what is a case study comparative

This page deals with the set of methods used in comparative case study analysis, which focuses on comparing a small or medium number of cases and qualitative data. Structured case study comparisons are a way to leverage theoretical lessons from particular cases and elicit general insights from a population of phenomena that share certain characteristics. The content on this page discusses variable-oriented analysis (guided by frameworks), formal concept analysis and qualitative comparative analysis.

The Chapter summary video gives a brief introduction and summary of this group of methods, what SES problems/questions they are useful for, and key resources needed to conduct the methods. The methods video/s introduce specific methods, including their origin and broad purpose, what SES problems/questions the specific method is useful for, examples of the method in use and key resources needed. The Case Studies demonstrate the method in action in more detail, including an introduction to the context and issue, how the method was used, the outcomes of the process and the challenges of implementing the method. The labs/activities give an example of a teaching activity relating to this group of methods, including the objectives of the activity, resources needed, steps to follow and outcomes/evaluation options.

More details can be found in Chapter 20 of the Routledge Handbook of Research Methods for Social-Ecological Systems.

Chapter summary:

Method Summaries

Case studies, comparative case study analysis: comparison of 6 fishing producer organizations.

Dudouet, B. (2023)

Lab teaching/ activity

Tips and tricks.

• Basurto, X., S. Gelcich, and E. Ostrom. 2013. ‘The Social-Ecological System Framework as a Knowledge Classificatory System for Benthic Small-Scale Fisheries.’ Global Environmental Change 23(6):  1366–1380.
• Binder, C., J. Hinkel, P.W.G. Bots, and C. Pahl-Wostl. 2013. ‘Comparison of Frameworks for Analyzing Social-Ecological Systems.’ Ecology and Society 18(4): 26. 
• Ragin, C. 2000. Fuzzy-Set Social Science . Chicago: University of Chicago Press.
• Schneider C.Q., and C. Wagemann. 2012. Set-theoretic Methods for the Social Sciences. A Guide to Qualitative Comparative Analysis . Cambridge: Cambridge University Press.
• Villamayor-Tomas, S., C. Oberlack, G. Epstein, S. Partelow, M. Roggero, E. Kellner, M. Tschopp, and M.  Cox. 2020. ‘Using Case Study Data to Understand SES Interactions: A Model-centered Meta-analysis of SES Framework Applications.’ Current Opinion in Environmental Sustainability .

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Comparative case studies

Comparative case studies can be useful to check variation in program implementation. 

Comparative case studies are another way of checking if results match the program theory. Each context and environment is different. The comparative case study can help the evaluator check whether the program theory holds for each different context and environment. If implementation differs, the reasons and results can be recorded. The opposite is also true, similar patterns across sites can increase the confidence in results.

Evaluators used a comparative case study method for the National Cancer Institute’s (NCI’s) Community Cancer Centers Program (NCCCP). The aim of this program was to expand cancer research and deliver the latest, most advanced cancer care to a greater number of Americans in the communities in which they live via community hospitals. The evaluation examined each of the program components (listed below) at each program site. The six program components were:

• increasing capacity to collect biospecimens per NCI’s best practices;
• enhancing clinical trials (CT) research;
• reducing disparities across the cancer continuum;
• improving the use of information technology (IT) and electronic medical records (EMRs) to support improvements in research and care delivery;
• improving quality of cancer care and related areas, such as the development of integrated, multidisciplinary care teams; and
• placing greater emphasis on survivorship and palliative care.

The evaluators use of this method assisted in providing recommendations at the program level as well as to each specific program site.

Advice for choosing this method

• Compare cases with the same outcome but differences in an intervention (known as MDD, most different design)
• Compare cases with the same intervention but differences in outcomes (known as MSD, most similar design)

Advice for using this method

• Consider the variables of each case, and which cases can be matched for comparison.
• Provide the evaluator with as much detail and background on each case as possible. Provide advice on possible criteria for matching.

National Cancer Institute, (2007).  NCI Community Cancer Centers Program Evaluation (NCCCP) . Retrieved from website: https://digitalscholarship.unlv.edu/jhdrp/vol8/iss1/4/

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• Broadening the range of designs and methods for impact evaluations

'Comparative case studies' is referenced in:

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• Rainbow Framework :  Check the results are consistent with causal contribution
• Sustained and Emerging Impacts Evaluation (SEIE)

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Comparative Case Study

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A comparative case study (CCS) is defined as ‘the systematic comparison of two or more data points (“cases”) obtained through use of the case study method’ (Kaarbo and Beasley 1999, p. 372). A case may be a participant, an intervention site, a programme or a policy. Case studies have a long history in the social sciences, yet for a long time, they were treated with scepticism (Harrison et al. 2017). The advent of grounded theory in the 1960s led to a revival in the use of case-based approaches. From the early 1980s, the increase in case study research in the field of political sciences led to the integration of formal, statistical and narrative methods, as well as the use of empirical case selection and causal inference (George and Bennett 2005), which contributed to its methodological advancement. Now, as Harrison and colleagues (2017) note, CCS:

“Has grown in sophistication and is viewed as a valid form of inquiry to explore a broad scope of complex issues, particularly when human behavior and social interactions are central to understanding topics of interest.”

It is claimed that CCS can be applied to detect causal attribution and contribution when the use of a comparison or control group is not feasible (or not preferred). Comparing cases enables evaluators to tackle causal inference through assessing regularity (patterns) and/or by excluding other plausible explanations. In practical terms, CCS involves proposing, analysing and synthesising patterns (similarities and differences) across cases that share common objectives.

What is involved?

Goodrick (2014) outlines the steps to be taken in undertaking CCS.

Key evaluation questions and the purpose of the evaluation: The evaluator should explicitly articulate the adequacy and purpose of using CCS (guided by the evaluation questions) and define the primary interests. Formulating key evaluation questions allows the selection of appropriate cases to be used in the analysis.

Propositions based on the Theory of Change: Theories and hypotheses that are to be explored should be derived from the Theory of Change (or, alternatively, from previous research around the initiative, existing policy or programme documentation).

Case selection: Advocates for CCS approaches claim an important distinction between case-oriented small n studies and (most typically large n) statistical/variable-focused approaches in terms of the process of selecting cases: in case-based methods, selection is iterative and cannot rely on convenience and accessibility. ‘Initial’ cases should be identified in advance, but case selection may continue as evidence is gathered. Various case-selection criteria can be identified depending on the analytic purpose (Vogt et al., 2011). These may include:

• Very similar cases
• Very different cases
• Typical or representative cases
• Extreme or unusual cases
• Deviant or unexpected cases
• Influential or emblematic cases

Identify how evidence will be collected, analysed and synthesised: CCS often applies mixed methods.

Test alternative explanations for outcomes: Following the identification of patterns and relationships, the evaluator may wish to test the established propositions in a follow-up exploratory phase. Approaches applied here may involve triangulation, selecting contradicting cases or using an analytical approach such as Qualitative Comparative Analysis (QCA). Download a Comparative Case Study here Download a longer briefing on Comparative Case Studies here

Useful resources

A webinar shared by Better Evaluation with an overview of using CCS for evaluation.

A short overview describing how to apply CCS for evaluation:

Goodrick, D. (2014). Comparative Case Studies, Methodological Briefs: Impact Evaluation 9 , UNICEF Office of Research, Florence.

An extensively used book that provides a comprehensive critical examination of case-based methods:

Byrne, D. and Ragin, C. C. (2009). The Sage handbook of case-based methods . Sage Publications.

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Rethinking Case Study Research

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Comparative case studies are an effective qualitative tool for researching the impact of policy and practice in various fields of social research, including education. Developed in response to the inadequacy of traditional case study approaches, comparative case studies are highly effective because of their ability to synthesize information across time and space. In Rethinking Case Study Research: A Comparative Approach , the authors describe, explain, and illustrate the horizontal, vertical, and transversal axes of comparative case studies in order to help readers develop their own comparative case study research designs. In six concise chapters, two experts employ geographically distinct case studies—from Tanzania to Guatemala to the U.S.—to show how this innovative approach applies to the operation of policy and practice across multiple social fields. With examples and activities from anthropology, development studies, and policy studies, this volume is written for researchers, especially graduate students, in the fields of education and the interpretive social sciences.

TABLE OF CONTENTS

Chapter 1 | 26  pages, follow the inquiry: an introduction, chapter 2 | 24  pages, case studies: an overview, chapter 3 | 22  pages, horizontal comparison, chapter 4 | 19  pages, vertical comparison, chapter 5 | 21  pages, tracing the transversal, chapter 6 | 16  pages, follow the inquiry: reflections on comparative case study research.

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What is Comparative Analysis and How to Conduct It? (+ Examples)

Appinio Research · 30.10.2023 · 36min read

Have you ever faced a complex decision, wondering how to make the best choice among multiple options? In a world filled with data and possibilities, the art of comparative analysis holds the key to unlocking clarity amidst the chaos.

In this guide, we'll demystify the power of comparative analysis, revealing its practical applications, methodologies, and best practices. Whether you're a business leader, researcher, or simply someone seeking to make more informed decisions, join us as we explore the intricacies of comparative analysis and equip you with the tools to chart your course with confidence.

What is Comparative Analysis?

Comparative analysis is a systematic approach used to evaluate and compare two or more entities, variables, or options to identify similarities, differences, and patterns. It involves assessing the strengths, weaknesses, opportunities, and threats associated with each entity or option to make informed decisions.

The primary purpose of comparative analysis is to provide a structured framework for decision-making by:

• Facilitating Informed Choices: Comparative analysis equips decision-makers with data-driven insights, enabling them to make well-informed choices among multiple options.
• Identifying Trends and Patterns: It helps identify recurring trends, patterns, and relationships among entities or variables, shedding light on underlying factors influencing outcomes.
• Supporting Problem Solving: Comparative analysis aids in solving complex problems by systematically breaking them down into manageable components and evaluating potential solutions.
• Enhancing Transparency: By comparing multiple options, comparative analysis promotes transparency in decision-making processes, allowing stakeholders to understand the rationale behind choices.
• Mitigating Risks : It helps assess the risks associated with each option, allowing organizations to develop risk mitigation strategies and make risk-aware decisions.
• Optimizing Resource Allocation: Comparative analysis assists in allocating resources efficiently by identifying areas where resources can be optimized for maximum impact.
• Driving Continuous Improvement: By comparing current performance with historical data or benchmarks, organizations can identify improvement areas and implement growth strategies.

Importance of Comparative Analysis in Decision-Making

• Data-Driven Decision-Making: Comparative analysis relies on empirical data and objective evaluation, reducing the influence of biases and subjective judgments in decision-making. It ensures decisions are based on facts and evidence.
• Objective Assessment: It provides an objective and structured framework for evaluating options, allowing decision-makers to focus on key criteria and avoid making decisions solely based on intuition or preferences.
• Risk Assessment: Comparative analysis helps assess and quantify risks associated with different options. This risk awareness enables organizations to make proactive risk management decisions.
• Prioritization: By ranking options based on predefined criteria, comparative analysis enables decision-makers to prioritize actions or investments, directing resources to areas with the most significant impact.
• Strategic Planning: It is integral to strategic planning, helping organizations align their decisions with overarching goals and objectives. Comparative analysis ensures decisions are consistent with long-term strategies.
• Resource Allocation: Organizations often have limited resources. Comparative analysis assists in allocating these resources effectively, ensuring they are directed toward initiatives with the highest potential returns.
• Continuous Improvement: Comparative analysis supports a culture of continuous improvement by identifying areas for enhancement and guiding iterative decision-making processes.
• Stakeholder Communication: It enhances transparency in decision-making, making it easier to communicate decisions to stakeholders. Stakeholders can better understand the rationale behind choices when supported by comparative analysis.
• Competitive Advantage: In business and competitive environments , comparative analysis can provide a competitive edge by identifying opportunities to outperform competitors or address weaknesses.
• Informed Innovation: When evaluating new products , technologies, or strategies, comparative analysis guides the selection of the most promising options, reducing the risk of investing in unsuccessful ventures.

In summary, comparative analysis is a valuable tool that empowers decision-makers across various domains to make informed, data-driven choices, manage risks, allocate resources effectively, and drive continuous improvement. Its structured approach enhances decision quality and transparency, contributing to the success and competitiveness of organizations and research endeavors.

How to Prepare for Comparative Analysis?

1. define objectives and scope.

Before you begin your comparative analysis, clearly defining your objectives and the scope of your analysis is essential. This step lays the foundation for the entire process. Here's how to approach it:

• Identify Your Goals: Start by asking yourself what you aim to achieve with your comparative analysis. Are you trying to choose between two products for your business? Are you evaluating potential investment opportunities? Knowing your objectives will help you stay focused throughout the analysis.
• Define Scope: Determine the boundaries of your comparison. What will you include, and what will you exclude? For example, if you're analyzing market entry strategies for a new product, specify whether you're looking at a specific geographic region or a particular target audience.
• Stakeholder Alignment: Ensure that all stakeholders involved in the analysis understand and agree on the objectives and scope. This alignment will prevent misunderstandings and ensure the analysis meets everyone's expectations.

2. Gather Relevant Data and Information

The quality of your comparative analysis heavily depends on the data and information you gather. Here's how to approach this crucial step:

• Data Sources: Identify where you'll obtain the necessary data. Will you rely on primary sources , such as surveys and interviews, to collect original data? Or will you use secondary sources, like published research and industry reports, to access existing data? Consider the advantages and disadvantages of each source.
• Data Collection Plan: Develop a plan for collecting data. This should include details about the methods you'll use, the timeline for data collection, and who will be responsible for gathering the data.
• Data Relevance: Ensure that the data you collect is directly relevant to your objectives. Irrelevant or extraneous data can lead to confusion and distract from the core analysis.

3. Select Appropriate Criteria for Comparison

Choosing the right criteria for comparison is critical to a successful comparative analysis. Here's how to go about it:

• Relevance to Objectives: Your chosen criteria should align closely with your analysis objectives. For example, if you're comparing job candidates, your criteria might include skills, experience, and cultural fit.
• Measurability: Consider whether you can quantify the criteria. Measurable criteria are easier to analyze. If you're comparing marketing campaigns, you might measure criteria like click-through rates, conversion rates, and return on investment.
• Weighting Criteria : Not all criteria are equally important. You'll need to assign weights to each criterion based on its relative importance. Weighting helps ensure that the most critical factors have a more significant impact on the final decision.

4. Establish a Clear Framework

Once you have your objectives, data, and criteria in place, it's time to establish a clear framework for your comparative analysis. This framework will guide your process and ensure consistency. Here's how to do it:

• Comparative Matrix: Consider using a comparative matrix or spreadsheet to organize your data. Each row in the matrix represents an option or entity you're comparing, and each column corresponds to a criterion. This visual representation makes it easy to compare and contrast data.
• Timeline: Determine the time frame for your analysis. Is it a one-time comparison, or will you conduct ongoing analyses? Having a defined timeline helps you manage the analysis process efficiently.
• Define Metrics: Specify the metrics or scoring system you'll use to evaluate each criterion. For example, if you're comparing potential office locations, you might use a scoring system from 1 to 5 for factors like cost, accessibility, and amenities.

With your objectives, data, criteria, and framework established, you're ready to move on to the next phase of comparative analysis: data collection and organization.

Comparative Analysis Data Collection

Data collection and organization are critical steps in the comparative analysis process. We'll explore how to gather and structure the data you need for a successful analysis.

1. Utilize Primary Data Sources

Primary data sources involve gathering original data directly from the source. This approach offers unique advantages, allowing you to tailor your data collection to your specific research needs.

Some popular primary data sources include:

• Surveys and Questionnaires: Design surveys or questionnaires and distribute them to collect specific information from individuals or groups. This method is ideal for obtaining firsthand insights, such as customer preferences or employee feedback.
• Interviews: Conduct structured interviews with relevant stakeholders or experts. Interviews provide an opportunity to delve deeper into subjects and gather qualitative data, making them valuable for in-depth analysis.
• Observations: Directly observe and record data from real-world events or settings. Observational data can be instrumental in fields like anthropology, ethnography, and environmental studies.
• Experiments: In controlled environments, experiments allow you to manipulate variables and measure their effects. This method is common in scientific research and product testing.

When using primary data sources, consider factors like sample size , survey design, and data collection methods to ensure the reliability and validity of your data.

2. Harness Secondary Data Sources

Secondary data sources involve using existing data collected by others. These sources can provide a wealth of information and save time and resources compared to primary data collection.

Here are common types of secondary data sources:

• Public Records: Government publications, census data, and official reports offer valuable information on demographics, economic trends, and public policies. They are often free and readily accessible.
• Academic Journals: Scholarly articles provide in-depth research findings across various disciplines. They are helpful for accessing peer-reviewed studies and staying current with academic discourse.
• Industry Reports: Industry-specific reports and market research publications offer insights into market trends, consumer behavior, and competitive landscapes. They are essential for businesses making strategic decisions.
• Online Databases: Online platforms like Statista , PubMed , and Google Scholar provide a vast repository of data and research articles. They offer search capabilities and access to a wide range of data sets.

When using secondary data sources, critically assess the credibility, relevance, and timeliness of the data. Ensure that it aligns with your research objectives.

3. Ensure and Validate Data Quality

Data quality is paramount in comparative analysis. Poor-quality data can lead to inaccurate conclusions and flawed decision-making. Here's how to ensure data validation and reliability:

• Cross-Verification: Whenever possible, cross-verify data from multiple sources. Consistency among different sources enhances the reliability of the data.
• Sample Size : Ensure that your data sample size is statistically significant for meaningful analysis. A small sample may not accurately represent the population.
• Data Integrity: Check for data integrity issues, such as missing values, outliers, or duplicate entries. Address these issues before analysis to maintain data quality.
• Data Source Reliability: Assess the reliability and credibility of the data sources themselves. Consider factors like the reputation of the institution or organization providing the data.

4. Organize Data Effectively

Structuring your data for comparison is a critical step in the analysis process. Organized data makes it easier to draw insights and make informed decisions. Here's how to structure data effectively:

• Data Cleaning: Before analysis, clean your data to remove inconsistencies, errors, and irrelevant information. Data cleaning may involve data transformation, imputation of missing values, and removing outliers.
• Normalization: Standardize data to ensure fair comparisons. Normalization adjusts data to a standard scale, making comparing variables with different units or ranges possible.
• Variable Labeling: Clearly label variables and data points for easy identification. Proper labeling enhances the transparency and understandability of your analysis.
• Data Organization: Organize data into a format that suits your analysis methods. For quantitative analysis, this might mean creating a matrix, while qualitative analysis may involve categorizing data into themes.

By paying careful attention to data collection, validation, and organization, you'll set the stage for a robust and insightful comparative analysis. Next, we'll explore various methodologies you can employ in your analysis, ranging from qualitative approaches to quantitative methods and examples.

Comparative Analysis Methods

When it comes to comparative analysis, various methodologies are available, each suited to different research goals and data types. In this section, we'll explore five prominent methodologies in detail.

Qualitative Comparative Analysis (QCA)

Qualitative Comparative Analysis (QCA) is a methodology often used when dealing with complex, non-linear relationships among variables. It seeks to identify patterns and configurations among factors that lead to specific outcomes.

• Case-by-Case Analysis: QCA involves evaluating individual cases (e.g., organizations, regions, or events) rather than analyzing aggregate data. Each case's unique characteristics are considered.
• Boolean Logic: QCA employs Boolean algebra to analyze data. Variables are categorized as either present or absent, allowing for the examination of different combinations and logical relationships.
• Necessary and Sufficient Conditions: QCA aims to identify necessary and sufficient conditions for a specific outcome to occur. It helps answer questions like, "What conditions are necessary for a successful product launch?"
• Fuzzy Set Theory: In some cases, QCA may use fuzzy set theory to account for degrees of membership in a category, allowing for more nuanced analysis.

QCA is particularly useful in fields such as sociology, political science, and organizational studies, where understanding complex interactions is essential.

Quantitative Comparative Analysis

Quantitative Comparative Analysis involves the use of numerical data and statistical techniques to compare and analyze variables. It's suitable for situations where data is quantitative, and relationships can be expressed numerically.

• Statistical Tools: Quantitative comparative analysis relies on statistical methods like regression analysis , correlation, and hypothesis testing. These tools help identify relationships, dependencies, and trends within datasets.
• Data Measurement: Ensure that variables are measured consistently using appropriate scales (e.g., ordinal, interval, ratio) for meaningful analysis. Variables may include numerical values like revenue, customer satisfaction scores, or product performance metrics.
• Data Visualization: Create visual representations of data using charts, graphs, and plots. Visualization aids in understanding complex relationships and presenting findings effectively.
• Statistical Significance : Assess the statistical significance of relationships. Statistical significance indicates whether observed differences or relationships are likely to be real rather than due to chance.

Quantitative comparative analysis is commonly applied in economics, social sciences, and market research to draw empirical conclusions from numerical data.

Case Studies

Case studies involve in-depth examinations of specific instances or cases to gain insights into real-world scenarios. Comparative case studies allow researchers to compare and contrast multiple cases to identify patterns, differences, and lessons.

• Narrative Analysis: Case studies often involve narrative analysis, where researchers construct detailed narratives of each case, including context, events, and outcomes.
• Contextual Understanding: In comparative case studies, it's crucial to consider the context within which each case operates. Understanding the context helps interpret findings accurately.
• Cross-Case Analysis: Researchers conduct cross-case analysis to identify commonalities and differences across cases. This process can lead to the discovery of factors that influence outcomes.
• Triangulation: To enhance the validity of findings, researchers may use multiple data sources and methods to triangulate information and ensure reliability.

Case studies are prevalent in fields like psychology, business, and sociology, where deep insights into specific situations are valuable.

SWOT Analysis

SWOT Analysis is a strategic tool used to assess the Strengths, Weaknesses, Opportunities, and Threats associated with a particular entity or situation. While it's commonly used in business, it can be adapted for various comparative analyses.

• Internal and External Factors: SWOT Analysis examines both internal factors (Strengths and Weaknesses), such as organizational capabilities, and external factors (Opportunities and Threats), such as market conditions and competition.
• Strategic Planning: The insights from SWOT Analysis inform strategic decision-making. By identifying strengths and opportunities, organizations can leverage their advantages. Likewise, addressing weaknesses and threats helps mitigate risks.
• Visual Representation: SWOT Analysis is often presented as a matrix or a 2x2 grid, making it visually accessible and easy to communicate to stakeholders.
• Continuous Monitoring: SWOT Analysis is not a one-time exercise. Organizations use it periodically to adapt to changing circumstances and make informed decisions.

SWOT Analysis is versatile and can be applied in business, healthcare, education, and any context where a structured assessment of factors is needed.

Benchmarking

Benchmarking involves comparing an entity's performance, processes, or practices to those of industry leaders or best-in-class organizations. It's a powerful tool for continuous improvement and competitive analysis.

• Identify Performance Gaps: Benchmarking helps identify areas where an entity lags behind its peers or industry standards. These performance gaps highlight opportunities for improvement.
• Data Collection: Gather data on key performance metrics from both internal and external sources. This data collection phase is crucial for meaningful comparisons.
• Comparative Analysis : Compare your organization's performance data with that of benchmark organizations. This analysis can reveal where you excel and where adjustments are needed.
• Continuous Improvement: Benchmarking is a dynamic process that encourages continuous improvement. Organizations use benchmarking findings to set performance goals and refine their strategies.

Benchmarking is widely used in business, manufacturing, healthcare, and customer service to drive excellence and competitiveness.

Each of these methodologies brings a unique perspective to comparative analysis, allowing you to choose the one that best aligns with your research objectives and the nature of your data. The choice between qualitative and quantitative methods, or a combination of both, depends on the complexity of the analysis and the questions you seek to answer.

How to Conduct Comparative Analysis?

Once you've prepared your data and chosen an appropriate methodology, it's time to dive into the process of conducting a comparative analysis. We will guide you through the essential steps to extract meaningful insights from your data.

1. Identify Key Variables and Metrics

Identifying key variables and metrics is the first crucial step in conducting a comparative analysis. These are the factors or indicators you'll use to assess and compare your options.

• Relevance to Objectives: Ensure the chosen variables and metrics align closely with your analysis objectives. When comparing marketing strategies, relevant metrics might include customer acquisition cost, conversion rate, and retention.
• Quantitative vs. Qualitative : Decide whether your analysis will focus on quantitative data (numbers) or qualitative data (descriptive information). In some cases, a combination of both may be appropriate.
• Data Availability: Consider the availability of data. Ensure you can access reliable and up-to-date data for all selected variables and metrics.
• KPIs: Key Performance Indicators (KPIs) are often used as the primary metrics in comparative analysis. These are metrics that directly relate to your goals and objectives.

2. Visualize Data for Clarity

Data visualization techniques play a vital role in making complex information more accessible and understandable. Effective data visualization allows you to convey insights and patterns to stakeholders. Consider the following approaches:

• Charts and Graphs: Use various types of charts, such as bar charts, line graphs, and pie charts, to represent data. For example, a line graph can illustrate trends over time, while a bar chart can compare values across categories.
• Heatmaps: Heatmaps are particularly useful for visualizing large datasets and identifying patterns through color-coding. They can reveal correlations, concentrations, and outliers.
• Scatter Plots: Scatter plots help visualize relationships between two variables. They are especially useful for identifying trends, clusters, or outliers.
• Dashboards: Create interactive dashboards that allow users to explore data and customize views. Dashboards are valuable for ongoing analysis and reporting.
• Infographics: For presentations and reports, consider using infographics to summarize key findings in a visually engaging format.

Effective data visualization not only enhances understanding but also aids in decision-making by providing clear insights at a glance.

3. Establish Clear Comparative Frameworks

A well-structured comparative framework provides a systematic approach to your analysis. It ensures consistency and enables you to make meaningful comparisons. Here's how to create one:

• Comparison Matrices: Consider using matrices or spreadsheets to organize your data. Each row represents an option or entity, and each column corresponds to a variable or metric. This matrix format allows for side-by-side comparisons.
• Decision Trees: In complex decision-making scenarios, decision trees help map out possible outcomes based on different criteria and variables. They visualize the decision-making process.
• Scenario Analysis: Explore different scenarios by altering variables or criteria to understand how changes impact outcomes. Scenario analysis is valuable for risk assessment and planning.
• Checklists: Develop checklists or scoring sheets to systematically evaluate each option against predefined criteria. Checklists ensure that no essential factors are overlooked.

A well-structured comparative framework simplifies the analysis process, making it easier to draw meaningful conclusions and make informed decisions.

4. Evaluate and Score Criteria

Evaluating and scoring criteria is a critical step in comparative analysis, as it quantifies the performance of each option against the chosen criteria.

• Scoring System: Define a scoring system that assigns values to each criterion for every option. Common scoring systems include numerical scales, percentage scores, or qualitative ratings (e.g., high, medium, low).
• Consistency: Ensure consistency in scoring by defining clear guidelines for each score. Provide examples or descriptions to help evaluators understand what each score represents.
• Data Collection: Collect data or information relevant to each criterion for all options. This may involve quantitative data (e.g., sales figures) or qualitative data (e.g., customer feedback).
• Aggregation: Aggregate the scores for each option to obtain an overall evaluation. This can be done by summing the individual criterion scores or applying weighted averages.
• Normalization: If your criteria have different measurement scales or units, consider normalizing the scores to create a level playing field for comparison.

5. Assign Importance to Criteria

Not all criteria are equally important in a comparative analysis. Weighting criteria allows you to reflect their relative significance in the final decision-making process.

• Relative Importance: Assess the importance of each criterion in achieving your objectives. Criteria directly aligned with your goals may receive higher weights.
• Weighting Methods: Choose a weighting method that suits your analysis. Common methods include expert judgment, analytic hierarchy process (AHP), or data-driven approaches based on historical performance.
• Impact Analysis: Consider how changes in the weights assigned to criteria would affect the final outcome. This sensitivity analysis helps you understand the robustness of your decisions.
• Stakeholder Input: Involve relevant stakeholders or decision-makers in the weighting process. Their input can provide valuable insights and ensure alignment with organizational goals.
• Transparency: Clearly document the rationale behind the assigned weights to maintain transparency in your analysis.

By weighting criteria, you ensure that the most critical factors have a more significant influence on the final evaluation, aligning the analysis more closely with your objectives and priorities.

With these steps in place, you're well-prepared to conduct a comprehensive comparative analysis. The next phase involves interpreting your findings, drawing conclusions, and making informed decisions based on the insights you've gained.

Comparative Analysis Interpretation

Interpreting the results of your comparative analysis is a crucial phase that transforms data into actionable insights. We'll delve into various aspects of interpretation and how to make sense of your findings.

• Contextual Understanding: Before diving into the data, consider the broader context of your analysis. Understand the industry trends, market conditions, and any external factors that may have influenced your results.
• Drawing Conclusions: Summarize your findings clearly and concisely. Identify trends, patterns, and significant differences among the options or variables you've compared.
• Quantitative vs. Qualitative Analysis: Depending on the nature of your data and analysis, you may need to balance both quantitative and qualitative interpretations. Qualitative insights can provide context and nuance to quantitative findings.
• Comparative Visualization: Visual aids such as charts, graphs, and tables can help convey your conclusions effectively. Choose visual representations that align with the nature of your data and the key points you want to emphasize.
• Outliers and Anomalies: Identify and explain any outliers or anomalies in your data. Understanding these exceptions can provide valuable insights into unusual cases or factors affecting your analysis.
• Cross-Validation: Validate your conclusions by comparing them with external benchmarks, industry standards, or expert opinions. Cross-validation helps ensure the reliability of your findings.
• Implications for Decision-Making: Discuss how your analysis informs decision-making. Clearly articulate the practical implications of your findings and their relevance to your initial objectives.
• Actionable Insights: Emphasize actionable insights that can guide future strategies, policies, or actions. Make recommendations based on your analysis, highlighting the steps needed to capitalize on strengths or address weaknesses.
• Continuous Improvement: Encourage a culture of continuous improvement by using your analysis as a feedback mechanism. Suggest ways to monitor and adapt strategies over time based on evolving circumstances.

Comparative Analysis Applications

Comparative analysis is a versatile methodology that finds application in various fields and scenarios. Let's explore some of the most common and impactful applications.

Business Decision-Making

Comparative analysis is widely employed in business to inform strategic decisions and drive success. Key applications include:

Market Research and Competitive Analysis

• Objective: To assess market opportunities and evaluate competitors.
• Methods: Analyzing market trends, customer preferences, competitor strengths and weaknesses, and market share.
• Outcome: Informed product development, pricing strategies, and market entry decisions.

Product Comparison and Benchmarking

• Objective: To compare the performance and features of products or services.
• Methods: Evaluating product specifications, customer reviews, and pricing.
• Outcome: Identifying strengths and weaknesses, improving product quality, and setting competitive pricing.

Financial Analysis

• Objective: To evaluate financial performance and make investment decisions.
• Methods: Comparing financial statements, ratios, and performance indicators of companies.
• Outcome: Informed investment choices, risk assessment, and portfolio management.

Healthcare and Medical Research

In the healthcare and medical research fields, comparative analysis is instrumental in understanding diseases, treatment options, and healthcare systems.

Clinical Trials and Drug Development

• Objective: To compare the effectiveness of different treatments or drugs.
• Methods: Analyzing clinical trial data, patient outcomes, and side effects.
• Outcome: Informed decisions about drug approvals, treatment protocols, and patient care.

Health Outcomes Research

• Objective: To assess the impact of healthcare interventions.
• Methods: Comparing patient health outcomes before and after treatment or between different treatment approaches.
• Outcome: Improved healthcare guidelines, cost-effectiveness analysis, and patient care plans.

Healthcare Systems Evaluation

• Objective: To assess the performance of healthcare systems.
• Methods: Comparing healthcare delivery models, patient satisfaction, and healthcare costs.
• Outcome: Informed healthcare policy decisions, resource allocation, and system improvements.

Social Sciences and Policy Analysis

Comparative analysis is a fundamental tool in social sciences and policy analysis, aiding in understanding complex societal issues.

Educational Research

• Objective: To compare educational systems and practices.
• Methods: Analyzing student performance, curriculum effectiveness, and teaching methods.
• Outcome: Informed educational policies, curriculum development, and school improvement strategies.

Political Science

• Objective: To study political systems, elections, and governance.
• Methods: Comparing election outcomes, policy impacts, and government structures.
• Outcome: Insights into political behavior, policy effectiveness, and governance reforms.

Social Welfare and Poverty Analysis

• Objective: To evaluate the impact of social programs and policies.
• Methods: Comparing the well-being of individuals or communities with and without access to social assistance.
• Outcome: Informed policymaking, poverty reduction strategies, and social program improvements.

Environmental Science and Sustainability

Comparative analysis plays a pivotal role in understanding environmental issues and promoting sustainability.

Environmental Impact Assessment

• Objective: To assess the environmental consequences of projects or policies.
• Methods: Comparing ecological data, resource use, and pollution levels.
• Outcome: Informed environmental mitigation strategies, sustainable development plans, and regulatory decisions.

Climate Change Analysis

• Objective: To study climate patterns and their impacts.
• Methods: Comparing historical climate data, temperature trends, and greenhouse gas emissions.
• Outcome: Insights into climate change causes, adaptation strategies, and policy recommendations.

Ecosystem Health Assessment

• Objective: To evaluate the health and resilience of ecosystems.
• Methods: Comparing biodiversity, habitat conditions, and ecosystem services.
• Outcome: Conservation efforts, restoration plans, and ecological sustainability measures.

Technology and Innovation

Comparative analysis is crucial in the fast-paced world of technology and innovation.

Product Development and Innovation

• Objective: To assess the competitiveness and innovation potential of products or technologies.
• Methods: Comparing research and development investments, technology features, and market demand.
• Outcome: Informed innovation strategies, product roadmaps, and patent decisions.

User Experience and Usability Testing

• Objective: To evaluate the user-friendliness of software applications or digital products.
• Methods: Comparing user feedback, usability metrics, and user interface designs.
• Outcome: Improved user experiences, interface redesigns, and product enhancements.

Technology Adoption and Market Entry

• Objective: To analyze market readiness and risks for new technologies.
• Methods: Comparing market conditions, regulatory landscapes, and potential barriers.
• Outcome: Informed market entry strategies, risk assessments, and investment decisions.

These diverse applications of comparative analysis highlight its flexibility and importance in decision-making across various domains. Whether in business, healthcare, social sciences, environmental studies, or technology, comparative analysis empowers researchers and decision-makers to make informed choices and drive positive outcomes.

Comparative Analysis Best Practices

Successful comparative analysis relies on following best practices and avoiding common pitfalls. Implementing these practices enhances the effectiveness and reliability of your analysis.

• Clearly Defined Objectives: Start with well-defined objectives that outline what you aim to achieve through the analysis. Clear objectives provide focus and direction.
• Data Quality Assurance: Ensure data quality by validating, cleaning, and normalizing your data. Poor-quality data can lead to inaccurate conclusions.
• Transparent Methodologies: Clearly explain the methodologies and techniques you've used for analysis. Transparency builds trust and allows others to assess the validity of your approach.
• Consistent Criteria: Maintain consistency in your criteria and metrics across all options or variables. Inconsistent criteria can lead to biased results.
• Sensitivity Analysis: Conduct sensitivity analysis by varying key parameters, such as weights or assumptions, to assess the robustness of your conclusions.
• Stakeholder Involvement: Involve relevant stakeholders throughout the analysis process. Their input can provide valuable perspectives and ensure alignment with organizational goals.
• Critical Evaluation of Assumptions: Identify and critically evaluate any assumptions made during the analysis. Assumptions should be explicit and justifiable.
• Holistic View: Take a holistic view of the analysis by considering both short-term and long-term implications. Avoid focusing solely on immediate outcomes.
• Documentation: Maintain thorough documentation of your analysis, including data sources, calculations, and decision criteria. Documentation supports transparency and facilitates reproducibility.
• Continuous Learning: Stay updated with the latest analytical techniques, tools, and industry trends. Continuous learning helps you adapt your analysis to changing circumstances.
• Peer Review: Seek peer review or expert feedback on your analysis. External perspectives can identify blind spots and enhance the quality of your work.
• Ethical Considerations: Address ethical considerations, such as privacy and data protection, especially when dealing with sensitive or personal data.

By adhering to these best practices, you'll not only improve the rigor of your comparative analysis but also ensure that your findings are reliable, actionable, and aligned with your objectives.

Comparative Analysis Examples

To illustrate the practical application and benefits of comparative analysis, let's explore several real-world examples across different domains. These examples showcase how organizations and researchers leverage comparative analysis to make informed decisions, solve complex problems, and drive improvements:

Retail Industry - Price Competitiveness Analysis

Objective: A retail chain aims to assess its price competitiveness against competitors in the same market.

Methodology:

• Collect pricing data for a range of products offered by the retail chain and its competitors.
• Organize the data into a comparative framework, categorizing products by type and price range.
• Calculate price differentials, averages, and percentiles for each product category.
• Analyze the findings to identify areas where the retail chain's prices are higher or lower than competitors.

Outcome: The analysis reveals that the retail chain's prices are consistently lower in certain product categories but higher in others. This insight informs pricing strategies, allowing the retailer to adjust prices to remain competitive in the market.

Healthcare - Comparative Effectiveness Research

Objective: Researchers aim to compare the effectiveness of two different treatment methods for a specific medical condition.

• Recruit patients with the medical condition and randomly assign them to two treatment groups.
• Collect data on treatment outcomes, including symptom relief, side effects, and recovery times.
• Analyze the data using statistical methods to compare the treatment groups.
• Consider factors like patient demographics and baseline health status as potential confounding variables.

Outcome: The comparative analysis reveals that one treatment method is statistically more effective than the other in relieving symptoms and has fewer side effects. This information guides medical professionals in recommending the more effective treatment to patients.

Environmental Science - Carbon Emission Analysis

Objective: An environmental organization seeks to compare carbon emissions from various transportation modes in a metropolitan area.

• Collect data on the number of vehicles, their types (e.g., cars, buses, bicycles), and fuel consumption for each mode of transportation.
• Calculate the total carbon emissions for each mode based on fuel consumption and emission factors.
• Create visualizations such as bar charts and pie charts to represent the emissions from each transportation mode.
• Consider factors like travel distance, occupancy rates, and the availability of alternative fuels.

Outcome: The comparative analysis reveals that public transportation generates significantly lower carbon emissions per passenger mile compared to individual car travel. This information supports advocacy for increased public transit usage to reduce carbon footprint.

Technology Industry - Feature Comparison for Software Development Tools

Objective: A software development team needs to choose the most suitable development tool for an upcoming project.

• Create a list of essential features and capabilities required for the project.
• Research and compile information on available development tools in the market.
• Develop a comparative matrix or scoring system to evaluate each tool's features against the project requirements.
• Assign weights to features based on their importance to the project.

Outcome: The comparative analysis highlights that Tool A excels in essential features critical to the project, such as version control integration and debugging capabilities. The development team selects Tool A as the preferred choice for the project.

Educational Research - Comparative Study of Teaching Methods

Objective: A school district aims to improve student performance by comparing the effectiveness of traditional classroom teaching with online learning.

• Randomly assign students to two groups: one taught using traditional methods and the other through online courses.
• Administer pre- and post-course assessments to measure knowledge gain.
• Collect feedback from students and teachers on the learning experiences.
• Analyze assessment scores and feedback to compare the effectiveness and satisfaction levels of both teaching methods.

Outcome: The comparative analysis reveals that online learning leads to similar knowledge gains as traditional classroom teaching. However, students report higher satisfaction and flexibility with the online approach. The school district considers incorporating online elements into its curriculum.

These examples illustrate the diverse applications of comparative analysis across industries and research domains. Whether optimizing pricing strategies in retail, evaluating treatment effectiveness in healthcare, assessing environmental impacts, choosing the right software tool, or improving educational methods, comparative analysis empowers decision-makers with valuable insights for informed choices and positive outcomes.

Conclusion for Comparative Analysis

Comparative analysis is your compass in the world of decision-making. It helps you see the bigger picture, spot opportunities, and navigate challenges. By defining your objectives, gathering data, applying methodologies, and following best practices, you can harness the power of Comparative Analysis to make informed choices and drive positive outcomes.

Remember, Comparative analysis is not just a tool; it's a mindset that empowers you to transform data into insights and uncertainty into clarity. So, whether you're steering a business, conducting research, or facing life's choices, embrace Comparative Analysis as your trusted guide on the journey to better decisions. With it, you can chart your course, make impactful choices, and set sail toward success.

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Comparative Case Studies: An Innovative Approach

What is a case study and what is it good for? In this article, we argue for a new approach—the comparative case study approach—that attends simultaneously to macro, meso, and micro dimensions of case-based research. The approach engages two logics of comparison: first, the more common compare and contrast; and second, a 'tracing across' sites or scales. As we explicate our approach, we also contrast it to traditional case study research. We contend that new approaches are necessitated by conceptual shifts in the social sciences, specifically in relation to culture, context, space, place, and comparison itself. We propose that comparative case studies should attend to three axes: horizontal, vertical, and transversal comparison. We conclude by arguing that this revision has the potential to strengthen and enhance case study research in Comparative and International Education, clarifying the unique contributions of qualitative research.

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Higher Education Quarterly

Anna Kosmützky , Terhi Nokkala

Abstract Finding the balance between adequately describing the uniqueness of the context of studied phenomena and maintaining sufficient common ground for comparability and analytical generalization has widely been recognized as a key challenge in international comparative research. Methodological reflections on how to adequately cover context and comparability have extensively been discussed for quantitative survey or secondary data research. In addition, most recently, promising methodological considerations for qualitative comparative research have been suggested in comparative fields related to higher education. The article's aim is to connect this discussion to comparative higher education research. Thus, the article discusses recent advancements in the methodology of qualitative international comparative research, connects them to older analytical methods that have been used within the field in the 1960s and 1970s, and demonstrates their analytical value based on their application to a qualitative small-N case study on research groups in diverse organizational contexts in three country contexts.

John C Weidman

This is the inaugural volume in the PSCIE (Pittsburgh Studies in Comparative and International Education) Series which expands on the life work of University of Pittsburgh professor Rolland G. Paulston (1929-2006). Recognized as a stalwart in the field of comparative and international education, Paulston's most widely recognized contribution is social cartography. He demonstrated that mapping comparative, international, and development education is no easy task and, depending on the perspective of the mapper, there may be multiple cartographies to chart. This collection of nineteen essays and research studies is a festschrift celebrating and developing Robert Paulston's scholarship in comparative, international, and development education (CIDE). Considering key international education issues, national education systems, and social and educational theories, essays in this volume explore and go beyond Paulston's seminal works in social cartography. Organized into three sec...

Ben Hawbaker , Candace Jones , Brooke Boren , Reut Livne-Tarandach

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case or comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

Eleanor Knott

This course focuses on how to design and conduct small-n case study and comparative research. Thinking outside of students' areas of interest and specialisms and topics, students will be encouraged to develop the concepts and comparative frameworks that underpin these phenomena. In other words, students will begin to develop their research topics as cases of something. The course covers questions of design and methods of case study research, from single-n to small-n case studies including discussions of process tracing and Mill's methods. The course addresses both the theoretical and methodological discussions that underpin research design as well as the practical questions of how to conduct case study research, including gathering, assessing and using evidence. Examples from the fields of comparative politics, IR, development studies, sociology and European studies will be used throughout the lectures and seminars.

Reut Livne-Tarandach , Candace Jones

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case and comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

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Case study methodology has long been a contested terrain in social sciences research which is characterized by varying, sometimes opposing, approaches espoused by many research methodologists. Despite being one of the most frequently used qualitative research methodologies in educational research, the methodologists do not have a full consensus on the design and implementation of case study, which hampers its full evolution. Focusing on the landmark works of three prominent methodologists, namely Robert Yin, Sharan Merriam, Robert Stake, I attempt to scrutinize the areas where their perspectives diverge, converge and complement one another in varying dimensions of case study research. I aim to help the emerging researchers in the field of education familiarize themselves with the diverse views regarding case study that lead to a vast array of techniques and strategies, out of which they can come up with a combined perspective which best serves their research purpose.

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case selection and the comparative method: introducing the case selector

• Published: 14 August 2017
• Volume 17 , pages 422–436, ( 2018 )

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• timothy prescott 1 &
• brian r. urlacher 1  

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We introduce a web application, the Case Selector ( http://und.edu/faculty/brian.urlacher ), that facilitates comparative case study research designs by creating an exhaustive comparison of cases from a dataset on the dependent, independent, and control variables specified by the user. This application was created to aid in systematic and transparent case selection so that researchers can better address the charge that cases are ‘cherry picked.’ An examination of case selection in a prominent study of rebel behaviour in civil war is then used to illustrate different applications of the Case Selector.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful comments and feedback over the course of the review processes. This project has been significantly improved by their suggestions. The authors have also agreed to provide access to the Case Selector through their faculty webpages at their affiliated institutions.

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prescott, t., urlacher, b.r. case selection and the comparative method: introducing the case selector. Eur Polit Sci 17 , 422–436 (2018). https://doi.org/10.1057/s41304-017-0128-5

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Writing a Comparative Case Study: Effective Guide

Table of Contents

As a researcher or student, you may be required to write a comparative case study at some point in your academic journey. A comparative study is an analysis of two or more cases. Where the aim is to compare and contrast them based on specific criteria. We created this guide to help you understand how to write a comparative case study . This article will discuss what a comparative study is, the elements of a comparative study, and how to write an effective one. We also include samples to help you get started.

What is a Comparative Case Study?

A comparative study is a research method that involves comparing two or more cases to analyze their similarities and differences . These cases can be individuals, organizations, events, or any other unit of analysis. A comparative study aims to gain a deeper understanding of the subject matter by exploring the differences and similarities between the cases.

Elements of a Comparative Study

Before diving into the writing process, it’s essential to understand the key elements that make up a comparative study. These elements include:

• Research Question : This is the central question the study seeks to answer. It should be specific and clear, and the basis of the comparison.
• Cases : The cases being compared should be chosen based on their significance to the research question. They should also be similar in some ways to facilitate comparison.
• Data Collection : Data collection should be comprehensive and systematic. Data collected can be qualitative, quantitative, or both.
• Analysis : The analysis should be based on the research question and collected data. The data should be analyzed for similarities and differences between the cases.
• Conclusion : The conclusion should summarize the findings and answer the research question. It should also provide recommendations for future research.

How to Write a Comparative Study

Now that we have established the elements of a comparative study, let’s dive into the writing process. Here is a detailed approach on how to write a comparative study:

Choose a Topic

The first step in writing a comparative study is to choose a topic relevant to your field of study. It should be a topic that you are familiar with and interested in.

Define the Research Question

Once you have chosen a topic, define your research question. The research question should be specific and clear.

Choose Cases

The next step is to choose the cases you will compare. The cases should be relevant to your research question and have similarities to facilitate comparison.

Collect Data

Collect data on each case using qualitative, quantitative, or both methods. The data collected should be comprehensive and systematic.

Analyze Data

Analyze the data collected for each case. Look for similarities and differences between the cases. The analysis should be based on the research question.

Write the Introduction

The introduction should provide background information on the topic and state the research question.

Write the Literature Review

The literature review should give a summary of the research that has been conducted on the topic.

Write the Methodology

The methodology should describe the data collection and analysis methods used.

Present Findings

Present the findings of the analysis. The results should be organized based on the research question.

Conclusion and Recommendations

Summarize the findings and answer the research question. Provide recommendations for future research.

Sample of Comparative Case Study

To provide a better understanding of how to write a comparative study , here is an example: Comparative Study of Two Leading Airlines: ABC and XYZ

Introduction

The airline industry is highly competitive, with companies constantly seeking new ways to improve customer experiences and increase profits. ABC and XYZ are two of the world’s leading airlines, each with a distinct approach to business. This comparative case study will examine the similarities and differences between the two airlines. And provide insights into what works well in the airline industry.

Research Questions

What are the similarities and differences between ABC and XYZ regarding their approach to business, customer experience, and profitability?

Data Collection and Analysis

To collect data for this comparative study, we will use a combination of primary and secondary sources. Primary sources will include interviews with customers and employees of both airlines, while secondary sources will include financial reports, marketing materials, and industry research. After collecting the data, we will use a systematic and comprehensive approach to data analysis. We will use a framework to compare and contrast the data, looking for similarities and differences between the two airlines. We will then organize the data into categories: customer experience, revenue streams, and operational efficiency.

After analyzing the data, we found several similarities and differences between ABC and XYZ. Similarities Both airlines offer a high level of customer service, with attentive flight attendants, comfortable seating, and in-flight entertainment. They also strongly focus on safety, with rigorous training and maintenance protocols in place. Differences ABC has a reputation for luxury, with features such as private suites and shower spas in first class. On the other hand, XYZ has a reputation for reliability and efficiency, with a strong emphasis on on-time departures and arrivals. In terms of revenue streams, ABC derives a significant portion of its revenue from international travel. At the same time, XYZ has a more diverse revenue stream, focusing on domestic and international travel. ABC also has a more centralized management structure, with decision-making authority concentrated at the top. On the other hand, XYZ has a more decentralized management structure, with decision-making authority distributed throughout the organization.

This comparative case study provides valuable insights into the airline industry and the approaches taken by two leading airlines, ABC and Delta. By comparing and contrasting the two airlines, we can see the strengths and weaknesses of each method. And identify potential strategies for improving the airline industry as a whole. Ultimately, this study shows that there is no one-size-fits-all approach to doing business in the airline industry. And that success depends on a combination of factors, including customer experience, operational efficiency, and revenue streams.

Wrapping Up

A comparative study is an effective research method for analyzing case similarities and differences. Writing a comparative study can be daunting, but proper planning and organization can be an effective research method. Define your research question, choose relevant cases, collect and analyze comprehensive data, and present the findings. The steps detailed in this blog post will help you create a compelling comparative study that provides valuable insights into your research topic . Remember to stay focused on your research question. And use the data collected to provide a clear and concise analysis of the cases being compared.

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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Case Studies and Comparative Analysis

A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. Generally, a case study can highlight an individual, group, organization, event, belief system, or action. A case study does not necessarily have to be one observation, but may include many observations (one or multiple individuals and entities across multiple time periods, all within the same case study). Comparative analysis, on the other hand, is a method that compares two or more of anything (documents, data sets, political systems etc.) – though sometimes a form of comparative analysis is used to compare two or more cases studies, demonstrating the links between these two methods. Text adapted from Wikipedia .

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• Published: 09 September 2024

Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining

• Zhengyu Wu 1 , 2 , 3 , 4 ,
• Ying Chen 5 &
• Dayou Luo 6  

Scientific Reports volume  14 , Article number:  21047 ( 2024 ) Cite this article

Metrics details

• Civil engineering
• Engineering
• Natural hazards

Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. To obtain a more robust and precise prediction of WCFZ in undersea mining, a WCFZ prediction dataset with 122 cases of fractured zones was constructed. Five machine learning algorithms (linear regression, XGBRegressor, RandomForestRegressor, LineareSVR, and KNeighborsRegressor) were employed to develop five corresponding predictive models, taking multiple factors into account.The optimal parameters for each model are obtained through ten-fold cross-validation (10CV). The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination (R 2 ) and mean squared error (MSE). A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria, showing a high R 2 value of up to 0.925 and a low MSE value of 3.61. The proposed model was validated in a recently established mining area on Sanshan Island, China. It shows low absolute and relative errors of 0.71 m and 2.01%, respectively, between the predicted value from the model and observation result from the field, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ.

Introduction

As mineral resources continue to be depleted, easily accessible shallow resources are gradually exhausting. Deep-seated deposits, high-altitude deposits, and complex mining conditions such as underwater or seabed deposits have become important targets for extraction 1 , 2 . Underground mining disrupts the original balance of the stress field in the overlying rock, which easily leads to collapse, fracture, separation, and deformation of the overlying rock. The strata above the excavation area will settle at different displacements and rates, resulting in separation between the fracture surfaces in these strata 3 , 4 , 5 . The adjacent strata in the vertical direction will be separated due to differential displacement, forming cracks parallel to the stratum plane, which are called transverse cracks. In the horizontal direction, subsidence will break the formation into rock blocks. These rock blocks will rotate and separate, resulting in cracks that are either perpendicular to or intersect the formation plane, known as longitudinal cracks. As mining operations continue, cracks will continue to develop upwards 6 . If the mining area is located under a lake, river, or ocean, cracks will become channels that facilitate the flow of water 7 . Once the water extends to the bottom of the water-conducting fractured zone (WCFZ), it can trigger disasters 8 . Especially with the advancement of technology, the extraction of underwater minerals has gradually increased, making it prone to the formation of WCFZ, leading to water inflow and water inrush accidents 9 . The occurrence of water inrush accidents not only results in casualties and property losses but also disrupts the existing ecological environment 10 , 11 . According to statistics from the National Mine Safety Supervision Bureau investigation system, from 2003 to 2019, a total of 592 mine water inrush accidents occurred in China, resulting in 2,890 fatalities, as shown in Fig.  1 . If the height of WCFZ is predicted before mining, and mining scale and methods are adjusted reasonably, it can prevent adverse interactions between the WCFZ and the overlying aquifer 12 . Therefore, accurately predicting the height of WCFZ in underwater mining, and achieving water-protected extraction, holds both theoretical and practical significance for safe mining and environmental protection.

Statistics on mine water inrush accidents in China from 2003 to 2019.

The estimation of WCFZ can be classified into two main categories: on-site measurement experiments and indoor prediction methods. In engineering practice, on-site measurements primarily include methods such as underground water injection leakage detection 13 , acoustic computed tomography (CT) 14 , borehole imaging technology 15 , resistivity scanning imaging technology 16 , 17 , and transient electromagnetic (TEM) 18 . On-site experiments can accurately determine the height of WCFZ. However, due to the complexity of underground or underwater mining environments, equipment operation, and cost constraints, On-site experiments are not widely used. Additionally, the data obtained from on-site measurements are real-time and can only be experimented with post-mining. Therefore it is challenging to accurately predict the height of WCFZ before extraction. Common indoor prediction methods include empirical formulas 19 , 20 , theoretical calculations 21 , 22 , 23 , numerical simulations 24 , 25 , physical models 26 , 27 , artificial intelligence 11 , 28 , 29 , etc. Normally summarized from on-site experiences, the use of empirical formulas is an easily operational method but only overly simplified factors are considered. Moreover, due to safety concerns, pillar thickness was normally exaggerated, which led to resource wastage. In theoretical calculations, mechanical theories simplify complex anisotropic rocks as ideal elastic–plastic bodies. However, influenced by assumptions and actual site conditions, these models often lack universality. It is a common challenge for numerical simulation methods to obtain precise geological condition parameters, which always require approximations or assumptions. Physical models demand high accuracy in material proportions and are unable to simulate specific complex geological conditions, in addition to being expensive and time-consuming.

In recent years, with the development of big data and machine learning, more and more researchers have been exploring the cross-integration of machine learning and various fields. For example, the Artificial neural network has been widely used to predict the height of a caving-fracturing zone 30 , 31 , 32 . Shahani et al. 33 used four gradient-boosting machine learning algorithms to predict the uniaxial compressive strength of soft sedimentary rocks, and found that the XGBoost algorithm performed best in predicting the uniaxial compressive strength of soft sedimentary rocks. Kamran et al. 34 developed a KNN-GWO model and successfully applied it to predict the stability of hard rock pillars. Kidega et al. 35 and Kamran et al. 36 applied machine learning to predict rockburst in underground engineering structures, which can further improve the prediction accuracy. A time-dependent model based on the energy balance was also proposed and validated to predict the height of the destressed zone in long-term condition 37 , 38 . Moreover, Razaei 39 developed three methods, namely radial basis function neural network, fuzzy inference system, and statistical analysis models to predict the stress concentration coefficient around a mined panel, which showed high agreement with the real values.

At the same time, the prediction of the height of the WCFZ based on machine learning has developed rapidly. Hou et al. 40 utilized a combination of genetic algorithms and a support vector machine to establish a predictive model for WFCZ height. Zhu et al. 41 achieved favorable results in predicting the height of WCFZ by combining the Improved Cuckoo Search (ICS) algorithm with the Extreme Learning Machine (ELM). Guo et al. 28 used a multi-population genetic algorithm (MPGA) to search for optimal SVR parameters, improving prediction accuracy and stability. Zhao et al. 42 optimized the ELM model using the Grey Wolf Optimization Algorithm (GOA), Whale Optimization Algorithm (WOA), and Salp Optimization Algorithm (SOA). These prediction results were validated using measured data from a borehole television logging tool, showing good consistency in the predictions.

The aforementioned achievements in predicting the height of WCFZ are all of significant importance. However, due to the uncertainty and extreme complexity of accurately predicting WCFZ, developing an accurate and reliable model for predicting the WCFZ in overlying strata during mining remains a massive challenge. The accuracy and reliability of case-based methods for predicting WCFZ in overlying strata during mining mainly depend on the quantity and quality of cases involved in the analysis. Currently, most research is based on tens of cases, leading to less reliable and less generalizable predictive models. There is a need to further collect cases related to WCFZ in overlying strata during mining and establish a large dataset. On the other hand, existing methods have their specific applicability, lacking a universal solution. There is a shortage of predictive models with satisfying classification performance for WCFZ in overlying strata during mining. To address this challenge, a detailed analysis of the problem, dataset characteristics, and thorough performance evaluation of models are required to identify the optimal algorithm, improving the applicability and accuracy of predictive models.

To fill the current research gaps, this study established a large dataset containing 122 sets of engineering cases from literature related to WCFZ in overlying strata during mining. By introducing five common intelligent algorithms (LinearRegression, XGBRegressor, RandomForestRegressor, LinearSVR, and KNeighborsRegressor), five comprehensive predictive models for WCFZ in overlying strata during mining were constructed, considering multiple factors. Subsequently, the regression performance of the five models was comprehensively evaluated using the correlation coefficient R 2 and Mean Squared Error (MSE). The predictive performance of these models was then compared with that of commonly used empirical formulas. Finally, based on the models developed in this study, an analysis of WCFZ in overlying strata during mining was conducted for the Xinli Mine Area on Sanshan Island, China, as depicted in Fig.  2 .

Schematic diagram of the performance evaluation for WCFZ prediction based on the five models.

Data acquisition and analysis

Although the mechanism of WCFZ in overlying strata during mining is highly complex, involving a multitude of influencing factors, certain factors such as mining depth ( H ), hard rock proportion coefficient ( c ), mining thickness ( d ), and working face length ( L ) 43 are generally considered worldwide. It should be noted that c is the ratio of the cumulative thickness of hard rock layers within the estimated height range ( H f ) of the WCFZ to H f itself. The formulas for calculating c and H f are given by Eqs. ( 1 ) and ( 2 ), respectively.

A total of 122 sets of non-duplicate and complete samples of WCFZ in overlying strata during mining were obtained from the available data, where 67 sets were sourced from Guo et al. 28 , 39 sets from Fan et al. 44 , and 16 sets from Chai et al. 45 , as shown in Table 1 . The first 80% of the samples were designated as the training set, while the remaining 20% were allocated as the testing set.

At the same time, in order to more intuitively display the distribution of 122 sets of data, histograms are used to visualize the data. Figure  3 shows the histogram of input parameters. It can be seen that the distribution of mining depth data presents a double peak, centered around 300 and 500, respectively. This distribution is relatively dispersed and exhibits a large standard deviation. The P value is greater than 0.05, indicating that the data does not significantly deviate from the normal distribution. The data distribution of the hard rock proportion coefficient is close to the normal distribution, but there is a slight right skew, with a peak between 0.4 and 0.6. The P value is less than 0.05, indicating that the data deviates from the normal distribution. The mining thickness data distribution is biased to the right, showing a positive skewed distribution, with a peak between 2 and 4, after which the frequency gradually decreases, and the P value is very small, indicating that the data significantly deviates from the normal distribution. The data distribution of working face length is close to the normal distribution, but there is a right-skewed long tail, with a peak between 130 and 170. The P value is very small, indicating that the data significantly deviates from the normal distribution.

Histogram of input data.

Figure  4 shows the histogram of output parameters. The data of WCFZ roughly presents an unimodal distribution, with the peak appearing in the range of 40–60. The data gradually decreases after 60, with a long tail on the right side, indicating that there may be some large values in the data. From the fitted normal distribution curve, the data distribution has some deviation from the normal distribution.

Histogram of output parameters.

Each attribute has different units and values, so it is necessary to normalize the sample data, as shown in Eq.  3 :

where, X ij represents the normalized value of the j -th input sample in the i- th attribute, and x ij is the j -th input sample value in the i -th attribute before normalization. Similarly, Yj is the normalized value of the j -th output sample and yj is the j-th output sample value before normalization.

Prior to predicting the height of WCFZ in overlying strata during mining, it is necessary to analyze the factors influencing these fracture zones. This study employs the Pearson correlation coefficient to measure the influencing factors. The correlation coefficient is a real number ranging between [− 1, + 1]. When the correlation coefficient is between − 1 and 0, it indicates a negative correlation between variables; when the correlation coefficient is between 0 and 1, it indicates a positive correlation between variables; when the correlation coefficient is 0, there is no correlation between them. However, discussing the correlation between two variables requires consideration of the significance level. It is meaningless to only discuss the size of the correlation coefficient without mentioning the p -value; the correlation between the two variables may be due to chance factors. Therefore, it is necessary to assess the significance level of the correlation between two variables.

From Fig.  5 and Table 2 , it can be observed that there is a weak positive correlation between d and L . However, the p -value in the table is 0.004 < 0.05, which indicates rejecting the null hypothesis. On the other hand, the weak negative correlation between d and H can be considered. However, the p -value is 0.017 < 0.05, indicating that the aforementioned statement needs further discussion. The correlation coefficient between the c and L is − 0.15 and the p -value is 0.11, indicating a weak negative correlation and a valid hypothesis in this case, respectively. Similarly, it is believed that there is a certain weak negative correlation between the c and H .

Correlation coefficients.

Prediction models for WCFZ

Machine learning algorithms, linearregression.

LinearRegression model is a commonly used regression algorithm to establish a linear relationship model between features and target variables. Its objective is to find the optimal model parameters by minimizing the difference between predicted values and actual values. This model is suitable for predicting continuous target variables with a simple and intuitive interpretability. It is widely applicable in many cases but requires additional attention when dealing with nonlinear problems and outliers 46 . Figure  6 shows the FLinearRegression flowchart.

Diagram of the LinearRegression process.

XGBRegressor

The XGBRegressor model is a regression model based on the Gradient Boosting Tree algorithm, which is widely used in modeling and predicting regression problems 47 . By integrating multiple weak learners and continuously optimizing the model's performance through iterative training. Its core involves optimizing the loss function through gradient descent in each iteration. However, when using the XGBRegressor model, it is important to set hyperparameters reasonably and handle the training time of large-scale datasets carefully. Figure  7 shows the structure diagram of the XGBRegressor model.

Schematic diagram of XGBRegressor structure.

RandomForestRegressor

The RandomForestRegressor model is a regression model based on the Random Forest algorithm. It constructs decision trees by randomly selecting features and samples, with each decision tree trained on different subsets of data and features 48 , as shown in Fig.  8 . During prediction, the RandomForestRegressor model aggregates the predictions of all decision trees, obtaining the final regression prediction result through averaging or voting.

Schematic diagram of RandomForestRegressor structure.

The LinearSVR model is a regression model based on the Support Vector Machine algorithm. It is a variant of the SVR algorithm used for solving regression problems. It works by finding an optimal hyperplane to divide the training data in the feature space into two parts, minimizing the difference between the predicted values of the target variable and the actual values. Unlike traditional SVR models, the Linear SVR model employs a linear kernel function, transforming nonlinear problems into linear ones 49 . Figure  9 is a structural diagram of the LinearSVR model.

Schematic diagram of LinearSVR structure.

KNeighborsRegressor

The KNeighborsRegressor model is a regression model based on the K-nearest neighbors’ algorithm. It works by finding the K nearest neighbors in the training data to a given test sample and making a regression prediction based on the target values of those neighbors 50 . This is a prediction based on similarity. When given a test sample unseen during training, the model calculates its distances to all samples in the training set and selects the K closest training samples. Then, it obtains the regression prediction for the test sample by averaging the target values of these K samples, weighted by their distances. Figure  10 provides a structural diagram of the KNeighborsRegressor model.

Schematic diagram of KNeighborsRegressor structure.

Algorithm optimization

During the model training process, ten-fold cross-validation (10CV) is employed to optimize the model parameters. Therefore, the optimal parameter combination within the given parameter space can be achieved, which enhances the performance and prediction accuracy of the regression model. In the grid search process, a series of prior candidate values for algorithm-related parameters are first provided. Through iterative traversal, all possible parameter value combinations are attempted to obtain the parameter value combination that yields the optimal algorithm performance, as depicted in Fig.  11 .

Flowchart of 10CV.

Regression performance evaluation

First, the data is preprocessed. Then, the Pearson method is used to analyze the correlation between the height of the WCFZ and the influencing factors. The influencing factors are used as inputs for the prediction model. The influencing factors include mining depth, hard rock proportion coefficient, mining thickness, and working face length. Finally, the performance of each model is reflected through the prediction evaluation module, and variable contribution and importance are introduced to focus on the impact of important information related to the height of the WCFZ.

To assess the generalization performance of the five models constructed in this study, evaluation metrics that measure the models' generalization ability are needed. MSE and Coefficient of Determination (R 2 ) are commonly used performance evaluation metrics in regression problems. The comparison between the predicted results of the models built using five different machine learning algorithms and the actual values is shown in Fig.  12 and Table 3 .

Predicted results from ( a ) LinearRegression; ( b ) XGBRegressor; ( c ) RandomForestRegressor; ( d ) linear SVR; and ( e ) KNeighborsRegressor.

The results indicate that LinearRegression has a relatively high R 2 value (0.862), explaining 86.2% of the variance in the target variable. It also has a low MSE of 10.15, indicating a small average deviation between predicted and actual values. Thus, the Linear Regression model performs well in prediction. XGBRegressor has the highest R 2 value (0.925), explaining 92.5% of the variance in the target variable. It also has the lowest MSE (3.61), indicating the smallest average deviation between predicted and actual values. This is because XGBRegressor can handle nonlinear relationships well, exhibiting high predictive performance. Overall, the XGBRegressor performs the best in prediction. RandomForestRegressor has a relatively high R 2 value (0.901), explaining 83.8% of the variance in the target variable. However, its MSE (7.81) is slightly higher than other models, indicating a larger average deviation between predicted and actual values. While performing well in prediction, it slightly lags behind the XGBoost regression model. LinearSVR has a relatively high R 2 (0.813), explaining 81.3% of the variance in the target variable. However, its MSE (25.76) is also highe, indicating a larger average deviation between predicted and actual values. LinearSVR performs moderately in prediction due to its poor fit to nonlinear relationships and sensitivity to outliers. KNeighborsRegressor has a lower R 2 value (0.793), indicating that the model explains 79.3% of the variance in the target variable. It also has a higher MSE (32.47), which means a larger average deviation between predicted and actual values. This model performs moderately in prediction, slightly inferior to other models.

Therefore, it can be concluded that the XGBRegressor performs the best in prediction, with the highest R 2 value and the lowest MSE. Following that, LinearRegression and RandomForestRegression models perform well in prediction. LinearSVR and KNeighborsRegressor models exhibit moderate performance in prediction, slightly inferior to other models.

The residual plot is an important tool for assessing the fit and error structure of a regression model. In this paper, the established regression model is evaluated by analyzing the residual plot (Fig.  13 ). The residual plot displays the differences between predicted and actual values.

Residual plots of ( a ) LinearRegression; ( b ) XGBRegressor; ( c ) RandomForestRegressor; ( d ) Linear SVR; and ( e ) KNeighborsRegressor.

It can be found from Fig.  13 a that the residual values vary within different ranges of predicted values. Although the MSE is slightly higher compared to some models, the LinearRegression model can also fit the data well overall. In contrast, the residual plot of the XGBRegressor model also exhibits characteristics of a random distribution, but compared to other models, its residual values are more concentrated around zero, indicating minimal deviation between predicted values and actual values. This further demonstrates the superiority of the XGBoost regression model in prediction. As for the RandomForestRegressor model, the residual plot presents a relatively random distribution, with residual values evenly distributed around zero, which suggests a good fitting can be obtained by this model. The residual plot of the LinearSVR model shows that most residual values are below zero, which might be due to systematic bias or failure to capture some important features in the data. However, additional feature engineering including feature transformation and selection, can be conducted to improve the predictive performance of the model. The residual plot of the KNeighborsRegressor model shows that the residual values decrease as the predicted values increase, and the residual values are relatively large, slightly inferior to other models.

It can be found from the residual plots that the XGBoost regression model performs the best in terms of prediction, followed by the LinearRegression and RandomForestRegressor models. In contrast, the predictive performance of the LinearSVR and KNeighborsRegressor models is relatively mediocre. The analysis of residual plots provides directions for improving model performance and further research to enhance predictive accuracy. For instance, since the residual plot of the KNeighborsRegressor model exhibits spatial autocorrelation, spatial regression models (such as SAR, SLM, SDM, etc.) can be considered to address the autocorrelation issue. For models with mediocre predictive performance (such as LinearSVR and KNeighborsRegressor), ensemble methods like Bagging and Boosting can be attempted, combining multiple models to improve overall predictive accuracy.

Meanwhile, this paper introduces variable contribution (Fig.  14 ) and importance (Fig.  15 ) to facilitate a quick understanding of the ranking of feature importance, which can achieve a more intuitive understanding of each feature’s contribution to a model’s prediction and their relative importance.

Contributions of ( a ) LinearRegression; ( b ) XGBRegressor; ( c ) RandomForestRegressor; ( d ) Linear SVR; and ( e ) KNeighborsRegressor.

Importance plots of ( a ) LinearRegression; ( b ) XGBRegressor; ( c ) RandomForestRegressor; ( d ) Linear SVR; and ( e ) KNeighborsRegressor.

Shap (Shapley Additive Explanations) generates a prediction value for each sample with the model, and the Shap value is the numerical value assigned to each feature in that sample. Similar to the additive method of linear models, assuming the model's baseline score (often the mean of the target variable for all samples) is y base , the i -th sample is x i , the j -th feature of the i -th sample is x i,j , and the Shap value of this feature is f ( x i,j ), then the model's prediction for sample x i is.

When f ( x i,j ) > 0, feature has a positive effect on the prediction of the target value; conversely, when f f ( x i,j ) < 0, the feature has a negative effect on the target prediction value. Therefore, Shap not only provides the magnitude of the feature's influence but also reflects the polarity of the influence of each feature in each sample.

In Fig.  14 , the vertical axis ranks the features based on the sum of Shap values for all samples, while the horizontal axis represents the Shap values (the distribution of feature effects on the model output). Each point represents a sample, with the sample count stacked vertically, and the color indicates the feature value (red corresponds to high values, blue corresponds to low values). In Fig.  15 , the absolute mean of Shap values for each feature is taken to obtain the distribution of feature importance, effectively blurring out the positive and negative influences seen in the previous figure.

From Figs. 14 a and 15 a, it can be observed that in the LinearRegression model, d is the most important feature. Points with lower d values have Shap values less than 0, indicating a negative impact on the prediction results, while higher values of d (red color) have a positive impact on the prediction results. The influence ranking of the other three features on the LinearRegression model is as follows: c  >  L  >  H . Both c and L have similar effects on d in the model, while H shows an opposite effect.

It is worth noting that most points of H are diffused around Shap = 0, indicating that it does not have a significant impact on most results but only affects a small portion of cases. From Figs. 14 b and 15 b, it can be seen that the influence of the first three features ( d  >  c  >  L ) on the XGBRegressor model is similar to that of the LinearRegression model. Higher values of H (red) have a negative impact on the prediction results. The importance ranking of the four features is the same. This may indicate that the features in the dataset have been adequately fitted to the target variable in both models, and the models have similar fitting effects. Therefore, it can be concluded that both models have similar abilities in explaining the variations in the dataset. Although the variable contribution rankings are the same, there are still differences in prediction performance and behavior between the models.

In the RandomForestRegressor model (Figs. 14 c and 15 c), d is the most important feature, with similar positive and negative impacts as described above. However, the importance of working face length, as indicated by the Shap values, is greater than that of the c . Both of them have points with low scores, where Shap values less than 0, indicating a negative impact on the prediction results. In the contrast, high scores (red) have a positive impact on the prediction results. For points with high values of d , the Shap values are less than 0, indicating a negative impact on the prediction results. For points with low values, the Shap values are greater than 0, indicating a positive effect. In the LinearSVR model (Figs. 14 d and 15 d), d has the most significant impact on the model, with effects similar to the three models mentioned above. However, the second most important feature is mining depth. Points with high scores have Shap values less than 0, indicating a negative impact on the prediction results, while low scores (red) have a positive impact on the prediction results. Following H in importance are L and c .

In the final KNeighborsRegressor model (Figs. 14 e and 15 e), it can be observed that H is the most important feature, but its Shap values are all on the right side of the vertical axis. This could indicate a nonlinear relationship between H and the prediction results, suggesting that the larger or smaller the value of the feature, the greater the impact on the prediction results. Following H in importance is L . It can be seen that points with low scores have Shap values less than 0, indicating a negative impact on the prediction results. Also, a positive effect can be indicated by thepoints with high scores and Shap values greater than 0. The d and c , thereafter, are diffused around Shap = 0, suggesting that they do not have a significant impact on most results but only affect a small portion of cases.

By comparing with traditional methods, a comparative study was conducted in the Xinli Mine area in the southwest of the Sanshan Island gold mine in Shandong Province, China. The height of the fractured zone in the mining-induced overlying strata was predicted to further validate the effectiveness and engineering value of each model.

The Xinli Mine area is located at the confluence of the Bohai Bay and Wang River, with the Bohai Bay surrounding the west and north sides of the mining area, as shown in Fig.  16 , with a total length of approximately 5000 km. The surface water of the mining area mainly comes from these two bodies of water. The ore body is mainly located below the Wang River and Bohai Bay, with an angle of approximately 60° with the coastline, and the dip angle of the ore is 46°. Based on the relationship between the bodies of water, it can be seen that the Xinli Mine area is largely influenced by seawater. The hard rock mainly consists of conglomerate and sandstone, and the aquifer is relatively abundant. The geological wells in the Xinli Mine area can illustrate the structure of the strata, as shown in Fig.  17 .

The geographical location of Xinli Mine area in Sanshan Island, China. The map was generated with CNSKnowall Version 01 51 and Gditu Version 2023 52 .

Geological column diagram of the Xinli Mine area.

There are many mining methods for undersea mining in the Sanshan Island Gold Mine, among which there are two main mining methods for mining close to the upper part, namely the point pillar upward layered filling mining method and the upward access mechanized filling mining method.

The point pillar upward horizontal layered filling mining method leaves point pillars to assist in supporting the roof, and the ore is mined in layers from bottom to top. After each layer of mining is completed, the layers are filled in a timely manner, and a working space of 2–3 m is always left between the filling body and the roof until the final layer of mining reaches the height of the mining stage. The interaction between the filling body and the point pillar forms a collaborative support system between the point pillar and the filling body, jointly maintaining the stability of the goaf and overlying rock layers.

Upward drift filling mining method refers to the filling mining method in which various layers are mined from bottom to top in the mining area, and the ore is mined using drift filling in the layers. Compared with the upward layered filling mining method, the characteristic of this method is that it uses a drift for backfilling, and during filling, the filling material is completely filled with the goaf, making it as close to the roof as possible. This mining method is suitable for mining inclined and steeply inclined ore bodies with unstable ore and surrounding rocks, high ore grade, and value.

In the process of mining undersea metal mines, there are many factors that affect the development height of the water-conducting fracture zone, mainly including the inclined length of the working face, the compressive strength of the roof, the mining depth, the mining height, the mining method, the mechanical properties and structural characteristics of the overburden rock, and the roof management method.

Underground mining may lead to the fracture of the upper rock mass, endangering the environment and causing death and property damage. As shown in Fig.  18 , underground mining usually leads to ground movement, and the strata above the excavation area will break with different displacements and rates, resulting in separation between the fracture surfaces in these strata. Due to the difference in displacement, vertically adjacent strata will separate, so fractures parallel to the plane of the strata can be referred to as transverse fractures. In the horizontal direction, the settlement will cause the strata to break into rock blocks, which will rotate and separate, resulting in fractures perpendicular to or intersecting with the ground plane, known as longitudinal fractures. As mining operations continue, fractures will continue to develop upwards. If the mining area is located under a lake, river, or ocean, the fractures will become channels for water movement.

Formation mechanism of overburden mining fractures.

Due to the underwater mining of metal ore under the sea, there is a certain chance that the rock fractures in the fracture zone will connect to the water above it. It is for this reason that the fractures that form a hydraulic connection with the mined-out area during mining are called water-conducting fractures. Due to the effect of pressure, the part of the rock zone that forms longitudinal and transverse fractures is called overburden mining fractures 53 .

Field observations

To validate the accuracy of the prediction results, a panoramic borehole camera 1 was used to measure the height of each fractured zone in the mining-induced overlying strata. With 360-degree borehole images and real-time videos, the condition of the borehole wall rocks can be clearly observed. Additionally, the device can record the characteristics of fractures above the caving zone to provide more detailed visual inspection information.

By increasing the depth of the borehole, it is possible to observe the connectivity of water apertures, the extent of fracture development, and even traces of water flow. Additionally, this device can determine the upper boundary of the fractured zone above the caving zone. In order to determine the height of the WCFZ at the top of the Xinli Mine area, four boreholes were drilled on the roadway, reaching a depth of − 200 m to measure the height of the fractured zone at a depth of − 240 m. Chen has already measured the height of the fractured zone at depths of − 200 m and − 165 m in references 6 , 54 .

In the first borehole, the fissure apertures are relatively large and connected above 33.64 m, and traces of water flow are also clearly visible, similar to the situation shown in 1 . However, in lower areas, the fissures are isolated and smaller in aperture size. In the second borehole, similar off-layers are located at distances of 34.18 m, 35.28 m, and 31.58 m from the bottom of the borehole. Based on this data, the height of the WCFZ at − 240 m depth can be determined. From these observations, it can be reasonably inferred that the height of the WFZ at − 240 m depth is similar. The conditions of different working faces in the Xinli Mine area are summarized in Fig.  19 .

Data of different working faces in Xinli mining area.

Commonly used theoretical criteria

Under conventional mining conditions, the height of the WCFZ is predicted using empirical formulas based on extensive regression analysis of measured data. These formulas comprehensively consider factors such as d , overlying strata type, and coal seam dip angle, and are widely applied. According to the empirical formula method 50 (see Table 4 ), the height of the WCFZ is predicted, where M represents the d.

In the Xinli Mine area, rock strata classification follows the "Three Downs" mining regulations and is divided into soft and weak categories. The classification is determined based on the coefficient of hard rock lithology, c , which quantifies two influencing factors: the uniaxial compressive strength of the rock layer and the overlying strata structure. The value of c refers to the ratio of the cumulative thickness of hard rock layers within the estimated height range of the WCFZ to the estimated height of the WCFZ itself 55 .

where, \(\sum \text{h}\) represents the cumulative thickness of hard rock layers in the overlying strata, the hard rock layers mainly include sandstone (fine sandstone, medium sandstone, coarse sandstone), igneous rocks, etc.; H f represents the estimated height of the WCFZ.

The on-site data from the Xinli Mine area in Sanshan Island is compared with the predictive results of five models (see Table 5 ). From Table 5 , it can be observed that the empirical formula predicts the height of the WCFZ between 26.84 and 35.01 m, with absolute errors ranging from − 2.91 to 13.66 and relative errors ranging from 0.83 to 33.74. Since the empirical formula method only considers a single influencing factor, it exhibits the highest error.

The predictive results of the five models are all superior to the traditional empirical formula. Among them, XGBRegressor performs the best. This is mainly because XGBRegressor effectively captures complex nonlinear relationships in the data through the ensemble of multiple weak classifiers, thereby improving the model's predictive accuracy. RandomForestRegressor exhibited a slightly worse performance, but still effectively captures complex nonlinear relationships in the data by utilizing an ensemble model of multiple decision trees. However, KNeighborsRegressor showed the worst prediction effectiveness. It might be due to the presence of outliers in the data, which leads to an inaccurate selection of nearest neighbors and further disruption of the model's predictive results.

The sample data on the WCFZ dataset were normalized, and then predicted, compared, and validated by five models, namely LinearRegression, XGBRegressor, RandomForestRegressor, LinearSVR, and KNeighborsRegressor. The predictive results are compared with the true values, as shown in Fig.  2 .

LinearRegression is a simple and intuitive model for linear relationships. Its advantages lie in its ease of understanding and interpretation, and it performs well on datasets with few features. However, it always involves complex nonlinear relationships in the prediction of the WCFZ. LinearRegression may be difficult in capturing the complexity of nonlinear features. Therefore, polynomial features or other nonlinear transformations may be considered to better fit the nonlinear relationships of complex geological conditions. Additionally, regularization methods such as Lasso and Ridge can be used to reduce the risk of overfitting. XGBRegressor performs well in this study, effectively capturing the complex nonlinear relationships in the overlying strata, demonstrating high flexibility. However, careful parameter tuning is required to avoid overfitting. Further research on parameter tuning and feature engineering methods can optimize the model's performance. RandomForestRegressor typically performs well in handling high-dimensional data and nonlinear relationships. Combining multiple decision trees provides good generalization performance and is relatively robust to outliers. As shown in Fig.  3 , RandomForestRegressor is only slightly inferior to XGBRegressor. However, the interpretability of the RandomForestRegressor model is relatively poor, and parameter tuning is relatively complex. LinearSVR is suitable for high-dimensional data and nonlinear problems. However, it is found to perform poorly in predicting the WCFZ, which may be because LinearSVR is sensitive to large-scale datasets and noise, and the original dataset has not been processed for noise. Therefore, in future research, dimensionality reduction, data cleaning, and noise processing may be needed, or distributed computing and faster optimization algorithms may be considered, along with exploring new kernel functions and regularization strategies to improve the model's performance.

KNeighborsRegressor is suitable for modeling small-scale datasets and local relationships. As shown in Figs. 4 and 5 , KNeighborsRegressor performs poorly in predicting the WCFZ, possibly due to limitations in performance when dealing with large-scale and high-dimensional data. In future research, dimensionality reduction techniques such as Principal Component Analysis (PCA) may be considered to reduce dimensions or attempt to improve performance using distance-weighted K-nearest neighbors.

Given the advantages and disadvantages of each model, the selection of sample data is crucial, and it needs to be representative and comprehensive. It is necessary to keep enriching sample data in practice and constantly improve and update models to make each model have better accuracy and generalization ability. At the same time, this study only studied the prediction performance of a single model in the height of the WCFZ and has not yet been optimized. Subsequent research should explore the impact of different optimization models on a single model to further improve the accuracy of machine learning in predicting the height of the water-conducting fracture zone.

The development of WCFZ in overburden strata due to mining is a complex process of movement and destruction in both time and space. Although it is difficult to convert qualitative factors into quantitative factors, it is still necessary to consider factors that have a significant impact on the high development of WCFZ, such as mining speed, time factors, and repeated mining. Considering more influencing factors and increasing the number of training samples can further improve the performance of the model.

Especially with the gradual depletion of shallow resources, underwater or seabed ore bodies with complex mining conditions have become important mining targets. If the highly developed WCFZ cannot be predicted in time, it is extremely easy to cause major personal and property damage accidents. Therefore, we should strengthen the research and prediction of the highly developed WCFZ, improve the accuracy and reliability of prediction, and provide more support and guarantee for sustainable development. At the same time, how to choose more objective and reasonable evaluation indicators for predicting the height of the WCFZ will become the focus and difficulty of future research.

As mineral resource exploitation extends into deeper sea areas, water influx caused by the formation of water-conducting fractured zones (WCFZ) has become a bottleneck problem restricting deep-sea safety production. In this study, we constructed a large dataset of WCFZ containing 122 engineering cases and established predictive models for WCFZ height based on five machine learning algorithms considering multiple factors. The main achievements and conclusions are as follows:

Mining depth ( H ), hard rock proportion coefficient ( c ), mining thickness ( d ), and working face length ( L ) were considered as the main influencing factors for the height of WCFZ in overlying strata during mining. The Pearson correlation coefficient analysis shows that the correlation between d and L , d and H are both weakly positive with p -values of 0.004 and 0.017, respectively. On the contrary, the weak negative correlation between c and L can be indicated by a correlation coefficient of − 0.15 and a p -value of 0.11.

The more accurate prediction for the height of WCFZ than the traditional empirical formula was obtained from the machine learning algorithms. Compared with the traditional empirical formula, the average relative errors of LinearRegression, XGBRegressor, RandomForestRegressor, LinearSVR, and KNeighborsRegressor were decreased by 10.27%, 12.9%, 10.03%, 5.61%, and 6.53%, respectively. XGBRegressor exhibited the most accurate prediction among all the models, which might be due to its superior capacity to handle nonlinear relationships.

Consisting with the regression performance evaluation, XGBRegressor showed the best performance in predicting the height of WCFZ in the Xinli Mine area of Sanshan Island, showing absolute and relative errors of 0.71 m and 2.01%, respectively.,

The accuracy of the five predictive models used in this study for WCFZ height can meet the requirements of practical engineering application.

In light of the results from this study, future research is recommended to address the remaining knowledge gaps and limitations. It is important to consider additional influencing factors such as mining speed, time factors, and repeated mining. Furthermore, the impact of incorporating different optimization techniques within a single model should be examined to enhance the accuracy of machine learning predictions.

This research is supported by financial grants from “the National Natural Science Foundation of China (52104122)”, “the Open Project of Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education (LKF2021007)”, and “the Natural Fund project of Fujian Province Science and Technology Department (2020J01943)”.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education, Wuhan, 430000, Hubei, China

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Contributions

Concept and design: D.L. and Z.W.; data collection and analysis: Z.W. and Y.C.; drafting of the article: Z.W.; critical revision of the article for important intellectual content: D.L. and Y.C.; study supervision: D.L. Funding acquisition: Z.W. All authors reviewed the manuscript.

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Correspondence to Dayou Luo .

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Wu, Z., Chen, Y. & Luo, D. Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining. Sci Rep 14 , 21047 (2024). https://doi.org/10.1038/s41598-024-71928-9

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DOI : https://doi.org/10.1038/s41598-024-71928-9

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Comparative analysis of machine learning techniques for water consumption prediction: a case study from kocaeli province.

1. Introduction

• Research Background
• Importance of Accurate Water Consumption Prediction
• Advantages of Machine Learning Methods
• Optimization Methods
• Purpose of the Research

2. Literature Review

3. materials and methods, 3.1. dataset description, 3.2. key features and variables.

• Consumption Data: Monthly water consumption figures for each subscriber.
• Weather Parameters: Daily measurements of rainfall, sunshine, humidity, temperatures, and wind speed.
• Subscriber Details: Type of subscriber (residential, commercial, official), activity type, and tariff type.
• Temporal Information: Number of weekends and holidays in each month, and the phase of the COVID-19 pandemic.

3.3. Data Preprocessing

3.3.1. handling missing values.

• Spatial Interpolation: For locations with missing data, we calculated the arithmetic mean from neighboring locations to fill gaps, ensuring that imputed values reflected local weather conditions.
• Forward Fill Method: We applied forward filling to maintain temporal continuity in time series data, carrying forward the last known value for any gaps.
• Mean or Median Imputation: For any remaining missing values that could not be filled through the above methods, we used mean or median imputation based on the respective parameter.

3.3.2. Normalization

3.3.3. feature selection, 3.4. machine learning techniques, 3.5. hyperparameter tuning, 3.6. cross-validation and evaluation metrics, 3.7. computational efficiency and feature importance, 3.8. data splitting for model training and testing.

• It ensures that each data point is used for both training and testing, providing a more comprehensive evaluation of the model’s performance.
• It helps mitigate the impact of data variability and reduces the risk of overfitting.
• It provides a more reliable estimate of the model’s performance on unseen data.

4. Results and Discussion

4.1. model performance, 4.2. discussion of results, 4.3. practical implications, 4.4. limitations and future research, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Görenekli, K.; Gülbağ, A. Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province. Sensors 2024 , 24 , 5846. https://doi.org/10.3390/s24175846

Görenekli K, Gülbağ A. Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province. Sensors . 2024; 24(17):5846. https://doi.org/10.3390/s24175846

Görenekli, Kasim, and Ali Gülbağ. 2024. "Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province" Sensors 24, no. 17: 5846. https://doi.org/10.3390/s24175846

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A Case Study Assessing the Cumulative Effects of Deepwater Horizon Restoration Projects on Barrier Island/Barrier Shoreline Ecosystem Resilience in the North-central Gulf of Mexico

USGS and partners will assess the potential cumulative effects of restoration projects on the resiliency of barrier islands and barrier shorelines in the north-central Gulf of Mexico.

The Science Issue and Relevance:   The Deepwater Horizon (DWH) mobile drilling unit explosion and associated oil spill in April 2010 substantially impacted northern Gulf of Mexico coastal ecosystems, exacerbating existing acute and chronic stressors. As part of the  Natural Resource Damage Assessment process, along with civil and criminal claims, and imposed fines and penalties, over $15 billion (USD) in funding was dedicated to addressing environmental and economic restoration. Since DWH, hundreds of projects have been planned and implemented across the coast with the overall goal of restoring ecosystem function and services. Given the unprecedented temporal, spatial, and funding scales associated with the DWH oil spill restoration effort, the need for robust monitoring was identified early on to help inform adaptive management and provide a means to assess restoration outcomes. Many restoration projects provide project-specific monitoring data (for example,  DIVER website ), which offer insight into project-specific outcomes. However, these data alone fall short of informing outcomes at the ecosystem or regional level that may incorporate cumulative, synergistic or antagonistic effects across a habitat type or geographic area. 

A recent  National Academies of Sciences Report focused on the need to develop approaches that specifically assess cumulative impacts of restoration across a geographic- or ecosystem-level scale. Such an approach requires understanding not just project-level outcomes, but also understanding the cumulative effects of multiple restoration projects on an ecosystem, and their interaction with impacts of on-going acute and chronic stressors (for example, sea-level rise). Over 85 restoration projects have been implemented across the north-central Gulf of Mexico coast as part of the DWH restoration response. These restoration activities provide an opportunity to examine cause and effect related to restoration actions, and more specifically, how on-going trends in the resilience and ecological changes in the barrier island/barrier shoreline (BI/BS) systems may differ from expected or predicted trends for this region.

Methodology for Addressing the Issue: The Louisiana State University, U.S. Fish and Wildlife Service, USGS, and the Water Institute of the Gulf are collaborating on a case study to assess the potential cumulative effects of DWH restoration projects on the resiliency of the BI/BS in the north-central Gulf of Mexico. The area of interest for this study will span from Dauphin Island in Alabama (Fig. 1) to Alligator Point in Florida (Point Bald State Park; Fig. 2). To achieve this objective, we will: 1) develop a conceptual model of BI/BS that identifies drivers, stressors, and outcome metrics to track BI/BS resiliency; 2) document changes in BI/BS resiliency indicators using available project and remotely sensed data; and 3) explore metrics to assess potential changes in these resiliency indicators in response to DWH restoration projects. 

Future Steps: The next steps include the assessment of potential cumulative effects of BI/BS and dissemination of results. This effort could be expanded via future updates with new or planned restoration activities and remote sensing data.

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His role is to help techUK members of all sizes and across all sectors to adopt AI at scale. This includes identifying the barriers to adoption, considering solutions and how best to maximise AI's potential.

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A Fiery Feeling

An Exploration of Stomach Acid Production

By Lacy M. Cleveland, Christine L. Savage, Elizabeth M. Kashmitter

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In this directed case study, students follow a discussion between “Jerry,” a diabetic and former smoker struggling with gastroesophageal reflex disease (GERD), and his primary care physician. Jerry’s diagnosis provides an opportunity for students to examine the process of stomach acid production and its regulation via the cells of the gastric pit and parasympathetic nervous system. Students learn how acetylcholine, histamine, and gastrin enhance, while somatostatin reduces, stomach acid production.   As Jerry explores his treatment options, students compare the mechanism and effectiveness of antacids, H2 blockers, and proton pump inhibitors. The case is designed for a lower-level undergraduate anatomy and physiology course and can be completed in one 50- or 75-minute class period or assigned as homework.   While it is recommended that students have prior knowledge of stomach acid production and the cells of the gastric pit, the design of the case allows for students to learn (not just reinforce) these concepts.  

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Date Posted

• Identify the roles and secretion of the cells associated with the gastric pit, including mucous cells, parietal cells, chief cells, D cells, enterochromaffin-like cells, and G cells.
• Describe regulation of stomach acid production.
• List the symptoms, risk factors, and causes of gastroesophageal reflex disease (GERD).
• Describe the criteria used by medical professionals to diagnose GERD.
• Compare and contrast the mechanism of action and effectiveness of antacids, H2 blockers, and proton pump inhibitors (PPIs).

Digestive system; parietal cells; stomach acid; gastrin; histamine; acid reflux; gastroesophageal reflux disease; GERD;

Subject Headings

EDUCATIONAL LEVEL

Undergraduate lower division

TOPICAL AREAS

TYPE/METHODS

Teaching Notes & Answer Key

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Case teaching notes are protected and access to them is limited to paid subscribed instructors. To become a paid subscriber, purchase a subscription here .

Teaching notes are intended to help teachers select and adopt a case. They typically include a summary of the case, teaching objectives, information about the intended audience, details about how the case may be taught, and a list of references and resources.

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Materials & Media

Supplemental materials.

The optional PowerPoint presentation below contains illustrations that may be used to support delivery of the case.

• stomach_acid_sup.pptx (~1 MB)
• Parietal Cell Acid Production This video outlines the process of stomach acid production by the parietal cell. Running time: 5:11 min. Produced by PhysioPathoPharmaco, 2017.

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Published on 9.9.2024 in Vol 26 (2024)

Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices

Authors of this article:

Original Paper

• Yae Won Tak 1 , MSc   ; 
• Jong Won Lee 2 , MD, PhD   ; 
• Junetae Kim 3 * , PhD   ; 
• Yura Lee 1 * , MD, PhD  

1 Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

2 Division of Breast Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

3 Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Republic of Korea

*these authors contributed equally

Corresponding Author:

Yura Lee, MD, PhD

Department of Information Medicine, Asan Medical Center

University of Ulsan College of Medicine

88, Olympic-Ro 43-Gil, Songpa-Gu

Seoul, 05505

Republic of Korea

Phone: 82 2 3010 1498

Email: [email protected]

Background: Digital health care apps, including digital therapeutics, have the potential to increase accessibility and improve patient engagement by overcoming the limitations of traditional facility-based medical treatments. However, there are no established tools capable of quantitatively measuring long-term engagement at present.

Objective: This study aimed to evaluate an existing engagement index (EI) in a commercial health management app for long-term use and compare it with a newly developed EI.

Methods: Participants were recruited from cancer survivors enrolled in a randomized controlled trial that evaluated the impact of mobile health apps on recovery. Of these patients, 240 were included in the study and randomly assigned to the Noom app (Noom Inc). The newly developed EI was compared with the existing EI, and a long-term use analysis was conducted. Furthermore, the new EI was evaluated based on adapted measurements from the Web Matrix Visitor Index, focusing on click depth, recency, and loyalty indices.

Results: The newly developed EI model outperformed the existing EI model in terms of predicting EI of a 6- to 9-month period based on the EI of a 3- to 6-month period. The existing model had a mean squared error of 0.096, a root mean squared error of 0.310, and an R 2 of 0.053. Meanwhile, the newly developed EI models showed improved performance, with the best one achieving a mean squared error of 0.025, root mean squared error of 0.157, and R 2 of 0.610. The existing EI exhibited significant associations: the click depth index (hazard ratio [HR] 0.49, 95% CI 0.29-0.84; P <.001) and loyalty index (HR 0.17, 95% CI 0.09-0.31; P <.001) were significantly associated with improved survival, whereas the recency index exhibited no significant association (HR 1.30, 95% CI 1.70-2.42; P =.41). Among the new EI models, the EI with a menu combination of menus available in the app’s free version yielded the most promising result. Furthermore, it exhibited significant associations with the loyalty index (HR 0.32, 95% CI 0.16-0.62; P <.001) and the recency index (HR 0.47, 95% CI 0.30-0.75; P <.001).

Conclusions: The newly developed EI model outperformed the existing model in terms of the prediction of long-term user engagement and compliance in a mobile health app context. We emphasized the importance of log data and suggested avenues for future research to address the subjectivity of the EI and incorporate a broader range of indices for comprehensive evaluation.

Introduction

Digital therapeutics (DTx) has the potential to expand accessibility and enhance engagement for patients by addressing the limitations associated with conventional facility-based medical treatments [ 1 ]. These interventions have gained considerable attention owing to their effectiveness in addressing various health challenges, which has led to their increasing adoption rate in health care settings [ 2 ]. Although DTx offers unique monitoring capabilities, enabling health care providers to remotely track patient progress and tailor interventions, their use remains controversial because of the ambiguity in terms of the DTx’s standpoint and effectiveness [ 3 ]. A diverse range of DTx, from smartphone apps for mental health support to wearable devices for chronic disease management, are available to meet the evolving needs of patients and health care providers alike with the availability of real-time and continuous log data for further improvements [ 4 , 5 ]. One such technology that has attracted research interest is mobile health (mHealth), which is used to monitor patients.

In recent years, the use of mHealth technologies in cancer care has steadily increased, offering a promising avenue for improving patient outcomes and revolutionizing health care delivery [ 6 ]. With the convenience of the high distribution rate of smartphones of over 86.11% globally, mHealth apps have been increasingly integrated into cancer management, providing patients with remote access to personalized care through physical fitness support, weight management, therapy, information provision, and social support [ 7 , 8 ]. Despite the growing adoption of mHealth solutions in cancer care, existing literature reviews have highlighted a significant challenge, that is, the absence of standardized measures for assessing the use of and compliance with these technologies [ 9 ].

The vast majority of studies evaluating intervention engagement rely on either postintervention surveys or interviews [ 10 - 13 ]. Furthermore, when assessing the effectiveness of therapeutic education systems, the methodology often involves twice-weekly clinical checkups and self-reports, despite the pioneering nature of the randomized controlled trial (RCT) for internet-delivered interventions [ 14 ]. This highlights the need for a more systematic methodology for evaluating mHealth intervention engagement rather than solely relying on subjective interviews.

This study evaluates the engagement index (EI) in commercial health management apps for long-term use by comparing the newly developed EI model with the existing model.

Study Design

This study aimed to confirm whether a newly developed EI better predicts long-term compliance than an existing EI by using the Web Matrix Visitor Index with modifications, focusing on indices such as click depth, recency, and loyalty based on the Noom app (Noom Inc) usage data. A new menu abundancy index (MI) was introduced, considering the survival time of each menu. In addition, the loyalty index (LI) was enriched by incorporating the final usage week, and the recency index (RI) was refined using permutation entropy to measure the regularity of app usage. This study analyzed data from 233 patients who used the Noom app, part of an RCT involving 960 cancer survivors (breast, colorectal, or lung cancer) aimed at assessing the impact of mHealth apps on recovery. The Noom app, a commercially available weight management tool, was used for its features such as meal logging, step count tracking, weight logging, exercise logging, engagement with health-related content, and messaging.

Study Population

Data obtained from patients who were recruited from an RCT that investigated the impact of mHealth apps on cancer survivors were used; research aimed to facilitate a smoother recovery for patients with breast, colorectal, or lung cancer as they transition back to their daily lives [ 15 - 17 ]. Written informed consent was obtained from all the participants before study participation. Subsequently, the participants were randomly assigned to 1 of 3 mHealth care groups, including the Noom app group. Of the 960 participants, 233 who used Noom were analyzed for this study.

Data Collection

Clinical and pathological information related to demographics were extracted from the electronic medical records of patients at the time of recruitment. The data collection was extended up to 18 months after the final patient enrollment, with follow-up assessments scheduled at 3 months and every 6 months after the initial baseline data collection.

Noom, a commercially available mobile app for weight management, can be downloaded from the Google Play Store and the Apple App Store [ 18 ]. Distinguished by its distinctive curriculum and human coaching intervention, Noom is a prominent feature in the realm of health and fitness apps [ 18 ]. It strives to be a versatile platform for behavioral change, serving as a potent tool for addressing diverse chronic and nonchronic conditions, with the goal of promoting healthier lifestyles for a wider population [ 19 ]. Noom has been shown to be an effective mHealth lifestyle platform, with positive results yielded in various clinical scenarios [ 20 - 22 ]. In this study, various features of Noom were used, including, but not limited to, meal logging, step count tracking, weight logging, exercise logging, engagement in health-related content, and messaging functionalities.

Data Analysis

This study aimed to confirm whether the newly developed EI better predicts long-term compliance than the existing EI. To achieve this, the goal was to predict compliance at 6-9 months or predict survival rates based on that at 3-6 months, with the aim of comparing the performance of the 2 indices. All data analyses were conducted using Python (version 3.8.5; Python Software Foundation).

New Engagement Index

At present, there is no established tool to measure engagement in health care apps, thus, we adopted the Web Matrix Visitor Index [ 23 ] to effectively measure engagement in the commercial health management app for cancer patients using 7 indices, that are click depth, duration, recency, loyalty, brand, feedback, and interaction. Of the 7 indices, we used 3 (click depth, recency, and loyalty) that were applicable; these could be calculated using the app access log data. Click depth measures the impact of page and event views, whereas recency indicates the speed at which visitors return to the website over time. Specifically, click depth is computed by dividing sessions with a reasonable threshold (eg, 4 pages viewed) by all sessions. Loyalty gauges the extent of long-term interaction with the brand, site, or products. Recency is calculated as 1 divided by the number of days since the most recent session, whereas loyalty is derived by subtracting 1 from the number of visitor sessions during the timeframe from 1.

As the app was not able to provide information when accessed each time, we defined the sessions as 1 day with each index ranging between 0 and 1. Click depth was calculated as the number of weeks with at least 2 menus accessed divided by the number of the current week. Loyalty was calculated as the number of accessed weeks divided by the number of this week. Recency was calculated as 1 over the average number of weeks between visits for each period. Finally, EI was calculated as the average (mean) between the click depth, loyalty, and recency indices.

Limitations of Engagement Index

Despite its generalizability, the EI encounters several limitations. First, it may not fully capture all dimensions of user engagement, thus leading to an incomplete representation of user patterns. Second, it is heavily influenced by the natural characteristics of app usage; particularly, over the long term, it can complicate the assessment of long-term app effectiveness. Third, it may fail to account for changes in engagement patterns over time, which limits its applicability in monitoring-maintained user involvement. Finally, its subjective nature could emerge in the metrics when calculating it, thus potentially introducing biases.

Calculation of the New Engagement Index

We aimed to enhance EI and its components based on its original characteristics. As the click depth index failed to account for the number of menus available in the app, we introduced the MI, which was devised from the click depth index. The MI considers the different menus offered by the app. By computing the survival time of each menu, with discontinuation defined as continuous nonusage for 45 days, we constructed a vector for each patient, where each co-ordinate represents the survival time of a menu. We chose 45 days as it represents the 75th percentile value of the nonusage period between usages. Subsequently, we computed the Euclidean distance from the origin for each vector. Consequently, menu abundancy is determined as the Euclidean distance between the patient and the patient with the minimum Euclidean distance, divided by the Euclidean distance between the largest and smallest vectors. The diagram and equation used for these calculations are presented in Figure 1 .

To enrich the existing LI, which solely considers the number of accessed weeks, we deemed it crucial to incorporate the number of the final weeks used. However, we aimed to prevent this addition from disproportionately influencing the overall index. Therefore, we formulated the LI as the sum of the number of accessed weeks and the natural logarithm of the final usage week number, divided by the total number of weeks used in the study. Due to some resulting values exceeding 1, we applied a simple linear transformation to all values. This involved dividing each value by the maximum value of the LI observed in the study, thus ensuring that the new LI ranged from 0 to 1.

The RI calculates the regularity of app usage. To quantify this, we used permutation entropy (PE), which is a robust time series tool. PE quantifies the complexity of a dynamic system by capturing order relations within a time series and deriving a probability distribution of ordinal patterns [ 24 ]. To ensure that PE falls within the range of 0-1, we applied a truncated normal distribution to the values. As lower PE values indicate higher regularity and vice versa, we subtracted the value from 1 to align with the existing RI, where a higher value corresponds to more regular visits. This adjustment maintains consistency with the mathematical representation of the RI.

Assumptions

We measured the effectiveness of the newly developed EI through multiple linear regression and survival analysis. These statistical methods were selected to provide deep insights into the interpretability and statistical significance of predictors.

Multiple linear regression reveals the linear relationships between the dependent variable (ie, EI) and the multiple independent variables. Considering these multiple independent variables as predictors could provide a better understanding of the multidimensional nature of usage engagement influenced by factors. Furthermore, this approach facilitates the identification of the most important drivers of long-term engagement, thereby contributing to the development of a more reliable and tailored EI capable of capturing the nuances of patient behavior and adherence patterns. The multiple linear regression assumptions were evaluated for our model. Linearity between the variables was assessed, with values indicating strong correlations: menu abundancy (0.93), LI (0.93), and RI (0.69), all close to 1. The normality of the residuals was supported by Shapiro-Wilk test results: menu abundancy (0.84), LI (0.83), RI (0.85), and new EI (0.90). However, the independence of residuals, as measured by the Durbin-Watson statistic, showed values of 1.02 (menu abundancy), 1.10 (LI), 0.95 (RI), and 0.88 (new EI2), indicating potential autocorrelation. Given that these indices are derived from the same app usage data, achieving complete independence is inherently challenging.

Multiple linear regression can be expressed in a generalized form, that is, formula 1

( EI m:m + 3 = β 0 + β 1 ⋅ MI m:m + 3 + β 2 ⋅ LI m:m + 3 + β 3 ⋅ RI m:m + 3 + ε ) (1)

where the dependent variable is defined as formula 2

( EI m:m + 3 := ( MI m:m + 3 + LI m:m + 3 + RI m:m + 3 )/3)  (2)

Moreover, β 0 denotes the intercept of the model; β 1 to β 3 , the coefficients for the predictors of the MI, LI, and RI, respectively, and ε , the error term, which accounts for the variance in the prediction that cannot be explained by the predictors. For regression models, we set 2-month durations, that is, 3-6 and 6-9 months; hence, the values of m for each model are 3 and 6, respectively.

Survival analysis, particularly through Cox regression, shows the discontinuation timing of app usage over a specified period. This method accounts for censored data and provides hazard ratios (HRs), quantifying the effect of different predictors on the likelihood of continued app usage. Thus, this approach helps identify which aspect of patient usage pattern is the most predictive and significant for long-term compliance.

( h ( t ) = h 0 ( t ) exp ( β 1 ⋅ MI 3:6 + β 2 ⋅ LI 3:6 + β 3 ⋅ RI 3:6 )) (3)

where h ( t ) denotes the hazard function at time t for predicting the survival days of each app user and h 0 ( t ) denotes the baseline hazard function, which is the hazard for an individual when all the covariates are 0.

Ethical Consideration

This study was approved by the Institutional Review Board of Asan Medical Center, Korea (Institutional Review Board 2021-1631). Stringent measures were in place to protect the privacy and confidentiality of study data, including secure storage within the hospital premises.

Demographic Traits

The demographic characteristics of the study cohort are presented in Table 1 . The average age of the patients was 53.55 years, with women constituting approximately two-thirds of the cohort. Each group of cancer type comprised a comparable number of participants, albeit colorectal cancer cases slightly outnumbered the others. Over half of the patients were diagnosed with stage 1, and approximately two-thirds had not undergone chemotherapy. The average BMI of the patients was 23.96 (SD 3.28) kg/m 2 .

Evaluation Between the Existing Engagement Index and New Engagement Index

Predicting 6-9 months engagement index based on the 3-6 months engagement index.

To predict the EI of patients with cancer between 6 and 9 months based on their EI between 3 and 6 months, we excluded the initial 0- to 3-month period as the patients were actively under hospital surveillance with ongoing follow-ups. For the existing EI, we observed a mean squared error (MSE) of 0.096, root mean squared error (RMSE) of 0.310, and R 2 of 0.053. For the new EI, we conducted 3 multiple linear regressions to identify the most significant menu combinations. The first combination (new EI1), comprising meal log, exercise log, message sent to the app, reading content, and weight log, exhibited an MSE of 0.036, RMSE of 0.190, and R 2 of 0.511. The second combination (new EI2), involving meal log, exercise log, weight log, and step count login, showed improved performance with an MSE of 0.025, RMSE of 0.157, and R 2 of 0.610. The third combination (new EI3), encompassing meal log, exercise log, message sent to the app, reading content, weight log, and step count login, yielded an MSE of 0.042, RMSE of 0.205, and R 2 of 0.374. The values of the multiple linear regression are presented in Table 2 .

a MSE: mean squared error.

b RMSE: root mean squared error.

c EI: engagement index.

Predicting Survival Rate From 3 to 6 Months

When predicting app usage survival using the individual index of the EI from 3 to 6 months through Cox regression, the existing EI exhibited a log rank test result of P <.05. The results indicated a significant association between click depth and loyalty indices, while the RI showed no significance. The click depth index exhibited an HR of 0.49 with a P value <.001, which indicates that a higher click depth index is significantly associated with the reduced hazard, thus yielding better outcomes. Similarly, the LI showed an HR of 0.17 and a P value <.001, demonstrating a strong and significant association with reduced hazard. Conversely, the RI showed an HR of 1.30 with a P value of .41, indicating no significant association. All the log rank test results were statistically significant. The values of the existing EI are presented in Table 3 .

a HR: hazard ratio.

For the new EI, we conducted 3 Cox regressions based on the three devised menu combinations. MI 1 incorporates the menus intended for active app users, encompassing those necessitating self-logging. It specifically encompasses meal log, exercise log, messages sent to the app, reading content, and weight log. MI 2 comprises menus available in the app’s free version. It consists of a meal log, exercise log, weight log, and step count login. MI 3 includes all available menus, such as meal log, exercise log, message sent to the app, reading content, weight log, and step count login. Hence, 3 new EIs were created (new EI1, new EI2, and new EI3), which includes each MI (MI1, MI2, and MI3).

New EI1 exhibited no significant association with the MI (HR 0.92; P =.81). However, it showed a strong and significant association with the LI (HR 0.28; P <.001). Furthermore, it showed a significant association with the RI (HR 0.48). Meanwhile, new EI2 exhibited a similar trend to new EI1, showing no significant association with the MI (HR 0.79; P =.50). However, it showed a strong and significant association with the LI (HR 0.3). Moreover, it exhibited a significant association with the RI (HR 0.47). Finally, new EI3 showed no significant association with the MI (HR 0.95; P =.82). However, it showed a significant and strong association with the LI (HR 0.26; P <.001), but it did not exhibit a significant association with the RI (HR 0.74; P =.23).

The MI did not exhibit a significant association with any of the new indices, whereas the LI showed a strong and significant association with all 3 indices. The RI was significantly associated with new EI1 and new EI2 but not with new EI3. All the log rank test results were significant for all the new indices. The values of the new EI are presented in Table 4 .

Principal Findings

We evaluated the existing EI in a commercial health management app for long-term use and compared it with the new EI. We evaluated the new EI by first predicting the EI of the 6- to 9-month period based on the EI of the 3- to 6-month period through multiple linear regression and by predicting the survival rate using the EI of the 3- to 6-month period. In both predictions, the new EI exhibited better performance than the existing EI, although the difference was marginal. Moreover, when the RI, the index that best represents the long-term use, was applied in the new EI, a statistically significant difference increased compared with the RI in the existing EI.

Comparison With Previous Work

Retention has been inconsistently measured across studies in the aspect of mHealth. For instance, a previous study [ 25 ] defined retention as continuous use of the app for 6 months after the first use, specifically between 150 and 210 days. Another study measured retention based solely on the number of logs [ 26 ]. In addition, 1 study [ 27 ] measured retention through follow-up interviews conducted 6 months post intervention. These variations highlight the lack of a standardized retention strategy in mHealth research, posing a significant limitation as results may hinge on a single participant’s interview response rather than reflecting overall trends and maintained use.

While the use of mHealth has the potential to enhance adherence to chronic disease management, research predominantly focuses on the assessment of the usability, feasibility, and acceptability of such apps rather than the direct measurement of adherence [ 28 ]. Similarly, studies addressing patient engagement in mHealth interventions in heart failure cases are often underreported and lacking consistency [ 29 ]. Moreover, a pressing need to evaluate user engagement in smartphone apps targeting other significant risk factors for cardiovascular disease, such as dietary behaviors, has been emphasized. Yang et al [ 30 ] identified 3 key issues concerning the measurement of adherence in mHealth programs. These include challenges in defining and measuring adherence, a tendency for adherence measurements to be grounded in empirical evidence or established theory, and the recognition that adherence is a multifaceted concept, thus requiring a comprehensive assessment rather than reliance on a 1-dimensional approach [ 30 ].

Although existing methodologies for measuring adherence to mHealth are limited, fewer measures of adherence with numerical results. Therefore, measurement using the EI has been considered a methodology that could be generally used and numerically measured. Taki et al [ 31 ] conducted a study that used the EI to measure engagement in the mHealth app. They used the click depth, loyalty, interaction, recency, and feedback indices and categorized the results into 3 groups to observe changes in the EI over time. However, they noted that some features were not measured by the EI, which may result in the underestimation of engagement of the participants. Similarly, White et al [ 32 ] used the EI to examine the demographic differences among 3 groups formed by the EI and used the reading, loyalty, interaction, recency, and feedback indices. However, they were unable to detect an association between the level of engagement and the duration of exclusive breastfeeding, which was possibly due to the limitations of the EI. Furthermore, Schepens Niemiec et al [ 33 ] used the loyalty, interaction, usability, and sentiment feedback indices with semistructured interviews to measure app engagement. They acknowledged that as only 4 indices were used, the statistical norm could not be determined to validate the evaluation of the mHealth apps. Despite its applicability to various programs offered by mHealth apps, EI exhibited similar limitations in each study, thereby raising uncertainties regarding its implications. However, despite the thorough investigation, with its simple characteristics, EI can effectively measure engagement in mHealth apps.

Reliance on postintervention surveys or interviews was common in other previous studies evaluating DTx engagement [ 10 - 14 ]. Alternatively, engagement with DTx was occasionally assessed simplistically, such as by marking the first date of a 28-day period without any data upload or by calculating the percentage of participants who completed follow-up at 8 weeks [ 34 , 35 ]. A review of various literature revealed that a more objective measure was evidently needed to evaluate patient engagement in DTx. Although valuable, manual interviews are difficult to replicate and are time consuming due to their labor-intensive nature, involving multiple coordinators. Therefore, the proposal and evaluation of an EI for DTx could enhance the quality of research in this field.

Limitations

This study represents the inaugural attempt to evaluate the existing EI. While the effectiveness of the index has not yet been evaluated, we have established its reliability despite the comprehensive evaluation for potential upgrades. Furthermore, we were able to demonstrate the importance of log data from a research viewpoint as well as its objectivity, reproducibility, and potential for use to evaluate adherence to mHealth.

EI has a subjective nature in the metrics that may potentially introduce biases, which cannot be overcome despite the update of the index. Furthermore, although the existing EI comprises 7 indices, this evaluation focused only on 3 indices due to the specific characteristics of the app under scrutiny. Also, while the results may indicate that the newly developed EI outperforms the existing EI, the calculation of the existing EI may be simpler than the newly developed EI. However, we believe that this approach is more effective in predicting and representing long-term use.

Conclusions

This study evaluated the new EI within the commercial health management app by comparing it with the existing EI. Despite thorough evaluation using 2 approaches (forecasting the EI of the 6- to 9-month period based on the EI of the 3- to 6-month period through multiple linear regression and predicting survival rates based on the EI of the 3- to 6-month period), the new EI exhibited a slightly superior performance to the existing EI in both approaches. Although the existing EI appeared too simplistic for evaluating mHealth app adherence, we were able to demonstrate that it effectively reflected adherence without the need for complex calculations, similar to the new EI.

Acknowledgments

This research was supported by a grant from the Korea Health Technology R and D Project through the Korea Health Industry Development Institute and funded by the Ministry of Health & Welfare, Republic of Korea (grant HI20C1058).

Data Availability

The data sets generated or analyzed during this study are not publicly available due to the need for institutional review board approval and privacy considerations but are available from the corresponding author upon reasonable request.

Authors' Contributions

All authors contributed to the conceptualization and reviewing and editing. YWT, JK, and YL handled the formal analysis. YWT, JK, and YL contributed to writing the original draft. JWL managed the resources.

Conflicts of Interest

None declared.

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Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 12.04.24; peer-reviewed by A Wani, D Ghosh; comments to author 09.05.24; revised version received 02.07.24; accepted 30.07.24; published 09.09.24.

©Yae Won Tak, Jong Won Lee, Junetae Kim, Yura Lee. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.09.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.