy composed of 99.2% Sn/0.8% Cu; air is used to cool and harden solder [ Malloy et al. 2013 with permission from Wiley Online Library http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1551-3793/homepage/Permissions.html )]. Note: Ag, silver; Cu, copper; Pb, lead; Sn, tin.

Some MCDA tools, such as MAUT, are optimization tools that seek to maximize the achievement of the decision maker’s preferences. These optimization approaches use utility functions, dimensionless scales that range from 0 to 1, to convert the measured performance of an alternative for a given decision criterion to a score between 0 and 1 ( Malloy et al. 2013 ). In contrast, outranking methods do not create utility functions or seek optimal alternatives. Instead, outranking methods seek the alternative that outranks other alternatives in terms of overall performance, also known as the dominant alternative ( Belton and Stewart 2002 ). The diverse MCDA tools use various approaches to address uncertainty regarding the performance of alternatives and the relative importance to be placed on respective attributes. Some, such as MAUT, use point values for performance and weighting and rely upon sensitivity analysis to evaluate the impact of uncertainty ( Malloy et al. 2013 ). Sensitivity analysis evaluates how different values of uncertain attributes or weights would affect the ranking of the alternatives. Others, such as stochastic multi-criteria acceptability analysis (SMAA), represent performance information and relative weights as probability distributions ( Lahdelma and Salminen 2010 ). Still others, such as multi-criteria mapping, rely on a part-quantitative, part-qualitative approach in which the analyst facilitates structured evaluation of alternatives by the ultimate decision maker, eliciting judgments from the decision maker regarding the performance of the respective alternatives on relevant attributes and on the relative importance of those attributes. The analyst then generates a ranking based upon that input ( SPRU 2014 ; Hansen 2010 ). MCDA has been used, though not extensively, in the related field of life-cycle assessment (LCA) ( Prado et al. 2012 ). For example, Wender et al. ( 2014 ) integrated LCA with MCDA methods to compare existing and emerging photovoltaic technologies.

Robust Scenario Approaches

Robust scenario analysis is particularly useful when decision makers face deep uncertainty, meaning situations in which the decision makers do not know or cannot agree upon the likely performance of one or more alternatives on important criteria ( Lempert and Collins 2007 ). Robust scenario analysis uses large ensembles of scenarios to visualize all plausible, relevant futures for each alternative. With this range of potential futures in mind, it helps decision makers to compare the alternatives in search of the most robust alternative. A robust alternative is one that performs well across a wide range of plausible scenarios even though it may not be optimal or dominant in any particular one ( Kalra et al. 2014 ).

Robust scenario decision making consists of four iterative steps. First, the decision makers define the decision context, identifying goals, uncertainties, and potential alternatives under consideration. Second, modelers generate ensembles of hundreds, thousands, or even more scenarios, each reflecting an outcome flowing from different plausible assumptions about how each alternative may perform. Third, quantitative analysis and visualization software is used to explore the benefits and drawbacks of the alternatives across the range of scenarios. Finally, trade-off analysis (i.e., comparative assessment of the relative pros and cons of the alternatives) is used to evaluate the alternatives and to identify a robust strategy ( Lempert et al. 2013 ).

Decision-Making Challenges Presented by Alternatives Analysis

Like many decisions involving multiple criteria, identifying a safer viable alternative or set of alternatives is often difficult. Finding potential alternatives, collecting information about their performance, and evaluating the trade-offs posed by each alternative are all laden with problems. Those difficulties are exacerbated in the regulatory setting because of additional constraints associated with that regulatory setting, such as the need for accountability, transparency, and consistency across similar cases ( Malloy et al. 2015 ). In this review, we focus on four challenges that are recognized in the decision analysis field to be of particular importance to regulatory AA:

• • Dealing with large numbers of attributes
• • Uncertainty in performance data
• • Poorly understood option space
• • Incorporating decision-maker values (sometimes called weighting of attributes)

Large Numbers of Attributes

In its essential form, AA focuses upon human health, environmental impacts, technical performance, and economic impact. But in fact, AA involves many more than four attributes. Each of the four major attributes, particularly human health, includes numerous sub-attributes, many more than any human can process without some form of heuristic or computational aid. An example is the case of California Safer Consumer Products regulations, which require that an AA consider all relevant “hazard traits” ( DTSC 2013 ). Hazard traits are “properties of chemicals that fall into broad categories of toxicological, environmental, exposure potential, and physical hazards that may contribute to adverse effects …” ( DTSC 2013 ). For human health alone, the California regulations identify twenty potentially relevant hazard traits ( DTSC 2013 ). Similarly, the U.S. EPA considers a total of twelve hazard end points in assessing impacts to human health in its alternatives assessment guidance ( U.S. EPA 2011 ).

Large numbers of attributes raise two types of difficulties. First, as the number of attributes increases, data collection regarding the performance of the baseline product and its alternatives becomes increasingly difficult, time-consuming, and expensive. Because not all attributes listed in regulations or guidance documents will be salient or have an impact in every case, decision-making approaches that judiciously sift out irrelevant or less-important attributes are desirable. Second, given humans’ cognitive limitations, larger numbers of relevant attributes complicate the often inevitable trade-off analysis that is needed in AA. Consider an example of two alternative solders, one of which performs best in terms of low carcinogenicity, neurotoxicity, acute aquatic toxicity, and wettability (a very desirable feature for solders), but not so well with respect to endocrine disruption, respiratory toxicity, chronic aquatic toxicity, and tensile strength (another advantageous feature for solders). Suppose the second alternative presents the opposite profile. Now, add dozens of other attributes relating to human health and safety, environmental impacts, and technical and economic performance to the mix. Even in the relatively simple case of one baseline product and two potential alternatives, evaluating and resolving the trade-offs can be treacherous. In assessing the alternatives, decision makers must determine whether and how to compensate for poor performance on some attributes with superior performance on other attributes. Similarly, the nature and scale of the performance data for the attributes varies wildly; using fundamentally different metrics for diverse attributes generates a mixture of quantitative and qualitative information.

Decision frameworks and methods should provide principled approaches to integrating or normalizing such information to support trade-off analysis. Elementary approaches often use ordinal measures of performance to normalize diverse types of data. For example, the U.S. EPA AA methodology under the Design for the Environment program characterizes performance on a variety of human health and environmental attributes as “low,” “medium,” or “high” ( U.S. EPA 2011 ). The increased tractability comes with some decrease in precision, potentially obscuring meaningful differences in performance or exaggerating differences at the margins. As the number of relevant attributes increases, it becomes more difficult to rely upon narrative and elementary approaches to manage the diverse types of data and to evaluate the trade-offs presented by the alternatives. MCDA approaches are well suited for handling large numbers of attributes and diverse forms of data. ( Kiker et al. 2005 ). In an AA case study using an MCDA method to evaluate alternatives to lead-based solder, researchers used an internal normalization approach to convert an alternative’s scores on each criterion to dimensionless units ranging from 0 to 1 and then applied an optimization algorithm to trade-offs across more than fifty attributes ( Malloy et al. 2013 ).

Uncertain Data Regarding Attributes

Uncertainty is not unique to AA; it presents challenges in conventional risk assessment and in many environmental decision-making situations. However, the diversity and number of the relevant data streams and potential trade-offs faced in AA exacerbate the problem of uncertainty. In thinking through uncertainty in this context, three considerations stand out: defining it, responding to it methodologically, and communicating about it to stakeholders.

The meaning of the term “uncertainty” is itself uncertain; definitions abound ( NAS 2009 ; Ascough et al. 2008 ). For our purposes, uncertainty includes a complete or partial lack of information, or the existence of conflicting information or variability, regarding an alternative’s performance on one or more attributes, such as health effects, potential exposure, or economic impact ( NAS 2009 ). Uncertainty includes “data gaps” resulting from a lack of experimental studies, measurements, or other empirical observations, along with situations in which available studies or modeling provide a range of differing data for the same attribute ( NAS 2014 ; Ascough et al. 2008 ). It also includes limitations inherent in data generation and modeling such as measurement error and use of modeling assumptions, as well as naturally occurring variability caused by heterogeneity or diversity in the relevant populations, materials, or systems. Uncertainty regarding the strength of the decision maker’s preferences, also known as value uncertainty, is discussed below.

There are a variety of methodological approaches for dealing with uncertainty. Some approaches (typically within narrative or elementary approaches) simply call for identification and discussion of missing data or use simple heuristics to deal with uncertainties, for example by assuming a worst-case performance for that attribute ( DTSC 2013 ; Rossi et al. 2006 ). Others rely upon expert judgment (often in the form of expert elicitation) to fill data gaps ( Rossi et al. 2012 ). Although MCDA approaches can make similar use of simple heuristics and expert estimations, they also provide a variety of more sophisticated mechanisms for dealing with uncertainty ( Malloy et al. 2013 ; Hyde et al. 2003 ). Simple forms of sensitivity analysis in which single input values are modified to observe the effect on the MCDA results are also often used at the conclusion of the decision analysis process—the lead-based solder study used this approach to assess the robustness of its outcomes—although this type of ad hoc analysis has significant limitations ( Malloy et al. 2013 ; Hyde et al. 2003 ).

Diverse MCDA methods also offer a variety of quantitative probabilistic approaches relying upon such tools as Monte Carlo analysis, fuzzy sets, and Bayesian networks to investigate the range of outcomes associated with different values for the uncertain attribute ( Lahdelma and Salminen 2010 ). Canis and colleagues used a stochastic decision-analytic technique to address uncertainty in an evaluation of four different processes for synthesizing carbon nanotubes (arc, high-pressure carbon monoxide, chemical vapor deposition, and laser) across five performance criteria. Rather than generating an ordered ranking of the alternatives from first to last, the method provided an estimate of the probability that each alternative would occupy each rank ( Canis et al. 2010 ). Robust scenario analysis takes a different approach, using large ensembles of scenarios in an attempt to visualize all plausible, relevant futures for each alternative. With this range of potential futures in mind, decision makers are enabled to compare the alternatives in search of the most robust alternative given the uncertainties ( Lempert and Collins 2007 ).

Choosing among these approaches to uncertainty is not trivial. Studies in the decision analysis literature (and in the context of multi-criteria choices in particular) demonstrate that the approach taken with respect to uncertainty can substantially affect decision outcomes ( Hyde et al. 2003 ; Durbach and Stewart 2011 ). For example, one heuristic approach—called the “uncertainty downgrade”—essentially penalizes an alternative with missing data by assuming the worst with respect to the affected attribute. In some cases, such a penalty default may encourage proponents of the alternative to generate more complete data, but it may also lead to the selection of less-safe but more-studied alternatives ( NAS 2014 ).

How the evaluation of uncertainties is presented to the decision maker can be as important as the substance of the evaluation itself. Decision-making methods and tools are of course meant to assist the decision maker; thus, the results of the uncertainty analysis must be salient and comprehensible. In simple cases, a comprehensive assessment of uncertainty may not be necessary. In complicated situations, however, simply identifying data gaps without providing qualitative or quantitative analysis of the scope or impact of the uncertainty can leave decision makers adrift. Alternatively, the door could be left open to strategic assessment of the uncertainties aimed at advancing the interests of the regulated entity rather than achieving the goals of the regulatory program. Providing point estimates for uncertain data can bias decision making, and presenting ranges of data in probability distributions without supporting analysis designed to facilitate understanding can lead to information overload ( Durbach and Stewart 2011 ). Decision analytical approaches such as MCDA can provide insightful, rigorous treatment of uncertainty, but that rigor comes at some potential cost in terms of resource intensity, complexity and reduced transparency ( NAS 2009 ).

Poorly Understood Option Space

The range of alternatives considered in AA (often referred to as the “option space” in decision analysis and engineering) can be quite wide ( Frye-Levine 2012 ; de Wilde et al. 2002 ). Alternatives may involve a ) the use of “drop‐in” chemical or material substitutes, b ) a redesign of the product or process to obviate the need for the chemical of concern, or c ) changes regarding the magnitude or nature of the use of the chemical ( Sinsheimer et al. 2007 ). Option generation is a core aspect of decision making; identifying an overly narrow set of alternatives undermines the value of the ultimate decision ( Del Missier et al. 2015 ; Adelman et al. 1995 ). Accordingly, existing regulatory programs emphasize the importance of considering a broad range of relevant potential alternatives ( DTSC 2013 ; ECHA 2011 ).

We highlight three issues that complicate the identification of viable alternatives. For these purposes, viability refers to technical and economic feasibility. First, information regarding the existence and performance of alternatives is often difficult to uncover, particularly when searching for alternatives other than straightforward drop-in chemical replacements. Existing government, academic, and private publications do offer general guidance on searching for alternatives ( NAS 2014 ; U.S. EPA 2011 ; IC2 2013 ; Rossi et al. 2012 ), and databases and reports provide specific listings of chemical alternatives for limited types of products [U.S. EPA Safer Chemical Ingredients List (SCIL)]. However, for many other products, information regarding chemical and nonchemical alternatives may not be available to the regulated firm. Rather, the information may reside with vendors, manufacturers, consultants, or academics outside the regulated entity’s normal commercial network.

Second, for any given product or process, alternatives will be at different stages of development: Some may be readily available, mature technologies, whereas others are emerging or in early stages of commercialization. Indeed, selection of a technology through a regulatory alternative analysis can itself accelerate commercialization or market growth of that technology. Because the option space can be so dynamic, AA frameworks that assume a static set of options may exclude innovative alternatives that could be available in the near term ( ECHA 2011 ). Thus, identifying the set of potential alternatives for consideration can itself be a difficult decision made under conditions of uncertainty.

Third, the regulated entity (or rather, its managers and staff) may be unable or reluctant to cast a broad net in identifying potential alternatives. Individuals face cognitive and disciplinary limitations that can substantially shape their evaluation of information and decision making. For example, cognitive biases and mental models that lead us to favor the status quo and to discount the importance of new information are well documented ( Samuelson and Zeckhauser 1988 ), even in business settings with high stakes ( Kunreuther et al. 2002 ); this status quo bias is amplified when executives have longer tenure within their industry ( Hambrick et al. 1993 ). These unconscious biases can be mitigated to some degree through training and the use of well-designed decision-making processes and aids. Thaler and Benartzi ( 2004 ) demonstrated how changing the default can influence behavior in the context of saving for retirement, and Croskerry ( 2002 ) provided an overview of biases that occur in clinical decision making with strategies of how to avoid them. However, such training, processes, and aids are largely ineffective when the decision maker is acting strategically to limit the set of alternatives to circumvent the goals of the regulatory process. Many regulated firms have strong business reasons to resist externally driven alterations to successful products, including costs, disruption, and the uncertainty of customer response to the revised product.

Incorporating Decision-Maker Preferences/Weighting of Attributes

By its very nature, AA involves the balancing of attributes against one another in evaluating potential alternatives. Consider the example of antifouling paint for marine applications: One paint may be safer for boatyard workers, whereas another may be more protective of aquatic vegetation. In most multi-criteria decision situations, however, the decision maker is not equally concerned about all decision attributes. An individual decision maker may place more importance on whether a given paint kills aquatic vegetation than on whether it contributes to smog formation. Weighting is a significant challenge. In many cases, the individual decision maker’s preferences are not clear, even to that individual. This so-called “value uncertainty” is compounded in situations such as regulatory settings, in which many stakeholders (and thus many sets of preferences) are involved ( Ascough et al. 2008 ).

Existing approaches to AA vary significantly in how they address incorporation of preferences/weighting. Narrative approaches typically provide no explicit weighting of the decision attributes, although in some instances, qualitative weighting may be provided for the analyst. More often, whether and how to weight the relevant attributes are left to the discretion of the analyst ( Jacobs et al. 2016 ; Linkov et al. 2005 ). Elementary approaches usually incorporate either implicit or explicit weighting of the decision attributes. For example, decision rules in elementary approaches that eliminate alternatives based on particular attributes by definition place greater weight upon those attributes. Most MCDA approaches confront weighting explicitly, using various methods to derive weights. Generally speaking, there are three methods for eliciting or establishing explicit attribute weights: the use of existing generic weights such as the set in the National Institute of Standards and Technology’s life cycle assessment software for building products; calculation of weights using objective criteria such as the distance-to-target method; or elicitation of weights from experts or stakeholders ( Hansen 2010 ; Zhou and Schoenung 2007 ; Gloria et al. 2007 ; SPRU 2004 ; Lippiatt 2002 ). The robust scenario approach does not attempt to weight attributes; instead, it generates outcomes reasonably expected from a set of plausible scenarios for each alternative, allowing the decision maker to select the most robust alternative; that is, the alternative that offers the best range of outcomes across the scenarios.

Each strategy for addressing value uncertainty raises its own issues. For example, in regulatory programs such as Superfund and the Clean Air Act, which use narrative decision making, weighting is typically performed on a largely ad hoc basis, generally without any direct, systematic discussion of the relative weights to be accorded to the relevant decision criteria ( U.S. EPA 1994 ; U.S. EPA 1990 ). Such ad hoc treatment of weighting raises concerns regarding the consistency of outcomes across similar cases. Over time, regulators may develop standard outcomes or rules of thumb, which provide some consistency in outcome, but such conventions and the tacit weighting embedded in them can undermine transparency in decision making. Moreover, a lack of clear guidance regarding the relative weight to be accorded to criteria could allow political or administrative factors to influence the decision. However, incorporation of explicit weighting in regulatory decisions creates complex political and methodological questions beyond dealing with value uncertainty. For example, agencies generating explicit weightings would have to deal with potentially inconsistent preferences among the regulated entity, the various stakeholder groups, and the public at large. Similarly, they must consider whether pragmatic and strategic considerations related to implementation and enforcement of the program are relevant in establishing weighting ( Department for Communities and Local Government 2009 ).

Principles for Developing Effective Alternatives Analysis Decision-Making Approaches

The previous section focused on the ways in which the various decision-making approaches can be used to address the four challenges presented by AA. However, integrating such decision making into AA itself raises thorny questions: for example, which of the decision approaches and tools should be used and in what circumstances. In this section, we propose four interrelated principles regarding the application of those approaches and tools in regulatory AA.

Different Decision Points within Alternatives Analysis May Require Different Decision Approaches and Tools

In the course of an AA, one must make a series of decisions. These decisions include selecting relevant attributes, identifying potential alternatives, assessing performance regarding attributes concerning human health impacts, ecological and environmental impacts, technical performance, and economic impacts; the preferred alternatives must also be ranked or selected. Different approaches and tools may be best suited for each of these decisions rather than a one-size-fits-all methodology. Consider decisions regarding the relative performance of alternatives on particular attributes. For some attributes such as production costs or technical performance, there may be well-established methods in industry for evaluating relative performance that can be integrated into a broader AA framework. Similarly, GreenScreen ® is a hazard assessment tool that is used by a variety of AA frameworks ( IC2 2013 ; Rossi et al. 2012 ). However, these individual tools are not designed to assist in the trade-off analysis across all of the disparate attributes; for this task, other approaches and tools will be needed. Some researchers recommend using multiple approaches for the same analysis with the aim of generating more robust analysis to inform the decision maker ( Kiker et al. 2005 ; Yatsalo et al. 2007 ).

Decision-Making Approaches and Tools Should Be as Simple as Possible

Not every AA will require sophisticated analysis. In some cases, the analyst may conclude after careful assessment that the data are relatively complete and the trade-offs fairly clear. In such cases, basic decision approaches and uncomplicated heuristics may be all that are necessary to support a sound decision. Thus, a simple case involving a drop-in chemical substitute with substantially better performance across most attributes may not call for sophisticated MCDA approaches. Other situations will present high uncertainty and complex trade-offs; thus, these situations will require more advanced approaches and tools. The evaluation of alternative processes for synthesizing carbon nanotubes, which involved substantial uncertainty regarding technical performance and health impacts was more suited for probabilistic MCDA ( Canis et al. 2010 ). Similarly, not every regulated business or regulatory agency will have the resources or the capacity to use high-level analytical tools. Accordingly, the decision-making approach/tool should be scaled to reflect the capacity of the decision maker and the task at hand while seeking to maximize the quality of the ultimate decision. Clearly, if the decision will have a major impact but the regulated entity is currently not equipped to apply the appropriate sophisticated tools, other entities such as nongovernmental organizations, trade associations, or regulatory agencies should support that firm with technical advice or resources rather than running the risk of regrettable outcomes.

The Decision-Making Approach and Tools Should Be Crafted to Reflect the Decision Context

Context matters in structuring decision processes. In particular, it is important to consider who will be performing the analysis and who will be making the decision. As discussed above, when AA is used in a regulatory setting, the regulated business will typically perform the initial alternative analysis and present a decision to the agency for review. These businesses will have a range of capabilities and objectives. Some will engage in a good faith or even a fervent effort to seek out safer alternatives. Others will reluctantly do the minimum required, and still others may engage in strategic behavior, appearing to perform a good faith AA but assiduously avoiding changes to their product. The decision-making process should be designed with all of these behaviors in mind. For example, it might include meaningful minimum standards to ensure rigor and consistency in the face of strategic behavior while incorporating flexibility to foster innovation among those firms more committed to adopting safer alternatives.

Multicriteria Decision Analysis Should Support but Not Supplant Deliberation

The output of MCDA is meant to inform rather than to replace deliberation, defined for these purposes as the process for communication and consideration of issues in which participants “discuss, ponder, exchange observations and views, reflect upon information and judgments concerning matters of mutual interest, and attempt to persuade each other” ( NAS 1996 ). MCDA provides analytical results that systematically evaluate the trade-offs between alternatives, allowing those engaged in deliberation to consider how their preferences and the alternatives’ respective performance on different attributes affect the decision ( Perez 2010 ). MCDA augments professional, political, and personal judgment as a guide and as a reference point for stakeholders affected by or otherwise interested in the decision. However, the output of many MCDA tools can appear conclusive, setting out quantified rankings and groupings of alternatives and striking visualizations. Therefore, care must be taken to ensure that MCDA does not supplant or distort the deliberative process and to ensure that decision makers and stakeholders understand the embedded assumptions in the MCDA tool used as well as the tool’s limitations. For example, multicriteria mapping methods specifically attempt to facilitate such deliberation through an iterative, facilitated process involving a series of interviews with identified stakeholders. ( SPRU 2004 ; Hansen 2010 ). Moreover, although MCDA tools summarize the performance of alternatives under clearly defined metrics and preferences, they do not define standards for determining when a difference between the performance of alternatives is sufficient to justify making a change. Consider a case in which a manufacturer finds an alternative that exhibits lower aquatic toxicity by an order of magnitude but does somewhat worse in terms of technical performance. Without explicit input regarding the preferences of the decision maker, the MCDA tool cannot answer the question of whether the distinction is sufficiently large to justify product redesign. Ultimately, the decision maker must determine whether the differences between the incumbent and an alternative are significant enough to justify a move to the alternative.

With these challenges and principles in mind, we now turn to the question of how decision analysis and related disciplines can best be incorporated into the developing field of AA.

Next Steps: Advancing Integration of Alternatives Analysis and Decision Analysis

Decision science is a well-developed discipline, offering a variety of tools to assist decision makers. However, many of those tools are not widely used in the environmental regulatory setting, much less in the emerging area of AA. The process of integration is complicated by several factors. First, AA is by nature deeply transdisciplinary, requiring extensive cross-discipline interaction. Second, choosing among the wide range of available approaches and tools, each with its own benefits and limitations, can be daunting to regulators, businesses, and other stakeholders. Moreover, many of the tools require significant expertise in decision analysis and are not within the existing capacities of entities engaged in AA. Third, given the limited experience with formal decision tools in AA (and in environmental regulation more generally), there is skepticism among some regarding the value added by the use of such tools. Nonetheless, we see value in exploring the integration of decision analysis and its tools into AA, and we provide four recommendations to advance this integration.

Recommendation 1: Engage in Systematic Development, Assessment, and Evaluation of Decision Approaches and Tools

Although there is a rich body of literature in decision science concerning the development and evaluation of various decision tools, there has been relatively little research focused on applications in the context of AA in particular or in regulatory settings more broadly. Although recent studies of decision making in AA provide some insights, they ultimately call for further attention to be paid to the question of how decision tools can be integrated ( NAS 2014 ; Jacobs et al. 2015). Such efforts may include, among other things:

• • Developing or adapting user-friendly decision tools specifically for use in AA, taking into account the capacities and resources of the likely users and the particular decision task at hand.
• • Analyzing how existing and emerging decision approaches and tools address the four decision challenges of dealing with large numbers of attributes, uncertainty in performance data, poorly understood option space, and weighting of attributes.
• • Evaluating the extent to which such approaches and tools are worthwhile and amenable to use in a regulatory setting by agencies, businesses, and other stakeholders.
• • Considering how to better bridge the gap between analysis (whether human health, environmental, engineering, economic, or other forms) and deliberation, with particular focus on the potential role of decision analysis and tools.
• • Articulating objective technical and normative standards for selecting decision approaches and tools for particular uses in AA.

The results of this effort could be guidance for selecting and using a decision approach or even a multi-tiered tool that offers increasing levels of sophistication depending on the needs of the user. The experience gained over the years with the implementation of LCA could be useful here. For instance, the development of methods such as top-down and streamlined LCA has emerged in response to the recognition that many entities do not have the capacity (or the need) to conduct a full-blown process-based LCA, and standards such as the International Organization for Standardization (ISO) 14,040 series have emerged for third-party verification of LCA studies.

Recommendation 2: Use Case Studies to Advance the Integration of Decision Analysis into AA

Systematic case studies offer the opportunity to answer specific questions about how to integrate decision analysis into AA, and they demonstrate the potential value and limitations of different decision tools in AA to stakeholders. Case studies could also build upon and test outcomes from the activities discussed in “Recommendation 1.” For example, a case study may apply different decision tools to the same data set to evaluate differences in the performance of the tools with respect to previously developed technical and normative standards. To ensure real-world relevance, the case studies should be based upon actual commercial products and processes of interest to regulators, businesses, and other stakeholders. Currently relevant case-study topics that could be used to examine one or more of the decision challenges discussed above include marine antifouling paint, chemicals used in hydraulic fracturing (fracking), flame retardant alternatives, carbon nanotubes, and bisphenol A alternatives.

Recommendation 3: Support Trans-Sector and Trans-Disciplinary Efforts to Integrate Decision Analysis and Other Relevant Disciplines into Alternatives Analysis

AA brings a range of disciplines to bear in evaluating the relative benefits and drawbacks of a set of potentially safer alternatives, including toxicology, public health, engineering, economics, chemistry, environmental science, decision analysis, computer science, business management and operations, risk communication, and law. Existing tools and methods for AA do not integrate these disciplines in a systematic or rigorous way. Advancing AA will require constructing connections across those disciplines. Although this paper focuses on decision analysis, engagement with other disciplines will also be needed. Existing initiatives such as the AA Commons, the Organisation for Economic Co-operation and Development (OECD) Working Group, the Health and Environmental Sciences Institute (HESI) Committee, and others provide a useful starting point, but more systematic, research-focused, broadly trans-disciplinary efforts are also needed ( BizNGO 2016 ; OECD 2016 ). The AA case studies from Recommendation 2 could promote transdisciplinary efforts by creating a vehicle for practitioners to combine data from different sectors into a decision model. A research coordination network would provide the necessary vehicle for systematic collaboration across disciplines and public and private entities and institutions.

Recommendation 4: Support Undergraduate, Graduate, and Postgraduate Education and Outreach Efforts Regarding Alternatives Analysis, Including Attention to Decision Making

Advancing AA research and application in the mid-to-long term will require training the next generation of scientists, policy makers, and practitioners regarding the scientific and policy aspects of this new field. With very limited exceptions ( Schoenung et al. 2009 ), existing curricula in relevant undergraduate, graduate, and professional programs do not cover AA or prevention-based regulation. Curricular development will be particularly challenging for two reasons: the relative emerging nature of AA and the transdisciplinary nature of the undertaking. Its emerging nature means that there is little in terms of curricular materials to begin with, requiring significant start-up efforts. In addition, the subject matter is something of a moving target as new research and methods become available and as regulatory programs develop. In terms of the many disciplines that affect AA and prevention-based policy, effective education will itself have to be transdisciplinary and will have to reach across disciplines in terms of readings and exercises and engage students and faculty from those various disciplines.

The societal value of research regarding AA methods depends largely on the extent to which research is accessible to and understood by its end users—policy makers at every level, nongovernmental organizations (NGOs), and businesses. Ultimately, adoption of the frameworks, methods, and tools developed by researchers also requires broader acceptance by the public. This acceptance requires systematic education and outreach: namely, nonformal education in structured learning environments such as in-service training and continuing education outside of formal degree programs and informal or community education facilitating personal and community growth and sociopolitical engagement ( Bell 2009 ). For some, the education and outreach will be at the conceptual level alone, informing stakeholders about the general scope and nature of AA. For others who are more deeply engaged in chemicals policy, the education and outreach will focus upon more technical and methodological aspects.

Conclusions

There is immediate demand for robust, effective approaches to regulatory AA to select alternatives to chemicals of concern. Translation of decision analysis tools used in other areas of environmental decision making to the chemical regulation sphere could strengthen existing AA approaches but also presents unique questions and challenges. For instance, AAs must meet evolving regulatory standards but also be nimble enough for the private sector to employ as a tool during product development. To be useful, different tools crafted for the particular context may be required. The decision approaches employed should be as simple as possible and are intended to support rather than supplant decision making. Transdisciplinary work, mainly organized around case studies designed to address specific questions, and increased access to education and training would advance the use of decision analysis to improve AA.

Supplemental Material

(145 kb) pdf, acknowledgments.

This paper came from discussions at a workshop that was supported by the University of California (UC) Sustainable Technology and Policy Program, a joint collaboration of the University of California, Los Angeles (UCLA) School of Law and the Center for Occupational and Environmental Health at the UCLA Fielding School of Public Health in partnership with the UC Center for Environmental Implications of Nanotechnology (UC CEIN). UC CEIN is funded by a cooperative agreement from the National Science Foundation and the U.S. Environmental Protection Agency (NSF DBI-0830117; NSF DBI-1266377). Support for this workshop was also provided by the Institute of the Environment and Sustainability and the Emmett Institute on Climate Change and the Environment, both at UCLA.

• Adelman L, Gualtieri J, Stanford S. 1995. Examining the effect of causal focus on the option generation process: An experiment using protocol analysis . Organ Behav Hum Decis Process 61 :54–66, 10.1006/obhd.1995.1005. [ CrossRef ] [ Google Scholar ]
• Ascough JC, Maier HR, Ravalico JK, Strudley MW. 2008. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making . Ecol Modell 219 :383–399, 10.1016/j.ecolmodel.2008.07.015. [ CrossRef ] [ Google Scholar ]
• Bell DVJ. 2009. Education for Sustainable Development: Cure Or Placebo? In: Innovation, Science, Environmental, Special Edition: Charting Sustainable Development in Canada 1987–2007 . Toner G, Meadowcroft J, eds. Montreal, Quebec, Canada: McGill-Queen’s University Press, 106–130. [ Google Scholar ]
• Belton V, Stewart TJ. 2002. Multiple Criteria Decision Analysis: An Integrated Approach . Norwell, MA: Kluwer Academic Publishers. [ Google Scholar ]
• BizNGO. 2016. The Commons Principles for Alternatives Assessment . http://www.bizngo.org/alternatives-assessment/commons-principles-alt-assessment [accessed August 22, 2016].
• Canis L, Linkov I, Seager TP. 2010. Application of stochastic multiattribute analysis to assessment of single walled carbon nanotube synthesis processes . Environ Sci Technol 44 :8704–8711, PMID: 20964398, 10.1021/es102117k. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carson RT, Damon M, Johnson LT, Gonzalez JA. 2009. Conceptual issues in designing a policy to phase out metal-based antifouling paints on recreational boats in San Diego Bay . J Environ Manage 90 ( 8 ):2460–2468, 10.1016/j.jenvman.2008.12.016. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• CEIN (Center for Environmental Implications of Nanotechnology). 2015. Project KNO-2: Developing or Transforming Nano Regulatory Approaches . http://www.cein.ucla.edu/new/p177.php [accessed 12 August 2016].
• Corrosion Proof Fittings v. EPA , 947 F.2d 1201 (5th Cir. 1991).
• Croskerry P. 2002. Achieving quality in clinical decision making: Cognitive strategies and detection of bias . Acad Emerg Med 9 :1184–1204, PMID: 12414468. [ PubMed ] [ Google Scholar ]
• Dafforn KA, Lewis JA, Johnston EL. 2011. Antifouling strategies: History and regulation, ecological impacts and mitigation . Mar Pollut Bull 62 :453–465, 10.1016/j.marpolbul.2011.01.012. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• de Wilde P, Augenbroe G, van der Voorden M. 2002. Design analysis integration: Supporting the selection of energy saving building components . Build Environ 37 :807–816, 10.1016/S0360-1323(02)00053-7. [ CrossRef ] [ Google Scholar ]
• Del Missier F, Visentini M, Mäntylä T. 2015. Option generation in decision making: Ideation beyond memory retrieval . Front Psychol 5 :1584, 10.3389/fpsyg.2014.01584. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Department for Communities and Local Government. 2009. Multi-Criteria Analysis: A Manual . http://eprints.lse.ac.uk/12761/1/Multi-criteria_Analysis.pdf [accessed 14 October 2015].
• Department of Ecology, Washington State. 2015. Alternatives Assessment Guide for Small and Medium Businesses . https://fortress.wa.gov/ecy/publications/documents/1504002.pdf [accessed 14 December 2015].
• DTSC (California Department of Toxic Substances Control). 2013. Safer Consumer Product Regulations (codified at 22 CCR Section 69506.2-.8.) ; R-2011 http://www.dtsc.ca.gov/LawsRegsPolicies/Regs/upload/Text-of-Final-Safer-Consumer-Products-Regulations-2.pdf [accessed 23 May 2017].
• Durbach IN, Stewart TJ. 2011. An experimental study of the effect of uncertainty representation in decision making . Eur J Oper Res 214 :380–392, 10.1016/j.ejor.2011.04.021. [ CrossRef ] [ Google Scholar ]
• Eason T, Meyer DE, Curran MA, Upadhyayula V. 2011. “ Guidance to Facilitate Decisions for Sustainable Nanotechnology .” EPA/600/R-11/107.
• ECHA (European Chemicals Agency). 2011. “ Guidance on the Preparation of an Application for Authorisation .” ECHA-11-G-01 https://echa.europa.eu/documents/10162/23036412/authorisation_application_en.pdf/6571a0df-9480-4508-98e1-ff807a80e3a9 [accessed 23 May 2017].
• European Parliament and Council. 2006. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), OJ L 396 ( 30 December 2006 ):1–849. [ Google Scholar ]
• Frye-Levine LA. 2012. Sustainability through design science: Re-imagining option spaces beyond eco-efficiency . Sustain Dev 20 :166–179, 10.1002/sd.1533. [ CrossRef ] [ Google Scholar ]
• Giove S, Brancia A, Satterstrom FK Linkov I.. 2009. Decision support systems and environment: Role of MCDA . In: Decision Support Systems for Risk-Based Management of Contaminated Sites . Marcomini A; Suter GW Jr.; Critto, eds. New York, NY: Springer, 53–74. [ Google Scholar ]
• Gloria TP, Lippiatt BC, Cooper J. 2007. Life cycle impact assessment weights to support environmentally preferable purchasing in the United States . Environ Sci Technol 41 :7551–7557, PMID: 18044540. [ PubMed ] [ Google Scholar ]
• Gregory R, Failing L, Harstone M, Long G, McDaniels T, Ohlson D. 2012. Structured Decision Making: A Practical Guide to Environmental Management Choices . Oxford, UK: Wiley-Blackwell. [ Google Scholar ]
• Hambrick DC, Geletkanycz MA, Fredrickson JW. 1993. Top executive commitment to the status quo . Strat Mgmt J 14 :401–418, 10.1002/smj.4250140602. [ CrossRef ] [ Google Scholar ]
• Hansen SF, Jensen KA, Baun A. 2014. NanoRiskCat: A conceptual tool for categorization and communication of exposure potentials and hazards of nanomaterials in consumer products . J Nanopart Res 16 :2195, 10.1007/s11051-013-2195-z. [ CrossRef ] [ Google Scholar ]
• Hansen SF. 2010. Multicriteria mapping of stakeholder preferences in regulating nanotechnology . J Nanopart Res 12 :1959–1970, PMID: 21170118, 10.1007/s11051-010-0006-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang IB, Keisler J, Linkov I. 2011. Multi-Criteria Decision Analysis in environmental sciences: Ten years of applications and trends . Sci Total Environ 409 :3578–3594, PMID: 21764422, 10.1016/j.scitotenv.2011.06.022. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hyde K, Maier HR, Colby C. 2003. Incorporating uncertainty in the PROMETHEE MCDA method . J Multi-Crit Decis Anal 12 :245–259, 10.1002/mcda.361. [ CrossRef ] [ Google Scholar ]
• IC2 (Interstate Chemicals Clearinghouse). 2013. Alternatives Assessment Guide . http://www.newmoa.org/prevention/ic2/IC2_AA_Guide-Version_1.pdf [accessed 14 Oct 2015].
• Jacobs MM, Malloy TF, Tickner JA, Edwards S. 2016. Alternatives assessment frameworks: Research needs for the informed substitution of hazardous chemicals . Environ Health Perspect 124 :265–280, 10.1289/ehp.1409581. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kalra N; Hallegatte S; Lempert RJ; Brown C; Fozzard A; Gill S; Shah A. 2014. “ Agreeing on Robust Decisions: New Processes for Decision Making Under Deep Uncertainty. World Bank Policy Research Paper No. 6906 .” http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2446310&download=yes## [accessed 24 November 2015].
• Kiker GA, Bridge TS, Varghese A, Seager TP, Linkov I. 2005. Application of multicriteria decision analysis in environmental decision making . Integr Environ Assess Manage 1 :95–108, 10.1897/IEAM_2004a-015.1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kunreuther H, Meyer R, Zeckhauser R, Slovic P, Schwartz B, et al. 2002. High stakes decision making: Normative, descriptive and prescriptive considerations . Mark Lett 13 :259–268, 10.1023/A:1020287225409. [ CrossRef ] [ Google Scholar ]
• Lahdelma R, Salminen P. 2010. Stochastic Multicriteria Acceptability Analysis (SMAA) . In: Trends in Multiple Criteria Decision Analysis . Ehrgott M, Figueira JR, Greco S, eds. New York, NY: Springer, 285–316. [ Google Scholar ]
• Lavoie ET, Heine LG, Holder H, Rossi MS, Lee RE, Connor EA, et al. 2010. Chemical alternatives assessment: Enabling substitution to safer chemicals . Environ Sci Technol 44 :9244–9249, 10.1021/es1015789. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lempert R, Collins M. 2007. Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches . Risk Anal 27 ( 4 ):1009–1026. [ PubMed ] [ Google Scholar ]
• Lempert RJ, Popper SW, Groves DG, Kalra N, Fischbach JR, Bankes SC. 2013. Making good decisions without predictions: Robust decision making for planning under deep uncertainty . http://www.rand.org/pubs/research_briefs/RB9701.html [accessed 14 October 2015].
• Linkov I, Sahay S, Kiker G, Bridges T, Seager TP. 2005. Multi-Criteria Decision Analysis: A framework for managing contaminated sediments . In: Strategic Management of Marine Ecosystems . Levner E, Linkov I, Proth J-M, eds. Dordrecht, The Netherlands: Springer, 271–297. [ Google Scholar ]
• Linkov I, Satterstrom FK, Kiker G, Batchelor C, Bridges T, Ferguson E. 2006. From comparative risk assessment to multi-criteria decision analysis and adaptive management: Recent developments and applications . Environ Int 32 :1072–1093, 10.1016/j.envint.2006.06.013. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Linkov I, Moberg E. 2012. Multi-Criteria Decision Analysis: Environmental Applications and Case Studies . Boca Raton, FL: CRC Press. [ Google Scholar ]
• Linkov I, Varghese, A, Jamil, S, Seager, T, Kiker, G, Bridges, T. 2004. Multi-criteria decision analysis: A framework for structuring remedial decisions and contaminated sites . In: Comparative Risk Assessment and Environmental Decision Making . Linkov I and Ramadan AB, eds. The Netherlands: Kluwer Academic Publishing, 15–54. [ Google Scholar ]
• Lippiatt B. 2002. “ Building for Environmental and Economic Stability Technical Manual and User Guide ” (NISTIR 6916). https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=9400081C.PDF [accessed 23 May 2017].
• Malloy TF, Blake A, Linkov I, Sinsheimer PJ. 2015. Decisions, science and values: Crafting regulatory alternatives analysis . Risk Analysis 35 ( 12 ):2137–2151, 10.1111/risa.12466. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malloy TF, Sinsheimer PJ, Blake A, Linkov I. 2013. Use of multi-criteria decision analysis in regulatory alternatives analysis: A case study of lead free solder . Integr Environ Assess Manag 9 ( 4 ):652–664, PMID: 23703936, 10.1002/ieam.1449. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malloy TF. 2014. Design for regulation: Integrating sustainable production into mainstream regulation . In: Law and the Transition to Business Sustainability . Cahoy DR and Colburn JE, eds. Cham, Switzerland: Springer International Publishing AG, 1–23. [ Google Scholar ]
• Malloy TF. 2014. Principled Prevention . Ariz State Law J 46 :105–190. [ Google Scholar ]
• MDEP (Maine Department of Environmental Protection). 2012. Regulation of Chemical Use in Children’s Products (codified at 06 096 CMR Chapter 880-888) http://www.maine.gov/dep/safechem/rules.html [accessed 19 August 2016].
• NAS (National Academy of Sciences). 2014. A Framework to Guide Selection of Chemical Alternatives . Washington, DC: National Academies Press. [ PubMed ] [ Google Scholar ]
• NAS. 1996. Understanding Risk: Informing Decisions in a Democratic Society . Washington, DC: National Academies Press. [ Google Scholar ]
• NAS. 2005. Decision Making for the Environment: Social and Behavioral Research Priorities . Washington, DC: National Academies Press. [ Google Scholar ]
• NAS. 2009. Science and Decisions: Advancing Risk Assessment . Washington, DC: National Academies Press. [ PubMed ] [ Google Scholar ]
• OECD (Organisation for Economic Co-operation and Development) Ad Hoc Group of Substitution of Harmful Chemicals. 2016. Substitution of hazardous chemicals . http://www.oecd.org/chemicalsafety/testing/substitution-of-hazardous-chemicals.htm [accessed 23 May 2017].
• Parnell GS, Metzger RE, Merrick J, Eilers R. 2001. Multiobjective decision analysis of theater missile defense architectures . Syst Engin 4 ( 1 ):24–34, 10.1002/1520-6858(2001)4:1<24::AID-SYS3>3.0.CO;2-M. [ CrossRef ] [ Google Scholar ]
• Perez O. 2010. Precautionary governance and the limits of scientific knowledge: A democratic framework for regulating nanotechnology . UCLA J Environ Law Policy 28 :30–76. [ Google Scholar ]
• Prado V, Rogers K, Seager TP. 2012. Integration of MCDA Tools in Valuation of Comparative Life Cycle Assessment , In: Life Cycle Assessment Handbook: A Guide for Environmentally Sustainable Products (Curran MA, ed). Beverly, MA: Scrivener, 413–432. [ Google Scholar ]
• Rossi M, Peele C, Thorpe B. 2012. BizNGO Chemical Alternatives Assessment Protocol . http://www.bizngo.org/alternatives-assessment/chemical-alternatives-assessment-protocol [accessed 14 October 2015].
• Rossi M, Tickner J, Geiser K. 2006. Alternatives Assessment Framework of the Lowell Center for Sustainable Production . http://www.sustainableproduction.org/downloads/FinalAltsAssess06_000.pdf [accessed 24 November 2015].
• Samuelson W, Zeckhauser R. 1988. Status quo bias in decision making . J Risk Uncertain 1 :7–59, 10.1007/BF00055564. [ CrossRef ] [ Google Scholar ]
• Schoenung JM, Ogunseitan OA, Eastmond DA. 2009. Research and education in green materials: A multi-disciplinary program to bridge the gaps, In: ISSST, 2009, IEEE International Symposium on Sustainable Systems and Technology , 18–20 May 2009 Tempe, AZ pp. 1–6, 10.1109/ISSST.2009.5156760. [ CrossRef ] [ Google Scholar ]
• Sinsheimer PJ, Grout C, Namkoong A, Gottlieb R, Latif A. 2007. The viability of professional wet cleaning as a pollution prevention alternative to perchlorethylene dry cleaning . J Air Waste Manage 57 :172–178. [ PubMed ] [ Google Scholar ]
• SPRU (Science Policy Research Unit). 2004. Briefing 5: Using the Multi-criteria Mapping Technique. Deliberative Mapping, University of Sussex . http://users.sussex.ac.uk/∼prfh0/DM%20Briefing%205.pdf [accessed 14 October 2015].
• Thaler RH, Benartzi S. 2004. Save More Tomorrow™: Using Behavioral Economics to Increase Employee Saving . J Polit Econ 112 :S164–S187, 10.1086/380085. [ CrossRef ] [ Google Scholar ]
• U.S. EPA (U.S. Environmental Protection Agency). 1990. National oil and hazardous substances pollution contingency plan . Final rule. Fed Reg 55 :8665–8865. [ Google Scholar ]
• U.S. EPA. 2011. “ Design for the Environment Program Alternatives Assessment Criteria for Hazard Evaluation, Version 2.0. ” https://www.epa.gov/sites/production/files/2014-01/documents/aa_criteria_v2.pdf [accessed 23 May 2017].
• U.S. EPA. 1994. National oil and hazardous substances pollution contingency plan . Final rule. Fed Reg 59 :13044–13161. [ Google Scholar ]
• U.S. EPA. “ Safer Chemical Ingredients List (SCIL) .” http://www2.epa.gov/saferchoice/safer-ingredients#scil [accessed 14 Oct 2015].
• Wender BA, Foley RW, Prado-Lopez V, Ravikumar D, Eisenberg DA, Hottle TA, et al. 2014. Illustrating anticipatory life cycle assessment for emerging photovoltaic technologies . Environ Sci Technol 48 :10531–10538, 10.1021/es5016923. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yatsalo BI, Kiker GA, Kim SJ, Bridges TS, Seager TP, Gardner K, et al. 2007. Application of multicriteria decision analysis tools to two contaminated sediment case studies . Integr Environ Assess Manag 3 :223–233, 10.1897/IEAM_2006-036.1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhou X, Schoenung JM. 2007. An integrated impact assessment and weighting methodology: Evaluation of the environmental consequences of computer display technology substitution . J Environ Manage 83 :1–24, PMID: 16714079, 10.1016/j.jenvman.2006.01.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]

Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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5 Compelling Alternatives to the Traditional Case Study Format

by John Cole | Aug 11, 2021 | Collateral , B2B Copywriting , Lead Generation , Case Studies | 0 comments

Case studies have long been one of the most popular and influential forms of marketing content.

In Eccolo Media’s annual B2B Technology Collateral Survey Report s (2008 to 2014), for example, technology buyers ranked case studies the second most influential content type – trailing only white papers – seven years in a row . 1 More recently, 60% of marketing influencers told Ascend2 that research and case studies are the content target audiences trust the most. 2

There’s really no surprise here. After all, case studies are short, quick reads. They’re familiar, easy to follow. They give technology buyers the information they need: solid evidence that they can succeed with your solution. And besides… everybody loves a good story.

But the traditional case study format has its drawbacks. It’s not always the perfect fit for every company, objective, audience, or customer story. And there’s a sameness to traditional case studies that makes it easy for them to get lost in the marketing message crowd.

So today, we’ll look at the advantages and disadvantages of a few “alternative” formats you may want to consider for your next success story project. Those I’ve chosen can all work well with technology-purchasing audiences. But before we explore the alternatives, let’s take a brief look at the pros and cons of the traditional case study.

The Traditional Case Study Format

We all know the traditional case study format. Four distinct sections under four well-known headings: Customer, Challenge (or Problem), Solution, and Results. They’re familiar to every B2B audience, easy to follow, easy to write.

So, what are the drawbacks of this tried-and-true formula?

Well, first of all, when you begin by describing the customer, it’s hard to get off to a compelling start. A good story provides some drama or intrigue right at the beginning to grab readers’ attention and pull them into the narrative. That drama comes from the customer’s challenge , not his background. Starting with a subject profile is not the best choice for some marketing objectives, like lead generation.

Second, the section headings offer no intrigue. They provide structure, but nothing to draw the attention of scanners. There’s no benefit. Besides, we’ve seen them all before.

Lastly, traditional case studies don’t appeal to trade journal editors. Editors want feature articles that resemble news stories, not academic papers or marketing pieces. If you want to get your case study placed in a trade magazine (or appeal to scanners or generate leads), you need a different format.

Five Alternative Case Study Formats

So, what are the alternatives to the traditional case study format? Here are five that can appeal to technology audiences.

1. The Feature Story

The “feature story” case study format is probably the most popular alternative to the traditional one. The reason it’s so popular? It addresses all the deficiencies of the traditional case study format.

As you’ve undoubtedly guessed, this type of case study is written like a feature story in a newspaper or magazine. It can follow the same logical sequence as the traditional form, but the information is not grouped under the standard subheads. Instead, the feature story case study employs techniques journalists use to engage readers, like descriptive subheads and an engaging opening paragraph, or “lead.”

To create drama in the lead, writers will typically start with the challenge, rather than a customer description. Background on the customer can either be sprinkled into the narrative—as a fiction author fleshes out characters—or placed in a sidebar. Descriptive subheads help to both summarize the story and pique the interest of scanners.

The big advantage of the feature story format is its engaging narrative flow. When well written, feature stories are more enjoyable to read and hold our attention better than traditional case studies. This makes them better for lead generation purposes. That’s also why trade editors like them. They look and read like other feature articles they publish.

The downside of the feature story format is that it requires greater writing skill. The writer must know how to handle key elements, like the headline, lead, and subheads. Story elements must be woven together into a cohesive narrative that flows relentlessly to a satisfying ending. If the reader gets lost, your success story will be a failure.

2. The Story-Within-A-Story

What could be better than a compelling, captivating success story? How about two?

The “story-within-a-story” is a variation of the feature story format. Along with describing why the customer chose your solution and how well it solved their problem, this case study format also includes an example of how your customer uses your solution to provide a better product or service to its own customers.

In other words, it contains a second case study that focuses on one of your customer’s customers.

This format can work very well if your market is OEMs, system integrators, or other vendors who incorporate your solution into their own. It’s also great for getting customer approval for your case study project and buy-in on joint marketing ventures; your customer gains publicity for one of their own successes. And like other feature stories, the story-within-a-story is ideal for trade journal placement and lead generation.

But with double the upside, you also get double the downside. This type of case study is more complex to produce. It involves additional interviews and approval cycles with your customer’s customer. Plus, crafting story-within-a-story calls for even greater writing skill than the feature story. Your writer needs to make sure the second story nests comfortably within the first without upsetting the flow of the narrative.

3. The Q&A

If you want a case study that can be created quickly and easily, consider the Q&A .

As the name suggests, a Q&A case study consists of a list of questions and the customer’s answers to each. While not a great lead-gen tool, Q&As can be very useful as website, blog, and newsletter content for nurturing leads and keeping customers engaged.

There are several advantages to the Q&A. The form is simple and doesn’t require great writing skills, so they’re quick and easy to produce. The questions, however, must be well thought out.

Q&A case studies are very appealing to technical audiences. Normally distrustful of marketing collateral, techies tend to like Q&As conducted with engineers or other technicians in roles similar to their own. They like getting no-nonsense information directly from their peers.

The downside here is that success is largely dependent on the quality of your customer’s responses to your questions. You need to pay a lot of attention to selecting the right customer rep for your interview. And your interviewer must be prepared to draw good information out of that person. There’s very little you can do in the editing process.

4. The First-Person Account

If your audience would respond well to a Q&A case study, but you want something you can place in a trade journal or use in lead generation campaigns, a “first-person” case study may fit the bill.

Like the Q&A, a first-person case study tells the story of a customer’s success with your solution in the customer’s own words. But the form is less rigid, more like that of a feature article. First-person case studies gain credibility by letting the reader hear the story “straight from the horse’s mouth” – like an extended testimonial.

First-person case studies are most often used by coaches and consultants who work with individuals. But they can succeed with corporate prospects as well, especially technical audiences. They tend to work best when the protagonist – the storyteller – had a big personal stake in the outcome of the story (had much to lose if the problem was not resolved, made or championed the purchase decision, etc).

What’s more, these individuals will often be more than willing to shepherd your case study through their own corporate approval process. After all, having a success story publicly documented can give a boost to one’s career.

Among the drawbacks of the first-person case study are that they can take longer to prepare, and they are not good for trade journal publication, due to the first-person perspective. They also have a potentially shorter shelf life. If the featured individual leaves the company, the customer might want the story discontinued.

Finally, a word of warning regarding first-person case studies: Don’t ask customers to write them themselves. Most won’t have the necessary writing skills or experience—let alone the time—to pull the project off. To create a story in the customer’s own words, your writer will need to prepare for a longer interview process and draw the full story out of the subject… without putting words in his or her mouth.

5. The Expected Results Story

Sometimes, it’s in a company’s interest to publish a case study before their customer has achieved any measurable results from their solution. This is called an “expected results” case study.

I wrote one of these recently. My client, an IT services company, had recently delivered Phase 1 of a three-phase project for a prestigious American university. We took an “expected results” approach for several reasons.

First, my client wanted to immediately leverage that success and the customer’s marquee name in their lead generation activities. But most of the measurable results of the project would not be realized until after the completion of Phase 3.

Second, Phase 1 had been the most critical phase of the project and held a very compelling story. It was a prime illustration of my client’s unique selling proposition and the reason the customer had chosen them for the job: the ability to deliver great results, on time and under budget, to an impossible deadline.

Third, while the customer had no problem with their name being used, the participation of a university representative in an interview was subject to a lengthy approval process. There was the possibility that customer participation and measurable results would never become available.

And finally, I would be interviewing members of my client’s technical staff, rather than the customer. Since staff members move quickly to other projects once a job is finished, my client wanted to document this project while it was still fresh in their minds.

Any of these circumstances would have been a good reason to proceed with an expected results story. Plus, there’s another great thing about this type of case study: it can be updated later, once the results are known.

The drawback of the expected results case study, of course, is that it has a weaker impact due to the lack of metrics. It forces you to make a case for your projected results. But if you have a compelling customer story and just lack hard results data, an expected results case study can let you leverage that story right away.

Takeaway Points

1. The traditional case study format (customer-challenge-solution-results) is still effective, but it can get lost in a crowd.

2. Traditional case studies are not always the best choice for every company, story, audience, or marketing objective.

3. Fortunately, you have a wide range of effective alternatives to the traditional case study format, including these five formats which work well with tech audiences:

• Feature story
• Story-within-a-story
• First-person
• Expected results

If you’d like help interviewing a customer and crafting a case study in any of these formats, including the traditional one, email me at [email protected] .

1   Eccolo Media 2008-2014 B2B Technology Collateral Surveys ,  www.eccolomedia.com .

2 Content Marketing Engagement Survey Summary Report , Ascend2, June 2019.

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• Open access
• Published: 24 May 2024

Integration of case-based learning and three-dimensional printing for tetralogy of fallot instruction in clinical medical undergraduates: a randomized controlled trial

• Jian Zhao 1   na1 ,
• Xin Gong 1   na1 ,
• Jian Ding 1 ,
• Kepin Xiong 2 ,
• Kangle Zhuang 3 ,
• Rui Huang 1 ,
• Shu Li 4 &
• Huachun Miao 1  

BMC Medical Education volume  24 , Article number:  571 ( 2024 ) Cite this article

288 Accesses

Metrics details

Case-based learning (CBL) methods have gained prominence in medical education, proving especially effective for preclinical training in undergraduate medical education. Tetralogy of Fallot (TOF) is a congenital heart disease characterized by four malformations, presenting a challenge in medical education due to the complexity of its anatomical pathology. Three-dimensional printing (3DP), generating physical replicas from data, offers a valuable tool for illustrating intricate anatomical structures and spatial relationships in the classroom. This study explores the integration of 3DP with CBL teaching for clinical medical undergraduates.

Sixty senior clinical medical undergraduates were randomly assigned to the CBL group and the CBL-3DP group. Computed tomography imaging data from a typical TOF case were exported, processed, and utilized to create four TOF models with a color 3D printer. The CBL group employed CBL teaching methods, while the CBL-3DP group combined CBL with 3D-printed models. Post-class exams and questionnaires assessed the teaching effectiveness of both groups.

The CBL-3DP group exhibited improved performance in post-class examinations, particularly in pathological anatomy and TOF imaging data analysis ( P  < 0.05). Questionnaire responses from the CBL-3DP group indicated enhanced satisfaction with teaching mode, promotion of diagnostic skills, bolstering of self-assurance in managing TOF cases, and cultivation of critical thinking and clinical reasoning abilities ( P  < 0.05). These findings underscore the potential of 3D printed models to augment the effectiveness of CBL, aiding students in mastering instructional content and bolstering their interest and self-confidence in learning.

The fusion of CBL with 3D printing models is feasible and effective in TOF instruction to clinical medical undergraduates, and worthy of popularization and application in medical education, especially for courses involving intricate anatomical components.

Peer Review reports

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease(CHD) [ 1 ]. Characterized by four structural anomalies: ventricular septal defect (VSD), pulmonary stenosis (PS), right ventricular hypertrophy (RVH), and overriding aorta (OA), TOF is a focal point and challenge in medical education. Understanding anatomical spatial structures is pivotal for learning and mastering TOF [ 2 ]. Given the constraints of course duration, medical school educators aim to provide students with a comprehensive and intuitive understanding of the disease within a limited timeframe [ 3 ].

The case-based learning (CBL) teaching model incorporates a case-based instructional approach that emphasizes typical clinical cases as a guide in student-centered and teacher-facilitated group discussions [ 4 ]. The CBL instructional methods have garnered widespread attention in medical education as they are particularly appropriate for preclinical training in undergraduate medical education [ 5 , 6 ]. The collection of case data, including medical records and examination results, is essential for case construction [ 7 ]. The anatomical and hemodynamic consequences of TOF can be determined using ultrasonography, computed tomography (CT), and magnetic resonance imaging techniques. However, understanding the anatomical structures from imaging data is a slow and challenging psychological reconstruction process for undergraduate medical students [ 8 ]. Three-dimensional (3D) visualization is valuable for depicting anatomical structures [ 9 ]. 3D printing (3DP), which creates physical replicas based on data, facilitates the demonstration of complex anatomical structures and spatial relationships in the classroom [ 10 ].

During the classroom session, 3D-printed models offer a convenient means for hands-on demonstration and communication, similar to facing a patient, enhancing the efficiency and specificity of intra-team communication and discussion [ 11 ]. In this study, we printed TOF models based on case imaging data, integrated them into CBL teaching, and assessed the effectiveness of classroom instruction.

Research participants

The study employed a prospective, randomized controlled design which received approval from the institutional ethics committee. Senior undergraduate students majoring in clinical medicine at Wannan Medical College were recruited for participation based on predefined inclusion criteria. The researchers implemented recruitment according to the recruitment criteria by contacting the class leaders of the target classes they had previously taught. Notably, these students were in their third year of medical education, with anticipation of progressing to clinical courses in the fourth year, encompassing Internal Medicine, Surgery, Obstetrics, Gynecology, and Pediatrics. Inclusion criteria for participants encompassed the following: (1) proficient communication and comprehension abilities, (2) consistent attendance without absenteeism or truancy, (3) absence of failing grades in prior examinations, and (4) capability to conscientiously fulfill assigned learning tasks. Exclusion criteria were (1) absence from lectures, (2) failure to complete pre-and post-tests, and (3) inadequate completion of questionnaires. For their participation in the study, Students were provided access to the e-book “Localized Anatomy,” authored by the investigators, as an incentive for their participation. Voluntary and anonymous participation was emphasized, with participants retaining the right to withdraw from the study at any time without providing a reason.

The study was conducted between May 1st, 2023, and June 30, 2023, from recruitment to completion of data collection. Drawing upon insights gained from a previous analogous investigation which yielded an effect size of 0.95 [ 10 ]. Sample size was computed, guided by a statistical consultant, with the aim of 0.85 power value, predicated on an effect size of 0.8 and a margin of error set at 0.05. A minimum of 30 participants per group was calculated using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), resulting in the recruitment of a total of 60 undergraduate students. Each participant was assigned an identification number, with codes placed in boxes. Codes drawn from the boxes determined allocation to either the CBL group or the CBL-3DP group. Subsequently, participants were randomly assigned to either the CBL group, receiving instruction utilizing the CBL methodology, or the CBL-3DP group, which received instruction integrating both CBL and 3D Printed models.

Printing of TOF models

Figure  1 A shows the printing flowchart of the TOF models. A typical TOF case was collected from the Yijishan Hospital of Wannan Medical College. The CT angiography imaging data of the case was exported. Mimics Research 20.0 software (Mimics Innovation Suite version 20, Materialize, Belgium) was used for data processing. The cardiovascular module of the CT-Heart tool was employed to adjust the threshold range, independently obtain the cardiac chambers and vessels, post-process the chambers and vessels to generate a hollow blood pool, and merge it with the myocardial volume to construct a complete heart model. The file was imported into Magics 24.0 software (version 24.0; Materialize, Belgium) for correction using the Shell tool page. After repairs, the model entered the smoothing page, where tools such as triangular surface simplification, local smoothing, refinement and smoothing, subdivision of components, and mesh painting were utilized to achieve varying degrees of smoothness. Finally, optimized data were obtained and exported as stereolithography (STL) files. An experienced cardiothoracic surgeon validated the anatomical accuracy of the digital model.

The STL files were imported into a 3D printer (J401Pro; Sailner 3D Technology, China) for model printing. This printer can produce full-color medical models using different materials. The models were fabricated using two distinct materials: rigid and flexible. Both materials are suitable for the observational discussion of the teaching objectives outlined in our study. From the perspective of observing pathological changes in the TOF, there is no significant difference between the two materials.

Experimental flow chart of this study. A TOF model printing flow chart. B The instructional framework

Teaching implementation

Figure  1 B illustrates the instructional framework employed in this study. One week preceding the class session, all the students were tasked with a 30-minute self-study session, focusing on the theoretical content related to TOF as outlined in the Pediatrics and Surgery textbooks, along with a review of pertinent academic literature. Both groups received co-supervision from two basic medicine lecturers boasting over a decade of teaching experience, alongside a senior cardiothoracic surgeon. Teaching conditions remained consistent across groups, encompassing uniform assessment criteria and adherence to predefined teaching time frames, all conducted in a Project-Based Learning (PBL) classroom at Wannan Medical College. Additionally, a pre-course examination was administered to gauge students’ preparedness for self-study.

In adherence to the curriculum guidelines, the teaching objectives aimed to empower students to master TOF’s clinical manifestations, diagnostic modalities, and differential diagnoses, while acquainting them with treatment principles and surgical methodologies. Additionally, the objectives sought to cultivate students’ clinical reasoning abilities and problem-solving skills. the duration of instruction for the TOF theory session was standardized to 25 min. The didactic content was integrated with the TOF case study to construct a coherent pedagogical structure.

During the instructional session, both groups underwent teaching utilizing the CBL methodology. Clinical manifestations and case details of TOF cases were presented to stimulate students’ interest and curiosity. Subsequently, the theory of TOF, including its etiology, pathogenesis, pathologic anatomy, clinical manifestations, diagnostic methods, and therapeutic interventions, was briefly elucidated. Emphasis was then placed on the case, wherein selected typical TOF cases were explained, guiding students in analysis and discussion. Students were organized into four teams under the instructors’ supervision, fostering cooperative learning and communication, thereby deepening their understanding of the disease through continuous inquiry and exploration (Fig.  2 L). In the routinely equipped PBL classroom with standard heart models (Fig.  2 J, K), all students had prior exposure to human anatomy and were familiar with these models. Both groups were provided with four standard heart models for reference, while the CBL-3DP group received additional four 3D-printed models depicting TOF anomalies, enriching their learning experience (Fig.  2 D, G). After the lesson, summarization, and feedback sessions were conducted to consolidate group discussions’ outcomes, evaluate teaching effectiveness, and assess learning outcomes.

Heart models utilized in instructional sessions. A External perspective of 3D digital models. B, C Cross-sectional views following trans-septal sagittal dissection of the 3D digital model (PS: Pulmonary Stenosis; OA: Overriding Aorta; VSD: Ventricular Septal Defect; RVH: Right Ventricular Hypertrophy). D External depiction of rigid 3D printed model. E, F Sagittal sections of the rigid 3D printed model. G External portrayal of flexible 3D printed model. H, I Sagittal sections of the flexible 3D printed model. J, K The normal heart model employed in the instruction of the CBL group. L Ongoing classroom session

Teaching effectiveness assessment

Following the instructional session, participants from the two groups underwent a theoretical examination to assess their comprehension of the taught material. This assessment covered domains such as pathological anatomy, clinical manifestations, imaging data interpretation, diagnosis, and treatment relevant to TOF. Additionally, structured questionnaires were administered to evaluate the efficacy of the pedagogical approach employed. The questionnaire consisted of six questions designed to gauge participants’ understanding of the teaching content, enhancement of diagnostic skills, cultivation of critical thinking and clinical reasoning abilities, bolstering of confidence in managing TOF cases, satisfaction with the teaching mode, and satisfaction with the CBL methodology.

The questionnaire employed a 5-point Likert scale to gauge responses, with 5 indicating “strongly satisfied/agree,” 4 for “satisfied/agree,” 3 denoting “neutral,” 2 reflecting “dissatisfied/disagree,” and 1 indicating “strongly dissatisfied/disagree.” It comprised six questions, with the initial two probing participants’ knowledge acquisition, questions 3 and 4 exploring satisfaction regarding enhanced competence, and the final two assessing satisfaction with teaching methods and modes. Additionally, participants were encouraged to provide suggestions at the end of the questionnaire. To ensure the questionnaire’s validity, five esteemed lecturers in basic medical sciences with more than 10 years of experience verified its content and assessed its Content Validity Ratio and Content Validity Index to ensure alignment with the study’s objectives.

Statistical analysis

Statistical analyses were conducted utilizing GraphPad Prism 9.0 software. Aggregate score data for both groups were presented as mean ± standard deviation (x ± s). The gender comparisons were analyzed with the chi-square (χ2) test, while the other variables were compared using the Mann-Whitney U test. The threshold for determining statistical significance was set at P  < 0.05.

Three-dimensional printing models

After configuring the structural colors of each component (Fig.  2 A, B, C), we printed four color TOF models using both rigid and flexible materials, resulting in four life-sized TOF models. Two color TOF models were created using rigid materials (Fig.  2 D, E, F). These models, exhibiting resistance to deformation, and with a firm texture, smooth and glossy surface, and good transparency, allowing visibility of the internal structures, were deemed conducive to teaching and observation. We also fabricated two color TOF models using flexible materials (Fig.  2 G, H, I), characterized by soft texture, opacity, and deformability, allowing for easy manipulation and cutting. It has potential utility beyond observational purposes. It can serve as a valuable tool for simulating surgical interventions and may be employed to create tomographic anatomical specimens. In this study, both material models were suitable for observation in the classroom. The participants were able to discern the four pathological changes characteristic of TOF from surface examination or cross-sectional analysis.

Baseline characteristics of the students

In total, 60 students were included in this study. The CBL group comprised 30 students (14 males and 16 females), with an average age of (21.20 ± 0.76) years. The CBL-3DP group consisted of 30 students (17 males and 13 females) with an average age of 20.96 years. All the students completed the study procedures. There were no significant differences in age, sex ratio, or pre-class exam scores between the two groups ( P  > 0.05), indicating that the baseline scores between the two groups were comparable (Table  1 ).

Theoretical examination results

All students completed the research procedures as planned. The post-class theoretical examination encompassed assessment of pathological anatomy, clinical presentations, imaging data interpretation, diagnosis, and treatment pertinent to TOF. Notably, no statistically significant disparities were observed in the scores on clinical manifestations, diagnosis and treatment components between the cohorts, as delineated in Table  2 . Conversely, discernible distinctions were evident whereby the CBL-3DP group outperformed the CBL group notably in pathological anatomy, imaging data interpretation, and overall aggregate scores ( P  < 0.05).

Results of the questionnaires

All the 60 participants submitted the questionnaire. Comparing the CBL and CBL-3DP groups, the scores from the CBL-3DP group showed significant improvements in many areas. This included satisfaction with the teaching mode, promotion of diagnostic skills, bolstering of self-assurance in managing TOF cases, and cultivation of critical thinking and clinical reasoning abilities (Fig.  3 B, C, D, E). All of which improved significantly ( P  < 0.05 for the first aspects and P  < 0.01 for the rest). However, the two groups were not comparable ( P  > 0.05) in terms of understanding of the teaching content and Satisfaction with the CBL methodology (Fig.  3 A, F).

Upon completion of the questionnaires, participants were invited to proffer recommendations. Notably, in the CBL group, seven students expressed challenges in comprehending TOF and indicated a need for additional time for consolidation to enhance understanding. Conversely, within the CBL-3DP group, twelve students advocated for the augmentation of model repertoire and the expansion of disease-related data collection to bolster pedagogical efficacy across other didactic domains.

Five-point Likert scores of students’ attitudes in CBL ( n  = 30) and CBL-3DP ( n  = 30) groups. A Understanding of teaching content. B Promotion of diagnostic skills. C Cultivation of critical thinking and clinical reasoning abilities. D Bolstering of self-assurance in managing TOF cases. E Satisfaction with the teaching mode. F Satisfaction with the CBL methodology. ns No significant difference, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

TOF presents a significant challenge in clinical practice, necessitating a comprehensive understanding for effective diagnosis and treatment [ 12 ]. Traditional teaching methods in medical schools have relied on conventional resources such as textbooks, 2D illustrations, cadaver dissections, and radiographic materials to impart knowledge about complex conditions like TOF [ 13 ]. However, the limitations of these methods in fully engaging students and bridging the gap between theoretical knowledge and practical application have prompted a need for innovative instructional approaches.

CBL has emerged as a valuable tool in medical education, offering students opportunities to engage with authentic clinical cases through group discussions and inquiry-based learning [ 14 ]. By actively involving students in problem-solving and decision-making processes, CBL facilitates the application of theoretical knowledge to real-world scenarios, thus better-preparing students for future clinical practice [ 15 ]. Our investigation revealed that both groups of students exhibited comparable levels of satisfaction with the CBL methodology, devoid of discernible disparities.

CHD presents a formidable challenge due to the intricate nature of anatomical anomalies, the diverse spectrum of conditions, and individual variations [ 16 ]. Utilizing 3D-printed physical models, derived from patient imaging data, can significantly enhance comprehension of complex anatomical structures [ 17 ]. These models have proven invaluable in guiding surgical planning, providing training for junior or inexperienced pediatric residents, and educating healthcare professionals and parents of patients [ 18 ]. Studies indicate that as much as 50% of pediatric surgical decisions can be influenced by the insights gained from 3D printed models [ 19 ]. By providing tangible, anatomically accurate models, 3D printing offers a unique opportunity for people to visualize complex structures and enhance their understanding of anatomical intricacies. Our study utilized full-color, to-scale 3D printed models to illustrate the structural abnormalities associated with TOF, thereby enriching classroom sessions and facilitating a deeper comprehension of the condition.

Comparative analysis between the CBL-3DP group and the CBL group revealed significant improvements in post-test performance, particularly in pathological anatomy and imaging data interpretation. Additionally, questionnaire responses indicated higher levels of satisfaction and confidence among students in the CBL-3DP group, highlighting the positive impact of incorporating 3D printed models into the learning environment, improving the effectiveness of CBL classroom instruction. Despite the merits, our study has limitations. Primarily, participants were exclusively drawn from the same grade level within a single college, possibly engendering bias owing to shared learning backgrounds. Future research could further strengthen these findings by expanding the sample size and including long-term follow-up to assess the retention of knowledge and skills. Additionally, the influence of the 3D models depicting a normal heart on the learning process and its potential to introduce bias into the results warrants consideration, highlighting a need for scrutiny in subsequent studies.

As medical science continues to advance, the need for effective teaching methods becomes increasingly paramount. Our study underscores the potential of combining active learning approaches like CBL with innovative technologies such as 3D printing to enhance teaching effectiveness, improve knowledge acquisition, and foster students’ confidence and enthusiasm in pursuing clinical careers. Moving forward, further research and integration of such methodologies are essential for meeting the evolving demands of medical education, especially in areas involving complex anatomical understanding.

Conclusions

Integrating 3D-printed models with the CBL method is feasible and effective in TOF instruction. The demonstrated success of this method warrants broad implementation in medical education, particularly for complex anatomical topics.

Data availability

All data supporting the conclusions of this research are available upon reasonable request from the corresponding author.

Apitz C, Webb GD, Redington AN. Tetralogy of Fallot. Lancet. 2009;374:1462–71.

Article   Google Scholar  

Ghosh RM, Jolley MA, Mascio CE, Chen JM, Fuller S, Rome JJ, et al. Clinical 3D modeling to guide pediatric cardiothoracic surgery and intervention using 3D printed anatomic models, computer aided design and virtual reality. 3D Print Med. 2022;8:11.

Chakrabarti R, Wardle K, Wright T, Bennie T, Gishen F. Approaching an undergraduate medical curriculum map: challenges and expectations. BMC Med Educ. 2021;21:341.

Donkin R, Yule H, Fyfe T. Online case-based learning in medical education: a scoping review. BMC Med Educ. 2023;23:564.

Novack JP. Designing cases for case-based immunology teaching in large medical school classes. Front Immunol. 2020;11:995.

Chen HC, Van Den Broek WES, Ten Cate O. The case for use of entrustable professional activities in undergraduate medical education. Acad Med. 2015;90:431–6.

Wang M, Sun Z, Jia M, Wang Y, Wang H, Zhu X, et al. Intelligent virtual case learning system based on real medical records and natural language processing. BMC Med Inf Decis Mak. 2022;22:60.

Yoo S-J, Thabit O, Kim EK, Ide H, Yim D, Dragulescu A, et al. 3D printing in medicine of congenital heart diseases. 3D Print Med. 2015;2:3.

Yammine K, Violato C. A meta-analysis of the educational effectiveness of three-dimensional visualization technologies in teaching anatomy. Anat Sci Educ. 2015;8:525–38.

Miao H, Ding J, Gong X, Zhao J, Li H, Xiong K, et al. Application of 3D-printed pulmonary segment specimens in experimental teaching of sectional anatomy. BMC Surg. 2023;23:109.

Sun Z, Wong YH, Yeong CH. Patient-specific 3D-printed low-cost models in medical education and clinical practice. Micromachines (Basel). 2023;14:464.

Downing TE, Kim YY. Tetralogy of Fallot: general principles of management. Cardiol Clin. 2015;33:531–41. vii–viii.

Jia X, Zeng W, Zhang Q. Combined administration of problem- and lecture-based learning teaching models in medical education in China: a meta-analysis of randomized controlled trials. Med (Baltim). 2018;97:e11366.

McLean SF. Case-based learning and its application in medical and health-care fields: a review of worldwide literature. J Med Educ Curric Dev. 2016;3:JMECD.S20377.

Zeng N, Lu H, Li S, Yang Q, Liu F, Pan H, et al. Application of the combination of CBL teaching method and SEGUE framework to improve the doctor-patient communication skills of resident physicians in otolaryngology department. Bmc Med Educ. 2024;24:201.

Sun Z. Patient-specific 3D-printed models in pediatric congenital heart disease. Children. 2023;10:319.

Meyer-Szary J, Luis MS, Mikulski S, Patel A, Schulz F, Tretiakow D, et al. The role of 3D printing in planning complex medical procedures and training of medical professionals—cross-sectional multispecialty review. IJERPH. 2022;19:3331.

Sun Z, Wee C. 3D printed models in cardiovascular disease: an exciting future to deliver personalized medicine. Micromachines-basel. 2022;13:1575.

Valverde I, Gomez-Ciriza G, Hussain T, Suarez-Mejias C, Velasco-Forte MN, Byrne N, et al. Three-dimensional printed models for surgical planning of complex congenital heart defects: an international multicentre study. Eur J Cardio-thorac. 2017;52:1139–48.

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Acknowledgements

We extend our sincere appreciation to the instructors and students whose invaluable participated in this study.

This paper received support from the Education Department of Anhui Province, China (Grant Numbers 2022jyxm1693, 2022jyxm1694, 2022xskc103, 2018jyxm1280).

Author information

Jian Zhao and Xin Gong are joint first authors.

Authors and Affiliations

Department of Human Anatomy, Wannan Medical College, No.22 West Wenchang Road, Wuhu, 241002, China

Jian Zhao, Xin Gong, Jian Ding, Rui Huang & Huachun Miao

Department of Cardio-Thoracic Surgery, Yijishan Hospital of Wannan Medical College, Wuhu, China

Kepin Xiong

Zhuhai Sailner 3D Technology Co., Ltd., Zhuhai, China

Kangle Zhuang

School of Basic Medical Sciences, Wannan Medical College, Wuhu, China

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Contributions

Jian Zhao and Huachun Miao designed the research. Jian Zhao, Xin Gong, Jian Ding, Kepin Xiong designed the tests and questionnaires. Kangle Zhuang processed the imaging data and printed the models. Xing Gong and Kepin Xiong implemented the teaching. Jian Zhao and Rui Huang collected the data and performed the statistical analysis. Jian Zhao and Huachun Miao prepared the manuscript. Shu Li and Huachun Miao revised the manuscript. Shu Li provided the Funding acquisition. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Shu Li or Huachun Miao .

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Ethics approval and consent to participate.

This investigation received ethical approval from the Ethical Committee of School of Basic Medical Sciences, Wannan Medical College (ECBMSWMC2022-1-12). All methodologies adhered strictly to established protocols and guidelines. Written informed consent was obtained from the study participants to take part in the study.

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Written informed consent was obtained from the individuals for the publication of any potentially identifiable images or data included in this article.

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The authors declare no competing interests.

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Zhao, J., Gong, X., Ding, J. et al. Integration of case-based learning and three-dimensional printing for tetralogy of fallot instruction in clinical medical undergraduates: a randomized controlled trial. BMC Med Educ 24 , 571 (2024). https://doi.org/10.1186/s12909-024-05583-z

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Received : 03 March 2024

Accepted : 21 May 2024

Published : 24 May 2024

DOI : https://doi.org/10.1186/s12909-024-05583-z

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• Medical education
• Case-based learning
• 3D printing
• Tetralogy of fallot
• Medical undergraduates

BMC Medical Education

ISSN: 1472-6920

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