comparative research data analysis

What is comparative analysis? A complete guide

Last updated

18 April 2023

Reviewed by

Jean Kaluza

Short on time? Get an AI generated summary of this article instead

Comparative analysis is a valuable tool for acquiring deep insights into your organization’s processes, products, and services so you can continuously improve them. 

Similarly, if you want to streamline, price appropriately, and ultimately be a market leader, you’ll likely need to draw on comparative analyses quite often.

When faced with multiple options or solutions to a given problem, a thorough comparative analysis can help you compare and contrast your options and make a clear, informed decision.

If you want to get up to speed on conducting a comparative analysis or need a refresher, here’s your guide.

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• What exactly is comparative analysis?

A comparative analysis is a side-by-side comparison that systematically compares two or more things to pinpoint their similarities and differences. The focus of the investigation might be conceptual—a particular problem, idea, or theory—or perhaps something more tangible, like two different data sets.

For instance, you could use comparative analysis to investigate how your product features measure up to the competition.

After a successful comparative analysis, you should be able to identify strengths and weaknesses and clearly understand which product is more effective.

You could also use comparative analysis to examine different methods of producing that product and determine which way is most efficient and profitable.

The potential applications for using comparative analysis in everyday business are almost unlimited. That said, a comparative analysis is most commonly used to examine

Emerging trends and opportunities (new technologies, marketing)

Competitor strategies

Financial health

Effects of trends on a target audience

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• Why is comparative analysis so important? 

Comparative analysis can help narrow your focus so your business pursues the most meaningful opportunities rather than attempting dozens of improvements simultaneously.

A comparative approach also helps frame up data to illuminate interrelationships. For example, comparative research might reveal nuanced relationships or critical contexts behind specific processes or dependencies that wouldn’t be well-understood without the research.

For instance, if your business compares the cost of producing several existing products relative to which ones have historically sold well, that should provide helpful information once you’re ready to look at developing new products or features.

• Comparative vs. competitive analysis—what’s the difference?

Comparative analysis is generally divided into three subtypes, using quantitative or qualitative data and then extending the findings to a larger group. These include

Pattern analysis —identifying patterns or recurrences of trends and behavior across large data sets.

Data filtering —analyzing large data sets to extract an underlying subset of information. It may involve rearranging, excluding, and apportioning comparative data to fit different criteria. 

Decision tree —flowcharting to visually map and assess potential outcomes, costs, and consequences.

In contrast, competitive analysis is a type of comparative analysis in which you deeply research one or more of your industry competitors. In this case, you’re using qualitative research to explore what the competition is up to across one or more dimensions.

For example

Service delivery —metrics like the Net Promoter Scores indicate customer satisfaction levels.

Market position — the share of the market that the competition has captured.

Brand reputation —how well-known or recognized your competitors are within their target market.

• Tips for optimizing your comparative analysis

Conduct original research

Thorough, independent research is a significant asset when doing comparative analysis. It provides evidence to support your findings and may present a perspective or angle not considered previously. 

Make analysis routine

To get the maximum benefit from comparative research, make it a regular practice, and establish a cadence you can realistically stick to. Some business areas you could plan to analyze regularly include:

Profitability

Competition

Experiment with controlled and uncontrolled variables

In addition to simply comparing and contrasting, explore how different variables might affect your outcomes.

For example, a controllable variable would be offering a seasonal feature like a shopping bot to assist in holiday shopping or raising or lowering the selling price of a product.

Uncontrollable variables include weather, changing regulations, the current political climate, or global pandemics.

Put equal effort into each point of comparison

Most people enter into comparative research with a particular idea or hypothesis already in mind to validate. For instance, you might try to prove the worthwhileness of launching a new service. So, you may be disappointed if your analysis results don’t support your plan.

However, in any comparative analysis, try to maintain an unbiased approach by spending equal time debating the merits and drawbacks of any decision. Ultimately, this will be a practical, more long-term sustainable approach for your business than focusing only on the evidence that favors pursuing your argument or strategy.

Writing a comparative analysis in five steps

To put together a coherent, insightful analysis that goes beyond a list of pros and cons or similarities and differences, try organizing the information into these five components:

1. Frame of reference

Here is where you provide context. First, what driving idea or problem is your research anchored in? Then, for added substance, cite existing research or insights from a subject matter expert, such as a thought leader in marketing, startup growth, or investment

2. Grounds for comparison Why have you chosen to examine the two things you’re analyzing instead of focusing on two entirely different things? What are you hoping to accomplish?

3. Thesis What argument or choice are you advocating for? What will be the before and after effects of going with either decision? What do you anticipate happening with and without this approach?

For example, “If we release an AI feature for our shopping cart, we will have an edge over the rest of the market before the holiday season.” The finished comparative analysis will weigh all the pros and cons of choosing to build the new expensive AI feature including variables like how “intelligent” it will be, what it “pushes” customers to use, how much it takes off the plates of customer service etc.

Ultimately, you will gauge whether building an AI feature is the right plan for your e-commerce shop.

4. Organize the scheme Typically, there are two ways to organize a comparative analysis report. First, you can discuss everything about comparison point “A” and then go into everything about aspect “B.” Or, you alternate back and forth between points “A” and “B,” sometimes referred to as point-by-point analysis.

Using the AI feature as an example again, you could cover all the pros and cons of building the AI feature, then discuss the benefits and drawbacks of building and maintaining the feature. Or you could compare and contrast each aspect of the AI feature, one at a time. For example, a side-by-side comparison of the AI feature to shopping without it, then proceeding to another point of differentiation.

5. Connect the dots Tie it all together in a way that either confirms or disproves your hypothesis.

For instance, “Building the AI bot would allow our customer service team to save 12% on returns in Q3 while offering optimizations and savings in future strategies. However, it would also increase the product development budget by 43% in both Q1 and Q2. Our budget for product development won’t increase again until series 3 of funding is reached, so despite its potential, we will hold off building the bot until funding is secured and more opportunities and benefits can be proved effective.”

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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 Analysis: What It Is & How to Conduct It

When a business wants to start a marketing campaign or grow, a comparative analysis can give them information that helps them make crucial decisions. This analysis gathers different data sets to compare different options so a business can make good decisions for its customers and itself. If you or your business want to make good decisions, learning about comparative analyses could be helpful. 

In this article, we’ll explain the comparative analysis and its importance. We’ll also learn how to do a good in-depth analysis .

What is comparative analysis?

Comparative analysis is a way to look at two or more similar things to see how they are different and what they have in common. 

It is used in many ways and fields to help people understand the similarities and differences between products better. It can help businesses make good decisions about key issues.

One meaningful way it’s used is when applied to scientific data. Scientific data is information that has been gathered through scientific research and will be used for a certain purpose.

When it is used on scientific data, it determines how consistent and reliable the data is. It also helps scientists make sure their data is accurate and valid.

Importance of comparative analysis 

Comparative analyses are important if you want to understand a problem better or find answers to important questions. Here are the main goals businesses want to reach through comparative analysis.

• It is a part of the diagnostic phase of business analytics. It can answer many of the most important questions a company may have and help you figure out how to fix problems at the company’s core to improve performance and even make more money.
• It encourages a deep understanding of the opportunities that apply to specific processes, departments, or business units. This analysis also ensures that we’re addressing the real reasons for performance gaps.
• It is used a lot because it helps people understand the challenges an organization has faced in the past and the ones it faces now. This method gives objective, fact-based information about performance and ways to improve it.

How to successfully conduct it

Consider using the advice below to carry out a successful comparative analysis:

Conduct research

Before doing an analysis, it’s important to do a lot of research . Research not only gives you evidence to back up your conclusions, but it might also show you something you hadn’t thought of before.

Research could also tell you how your competitors might handle a problem.

Make a list of what’s different and what’s the same.

When comparing two things in a comparative analysis, you need to make a detailed list of the similarities and differences.

Try to figure out how a change to one thing might affect another. Such as how increasing the number of vacation days affects sales, production, or costs. 

A comparative analysis can also help you find outside causes, such as economic conditions or environmental analysis problems.

Describe both sides

Comparative analysis may try to show that one argument or idea is better, but the analysis must cover both sides equally. The analysis shows both sides of the main arguments and claims. 

For example, to compare the benefits and drawbacks of starting a recycling program, one might examine both the positive effects, such as corporate responsibility and the potential negative effects, such as high implementation costs, to make wise, practical decisions or come up with alternate solutions.

Include variables

A thorough comparison unit of analysis is usually more than just a list of pros and cons because it usually considers factors that affect both sides.

Variables can be both things that can’t be changed, like how the weather in the summer affects shipping speeds, and things that can be changed, like when to work with a local shipper.

Do analyses regularly

Comparative analyses are important for any business practice. Consider the different areas and factors that a comparative analysis looks at:

• Competitors
• How well do stocks
• Financial position
• Profitability
• Dividends and revenue
• Development and research

Because a comparative analysis can help more than one department in a company, doing them often can help you keep up with market changes and stay relevant.

We’ve talked about how good a comparative analysis is for your business. But things always have two sides. It is a good workaround, but still do your own user interviews or user tests if you can. 

We hope you have fun doing comparative analyses! Comparative analysis is always a method you like to use, and the point of learning from competitors is to add your own ideas. In this way, you are not just following but also learning and making.

QuestionPro can help you with your analysis process, create and design a survey to meet your goals, and analyze data for your business’s comparative analysis.

At QuestionPro, we give researchers tools for collecting data, like our survey software and a library of insights for all kinds of l ong-term research . If you want to book a demo or learn more about our platform, just click here.

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• v.4(2); 2014 Jun

Using qualitative comparative analysis to understand and quantify translation and implementation

Heather kane.

RTI International, 3040 Cornwallis Road, Research Triangle Park, P.O. Box 12194, Durham, NC 27709 USA

Megan A Lewis

Pamela a williams, leila c kahwati.

Understanding the factors that facilitate implementation of behavioral medicine programs into practice can advance translational science. Often, translation or implementation studies use case study methods with small sample sizes. Methodological approaches that systematize findings from these types of studies are needed to improve rigor and advance the field. Qualitative comparative analysis (QCA) is a method and analytical approach that can advance implementation science. QCA offers an approach for rigorously conducting translational and implementation research limited by a small number of cases. We describe the methodological and analytic approach for using QCA and provide examples of its use in the health and health services literature. QCA brings together qualitative or quantitative data derived from cases to identify necessary and sufficient conditions for an outcome. QCA offers advantages for researchers interested in analyzing complex programs and for practitioners interested in developing programs that achieve successful health outcomes.

INTRODUCTION

In this paper, we describe the methodological features and advantages of using qualitative comparative analysis (QCA). QCA is sometimes called a “mixed method.” It refers to both a specific research approach and an analytic technique that is distinct from and offers several advantages over traditional qualitative and quantitative methods [ 1 – 4 ]. It can be used to (1) analyze small to medium numbers of cases (e.g., 10 to 50) when traditional statistical methods are not possible, (2) examine complex combinations of explanatory factors associated with translation or implementation “success,” and (3) combine qualitative and quantitative data using a unified and systematic analytic approach.

This method may be especially pertinent for behavioral medicine given the growing interest in implementation science [ 5 ]. Translating behavioral medicine research and interventions into useful practice and policy requires an understanding of the implementation context. Understanding the context under which interventions work and how different ways of implementing an intervention lead to successful outcomes are required for “T3” (i.e., dissemination and implementation of evidence-based interventions) and “T4” translations (i.e., policy development to encourage evidence-based intervention use among various stakeholders) [ 6 , 7 ].

Case studies are a common way to assess different program implementation approaches and to examine complex systems (e.g., health care delivery systems, interventions in community settings) [ 8 ]. However, multiple case studies often have small, naturally limited samples or populations; small samples and populations lack adequate power to support conventional, statistical analyses. Case studies also may use mixed-method approaches, but typically when researchers collect quantitative and qualitative data in tandem, they rarely integrate both types of data systematically in the analysis. QCA offers solutions for the challenges posed by case studies and provides a useful analytic tool for translating research into policy recommendations. Using QCA methods could aid behavioral medicine researchers who seek to translate research from randomized controlled trials into practice settings to understand implementation. In this paper, we describe the conceptual basis of QCA, its application in the health and health services literature, and its features and limitations.

CONCEPTUAL BASIS OF QCA

QCA has its foundations in historical, comparative social science. Researchers in this field developed QCA because probabilistic methods failed to capture the complexity of social phenomena and required large sample sizes [ 1 ]. Recently, this method has made inroads into health research and evaluation [ 9 – 13 ] because of several useful features as follows: (1) it models equifinality , which is the ability to identify more than one causal pathway to an outcome (or absence of the outcome); (2) it identifies conjunctural causation , which means that single conditions may not display their effects on their own, but only in conjunction with other conditions; and (3) it implies asymmetrical relationships between causal conditions and outcomes, which means that causal pathways for achieving the outcome differ from causal pathways for failing to achieve the outcome.

QCA is a case-oriented approach that examines relationships between conditions (similar to explanatory variables in regression models) and an outcome using set theory; a branch of mathematics or of symbolic logic that deals with the nature and relations of sets. A set-theoretic approach to modeling causality differs from probabilistic methods, which examines the independent, additive influence of variables on an outcome. Regression models, based on underlying assumptions about sampling and distribution of the data, ask “what factor, holding all other factors constant at each factor’s average, will increase (or decrease) the likelihood of an outcome .” QCA, an approach based on the examination of set, subset, and superset relationships, asks “ what conditions —alone or in combination with other conditions—are necessary or sufficient to produce an outcome .” For additional QCA definitions, see Ragin [ 4 ].

Necessary conditions are those that exhibit a superset relationship with the outcome set and are conditions or combinations of conditions that must be present for an outcome to occur. In assessing necessity, a researcher “identifies conditions shared by cases with the same outcome” [ 4 ] (p. 20). Figure  1 shows a hypothetical example. In this figure, condition X is a necessary condition for an effective intervention because all cases with condition X are also members of the set of cases with the outcome present; however, condition X is not sufficient for an effective intervention because it is possible to be a member of the set of cases with condition X, but not be a member of the outcome set [ 14 ].

Necessary and sufficient conditions and set-theoretic relationships

Sufficient conditions exhibit subset relationships with an outcome set and demonstrate that “the cause in question produces the outcome in question” [ 3 ] (p. 92). Figure  1 shows the multiple and different combinations of conditions that produce the hypothetical outcome, “effective intervention,” (1) by having condition A present, (2) by having condition D present, or (3) by having the combination of conditions B and C present. None of these conditions is necessary and any one of these conditions or combinations of conditions is sufficient for the outcome of an effective intervention.

QCA AS AN APPROACH AND AS AN ANALYTIC TECHNIQUE

The term “QCA” is sometimes used to refer to the comparative research approach but also refers to the “analytic moment” during which Boolean algebra and set theory logic is applied to truth tables constructed from data derived from included cases. Figure  2 characterizes this distinction. Although this figure depicts steps as sequential, like many research endeavors, these steps are somewhat iterative, with respecification and reanalysis occurring along the way to final findings. We describe each of the essential steps of QCA as an approach and analytic technique and provide examples of how it has been used in health-related research.

QCA as an approach and as an analytic technique

Operationalizing the research question

Like other types of studies, the first step involves identifying the research question(s) and developing a conceptual model. This step guides the study as a whole and also informs case, condition (c.f., variable), and outcome selection. As mentioned above, QCA frames research questions differently than traditional quantitative or qualitative methods. Research questions appropriate for a QCA approach would seek to identify the necessary and sufficient conditions required to achieve the outcome. Thus, formulating a QCA research question emphasizes what program components or features—individually or in combination—need to be in place for a program or intervention to have a chance at being effective (i.e., necessary conditions) and what program components or features—individually or in combination—would produce the outcome (i.e., sufficient conditions). For example, a set theoretic hypothesis would be as follows: If a program is supported by strong organizational capacity and a comprehensive planning process, then the program will be successful. A hypothesis better addressed by probabilistic methods would be as follows: Organizational capacity, holding all other factors constant, increases the likelihood that a program will be successful.

For example, Longest and Thoits [ 15 ] drew on an extant stress process model to assess whether the pathways leading to psychological distress differed for women and men. Using QCA was appropriate for their study because the stress process model “suggests that particular patterns of predictors experienced in tandem may have unique relationships with health outcomes” (p. 4, italics added). They theorized that predictors would exhibit effects in combination because some aspects of the stress process model would buffer the risk of distress (e.g., social support) while others simultaneously would increase the risk (e.g., negative life events).

Identify cases

The number of cases in a QCA analysis may be determined by the population (e.g., 10 intervention sites, 30 grantees). When particular cases can be chosen from a larger population, Berg-Schlosser and De Meur [ 16 ] offer other strategies and best practices for choosing cases. Unless the number of cases relies on an existing population (i.e., 30 programs or grantees), the outcome of interest and existing theory drive case selection, unlike variable-oriented research [ 3 , 4 ] in which numbers are driven by statistical power considerations and depend on variation in the dependent variable. For use in causal inference, both cases that exhibit and do not exhibit the outcome should be included [ 16 ]. If a researcher is interested in developing typologies or concept formation, he or she may wish to examine similar cases that exhibit differences on the outcome or to explore cases that exhibit the same outcome [ 14 , 16 ].

For example, Kahwati et al. [ 9 ] examined the structure, policies, and processes that might lead to an effective clinical weight management program in a large national integrated health care system, as measured by mean weight loss among patients treated at the facility. To examine pathways that lead to both better and poorer facility-level weight loss, 11 facilities from among those with the largest weight loss outcomes and 11 facilities from among those with the smallest were included. By choosing cases based on specific outcomes, Kahwati et al. could identify multiple patterns of success (or failure) that explain the outcome rather than the variability associated with the outcome.

Identify conditions and outcome sets

Selecting conditions relies on the research question, conceptual model, and number of cases similar to other research methods. Conditions (or “sets” or “condition sets”) refer to the explanatory factors in a model; they are similar to variables. Because QCA research questions assess necessary and sufficient conditions, a researcher should consider which conditions in the conceptual model would theoretically produce the outcome individually or in combination. This helps to focus the analysis and number of conditions. Ideally, for a case study design with a small (e.g., 10–15) or intermediate (e.g., 16–100) number of cases, one should aim for fewer than five conditions because in QCA a researcher assesses all possible configurations of conditions. Adding conditions to the model increases the possible number of combinations exponentially (i.e., 2 k , where k = the number of conditions). For three conditions, eight possible combinations of the selected conditions exist as follows: the presence of A, B, C together, the lack of A with B and C present, the lack of A and lack of B with C present, and so forth. Having too many conditions will likely mean that no cases fall into a particular configuration, and that configuration cannot be assessed by empirical examples. When one or more configurations are not represented by the cases, this is known as limited diversity, and QCA experts suggest multiple strategies for managing such situations [ 4 , 14 ].

For example, Ford et al. [ 10 ] studied health departments’ implementation of core public health functions and organizational factors (e.g., resource availability, adaptability) and how those conditions lead to superior and inferior population health changes. They operationalized three core public functions (i.e., assessment of environmental and population public health needs, capacity for policy development, and authority over assurance of healthcare operations) and operationalized those for their study by using composite measures of varied health indicators compiled in a UnitedHealth Group report. In this examination of 41 state health departments, the authors found that all three core public health functions were necessary for population health improvement. The absence of any of the core public health functions was sufficient for poorer population health outcomes; thus, only the health departments with the ability to perform all three core functions had improved outcomes. Additionally, these three core functions in combination with either resource availability or adaptability were sufficient combinations (i.e., causal pathways) for improved population health outcomes.

Calibrate condition and outcome sets

Calibration refers to “adjusting (measures) so that they match or conform to dependably known standards” and is a common way of standardizing data in the physical sciences [ 4 ] (p. 72). Calibration requires the researcher to make sense of variation in the data and apply expert knowledge about what aspects of the variation are meaningful. Because calibration depends on defining conditions based on those “dependably known standards,” QCA relies on expert substantive knowledge, theory, or criteria external to the data themselves [ 14 ]. This may require researchers to collaborate closely with program implementers.

In QCA, one can use “crisp” set or “fuzzy” set calibration. Crisp sets, which are similar to dichotomous categorical variables in regression, establish decision rules defining a case as fully in the set (i.e., condition) or fully out of the set; fuzzy sets establish degrees of membership in a set. Fuzzy sets “differentiate between different levels of belonging anchored by two extreme membership scores at 1 and 0” [ 14 ] (p.28). They can be continuous (0, 0.1, 0.2,..) or have qualitatively defined anchor points (e.g., 0 is fully out of the set; 0.33 is more out than in the set; 0.66 is more in than out of the set; 1 is fully in the set). A researcher selects fuzzy sets and the corresponding resolution (i.e., continuous, four cutoff points, six cutoff) based on theory and meaningful differences between cases and must be able to provide a verbal description for each cutoff point [ 14 ]. If, for example, a researcher cannot distinguish between 0.7 and 0.8 membership in a set, then a more continuous scoring of cases would not be useful, rather a four point cutoff may better characterize the data. Although crisp and fuzzy sets are more commonly used, new multivariate forms of QCA are emerging as are variants that incorporate elements of time [ 14 , 17 , 18 ].

Fuzzy sets have the advantage of maintaining more detail for data with continuous values. However, this strength also makes interpretation more difficult. When an observation is coded with fuzzy sets, a particular observation has some degree of membership in the set “condition A” and in the set “condition NOT A.” Thus, when doing analyses to identify sufficient conditions, a researcher must make a judgment call on what benchmark constitutes recommendation threshold for policy or programmatic action.

In creating decision rules for calibration, a researcher can use a variety of techniques to identify cutoff points or anchors. For qualitative conditions, a researcher can define decision rules by drawing from the literature and knowledge of the intervention context. For conditions with numeric values, a researcher can also employ statistical approaches. Ideally, when using statistical approaches, a researcher should establish thresholds using substantive knowledge about set membership (thus, translating variation into meaningful categories). Although measures of central tendency (e.g., cases with a value above the median are considered fully in the set) can be used to set cutoff points, some experts consider the sole use of this method to be flawed because case classification is determined by a case’s relative value in regard to other cases as opposed to its absolute value in reference to an external referent [ 14 ].

For example, in their study of National Cancer Institutes’ Community Clinical Oncology Program (NCI CCOP), Weiner et al. [ 19 ] had numeric data on their five study measures. They transformed their study measures by using their knowledge of the CCOP and by asking NCI officials to identify three values: full membership in a set, a point of maximum ambiguity, and nonmembership in the set. For their outcome set, high accrual in clinical trials, they established 100 patients enrolled accrual as fully in the set of high accrual, 70 as a point of ambiguity (neither in nor out of the set), and 50 and below as fully out of the set because “CCOPs must maintain a minimum of 50 patients to maintain CCOP funding” (p. 288). By using QCA and operationalizing condition sets in this way, they were able to answer what condition sets produce high accrual, not what factors predict more accrual. The advantage is that by using this approach and analytic technique, they were able to identify sets of factors that are linked with a very specific outcome of interest.

Obtain primary or secondary data

Data sources vary based on the study, availability of the data, and feasibility of data collection; data can be qualitative or quantitative, a feature useful for mixed-methods studies and systematically integrating these different types of data is a major strength of this approach. Qualitative data include program documents and descriptions, key informant interviews, and archival data (e.g., program documents, records, policies); quantitative data consists of surveys, surveillance or registry data, and electronic health records.

For instance, Schensul et al. [ 20 ] relied on in-depth interviews for their analysis; Chuang et al. [ 21 ] and Longest and Thoits [ 15 ] drew on survey data for theirs. Kahwati et al. [ 9 ] used a mixed-method approach combining data from key informant interviews, program documents, and electronic health records. Any type of data can be used to inform the calibration of conditions.

Assign set membership scores

Assigning set membership scores involves applying the decision rules that were established during the calibration phase. To accomplish this, the research team should then use the extracted data for each case, apply the decision rule for the condition, and discuss discrepancies in the data sources. In their study of factors that influence health care policy development in Florida, Harkreader and Imershein [ 22 ] coded contextual factors that supported state involvement in the health care market. Drawing on a review of archival data and using crisp set coding, they assigned a value of 1 for the presence of a contextual factor (e.g., presence of federal financial incentives promoting policy, unified health care provider policy position in opposition to state policy, state agency supporting policy position) and 0 for the absence of a contextual factor.

Construct truth table

After completing the coding, researchers create a “truth table” for analysis. A truth table lists all of the possible configurations of conditions, the number of cases that fall into that configuration, and the “consistency” of the cases. Consistency quantifies the extent to which cases that share similar conditions exhibit the same outcome; in crisp sets, the consistency value is the proportion of cases that exhibit the outcome. Fuzzy sets require a different calculation to establish consistency and are described at length in other sources [ 1 – 4 , 14 ]. Table  1 displays a hypothetical truth table for three conditions using crisp sets.

Sample of a hypothetical truth table for crisp sets

1 fully in the set, 0 fully out of the set

QCA AS AN ANALYTIC TECHNIQUE

The research steps to this point fall into QCA as an approach to understanding social and health phenomena. Analysis of the truth table is the sine qua non of QCA as an analytic technique. In this section, we provide an overview of the analysis process, but analytic techniques and emerging forms of analysis are described in multiple texts [ 3 , 4 , 14 , 17 ]. The use of computer software to conduct truth table analysis is recommended and several software options are available including Stata, fsQCA, Tosmana, and R.

A truth table analysis first involves the researcher assessing which (if any) conditions are individually necessary or sufficient for achieving the outcome, and then second, examining whether any configurations of conditions are necessary or sufficient. In instances where contradictions in outcomes from the same configuration pattern occur (i.e., one case from a configuration has the outcome; one does not), the researcher should also consider whether the model is properly specified and conditions are calibrated accurately. Thus, this stage of the analysis may reveal the need to review how conditions are defined and whether the definition should be recalibrated. Similar to qualitative and quantitative research approaches, analysis is iterative.

Additionally, the researcher examines the truth table to assess whether all logically possible configurations have empiric cases. As described above, when configurations lack cases, the problem of limited diversity occurs. Configurations without representative cases are known as logical remainders, and the researcher must consider how to deal with those. The analysis of logical remainders depends on the particular theory guiding the research and the research priorities. How a researcher manages the logical remainders has implications for the final solution, but none of the solutions based on the truth table will contradict the empirical evidence [ 14 ]. To generate the most conservative solution term, a researcher makes no assumptions about truth table rows with no cases (or very few cases in larger N studies) and excludes them from the logical minimization process. Alternately, a researcher can choose to include (or exclude) rows with no cases from analysis, which would generate a solution that is a superset of the conservative solution. Choosing inclusion criteria for logical remainders also depends on theory and what may be empirically possible. For example, in studying governments, it would be unlikely to have a case that is a democracy (“condition A”), but has a dictator (“condition B”). In that circumstance, the researcher may choose to exclude that theoretically implausible row from the logical minimization process.

Third, once all the solutions have been identified, the researcher mathematically reduces the solution [ 1 , 14 ]. For example, if the list of solutions contains two identical configurations, except that in one configuration A is absent and in the other A is present, then A can be dropped from those two solutions. Finally, the researcher computes two parameters of fit: coverage and consistency. Coverage determines the empirical relevance of a solution and quantifies the variation in causal pathways to an outcome [ 14 ]. When coverage of a causal pathway is high, the more common the solution is, and more of the outcome is accounted for by the pathway. However, maximum coverage may be less critical in implementation research because understanding all of the pathways to success may be as helpful as understanding the most common pathway. Consistency assesses whether the causal pathway produces the outcome regularly (“the degree to which the empirical data are in line with a postulated subset relation,” p. 324 [ 14 ]); a high consistency value (e.g., 1.00 or 100 %) would indicate that all cases in a causal pathway produced the outcome. A low consistency value would suggest that a particular pathway was not successful in producing the outcome on a regular basis, and thus, for translational purposes, should not be recommended for policy or practice changes. A causal pathway with high consistency and coverage values indicates a result useful for providing guidance; a high consistency with a lower coverage score also has value in showing a causal pathway that successfully produced the outcome, but did so less frequently.

For example, Kahwati et al. [ 9 ] examined their truth table and analyzed the data for single conditions and combinations of conditions that were necessary for higher or lower facility-level patient weight loss outcomes. The truth table analysis revealed two necessary conditions and four sufficient combinations of conditions. Because of significant challenges with logical remainders, they used a bottom-up approach to assess whether combinations of conditions yielded the outcome. This entailed pairing conditions to ensure parsimony and maximize coverage. With a smaller number of conditions, a researcher could hypothetically find that more cases share similar characteristics and could assess whether those cases exhibit the same outcome of interest.

At the completion of the truth table analysis, Kahwati et al. [ 9 ] used the qualitative data from site interviews to provide rich examples to illustrate the QCA solutions that were identified, which explained what the solutions meant in clinical practice for weight management. For example, having an involved champion (usually a physician), in combination with low facility accountability, was sufficient for program success (i.e., better weight loss outcomes) and was related to better facility weight loss. In reviewing the qualitative data, Kahwati et al. [ 9 ] discovered that involved champions integrate program activities into their clinical routines and discuss issues as they arise with other program staff. Because involved champions and other program staff communicated informally on a regular basis, formal accountability structures were less of a priority.

ADVANTAGES AND LIMITATIONS OF QCA

Because translational (and other health-related) researchers may be interested in which intervention features—alone or in combination—achieve distinct outcomes (e.g., achievement of program outcomes, reduction in health disparities), QCA is well suited for translational research. To assess combinations of variables in regression, a researcher relies on interaction effects, which, although useful, become difficult to interpret when three, four, or more variables are combined. Furthermore, in regression and other variable-oriented approaches, independent variables are held constant at the average across the study population to isolate the independent effect of that variable, but this masks how factors may interact with each other in ways that impact the ultimate outcomes. In translational research, context matters and QCA treats each case holistically, allowing each case to keep its own values for each condition.

Multiple case studies or studies with the organization as the unit of analysis often involve a small or intermediate number of cases. This hinders the use of standard statistical analyses; researchers are less likely to find statistical significance with small sample sizes. However, QCA draws on analyses of set relations to support small-N studies and to identify the conditions or combinations of conditions that are necessary or sufficient for an outcome of interest and may yield results when probabilistic methods cannot.

Finally, QCA is based on an asymmetric concept of causation , which means that the absence of a sufficient condition associated with an outcome does not necessarily describe the causal pathway to the absence of the outcome [ 14 ]. These characteristics can be helpful for translational researchers who are trying to study or implement complex interventions, where more than one way to implement a program might be effective and where studying both effective and ineffective implementation practices can yield useful information.

QCA has several limitations that researchers should consider before choosing it as a potential methodological approach. With small- and intermediate-N studies, QCA must be theory-driven and circumscribed by priority questions. That is, a researcher ideally should not use a “kitchen sink” approach to test every conceivable condition or combination of conditions because the number of combinations increases exponentially with the addition of another condition. With a small number of cases and too many conditions, the sample would not have enough cases to provide examples of all the possible configurations of conditions (i.e., limited diversity), or the analysis would be constrained to describing the characteristics of the cases, which would have less value than determining whether some conditions or some combination of conditions led to actual program success. However, if the number of conditions cannot be reduced, alternate QCA techniques, such as a bottom-up approach to QCA or two-step QCA, can be used [ 14 ].

Another limitation is that programs or clinical interventions involved in a cross-site analysis may have unique programs that do not seem comparable. Cases must share some degree of comparability to use QCA [ 16 ]. Researchers can manage this challenge by taking a broader view of the program(s) and comparing them on broader characteristics or concepts, such as high/low organizational capacity, established partnerships, and program planning, if these would provide meaningful conclusions. Taking this approach will require careful definition of each of these concepts within the context of a particular initiative. Definitions may also need to be revised as the data are gathered and calibration begins.

Finally, as mentioned above, crisp set calibration dichotomizes conditions of interest; this form of calibration means that in some cases, the finer grained differences and precision in a condition may be lost [ 3 ]. Crisp set calibration provides more easily interpretable and actionable results and is appropriate if researchers are primarily interested in the presence or absence of a particular program feature or organizational characteristic to understand translation or implementation.

QCA offers an additional methodological approach for researchers to conduct rigorous comparative analyses while drawing on the rich, detailed data collected as part of a case study. However, as Rihoux, Benoit, and Ragin [ 17 ] note, QCA is not a miracle method, nor a panacea for all studies that use case study methods. Furthermore, it may not always be the most suitable approach for certain types of translational and implementation research. We outlined the multiple steps needed to conduct a comprehensive QCA. QCA is a good approach for the examination of causal complexity, and equifinality could be helpful to behavioral medicine researchers who seek to translate evidence-based interventions in real-world settings. In reality, multiple program models can lead to success, and this method accommodates a more complex and varied understanding of these patterns and factors.

Implications

Practice : Identifying multiple successful intervention models (equifinality) can aid in selecting a practice model relevant to a context, and can facilitate implementation.

Policy : QCA can be used to develop actionable policy information for decision makers that accommodates contextual factors.

Research : Researchers can use QCA to understand causal complexity in translational or implementation research and to assess the relationships between policies, interventions, or procedures and successful outcomes.

What is Qualitative Comparative Analysis (QCA)?

Introduction

A brief introduction to qualitative comparative analysis, what does qca do, when do researchers use qca, examples of qualitative comparative analysis, what is the qualitative comparative analysis method, strengths of qualitative comparative analysis, weaknesses of qualitative comparative analysis.

Qualitative comparative analysis (QCA) stands as a pivotal approach in the realm of social science research. Designed to bridge the gap between qualitative and quantitative analysis , QCA offers a unique way to systematically study complex social phenomena by analyzing qualitative data. This article aims to provide a comprehensive overview of its concepts, applications, strengths, and weaknesses to give you a clearer grasp of what QCA is and why it's essential in today's research landscape.

Qualitative comparative analysis is a research methodology primarily rooted in the social sciences, yet its applicability spans across diverse fields. It was originally developed by Charles Ragin in the 1980s as a method to address challenges faced when analyzing complex social situations. At its core, QCA is designed to systematically compare cases to identify patterns.

Unlike traditional qualitative research methods that focus on understanding individual cases in depth, or quantitative methods that seek generalizations from large datasets, QCA finds its niche in the middle ground. It aims to derive general patterns from a limited number of cases by treating them as configurations of attributes or conditions. Through this, qualitative researchers can identify which combinations of conditions lead to an outcome of interest, allowing for a nuanced understanding that both respects case specificity and seeks broader patterns.

Moreover, QCA models use Boolean algebra and set theory to make multiple comparisons. This mathematical approach ensures that the method remains rigorous and structured, granting researchers a solid foundation for building analyses and conclusions. As such, QCA is not just a method, but a fusion of deep insights from both qualitative and quantitative analysis .

At its essence, QCA allows researchers to discern relationships between conditions and outcomes across various cases. It serves a dual purpose: simplifying complex data while preserving the depth and richness of each case.

QCA helps in identifying "causal recipes." Unlike traditional variable oriented methods that seek a singular cause for an outcome, QCA acknowledges that multiple paths can lead to the same result. These paths or "recipes" are different configurations of conditions that lead to a particular outcome.

QCA emphasizes the importance of "conjunctural causation." This means that it's not just the presence or absence of individual conditions that matter, but the specific combination of these conditions. QCA thus recognizes the role of "equifinality" in social phenomena. This principle asserts that there can be multiple, equally valid paths leading to the same outcome.

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Researchers often turn to QCA when they're faced with a complex interplay of conditions and outcomes. Given its unique blend of quantitative and qualitative methods , QCA provides a framework to embrace and understand this complexity.

In the realm of political science, for instance, research may want to study how policy-making, governance, and societal structures are intertwined. Imagine a study aiming to understand the factors leading to successful democratic transitions. Here, various combinations of historical, cultural, economic, and social conditions can be assessed to determine which specific combinations lead to a democracy.

Similarly, in health research, the factors affecting health outcomes can be manifold. For instance, when studying the impact of health campaigns hosted on web sites aiming to reduce smoking rates, researchers might find that cultural background, age, frequency of website interaction, and existing health beliefs all play a part. Instead of trying to find a single dominant factor, scholars can identify multiple pathways through which these campaigns might succeed or fail.

Additionally, this method can facilitate systematic cross case analysis in comparative research with multiple cases. Researchers can highlight patterns and relationships without losing sight of the unique intricacies of each case. Moreover, fuzzy set analysis enables researchers to deal with cases that don't fit neatly into binary categories. For instance, instead of classifying a country as simply democratic or not in the above example, fuzzy sets are based on degrees of membership, acknowledging the continuum of political systems.

Qualitative comparative analysis finds its utility in a diverse range of fields, and its flexibility makes it a favorite among researchers tackling intricate questions. Within research on politics and democratic transitions, the use of QCA, particularly "crisp set QCA", is evident. This version of QCA, which relies on binary distinctions (e.g., democratic vs. non-democratic), aids researchers in understanding the myriad conditions—such as civil unrest, economic stability, international influences, and historical legacies—that lead to a nation's democratic evolution. Utilizing crisp set QCA, researchers pinpoint combinations of these conditions that consistently catalyze democratic shifts.

In health care research, specifically studies analyzing the effectiveness of web-based campaigns promoting vaccination, "multi-value QCA" may be more suitable. Unlike its binary counterpart, multi-value QCA allows for more than two values in the causal conditions. This is particularly useful when examining a variety of factors, such as age groups, different socioeconomic brackets, and varying levels of prior beliefs. With this nuanced approach, researchers can systematically determine which combination of conditions are related to heightened vaccination rates.

Conducting QCA involves a series of structured steps that guide researchers from the initial phase of conceptualizing their study to the final interpretation of results . Here's a simplified breakdown of the process:

• Case selection : Begin by choosing the cases you wish to study. These cases should have varying outcomes concerning the research question , ensuring a mix of both positive and negative results.
• Define conditions and outcomes : Clearly define the causal conditions you believe influence the outcome. These can be binary (e.g., success/failure) in crisp set QCA or more nuanced in fuzzy set or multi-value QCA. Additionally, identify the outcome or outcomes of interest.
• Calibration : Assign values to each causal condition within each case. In crisp set QCA, this is a straightforward binary distinction. However, in fuzzy set QCA, the causal conditions need to be calibrated to indicate the degree of membership of each case in a given condition (i.e., given a value between 0 and 1, which refers to full membership). These set membership scores depend on each condition and the dataset, such that researchers' chosen cutoff points are a crucial aspect of fuzzy set analysis.
• Construct a truth table : After assigning values to each causal condition, create a truth table. This data matrix lists all possible combinations of conditions and their associated outcomes. It's a visual representation of how different conditions are related to the desired outcome.
• Analyze patterns : With the truth table at hand, identify patterns that lead to the outcome of interest. Look for combinations of conditions that consistently result in a particular outcome. Dedicated computer software for QCA can greatly facilitate this process by calculating and setting frequency and consistency values. Determining cutoff points (both for determining set membership and which possible configurations are related to the presence of the outcome) is often an iterative process, as researchers can try different combinations based on their causal inferences.
• Interpretation and presentation : After setting up the truth table and indicating the positive or negative outcomes of each combination, run the analysis and interpret the findings . The results convey which combinations of causal conditions are necessary or sufficient for the desired outcome. These findings can be presented in a manner that highlights the causal complexity and provides insights into the phenomenon under study. Researchers typically present the results of QCA in a table displaying the different causal configurations with symbols indicating the absence or presence of each condition within each configuration.

QCA boasts several strengths that make it a favored method in various research domains. Chief among these is its ability to bridge the gap between qualitative and quantitative research , allowing for in-depth case understanding while drawing broader, systematic conclusions. QCA analysis does not depend on having a high number of cases to assess causality. It adeptly handles the complexity of real-world scenarios by acknowledging multiple pathways to the same outcome (equifinality) and asymmetric causality, ensuring researchers capture the full spectrum of causal dynamics. Its emphasis on conjunctural causation enables the identification of unique combinations of conditions leading to outcomes, offering richer insights than traditional linear regression based on quantitative measures. Additionally, with set theory and robust statistical techniques at its foundation, QCA provides a structured and rigorous analytic technique.

While QCA offers a myriad of benefits, it's essential to recognize its limitations as well. Firstly, QCA can be data-intensive; each case requires meticulous detailing, which can be demanding when dealing with a large number of cases. The method's reliance on Boolean algebra and set theory, while providing structure, can also be a double-edged sword. Oversimplification or incorrect calibration can lead to misleading results. Furthermore, QCA, being primarily a cross-sectional analysis tool, might not be ideal for studies requiring a temporal or longitudinal perspective . Also, while it excels in identifying combinations of causal conditions, it may not always elucidate the deeper mechanisms or processes underlying those causalities. As with any research method, it's imperative for researchers to understand these constraints and apply QCA judiciously, ensuring that its application aligns with the research question and context.

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• Comparative Analysis

What It Is and Why It's Useful

Comparative analysis asks writers to make an argument about the relationship between two or more texts. Beyond that, there's a lot of variation, but three overarching kinds of comparative analysis stand out:

• Coordinate (A ↔ B): In this kind of analysis, two (or more) texts are being read against each other in terms of a shared element, e.g., a memoir and a novel, both by Jesmyn Ward; two sets of data for the same experiment; a few op-ed responses to the same event; two YA books written in Chicago in the 2000s; a film adaption of a play; etc. 
• Subordinate (A  → B) or (B → A ): Using a theoretical text (as a "lens") to explain a case study or work of art (e.g., how Anthony Jack's The Privileged Poor can help explain divergent experiences among students at elite four-year private colleges who are coming from similar socio-economic backgrounds) or using a work of art or case study (i.e., as a "test" of) a theory's usefulness or limitations (e.g., using coverage of recent incidents of gun violence or legislation un the U.S. to confirm or question the currency of Carol Anderson's The Second ).
• Hybrid [A  → (B ↔ C)] or [(B ↔ C) → A] , i.e., using coordinate and subordinate analysis together. For example, using Jack to compare or contrast the experiences of students at elite four-year institutions with students at state universities and/or community colleges; or looking at gun culture in other countries and/or other timeframes to contextualize or generalize Anderson's main points about the role of the Second Amendment in U.S. history.

"In the wild," these three kinds of comparative analysis represent increasingly complex—and scholarly—modes of comparison. Students can of course compare two poems in terms of imagery or two data sets in terms of methods, but in each case the analysis will eventually be richer if the students have had a chance to encounter other people's ideas about how imagery or methods work. At that point, we're getting into a hybrid kind of reading (or even into research essays), especially if we start introducing different approaches to imagery or methods that are themselves being compared along with a couple (or few) poems or data sets.

Why It's Useful

In the context of a particular course, each kind of comparative analysis has its place and can be a useful step up from single-source analysis. Intellectually, comparative analysis helps overcome the "n of 1" problem that can face single-source analysis. That is, a writer drawing broad conclusions about the influence of the Iranian New Wave based on one film is relying entirely—and almost certainly too much—on that film to support those findings. In the context of even just one more film, though, the analysis is suddenly more likely to arrive at one of the best features of any comparative approach: both films will be more richly experienced than they would have been in isolation, and the themes or questions in terms of which they're being explored (here the general question of the influence of the Iranian New Wave) will arrive at conclusions that are less at-risk of oversimplification.

For scholars working in comparative fields or through comparative approaches, these features of comparative analysis animate their work. To borrow from a stock example in Western epistemology, our concept of "green" isn't based on a single encounter with something we intuit or are told is "green." Not at all. Our concept of "green" is derived from a complex set of experiences of what others say is green or what's labeled green or what seems to be something that's neither blue nor yellow but kind of both, etc. Comparative analysis essays offer us the chance to engage with that process—even if only enough to help us see where a more in-depth exploration with a higher and/or more diverse "n" might lead—and in that sense, from the standpoint of the subject matter students are exploring through writing as well the complexity of the genre of writing they're using to explore it—comparative analysis forms a bridge of sorts between single-source analysis and research essays.

Typical learning objectives for single-sources essays: formulate analytical questions and an arguable thesis, establish stakes of an argument, summarize sources accurately, choose evidence effectively, analyze evidence effectively, define key terms, organize argument logically, acknowledge and respond to counterargument, cite sources properly, and present ideas in clear prose.

Common types of comparative analysis essays and related types: two works in the same genre, two works from the same period (but in different places or in different cultures), a work adapted into a different genre or medium, two theories treating the same topic; a theory and a case study or other object, etc.

How to Teach It: Framing + Practice

Framing multi-source writing assignments (comparative analysis, research essays, multi-modal projects) is likely to overlap a great deal with "Why It's Useful" (see above), because the range of reasons why we might use these kinds of writing in academic or non-academic settings is itself the reason why they so often appear later in courses. In many courses, they're the best vehicles for exploring the complex questions that arise once we've been introduced to the course's main themes, core content, leading protagonists, and central debates.

For comparative analysis in particular, it's helpful to frame assignment's process and how it will help students successfully navigate the challenges and pitfalls presented by the genre. Ideally, this will mean students have time to identify what each text seems to be doing, take note of apparent points of connection between different texts, and start to imagine how those points of connection (or the absence thereof)

• complicates or upends their own expectations or assumptions about the texts
• complicates or refutes the expectations or assumptions about the texts presented by a scholar
• confirms and/or nuances expectations and assumptions they themselves hold or scholars have presented
• presents entirely unforeseen ways of understanding the texts

—and all with implications for the texts themselves or for the axes along which the comparative analysis took place. If students know that this is where their ideas will be heading, they'll be ready to develop those ideas and engage with the challenges that comparative analysis presents in terms of structure (See "Tips" and "Common Pitfalls" below for more on these elements of framing).

Like single-source analyses, comparative essays have several moving parts, and giving students practice here means adapting the sample sequence laid out at the " Formative Writing Assignments " page. Three areas that have already been mentioned above are worth noting:

• Gathering evidence : Depending on what your assignment is asking students to compare (or in terms of what), students will benefit greatly from structured opportunities to create inventories or data sets of the motifs, examples, trajectories, etc., shared (or not shared) by the texts they'll be comparing. See the sample exercises below for a basic example of what this might look like.
• Why it Matters: Moving beyond "x is like y but also different" or even "x is more like y than we might think at first" is what moves an essay from being "compare/contrast" to being a comparative analysis . It's also a move that can be hard to make and that will often evolve over the course of an assignment. A great way to get feedback from students about where they're at on this front? Ask them to start considering early on why their argument "matters" to different kinds of imagined audiences (while they're just gathering evidence) and again as they develop their thesis and again as they're drafting their essays. ( Cover letters , for example, are a great place to ask writers to imagine how a reader might be affected by reading an their argument.)
• Structure: Having two texts on stage at the same time can suddenly feel a lot more complicated for any writer who's used to having just one at a time. Giving students a sense of what the most common patterns (AAA / BBB, ABABAB, etc.) are likely to be can help them imagine, even if provisionally, how their argument might unfold over a series of pages. See "Tips" and "Common Pitfalls" below for more information on this front.

Sample Exercises and Links to Other Resources

• Common Pitfalls
• Advice on Timing
• Try to keep students from thinking of a proposed thesis as a commitment. Instead, help them see it as more of a hypothesis that has emerged out of readings and discussion and analytical questions and that they'll now test through an experiment, namely, writing their essay. When students see writing as part of the process of inquiry—rather than just the result—and when that process is committed to acknowledging and adapting itself to evidence, it makes writing assignments more scientific, more ethical, and more authentic. 
• Have students create an inventory of touch points between the two texts early in the process.
• Ask students to make the case—early on and at points throughout the process—for the significance of the claim they're making about the relationship between the texts they're comparing.
• For coordinate kinds of comparative analysis, a common pitfall is tied to thesis and evidence. Basically, it's a thesis that tells the reader that there are "similarities and differences" between two texts, without telling the reader why it matters that these two texts have or don't have these particular features in common. This kind of thesis is stuck at the level of description or positivism, and it's not uncommon when a writer is grappling with the complexity that can in fact accompany the "taking inventory" stage of comparative analysis. The solution is to make the "taking inventory" stage part of the process of the assignment. When this stage comes before students have formulated a thesis, that formulation is then able to emerge out of a comparative data set, rather than the data set emerging in terms of their thesis (which can lead to confirmation bias, or frequency illusion, or—just for the sake of streamlining the process of gathering evidence—cherry picking). 
• For subordinate kinds of comparative analysis , a common pitfall is tied to how much weight is given to each source. Having students apply a theory (in a "lens" essay) or weigh the pros and cons of a theory against case studies (in a "test a theory") essay can be a great way to help them explore the assumptions, implications, and real-world usefulness of theoretical approaches. The pitfall of these approaches is that they can quickly lead to the same biases we saw here above. Making sure that students know they should engage with counterevidence and counterargument, and that "lens" / "test a theory" approaches often balance each other out in any real-world application of theory is a good way to get out in front of this pitfall.
• For any kind of comparative analysis, a common pitfall is structure. Every comparative analysis asks writers to move back and forth between texts, and that can pose a number of challenges, including: what pattern the back and forth should follow and how to use transitions and other signposting to make sure readers can follow the overarching argument as the back and forth is taking place. Here's some advice from an experienced writing instructor to students about how to think about these considerations:

a quick note on STRUCTURE

     Most of us have encountered the question of whether to adopt what we might term the “A→A→A→B→B→B” structure or the “A→B→A→B→A→B” structure.  Do we make all of our points about text A before moving on to text B?  Or do we go back and forth between A and B as the essay proceeds?  As always, the answers to our questions about structure depend on our goals in the essay as a whole.  In a “similarities in spite of differences” essay, for instance, readers will need to encounter the differences between A and B before we offer them the similarities (A d →B d →A s →B s ).  If, rather than subordinating differences to similarities you are subordinating text A to text B (using A as a point of comparison that reveals B’s originality, say), you may be well served by the “A→A→A→B→B→B” structure.  

     Ultimately, you need to ask yourself how many “A→B” moves you have in you.  Is each one identical?  If so, you may wish to make the transition from A to B only once (“A→A→A→B→B→B”), because if each “A→B” move is identical, the “A→B→A→B→A→B” structure will appear to involve nothing more than directionless oscillation and repetition.  If each is increasingly complex, however—if each AB pair yields a new and progressively more complex idea about your subject—you may be well served by the “A→B→A→B→A→B” structure, because in this case it will be visible to readers as a progressively developing argument.

As we discussed in "Advice on Timing" at the page on single-source analysis, that timeline itself roughly follows the "Sample Sequence of Formative Assignments for a 'Typical' Essay" outlined under " Formative Writing Assignments, " and it spans about 5–6 steps or 2–4 weeks. 

Comparative analysis assignments have a lot of the same DNA as single-source essays, but they potentially bring more reading into play and ask students to engage in more complicated acts of analysis and synthesis during the drafting stages. With that in mind, closer to 4 weeks is probably a good baseline for many single-source analysis assignments. For sections that meet once per week, the timeline will either probably need to expand—ideally—a little past the 4-week side of things, or some of the steps will need to be combined or done asynchronously.

What It Can Build Up To

Comparative analyses can build up to other kinds of writing in a number of ways. For example:

• They can build toward other kinds of comparative analysis, e.g., student can be asked to choose an additional source to complicate their conclusions from a previous analysis, or they can be asked to revisit an analysis using a different axis of comparison, such as race instead of class. (These approaches are akin to moving from a coordinate or subordinate analysis to more of a hybrid approach.)
• They can scaffold up to research essays, which in many instances are an extension of a "hybrid comparative analysis."
• Like single-source analysis, in a course where students will take a "deep dive" into a source or topic for their capstone, they can allow students to "try on" a theoretical approach or genre or time period to see if it's indeed something they want to research more fully.
• DIY Guides for Analytical Writing Assignments

• Types of Assignments
• Unpacking the Elements of Writing Prompts
• Formative Writing Assignments
• Single-Source Analysis
• Research Essays
• Multi-Modal or Creative Projects
• Giving Feedback to Students

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Enhancing extreme precipitation forecasts through machine learning quality control of precipitable water data from satellite fengyun-2e: a comparative study of minimum covariance determinant and isolation forest methods.

1. Introduction

2. materials and methods, 2.1. case review, 2.2. nwp model and assimilation system, 2.3. data description.

• Conventional surface and upper-air observational data PREPBUFR provided by the U.S. National Centers for Environmental Prediction (NCEP) include multiple subsets of data, such as upper-air observation reports (ADPUPA), satellite-derived wind reports (SATWND), sea surface observation reports (SFCSHP), land surface observation reports (ADPSFC), vertical azimuth display wind observations (VADWND), and ASCAT scatterometer data (ASCATW). These data were subjected to NCEP preprocessing, including QC, format unification, and bias correction, to ensure data quality and consistency. These preprocessed data are widely applied in the data assimilation processes for global and regional NWP models to enhance their forecasting capabilities. In this study, these data served as baseline assimilation data to provide stable and reliable observational inputs for the model.
• Quality-controlled IPW data (hereafter referred to as CMA IPW) from conventional observation stations in China, which were provided by the Atmospheric Sounding Center of the China Meteorological Administration (CMA). After undergoing rigorous quality control procedures, the accuracy and reliability of the data were fully guaranteed. In this study, these data served as a reference dataset to help us understand normal meteorological patterns and to assist in fine-tuning our unsupervised ML models for quality control of FY2E TPW data. This approach allowed us to leverage both ML techniques and domain knowledge in the two processes, aiming to improve the accuracy of precipitation event predictions.
• The TPW data observed by FY2E covering China and its surrounding areas were provided by the National Satellite Meteorological Center of China. Note that IPW and TPW both refer to the total amount of water vapor in a vertical column of the atmosphere. Although these terms are often used to describe the same physical quantity, the choice of terminology may vary depending on the specific research context, measurement technique, and instrumentation. In this study, we used the IPW when referring to ground-based measurements and the TPW for satellite observations, which is consistent with the conventions in our data sources. The FY2E TPW offers more comprehensive water vapor distribution information than ground observations. In this study, the FY2E TPW data were used to train the unsupervised ML models. Figure 3 provides an overview of the spatial distributions of the FY2E TPW and CMA IPW data at 12:00 UTC on 8 July 2013, illustrating the typical patterns observed during the study period.

2.4. QC Process

2.4.1. introduction to ml-based qc methods.

• ψ sample points were randomly selected from the given dataset to form a subset X ′ of X = { x 1 , … , x n } , which was placed in the root node.
• A dimension q was randomly designated from d dimensions, and a split point p was randomly generated in the current data, satisfying min ⁡ x i j , j = q , x i j ∈ X ’ < p < max ⁡ ( x i j , j = q , x i j ∈ X ’ ) .
• The split point p generated a hyperplane that divided the current data space into two subspaces: sample points with a specified dimension less than p were placed in the left child node T l , whereas those greater than or equal to p were placed in the right child node T r .
• Steps b and c were recursively executed until all leaf nodes contained only one sample point or the Isolation Tree reached the specified height.
• Steps a to d were repeated until t Isolation Trees were generated.

2.4.2. Data Preprocessing and QC Experiments’ Design

3.1. qc results, 3.2. analysis of simulated circulation fields, 3.3. analysis of precipitation forecasts, 3.3.1. simulated precipitation distribution, 3.3.2. quantitative precipitation verification, 4. discussion, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Shen, W.; Chen, S.; Xu, J.; Zhang, Y.; Liang, X.; Zhang, Y. Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods. Remote Sens. 2024 , 16 , 3104. https://doi.org/10.3390/rs16163104

Shen W, Chen S, Xu J, Zhang Y, Liang X, Zhang Y. Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods. Remote Sensing . 2024; 16(16):3104. https://doi.org/10.3390/rs16163104

Shen, Wenqi, Siqi Chen, Jianjun Xu, Yu Zhang, Xudong Liang, and Yong Zhang. 2024. "Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods" Remote Sensing 16, no. 16: 3104. https://doi.org/10.3390/rs16163104

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Bibliometric analysis and visualisation of research hotspots and frontiers on omics in osteosarcoma

• Open access
• Published: 22 August 2024
• Volume 150 , article number  393 , ( 2024 )

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You have full access to this open access article

• Xinyu Wang 1 , 4   na1 ,
• Xin Cao 2   na1 ,
• Zhongshang Dai 3 &
• Zhehao Dai 1  

Background/objective

Omics technology has become a widely applied biological science that can be used to study the etiology, pathogenesis, and treatment of osteosarcoma(OS). Bibliometric analysis is still blank in this field.This study aimed to access the trends and hotspots of omics in OS research through the bibliometric analysis method.

Relevant articles and reviews from 1999 to 2023 were retrieved from the Web of Science Core Collection. The data were processed with CiteSpace, and some graphs were generated with Graphpad, VOSviewer, Scimago Graphica, Bibliometrix and R Studio.

A total of 1581 papers were included. China (569, 36.0%) and the United States (523, 33.1%) took the dominant position in the number of published papers, and the links between countries most frequently occurred between North America and East Asia, and between Australia and Europe. Top institutions with the highest number of publications were almost located in the United States, with The University of Texas MD Anderson Cancer Center contributing the most (44, 2.78%). Among the researchers in this field, Cleton-Jansen AM was the author with the highest number of articles in the field (20, 1.27%). According to the keyword cluster analysis, most studies focused on the “comparative genomic hybridization” before 2012. The latest surge words “tumor microenvironment” and “immune infiltration” in the keyword heatmap indicate future research directions.

Our study provided the current status of the omics research in OS on a global level and the hottest directions. The field of omics in OS was developing rapidly, and the main focuses of research were revealing the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. Research on the immune microenvironment and its relationship with genetic aberrations of OS will be a priority in the future.

The research of omics in osteosarcoma has been exploding since 1998 due to the development of omics technology and the emphasis of researchers.

The research hotspots had shifted from the discovering of new molecular target to revealing the characteristics of tumor microenvironment of osteosarcoma and how to activate the immune system to fight cancer cells.

According to the present trend, research on the immune microenvironment and its molecular mechanisms will be a priority in the future.

Avoid common mistakes on your manuscript.

Introduction

Osteosarcoma (OS) is the most common malignant bone tumor, accounting for approximately 20% of all bone tumors and about 5% of pediatric tumors overall (Tang et al. 2008 ). Despite the fact that the precise identity of the cell at the origin of the tumor remains unknown, OS is characterized by the presence of transformed osteoblastic cells that produce osteoid matrix. The current treatment combines surgery with preoperative and postoperative multi-drug chemotherapy using three or four cytotoxic agents (cisplatin, doxorubicin, high-dose methotrexate/ifosfamide). However, the survival rate has not notably improved in the past 50 years after the introduction of chemotherapy. Patients with localized OS have a 5-year survival rate of 70–75%, while patients with metastatic disease or recurrence have a long-term survival rate of only 30% (Meltzer et al. 2021 ). Therefore, revealing the molecular mechanisms and improving the prognosis of OS remains a constant and major goal for many worldwide research and clinical groups.

Omics technology is used to study a series of molecules and their interactions involved in the whole process of gene expression from a systems-level, mainly including genomics, transcriptomics, proteomics and metabolomics (Jeong et al. 2023 ). More specifically, the study of genomes involves understanding the structure, function, and inheritance of an organism’s entire genome. Transcriptomics evaluates all messenger RNA molecules in a single cell, tissue, or organism in terms of their quality or quantity. In contrast, proteomics was created from genomics and is centered on measuring proteins/peptides, modifications, and interactions in various sample types using MS-based methods or high-throughput analyses. Metabolomics involves the large-scale study of many small molecules, including amino acids, fatty acids, organic acids, and ketones, which are the end products of complex biochemical processes. On their respective scales, each type of omics science is used to identify, characterize, and quantify all biological molecules associated with diseases.With the advances in the technologies and tools for generating and processing large omics data, and the application of artificial intelligence methodologies for deciphering complex multi-omics interaction, omics technology becomes a powerful approach to decipher the mechanistic details of gene expression (Lee et al. 2022 ). Omics technology is expected to complement current clinical and pathology evaluations and guide personalized cancer management by discovering previously obscure sub-types with clinical implications and identifying patients’ prognoses, which help in revealing the molecular mechanisms and heterogeneity of OS, thereby improving the prognosis of patients (Pan et al. 2021 ).

Annually, a large number of original research articles on omics in OS were published and many conventional reviews had been conducted in this field to elucidate the research status and trends too. However, most of these review studies focused on the application or the outcome of a certain kind of omics technologies to attempt to answer a specific research question. For example, Dylan C Dean and colleagues searched the articles on metabolomics of OS and discussed the new founding of metabolic pathways only (Esperança-Martins et al. 2021 ). In another simple example, based on the outcome of genomics, Fuloria S and colleagues analyzed the intricate interplay between ncRNAs and the Wnt/β-catenin cascade in OS and proposed ncRNAs as biomarkers and therapeutics approaches in their review (Fuloria et al. 2024 ). Thus it can be seen that the inclusion–exclusion criteria of these conventional reviews were formulated according to the specific research question and were particularly stringent, the coverage was not comprehensive. Compared with conventional narrative reviews by experts, bibliometric analysis was an objectively quantitative method which applied mathematical and statistical tools to extract and analyze the metrics of each publication including author, institution, country and keywords, in order to evaluate their inter-relationships and impacts (Donthu et al. 2021 ). More important, the results of bibliometric analysis can be displayed in a more intuitive and comprehensible way. Due to these benefits, bibliometric analysis had gained considerable popularity in biomedical research in recent years. In the field of OS, some bibliometric analyses had been published regarding to limb salvage surgery (Raj et al. 2023 ), immunotherapy (Shen et al. 2024 ), the application of graphene oxide (Barba-Rosado et al. 2024 ), prognosis (Yin et al. 2024 ), immune microenvironment (Zhang et al. 2023 ), extracellular vesicles (Pei et al. 2024 ), and non-coding RNA (Chen et al. 2024 ). However, none has conducted bibliometric analysis on omics in OS. Therefore, in this study, we carried out the bibliometric analysis method analyze articles on omics in OS for the following three purposes: (1) identifying the cooperation and impact of various authors, countries, institutions, and journals, (2) displaying the basic knowledge and development trends through a co-cited reference analysis, and (3) detecting research frontiers through a keyword analysis. Our bibliometric analysis will give researchers an all-encompassing view of omics in OS research over the past twenty years, and lay a foundation for future research.

Data source and search strategy

The flowchart of publications screening

The literature was obtained from the Web of Science Core Collection (WoSCC) database ( https://www.webofscience.com/wos/woscc/basic-search ). The publication date was restricted from January 1,1999 to October 12, 2023. The search strategy was TS = (genom* OR transcriptom* OR proteom* OR metabolom* OR metabonom* OR microbiom* OR “multi omic*”) AND TS = (osteosarcoma OR “bone sarcoma”) and LA = (English). In this study, only research and review articles were selected, and editorial materials, corrections, letters, news items, meeting abstracts, and retractions were eliminated from the search results. Due to the daily update to the database, all searches were conducted on the same day to avoid bias.All records were saved and kept in plain text and tab-delimited file for the purpose of drawing and analyzing the scientific atlas.The literature screening procedure was depicted in Fig.  1 .

Bibliometric analysis

The literature’s year, authors, organizations, titles, abstracts, keywords, journals, and cited references were downloaded in plain text. An Excel spreadsheet was used to collect the following data as bibliometric indicators: total number of publications, year of publication, publication types, top ten countries, top ten institutions, top ten journals, and top ten citations.

Visualize analysis

The VOSviewer software tool (version 1.6.16) was used to explore the co-authorship (authors, organizations, and countries), co-occurrence (author and keywords), bibliographic coupling (sources), and co-citation (cited references, cited sources, and cited authors), was applied to create network visualization maps of the most co-occurrence terms to analysis the research hotspots and the most co-authorship of countries (Song et al. 2022 ).In this map, items were called nodes reflecting author, country, organization, and keywords, and links reflecting the degree of collaboration of each item were represented by edges (Zhang et al. 2023 ).

Simultaneously, the dual-map overlay of journals and citation bursts were built based on CiteSpace(version 6.2.R4), which helped to identify emerging trends and the distribution of academic journals in real time (Chen et al. 2019 ). In this dual-map, network nodes usually represent authors, and the size of the nodes was proportional to the number of studies posted by them. Link colors varied with the years articles were published, and link clusters represented author cooperation relationships. In the analysis of keywords, selection criteria were set as follows: g-index(k = 8), LRF = 2.0, L/ N  = 10, LBY = 8, e = 2.0.

In particular, R package “Bibliometrix”(version 3.2.1) ( https://www.bibliometrix.org ) was adopted to put out the popularity of key words each year. The 2022 impact factor (IF) and Journal Citation Reports (JCR) were also gained from the Web of Science group.

Literature search results

After scanning, a total of 1581 English articles related to omics in OS were included, of which 1406 research articles and 175 reviews.These were from 64 countries, 2082 constitutions, 540 journals and 9832 authors with 61,361 references from 5048 journals.

The annual number of publication and citation from 1999 to 2023 were presented in Fig.  2 , the average h-index is 94, and the average citation is 28.88. The growth of the publication and citation showed two stage: the first (1999–2017), which had a very slow and unstable growth, and the second (2018–2022), which had a explosive growth. The number of published paper reached 179 in 2022, exactly 10 times the previous amount in 1999.

Annual number of publication and citation in omics in OS

Distribution of countries

In total, 64 countries were involved in the research of omics in OS, of which top 10 leading countries were shown in Table  1 . China had the largest number of publications and the number was 569, accounting 36.0%, followed by United States (523, 33.1%). But Germany, in third place, had only 106 articles, a sharp drop, followed by Italy (104, 6.6%). The network of countries in this field of omics in OS were presented in Fig.  3 . This map offered a clear imagine of eight clusters in these countries, with the strongest links showing the frequent association among China, United States, Australia and Canada.

Country collaboration map

Institution and authors analysis

Table  2 Showed the most productive research areas, institutions and authors. In the field of omics in OS, articles on oncology (558, 35.29%) were more than twice as many as articles on biochemistry molecular biology. Among the 2082 institutions, the University of Texas MD Anderson Cancer Center published the greatest number of article (44, 2.78%), followed by Baylor College of Medicine (40, 2.53%). A total of 9832 authors was involved in omics research in OS. Based on the number of publications, the number of records and the proportion of the top 6 authors were shown in the table  2 . The top three authors were Cleton-Jansen AM (20, 1.27%), Baumhoer D (18, 1.14%) and Modiano JF (18, 1.14%). The relationship between affiliations, authors and keywords in the field of omics in OS is shown in Fig.  4 .

The alluvial diagram of author, institution and article subject

Journals and co-cited journals

Publications related to omics in OS were published in 540 journals, and the top 10 journals and co-cited journals were shown in Table  3 . Plos One published the highest number of articles, 46, and also had the highest number of citations, 1256. Of all the top 10 journals, only the International Journal of Molecular Sciences and Cancer Research appeared in the Web of Science’s 2023 edition of Journal Citation Reports Category Quartile (JCR-c) Q1. The co-cited journals shown in the Table  3 had an impact factor (IF) ranging from 3.7 to 64.8, and four out of five were included in JCR-c Q1. The double map overlay of the journals (Fig.  5 ) showed the distribution of the journals with regard to the topic. The target (citing journals) was placed on the left side and the source (cited journals) on the other side, with the coloured paths indicating the citation correlations. An orange path was clearly identified in the figure.

Dual-map overlay of journals

Cited references

The citation analysis of journals was applied to assess the influence usually within a particular field. In the research, articles related to omics in OS cited 61,361 references from 5048 journals. The top 10 cited references were presented in Table  4 , and all of these articles had no less than 283 citations. Four out of five journals belong to JCR Q1, and one appeared in JCR Q3. The article with the most citation ( n  = 818, IF = 78.5, JCR-c = Q1) entitled “Translational biology of osteosarcoma” was published in Nature Reviews Cancer in 2014. The second article ( n  = 732, IF = 4.8, JCR-c = Q2) entitled “Role of Poly (ADP-ribose) Polymerase (PARP) Cleavage in Apoptosis” was published in Journal of Biological Chemistry in 1999.

To capture the research hotspots, a keyword co-occurrence map was created using Citespace, as shown in Fig.  6 A. The high-frequency terms included “expression”, “gene”, “proliferation”, “identification”, “apoptosis”, “survival”, “metastasis” and “comparative genomic hybridization”. In addition, the cluster of the keywords was analyzed to gain further insight into the hotspots in the field of omics in OS. The results of the keyword cluster analysis were presented in Fig.  6 B, and a total of ten clusters were obtained, with the largest cluster being Cluster #0 named “prognosis”, followed by “cancer”, “comparative genomic hybridization”, “gene expression” and so on. A timeline plot of the keyword clusters was shown in Fig.  7 , “prognosis” is the essential topic consistently, and “6-methoxyflavone” aroused the attention of scholars.

More specifically, the top 25 keywords with the strongest citation bursts were presented in Fig.  8 . Keyword with the burst strength (32.14) and longest duration (1999–2012) was “comparative genomic hybridization”. Latest keywords in the outbreak contained"resistance” (2020–2023), “target therapy“(2020–2023) and “progression“(2021–2023). The R Studio heatmap (Fig. 9) verifying these results showed that the keywords"chondrosarcoma”, “gene expression” and “cell cycle"were the earliest burst keywords in 2011, with “gene expression”, “p53” and “microarray” having the longest citation duration, and the recent outbreaks were associated with “immune infiltration”, “tumor microenvironment” and “biomarkers”, indicating the latest trends of the study.

Visualization of keyword clustering map from 1999 to 2023. A : keyword co-occurrence map, B : keyword cluster map

Keywords clustering timeline view between 1999 and 2023

Top 25 keywords with the strongest citation bursts

Heatmap and accumulated frequency heatmap of keywords from 1999 to 2023

With the rapid development of research in various fields, it has become increasingly important for researchers to understand the current advancements in their respective research areas. Compared to meta-analysis and systematic review methods, bibliometric analysis provides a more objective and simpler visualization method to validate and analyze existing literature (Donthu et al. 2021 ). Our research is the first bibliometric study to evaluate and visualize research of omics in OS. A total of 1581 articles from the WoSCC database were analyzed to identify the hotspots and global trends. According to our study, the annual publications from 1999 to 2017 were relatively rare, with an average annual publication of 41 articles, indicating that the research of omics in OS was not adequate. The number of related publications and the citation frequency had increased rapidly since 2018, indicating that omics technology developed rapidly and captured more and more researchers’ attention in the past 5 years, which is consistent with the overall trend of biological technology development. Especially, the recent traction of integrated multi-omics analysis had seen the focus of research, which had empowered to characterize different molecular layers at unprecedented scale and resolution, fueling OS precision medicine.

Regarding the distribution of research, the data indicated that the majority of studies on omics in OS were concentrated in China and the United States, with over five times as many papers in each nation as in the third country, pointing to an uneven global development. Besides, we noticed that China, the United States and Europe (including Germany, Italy, United Kingdom and so forth) were the gathering place of the current related research, and the cooperative research in this field was relatively extensive, and many authors had participated in international cooperation, which indicated a well-established international framework. The top 7 leading institutions were listed in Table  2 , hoping to recommend platforms for collaboration and further learning. A diverse group of experts in the field and a large quantity of financial assistance for researchers were key factors in the success of research in these nations and institutions.

To further understand the research of omics in OS, the top 10 cited articles, including 7 research articles and 3 reviews are summarized. The multiple somatic chromosomal lesions was the primary topic. The most cited paper was a study by Kansara M et al. in Nature Reviews Cancer as a review (Kansara et al. 2014 ), with 818 total citations, which discusses normal bone biology relevant to OS, and argued that genetic features of OS were characterized by chromosomal instability, so that the effect of targeted therapy was uncertain and immunotherapy may be more suitable for OS patients. The forth most cited paper was by Chen X et al. from Cell Report (Chen et al. 2014 ). This study reported that chromosomal lesions, rather than single-nucleotide variations (SNVs), were the major mechanism of recurrent mutations in OS, and many of the most significant chromosomal lesions were found in known cancer genes, including TP53, RB1, and ATRX. The paper by Ma X et al. (Ma X et al. 2018 ) and the paper by Pierron G et al.(Pierron et al. 2012 ) discussed somatic chromosomal lesions from different perspectives. The immune microenvironment was another hot topic. The article was Buddingh EP et al.(Buddingh et al. 2011 ), published in Clinical Cancer Research in 2011, with 313 total citations. This study reported that tumor-infiltrating macrophages were associated with metastasis suppression in high-grade OS. All three reviews (Kansara et al. 2014 ; Gianferante et al. 2017 ; Lindsey et al. 2017 ) summarized the characteristics of immune microenvironment in OS and suggested that newer immune-based treatments may offer a more comprehensive approach to battling cancer pleomorphism. From the perspective of the “seed and soil” theory (Fidler 2003 ), it was easy to understand the reasons why “multiple somatic chromosomal lesions” and “ immune microenvironment” had become hot topics. Genetic aberrations cell was “seed”, and the immune microenvironment was “soil”. The crosstalk established between them fueled the tumor growth by inducing a local immunosuppressive environment. For this reason, there has been a long-standing interest in targeting this interaction and modulating the host’s immune response as a strategy to eliminate cancer. Targeting immune checkpoints, such as cytotoxic T-lymphocyte- associated antigen 4 (CTLA-4) and programmed cell death 1 (PD-1)/ligand 1 (PD-L1), has been an overwhelmingly successful step forward for immunotherapy in the treatment of cancer, but clinical trials in OS was disappointing. Osteosarcoma demonstrates significant genetic complexity and genome instability with resultant high levels of genomic rearrangements and the highest point mutation burden as compared to other pediatric cancers, suggesting that these genomics factors may yield neoantigens capable of eliciting an immune response. However, the rsults of clinical trials did not match this rationale, indicating the existence of other unknown factors. In the MDACC OS cohort, Wu cc and Livingston JA further found that copy number loss has a significant negative correlation with the immune scores, but such a correlation was not observed between copy number gains and the immune scores (Wu et al. 2020 ). The copy number loss may lead to permanent loss of many genes and eventually impact immune response and the effect of immunotherapy in OS. Therefore, the future challenge in OS will be to comprehensively describe the relationship between genetic aberrations and immune microenvironment, and clarify the reasons for disappointing clinical trials in order to break immunosuppressive mechanisms and enhance antitumor immune responses.

Keyword co-occurrence analysis helped to understand the distribution and evolution of multiple research hotspots in a certain field. In co-occurrence clustering analysis, expression, gene, proliferation, identification, apoptosis, survival, metastasis, comparative genomic hybridization and other high-frequency keywords ranked in the top ten, indicating that prognostic evaluation and pathogenesis were research hotspots in OS. Furthermore, “prognosis” was the largest cluster, followed by “cancer”, “comparative genomic hybridization”, “gene expression” and so on, according to the keyword cluster analysis. OS was characterized by marked instability of its somatic genome, which frequently featured chromothripsis, chromosomal aneuploidy and chromosomal rearrangements. The structural variations produced by frequent chromosomal rearrangements contribute to the majority of genetic lesions in OS (Liao et al. 2020 ) and also the fundamental reason for the lack of significant improvement in the treatment strategy and long-term survival of OS in the past 50 years. Therefore, the discovery of genetic abnormalities in OS through various omics sequencing techniques to improve the prognosis of OS patients was a hot topic for researchers, which was consistent with our research findings. However, few recurrent genetic alterations was identified with the exception of the tumor suppressors TP53 and RB1. As was well known, somatic mutations in TP53 were one of the most frequent alterations in human cancers, with the majority of these alterations being missense mutation (Petitjean et al. 2007 ). It had been estimated previously that only 20-50% of OS carried TP53 mutations, and other portion were wild type(Kovac et al. 2015 ; Chen et al. 2016 ). With further research of sequencing techniques, more and more TP53 structural variations were identified (Chen et al. 2014 ). Therefore, it now was suspected that up to 75–90% of OS patients harbored various types of TP53 genetic alterations. Loss of the p53 pathways that disabled the cell’s ability to respond to DNA damage mediated genome instability and triggered OS oncogenesis. RB1 was a key regulator of cell cycle progression by controlling the G1/S phase transition. RB1 alterations had been identified in 50–78% of OS by sequencing studies (Wu et al. 2020 ). Unlike TP53, the depletion of RB1 alone was not sufficient to induce OS formation in animal models. Therefore, it was speculated that Rb1 alterations may synergize with TP53 inactivation during OS oncogenesis. The model of the natural history of OS, proposed by Kovac M and colleagues, theorized that a mutation of TP53 and/or RB1 led to secondary genetic aberrations, which in turn resulted in the emergence of OS (Kovac et al. 2015 ). So, it was not difficult to understand that TP53 was constantly mentioned in the majority of articles (Liu et al. 2013 ; Sorimachi et al. 2023 ).

CiteSpace for burst detection for high-frequency keywords showed that the focus of research was gradually shifting from “comparative genomic hybridization” (1999–2012) to “tumor microenvironment” (2021–2023) and “progression”(2021–2023). Comparative genomic hybridization allowed detection of DNA sequence copy number changes throughout the genome in a single hybridization and it mapped these sometimes very complex changes onto normal metaphase chromosomes, which was an ideal screening tool for instable somatic genome of OS. However, the current and future research hotspots were not about discovering new molecular target in targeted therapy from omics sequencing in the past, but turning to reveal the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. The poor therapeutic effects with side effects of targeted therapy for OS in many clinical trials can partially explain this transitions (Wang et al. 2024 ). The keyword visualization view over time showed that the frequency of use of the main keywords was increasing year by year, which was consistent with the increase in the number of related publications.

Limitations

This study has limitations inherent in bibliometrics. Firstly, not all relevant articles were in the WoSCC database, though WoSCC database was a core journal citation index database and its concept of scientific citation indexing (SCI) was relatively normalized and provided metadata with further distributive refinement (Qiu et al. 2018 ). Secondly, from the filters, only articles published in English were included, which may indicate that non-English publications were underestimated. Thirdly, from the way of analyzing, lots of information in the articles was ignored. For example, the publications with high frequency and citations may be cited for both negative and positive reasons which our research cannot distinguish, and the recent publications were underrepresented due to time constraints. Another example was that some articles on omics in other fields were also imported into software tools for further analysis just because they mentioned the function of the gene in OS, which may have an effect on the outcome. Despite these, our study still provided excellent objective information and insights, with the aim of facilitating the research of omics in OS.

Conclusions

This study showed the current status of the research of omics in OS on a global level and the hottest directions. According to the pattern in recent years, there will be a explosive rise in the number of publications in this field. Until now, China and the United States had made the most significant contributions in this field. The research hotspots had shifted from the discovering of new molecular target to revealing the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. In the future, research on the immune microenvironment and its relationship with genetic aberrations of OS will be a priority.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Impact factors

Journal citation reports

Journal Citation Reports Category Quartile

• Osteosarcoma

Single-nucleotide variations

Web of Science Core Collection

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Acknowledgements

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This research was supported by National Natural Science Foundation of China (Grant number 82373108), Research Project of Health Commission of Hunan Province (Grant number C202304077236), Natural Science Foundation of Hunan Province (Grant number 2024JJ5482) and Innovation and entrepreneurship education teaching reform research project of Central South University (Grant number 2022CG035).

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Xinyu Wang and Xin Cao have contributed to this paper equally.

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Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People’s Republic of China

Xinyu Wang & Zhehao Dai

Department of Rheumatology & Immunology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People’s Republic of China

Department of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People’s Republic of China

Zhongshang Dai

Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, People’s Republic of China

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Zhehao Dai design this study. Xin Cao collected and analyzed the data. Xinyu Wang participated in writing the original draft. Zhongshang Dai reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.

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Wang, X., Cao, X., Dai, Z. et al. Bibliometric analysis and visualisation of research hotspots and frontiers on omics in osteosarcoma. J Cancer Res Clin Oncol 150 , 393 (2024). https://doi.org/10.1007/s00432-024-05898-w

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Received : 26 May 2024

Accepted : 15 July 2024

Published : 22 August 2024

DOI : https://doi.org/10.1007/s00432-024-05898-w

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