synthesis of analysis

How to Synthesize Written Information from Multiple Sources

Shona McCombes

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B.A., English Literature, University of Glasgow

Shona McCombes is the content manager at Scribbr, Netherlands.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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When you write a literature review or essay, you have to go beyond just summarizing the articles you’ve read – you need to synthesize the literature to show how it all fits together (and how your own research fits in).

Synthesizing simply means combining. Instead of summarizing the main points of each source in turn, you put together the ideas and findings of multiple sources in order to make an overall point.

At the most basic level, this involves looking for similarities and differences between your sources. Your synthesis should show the reader where the sources overlap and where they diverge.

Unsynthesized Example

Franz (2008) studied undergraduate online students. He looked at 17 females and 18 males and found that none of them liked APA. According to Franz, the evidence suggested that all students are reluctant to learn citations style. Perez (2010) also studies undergraduate students. She looked at 42 females and 50 males and found that males were significantly more inclined to use citation software ( p < .05). Findings suggest that females might graduate sooner. Goldstein (2012) looked at British undergraduates. Among a sample of 50, all females, all confident in their abilities to cite and were eager to write their dissertations.

Synthesized Example

Studies of undergraduate students reveal conflicting conclusions regarding relationships between advanced scholarly study and citation efficacy. Although Franz (2008) found that no participants enjoyed learning citation style, Goldstein (2012) determined in a larger study that all participants watched felt comfortable citing sources, suggesting that variables among participant and control group populations must be examined more closely. Although Perez (2010) expanded on Franz’s original study with a larger, more diverse sample…

Step 1: Organize your sources

After collecting the relevant literature, you’ve got a lot of information to work through, and no clear idea of how it all fits together.

Before you can start writing, you need to organize your notes in a way that allows you to see the relationships between sources.

One way to begin synthesizing the literature is to put your notes into a table. Depending on your topic and the type of literature you’re dealing with, there are a couple of different ways you can organize this.

Summary table

A summary table collates the key points of each source under consistent headings. This is a good approach if your sources tend to have a similar structure – for instance, if they’re all empirical papers.

Each row in the table lists one source, and each column identifies a specific part of the source. You can decide which headings to include based on what’s most relevant to the literature you’re dealing with.

For example, you might include columns for things like aims, methods, variables, population, sample size, and conclusion.

For each study, you briefly summarize each of these aspects. You can also include columns for your own evaluation and analysis.

The summary table gives you a quick overview of the key points of each source. This allows you to group sources by relevant similarities, as well as noticing important differences or contradictions in their findings.

Synthesis matrix

A synthesis matrix is useful when your sources are more varied in their purpose and structure – for example, when you’re dealing with books and essays making various different arguments about a topic.

Each column in the table lists one source. Each row is labeled with a specific concept, topic or theme that recurs across all or most of the sources.

Then, for each source, you summarize the main points or arguments related to the theme.

The purposes of the table is to identify the common points that connect the sources, as well as identifying points where they diverge or disagree.

Step 2: Outline your structure

Now you should have a clear overview of the main connections and differences between the sources you’ve read. Next, you need to decide how you’ll group them together and the order in which you’ll discuss them.

For shorter papers, your outline can just identify the focus of each paragraph; for longer papers, you might want to divide it into sections with headings.

There are a few different approaches you can take to help you structure your synthesis.

If your sources cover a broad time period, and you found patterns in how researchers approached the topic over time, you can organize your discussion chronologically .

That doesn’t mean you just summarize each paper in chronological order; instead, you should group articles into time periods and identify what they have in common, as well as signalling important turning points or developments in the literature.

If the literature covers various different topics, you can organize it thematically .

That means that each paragraph or section focuses on a specific theme and explains how that theme is approached in the literature.

Source Used with Permission: The Chicago School

If you’re drawing on literature from various different fields or they use a wide variety of research methods, you can organize your sources methodologically .

That means grouping together studies based on the type of research they did and discussing the findings that emerged from each method.

If your topic involves a debate between different schools of thought, you can organize it theoretically .

That means comparing the different theories that have been developed and grouping together papers based on the position or perspective they take on the topic, as well as evaluating which arguments are most convincing.

Step 3: Write paragraphs with topic sentences

What sets a synthesis apart from a summary is that it combines various sources. The easiest way to think about this is that each paragraph should discuss a few different sources, and you should be able to condense the overall point of the paragraph into one sentence.

This is called a topic sentence , and it usually appears at the start of the paragraph. The topic sentence signals what the whole paragraph is about; every sentence in the paragraph should be clearly related to it.

A topic sentence can be a simple summary of the paragraph’s content:

“Early research on [x] focused heavily on [y].”

For an effective synthesis, you can use topic sentences to link back to the previous paragraph, highlighting a point of debate or critique:

“Several scholars have pointed out the flaws in this approach.” “While recent research has attempted to address the problem, many of these studies have methodological flaws that limit their validity.”

By using topic sentences, you can ensure that your paragraphs are coherent and clearly show the connections between the articles you are discussing.

As you write your paragraphs, avoid quoting directly from sources: use your own words to explain the commonalities and differences that you found in the literature.

Don’t try to cover every single point from every single source – the key to synthesizing is to extract the most important and relevant information and combine it to give your reader an overall picture of the state of knowledge on your topic.

Step 4: Revise, edit and proofread

Like any other piece of academic writing, synthesizing literature doesn’t happen all in one go – it involves redrafting, revising, editing and proofreading your work.

Checklist for Synthesis

•   Do I introduce the paragraph with a clear, focused topic sentence?
•   Do I discuss more than one source in the paragraph?
•   Do I mention only the most relevant findings, rather than describing every part of the studies?
•   Do I discuss the similarities or differences between the sources, rather than summarizing each source in turn?
•   Do I put the findings or arguments of the sources in my own words?
•   Is the paragraph organized around a single idea?
•   Is the paragraph directly relevant to my research question or topic?
•   Is there a logical transition from this paragraph to the next one?

Further Information

How to Synthesise: a Step-by-Step Approach

Help…I”ve Been Asked to Synthesize!

Learn how to Synthesise (combine information from sources)

How to write a Psychology Essay

Writing Resources

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Learning about Synthesis Analysis

What D oes Synthesis and Analysis Mean?

Synthesis: the combination of ideas to

• show commonalities or patterns

Analysis: a detailed examination

• of elements, ideas, or the structure of something
• can be a basis for discussion or interpretation

Synthesis and Analysis: combine and examine ideas to

• show how commonalities, patterns, and elements fit together
• form a unified point for a theory, discussion, or interpretation
• develop an informed evaluation of the idea by presenting several different viewpoints and/or ideas

Key Resource: Synthesis Matrix

Synthesis Matrix

A synthesis matrix is an excellent tool to use to organize sources by theme and to be able to see the similarities and differences as well as any important patterns in the methodology and recommendations for future research. Using a synthesis matrix can assist you not only in synthesizing and analyzing,  but it can also aid you in finding a researchable problem and gaps in methodology and/or research.

Use the Synthesis Matrix Template attached below to organize your research by theme and look for patterns in your sources .Use the companion handout, "Types of Articles" to aid you in identifying the different article types for the sources you are using in your matrix. If you have any questions about how to use the synthesis matrix, sign up for the synthesis analysis group session to practice using them with Dr. Sara Northern!

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Module 8: Analysis and Synthesis

Putting it together: analysis and synthesis.

The ability to analyze effectively is fundamental to success in college and the workplace, regardless of your major or your career plans. Now that you have an understanding of what analysis is, the keys to effective analysis, and the types of analytic assignments you may face, work on improving your analytic skills by keeping the following important concepts in mind:

• Recognize that analysis comes in many forms. Any assignment that asks how parts relate to the whole, how something works, what something means, or why something is important is asking for analysis.
• Suspend judgment before undertaking analysis.
• Craft analytical theses that address how, why, and so what.
• Support analytical interpretations with clear, explicitly cited evidence.
• Remember that all analytical tasks require you to break down or investigate something.

Analysis is the first step towards synthesis, which requires not only thinking critically and investigating a topic or source, but combining thoughts and ideas to create new ones. As you synthesize, you will draw inferences and make connections to broader themes and concepts. It’s this step that will really help add substance, complexity, and interest to your essays.

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A Guide to Evidence Synthesis: What is Evidence Synthesis?

• Meet Our Team
• Our Published Reviews and Protocols
• What is Evidence Synthesis?
• Types of Evidence Synthesis
• Evidence Synthesis Across Disciplines
• Finding and Appraising Existing Systematic Reviews
• 0. Develop a Protocol
• 1. Draft your Research Question
• 2. Select Databases
• 3. Select Grey Literature Sources
• 4. Write a Search Strategy
• 5. Register a Protocol
• 6. Translate Search Strategies
• 7. Citation Management
• 8. Article Screening
• 9. Risk of Bias Assessment
• 10. Data Extraction
• 11. Synthesize, Map, or Describe the Results
• Evidence Synthesis Institute for Librarians
• Open Access Evidence Synthesis Resources

What are Evidence Syntheses?

What are evidence syntheses.

According to the Royal Society, 'evidence synthesis' refers to the process of bringing together information from a range of sources and disciplines to inform debates and decisions on specific issues. They generally include a methodical and comprehensive literature synthesis focused on a well-formulated research question.  Their aim is to identify and synthesize all  of the scholarly research on a particular topic, including both published and unpublished studies. Evidence syntheses are conducted in an unbiased, reproducible way to provide evidence for practice and policy-making, as well as to identify gaps in the research. Evidence syntheses may also include a meta-analysis, a more quantitative process of synthesizing and visualizing data retrieved from various studies. 

Evidence syntheses are much more time-intensive than traditional literature reviews and require a multi-person research team. See this PredicTER tool to get a sense of a systematic review timeline (one type of evidence synthesis). Before embarking on an evidence synthesis, it's important to clearly identify your reasons for conducting one. For a list of types of evidence synthesis projects, see the next tab.

How Does a Traditional Literature Review Differ From an Evidence Synthesis?

How does a systematic review differ from a traditional literature review.

One commonly used form of evidence synthesis is a systematic review.  This table compares a traditional literature review with a systematic review.

Video: Reproducibility and transparent methods (Video 3:25)

Reporting Standards

There are some reporting standards for evidence syntheses. These can serve as guidelines for protocol and manuscript preparation and journals may require that these standards are followed for the review type that is being employed (e.g. systematic review, scoping review, etc). ​

• PRISMA checklist Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses.
• PRISMA-P Standards An updated version of the original PRISMA standards for protocol development.
• PRISMA - ScR Reporting guidelines for scoping reviews and evidence maps
• PRISMA-IPD Standards Extension of the original PRISMA standards for systematic reviews and meta-analyses of individual participant data.
• EQUATOR Network The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature by promoting transparent and accurate reporting and wider use of robust reporting guidelines. They provide a list of various standards for reporting in systematic reviews.

Video: Guidelines and reporting standards

PRISMA Flow Diagram

The  PRISMA  flow diagram depicts the flow of information through the different phases of an evidence synthesis. It maps the search (number of records identified), screening (number of records included and excluded), and selection (reasons for exclusion).  Many evidence syntheses include a PRISMA flow diagram in the published manuscript.

See below for resources to help you generate your own PRISMA flow diagram.

• PRISMA Flow Diagram Tool
• PRISMA Flow Diagram Word Template
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Analysis vs. Synthesis

What's the difference.

Analysis and synthesis are two fundamental processes in problem-solving and decision-making. Analysis involves breaking down a complex problem or situation into its constituent parts, examining each part individually, and understanding their relationships and interactions. It focuses on understanding the components and their characteristics, identifying patterns and trends, and drawing conclusions based on evidence and data. On the other hand, synthesis involves combining different elements or ideas to create a new whole or solution. It involves integrating information from various sources, identifying commonalities and differences, and generating new insights or solutions. While analysis is more focused on understanding and deconstructing a problem, synthesis is about creating something new by combining different elements. Both processes are essential for effective problem-solving and decision-making, as they complement each other and provide a holistic approach to understanding and solving complex problems.

Further Detail

Introduction.

Analysis and synthesis are two fundamental processes in various fields of study, including science, philosophy, and problem-solving. While they are distinct approaches, they are often interconnected and complementary. Analysis involves breaking down complex ideas or systems into smaller components to understand their individual parts and relationships. On the other hand, synthesis involves combining separate elements or ideas to create a new whole or understanding. In this article, we will explore the attributes of analysis and synthesis, highlighting their differences and similarities.

Attributes of Analysis

1. Focus on details: Analysis involves a meticulous examination of individual components, details, or aspects of a subject. It aims to understand the specific characteristics, functions, and relationships of these elements. By breaking down complex ideas into smaller parts, analysis provides a deeper understanding of the subject matter.

2. Objective approach: Analysis is often driven by objectivity and relies on empirical evidence, data, or logical reasoning. It aims to uncover patterns, trends, or underlying principles through systematic observation and investigation. By employing a structured and logical approach, analysis helps in drawing accurate conclusions and making informed decisions.

3. Critical thinking: Analysis requires critical thinking skills to evaluate and interpret information. It involves questioning assumptions, identifying biases, and considering multiple perspectives. Through critical thinking, analysis helps in identifying strengths, weaknesses, opportunities, and threats, enabling a comprehensive understanding of the subject matter.

4. Reductionist approach: Analysis often adopts a reductionist approach, breaking down complex systems into simpler components. This reductionist perspective allows for a detailed examination of each part, facilitating a more in-depth understanding of the subject matter. However, it may sometimes overlook the holistic view or emergent properties of the system.

5. Diagnostic tool: Analysis is commonly used as a diagnostic tool to identify problems, errors, or inefficiencies within a system. By examining individual components and their interactions, analysis helps in pinpointing the root causes of issues, enabling effective problem-solving and optimization.

Attributes of Synthesis

1. Integration of ideas: Synthesis involves combining separate ideas, concepts, or elements to create a new whole or understanding. It aims to generate novel insights, solutions, or perspectives by integrating diverse information or viewpoints. Through synthesis, complex systems or ideas can be approached holistically, considering the interconnections and interdependencies between various components.

2. Creative thinking: Synthesis requires creative thinking skills to generate new ideas, concepts, or solutions. It involves making connections, recognizing patterns, and thinking beyond traditional boundaries. By embracing divergent thinking, synthesis enables innovation and the development of unique perspectives.

3. Systems thinking: Synthesis often adopts a systems thinking approach, considering the interactions and interdependencies between various components. It recognizes that the whole is more than the sum of its parts and aims to understand emergent properties or behaviors that arise from the integration of these parts. Systems thinking allows for a comprehensive understanding of complex phenomena.

4. Constructive approach: Synthesis is a constructive process that builds upon existing knowledge or ideas. It involves organizing, reorganizing, or restructuring information to create a new framework or understanding. By integrating diverse perspectives or concepts, synthesis helps in generating comprehensive and innovative solutions.

5. Design tool: Synthesis is often used as a design tool to create new products, systems, or theories. By combining different elements or ideas, synthesis enables the development of innovative and functional solutions. It allows for the exploration of multiple possibilities and the creation of something new and valuable.

Interplay between Analysis and Synthesis

While analysis and synthesis are distinct processes, they are not mutually exclusive. In fact, they often complement each other and are interconnected in various ways. Analysis provides the foundation for synthesis by breaking down complex ideas or systems into manageable components. It helps in understanding the individual parts and their relationships, which is essential for effective synthesis.

On the other hand, synthesis builds upon the insights gained from analysis by integrating separate elements or ideas to create a new whole. It allows for a holistic understanding of complex phenomena, considering the interconnections and emergent properties that analysis alone may overlook. Synthesis also helps in identifying gaps or limitations in existing knowledge, which can then be further analyzed to gain a deeper understanding.

Furthermore, analysis and synthesis often involve an iterative process. Initial analysis may lead to the identification of patterns or relationships that can inform the synthesis process. Synthesis, in turn, may generate new insights or questions that require further analysis. This iterative cycle allows for continuous refinement and improvement of understanding.

Analysis and synthesis are two essential processes that play a crucial role in various fields of study. While analysis focuses on breaking down complex ideas into smaller components to understand their individual parts and relationships, synthesis involves integrating separate elements or ideas to create a new whole or understanding. Both approaches have their unique attributes and strengths, and they often complement each other in a cyclical and iterative process. By employing analysis and synthesis effectively, we can gain a comprehensive understanding of complex phenomena, generate innovative solutions, and make informed decisions.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Library Guides

Literature reviews: synthesis.

• Criticality

Synthesise Information

So, how can you create paragraphs within your literature review that demonstrates your knowledge of the scholarship that has been done in your field of study?  

You will need to present a synthesis of the texts you read.  

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains synthesis for us in the following video:  

Synthesising Texts  

What is synthesis? 

Synthesis is an important element of academic writing, demonstrating comprehension, analysis, evaluation and original creation.  

With synthesis you extract content from different sources to create an original text. While paraphrase and summary maintain the structure of the given source(s), with synthesis you create a new structure.  

The sources will provide different perspectives and evidence on a topic. They will be put together when agreeing, contrasted when disagreeing. The sources must be referenced.  

Perfect your synthesis by showing the flow of your reasoning, expressing critical evaluation of the sources and drawing conclusions.  

When you synthesise think of "using strategic thinking to resolve a problem requiring the integration of diverse pieces of information around a structuring theme" (Mateos and Sole 2009, p448). 

Synthesis is a complex activity, which requires a high degree of comprehension and active engagement with the subject. As you progress in higher education, so increase the expectations on your abilities to synthesise. 

How to synthesise in a literature review: 

Identify themes/issues you'd like to discuss in the literature review. Think of an outline.  

Read the literature and identify these themes/issues.  

Critically analyse the texts asking: how does the text I'm reading relate to the other texts I've read on the same topic? Is it in agreement? Does it differ in its perspective? Is it stronger or weaker? How does it differ (could be scope, methods, year of publication etc.). Draw your conclusions on the state of the literature on the topic.  

Start writing your literature review, structuring it according to the outline you planned.  

Put together sources stating the same point; contrast sources presenting counter-arguments or different points.  

Present your critical analysis.  

Always provide the references. 

The best synthesis requires a "recursive process" whereby you read the source texts, identify relevant parts, take notes, produce drafts, re-read the source texts, revise your text, re-write... (Mateos and Sole, 2009). 

What is good synthesis?  

The quality of your synthesis can be assessed considering the following (Mateos and Sole, 2009, p439):  

Integration and connection of the information from the source texts around a structuring theme. 

Selection of ideas necessary for producing the synthesis. 

Appropriateness of the interpretation.  

Elaboration of the content.  

Example of Synthesis

Original texts (fictitious): 

Synthesis: 

Animal experimentation is a subject of heated debate. Some argue that painful experiments should be banned. Indeed it has been demonstrated that such experiments make animals suffer physically and psychologically (Chowdhury 2012; Panatta and Hudson 2016). On the other hand, it has been argued that animal experimentation can save human lives and reduce harm on humans (Smith 2008). This argument is only valid for toxicological testing, not for tests that, for example, merely improve the efficacy of a cosmetic (Turner 2015). It can be suggested that animal experimentation should be regulated to only allow toxicological risk assessment, and the suffering to the animals should be minimised.   

Bibliography

Mateos, M. and Sole, I. (2009). Synthesising Information from various texts: A Study of Procedures and Products at Different Educational Levels. European Journal of Psychology of Education,  24 (4), 435-451. Available from https://doi.org/10.1007/BF03178760 [Accessed 29 June 2021].

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Analysis has always been at the heart of philosophical method, but it has been understood and practised in many different ways. Perhaps, in its broadest sense, it might be defined as a process of isolating or working back to what is more fundamental by means of which something, initially taken as given, can be explained or reconstructed. The explanation or reconstruction is often then exhibited in a corresponding process of synthesis. This allows great variation in specific method, however. The aim may be to get back to basics, but there may be all sorts of ways of doing this, each of which might be called ‘analysis’. The dominance of ‘analytic’ philosophy in the English-speaking world, and increasingly now in the rest of the world, might suggest that a consensus has formed concerning the role and importance of analysis. This assumes, though, that there is agreement on what ‘analysis’ means, and this is far from clear. On the other hand, Wittgenstein's later critique of analysis in the early (logical atomist) period of analytic philosophy, and Quine's attack on the analytic-synthetic distinction, for example, have led some to claim that we are now in a ‘post-analytic’ age. Such criticisms, however, are only directed at particular conceptions of analysis. If we look at the history of philosophy, and even if we just look at the history of analytic philosophy, we find a rich and extensive repertoire of conceptions of analysis which philosophers have continually drawn upon and reconfigured in different ways. Analytic philosophy is alive and well precisely because of the range of conceptions of analysis that it involves. It may have fragmented into various interlocking subtraditions, but those subtraditions are held together by both their shared history and their methodological interconnections. It is the aim of this article to indicate something of the range of conceptions of analysis in the history of philosophy and their interconnections, and to provide a bibliographical resource for those wishing to explore analytic methodologies and the philosophical issues that they raise.

1.1 Characterizations of Analysis

1.2 guide to this entry.

• Supplementary Document: Definitions and Descriptions of Analysis
• 1. Introduction
• 2. Ancient Greek Geometry
• 4. Aristotle
• 1. Medieval Philosophy
• 2. Renaissance Philosophy
• 2. Descartes and Analytic Geometry
• 3. British Empiricism

5. Modern Conceptions of Analysis, outside Analytic Philosophy

• 5. Wittgenstein
• 6. The Cambridge School of Analysis
• 7. Carnap and Logical Positivism
• 8. Oxford Linguistic Philosophy
• 9. Contemporary Analytic Philosophy

7. Conclusion

Other internet resources, related entries, 1. general introduction.

This section provides a preliminary description of analysis—or the range of different conceptions of analysis—and a guide to this article as a whole.

If asked what ‘analysis’ means, most people today immediately think of breaking something down into its components; and this is how analysis tends to be officially characterized. In the Concise Oxford Dictionary , for example, ‘analysis’ is defined as the “resolution into simpler elements by analysing (opp. synthesis )”, the only other uses mentioned being the mathematical and the psychological [ Quotation ]. And in the Oxford Dictionary of Philosophy , ‘analysis’ is defined as “the process of breaking a concept down into more simple parts, so that its logical structure is displayed” [ Quotation ]. The restriction to concepts and the reference to displaying ‘logical structure’ are important qualifications, but the core conception remains that of breaking something down.

This conception may be called the decompositional conception of analysis (see Section 4 ). But it is not the only conception, and indeed is arguably neither the dominant conception in the pre-modern period nor the conception that is characteristic of at least one major strand in ‘analytic’ philosophy. In ancient Greek thought, ‘analysis’ referred primarily to the process of working back to first principles by means of which something could then be demonstrated. This conception may be called the regressive conception of analysis (see Section 2 ). In the work of Frege and Russell, on the other hand, before the process of decomposition could take place, the statements to be analyzed had first to be translated into their ‘correct’ logical form (see Section 6 ). This suggests that analysis also involves a transformative or interpretive dimension. This too, however, has its roots in earlier thought (see especially the supplementary sections on Ancient Greek Geometry and Medieval Philosophy ).

These three conceptions should not be seen as competing. In actual practices of analysis, which are invariably richer than the accounts that are offered of them, all three conceptions are typically reflected, though to differing degrees and in differing forms. To analyze something, we may first have to interpret it in some way, translating an initial statement, say, into the privileged language of logic, mathematics or science, before articulating the relevant elements and structures, and all in the service of identifying fundamental principles by means of which to explain it. The complexities that this schematic description suggests can only be appreciated by considering particular types of analysis.

Understanding conceptions of analysis is not simply a matter of attending to the use of the word ‘analysis’ and its cognates—or obvious equivalents in languages other than English, such as ‘ analusis ’ in Greek or ‘ Analyse ’ in German. Socratic definition is arguably a form of conceptual analysis, yet the term ‘ analusis ’ does not occur anywhere in Plato's dialogues (see Section 2 below). Nor, indeed, do we find it in Euclid's Elements , which is the classic text for understanding ancient Greek geometry: Euclid presupposed what came to be known as the method of analysis in presenting his proofs ‘synthetically’. In Latin, ‘ resolutio ’ was used to render the Greek word ‘ analusis ’, and although ‘resolution’ has a different range of meanings, it is often used synonymously with ‘analysis’ (see the supplementary section on Renaissance Philosophy ). In Aristotelian syllogistic theory, and especially from the time of Descartes, forms of analysis have also involved ‘reduction’; and in early analytic philosophy it was ‘reduction’ that was seen as the goal of philosophical analysis (see especially the supplementary section on The Cambridge School of Analysis ).

Further details of characterizations of analysis that have been offered in the history of philosophy, including all the classic passages and remarks (to which occurrences of ‘[ Quotation ]’ throughout this entry refer), can be found in the supplementary document on

Definitions and Descriptions of Analysis .

A list of key reference works, monographs and collections can be found in the

Annotated Bibliography, §1 .

This entry comprises three sets of documents:

• The present document
• Six supplementary documents (one of which is not yet available)
• An annotated bibliography on analysis, divided into six documents

The present document provides an overview, with introductions to the various conceptions of analysis in the history of philosophy. It also contains links to the supplementary documents, the documents in the bibliography, and other internet resources. The supplementary documents expand on certain topics under each of the six main sections. The annotated bibliography contains a list of key readings on each topic, and is also divided according to the sections of this entry.

2. Ancient Conceptions of Analysis and the Emergence of the Regressive Conception

The word ‘analysis’ derives from the ancient Greek term ‘ analusis ’. The prefix ‘ ana ’ means ‘up’, and ‘ lusis ’ means ‘loosing’, ‘release’ or ‘separation’, so that ‘ analusis ’ means ‘loosening up’ or ‘dissolution’. The term was readily extended to the solving or dissolving of a problem, and it was in this sense that it was employed in ancient Greek geometry and philosophy. The method of analysis that was developed in ancient Greek geometry had an influence on both Plato and Aristotle. Also important, however, was the influence of Socrates's concern with definition, in which the roots of modern conceptual analysis can be found. What we have in ancient Greek thought, then, is a complex web of methodologies, of which the most important are Socratic definition, which Plato elaborated into his method of division, his related method of hypothesis, which drew on geometrical analysis, and the method(s) that Aristotle developed in his Analytics . Far from a consensus having established itself over the last two millennia, the relationships between these methodologies are the subject of increasing debate today. At the heart of all of them, too, lie the philosophical problems raised by Meno's paradox, which anticipates what we now know as the paradox of analysis, concerning how an analysis can be both correct and informative (see the supplementary section on Moore ), and Plato's attempt to solve it through the theory of recollection, which has spawned a vast literature on its own.

‘Analysis’ was first used in a methodological sense in ancient Greek geometry, and the model that Euclidean geometry provided has been an inspiration ever since. Although Euclid's Elements dates from around 300 BC, and hence after both Plato and Aristotle, it is clear that it draws on the work of many previous geometers, most notably, Theaetetus and Eudoxus, who worked closely with Plato and Aristotle. Plato is even credited by Diogenes Laertius ( LEP , I, 299) with inventing the method of analysis, but whatever the truth of this may be, the influence of geometry starts to show in his middle dialogues, and he certainly encouraged work on geometry in his Academy.

The classic source for our understanding of ancient Greek geometrical analysis is a passage in Pappus's Mathematical Collection , which was composed around 300 AD, and hence drew on a further six centuries of work in geometry from the time of Euclid's Elements :

Now analysis is the way from what is sought—as if it were admitted—through its concomitants ( akolouthôn ) in order[,] to something admitted in synthesis. For in analysis we suppose that which is sought to be already done, and we inquire from what it results, and again what is the antecedent of the latter, until we on our backward way light upon something already known and being first in order. And we call such a method analysis, as being a solution backwards ( anapalin lysin ). In synthesis, on the other hand, we suppose that which was reached last in analysis to be already done, and arranging in their natural order as consequents ( epomena ) the former antecedents and linking them one with another, we in the end arrive at the construction of the thing sought. And this we call synthesis. [ Full Quotation ]

Analysis is clearly being understood here in the regressive sense—as involving the working back from ‘what is sought’, taken as assumed, to something more fundamental by means of which it can then be established, through its converse, synthesis. For example, to demonstrate Pythagoras's theorem—that the square on the hypotenuse of a right-angled triangle is equal to the sum of the squares on the other two sides—we may assume as ‘given’ a right-angled triangle with the three squares drawn on its sides. In investigating the properties of this complex figure we may draw further (auxiliary) lines between particular points and find that there are a number of congruent triangles, from which we can begin to work out the relationship between the relevant areas. Pythagoras's theorem thus depends on theorems about congruent triangles, and once these—and other—theorems have been identified (and themselves proved), Pythagoras's theorem can be proved. (The theorem is demonstrated in Proposition 47 of Book I of Euclid's Elements .)

The basic idea here provides the core of the conception of analysis that one can find reflected, in its different ways, in the work of Plato and Aristotle (see the supplementary sections on Plato and Aristotle ). Although detailed examination of actual practices of analysis reveals more than just regression to first causes, principles or theorems, but decomposition and transformation as well (see especially the supplementary section on Ancient Greek Geometry ), the regressive conception dominated views of analysis until well into the early modern period.

Ancient Greek geometry was not the only source of later conceptions of analysis, however. Plato may not have used the term ‘analysis’ himself, but concern with definition was central to his dialogues, and definitions have often been seen as what ‘conceptual analysis’ should yield. The definition of ‘knowledge’ as ‘justified true belief’ (or ‘true belief with an account’, in more Platonic terms) is perhaps the classic example. Plato's concern may have been with real rather than nominal definitions, with ‘essences’ rather than mental or linguistic contents (see the supplementary section on Plato ), but conceptual analysis, too, has frequently been given a ‘realist’ construal. Certainly, the roots of conceptual analysis can be traced back to Plato's search for definitions, as we shall see in Section 4 below.

Further discussion can be found in the supplementary document on

Ancient Conceptions of Analysis .

Further reading can be found in the

Annotated Bibliography, §2 .

3. Medieval and Renaissance Conceptions of Analysis

Conceptions of analysis in the medieval and renaissance periods were largely influenced by ancient Greek conceptions. But knowledge of these conceptions was often second-hand, filtered through a variety of commentaries and texts that were not always reliable. Medieval and renaissance methodologies tended to be uneasy mixtures of Platonic, Aristotelian, Stoic, Galenic and neo-Platonic elements, many of them claiming to have some root in the geometrical conception of analysis and synthesis. However, in the late medieval period, clearer and more original forms of analysis started to take shape. In the literature on so-called ‘syncategoremata’ and ‘exponibilia’, for example, we can trace the development of a conception of interpretive analysis. Sentences involving more than one quantifier such as ‘Some donkey every man sees’, for example, were recognized as ambiguous, requiring ‘exposition’ to clarify.

In John Buridan's masterpiece of the mid-fourteenth century, the Summulae de Dialectica , we can find all three of the conceptions outlined in Section 1.1 above. He distinguishes explicitly between divisions, definitions and demonstrations, corresponding to decompositional, interpretive and regressive analysis, respectively. Here, in particular, we have anticipations of modern analytic philosophy as much as reworkings of ancient philosophy. Unfortunately, however, these clearer forms of analysis became overshadowed during the Renaissance, despite—or perhaps because of—the growing interest in the original Greek sources. As far as understanding analytic methodologies was concerned, the humanist repudiation of scholastic logic muddied the waters.

Medieval and Renaissance Conceptions of Analysis .

Annotated Bibliography, §3 .

4. Early Modern Conceptions of Analysis and the Development of the Decompositional Conception

The scientific revolution in the seventeenth century brought with it new forms of analysis. The newest of these emerged through the development of more sophisticated mathematical techniques, but even these still had their roots in earlier conceptions of analysis. By the end of the early modern period, decompositional analysis had become dominant (as outlined in what follows), but this, too, took different forms, and the relationships between the various conceptions of analysis were often far from clear.

In common with the Renaissance, the early modern period was marked by a great concern with methodology. This might seem unsurprising in such a revolutionary period, when new techniques for understanding the world were being developed and that understanding itself was being transformed. But what characterizes many of the treatises and remarks on methodology that appeared in the seventeenth century is their appeal, frequently self-conscious, to ancient methods (despite, or perhaps—for diplomatic reasons—because of, the critique of the content of traditional thought), although new wine was generally poured into the old bottles. The model of geometrical analysis was a particular inspiration here, albeit filtered through the Aristotelian tradition, which had assimilated the regressive process of going from theorems to axioms with that of moving from effects to causes (see the supplementary section on Aristotle ). Analysis came to be seen as a method of discovery, working back from what is ordinarily known to the underlying reasons (demonstrating ‘the fact’), and synthesis as a method of proof, working forwards again from what is discovered to what needed explanation (demonstrating ‘the reason why’). Analysis and synthesis were thus taken as complementary, although there remained disagreement over their respective merits.

There is a manuscript by Galileo, dating from around 1589, an appropriated commentary on Aristotle's Posterior Analytics , which shows his concern with methodology, and regressive analysis, in particular (see Wallace 1992a and 1992b). Hobbes wrote a chapter on method in the first part of De Corpore , published in 1655, which offers his own interpretation of the method of analysis and synthesis, where decompositional forms of analysis are articulated alongside regressive forms [ Quotations ]. But perhaps the most influential account of methodology, from the middle of the seventeenth century until well into the nineteenth century, was the fourth part of the Port-Royal Logic , the first edition of which appeared in 1662 and the final revised edition in 1683. Chapter 2 (which was the first chapter in the first edition) opens as follows:

The art of arranging a series of thoughts properly, either for discovering the truth when we do not know it, or for proving to others what we already know, can generally be called method. Hence there are two kinds of method, one for discovering the truth, which is known as analysis , or the method of resolution , and which can also be called the method of discovery . The other is for making the truth understood by others once it is found. This is known as synthesis , or the method of composition , and can also be called the method of instruction . [ Fuller Quotations ]

That a number of different methods might be assimilated here is not noted, although the text does go on to distinguish four main types of ‘issues concerning things’: seeking causes by their effects, seeking effects by their causes, finding the whole from the parts, and looking for another part from the whole and a given part ( ibid ., 234). While the first two involve regressive analysis and synthesis, the third and fourth involve decompositional analysis and synthesis.

As the authors of the Logic make clear, this particular part of their text derives from Descartes's Rules for the Direction of the Mind , written around 1627, but only published posthumously in 1684. The specification of the four types was most likely offered in elaborating Descartes's Rule Thirteen, which states: “If we perfectly understand a problem we must abstract it from every superfluous conception, reduce it to its simplest terms and, by means of an enumeration, divide it up into the smallest possible parts.” ( PW , I, 51. Cf. the editorial comments in PW , I, 54, 77.) The decompositional conception of analysis is explicit here, and if we follow this up into the later Discourse on Method , published in 1637, the focus has clearly shifted from the regressive to the decompositional conception of analysis. All the rules offered in the earlier work have now been reduced to just four. This is how Descartes reports the rules he says he adopted in his scientific and philosophical work:

The first was never to accept anything as true if I did not have evident knowledge of its truth: that is, carefully to avoid precipitate conclusions and preconceptions, and to include nothing more in my judgements than what presented itself to my mind so clearly and so distinctly that I had no occasion to doubt it. The second, to divide each of the difficulties I examined into as many parts as possible and as may be required in order to resolve them better. The third, to direct my thoughts in an orderly manner, by beginning with the simplest and most easily known objects in the order to ascend little by little, step by step, to knowledge of the most complex, and by supposing some order even among objects that have no natural order of precedence. And the last, throughout to make enumerations so complete, and reviews so comprehensive, that I could be sure of leaving nothing out. ( PW , I, 120.)

The first two are rules of analysis and the second two rules of synthesis. But although the analysis/synthesis structure remains, what is involved here is decomposition/composition rather than regression/progression. Nevertheless, Descartes insisted that it was geometry that influenced him here: “Those long chains composed of very simple and easy reasonings, which geometers customarily use to arrive at their most difficult demonstrations, had given me occasion to suppose that all the things which can fall under human knowledge are interconnected in the same way.” ( Ibid . [ Further Quotations ])

Descartes's geometry did indeed involve the breaking down of complex problems into simpler ones. More significant, however, was his use of algebra in developing ‘analytic’ geometry as it came to be called, which allowed geometrical problems to be transformed into arithmetical ones and more easily solved. In representing the ‘unknown’ to be found by ‘ x ’, we can see the central role played in analysis by the idea of taking something as ‘given’ and working back from that, which made it seem appropriate to regard algebra as an ‘art of analysis’, alluding to the regressive conception of the ancients. Illustrated in analytic geometry in its developed form, then, we can see all three of the conceptions of analysis outlined in Section 1.1 above, despite Descartes's own emphasis on the decompositional conception. For further discussion of this, see the supplementary section on Descartes and Analytic Geometry .

Descartes's emphasis on decompositional analysis was not without precedents, however. Not only was it already involved in ancient Greek geometry, but it was also implicit in Plato's method of collection and division. We might explain the shift from regressive to decompositional (conceptual) analysis, as well as the connection between the two, in the following way. Consider a simple example, as represented in the diagram below, ‘collecting’ all animals and ‘dividing’ them into rational and non-rational , in order to define human beings as rational animals.

On this model, in seeking to define anything, we work back up the appropriate classificatory hierarchy to find the higher (i.e., more basic or more general) ‘Forms’, by means of which we can lay down the definition. Although Plato did not himself use the term ‘analysis’—the word for ‘division’ was ‘ dihairesis ’—the finding of the appropriate ‘Forms’ is essentially analysis. As an elaboration of the Socratic search for definitions, we clearly have in this the origins of conceptual analysis. There is little disagreement that ‘Human beings are rational animals’ is the kind of definition we are seeking, defining one concept, the concept human being , in terms of other concepts, the concepts rational and animal . But the construals that have been offered of this have been more problematic. Understanding a classificatory hierarchy extensionally , that is, in terms of the classes of things denoted, the classes higher up are clearly the larger, ‘containing’ the classes lower down as subclasses (e.g., the class of animals includes the class of human beings as one of its subclasses). Intensionally , however, the relationship of ‘containment’ has been seen as holding in the opposite direction. If someone understands the concept human being , at least in the strong sense of knowing its definition, then they must understand the concepts animal and rational ; and it has often then seemed natural to talk of the concept human being as ‘containing’ the concepts rational and animal . Working back up the hierarchy in ‘analysis’ (in the regressive sense) could then come to be identified with ‘unpacking’ or ‘decomposing’ a concept into its ‘constituent’ concepts (‘analysis’ in the decompositional sense). Of course, talking of ‘decomposing’ a concept into its ‘constituents’ is, strictly speaking, only a metaphor (as Quine was famously to remark in §1 of ‘Two Dogmas of Empiricism’), but in the early modern period, this began to be taken more literally.

For further discussion, see the supplementary document on

Early Modern Conceptions of Analysis ,

which contains sections on Descartes and Analytic Geometry, British Empiricism, Leibniz, and Kant.

For further reading, see the

Annotated Bibliography, §4 .

As suggested in the supplementary document on Kant , the decompositional conception of analysis found its classic statement in the work of Kant at the end of the eighteenth century. But Kant was only expressing a conception widespread at the time. The conception can be found in a very blatant form, for example, in the writings of Moses Mendelssohn, for whom, unlike Kant, it was applicable even in the case of geometry [ Quotation ]. Typified in Kant's and Mendelssohn's view of concepts, it was also reflected in scientific practice. Indeed, its popularity was fostered by the chemical revolution inaugurated by Lavoisier in the late eighteenth century, the comparison between philosophical analysis and chemical analysis being frequently drawn. As Lichtenberg put it, “Whichever way you look at it, philosophy is always analytical chemistry” [ Quotation ].

This decompositional conception of analysis set the methodological agenda for philosophical approaches and debates in the (late) modern period (nineteenth and twentieth centuries). Responses and developments, very broadly, can be divided into two. On the one hand, an essentially decompositional conception of analysis was accepted, but a critical attitude was adopted towards it. If analysis simply involved breaking something down, then it appeared destructive and life-diminishing, and the critique of analysis that this view engendered was a common theme in idealism and romanticism in all its main varieties—from German, British and French to North American. One finds it reflected, for example, in remarks about the negating and soul-destroying power of analytical thinking by Schiller [ Quotation ], Hegel [ Quotation ] and de Chardin [ Quotation ], in Bradley's doctrine that analysis is falsification [ Quotation ], and in the emphasis placed by Bergson on ‘intuition’ [ Quotation ].

On the other hand, analysis was seen more positively, but the Kantian conception underwent a certain degree of modification and development. In the nineteenth century, this was exemplified, in particular, by Bolzano and the neo-Kantians. Bolzano's most important innovation was the method of variation, which involves considering what happens to the truth-value of a sentence when a constituent term is substituted by another. This formed the basis for his reconstruction of the analytic/synthetic distinction, Kant's account of which he found defective. The neo-Kantians emphasized the role of structure in conceptualized experience and had a greater appreciation of forms of analysis in mathematics and science. In many ways, their work attempts to do justice to philosophical and scientific practice while recognizing the central idealist claim that analysis is a kind of abstraction that inevitably involves falsification or distortion. On the neo-Kantian view, the complexity of experience is a complexity of form and content rather than of separable constituents, requiring analysis into ‘moments’ or ‘aspects’ rather than ‘elements’ or ‘parts’. In the 1910s, the idea was articulated with great subtlety by Ernst Cassirer [ Quotation ], and became familiar in Gestalt psychology.

In the twentieth century, both analytic philosophy and phenomenology can be seen as developing far more sophisticated conceptions of analysis, which draw on but go beyond mere decompositional analysis. The following Section offers an account of analysis in analytic philosophy, illustrating the range and richness of the conceptions and practices that arose. But it is important to see these in the wider context of twentieth-century methodological practices and debates, for it is not just in ‘analytic’ philosophy—despite its name—that analytic methods are accorded a central role. Phenomenology, in particular, contains its own distinctive set of analytic methods, with similarities and differences to those of analytic philosophy. Phenomenological analysis has frequently been compared to conceptual clarification in the ordinary language tradition, for example, and the method of ‘phenomenological reduction’ that Husserl invented in 1905 offers a striking parallel to the reductive project opened up by Russell's theory of descriptions, which also made its appearance in 1905.

Just like Frege and Russell, Husserl's initial concern was with the foundations of mathematics, and in this shared concern we can see the continued influence of the regressive conception of analysis. According to Husserl, the aim of ‘eidetic reduction’, as he called it, was to isolate the ‘essences’ that underlie our various forms of thinking, and to apprehend them by ‘essential intuition’ (‘ Wesenserschauung ’). The terminology may be different, but this resembles Russell's early project to identify the ‘indefinables’ of philosophical logic, as he described it, and to apprehend them by ‘acquaintance’ (cf. POM , xx). Furthermore, in Husserl's later discussion of ‘explication’ (cf. EJ , §§ 22-4 [ Quotations ]), we find appreciation of the ‘transformative’ dimension of analysis, which can be fruitfully compared with Carnap's account of explication (see the supplementary section on Carnap and Logical Positivism ). Carnap himself describes Husserl's idea here as one of “the synthesis of identification between a confused, nonarticulated sense and a subsequently intended distinct, articulated sense” (1950, 3 [ Quotation ]).

Phenomenology is not the only source of analytic methodologies outside those of the analytic tradition. Mention might be made here, too, of R. G. Collingwood, working within the tradition of British idealism, which was still a powerful force prior to the Second World War. In his Essay on Philosophical Method (1933), for example, he criticizes Moorean philosophy, and develops his own response to what is essentially the paradox of analysis (concerning how an analysis can be both correct and informative), which he recognizes as having its root in Meno's paradox. In his Essay on Metaphysics (1940), he puts forward his own conception of metaphysical analysis, in direct response to what he perceived as the mistaken repudiation of metaphysics by the logical positivists. Metaphysical analysis is characterized here as the detection of ‘absolute presuppositions’, which are taken as underlying and shaping the various conceptual practices that can be identified in the history of philosophy and science. Even among those explicitly critical of central strands in analytic philosophy, then, analysis in one form or another can still be seen as alive and well.

Annotated Bibliography, §5 .

6. Conceptions of Analysis in Analytic Philosophy and the Introduction of the Logical (Transformative) Conception

If anything characterizes ‘analytic’ philosophy, then it is presumably the emphasis placed on analysis. But as the foregoing sections have shown, there is a wide range of conceptions of analysis, so such a characterization says nothing that would distinguish analytic philosophy from much of what has either preceded or developed alongside it. Given that the decompositional conception is usually offered as the main conception today, it might be thought that it is this that characterizes analytic philosophy. But this conception was prevalent in the early modern period, shared by both the British Empiricists and Leibniz, for example. Given that Kant denied the importance of decompositional analysis, however, it might be suggested that what characterizes analytic philosophy is the value it places on such analysis. This might be true of Moore's early work, and of one strand within analytic philosophy; but it is not generally true. What characterizes analytic philosophy as it was founded by Frege and Russell is the role played by logical analysis , which depended on the development of modern logic. Although other and subsequent forms of analysis, such as linguistic analysis, were less wedded to systems of formal logic, the central insight motivating logical analysis remained.

Pappus's account of method in ancient Greek geometry suggests that the regressive conception of analysis was dominant at the time—however much other conceptions may also have been implicitly involved (see the supplementary section on Ancient Greek Geometry ). In the early modern period, the decompositional conception became widespread (see Section 4 ). What characterizes analytic philosophy—or at least that central strand that originates in the work of Frege and Russell—is the recognition of what was called earlier the transformative or interpretive dimension of analysis (see Section 1.1 ). Any analysis presupposes a particular framework of interpretation, and work is done in interpreting what we are seeking to analyze as part of the process of regression and decomposition. This may involve transforming it in some way, in order for the resources of a given theory or conceptual framework to be brought to bear. Euclidean geometry provides a good illustration of this. But it is even more obvious in the case of analytic geometry, where the geometrical problem is first ‘translated’ into the language of algebra and arithmetic in order to solve it more easily (see the supplementary section on Descartes and Analytic Geometry ). What Descartes and Fermat did for analytic geometry, Frege and Russell did for analytic philosophy. Analytic philosophy is ‘analytic’ much more in the sense that analytic geometry is ‘analytic’ than in the crude decompositional sense that Kant understood it.

The interpretive dimension of modern philosophical analysis can also be seen as anticipated in medieval scholasticism (see the supplementary section on Medieval Philosophy ), and it is remarkable just how much of modern concerns with propositions, meaning, reference, and so on, can be found in the medieval literature. Interpretive analysis is also illustrated in the nineteenth century by Bentham's conception of paraphrasis , which he characterized as “that sort of exposition which may be afforded by transmuting into a proposition, having for its subject some real entity, a proposition which has not for its subject any other than a fictitious entity” [ Full Quotation ]. He applied the idea in ‘analyzing away’ talk of ‘obligations’, and the anticipation that we can see here of Russell's theory of descriptions has been noted by, among others, Wisdom (1931) and Quine in ‘Five Milestones of Empiricism’ [ Quotation ].

What was crucial in the emergence of twentieth-century analytic philosophy, however, was the development of quantificational theory, which provided a far more powerful interpretive system than anything that had hitherto been available. In the case of Frege and Russell, the system into which statements were ‘translated’ was predicate logic, and the divergence that was thereby opened up between grammatical and logical form meant that the process of translation itself became an issue of philosophical concern. This induced greater self-consciousness about our use of language and its potential to mislead us, and inevitably raised semantic, epistemological and metaphysical questions about the relationships between language, logic, thought and reality which have been at the core of analytic philosophy ever since.

Both Frege and Russell (after the latter's initial flirtation with idealism) were concerned to show, against Kant, that arithmetic is a system of analytic and not synthetic truths. In the Grundlagen , Frege had offered a revised conception of analyticity, which arguably endorses and generalizes Kant's logical as opposed to phenomenological criterion, i.e., (AN L ) rather than (AN O ) (see the supplementary section on Kant ):

(AN) A truth is analytic if its proof depends only on general logical laws and definitions.

The question of whether arithmetical truths are analytic then comes down to the question of whether they can be derived purely logically. (Here we already have ‘transformation’, at the theoretical level—involving a reinterpretation of the concept of analyticity.) To demonstrate this, Frege realized that he needed to develop logical theory in order to formalize mathematical statements, which typically involve multiple generality (e.g., ‘Every natural number has a successor’, i.e. ‘For every natural number x there is another natural number y that is the successor of x ’). This development, by extending the use of function-argument analysis in mathematics to logic and providing a notation for quantification, was essentially the achievement of his first book, the Begriffsschrift (1879), where he not only created the first system of predicate logic but also, using it, succeeded in giving a logical analysis of mathematical induction (see Frege FR , 47-78).

In his second book, Die Grundlagen der Arithmetik (1884), Frege went on to provide a logical analysis of number statements. His central idea was that a number statement contains an assertion about a concept. A statement such as ‘Jupiter has four moons’ is to be understood not as predicating of Jupiter the property of having four moons, but as predicating of the concept moon of Jupiter the second-level property has four instances , which can be logically defined. The significance of this construal can be brought out by considering negative existential statements (which are equivalent to number statements involving the number 0). Take the following negative existential statement:

(0a) Unicorns do not exist.

If we attempt to analyze this decompositionally , taking its grammatical form to mirror its logical form, then we find ourselves asking what these unicorns are that have the property of non-existence. We may then be forced to posit the subsistence —as opposed to existence —of unicorns, just as Meinong and the early Russell did, in order for there to be something that is the subject of our statement. On the Fregean account, however, to deny that something exists is to say that the relevant concept has no instances: there is no need to posit any mysterious object . The Fregean analysis of (0a) consists in rephrasing it into (0b), which can then be readily formalized in the new logic as (0c):

(0b) The concept unicorn is not instantiated. (0c) ~(∃ x ) Fx .

Similarly, to say that God exists is to say that the concept God is (uniquely) instantiated, i.e., to deny that the concept has 0 instances (or 2 or more instances). On this view, existence is no longer seen as a (first-level) predicate, but instead, existential statements are analyzed in terms of the (second-level) predicate is instantiated , represented by means of the existential quantifier. As Frege notes, this offers a neat diagnosis of what is wrong with the ontological argument, at least in its traditional form ( GL , §53). All the problems that arise if we try to apply decompositional analysis (at least straight off) simply drop away, although an account is still needed, of course, of concepts and quantifiers.

The possibilities that this strategy of ‘translating’ into a logical language opens up are enormous: we are no longer forced to treat the surface grammatical form of a statement as a guide to its ‘real’ form, and are provided with a means of representing that form. This is the value of logical analysis: it allows us to ‘analyze away’ problematic linguistic expressions and explain what it is ‘really’ going on. This strategy was employed, most famously, in Russell's theory of descriptions, which was a major motivation behind the ideas of Wittgenstein's Tractatus (see the supplementary sections on Russell and Wittgenstein ). Although subsequent philosophers were to question the assumption that there could ever be a definitive logical analysis of a given statement, the idea that ordinary language may be systematically misleading has remained.

To illustrate this, consider the following examples from Ryle's classic 1932 paper, ‘Systematically Misleading Expressions’:

(Ua) Unpunctuality is reprehensible. (Ta) Jones hates the thought of going to hospital.

In each case, we might be tempted to make unnecessary reifications, taking ‘unpunctuality’ and ‘the thought of going to hospital’ as referring to objects. It is because of this that Ryle describes such expressions as ‘systematically misleading’. (Ua) and (Ta) must therefore be rephrased:

(Ub) Whoever is unpunctual deserves that other people should reprove him for being unpunctual. (Tb) Jones feels distressed when he thinks of what he will undergo if he goes to hospital.

In these formulations, there is no overt talk at all of ‘unpunctuality’ or ‘thoughts’, and hence nothing to tempt us to posit the existence of any corresponding entities. The problems that otherwise arise have thus been ‘analyzed away’.

At the time that Ryle wrote ‘Systematically Misleading Expressions’, he, too, assumed that every statement had an underlying logical form that was to be exhibited in its ‘correct’ formulation [ Quotations ]. But when he gave up this assumption (for reasons indicated in the supplementary section on The Cambridge School of Analysis ), he did not give up the motivating idea of logical analysis—to show what is wrong with misleading expressions. In The Concept of Mind (1949), for example, he sought to explain what he called the ‘category-mistake’ involved in talk of the mind as a kind of ‘Ghost in the Machine’. His aim, he wrote, was to “rectify the logical geography of the knowledge which we already possess” (1949, 9), an idea that was to lead to the articulation of connective rather than reductive conceptions of analysis, the emphasis being placed on elucidating the relationships between concepts without assuming that there is a privileged set of intrinsically basic concepts (see the supplementary section on Oxford Linguistic Philosophy ).

What these various forms of logical analysis suggest, then, is that what characterizes analysis in analytic philosophy is something far richer than the mere ‘decomposition’ of a concept into its ‘constituents’. But this is not to say that the decompositional conception of analysis plays no role at all. It can be found in the early work of Moore, for example (see the supplementary section on Moore ). It might also be seen as reflected in the approach to the analysis of concepts that seeks to specify the necessary and sufficient conditions for their correct employment. Conceptual analysis in this sense goes back to the Socrates of Plato's early dialogues (see the supplementary section on Plato ). But it arguably reached its heyday in the 1950s and 1960s. As mentioned in Section 2 above, the definition of ‘knowledge’ as ‘justified true belief’ is perhaps the most famous example; and this definition was criticised in Gettier's classic paper of 1963. (For details of this, see the entry in this Encyclopedia on The Analysis of Knowledge .) The specification of necessary and sufficient conditions may no longer be seen as the primary aim of conceptual analysis, especially in the case of philosophical concepts such as ‘knowledge’, which are fiercely contested; but consideration of such conditions remains a useful tool in the analytic philosopher's toolbag.

For a more detailed account of the these and related conceptions of analysis, see the supplementary document on

Conceptions of Analysis in Analytic Philosophy .

Annotated Bibliography, §6 .

The history of philosophy reveals a rich source of conceptions of analysis. Their origin may lie in ancient Greek geometry, and to this extent the history of analytic methodologies might be seen as a series of footnotes to Euclid. But analysis developed in different though related ways in the two traditions stemming from Plato and Aristotle, the former based on the search for definitions and the latter on the idea of regression to first causes. The two poles represented in these traditions defined methodological space until well into the early modern period, and in some sense is still reflected today. The creation of analytic geometry in the seventeenth century introduced a more reductive form of analysis, and an analogous and even more powerful form was introduced around the turn of the twentieth century in the logical work of Frege and Russell. Although conceptual analysis, construed decompositionally from the time of Leibniz and Kant, and mediated by the work of Moore, is often viewed as characteristic of analytic philosophy, logical analysis, taken as involving translation into a logical system, is what inaugurated the analytic tradition. Analysis has also frequently been seen as reductive, but connective forms of analysis are no less important. Connective analysis, historically inflected, would seem to be particularly appropriate, for example, in understanding analysis itself.

What follows here is a selection of thirty classic and recent works published over the last half-century that together cover the range of different conceptions of analysis in the history of philosophy. A fuller bibliography, which includes all references cited, is provided as a set of supplementary documents, divided to correspond to the sections of this entry:

Annotated Bibliography on Analysis

• Baker, Gordon, 2004, Wittgenstein's Method , Oxford: Blackwell, especially essays 1, 3, 4, 10, 12
• Baldwin, Thomas, 1990, G.E. Moore , London: Routledge, ch. 7
• Beaney, Michael, 2004, ‘Carnap's Conception of Explication: From Frege to Husserl?’, in S. Awodey and C. Klein, (eds.), Carnap Brought Home: The View from Jena , Chicago: Open Court, pp. 117-50
• –––, 2005, ‘Collingwood's Conception of Presuppositional Analysis’, Collingwood and British Idealism Studies 11, no. 2, 41-114
• –––, (ed.), 2007, The Analytic Turn: Analysis in Early Analytic Philosophy and Phenomenology , London: Routledge [includes papers on Frege, Russell, Wittgenstein, C.I. Lewis, Bolzano, Husserl]
• Byrne, Patrick H., 1997, Analysis and Science in Aristotle , Albany: State University of New York Press
• Cohen, L. Jonathan, 1986, The Dialogue of Reason: An Analysis of Analytical Philosophy , Oxford: Oxford University Press, chs. 1-2
• Dummett, Michael, 1991, Frege: Philosophy of Mathematics , London: Duckworth, chs. 3-4, 9-16
• Engfer, Hans-Jürgen, 1982, Philosophie als Analysis , Stuttgart-Bad Cannstatt: Frommann-Holzboog [Descartes, Leibniz, Wolff, Kant]
• Garrett, Aaron V., 2003, Meaning in Spinoza's Method , Cambridge: Cambridge University Press, ch. 4
• Gaukroger, Stephen, 1989, Cartesian Logic , Oxford: Oxford University Press, ch. 3
• Gentzler, Jyl, (ed.), 1998, Method in Ancient Philosophy , Oxford: Oxford University Press [includes papers on Socrates, Plato, Aristotle, mathematics and medicine]
• Gilbert, Neal W., 1960, Renaissance Concepts of Method , New York: Columbia University Press
• Hacker, P.M.S., 1996, Wittgenstein's Place in Twentieth-Century Analytic Philosophy , Oxford: Blackwell
• Hintikka, Jaakko and Remes, Unto, 1974, The Method of Analysis , Dordrecht: D. Reidel [ancient Greek geometrical analysis]
• Hylton, Peter, 2005, Propositions, Functions, Analysis: Selected Essays on Russell's Philosophy , Oxford: Oxford University Press
• –––, 2007, Quine , London: Routledge, ch. 9
• Jackson, Frank, 1998, From Metaphysics to Ethics: A Defence of Conceptual Analysis , Oxford: Oxford University Press, chs. 2-3
• Kretzmann, Norman, 1982, ‘Syncategoremata, exponibilia, sophistimata’, in N. Kretzmann et al. , (eds.), The Cambridge History of Later Medieval Philosophy , Cambridge: Cambridge University Press, 211-45
• Menn, Stephen, 2002, ‘Plato and the Method of Analysis’, Phronesis 47, 193-223
• Otte, Michael and Panza, Marco, (eds.), 1997, Analysis and Synthesis in Mathematics , Dordrecht: Kluwer
• Rorty, Richard, (ed.), 1967, The Linguistic Turn , Chicago: University of Chicago Press [includes papers on analytic methodology]
• Rosen, Stanley, 1980, The Limits of Analysis , New York: Basic Books, repr. Indiana: St. Augustine's Press, 2000 [critique of analytic philosophy from a ‘continental’ perspective]
• Sayre, Kenneth M., 1969, Plato's Analytic Method , Chicago: University of Chicago Press
• –––, 2006, Metaphysics and Method in Plato's Statesman , Cambridge: Cambridge University Press, Part I
• Soames, Scott, 2003, Philosophical Analysis in the Twentieth Century , Volume 1: The Dawn of Analysis , Volume 2: The Age of Meaning , New Jersey: Princeton University Press [includes chapters on Moore, Russell, Wittgenstein, logical positivism, Quine, ordinary language philosophy, Davidson, Kripke]
• Strawson, P.F., 1992, Analysis and Metaphysics: An Introduction to Philosophy , Oxford: Oxford University Press, chs. 1-2
• Sweeney, Eileen C., 1994, ‘Three Notions of Resolutio and the Structure of Reasoning in Aquinas’, The Thomist 58, 197-243
• Timmermans, Benoît, 1995, La résolution des problèmes de Descartes à Kant , Paris: Presses Universitaires de France
• Urmson, J.O., 1956, Philosophical Analysis: Its Development between the Two World Wars , Oxford: Oxford University Press

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

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abstract objects | analytic/synthetic distinction | Aristotle | Bolzano, Bernard | Buridan, John [Jean] | Descartes, René | descriptions | Frege, Gottlob | Kant, Immanuel | knowledge: analysis of | Leibniz, Gottfried Wilhelm | logical constructions | logical form | Moore, George Edward | necessary and sufficient conditions | Ockham [Occam], William | Plato | Russell, Bertrand | Wittgenstein, Ludwig

Acknowledgments

In first composing this entry (in 2002-3) and then revising the main entry and bibliography (in 2007), I have drawn on a number of my published writings (especially Beaney 1996, 2000, 2002, 2007b, 2007c; see Annotated Bibliography §6.1 , §6.2 ). I am grateful to the respective publishers for permission to use this material. Research on conceptions of analysis in the history of philosophy was initially undertaken while a Research Fellow at the Institut für Philosophie of the University of Erlangen-Nürnberg during 1999-2000, and further work was carried out while a Research Fellow at the Institut für Philosophie of the University of Jena during 2006-7, in both cases funded by the Alexander von Humboldt-Stiftung. In the former case, the account was written up while at the Open University (UK), and in the latter case, I had additional research leave from the University of York. I acknowledge the generous support given to me by all five institutions. I am also grateful to the editors of this Encyclopedia, and to Gideon Rosen and Edward N. Zalta, in particular, for comments and suggestions on the content and organisation of this entry in both its initial and revised form. I would like to thank John Ongley, too, for reviewing the first version of this entry, which has helped me to improve it (see Annotated Bibliography §1.3 ). In updating the bibliography (in 2007), I am indebted to various people who have notified me of relevant works, and especially, Gyula Klima (regarding §2.1), Anna-Sophie Heinemann (regarding §§ 4.2 and 4.4), and Jan Wolenski (regarding §5.3). I invite anyone who has further suggestions of items to be included or comments on the article itself to email me at the address given below.

Copyright © 2014 by Michael Beaney < michael . beaney @ hu-berlin . de >

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Using Evidence: Synthesis

Synthesis video playlist.

Note that these videos were created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

Basics of Synthesis

As you incorporate published writing into your own writing, you should aim for synthesis of the material.

Synthesizing requires critical reading and thinking in order to compare different material, highlighting similarities, differences, and connections. When writers synthesize successfully, they present new ideas based on interpretations of other evidence or arguments. You can also think of synthesis as an extension of—or a more complicated form of—analysis. One main difference is that synthesis involves multiple sources, while analysis often focuses on one source.

Conceptually, it can be helpful to think about synthesis existing at both the local (or paragraph) level and the global (or paper) level.

Local Synthesis

Local synthesis occurs at the paragraph level when writers connect individual pieces of evidence from multiple sources to support a paragraph’s main idea and advance a paper’s thesis statement. A common example in academic writing is a scholarly paragraph that includes a main idea, evidence from multiple sources, and analysis of those multiple sources together.

Global Synthesis

Global synthesis occurs at the paper (or, sometimes, section) level when writers connect ideas across paragraphs or sections to create a new narrative whole. A literature review , which can either stand alone or be a section/chapter within a capstone, is a common example of a place where global synthesis is necessary. However, in almost all academic writing, global synthesis is created by and sometimes referred to as good cohesion and flow.

Synthesis in Literature Reviews

While any types of scholarly writing can include synthesis, it is most often discussed in the context of literature reviews. Visit our literature review pages for more information about synthesis in literature reviews.

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Analysis and Synthesis Explained

Posted by Thomas DeMichele on August 8, 2017 in Reference

Understanding Analysis and Synthesis

In simple terms , analysis examines a system by dividing a whole into its parts, and synthesis examines a system by combining and comparing parts.

In more complete terms , analysis is the top-down process of examining a rational or material system by analyzing its properties to better understand a system and its parts, and synthesis is the bottom-up process of examining the parts of systems and systems themselves, and comparing them to each other, to better understand systems whole, properties of systems, and their relations. [1] [2] [3]

The origin of the terms analysis and synthesis : The terms analysis and synthesis come from classical Greek though and literally mean “to loosen up” and “to put together” respectively. Analysis is “to loosen up” to examine from the top down, from the system to its properties.  Synthesis is to “to put together,” to examine from the bottom-up, to start with the pieces and see how they fit together. Both are simply different directions from which we can approach examining a rational or material (idea-based or real) system. See:  Analysis and Synthesis On Scientific Method – Based on a Study by Bernhard Riemann  for more reading after you check out the quick introduction to analysis and synthesis below.

Analysis and Synthesis Defined

To define the above in more complete terms:

A system  is a collection of properties . It can either be simple, like a binary system, or complex, like the ecosystem of earth. With that said, a system as a whole and its properties can be rational (idea-based and “formal”), empirical (real and “material”), or mixed (for example, a computer contains rational properties like software and material properties like hardware). Thus, a system in this sense is simply any collection of real or imagined properties from a system of numbers, to an atom, to a collection of atoms as an object, to a collection of objects, to an essay, to the universe.

Analysis breaks down systems into this simplest parts in order to understand each individual part and the system as a whole.

Synthesis compares the parts of one or more systems, or even whole systems themselves, in order to understand the relations between systems and parts of systems.

Analysis and Synthesis Explained in More Detail With Examples

To further illustrate Analysis and Synthesis, we can offer the following example.

Say we have 3 systems, let’s call them X, Y, and Z.

• Analysis would take each system and dissect that system to see what was true about each system respectively, defining the properties of X, Y, and Z, then defining the properties of those properties until no further analysis could be done.
• Synthesis on the other hand would look at each part of each system individually (from the most basic property to the most complex) to see how those parts related to other parts in the system, to see how those parts related to parts of the other systems, and to see how the systems related to each other. In doing this it would attempt to consider all possible relations, considering two given properties at a time, or considering two or more properties or systems together.

Since analysis deals with what is true about a system (as it examines what properties are contained in the system), it generally uses top-down deductive reasoning to understand what is certain about a system (unless we observed or measured the system incorrectly, what we glean from the system should have a degree of certainty to it; ex. we look at the black cat, we notice the property blackness, we can thus be fairly sure “the black cat is black.”).

Since synthesis deals with the relations between systems and properties of systems, it generally uses bottom-up inductive reasoning to see what is likely true about a property of a system or system of properties in terms of their relation to other systems and/or properties. Because it is comparing systems, and inferring relations based on a sort of reasoning by analogy, it can tend to produce insights that while valuable are less certain than those gleaned by analysis.

Both of these reasoning types, analysis and synthesis, can work in tandem or in isolation. After-all, to preform synthesis we must have first analyzed a system, and once we have preformed synthesis, we could very well uncover new aspects of a system to analyze.

In other words, analysis and synthesis are the two main directions from which we can approach studying any rational (idea-based and “formal”) or empirical (real and “material”) system.

TIP : Another idea from Greek thought is Plato’s Dialectic and Aristotle’s Golden Mean . Learn how we can use analysis and synthesis to explore dualities. Or, learn about rationalism and empiricism and how we can apply analysis and synthesis to logic and reason . These concepts are at the heart of not only human reason , but well known systems like the scientific method. So there is a lot to learn about.

• Critical Thinking: Analysis and Synthesis
• Analysis, Synthesis, and Doing Homework
• Analysis and Synthesis On Scientific Method – Based on a Study by Bernhard Riemann

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Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Synthesizing Sources

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When you look for areas where your sources agree or disagree and try to draw broader conclusions about your topic based on what your sources say, you are engaging in synthesis. Writing a research paper usually requires synthesizing the available sources in order to provide new insight or a different perspective into your particular topic (as opposed to simply restating what each individual source says about your research topic).

Note that synthesizing is not the same as summarizing.  

• A summary restates the information in one or more sources without providing new insight or reaching new conclusions.
• A synthesis draws on multiple sources to reach a broader conclusion.

There are two types of syntheses: explanatory syntheses and argumentative syntheses . Explanatory syntheses seek to bring sources together to explain a perspective and the reasoning behind it. Argumentative syntheses seek to bring sources together to make an argument. Both types of synthesis involve looking for relationships between sources and drawing conclusions.

In order to successfully synthesize your sources, you might begin by grouping your sources by topic and looking for connections. For example, if you were researching the pros and cons of encouraging healthy eating in children, you would want to separate your sources to find which ones agree with each other and which ones disagree.

After you have a good idea of what your sources are saying, you want to construct your body paragraphs in a way that acknowledges different sources and highlights where you can draw new conclusions.

As you continue synthesizing, here are a few points to remember:

• Don’t force a relationship between sources if there isn’t one. Not all of your sources have to complement one another.
• Do your best to highlight the relationships between sources in very clear ways.
• Don’t ignore any outliers in your research. It’s important to take note of every perspective (even those that disagree with your broader conclusions).

Example Syntheses

Below are two examples of synthesis: one where synthesis is NOT utilized well, and one where it is.

Parents are always trying to find ways to encourage healthy eating in their children. Elena Pearl Ben-Joseph, a doctor and writer for KidsHealth , encourages parents to be role models for their children by not dieting or vocalizing concerns about their body image. The first popular diet began in 1863. William Banting named it the “Banting” diet after himself, and it consisted of eating fruits, vegetables, meat, and dry wine. Despite the fact that dieting has been around for over a hundred and fifty years, parents should not diet because it hinders children’s understanding of healthy eating.

In this sample paragraph, the paragraph begins with one idea then drastically shifts to another. Rather than comparing the sources, the author simply describes their content. This leads the paragraph to veer in an different direction at the end, and it prevents the paragraph from expressing any strong arguments or conclusions.

An example of a stronger synthesis can be found below.

Parents are always trying to find ways to encourage healthy eating in their children. Different scientists and educators have different strategies for promoting a well-rounded diet while still encouraging body positivity in children. David R. Just and Joseph Price suggest in their article “Using Incentives to Encourage Healthy Eating in Children” that children are more likely to eat fruits and vegetables if they are given a reward (855-856). Similarly, Elena Pearl Ben-Joseph, a doctor and writer for Kids Health , encourages parents to be role models for their children. She states that “parents who are always dieting or complaining about their bodies may foster these same negative feelings in their kids. Try to keep a positive approach about food” (Ben-Joseph). Martha J. Nepper and Weiwen Chai support Ben-Joseph’s suggestions in their article “Parents’ Barriers and Strategies to Promote Healthy Eating among School-age Children.” Nepper and Chai note, “Parents felt that patience, consistency, educating themselves on proper nutrition, and having more healthy foods available in the home were important strategies when developing healthy eating habits for their children.” By following some of these ideas, parents can help their children develop healthy eating habits while still maintaining body positivity.

In this example, the author puts different sources in conversation with one another. Rather than simply describing the content of the sources in order, the author uses transitions (like "similarly") and makes the relationship between the sources evident.

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A 7-Step Guideline for Qualitative Synthesis and Meta-Analysis of Observational Studies in Health Sciences

Marija glisic.

1 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

2 Swiss Paraplegic Research, Nottwil, Switzerland

Peter Francis Raguindin

3 Graduate School for Health Sciences, University of Bern, Bern, Switzerland

4 Faculty of Health Science and Medicine, University of Lucerne, Lucerne, Switzerland

Armin Gemperli

5 Institute of Primary and Community Care, University of Lucerne, Lucerne, Switzerland

Petek Eylul Taneri

6 HRB-Trials Methodology Research Network, National University of Ireland, Galway, Ireland

Dante Jr. Salvador

Trudy voortman.

7 Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, Netherlands

8 Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands

Pedro Marques Vidal

9 Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland

Stefania I. Papatheodorou

10 Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States

Setor K. Kunutsor

11 Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, United Kingdom

12 Translational Health Sciences, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol, United Kingdom

Arjola Bano

13 Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

John P. A. Ioannidis

14 Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States

15 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, United States

16 Department of Statistics, Stanford University, Stanford, CA, United States

17 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, United States

Taulant Muka

18 Epistudia, Bern, Switzerland

Associated Data

Objectives: To provide a step-by-step, easy-to-understand, practical guide for systematic review and meta-analysis of observational studies.

Methods: A multidisciplinary team of researchers with extensive experience in observational studies and systematic review and meta-analysis was established. Previous guidelines in evidence synthesis were considered.

Results: There is inherent variability in observational study design, population, and analysis, making evidence synthesis challenging. We provided a framework and discussed basic meta-analysis concepts to assist reviewers in making informed decisions. We also explained several statistical tools for dealing with heterogeneity, probing for bias, and interpreting findings. Finally, we briefly discussed issues and caveats for translating results into clinical and public health recommendations. Our guideline complements “A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research” and addresses peculiarities for observational studies previously unexplored.

Conclusion: We provided 7 steps to synthesize evidence from observational studies. We encourage medical and public health practitioners who answer important questions to systematically integrate evidence from observational studies and contribute evidence-based decision-making in health sciences.

Introduction

Observational studies are more common than experimental studies ( 1 , 2 ). Moreover, many systematic reviews and meta-analyses (SRMA) integrate evidence from observational studies. When undertaking synthesis and MA, it is crucial to understand properties, methodologies, and limitations among different observational study designs and association estimates derived from these studies. Different study designs influence variability in results among studies, and thus heterogeneity and conclusions ( Supplementary Material S1 ). Specific study type considerations and methodological features include (among others): study participant selection and study sample representation; measurement and characterization methods for exposure and extent of information bias; potential confounders and outcomes; design-specific contributions leading to bias; and methods used to analyze the data. Furthermore, observational studies may have a wider array of selective reporting biases than randomized trials. Most observational studies are unregistered, and typically more degrees of analytical flexibility and choice of analyses report such designs compared with randomized trials, leading to more variable results and potential bias ( 3 ). These methodological components influence study design suitability and result in trustworthiness for SRMA. Indeed, evidence shows that MAs of observational studies often suffer methodologically ( 1 ), and despite statistical or other summary result significance, many observational studies demonstrate low credibility ( 2 ). Observational data often complement evidence from randomized controlled trials (RCTs) when shaping public health and clinical guidelines and recommendations. Yet, observational data for informing public health and clinical decision-making are inconsistently available in SRMAs. Therefore, we provide concise guidance for combining results in a MA of observational studies.

The current guideline was developed by a multidisciplinary team of researchers with extensive experience in SRMAs. The guide extends a previous guideline ( 4 ) and provides further recommendations for synthesizing and pooling results from observational data. For this, we considered previous guidances for SRMA of observational studies ( 5 – 7 ), and acknowledged several contentious points concerning optimal methods for MA of observational studies ( 8 ). We explicitly address such uncertainties and offer definitive recommendations for uncontested best practices. Finally, we offer guidance relevant to diverse types of observational data subject to SRMA. However, the range of observational data types, such as adverse drug events, genetic associations, effectiveness studies, nutritional associations, air pollution, and prevalence studies, is broad. Therefore, proper evidence synthesis requires knowledge of best SR practices and field-specific subject matter.

Step-by-Step Guide

The overall step-by-step guidance is visualized in Figure 1 .

The 7-Step Guide which illustrates the steps for synthesis and meta-analysis of observational studies (Bern, Switzerland. 2023).

Step 1. Decide Whether Narrative or Descriptive Data Synthesis or Meta-Analysis is Suitable

When summarizing evidence from observational studies, narrative or descriptive data synthesis is desirable when: a) the number of studies is insufficient to perform MA; b) essential information to combine results from different studies is missing across studies; or c) the evidence is judged as too heterogeneous, such as clinical heterogeneity, based on a priori decision. We provide tips for determining when clinical heterogeneity is too high in Figure 2 . We caution early, careful thinking and decision-making about handling complex patterns of bias in available evidence and pre-specified protocols. Otherwise, observed results can drive included study choices prematurely.

• a. How many studies are sufficient for MA? MA is possible if association estimates from two studies are available. However, deciding to perform a MA ( 9 )—see Step 2 for choosing statistical models—is influenced by differences in study design, exposure, adjustment, outcome assessment, study population, risk of bias, and other methodological features across studies.
• b. What information is essential for MA? To combine study results, measurements of association estimates from individual studies and standard errors or 95% confidence intervals (CIs) of the estimate are needed. For details about combining different estimates and information needed, see Step 3 and Supplementary Table S1 . We suggest contacting the corresponding authors for missing essential information.
• c. When is heterogeneity too large? Without widely accepted, automated quantitative measures to grade it, determining whether clinical or methodological heterogeneity is too high is subjective. Heterogeneity can result from methodological differences, such as different study designs, analytical assessments of exposures/outcomes, or variations among populations across different studies; it requires restricting MA based on study population, design, exposure, or outcome characteristics. To see how statistical heterogeneity is explored quantitatively using I 2 or Cochran Q statistics, see Step 6. Deciding to perform MA should not be based on statistical heterogeneity.
• d. Do “study quality” and methodological rigor determine whether to meta-analyze the evidence? “Study quality” is a complex term; it involves assessing methodological rigor (what was done) and completeness or accuracy of reporting (what is reported to have been done) within individual studies. Established and validated risk of bias tools can evaluate individual studies included in SR, which can inform the synthesis and interpretation of results. Poor methodological rigor and incomplete or inaccurate reporting of individual studies can bias synthesized results and limit MA interpretation and generalizability. Thus, potential biases across included studies should be systematically assessed. Various tools and scales can be used to assess methodological rigor and reporting. We summarize these scales in Supplementary Material S2 , Supplementary Table S2 .
• e. Does the study design determine whether to meta-analyze the evidence?

Factors to consider on whether to perform a meta-analysis or not (Bern, Switzerland. 2023).

Including all study designs in SRs reduces subjective interpretations of potential biases and inappropriate study exclusions ( 6 ); however, the decision to meta-analyze results across all study designs depends on research questions. For example, cross-sectional designs are likely inappropriate for research questions dealing with temporality but could be used to summarize prevalence estimates of diseases. If different study designs are included in SRs, address heterogeneity by study design in the MA step and perform subgroup analyses by study design otherwise, misleading results can follow ( 10 ).

Overall, when deciding to remove studies from MA due to poor methodology, it is crucial to evaluate the extent of bias across available evidence (i.e., bias in single or multiple studies). If all available studies provide biased estimates, MA simply provide a composite of these errors with low-reliability results perpetuating these biases. If only a proportion of studies are biased and subsequently included in MA, stratification by methodological features may be a solution. However, even with enough studies in the synthesis to perform subgroup analysis, it is informative only. More details are provided in Steps 6 and 7.

After carefully considering Step 1 items a–e, if MA is not feasible or meaningful, summarize findings qualitatively with narrative or descriptive data synthesis. Descriptive data synthesis is not necessarily worse or lower quality compared with MA. Depending on the number of included studies and methodological differences across studies in a descriptive synthesis, writing a narrative data summary can prove more difficult compared with MA. In Table 1 , we provide insights for simplifying the process of descriptive data synthesis. We use examples, such as grouping studies and presenting data from previously published SRs summarizing evidence without MA ( 12 – 15 ).

Steps to consider when conducting a narrative summary of evidence (Bern, Switzerland. 2023).

We suggest providing graphical summaries of important findings, especially when tables and figures amass complex, convoluted information [e.g., second figure of SR by Oliver-Williams et al. ( 12 )]. If MA is inappropriate, another graphical option is a forest plot without the overall association estimate 15 —a display that promotes reader insights on association estimate size and 95% CIs across studies. We also recommend synthesis without MA (SWiM) reporting guidelines ( 11 ) to assist in reporting findings from SRs without MA. Finally, although narrative synthesis of evidence is the default choice when performing an SR of qualitative research, it extends beyond the scope of our guidelines. Several guidelines exist on SR of qualitative studies ( 16 , 17 ).

Step 2. Understand the Concept of Meta-Analysis and Different Models

Combining results from different observational studies can lead to more comprehensive evaluations of evidence, greater external validity, and higher precision due to larger sample sizes. However, higher precision can be misleading, especially if studies are biased.

MA mathematically combines different study results ( 18 ); it computes summary statistics for each study, then further summarizes and interprets study-level summary statistics. Summary association estimates allow for overall judgments on investigated associations; however, the interpretation depends on assumptions and models used when combining data across studies. Observational studies are far more susceptible to confounding and bias; therefore, they have additional degrees of imprecision beyond observed CIs. Furthermore, many associations differ by study characteristics and exposure levels and types; thus, true effect size genuinely varies. Weighting studies in meta-analyses typically considers study imprecision and heterogeneity between studies, yet some also weigh quality scores ( 19 ). We generally discourage including quality scores because they are subjective, and it is difficult to summarize quality in a single number or weight. Nevertheless, when combining studies of different designs or identifying large discrepancies in risks of bias, additional subgroup or sensitivity analyses such as excluding studies with lower credibility and identifying influences of such studies in summary estimates. More sophisticated methods try to “correct” results for different types of bias related to internal validity and generalizability features in each study ( 20 ). Yet, they are not widely used and worthy of skepticism for claims to correct bias ( 21 ).

Fixed-Effects Model

If a single effect underlies an investigated association and all studies are homogenous, obtain a summary estimate by weighted mean by measuring that effect in fixed-effects models. The weights reflect each study’s precision. Precision is the degree of resemblance among study results if the study is repeated under similar circumstances.

Estimate precision is mainly related to variations of random error, such as sample size or the number of events of interest; measurement uncertainty—accurate and calibrated measurements—and the nature of measured phenomenon (where some events are simply more variable than others in occurrence) also affect estimate precision. The precision of estimates is expressed as the inverse variance of association estimates—or 1 divided by the square root of its standard error. Summary estimates are referred to as the fixed-effects association estimate. Fixed-effects models assume all studies have a common true (“fixed”) overall effect and any differences in observed estimates between studies are due to random error—a strong assumption typically unsuitable for most observational data.

Random-Effects Model

The random-effects model allows each study its own exposure or treatment association estimate with distributed associations varied across different individual and population characteristics, as well as dependent on exposure and treatment characteristics, such as dose or category. We expect sufficient statistical commonalities across studies when combining information; however, identical true association estimates are unnecessary for included studies. For example, the association between hormone therapy and the risk of cardiovascular disease among women depends on menopausal status and the type of hormone therapy. Although studies investigating hormone therapy and cardiovascular disease have exposure, population, and outcome in common, there are different true effects across different reproductive stages and formulations of hormone therapies ( 22 ).

The random-effects model is an extension of the fixed-effects model, where each study estimate combines the true total effect and difference from variation between studies and random errors. Therefore, an additional parameter represents variability between studies around the true common effect and distinguishes random-effects models from fixed-effects models. To simplify, random-effects models distribute true effect sizes represented across different studies. The combined random-effects estimates represent the mean of the population of true effects. Thus, we can generalize findings to broader phenotypes and populations beyond specific, well-defined phenotypes and populations analyzed in individual studies. For instance, an MA of observational studies on hormone therapy and cardiovascular disease provides an overall measure of association estimates between hormone therapy and cardiovascular disease; however, random-effects estimates are summary estimates of the overall true measure of association estimates of different types of hormone therapies and true measured of observed association estimates among different women’s reproductive stages. As a result, random-effects models incorporate higher degrees of heterogeneity between studies. It also gives proportionally higher weights to smaller studies and lower weights to larger studies than the fixed-effects association estimates, resulting in differences in summary estimates between the two models.

The random-effects model incorporates study variance and results to wider CIs. However, random and fixed-effects estimates would be similar, with no observed between-study variability and zero estimated between-study variance. There are many variants of random-effects models ( 23 ). Inverse variance and DerSimonian-Laird methods are the most widely used, yet these are not methods with the best statistical properties in most circumstances. Therefore, accurate working knowledge of alternatives and choosing the best-fit methods is essential ( 23 ).

We previously compared different characteristics of fixed-effects vs. random-effects in Supplementary Table S3 . Since observational studies typically involve variable study populations, different levels of adjustments and analyses than RCTs, and participants under different conditions, they are usually better represented by random-effects than fixed-effects models. It is even more true when different study designs are combined or when observational studies are combined with RCTs. However, random-effects models also come with several caveats. For example, estimates of between-study variance in calculations of limited numbers of studies are very uncertain; different random-effects methods yield substantially different results; in the presence of publication selection bias (mostly affecting smaller studies), random-effects models give even more importance to smaller studies and summary estimates are more biased than fixed-effects models. Some methodologists propose methods to overcome these issues, such as only combining large enough studies, using other forms of weighting, or correcting for publication and other selective reporting biases ( 24 – 26 ). Familiarity with the data at hand and the suitability of methods related to specific MAs is crucial.

Step 3. Follow the Statistical Analysis Plan

Statistical analysis plans are designed during SR protocol preparation; we describe such plans in Step 6 of our previously published guideline ( 4 ). In addition to detailed descriptions of planned analyses, SR protocols provide descriptions of research questions, study designs, inclusion and exclusion criteria, electronic databases, and preliminary search strategies. We previously discussed review protocol preparation in detail ( 4 ). Further detailed instructions on how to prepare a statistical analysis plan can be found in Supplementary Material S3 .

Step 4. Prepare Datasets for Meta-Analysis

Prior to MA, examine the results extracted from each study with either a dichotomous or continuous outcome ( Supplementary Material S4 ).

If studies use different units when reporting findings, convert units for consistency before combining. Decide units (SI or conventional units) and scales (meter, centimeter, millimeter) before mathematically combining study estimates. Resolve differences in reporting summary statistics, such as measures of central tendency (mean or median) and spread (range or standard deviation). Convert studies reporting median and interquartile range (or range) to mean and standard deviation through a priori -defined methods, such as those described by Hozo or Wan ( 27 , 28 ). Although studies not reporting summary statistics or central tendency and spread are excluded from meta-analyses, keep track of them and discuss unusable evidence and inference effects. Determine if outcomes are normally distributed. Transform values from studies reporting non-normal distributions for combination, such as log transformation.

Data reflecting risk at multiple levels of exposure, such as quantiles, present special challenges. By only extracting estimates of risk in upper versus lower levels of exposure, such as nutrient levels in nutritional associations, valuable information is lost. We suggest an interval collapsing method ( 29 ) that allows using information from all levels of exposure. Consider issues of dose-response relationships and non-linearity. Prespecify the plans for extracting and synthesizing relevant data. We advise reading and discussing articles about common MA methods on trends and dose-response ( 30 – 32 ). If studies use different cut-points to define exposure categories for continuous exposures, carefully record and consider them in the analysis ( 33 ).

Since most SR involves fewer than 100 studies, use simple spreadsheet applications to encode study details and association estimates. Use dedicated database management software, such as RedCap (free) or Microsoft Access (commercial). Recently popularized machine-learning-based software, such as Covidence (with limited validity), helps extract data, screen abstracts, and assess the quality and allows data transfer to RevMan (Cochrane Collaboration). RevMan is a multifunctional MA software performing qualitative and quantitative analyses and may be suitable for beginners. However, many MA methods are unavailable in RevMan, which limits analysis options. R (free) and Stata (commercial) are other softwares one may consider for data analysis ( Supplementary Table S4 ), We also recommend mapping adjusted variables from in each study and the analyses done (main analyses and subgroup or restricted analyses). It allows a bird’s eye view of what adjustments were made, how consistent or different adjustments considered for inclusion in the MA were across different studies, and whether different unadjusted and adjusted estimates were provided in specific studies. Adjusted and unadjusted or crude association estimates across studies are often available, and differences should be accounted and explained. When preparing data analysis plans, common dilemmas include choosing among several models and the provided variably adjusted estimates. When undertaking a synthesis or review for a particular research question using causal structures, such as through DAGs, identify confounders ideally included in studies’ adjusted models in the selection criterion. When selecting estimates for MA, limit analysis to studies adjusting for confounders defined important a priori . Alternatively, combine different covariate-conditional estimates, such as conducting minimally adjusted and maximally adjusted analyses and comparing summary results. When combining estimates from studies with estimated different covariate-conditional effects, we advise caution regarding the non-collapsibility of odds and hazard ratios, where covariate-conditional odds ratios may differ from crude odds ratios even in the absence of confounding; however, estimates of risk ratios do not exhibit this problem ( 34 ). Ultimately, compare sensitivity analysis results between meta-analyses of adjusted and unadjusted data to indicate the presence of biases.

Step 5. Run the Meta-Analysis

Meta-analysis for dichotomous outcome.

The most common measures of associations for dichotomous outcomes are proportions and prevalences, risk ratios, odds ratios, relative risks, hazard ratios, or risk differences. Mathematically transform and approximately normally distribute each of these association measures into new measures on a continuous scale. Meta-analyze transformed measures using standard tools for continuous effect sizes where derived summary effects may be finally back-transformed into its original scale. We provide an overview of study designs and common transformations in Supplementary Tables S5–S7 .

Originally developed as a technique for examining odds ratios with stratification in mind, the Mantel-Haenszel method was not originally developed for MA. The Mantel-Haenszel approach bypasses the need to first transform risk estimates, performs an inverse-variance weighted MA, and then back transforms summary estimators. With a weighted average of the effect sizes result, applying it directly to study risk ratios, odds ratios, or risk differences is advised. It provides robust estimates when data are sparse and produces estimates similar to the inverse variance method in other situations. Therefore, the method can be widely used. Peto’s approach is an alternative to the Mantel-Haenszel method for combining odds ratios. Peto’s summary odds ratio can be biased, especially when there are large differences in sample sizes between treatment arms; however, it generally works well in other situations. Although the Mantel-Haenszel and Peto methods pertain to raw counts with no applicability in most meta-analyses of observational data where adjusted estimates are considered, they may apply to types of observational data where raw counts are involved, such as adverse events.

When outcomes in comparison groups are either 100% or zero, computational difficulties arise in one (single-zero studies) or both (double-zero studies) comparison groups. Some studies purposely remove double-zero studies from their analyses. However, such approaches are problematic when meta-analyzing rare events, such as surgical complications and adverse medication effects. In these instances, a corrective count—typically 0.5—is added to the group with an otherwise zero count. The metan package in Stata and the metabin command from the meta library in R correct these by default. Nyaga et al. ( 35 ) provide a guide for Stata. Such arbitrary corrections possibly introduce bias or even reverse MA results, especially when the number of samples in two groups is unbalanced ( 10 ). We advise avoiding altogether or extreme caution when using methods that ignore information from double-zero studies or use continuity corrections. Beta-binomial regression methods may be the best approach for treating such studies when computing summary estimates for relative risks, odds ratios, or risk differences ( 36 ).

Meta-Analysis for Continuous Outcomes

For continuous outcomes, investigate two exposure groups (exposed vs. unexposed) or per unit increase in exposure in terms of their mean outcome level. The association is quantified as the mean difference—for example, the difference between study groups in mean weight loss—or as beta-coefficient from univariable or multivariable regression models. A MA can then directly summarize mean differences for each study. If different measurement scales, such as different instruments or different formulas to derive outcomes, are available, we advise using standardized mean differences as measures of association estimates in MA—the mean difference divided by pooled standard deviation. Use one of several ways to calculate pooled standard deviation, such as the most popular methods for standardized effect sizes: Cohen’s D, Hedge’s g, and Glass’ delta ( 37 – 39 ).

To measure standardized size effects, combine mean, standard deviation, and sample size of exposed and non-exposed groups as input with different weights. If using software, select the standardization method. Hedge’s g includes a correction factor for small sample bias; it is preferred over Cohen’s D for very small sample sizes (fewer than 20) ( 39 ). Otherwise, the two methods give very similar results. Expressing the standardized effect measure demonstrates differences between exposed and non-exposed groups by standard deviation. For example, if Hedge’s g is 1, groups differ by 1 standard deviation and so on. When standard errors are very different between study groups, Glass’s delta—a measure using only the standard deviation of the unexposed group—is usually used to measure effect size ( 38 ). If mean differences or standardized mean differences are combined, calculate with only the effect size and standard deviation of individual groups. The software calculates the differences and associated variance of differences for weighting—the standardized mean differences with appropriate variance estimation ( Supplementary Tables S6, S7 , example Supplementary Figures S1, S2 ).

95% Confidence Intervals (CIs) and Prediction Intervals

Providing 95% CIs and prediction intervals is desirable when performing a MA. CIs reflect sampling uncertainty and quantify the precision of mean summary measures of association estimates; prediction intervals reflect expected uncertainty in summary estimates when including a new study in meta-analyses. Prediction intervals—along with sampling uncertainty—reflect inherent uncertainty about specific estimates and estimate the interval of a new study if randomly selected from the same population of studies already included in meta-analyses ( 40 , 41 ). Implement prediction intervals in random-effects MA frameworks. To calculate prediction intervals, 3 studies are required; however, considering prediction intervals account for the variance of summary estimates and heterogeneity, they can be imprecise for MA of few studies.

Step 6. Explore Heterogeneity

Cochran’s Q homogeneity test and its related metric—the Higgin’s & Thompson’s I 2 index—are commonly used in most statistical software (Stata, R, and RevMan). Under the hypothesis of homogeneity among the effect sizes ( 42 ), the Q test follows a Chi-square distribution (with k-1 degrees of freedom, where k is number of studies). The Q test is used to evaluate the presence or absence of statistically significant heterogeneity based on α threshold of statistical significance ( 43 ). Calculated as [Q−df]/x 100, the I 2 measures the proportion of the total variability in effect size due to between-study heterogeneity rather than sampling error. I 2 is highly influenced by the size of the studies (within-study variability), not just the size of between-study heterogeneity. A higher percentage indicates higher heterogeneity. H is the square root of the Chi-square heterogeneity statistic divided by its degrees of freedom. It describes relative differences between observed and expected Q in the absence of heterogeneity. The H value of 1 indicates perfect homogeneity. R is the ratio of the standard error of the underlying mean from random-effects meta-analyses to standard errors of a fixed-effects meta-analytic estimate. Similar to H, the R 2 value of 1 indicates perfect homogeneity. Finally, τ 2 is the estimate of between-study variance under random-effects models. τ 2 is an absolute measure of between-study heterogeneity; in contrast to other measures (Q, I 2 , H, and R), it does not depend on study precision ( 44 ). Further information about heterogeneity can be found here ( 45 ).

Classification of Heterogeneity

Assessing heterogeneity in SRs is crucial in the synthesis of observational studies. Recall that the reliability of heterogeneity tests hinges on the number of studies. Thus, fewer studies make I 2 estimates unreliable. To classify heterogeneity, different categorizations are used across different meta-analyses. The Cochrane Collaboration recommends classifying 0%–40% as likely unimportant heterogeneity; 30%–60% as likely moderate heterogeneity; 50%–90% as likely substantial heterogeneity; and 75%–100% as likely considerable heterogeneity ( 18 ). Although there is no rule of thumb for I 2 cut-offs to classify studies as low, medium, or high heterogeneity, categorize using a priori protocol definitions. Provide CIs for I 2 since estimates of heterogeneity have large uncertainty ( 46 ) (See Supplementary Figures S1, S2 for examples).

Subgroup or Restricted Analysis

Ideally, all studies compared in meta-analyses should be similar; however, it is almost impossible for observational studies. When performing subgroup analyses, look at factors explaining between-study heterogeneity. Explore subgroups, including patient or individual characteristics, study methods, and exposure or outcome definitions. Define subgroup characteristics a priori . Group studies according to study characteristics. We outline a subgroup analysis essential guide in Supplementary Table S8 ( Supplementary Figure S3 provides example).

Meta-Regression

Meta-regression applies basic regression concepts using study-level association estimates ( 42 , 47 , 48 ). Examining the association—typically linear, yet not in all cases—between the outcome of interest and covariates determines the contribution of covariates (study characteristics) in the heterogeneity of the association estimates. In common regression analyses, patient-level information is used when comparing outcomes and exposures alongside various covariates. In meta-regression (instead of patient-level information) use population-level information, such as mean age, location, mean body mass index, percentage of females, mean follow-up time, and risk of bias, to explore association estimates. The common practice of visualizing meta-regressions is with bubble plots ( Supplementary Figure S4 ) using the metareg package in Stata ( 49 ).

In meta-regression, variables under investigation are potential effect modifiers. Beta-coefficient refers to incremental changes in outcomes with increasing levels of the covariate. Positive coefficients signify an increase in the outcome with increasing levels of the covariate variable; negative coefficients mean a decrease in the outcome.

It is important to understand that meta-regression explores consistency of findings and does not make causal inferences on associations. Meta-regression results are based on observational data across different studies. Thus, it suffers from similar pitfalls in causality and biases. A statistically significant association between an outcome and covariate (beta coefficient) may have a confounding variable that drives the association, albeit occasionally mitigated by multivariate analysis. In addition, covariates, in some cases, can be highly collinear. Since most SR involve fewer studies capable of meta-regression, power is also an issue. The number of studies is one major stumbling block when performing meta-regression. In multivariable analysis, the number of studies becomes more important since more studies are required. Based on recommendations from the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy, do not consider meta-regression with fewer than 10 studies in a MA. For multivariable regression, they advise at least 10 studies per covariate ( 50 ), which means multivariable analysis requires at least 20 studies ( 47 ). Meta-regression may also be subject to ecological fallacy. In meta-regression, we use average study participant characteristics; therefore, the association between average study participant characteristics and measures of association estimate may not be the same within and between analyzed studies. Common covariates prone to ecological fallacy are age and sex. Using individual-level data is the only way to avoid ecological fallacies ( 51 ). Use caution if concluding causality from meta-regression and interpreting results ( 52 ). False positive claims are common in meta-regression ( 50 ).

While the most commonly used meta-regression is the random-effects meta-regression, other models, such as fixed-effects meta-regression, control rate meta-regression, multivariate meta-regression and Bayesian hierarchical modeling, can be used. These methods will depend on the specifics of analysis, such as the type of data, the number of studies, and the research question. More information can be found elsewhere ( 53 , 54 ).

Perform Leave-One-Out Analysis (Influence Analysis)

An MA may include studies providing extreme positive or negative associations. Sometimes it is possible to identify such outliers visually by expecting the forest plot, but often the situation is more complex due to sampling variances across included studies ( 55 ). To explore whether the outlier influences the summary effect estimate, one can explore whether the exclusion of such study from the analysis leads to considerable changes in the summary effect estimate. In case of small number of studies, the exclusion may be done manually; yet the most commonly used statistical software provide a possibility to perform a leave-one-out analysis, which iteratively removes one study at a time from the analysis and provides recomputed summary association estimates ( 48 ). For instance, in STATA, use the metaninf package ( 56 ) or in R, use the metafor package to perform a leave-one-out analysis (example shown in Supplementary Figure S5 ). For further reading, we suggest the article on outlier and influence diagnostics for MA ( 55 ).

Step 7. Explore Publication Selection Bias

Selection bias related to the publication process—or publication selection bias—arises when disseminating study results influences the nature and direction of results ( 57 ). Publication selection biases include: a) classic publication bias or file drawer bias when entire studies remain unpublished; time-lag bias when rapid publication depends on results; b) duplicate publication bias when some data are published more than once; c) location bias or citation bias when citations and study visibility depend on results; d) language bias when study publication in different languages is differentially driven by results; and e) outcome reporting bias when only some outcomes and/or analyses are published preferentially.

A thorough literature search is the first step in preventing publication bias (explained in our previous publication) ( 4 ). In addition to bibliographic database search, rigorous search of the gray literature and study registries (for preliminary data or for unpublished results) should be done to identify other studies of interest. We summarized the most important databases in Supplementary Table S9 . In addition, one should consider whether highly specialized or very large numbers of studies without any special planning (e.g., when exposures and outcomes are commonly and routinely measured in datasets such as ubiquitous electronic health records) readily address the question of interest. Selective reporting bias is very easy to be introduced in the latter situation.

Several methods exist for exploring publication selection bias; however, no method definitively proves or disproves publication selection bias. We comment on several widely popular, yet often over-interpreted methods in the next two subsections and in Supplementary Table S10 and we urge caution against their misuse and misinterpretation. Based on statistical properties (sensitivity and specificity for detecting publication selection bias), newer tests, such as those based on evaluating excess statistical significance ( 26 ), may perform better. When less biased summary estimates of effects are desired, the Weighted Average of Adequately Powered Studies (WAAP) ( 24 ) (that focuses on studies with >80% power) may have the best performance. However, many MA has few studies and not well-powered studies at all; then any test for publication selection bias and attempt to adjust for such bias may be in vain. Even greater caution is needed in such circumstances.

Visual Inspection of Study Results

To help understand whether effect sizes differ systematically between small and large studies, funnel plots provide the simplest technique and a graphical representation ( Supplementary Figure S5 ). Funnel plot graphs demonstrate association sizes or estimates on the horizontal axis (x-axis) and the study precision, sample size, or the inverse of the standard error on the vertical axis (y-axis)—an inverted funnel. Ideally, symmetry around the estimates provided by larger studies (the tip of the inverted funnel) extends to the smaller studies (the foot of the inverted funnel). An asymmetrical funnel shape with larger estimates for smaller rather than larger studies hints at publication selection bias, yet other possible reasons exist for the same pattern. Draw cautious inferences ( 58 , 59 ). Since plain visual assessment is subjective, we do not recommend using it as the sole criterion to arbitrate publication bias.

In some observational studies, observed differences between large and small studies arise from methodological differences. Different study characteristics in study sizes can lead to heterogeneity in the analysis. For example, smaller studies can have more stringent disease criteria for inclusion (lower risk for misclassification bias) and more intricate methods for data collection (lower risk for recall bias) compared with larger studies. More commonly, smaller studies are subject to more selective analysis and reporting pressure with possibly more bias than well-designed large studies. There is no way to generalize a priori for all topics, and studies should be examined carefully in each case. Thus, in the context of observational studies, it holds even more than funnel plot asymmetry should not automatically indicate publication bias ( 9 , 10 ). In particular, any factor associated with both study effect and study size could confound the true association and cause an asymmetrical funnel. Contour-enhanced funnel plots may help interpret funnels and differentiate funnel plot asymmetry caused by statistical significance-related publication bias from other factors; however, most of these caveats still apply ( 60 ).

Statistical Tests to Explore Publication Selection Bias

Several tests and statistical methods are developed to detect (and potentially correct) publication selection bias. Egger’s test remains the most popular. It is based on linear regression of normalized association or effect estimates (using association estimates divided by standard errors) and study precision (inverse of the standard error) ( 61 , 62 ). The intercept of regression lines measures the asymmetry—the larger its deviation from zero, the bigger the funnel plot asymmetry. A p-value <0.05 indicates the presence of publication bias, which means estimates of smaller studies do not mimic estimates of larger studies. Egger’s test may be unreliable for fewer than 10 studies. We advise caution when interpreting estimates of fewer than 10 studies. Further, for log odds ratios, even in the absence of selective outcome reporting, the test inflates Type I errors (false positive findings) ( 58 , 63 ). When all studies have similar variances, test results have no meaning. Egger’s test (and other modifications) as small study effect tests (i.e., whether small and larger studies give different results) should be used rather than strictly as a test of publication selection bias (See Supplementary Figures S6, S7 for example).

Other methods have been developed to address the limitations of existing popular approaches, such as the three-parameter selection model ( 64 ), the proportion of statistical significance test ( 26 ), and variants thereof. The three-parameter selection model’s main assumption is the likelihood of publication is an increasing step function of the complement of a study’s p-value . Maximum likelihood methods estimate corrected effect sizes and the relative probability that insignificant results are published. Whereas the proportion of statistical significance test compares expected with observed proportions of statistically significant findings. Find detailed explanation elsewhere ( 26 ). Some methodologists propose the most reliable summary results are obtained by methods accommodating possibilities of publication selection bias. With proven, good statistical properties, some of these methods may be used more in the future ( 26 ). However, for typical meta-analyses with limited available data, mostly small studies, and no formal pre-registration, no methods are likely perfect. Even when not formally demonstrated, consider publication selection bias as a definite possibility.

Synthesizing data from high-quality observational studies, at low risk of bias, complements data from RCTs and may provide insight into prevalence, the generalizability of findings for different populations, and information on long-term effects and desirable or adverse events (harms) when dealing with interventions. SRs and MA help quantify associations not testable in RCTs, such as quantifying the association between age at menopause onset or obesity with health outcomes. For observational evidence which assess interventions, we recommend applying the grading of recommendations, assessment, development, and evaluation (GRADE) tool to translate results from SRs and MA into evidence-based recommendations for research and clinical and public health impact ( 65 ). Applying GRADE addresses a range of research questions related to diagnosing, screening, preventing, treating, and public health. A panel of experts formulates recommendations, ideally experienced information specialists and subject matter experts. For observational evidence pertaining to putative protective and risk factors, use a series of criteria focused on the amount of evidence, statistical support, the extent of heterogeneity, and hints of bias ( 66 ). Eventually, systematic reviews and meta-analyses are observational studies themselves. Therefore, always cautiously interpret and take special care when claiming causality and framing strong recommendations for policy and clinical decision-making.

Funding Statement

PFR and DS received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 801076, through the SSPH+ Global PhD Fellowship Programme in Public Health Sciences (GlobalP3HS) of the Swiss School of Public Health.

Author Contributions

MG and TM conceptualized the study. MG, PFR, AG, PET, DS, AB, JPAI contributed to the writing of the manuscript. TV, PMV, SP, and SK provided critical inputs on the draft. JPAI and TM supervised the study conduct. All approved the final version of the manuscript.

Conflict of Interest

TM is Chief Scientific Officer at Epistudia, a start-up company on online learning and evidence synthesis. DS is a Co-founder and Director at CrunchLab Health Analytics, Inc., a health technology assessment consulting firm.

The remaining authors declare that they do not have any conflicts of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.ssph-journal.org/articles/10.3389/phrs.2023.1605454/full#supplementary-material

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“We are drowning in information while starving for wisdom. The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely.”

– E.O. Wilson, American Biologist & Pulitzer Prize Winner

I vividly remember almost 20 years ago sitting 30 floors up in a conference room overlooking San Francisco’s Bay Bridge. As a new Business Analyst at Mercer Management Consulting , I was the cream of the crop, top of the 21-year-old career pyramid. I was proud of my labor over the previous 48 hours, having cranked out tons of analysis and dozens of charts . The partner on my project quickly flipped through my dozens of charts, looked up at me, and declared, “What the hell is all of this? Joe, give me the three insights from all this crap.” I was frozen, having just free fallen from the top of my pyramid with a massive figurative thud. All I could muster was, “Ummm, I don’t know…..” Then the project manager dove in covering me from any more mortar fire. After that embarrassing experience, I vowed never to be in that situation again, which drove me to embrace synthesis, which is one of the most difficult but important tools to learn when it comes to analysis.

What is synthesizing?

One of the core skills of strategic leaders is the ability to continually process data, facts, analysis, and conversations, and synthesize it all into insight. Whether it is synthesizing an analysis, a meeting, a discussion, or a situation, having your brain derive and prioritize insight from context is a critical skill that will always pay big dividends.

I can’t start any section called analytics , without first covering the concept of synthesis.

Remember those reading comprehension sections of those standardized tests we took every few years from about 3rd grade through high school? You read a few paragraphs about some random topic, and then there is the question, “What is the main takeaway from what you just read?” Your brain processes the answers and thinks through what you just read, and aha, you pick an answer, which hopefully is the right one. That is synthesis; combining ideas and facts and deriving higher-level meaning, understanding, and knowledge.

At McKinsey, synthesis was another top 5 word used in projects . “What is your synthesis of the analysis…your takeaway from the interviews…what have you learned so far?” Clients don’t pay McKinsey just to analyze stuff. Clients pay McKinsey to synthesize stuff into insight, knowledge, new perspectives, recommendations, and strategy.

How do you improve at synthesizing?

Your brain synthesizes, it takes facts, stimuli, and ideas, and creates meaning out of them. And, while analysis is the detailed examination of something, synthesis should always be the byproduct of analysis. Over time, there are many ways to improve your ability to synthesize, and here are some of the best tips:

Focus on the Purpose

There are many good reasons why McKinsey teams focus their efforts on solving the problem statement of a project, and one of those is that it provides a useful framing and focus for analysis. Before conducting an analysis, define what the purpose of the analysis is, and during the analysis, remind yourself of the purpose. Your mind will create takeaways and hypotheses from the analysis within the framing of the purpose.

Practice the “3 Takeaways”

One of the easiest ways to train your brain to synthesize is to constantly practice the “3 Takeaways”, which is simply, at the end of any analysis, meeting or conversation, think through and potentially verbalize, “Here are my three takeaways from this meeting…” You’ll not only help provide direction, but you will quickly improve your ability to synthesize. Over the past 20 years working in strategy, I’ve used this technique to master synthesizing brainstorming sessions, analysis, action items, and any other information that can be synthesized.

Constantly frame and reframe things

I’ve always found it extremely helpful to mentally process analysis, meetings, ideas, decisions , and conversations through a few framing questions , including:

o What are the implications for revenues, costs, and resources?

o What question are we trying to answer?

o Why is this potentially important?

o Is this a high priority or low priority?

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Gifted and Talented Chemistry - Synthesis and Analysis

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Synthesis and analysis are two key aspects of chemistry, particularly when exploring the role of chemistry in an industrial context and relating the products formed to applications in everyday life.  In order to successfully synthesise any material it is necessary to have an understanding of the starting materials, the product(s) and the mechanisms and conditions needed to move from one to the other. This pulls together many different aspects of chemistry learning. This programme is designed to develop students understanding of these topics from basic concepts to higher level thinking. It also aims to show that understanding how aspects of chemistry link together gives fuller understanding of the chemical processes as a whole. Working through the activities will also develop thinking and research skills.

Synthesis and analysis student pack

Synthesis and analysis teacher pack, additional information.

The RSC would like to thank Tim Jolliff for the use of his materials in these resources. Tim’s book ‘Chemistry for the Gifted and Talented’ is also available on Learn Chemistry.

• 11-14 years
• 14-16 years
• 16-18 years
• Practical experiments
• Teacher notes
• Organic chemistry
• Analytical chemistry
• Reactions and synthesis
• Able and talented
• Chromatography

Specification

• The industrial advantages of ethanoic anhydride over ethanoyl chloride in the manufacture of the drug aspirin.
• The synthesis of an organic compound can involve several steps.
• a) the techniques and procedures used for the preparation and purification of organic solids involving use of a range of techniques including: i) organic preparation: use of Quickfit apparatus; distillation and heating under reflux
• a) the techniques and procedures used for the preparation and purification of organic solids involving use of a range of techniques including: ii) purification of an organic solid: filtration under reduced pressure; recrystallisation; measurement of melt…
• b) for an organic molecule containing several functional groups: identification of individual functional groups; prediction of properties and reactions
• c) multi-stage synthetic routes for preparing organic compounds.
• 16. The preparation of aspirin
• 7B. Determination of acetyl salicylic acid in a commercial tablet, using pure aspirin as a control.
• The reaction mechanism for SN1 and SN2 reactions
• (a) synthesis of organic compounds by a sequence of reactions
• (b) principles underlying the techniques of manipulation, separation and purification used in organic chemistry
• (g) use of chromatographic data from TLC/paper chromatography, GC and HPLC to find the composition of mixtures
• 1.9.6 describe paper chromatography as the separation of mixtures of soluble substances by running a solvent (mobile phase) through the mixture on the paper (stationary phase), which causes the substances to move at different rates over the paper;
• prepare aspirin using salicylic acid and ethanoic anhydride; and
• use chromatography to compare the purity of laboratory-made aspirin with commercial tablets.
• 2.4.8 recall the mechanism of electrophilic addition between chlorine, bromine, hydrogen chloride and hydrogen bromide with alkenes using curly arrows;
• 4.10.5 prepare methyl-3-nitrobenzoate from methyl benzoate to illustrate nitration of the benzene ring.
• 2. Develop and use models to describe the nature of matter; demonstrate how they provide a simple way to to account for the conservation of mass, changes of state, physical change, chemical change, mixtures, and their separation.
• Mechanisms of ionic addition (addition of HCl, Br₂, Cl₂ only to ethene).
• Chromatography as a separation technique in which a mobile phase carrying a mixture is caused to move in contact with a selectively absorbent stationary phase.

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Chapter Six: Analysis and Synthesis

What does it mean to know something? How would you explain the process of thinking? In the 1950s, educational theorist Benjamin Bloom proposed that human cognition, thinking and knowing, could be classified by six categories. 1 Hierarchically arranged in order of complexity, these steps were:

Since his original model, the taxonomy has been revised, as illustrated in the diagram below:

• Each word is an action verb instead of a noun (e.g., “applying” instead of “application”);
• Some words have been changed for different synonyms;
• One version holds “creating” above “evaluating”;
• And, most importantly, other versions are reshaped into a circle, as pictured above. 2

What do you think the significance of these changes is?

I introduce this model of cognition to contextualize analysis as a cognitive tool which can work in tandem with other cognitive tasks and behaviors. Analysis is most commonly used alongside synthesis . To proceed with the LEGO® example from Chapter 4, consider my taking apart the castle as an act of analysis. I study each face of each block intently, even those parts that I can’t see when the castle is fully constructed. In the process of synthesis, I bring together certain blocks from the castle to instead build something else—let’s say, a racecar. By unpacking and interpreting each part , I’m able to build a new whole . 3

In a text wrestling essay, you’re engaging in a process very similar to my castle-to-racecar adventure. You’ll encounter a text and unpack it attentively, looking closely at each piece of language, its arrangement, its signification, and then use it to build an insightful, critical insight about the original text. I might not use every original block, but by exploring the relationship of part-to-whole, I better understand how the castle is a castle. In turn, I am better positioned to act as a sort of tour guide for the castle or a mechanic for the racecar, able to show my readers what about the castle or racecar is important and to explain how it works.

In this chapter, you’ll learn about crafting a thesis for a text wrestling essay and using evidence to support that thesis . As you will discover, an analytical essay involves every tier of Bloom’s Taxonomy, arguably even including “judgement” because your thesis will present an interpretation that is evidence-based and arguable.

Chapter Vocabulary

So What? Turning Observations into a Thesis

It’s likely that you’ve heard the term “thesis statement” multiple times in your writing career. Even though you may have some idea what a thesis entails already, it is worth reviewing and unpacking the expectations surrounding a thesis, specifically in a text wrestling essay.

A thesis statement is a central, unifying insight that drives your analysis or argument. In a typical college essay, this insight should be articulated in one to three sentences, placed within the introductory paragraph or section. As we’ll see below, this is not always the case, but it is what many of your audiences will expect. To put it simply, a thesis is the “So what?” of an analytical or persuasive essay. It answers your audience when they ask, Why does your writing matter? What bigger insights does it yield about the subject of analysis? About our world?

Thesis statements in most rhetorical situations advocate for a certain vision of a text, phenomenon, reality, or policy. Good thesis statements support such a vision using evidence and thinking that confirms, clarifies, demonstrates, nuances, or otherwise relates to that vision. In other words, a thesis is “a proposition that you can prove with evidence…, yet it’s one you have to prove, that isn’t obviously true or merely factual.” 4

In a text wrestling analysis, a thesis pushes beyond basic summary and observation. In other words, it’s the difference between:

If you think of your essay as the human body, the thesis is the spine. Yes, the body can still exist without a spine, but its functionings will be severely limited. Furthermore, everything comes back to and radiates out from the spine: trace back from your fingertips to your backbone and consider how they relate. In turn, each paragraph should tie back to your thesis, offering support and clear connections so your reader can see the entire “body” of your essay. In this way, a thesis statement serves two purposes: it is not only about the ideas of your paper, but also the structure .

The Purdue Online Writing Lab (OWL) 5 suggests this specific process for developing your thesis statement:

• Once you’ve read the story or novel closely, look back over your notes for patterns of questions or ideas that interest you. Have most of your questions been about the characters, how they develop or change?

For example: If you are reading Conrad’s  The Secret Agent , do you seem to be most interested in what the author has to say about society? Choose a pattern of ideas and express it in the form of a question and an answer such as the following:

Question:  What does Conrad seem to be suggesting about early twentieth-century London society in his novel  The Secret Agent ? Answer:  Conrad suggests that all classes of society are corrupt.

Pitfalls: Choosing too many ideas. Choosing an idea without any support.

• Once you have some general points to focus on, write your possible ideas and answer the questions that they suggest.

For example: Question :  How does Conrad develop the idea that all classes of society are corrupt? Answer:  He uses images of beasts and cannibalism whether he’s describing socialites, policemen or secret agents.

• To write your thesis statement, all you have to do is turn the question and answer around. You’ve already given the answer, now just put it in a sentence (or a couple of sentences) so that the thesis of your paper is clear.

For example: In his novel,  The Secret Agent , Conrad uses beast and cannibal imagery to describe the characters and their relationships to each other. This pattern of images suggests that Conrad saw corruption in every level of early twentieth-century London society.

• Now that you’re familiar with the story or novel and have developed a thesis statement, you’re ready to choose the evidence you’ll use to support your thesis. There are a lot of good ways to do this, but all of them depend on a strong thesis for their direction.

For example: Here’s a student’s thesis about Joseph Conrad’s  The Secret Agent .

In his novel, The Secret Agent, Conrad uses beast and cannibal imagery to describe the characters and their relationships to each other. This pattern of images suggests that Conrad saw corruption in every level of early twentieth-century London society.

This thesis focuses on the idea of social corruption and the device of imagery. To support this thesis, you would need to find images of beasts and cannibalism within the text.

There are many ways to write a thesis, and your construction of a thesis statement will become more intuitive and nuanced as you become a more confident and competent writer. However, there are a few tried-and-true strategies that I’ll share with you over the next few pages.

The T3 Strategy

T3 is a formula to create a thesis statement. The T (for Thesis) should be the point you’re trying to make—the “So what?” In a text wrestling analysis, you are expected to advocate for a certain interpretation of a text: this is your “So what?” Examples might include:

In “A Wind from the North,” Bill Capossere conveys the loneliness of isolated life or Kate Chopin’s “The Story of an Hour” suggests that marriage can be oppressive to women

But wait—there’s more! In a text wrestling analysis, your interpretation must be based on evidence from that text. Therefore, your thesis should identify both a focused statement of the interpretation (the whole) and also the particular subjects of your observation (the parts of the text you will focus on support that interpretation). A complete T3 thesis statement for a text wrestling analysis might look more like this:

In “A Wind from the North,” Bill Capossere conveys the loneliness of an isolated lifestyle using the motif of snow, the repeated phrase “five or six days” (104), and the symbol of his uncle’s car. or “The Story of an Hour” suggests that marriage can be oppressive to women. To demonstrate this theme, Kate Chopin integrates irony, foreshadowing, and symbols of freedom in the story.

Notice the way the T3 allows for the part-to-whole thinking that underlies analysis:

This is also a useful strategy because it can provide structure for your paper: each justifying support for your thesis should be one section of your paper.

• Thesis: In “A Wind from the North,” Bill Capossere conveys the loneliness of an isolated lifestyle using the motif of snow, the repeated phrase “five or six days” (104), and the symbol of his uncle’s car.
• Section on ‘the motif of snow.’ Topic sentence: The recurring imagery of snow creates a tone of frostiness and demonstrates the passage of time.
• Section on ‘the repeated phrase “five or six days” (104).’ Topic sentence: When Capossere repeats “five or six days” (104), he reveals the ambiguity of death in a life not lived.
• Section on ‘the symbol of his uncle’s car.’ Topic sentence: Finally, Capossere’s uncle’s car is symbolic of his lifestyle.

Once you’ve developed a T3 statement, you can revise it to make it feel less formulaic. For example:

In “A Wind from the North,” Bill Capossere conveys the loneliness of an isolated lifestyle by symbolizing his uncle with a “untouchable” car. Additionally, he repeats images and phrases in the essay to reinforce his uncle’s isolation. or “The Story of an Hour,” a short story by Kate Chopin, uses a plot twist to imply that marriage can be oppressive to women. The symbols of freedom in the story create a feeling of joy, but the attentive reader will recognize the imminent irony.

The O/P Strategy

An occasion/position thesis statement is rhetorically convincing because it explains the relevance of your argument and concisely articulates that argument. Although you should already have your position in mind, your rhetorical occasion will lead this statement off: what sociohistorical conditions make your writing timely, relevant, applicable? Continuing with the previous examples:

As our society moves from individualism to isolationism, Bill Capossere’s “A Wind from the North” is a salient example of a life lived alone. or Although Chopin’s story was written over 100 years ago, it still provides insight to gender dynamics in American marriages.

Following your occasion, state your position—again, this is your “So What?” It is wise to include at least some preview of the parts you will be examining.

As our society moves from individualism to isolationism, Bill Capossere’s “A Wind from the North” is a salient example of a life lived alone. Using recurring images and phrases, Capossere conveys the loneliness of his uncle leading up to his death. or Although Chopin’s story was written over 100 years ago, it still provides insight to gender dynamics in American marriages. “The Story of an Hour” reminds us that marriage has historically meant a surrender of freedom for women.

Research Question and Embedded Thesis

There’s one more common style of thesis construction that’s worth noting, and that’s the inquiry-based thesis. (Read more about inquiry-based research writing in Chapter Eight). For this thesis, you’ll develop an incisive and focused question which you’ll explore throughout the course of the essay. By the end of the essay, you will be able to offer an answer (perhaps a complicated or incomplete answer, but still some kind of answer) to the question. This form is also referred to as the “embedded thesis” or “delayed thesis” organization.

Although this model of thesis can be effectively applied in a text wrestling essay, it is often more effective when combined with one of the other methods above.

Consider the following examples:

Bill Capossere’s essay “A Wind from the North” suggests that isolation results in sorrow and loneliness; is this always the case? How does Capossere create such a vision of his uncle’s life? or Many people would believe that Kate Chopin’s story reflects an outdated perception of marriage—but can “The Story of an Hour” reveal power imbalances in modern relationships, too?

Synthesis: Using Evidence to Explore Your Thesis

Now that you’ve considered what your analytical insight might be (articulated in the form of a thesis), it’s time to bring evidence in to support your analysis—this is the synthesis part of Bloom’s Taxonomy earlier in this chapter. Synthesis refers to the creation of a new whole (an interpretation) using smaller parts (evidence from the text you’ve analyzed).

There are essentially two ways to go about collecting and culling relevant support from the text with which you’re wrestling. In my experience, students are split about evenly on which option is better for them:

Option #1: Before writing your thesis, while you’re reading and rereading your text, annotate the page and take notes. Copy down quotes, images, formal features, and themes that are striking, exciting, or relatable. Then, try to group your collection of evidence according to common traits. Once you’ve done so, choose one or two groups on which to base your thesis. Or Option #2: After writing your thesis , revisit the text looking for quotes, images, and themes that support, elaborate, or explain your interpretation. Record these quotes, and then return to the drafting process.

Once you’ve gathered evidence from your focus text, you should weave quotes, paraphrases, and summaries into your own writing. A common misconception is that you should write “around” your evidence, i.e. choosing the direct quote you want to use and building a paragraph around it. Instead, you should foreground your interpretation and analysis, using evidence in the background to explore and support that interpretation. Lead with your idea, then demonstrate it with evidence; then, explain how your evidence demonstrates your idea.

The appropriate ratio of evidence (their writing) to exposition (your writing) will vary depending on your rhetorical situation, but I advise my students to spend at least as many words unpacking a quote as that quote contains. (I’m referring here to Step #4 in the table below.) For example, if you use a direct quote of 25 words, you ought to spend at least 25 words explaining how that quote supports or nuances your interpretation.

There are infinite ways to bring evidence into your discussion, 6 but for now, let’s take a look at a formula that many students find productive as they find their footing in analytical writing: Front-load + Quote/Paraphrase/Summarize + Cite + Explain/elaborate/analyze.

What might this look like in practice?

The recurring imagery of snow creates a tone of frostiness and demonstrates the passage of time. (1) Snow brings to mind connotations of wintery cold, quiet, and death (2) as a “sky of utter clarity and simplicity” lingers over his uncle’s home and “it [begins] once more to snow” ( (3) Capossere 104). (4) Throughout his essay, Capossere returns frequently to weather imagery, but snow especially, to play on associations the reader has. In this line, snow sets the tone by wrapping itself in with “clarity,” a state of mind. Even though the narrator still seems ambivalent about his uncle, this clarity suggests that he is reflecting with a new and somber understanding.

• Front-load Snow brings to mind connotations of wintery cold, quiet, and death
• Quote as a “sky of utter clarity and simplicity” lingers over his uncle’s home and “it [begins] once more to snow”
• Cite (Capossere 104).
• Explain/elaborate/analysis Throughout his essay, Capossere returns frequently to weather imagery, but snow especially, to play on associations the reader has. In this line, snow sets the tone by wrapping itself in with “clarity,” a state of mind. Even though the narrator still seems ambivalent about his uncle, this clarity suggests that he is reflecting with a new and somber understanding.

This might feel formulaic and forced at first, but following these steps will ensure that you give each piece of evidence thorough attention. Some teachers call this method a “quote sandwich” because you put your evidence between two slices of your own language and interpretation.

For more on front-loading (readerly signposts or signal phrases), see the subsection titled “Readerly Signposts” in Chapter Nine.

Idea Generation: Close Reading Graphic Organizer

The first time you read a text, you most likely will not magically stumble upon a unique, inspiring insight to pursue as a thesis. As discussed earlier in this section, close reading is an iterative process, which means that you must repeatedly encounter a text (reread, re-watch, re-listen, etc.) trying to challenge it, interrogate it, and gradually develop a working thesis.

Very often, the best way to practice analysis is collaboratively, through discussion. Because other people will necessarily provide different perspectives through their unique interpretive positions, reading groups can help you grow your analysis. By discussing a text, you open yourself up to more nuanced and unanticipated interpretations influenced by your peers. Your teacher might ask you to work in small groups to complete the following graphic organizer in response to a certain text. (You can also complete this exercise independently, but it might not yield the same results.)

Thesis Builder

Your thesis statement can and should evolve as you continue writing your paper: teachers will often refer to a thesis as a “working thesis” because the revision process should include tweaking, pivoting, focusing, expanding, and/or rewording your thesis. The exercise on the next two pages, though, should help you develop a working thesis to begin your project. Following the examples, identify the components of your analysis that might contribute to a thesis statement.

Model Texts by Student Authors

(A text wrestling analysis of “Proofs” by Richard Rodriguez)

Songs are culturally important. In the short story “Proofs” by Richard Rodriguez, a young Mexican American man comes to terms with his bi-cultural life. This young man’s father came to America from a small and poverty-stricken Mexican village. The young man flashes from his story to his father’s story in order to explore his Mexican heritage and American life. Midway through the story Richard Rodriguez utilizes the analogies of songs to represent the cultures and how they differ. Throughout the story there is a clash of cultures. Because culture can be experienced through the arts and teachings of a community, Rodriguez uses the songs of the two cultures to represent the protagonist’s bi-cultural experience.

According to Rodriguez, the songs that come from Mexico express an emotional and loving culture and community: “But my mama says there are no songs like the love songs of Mexico” (50). The songs from that culture can be beautiful. It is amazing the love and beauty that come from social capital and community involvement. The language Richard Rodriguez uses to explain these songs is beautiful as well. “—it is the raw edge of sentiment” (51). The author explains how it is the men who keep the songs. No matter how stoic the men are, they have an outlet to express their love and pain as well as every emotion in between. “The cry of a Jackal under the moon, the whistle of a phallus, the maniacal song of the skull” (51). This is an outlet for men to express themselves that is not prevalent in American culture. It expresses a level of love and intimacy between people that is not a part of American culture. The songs from the American culture are different. In America the songs get lost. There is assimilation of cultures. The songs of Mexico are important to the protagonist of the story. There is a clash between the old culture in Mexico and the subject’s new American life represented in these songs.

A few paragraphs later in the story, on page 52, the author tells us the difference in the American song. America sings a different tune. America is the land of opportunity. It represents upward mobility and the ability to “make it or break it.” But it seems there is a cost for all this material gain and all this opportunity. There seems to be a lack of love and emotion, a lack of the ability to express pain and all other feelings, the type of emotion which is expressed in the songs of Mexico. The song of America says, “You can be anything you want to be” (52). The song represents the American Dream. The cost seems to be the loss of compassion, love and emotion that is expressed through the songs of Mexico. There is no outlet quite the same for the stoic men of America. Rodriguez explains how the Mexican migrant workers have all that pain and desire, all that emotion penned up inside until it explodes in violent outbursts. “Or they would come into town on Monday nights for the wrestling matches or on Tuesdays for boxing. They worked over in Yolo County. They were men without women. They were Mexicans without Mexico” (49).

Rodriguez uses the language in the story almost like a song in order to portray the culture of the American dream. The phrase “I will send for you or I will come home rich,” is repeated twice throughout the story. The gain for all this loss of love and compassion is the dream of financial gain. “You have come into the country on your knees with your head down. You are a man” (48). That is the allure of the American Dream.

The protagonist of the story was born in America. Throughout the story he is looking at this illusion of the American Dream through a different frame. He is also trying to come to terms with his own manhood in relation to his American life and Mexican heritage. The subject has the ability to see the two songs in a different light. “The city will win. The city will give the children all the village could not-VCR’s, hairstyles, drumbeat. The city sings mean songs, dirty songs” (52). Part of the subject’s reconciliation process with himself is seeing that all the material stuff that is dangled as part of the American Dream is not worth the love and emotion that is held in the old Mexican villages and expressed in their songs.

Rodriguez represents this conflict of culture on page 53. The protagonist of the story is taking pictures during the arrest of illegal border-crossers. “I stare at the faces. They stare at me. To them I am not bearing witness; I am part of the process of being arrested”(53). The subject is torn between the two cultures in a hazy middle ground. He is not one of the migrants and he is not one of the police. He is there taking pictures of the incident with a connection to both of the groups and both of the groups see him connected with the other.

The old Mexican villages are characterized by a lack of : “Mexico is poor” (50). However, this is not the reason for the love and emotion that is held. The thought that people have more love and emotion because they are poor is a misconception. There are both rich people and poor people who have multitudes of love and compassion. The defining elements in creating love and emotion for each other comes from the level of community interaction and trust—the ability to sing these love songs and express emotion towards one another. People who become caught up in the American Dream tend to be obsessed with their own personal gain. This diminishes the social interaction and trust between fellow humans. There is no outlet in the culture of America quite the same as singing love songs towards each other. It does not matter if they are rich or poor, lack of community, trust, and social interaction; lack of songs can lead to lack of love and emotion that is seen in the old songs of Mexico.

The image of the American Dream is bright and shiny. To a young boy in a poor village the thought of power and wealth can dominate over a life of poverty with love and emotion. However, there is poverty in America today as well as in Mexico. The poverty here looks a little different but many migrants and young men find the American Dream to be an illusion. “Most immigrants to America came from villages.

The America that Mexicans find today, at the decline of the century, is a closed-circuit city of ramps and dark towers, a city without God. The city is evil. Turn. Turn” (50). The song of America sings an inviting tune for young men from poor villages. When they arrive though it is not what they dreamed about. The subject of the story can see this. He is trying to come of age in his own way, acknowledging America and the Mexico of old. He is able to look back and forth in relation to the America his father came to for power and wealth and the America that he grew up in. All the while, he watches this migration of poor villages, filled with love and emotion, to a big heartless city, while referring back to his father’s memory of why he came to America and his own memories of growing up in America. “Like wandering Jews. They carried their home with them, back and forth: they had no true home but the tabernacle of memory” (51). The subject of the story is experiencing all of this conflict of culture and trying to compose his own song.

Works Cited

Rodriguez, Richard. “Proofs.” In Short: A Collection of Brief Creative Nonfiction , edited by Judith Kitchen and Mary Paumier Jones, Norton, 1996, pp. 48-54.

Normal Person: An Analysis of the Standards of Normativity in “A Plague of Tics” 9

David Sedaris’ essay “A Plague of Tics” describes Sedaris’ psychological struggles he encountered in his youth, expressed through obsessive-compulsive tics. These abnormal behaviors heavily inhibited his functionings, but more importantly, isolated and embarrassed him during his childhood, adolescence, and young adult years. Authority figures in his life would mock him openly, and he constantly struggled to perform routine simple tasks in a timely manner, solely due to the amount of time that needed to be set aside for carrying out these compulsive tics. He lacked the necessary social support an adolescent requires because of his apparent abnormality. But when we look at the behaviors of his parents, as well as the socially acceptable tics of our society more generally, we see how Sedaris’ tics are in fact not too different, if not less harmful than those of the society around him. By exploring Sedaris’ isolation, we can discover that socially constructed standards of normativity are at best arbitrary, and at worst violent.

As a young boy, Sedaris is initially completely unaware that his tics are not socially acceptable in the outside world. He is puzzled when his teacher, Miss Chestnut, correctly guesses that he is “going to hit [himself] over the head with [his] shoe” (361), despite the obvious removal of his shoe during their private meeting. Miss Chestnut continues by embarrassingly making fun out of the fact that Sedaris’ cannot help but “bathe her light switch with [his] germ-ridden tongue” (361) repeatedly throughout the school day. She targets Sedaris with mocking questions, putting him on the spot in front of his class; this behavior is not ethical due to Sedaris’ age. It violates the trust that students should have in their teachers and other caregivers. Miss Chestnut criticizes him excessively for his ambiguous, child-like answers. For example, she drills him on whether it is “healthy to hit ourselves over the head with our shoes” (361) and he “guess[es] that it was not,” (361) as a child might phrase it. She ridicules his use of the term “guess,” using obvious examples of instances when guessing would not be appropriate, such as “[running] into traffic with a paper sack over [her] head” (361). Her mockery is not only rude, but ableist and unethical. Any teacher—at least nowadays—should recognize that Sedaris needs compassion and support, not emotional abuse.

These kinds of negative responses to Sedaris’ behavior continue upon his return home, in which the role of the insensitive authority figure is taken on by his mother. In a time when maternal support is crucial for a secure and confident upbringing, Sedaris’ mother was never understanding of his behavior, and left little room for open, honest discussion regarding ways to cope with his compulsiveness. She reacted harshly to the letter sent home by Miss Chestnut, nailing Sedaris, exclaiming that his “goddamned math teacher” (363) noticed his strange behaviors, as if it should have been obvious to young, egocentric Sedaris. When teachers like Miss Chestnut meet with her to discuss young David’s problems, she makes fun of him, imitating his compulsions; Sedaris is struck by “a sharp, stinging sense of recognition” upon viewing this mockery (365). Sedaris’ mother, too, is an authority figure who maintains ableist standards of normativity by taunting her own son. Meeting with teachers should be an opportunity to truly help David, not tease him.

On the day that Miss Chestnut makes her appearance in the Sedaris household to discuss his behaviors with his mother, Sedaris watches them from the staircase, helplessly embarrassed. We can infer from this scene that Sedaris has actually become aware of that fact that his tics are not considered to be socially acceptable, and that he must be “the weird kid” among his peers—and even to his parents and teachers. His mother’s cavalier derision demonstrates her apparent disinterest in the well-being of he son, as she blatantly brushes off his strange behaviors except in the instance during which she can put them on display for the purpose of entertaining a crowd. What all of these pieces of his mother’s flawed personality show us is that she has issues too—drinking and smoking, in addition to her poor mothering—but yet Sedaris is the one being chastised while she lives a normal life. Later in the essay, Sedaris describes how “a blow to the nose can be positively narcotic” (366), drawing a parallel to his mother’s drinking and smoking. From this comparison, we can begin to see flawed standards of “normal behavior”: although many people drink and smoke (especially at the time the story takes place), these habits are much more harmful than what Sedaris does in private.

Sedaris’ father has an equally harmful personality, but it manifests differently. Sedaris describes him as a hoarder, one who has, “saved it all: every last Green Stamp and coupon, every outgrown bathing suit and scrap of linoleum” (365). Sedaris’ father attempts to “cure [Sedaris] with a series of threats” (366). In one scene, he even enacts violence upon David by slamming on the brakes of the car while David has his nose pressed against a windshield. Sedaris reminds us that his behavior might have been unusual, but it wasn’t violent: “So what if I wanted to touch my nose to the windshield? Who was I hurting?” (366). In fact, it is in that very scene that Sedaris draws the aforementioned parallel to his mother’s drinking: when Sedaris discovers that “a blow to the nose can be positively narcotic,” it is while his father is driving around “with a lapful of rejected, out-of-state coupons” (366). Not only is Sedaris’ father violating the trust David places in him as a caregiver; his hoarding is an arguably unhealthy habit that simply happens to be more socially acceptable than licking a concrete toadstool. Comparing Sedaris’s tics to his father’s issues, it is apparent that his father’s are much more harmful than his own. None of the adults in Sedaris’ life are innocent—“mother smokes and Miss Chestnut massaged her waist twenty, thirty times a day—and here I couldn’t press my nose against the windshield of a car” (366)—but nevertheless, Sedaris’s problems are ridiculed or ignored by the ‘normal’ people in his life, again bringing into question what it means to be a normal person.

In high school, Sedaris’ begins to take certain measures to actively control and hide his socially unacceptable behaviors. “For a time,” he says, “I thought that if I accompanied my habits with an outlandish wardrobe, I might be viewed as eccentric rather than just plain retarded” (369). Upon this notion, Sedaris starts to hang numerous medallions around his neck, reflecting that he “might as well have worn a cowbell” (369) due to the obvious noises they made when he would jerk his head violently, drawing more attention to his behaviors (the opposite of the desired effect). He also wore large glasses, which he now realizes made it easier to observe his habit of rolling his eyes into his head, and “clunky platform shoes [that] left lumps when used to discreetly tap [his] forehead” (369). Clearly Sedaris was trying to appear more normal, in a sense, but was failing terribly. After high school, Sedaris faces the new wrinkle of sharing a college dorm room. He conjures up elaborate excuses to hide specific tics, ensuring his roommate that “there’s a good chance the brain tumor will shrink” (369) if he shakes his head around hard enough and that specialists have ordered him to perform “eye exercises to strengthen what they call he ‘corneal fibers’” (369). He eventually comes to a point of such paranoid hypervigilance that he memorizes his roommate’s class schedule to find moments to carry out his tics in privacy. Sedaris worries himself sick attempting to approximate ‘normal’: “I got exactly fourteen minutes of sleep during my entire first year of college” (369). When people are pressured to perform an identity inconsistent with their own—pressured by socially constructed standards of normativity—they harm themselves in the process. Furthermore, even though the responsibility does not necessarily fall on Sedaris’ peers to offer support, we can assume that their condemnation of his behavior reinforces the standards that oppress him.

Sedaris’ compulsive habits peak and begin their slow decline when he picks up the new habit of smoking cigarettes, which is of course much more socially acceptable while just as compulsive in nature once addiction has the chance to take over. He reflects, from the standpoint of an adult, on the reason for the acquired habit, speculating that “maybe it was coincidental, or perhaps … much more socially acceptable than crying out in tiny voices” (371). He is calmed by smoking, saying that “everything’s fine as long I know there’s a cigarette in my immediate future” (372). (Remarkably, he also reveals that he has not truly been cured, as he revisits his former tics and will “dare to press [his] nose against the doorknob or roll his eyes to achieve that once-satisfying ache” [372.]) Sedaris has officially achieved the tiresome goal of appearing ‘normal’, as his compulsive tics seemed to “[fade] out by the time [he] took up with cigarettes” (371). It is important to realize, however, that Sedaris might have found a socially acceptable way to mask his tics, but not a healthy one. The fact that the only activity that could take place of his compulsive tendencies was the dangerous use of a highly addictive substance, one that has proven to be dangerously harmful with frequent and prolonged use, shows that he is conforming to the standards of society which do not correspond with healthy behaviors.

In a society full of dangerous, inconvenient, or downright strange habits that are nevertheless considered socially acceptable, David Sedaris suffered through the psychic and physical violence and negligence of those who should have cared for him. With what we can clearly recognize as a socially constructed disability, Sedaris was continually denied support and mocked by authority figures. He struggled to socialize and perform academically while still carrying out each task he was innately compelled to do, and faced consistent social hardship because of his outlandish appearance and behaviors that are viewed in our society as “weird.” Because of ableist, socially constructed standards of normativity, Sedaris had to face a long string of turmoil and worry that most of society may never come to completely understand. We can only hope that as a greater society, we continue sharing and studying stories like Sedaris’ so that we critique the flawed guidelines we force upon different bodies and minds, and attempt to be more accepting and welcoming of the idiosyncrasies we might deem to be unfavorable.

Teacher Takeaways

“The student clearly states their thesis in the beginning, threading it through the essay, and further developing it through a synthesized conclusion. The student’s ideas build logically through the essay via effective quote integration: the student sets up the quote, presents it clearly, and then responds to the quote with thorough analysis that links it back to their primary claims. At times this thread is a bit difficult to follow; as one example, when the student talks about the text’s American songs, it’s not clear how Rodriguez’s text illuminates the student’s thesis. Nor is it clear why the student believes Rodriguez is saying the “American Dream is not worth the love and emotion.” Without this clarification, it’s difficult to follow some of the connections the student relies on for their thesis, so at times it seems like they may be stretching their interpretation beyond what the text supplies.”– Professor Dannemiller

“I like how this student follows their thesis through the text, highlighting specific instances from Sedaris’s essay that support their analysis. Each instance of this evidence is synthesized with the student’s observations and connected back to their thesis statement, allowing for the essay to capitalize on the case being built in their conclusion. At the ends of some earlier paragraphs, some of this ‘spine-building’ is interrupted with suggestions of how characters in the essay should behave, which doesn’t always clearly link to the thesis’s goals. Similarly, some information isn’t given a context to help us understand its relevance, such as what violating the student-teacher trust has to do with normativity being a social construct, or how Sedaris’s description of ‘a blow to the nose’ being a narcotic creates a parallel to his mother’s drinking and smoking. Without further analysis and synthesis of this information the reader is left to guess how these ideas connect.”– Professor Dannemiller

Sedaris, David. “A Plague of Tics.” 50 Essays: A Portable Anthology , 4 th edition, edited by Samuel Cohen, Bedford, 2013, pp. 359-372.

Analyzing “Richard Cory” 10

In the poem “Richard Cory” by Edward Arlington Robinson, a narrative is told about the character Richard Cory by those who admired him. In the last stanza, the narrator, who uses the pronoun “we,” tells us that Richard Cory commits suicide. Throughout most of the poem, though, Cory had been described as a wealthy gentleman. The “people on the pavement” (2), the speakers of the poem, admired him because he presented himself well, was educated, and was wealthy. The poem presents the idea that, even though Cory seemed to have everything going for him, being wealthy does not guarantee happiness or health.

Throughout the first three stanzas Cory is described in a positive light, which makes it seem like he has everything that he could ever need. Specifically, the speaker compares Cory directly and indirectly to royalty because of his wealth and his physical appearance: “He was a gentleman from sole to crown, / Clean favored and imperially slim” (Robinson 3-4). In line 3, the speaker is punning on “soul” and “crown.” At the same time, Cory is both a gentleman from foot (sole) to head (crown) and also soul to crown. The use of the word “crown” instead of head is a clever way to show that Richard was thought of as a king to the community. The phrase “imperially slim” can also be associated with royalty because imperial comes from “empire.” The descriptions used gave clear insight that he was admired for his appearance and manners, like a king or emperor.

In other parts of the poem, we see that Cory is ‘above’ the speakers. The first lines, “When Richard Cory went down town, / We people on the pavement looked at him” (1-2), show that Cory is not from the same place as the speakers. The words “down” and “pavement” also suggest a difference in status between Cory and the people. The phrase “We people on the pavement” used in the first stanza (Robinson 2), tells us that the narrator and those that they are including in their “we” may be homeless and sleeping on the pavement; at the least, this phrase shows that “we” are below Cory.

In addition to being ‘above,’ Cory is also isolated from the speakers. In the second stanza, we can see that there was little interaction between Cory and the people on the pavement: “And he was always human when he talked; / But still fluttered pulses when he said, / ‘Good- morning’” (Robinson 6-8). Because people are “still fluttered” by so little, we can speculate that it was special for them to talk to Cory. But these interactions gave those on the pavement no insight into Richard’s real feelings or personality. Directly after the descriptions of the impersonal interactions, the narrator mentions that “he was rich—yes, richer than a king” (Robinson 9). At the same time that Cory is again compared to royalty, this line reveals that people were focused on his wealth and outward appearance, not his personal life or wellbeing.

The use of the first-person plural narration to describe Cory gives the reader the impression that everyone in Cory’s presence longed to have the life that he did. Using “we,” the narrator speaks for many people at once. From the end of the third stanza to the end of the poem, the writing turns from admirable description of Richard to a noticeably more melancholy, dreary description of what those who admired Richard had to do because they did not have all that Richard did. These people had nothing, but they thought that he was everything. To make us wish that we were in his place. So on we worked, and waited for the light,

And went without the meat, and cursed the bread…. (Robinson 9-12)

They sacrificed their personal lives and food to try to rise up to Cory’s level. They longed to not be required to struggle. A heavy focus on money and materialistic things blocked their ability to see what Richard Cory was actually feeling or going through. I suggest that “we” also includes the reader of the poem. If we read the poem this way, “Richard Cory” critiques the way we glorify wealthy people’s lives to the point that we hurt ourselves. Our society values financial success over mental health and believes in a false narrative about social mobility.

Though the piece was written more than a century ago, the perceived message has not been lost. Money and materialistic things do not create happiness, only admiration and alienation from those around you. Therefore, we should not sacrifice our own happiness and leisure for a lifestyle that might not make us happy. The poem’s message speaks to our modern society, too, because it shows a stigma surrounding mental health: if people have “everything / To make us wish that we were in [their] place” (11-12), we often assume that they don’t deal with the same mental health struggles as everyone. “Richard Cory” reminds us that we should take care of each other, not assume that people are okay because they put up a good front.

“I enjoy how this author uses evidence: they use a signal phrase (front-load) before each direct quote and take plenty of time to unpack the quote afterward. This author also has a clear and direct thesis statement which anticipates the content of their analysis. I would advise them, though, to revise that thesis by ‘previewing’ the elements of the text they plan to analyze. This could help them clarify their organization, since a thesis should be a road-map.”– Professor Wilhjelm

Robinson, Edward Arlington. “Richard Cory.” The Norton Introduction to Literature , Shorter 12 th edition, edited by Kelly J. Mays, Norton, 2017, p. 482.

the cognitive process and/or rhetorical mode of studying constituent parts to demonstrate an interpretation of a larger whole.

a part or combination of parts that lends support or proof to an arguable topic, idea, or interpretation.

a cognitive and rhetorical process by which an author brings together parts of a larger whole to create a unique new product. Examples of synthesis might include an analytical essay, found poetry, or a mashup/remix.

a 1-3 sentence statement outlining the main insight(s), argument(s), or concern(s) of an essay; not necessary in every rhetorical situation; typically found at the beginning of an essay, though sometimes embedded later in the paper. Also referred to as a “So what?” statement.

EmpoWORD: A Student-Centered Anthology and Handbook for College Writers Copyright © 2018 by Shane Abrams is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Synthesis vs. Analysis: Breaking Down the Difference

Both synthesis and analysis play an important role in market and competitive intelligence (M/CI), but are two markedly different stages of a broader CI process. All too often, business leaders conflate synthesis and analysis, a mistake that can be very damaging to the overall success of M/CI efforts within an organization.

In this guide, we’ll break down the key differences between synthesis and analysis, discuss where you should focus the majority of your time, and explore ways to improve both your synthesis and analysis processes in the competitive intelligence infrastructure at your organization.

But first, let’s start with definitions for both synthesis and analysis as they relate to competitive intelligence activities.

What is synthesis?

Synthesis is the process of combining simple things into something more complex in order to understand their shared qualities.

What is analysis?

Analysis is the process of breaking down something into its basic parts to understand the nature, function or meaning of the relationships among the parts.

It is the understanding of the meaning that allows CI practitioners to create insights, intelligence, and knowledge.

It’s crucial to note that analysis can only be conducted by humans – not software. Too often, the makers of software programs or the latest technology claim that their product has the ability to conduct analysis. The reality is that today this just isn’t possible. Claims of non-human analysis are at best misleading and more likely fraudulent.

Synthesis vs. Analysis: Why Does It Matter?

To a layperson, these differences might seem trivial or a matter of semantics, but nothing could be further from the truth. Understanding this distinction is actually crucially important in helping competitive intelligence practitioners to educate their end customers. These end customers likely belong to a variety of departments or business units scattered around the organization, and consume M/CI insights to help them make better decisions. It’s fair to say that many end users’ understanding of M/CI is rudimentary at best.

Consumers of competitive intelligence should understand that analysis isn’t just something that happens with technology. Meaningful analysis requires a great deal of work to be performed by humans, and it’s important to recognize that this work takes time.

Yes, it’s possible to summarize information quickly. But analyzing information and transforming it into something valuable with context and meaning for your organization takes time.

Drawing a clear line between synthesis and analysis also helps to better align expectations across the organization. Many times, stakeholders might think they want a synthesis, but what they really want is an analysis. Let’s look at a quick example:

The leader of a sales organization reaches out to the M/CI team and asks for a report with the ten most recent deals that the sales team lost to a major competitor. This is a synthesis, and while the report does have value, it probably doesn’t provide any particularly meaningful information or helpful insights. In reality, what the sales leader likely wants to know is WHY their team lost those deals. Uncovering insights in this area requires an analysis of the data involved. This analysis report will certainly provide greater insights into the sales teams performance and will likely require more time to produce.

Making sure that the end users of competitive intelligence across the organization understand what they’re asking for, and the work involved in delivering it, enables the M/CI team to serve end users much more effectively.

Technology for Synthesis, Humans for Analysis

Another helpful way to think about the distinction between synthesis and analysis is the way in which the work is completed.

Today, the level of data that M/CI teams have access to continues to grow rapidly, and shows no signs of letting up. Compounding that issue is the increasing diversity of the requests that flow into the M/CI team from across the organization.

As a general rule, technology can perform synthesis much more effectively than humans can and M/CI teams should deploy technology to perform synthesis. Making sense of the information gathered and how it impacts certain areas of the business or markets (aka analysis) is work best performed by talented, well-qualified M/CI professionals. Take an example of an organization who want to track news about their competitors:

By setting up an automated monitoring tool that synthesizes competitor news, organizations can track news sources, social media platforms, and press releases for any news related to a set of pre-selected competitors. Any news will be pulled into a central platform, which will display all relevant news items in a real-time dashboard, email report, or some other format. This is far more efficient than relying on a human to track all these sources of information, and ensures key news items are never missed by the CI team.

Without software to assist M/CI teams overcome the deluge of data and inbound requests, it’s all too easy for even the most talented of M/CI professionals to get bogged down with low-value administrative tasks. . Organizations should look to incorporate sophisticated M/CI software platforms that employ technologies like Natural Language Processing (NLP) and Artificial Intelligence (AI) to effectively source, tag, and categorize competitive intelligence data. A lack of software and infrastructure around M/CI efforts often leads organizations to enter a cycle of competitive intelligence failure, where the CI function fails to prove their value to the wider organization and is eventually shut down.

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Difference Between Analysis and Synthesis

• Categorized under Science | Difference Between Analysis and Synthesis

Analysis Vs Synthesis

Analysis is like the process of deduction wherein you cut down a bigger concept into smaller ones. As such, analysis breaks down complex ideas into smaller fragmented concepts so as to come up with an improved understanding. Synthesis, on the other hand, resolves a conflict set between an antithesis and a thesis by settling what truths they have in common. In the end, the synthesis aims to make a new proposal or proposition.

Derived from the Greek word ‘analusis’ which literally means ‘a breaking up,’ analysis is, by far, mostly used in the realm of logic and mathematics even before the time of the great philosopher Aristotle. When learners are asked to analyze a certain concept or subject matter, they are encouraged to connect different ideas or examine how each idea was composed. The relation of each idea that connects to the bigger picture is studied. They are also tasked to spot for any evidences that will help them lead into a concrete conclusion. These evidences are found by discovering the presence of biases and assumptions.

Synthesizing is different because when the learners are asked to synthesize, they already try to put together the separate parts that have already been analyzed with other ideas or concepts to form something new or original. It’s like they look into varied resource materials to get insights and bright ideas and from there, they form their own concepts.

Similar definitions of synthesis (from other sources) state that it is combining two (or even more) concepts that form something fresh. This may be the reason why synthesis in chemistry means starting a series of chemical reactions in order to form a complex molecule out of simpler chemical precursors. In botany, plants perform their basic function of photosynthesis wherein they use the sunlight’s energy as catalyst to make an organic molecule from a simple carbon molecule. In addition, science professors use this term like bread and butter to denote that something is being made. When they mention about amino acid (the building blocks of proteins) synthesis, then it is the process of making amino acids out of its many basic elements or constituents. But in the field of Humanities, synthesis (in the case of philosophy) is the end product of dialectic (i.e. a thesis) and is considered as a higher process compared to analysis.

When one uses analysis in Chemistry, he will perform any of the following: (quantitative analysis) search for the proportionate components of a mixture, (qualitative analysis) search for the components of a specific chemical, and last is to split chemical processes and observe any reactions that occur between the individual elements of matter.

1. Synthesis is a higher process that creates something new. It is usually done at the end of an entire study or scientific inquiry. 2. Analysis is like the process of deduction wherein a bigger concept is broken down into simpler ideas to gain a better understanding of the entire thing.

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Cite APA 7 , . (2011, March 19). Difference Between Analysis and Synthesis. Difference Between Similar Terms and Objects. http://www.differencebetween.net/science/difference-between-analysis-and-synthesis/. MLA 8 , . "Difference Between Analysis and Synthesis." Difference Between Similar Terms and Objects, 19 March, 2011, http://www.differencebetween.net/science/difference-between-analysis-and-synthesis/.

It’s very useful to understand the science and other subjects. Thanks

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Thanks so much…. You explained so beautifully and simply….. Thanks again a lot

Thank you sir for your good explanation

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The Peak Performance Center

The pursuit of performance excellence, critical thinking.

Critical thinking refers to the process of actively analyzing, assessing, synthesizing, evaluating and reflecting on information gathered from observation, experience, or communication. It is thinking in a clear, logical, reasoned, and reflective manner to solve problems or make decisions. Basically, critical thinking is taking a hard look at something to understand what it really means.

Critical Thinkers

Critical thinkers do not simply accept all ideas, theories, and conclusions as facts. They have a mindset of questioning ideas and conclusions. They make reasoned judgments that are logical and well thought out by assessing the evidence that supports a specific theory or conclusion.

When presented with a new piece of new information, critical thinkers may ask questions such as;

“What information supports that?”

“How was this information obtained?”

“Who obtained the information?”

“How do we know the information is valid?”

“Why is it that way?”

“What makes it do that?”

“How do we know that?”

“Are there other possibilities?”

Combination of Analytical and Creative Thinking

Many people perceive critical thinking just as analytical thinking. However, critical thinking incorporates both analytical thinking and creative thinking. Critical thinking does involve breaking down information into parts and analyzing the parts in a logical, step-by-step manner. However, it also involves challenging consensus to formulate new creative ideas and generate innovative solutions. It is critical thinking that helps to evaluate and improve your creative ideas.

Elements of Critical Thinking

Critical thinking involves:

• Gathering relevant information
• Evaluating information
• Asking questions
• Assessing bias or unsubstantiated assumptions
• Making inferences from the information and filling in gaps
• Using abstract ideas to interpret information
• Formulating ideas
• Weighing opinions
• Reaching well-reasoned conclusions
• Considering alternative possibilities
• Testing conclusions
• Verifying if evidence/argument support the conclusions

Developing Critical Thinking Skills

Critical thinking is considered a higher order thinking skills, such as analysis, synthesis, deduction, inference, reason, and evaluation. In order to demonstrate critical thinking, you would need to develop skills in;

Interpreting : understanding the significance or meaning of information

Analyzing : breaking information down into its parts

Connecting : making connections between related items or pieces of information.

Integrating : connecting and combining information to better understand the relationship between the information.

Evaluating : judging the value, credibility, or strength of something

Reasoning : creating an argument through logical steps

Deducing : forming a logical opinion about something based on the information or evidence that is available

Inferring : figuring something out through reasoning based on assumptions and ideas

Generating : producing new information, ideas, products, or ways of viewing things.

Blooms Taxonomy

Bloom’s Taxonomy Revised

Mind Mapping

Chunking Information

Brainstorming

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• Published: 21 March 2022

Facilitators and barriers for lifestyle change in people with prediabetes: a meta-synthesis of qualitative studies

• Gyri Skoglund 1 ,
• Birgitta Blakstad Nilsson 1 , 2 ,
• Cecilie Fromholt Olsen 1 ,
• Astrid Bergland 1 &
• Gunvor Hilde 1  

BMC Public Health volume  22 , Article number:  553 ( 2022 ) Cite this article

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The increasing prevalence of type 2 diabetes worldwide is a major global public health concern. Prediabetes is a reversible condition and is seen as the critical phase for the prevention of type 2 diabetes. The aim of this study is to identify and synthesize current evidence on the perceived barriers and facilitators of lifestyle change among people with prediabetes in terms of both initial change and lifestyle change maintenance.

A systematic literature search in six bibliographic databases was conducted in April 2021. Potential studies were assessed for eligibility based on pre-set criteria. Quality appraisal was done on the included studies, and the thematic synthesis approach was applied to synthesize and analyse the data from the included studies.

Twenty primary studies were included, containing the experiences of 552 individuals. Thirteen studies reported participants perceived facilitators and barriers of lifestyle change when taking part in community-based lifestyle intervention programs, while seven studies reported on perceived facilitators and barriers of lifestyle change through consultations with health care professionals (no intervention involved).

Three analytical themes illuminating perceived barriers and facilitators for lifestyle change were identified: 1) the individual’s evaluation of the importance of initiating lifestyle change , 2) the second theme was strategies and coping mechanisms for maintaining lifestyle changes and 3) the last theme was the significance of supportive relations and environments in initiating and maintaining lifestyle change .

Awareness of prediabetes and the perception of its related risks affects the motivation for lifestyle change in people at risk of type 2 diabetes; but this does not necessarily lead to lifestyle changes. Facilitators and barriers of lifestyle change are found to be in a complex interplay within multiple ecological levels, including the interpersonal, intrapersonal, environmental and policy level. An integrated understanding and analysis of the perceived barriers and facilitators of lifestyle change might inform people with prediabetes, healthcare professionals, and policy makers in terms of the need for psychological, social, and environmental support for this population.

Peer Review reports

Type 2 diabetes represents a significant global health burden, with great impact on individuals, families, and societies. The prevalence of type 2 diabetes is increasing worldwide. Reports estimate that 578 million people will have diabetes in 2030, and the number will increase by 51% (700 million) in 2045 without urgent and sufficient action [ 1 ]. Considering the growing epidemic of diabetes and its complications, the increasing prevalence of prediabetes is a major global public health concern [ 2 ]. The term prediabetes is used to identify those individuals who are at risk of future diabetes and it is also associated with an increased cardiometabolic risk [ 2 ]. Prediabetes is a condition characterized by elevated blood glucose levels, below the threshold limit for type 2 diabetes but above normal levels, and it is estimated that 70% of individuals with prediabetes will eventually develop diabetes [ 2 , 3 ]. Prediabetes is seen as the critical phase for prevention, as the patients’ condition at this stage is reversible and could therefore serve as a window of opportunity to combat type 2 diabetes [ 3 ].

The risk of developing prediabetes increases with being overweight, living a sedentary lifestyle, age, and having a family history of diabetes [ 4 ]. Lifestyle changes aiming for healthy behaviour in terms of healthy diet, regular physical activity, and maintaining a healthy body weight are the cornerstones of prevention or the delayed onset of type 2 diabetes [ 4 , 5 ]. Weight reduction is shown to be the single-most important factor in reducing diabetes incidence: for every kilogram of weight loss, diabetes incidence has been reduced by 16 percent [ 6 ]. Several studies have shown the efficacy of lifestyle intervention with regards to diabetes prevention, with a relative risk reduction of 36–54% in those with prediabetes [ 7 ]. The positive outcomes of lifestyle changes have been observed in diverse populations [ 7 , 8 ], and diabetes prevention has therefore become a key priority for many nations, forming the basis of many national and international practice guidelines [ 9 , 10 , 11 ]. Although research has shown that lifestyle intervention programs are effective [ 7 , 8 , 12 , 13 ], improvements over the long term have been shown to deteriorate, highlighting challenges with long-term adherence and the maintenance of lifestyle changes [ 5 ]. A systematic review of obesity-related lifestyle change interventions, has shown that health behaviours that are initiated and regulated via autonomous motivation are more likely to be maintained over time through autonomous motivation, self-efficacy, and self-regulation skills [ 14 ].

Theoretical framework

In addition to previous research, the theoretical understanding of lifestyle and behavior change is important. A systematic review by Kwasnicka et al. [ 15 ] identified and synthesized 100 current theoretical explanations for behavioral change and maintenance. The review stated that there are distinct patterns of theoretical explanation for initial change and change maintenance and they highlighted the differential nature and role of five overarching, interconnected themes: maintenance motives, self-regulation, resources (psychological and physical), habits, and environmental and social influences. The individual’s motivation is crucial for behaviour change and maintenance, and motives that initiate change may differ from those maintaining change [ 15 ]. Approaches to initiate behaviour change can include motivation in the form of external pressure or control or the positive use of incentives or rewards, but these approaches are often insufficient in order to enhance maintenance of lifestyle change [ 16 ].

The ecological model

In addition to the theoretical explanations of Kwasnicka et al. [ 15 ] the ecological model can be a helpful framework in understanding the facilitators and barriers of lifestyle change in people with prediabetes in a larger context, and within a comprehensive understanding of the multiple determinants of health behaviours [ 17 ]. Health behaviours are dynamic, varying over individual’s lifespans, across settings, and over time [ 18 ], and the complex interplay of facilitators and barriers for healthy behaviours make lifestyle changes challenging to perform [ 19 , 20 ]. According to ecological models of health there are multiple levels that influence on health behaviour and these are the intrapersonal, interpersonal, environmental, and societal level [ 21 ] and the barriers and facilitators for healthy behaviours constantly interact across all these levels [ 17 ]. In addition to the individual motivation and skills for lifestyle change, the ecological perspective further addresses the environmental aspect in understanding the facilitators and barriers in play, and how they impact on lifestyle change and maintenance [ 21 ].

In a review of qualitative studies by Kelly et al. [ 22 ] on the facilitators and barriers for healthy behaviours in midlife (40–64 years), they found that examples of consistent barriers included entrenched attitudes and behaviours, a lack of knowledge, a lack of time, lack of access to transport to facilities and resources, restrictions in the physical environment, and financial costs. The facilitators of healthy behaviour included enjoyment, health benefits, social support, and clear messages. Among the included qualitative studies, however, there were none specifically addressing those with prediabetes.

Former research has found that people who were aware of their prediabetes status were more likely to report a perceived threat of developing diabetes, but they did not report increased engagement in health behaviours [ 23 , 24 , 25 ]. This indicates the need to better understand what characterizes the facilitators and barriers for lifestyle change and maintenance in people with prediabetes, and by identifying this, research on lifestyle change and the implementation of health interventions can be optimally tailored and effective.

Aim of the meta-synthesis

To our knowledge, no previous meta-syntheses examining perceived barriers and facilitators of lifestyle change among people at risk of developing type 2 diabetes have been performed. Hence, the current study aimed to identify and synthesize current qualitative evidence on facilitators and barriers of initial lifestyle change and maintenance based on the experiences of people with prediabetes.

Meta-synthesis, or qualitative evidence synthesis, is the synthesis of primary research studies that relate to a specific topic in order to arrive at a new or enhanced understanding of a specific phenomenon being explored [ 26 ]. One approach to the synthesis of the findings of qualitative research is thematic synthesis as described by Thomas and Harden [ 27 ]. This method combines approaches from both meta-ethnography and grounded theory and was originally developed to guide review of intervention needs, appropriateness, and effectiveness [ 26 , 28 ]. The approach of thematic synthesis is based on the method of thematic analysis used in primary qualitative research, however thematic synthesis enables new insights, interpretations and theories to be developed that has not been seen in the primary studies [ 29 ]. This meta-synthesis was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42020180051). We followed the Enhancing Transparency of Reporting the Synthesis of Qualitative Research (ENTREQ) framework [ 30 ].

Search strategy

Systematic comprehensive literature searches were conducted in six bibliographical databases: Medline, Embase PsychInfo, CINAHL, Web of Science, and Cochrane. This choice of databases is in line with suggestions presented in the systematic review on optimal database combinations for literature searches in systematic reviews [ 31 ]. The searches were done by the first author (GS) with close assistance from a health research librarian. The search strategy aimed to cover primary studies addressing the study population of interest, phenomena of interest, and setting of interest; we limited the search to qualitative studies (see Additional file 1 ). The literature search was initially developed in Medline and afterwards translated to the other databases’ search syntax with both text words and adapted thesaurus terms. We also screened the reference lists of the included studies and related systematic reviews to identify further papers. Non-English studies were excluded to prevent cultural and linguistic bias in translations, and there was no publication year limit. The review includes data for studies identified in searches up to April 21st, 2021.

Selection criteria

The primary studies were selected according to the study population, phenomenon of interest, setting and study design. An explicit description of criteria for inclusion and exclusion is presented in Table 1 . The phenomenon of interest of this meta-synthesis was facilitators and barriers to lifestyle change and maintenance in people with prediabetes. When selecting the primary studies, we presumed that the facilitators and barriers could be identified from the data in the studies, but it did not necessarily have to be explicitly mentioned. The primary studies included according to the setting criteria, involved several studies where experiences from participation in a structured lifestyle intervention program were reported. The lifestyle interventions described in these studies mainly focused on physical activity and dietary change and weight loss.

One researcher (GS) screened all titles and abstracts retrieved from the literature search results, excluding studies that did not meet the inclusion criteria. The full texts of potentially relevant articles were then screened independently by two authors in groups of pairs (GS and AB, GS and GH, GS and BBN), and additional information was sought from the authors of the full text articles where necessary. If consensus was not reached between the two researchers, a third reviewer was consulted.

Quality appraisal

Two authors in groups of pairs (GS and AB, GS and GH, GS and BBN) conducted a quality assessment of the included studies independently according to the Critical Appraisal Skills Program (CASP) checklist for qualitative research [ 33 ]. The checklist of ten questions allowed for the systematic appraisal of the qualitative research evidence included in our review (Table 2 ). The checklist guides the reviewer when assessing the validity, result and relevance of each study. After this initial independent assessment, the results of the appraisal were discussed, and a third reviewer was consulted to resolve any disagreements. There was an agreement that no studies were to be excluded based on the quality appraisal. However, an assessment of methodological quality would provide transparency and understanding of the relative strength and weaknesses of the body of evidence included [ 29 ].

Data extraction and synthesis

The data extracted from the primary studies included all the text in the studies’ results chapters, including participant quotations. The extracted text was entered verbatim into NVivo Pro 12 (NVivo qualitative data analysis software; Melbourne, Australia: QSR International Pty Ltd., 2018). Each study was read several times to ensure that all the extracted text was related to the perspectives and experiences of people with prediabetes.

We used the thematic synthesis approach by Thomas and Harden [ 27 ], and this involved three main stages:

1) Line-by-line coding of the findings of the primary studies:

Two independent reviewers performed an inductive line-by-line coding of the extracted material. New codes were generated independently of the original codes used in the primary studies. The codes were compared, and all codes that represented similarities across the primary studies and belonged to the same concept were organized into categories.

2) Development of descriptive themes:

Descriptive subthemes were formed through the merging and grouping of categories in an iterative process, staying close to the primary data in the included studies. The primary studies were read and reviewed by GS to ensure that the descriptive themes captured and reflected the depth of the data reported in the primary studies.

3) Development of analytical themes:

The descriptive themes were discussed in the research team in relation to the research question and organized within the main analytical themes. This was an iterative and cyclic process. In the analytical stage of the synthesis, we wanted to go beyond the descriptive findings trying to generate new understanding. After the development of the analytical themes, we related this to a higher-level theoretical framework to illuminate the central themes in the synthesis.

Meta-synthesis researchers’ background and preconceptions

The research team consisted of two PhD students (GS and CFO) and three researchers with a clinical and academic background, all of whom were physiotherapists (AB, GH, and BBN). Although the authors acknowledge that there has been much debate regarding the definition of prediabetes and share some of the expressed concerns in the literature regarding the usefulness of this label [ 54 , 55 ], the present analysis did not assume a critical stance toward this diagnosis, as our main aim was to use it as a descriptive category that would allow us to identify and review the existing literature in this area and on this population. It was the first author’s preunderstanding that risk perception is crucial in the initiation of lifestyle changes and that prediabetes might be a particularly challenging state in this respect. Furthermore, the researchers shared the preunderstanding that lifestyle change is complex and cannot be completely understood within a biomedical perspective. We used reflexive discussions to become aware of these preconceptions and reduce their influence on the analysis. However, in line with the qualitative research paradigm [ 56 ], we also acknowledge that they inevitably influenced the synthesis.

Literature search results

The literature search resulted in 9058 identified studies and, after duplicates were removed, 6035 studies. Titles and abstracts were screened by the first author (GS), and, of these, 54 full-text articles were found to be considered eligible. These were screened by two independent reviewers according to pre-set criteria for inclusion and exclusion, and 20 studies were finally included; see PRISMA flow diagram (Fig.  1 ).

PRISMA Flow Diagram-identification and selection of studies [ 57 ]

Study characteristics

The 20 included studies were published between 2008 and 2021 and involved 552 participants in total. The age of the participants ranged from 21–79 years; 312 participants were women and 240 were men. All participants had been diagnosed with prediabetes within the last year (when the data was collected). Eight studies were from Europe, three from Asia, two from the South Pacific, four from the USA, two from Canada, and one from Africa. Each study was systematically assessed for its research question or statement of purpose, research method, theoretical framework, sample size, and setting. The characteristics of the 20 studies included in the thematic synthesis are presented in Table 3 .

Thirteen studies reported on the participant perceived facilitators and barriers of lifestyle change when taking part in community-based lifestyle intervention programs [ 34 , 36 , 37 , 38 , 39 , 40 , 46 , 47 , 48 , 49 , 50 , 51 , 53 ], while seven studies reported on the participants perceived facilitators and barriers of lifestyle change through consultations with health care providers (no intervention involved) [ 35 , 41 , 42 , 43 , 44 , 45 , 52 ]. Thirteen studies [ 35 , 36 , 37 , 39 , 41 , 45 , 46 , 47 , 48 , 49 , 50 , 52 , 53 ] reported on the barriers and facilitators of lifestyle change and behavioural change maintenance, addressing both exercise and diet (participants exposed to an lifestyle intervention in nine studies, whereas no intervention in four studies), four studies [ 38 , 40 , 42 , 43 ] reported on exercise only (participants exposed to an lifestyle intervention in two studies, whereas no intervention in two), and three studies [ 34 , 44 , 51 ] reported on diet only (participants exposed to an lifestyle intervention in two studies, whereas no intervention in one).

Quality assessment

Of the then criteria used to assess the methodological quality [ 33 ], all the included studies met seven or more of these criteria. Two studies [ 36 , 51 ] were graded with seven out of ten points, three studies [ 37 , 38 , 53 ] were graded with eight points, six studies [ 39 , 41 , 45 , 46 , 47 , 52 ] were graded with nine points and nine studies [ 34 , 35 , 40 , 42 , 43 , 44 , 48 , 49 , 50 ] with ten points (Table 2 ). The relationship between the researcher and participants were one domain that was assessed not to be adequately described in several of the included studies [ 37 , 38 , 39 , 41 , 45 , 47 , 51 , 52 , 53 ].

Thematic synthesis of the qualitative studies

In total 986 codes were recorded from the extracted data, from which eight descriptive themes emerged. From the synthesis and analysis of the included primary studies, three main themes illuminating the perceived barriers and facilitators of lifestyle change among people with prediabetes were identified: 1) the individual’s evaluation of the importance of initiating lifestyle change; 2) strategies and coping mechanisms for maintaining lifestyle change; and 3) the significance of supportive relations and environments in initiating and maintaining lifestyle change (Fig.  2 ).

Emergent descriptive and analytical themes

In general, the primary studies demonstrated that there are multiple barriers and facilitators in the process of lifestyle change, and they exist in a complex interplay. Table 4 presents how the different primary studies are distributed across the main themes and subthemes based on whether they included lifestyle intervention programs or not, and the area of lifestyle change, being exercise or diet, or both. The presentation of the results is supplemented with quotes from participants in the included primary studies.

Theme 1: The individual’s evaluation of the importance of initiating lifestyle change

The first theme focused on the impact of the awareness and perception of risk on the individual’s evaluation of the importance of initiating lifestyle change, specifically considering reactions to the diagnosis of prediabetes and the internal struggle during the process of lifestyle change.

The impact of the awareness and perception of risk when diagnosed with prediabetes

Our analysis revealed that a vital facilitator in healthy lifestyle changes was when people became aware of being at a high risk of developing type 2 diabetes and realized the potential threat to their health. They experienced fear regarding the consequences of disease and facing an uncertain future [ 34 , 36 , 38 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 52 , 53 ]. Several participants in the primary studies reflected on the experience of having family members diagnosed with diabetes and expressed the desire to stay healthy and alive for their children and grandchildren to not become a burden to their family [ 34 , 36 , 38 , 41 , 43 , 44 , 46 , 47 , 48 ]. For example, one individual said:

There’s a big element of worry . . . like I’m on the train and I can’t stop it. You get that worry of ‘are you going to be able to stop this from getting worse?’ . . . like ‘whoa, what’s going on here?’ . . . I don’t want to become diabetic, that would be my main concern, I don’t want what comes with that. [ 48 ]

Several participants in the reviewed studies were aware of the increased risk of the progression to type 2 diabetes if lifestyle changes were not made and they were determined to stay ahead of their disease development [ 34 , 36 , 38 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 50 , 53 ]. In one of the included studies, participants reported that, at the time of their prediabetes diagnosis, their health care consultations provided little to no information on how to comprehend and understand the impact of its risk [ 52 ]. Several participants described shock when diagnosed with prediabetes [ 34 , 36 , 38 , 49 , 50 , 52 , 53 ]. For some participants this shock motivated them for lifestyle change, others found it difficult to identify themselves as being in an ‘at risk state’, as this conflicted with their own perceptions of having a healthy lifestyle creating a distance to future risk [ 42 , 45 , 48 , 52 , 53 ]. Hence, the findings illustrated how the recognition of prediabetes as asymptomatic and not associated with a medical condition or equated with severe illness led to a downplaying of the risk by the participants in the reviewed studies [ 42 , 45 , 48 , 52 , 53 ].

The internal struggle in the process of lifestyle change

Feelings of both guilt and self-blame arose with a diagnosis of prediabetes. The findings illustrated this phenomenon by describing how participants in our included studies accepted a personal responsibility for their outcomes [ 34 , 35 , 38 , 39 , 42 , 47 , 48 , 50 , 51 , 52 , 53 ]. In one study, a participant expressed a sense of commitment and personal responsibility to society in terms of lifestyle change and preventive behaviours [ 50 ]. Internal struggles with self-criticism and self-blame, especially when it came to dietary changes, were described by several participants in the included studies as leading to lower self-esteem and a lack of confidence, which, in turn, inhibited the driving force for change [ 35 , 39 , 42 , 47 , 48 , 53 ]. An individual described this feeling in the following way:

How am I going to do this? It seems so overwhelming. I know I should ideally lose a hundred pounds to get back to…my ideal weight, but it seems like such an insurmountable mountain to climb that why even try? [ 48 ]

A recurrent theme in our findings was how the gap between behavioural intentions and actual behaviour change amplified the negative feelings of guilt and self-blame that, in turn, lead to stress [ 34 , 35 , 39 , 48 , 52 , 53 ]. One of the studies demonstrated that stress affected behaviour change in terms of different emotional and cognitive responses for the participants in the included studies, with participants describing how this challenged their self-control, decision-making, and self-regulation [ 53 ]. One participant stated:

Sometimes I get very angry at myself because I don’t have the self-control to say: ‘stop eating that and go and exercise.’ Typically, I intend to do it, but then I feel anxious and I go and eat a pastry or something like that. Then after I feel terrible and I start thinking, how is it possible that I cannot get over this stress? [ 53 ]

Several of the studies described how temptation for sweet foods challenged the participants’ sense of self-control, making it difficult for them to implement healthy changes in their diet [ 34 , 36 , 44 , 45 , 47 , 51 , 52 , 53 ]. One study described how increased awareness regarding the necessity of dietary change created new cravings and temptations [ 53 ]. For some participants, having to reduce sugar and missing the sweet taste of foods were particularly challenging [ 34 , 36 , 45 , 48 , 51 , 53 ], describing it as a feeling of sacrificing the good life [ 45 ].

In some studies, the participants described that the stress and energy involved in making lifestyle changes would compromise their quality of life, also noting that they had greater concerns than progressing to diabetes [ 34 , 39 , 40 , 42 , 47 , 48 , 52 , 53 ]. One participant expressed the following:

I think there’s always a risk, I think there’s always some sort of risk, but it’s a very . . . I put it really on the backburner. If you think of priorities, it’s falling downstairs or tripping over, and I do try and eliminate risk. This is why I’ve started off with this Pilates teacher, which is definitely making me more aware of balance. Diabetes, it doesn’t worry me particularly. [ 52 ]

The importance of internal motivation and positive health feedback

Our findings demonstrated that experienced positive health feedback among the participants facilitated lifestyle change. For example, participants from several of the studies experienced benefits from exercising, such as improved physical condition and mental well-being. This encouraged them and led to a sense of accomplishment [ 41 , 43 , 47 , 49 , 53 ]. Improved physical condition, mental well-being, the enjoyment of different activities, and taking pleasure in nature were described as drivers of the maintenance of exercise change [ 38 , 40 , 41 , 43 , 46 , 47 , 49 , 52 , 53 ]. This sense of overall well-being and enjoyment was depicted as a central autonomous motivation for exercise, and, for many participants, exercise was also connected with being outdoors and taking pleasure in nature [ 38 , 40 , 41 , 43 , 49 ]. Accordingly, one individual described the following:

So, when you go outside to exercise, you feel the sunshine, you breathe in the fresh air, your body will then be good. It is for our wellbeing. [ 43 ]

Several participants in the included studies highlighted the value of former experience with exercise and how this facilitated their self-confidence to seek new activities that gave them further positive experiences with exercise [ 40 , 42 , 43 , 46 , 49 , 52 , 53 ]. Some participants explained that exercise also became integrated into their sense of self when it became a routine and a habit. Being able to identify oneself as a person with an active lifestyle and the desire to be a good role model for one’s children were facilitators for lifestyle change [ 38 , 40 , 47 , 49 ]. Participants also reported experiencing a sense of self-control that strengthened their motivation to adhere to a regular exercise regimen [ 43 , 46 , 50 , 51 ].

As with exercise, receiving positive health feedback from dietary change was described as giving a sense of mastery and self-control that facilitated maintenance. The participants in some of the studies experienced weight loss, a decrease in blood pressure, and a reduction in medication use in terms of dosage, as well as increased energy and improved sleep [ 34 , 42 , 44 , 46 , 51 , 53 ].

Theme 2: Strategies and coping mechanisms for maintaining lifestyle change

The focus in the second theme was on the strategies and coping mechanisms involved in lifestyle change maintenance, including making plans and setting attainable goals and the importance of knowledge and skills in mastering lifestyle change maintenance.

The motivation in making plans and setting goals

Making plans and setting goals were helpful facilitators of initiating and maintaining lifestyle change. Several studies emphasized that the process of guiding one’s own thoughts, behaviours, and feelings was important in order to make more concrete plans and set realistic and specific goals [ 34 , 36 , 38 , 39 , 40 , 41 , 47 , 49 , 51 , 53 ]. One participant noted:

I established a goal. I force myself to run three laps no matter how sluggish I feel. . . If I run today, I feel that I have paid attention to my health and I feel at peace. [ 39 ]

In two of the studies, self-compassion was highlighted as a strategy for making plans and setting goals [ 48 , 49 ]. Being kind to oneself was also put forward as making it easier to set attainable goals and prioritize oneself in finding the space, energy, and time for healthy changes [ 48 , 49 , 52 , 53 ]. Making time for lifestyle change was presented as a challenge in the process of making plans and reaching goals. Obligations regarding time, such as family commitments and workload, were often mentioned as barriers to participants being more physically active [ 34 , 38 , 40 , 41 , 42 , 43 , 46 , 47 , 49 , 50 , 51 , 53 ]. In several studies, female participants described how they found it difficult to find the time for and prioritize exercise when fulfilling their various responsibilities as wives, mothers, daughters, and, in some cases, caregivers [ 42 , 43 , 46 , 47 , 51 , 53 ]. One participant described their obligations as follows:

From Monday to Friday, I’m working . . . then Saturday and weekend I need to run errands for my children, my husband, and on top of that there is the housework. I also need to spend some time to visit my parents. Time is very important to me, I have so many duties and roles to fulfil, my first priority is always my family. [ 43 ]

Male participants, however, more often cited work as their reason for having ‘no time’ [ 43 ]. For example, one explained:

I am always so busy . . . in the evenings there are always papers to look at, I have no time for exercise. . . I simply don’t have the time. [ 41 ]

Knowledge and skills in mastering lifestyle change maintenance

The included studies presented a broad range of accounts about how one strategy for coping with lifestyle changes involves attaining knowledge, competence, and skills regarding exercise and a healthy diet for managing change [ 34 , 35 , 36 , 39 , 41 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 53 ]. Some of the studies demonstrated how knowledge and understanding affected how the participants behaved, enabling them to re-evaluate former habits [ 35 , 41 , 43 , 44 , 49 , 51 , 52 ].

The importance of skills and competence was highlighted in our included studies [ 43 , 44 , 46 , 47 , 49 , 51 , 53 ], with one woman describing the following:

. . . my cooking is all standard, you add the oil, the salt, and the sauce. But if you ask me to cook healthy food, like reduce the oil, reduce the salt, don’t use the sauce, then I don’t know how to cook already. Also, I have been cooking white rice all my life, now you tell me change to brown or red rice, I don’t know how to cook, how to make it tasty like white rice. [ 44 ]

Health care providers can help people with prediabetes by supplying them with information and guidance that will equip them with the knowledge, competence, and skills they need to facilitate and manage lifestyle changes and the risk they are facing [ 34 , 36 , 37 , 41 , 42 , 46 , 47 , 49 , 50 ]. Specifically, one participant mentioned the following:

It wasn’t stop this, stop that. It was cut down on this, cut down, little steps. . .The favourite saying is ‘little steps.’ And that’s probably one of the most helpful sayings I’ve ever heard. Not trying to do it in a week or two weeks, or two months or three months. It’s over a period of time, you know? [ 34 ]

Because of the perceived complexity of information regarding lifestyle change, several participants emphasized the importance of clarity and simplicity as well as pedagogical and empowering dialogue [ 34 , 35 , 36 , 37 , 41 , 46 , 47 , 49 , 50 ]. Access to information and guidance in developing manageable strategies were also deemed vital for coping with lifestyle changes [ 34 , 35 , 36 , 37 , 41 , 46 , 47 , 49 , 50 ].

Theme 3: The significance of supportive relations and environments in initiating and maintaining lifestyle change

The third theme focuses on the role of supportive relations being support from family, health care providers and peers in initiating and maintaining lifestyle change. In this final theme, supportive environments include external monitoring and support from lifestyle intervention programs, facilitating surroundings, and the availability of health promoting options for lifestyle change.

Family as allies for change and the importance of support from health care providers and peers

In the included studies, the spouse or children of the participants were described as important allies when it came to motivation for initiating and continuing lifestyle changes. Several participants highlighted how support from family members acted as a form of supervision, with family members checking up on them and encouraging shared decisions in facilitating healthy behaviours [ 40 , 42 , 49 , 51 , 53 ]. In terms of making dietary changes, the influence of one’s spouse and children was also noted as playing an important role in whether recommendations from health care providers were met or not. This influence could take the form of informative reminders from family members in meal situations [ 34 , 36 , 44 , 47 , 51 , 53 ]. For example, one woman mentioned:

My children will say, ‘mom that’s salty, don’t eat’ or you know, they will say ‘this is too fat, don’t eat’, you know what I mean? They will remind me and keep a look-out on my diet. [ 44 ]

Acceptance of the necessity of change within the family was another important factor for participants. A mutual understanding of the process of change was described as leading to increased involvement and support from family members, which, in turn motivated and encouraged participants [ 34 , 36 , 42 , 44 , 51 , 53 ]. Some studies also pointed out that family norms regarding being active could be part of participants’ identities and family cultures. In our findings, this was demonstrated to facilitate attempts to make lifestyle changes [ 41 , 43 , 51 , 53 ]. On the other hand, family norms, traditions, and culture could sometimes be barriers to lifestyle change, especially in terms of dietary changes [ 34 , 36 , 44 , 45 , 46 , 51 , 53 ]. The studies found that the participants described social expectations and pressure around providing and being offered foods as a challenge, with family gatherings and parties presented as examples of challenging settings with fewer healthy food options [ 34 , 36 , 44 , 45 , 46 , 51 , 53 ]. In the context of everyday life, food traditions and eating norms in families could also sometimes make dietary change difficult [ 34 , 36 , 44 , 51 , 53 ]. One individual described the following:

My whole family eats white rice since young, it has become a habit, a culture in us. Now say change to brown rice, not easy, it takes time for us to adjust to the new taste of brown rice. [ 44 ]

Receiving support and encouragement and not feeling alone in making lifestyle changes were described as positive effects of joining a group with other people with prediabetes [ 34 , 36 , 37 , 39 , 41 , 48 , 50 ]. Participants specifically described the benefits of sharing their experiences, exchanging ideas and strategies, and being motivated by each other [ 34 , 36 , 37 , 39 , 41 , 48 , 49 , 50 ]. Some participants highlighted that, when participating in a lifestyle program and joining a group with peers, external support from peers led to more physical activity and exercise on their part [ 39 , 41 ]. In one study, female participants described the importance of support from other women in a female-only setting, emphasizing the mutual understanding of barriers and other experiences that are specific to women [ 37 ].

Empowering communication was highlighted by participants in all studies as a key factor facilitating the supportive function of health care providers [ 34 , 36 , 37 , 39 , 41 , 44 , 46 , 47 , 49 , 51 ]. Participants in most of the studies emphasized how health care providers could facilitate lifestyle change [ 34 , 36 , 37 , 39 , 41 , 44 , 46 , 47 , 51 ]. Feeling accountable, receiving trusted communication and care, and being addressed with respect and empathy were also identified as important characteristics of this support [ 34 , 36 , 37 , 38 , 41 , 44 , 46 , 47 ]. One woman, when explaining how her health care professional helped her, stated the following:

It was the way she encouraged me, how she uplifted me. I am so grateful . . . So, I think having the right people at the forefront there just to open you up, you know, and acknowledging where I am at. [ 34 ]

The motivation of external monitoring in maintaining lifestyle change

In several studies, the participants highlighted that a successful facilitator they strongly valued was being monitored in intervention programs during the process of lifestyle change [ 34 , 36 , 37 , 38 , 39 , 40 , 46 , 47 , 49 , 50 ]. Participating in a program imparted a sense of commitment on them, and the participants were held accountable for their attempts to make healthy changes [ 34 , 36 , 37 , 38 , 39 , 40 , 46 , 47 , 49 , 50 ]. Having to report on their progress to a supervisor or having official measurements of their weight loss or improved physical condition taken in the near future, were described as strong motivators encouraging the participants to push themselves [ 36 , 37 , 38 , 40 , 46 , 47 ]. Tailoring lifestyle interventions to individuals also seemed to facilitate the process of making healthy changes. The freedom of choice and flexibility in a tailored program was seen to allow participants to set personalized and meaningful goals [ 34 , 37 , 46 , 47 , 50 ].

Five studies highlighted the importance of technological devices in monitoring healthy lifestyle change and how such devices could provide support for those not participating in a lifestyle intervention program. The data from step-counter technology and the feedback provided from this was described as motivating and inspiring [ 37 , 41 , 49 , 53 ]. For example, a user of a Fitbit stated:

I have a Fitbit that makes it easier, because I like to challenge myself to make sure I get my steps every day. So, lots of times, I’ll get home in the evening and I’ll see them at 9000 steps, and I’ll like go out and walk up and down the driveway. [ 41 ]

The value of using digital tracking and apps to document the process of change and regulate food consumption was also described as an external motivation in terms of dietary change [ 53 ], with one participant expressing the following:

I must not just settle with reducing carbohydrates, but I must, as we say, document it. I had a friend that believed that, for everything you did, you had to keep a record of it and said, ‘It’s like sports; if you don’t keep a record, you’re only practicing. [ 53 ]

In a study that used an online-modality lifestyle intervention program, the participants highlighted the logistical benefits of the flexibility and convenience of a digital follow-up [ 37 ], showing how this could make lifestyle intervention programs more accessible regarding distance and geography or according to work schedule or family obligations.

The availability of health promoting options and facilitating surroundings

Participants described experiencing barriers and facilitators of lifestyle change in their work environments, in their neighbourhoods, in their local communities, and at the societal level [ 34 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 49 , 50 , 51 , 53 ]. For example, three of the studies described how making healthy changes to one’s diet was challenging when there were limited healthy options at the workplace or local restaurants [ 34 , 44 ]. Several participants cited financial restraints as barriers to lifestyle change [ 34 , 36 , 44 , 45 , 47 , 51 , 53 ], with the high cost of healthy food leading some to choose unhealthy food because it was the more affordable option [ 34 , 36 , 44 , 45 , 47 , 51 , 53 ]. For example, one individual stated:

Look, the barrier to those goal settings is budget, you know . . . So, when you see on TV people saying they’re eating unhealthily, what they’re doing, what we’re doing is we’re eating to a budget planned to survive for the week.... So, don’t go telling poor people ‘you’re going to get diabetes if you eat this and this and this’; so we want you to eat this food, but it’s too expensive for you to buy, you know. [ 36 ]

In several studies, we found that having access to exercise facilities and organized activities in local communities, parks, and green areas made it easier to initiate and maintain physical activity and exercise [ 35 , 38 , 40 , 41 , 43 , 46 , 47 , 49 ]. However, climate and weather conditions could affect access to those spaces and some participants experienced bad weather and climate as a barrier to exercise [ 38 , 40 , 41 , 43 , 46 ]. Having access to nature and outdoor life was also described as an important facilitator for physical activity [ 41 , 43 , 49 ]. Moreover, some participants pointed out that it was too expensive for them to use indoor training facilities. In one study, participants acknowledged a governmental health promotion strategy to lower the cost of accessing different indoor training facilities as a positive solution [ 47 ].

This meta-synthesis aimed to explore, synthesize, and interpret qualitative research on facilitators and barriers of lifestyle change and maintenance among people with prediabetes. In line with the ecological framework, our findings indicate that the relevant barriers and facilitators are found within the intrapersonal, interpersonal, environmental, and policy level. We identified three main themes within these ecological levels being the individual’s evaluation of the importance of lifestyle change, strategies and coping mechanisms for maintaining lifestyle change and the importance of supportive relations and environments in initiating and maintaining lifestyle change. These themes are not independent, they exist in a complex interplay, which our discussion will reflect. In addition to the ecological framework [ 17 , 21 ] the findings will be discussed in light of the central themes in the theoretical explanations of behavioural change maintenance presented in the review by Kwasnicka et al. [ 15 ].

The individual’s evaluation of the importance of initiating lifestyle change

At the intrapersonal level, individual motives are crucial for initiating and maintaining behaviour change and are the drivers of volitional behaviour [ 15 ]. Our findings indicate that getting the diagnosis of prediabetes, affected the participants’ perception of risk and motivation towards initiating lifestyle change, but the internal struggle experienced by many participants also affected the individual’s evaluation of the importance of initiating lifestyle change. These findings align with the review by Kwasnicka et al. [ 15 ] in highlighting the importance of intrinsic motivation and autonomy in facilitating the maintenance of initial lifestyle change.

Using the label ‘prediabetes’ on individuals at high risk of type 2 diabetes may increase the perceived threat of developing diabetes [ 55 ]. Our findings illustrate that the recognition of prediabetes as asymptomatic and not equating it with severe illness in some cases led to a downplaying of the associated risk [ 48 , 52 , 53 ]. This reveals some of the complexity of initiating lifestyle change in the face of an invisible disease; thus, this is perhaps what sets the prediabetes population apart from other high-risk populations. Our findings and previous research [ 23 , 24 ] suggest that health care providers should emphasize illness severity and provide cues to action to encourage health behaviours, whilst at the same time acknowledging the fear and insecurity that might arise when dealing with the diagnosis of prediabetes.

According to a systematic review and meta-analysis by Hennessey et al. [ 58 ], struggle in the process of lifestyle change may create stress and deplete one’s cognitive and emotional capacity, which, in turn, challenges or disrupts the self-regulatory capacity. Kwasnicka et al. [ 15 ] state that self-regulation is a limited resource, and coping with behavioural barriers, overcoming temptations, managing lapses, and avoiding relapses is a demanding process and requires sustained effort. This might explain why participants in the included studies searched for a balance between preserving their mental needs and focusing on preventive behaviours [ 34 , 39 , 40 , 42 , 47 , 48 , 52 , 53 ]. According to Kwasnicka et al. [ 15 ] individuals are more likely to initiate behaviour change at times when their psychological and physical resources are plentiful, and the opportunity costs are low. Our findings reflected that when resources are low, individuals need more guidance and support in order to cope with the initiation and maintenance of lifestyle changes, especially when it comes to setting attainable goals and maintaining a balanced effort in everyday life.

The importance of intrinsic motivation and positive health feedback

According to the review by Kwasnicka et al. [ 15 ], the motivation to avoid negative health consequences is hypothesized to be insufficient to maintain preventive behaviour requiring maintained effort. In line with our findings, individuals are intrinsically motivated when lifestyle change is perceived as personally relevant and resembling one’s values and beliefs [ 16 ]. To support individuals with prediabetes in the process of initiating and maintaining lifestyle change, as well as to enhance intrinsic and autonomous motivation, it seems important that health care providers explore the individual’s perceptions of risk, their beliefs, and their personal values. In line with the ecological model this also pertains to the individual differences in culture and their different social and environmental contexts [ 21 ].

Several participants in the included studies experienced success with exercise and dietary changes after lifestyle change interventions. This was experienced through perceived positive health feedback, such as improved physical condition, weight loss, and this enhanced self-efficacy in the participants [ 41 , 43 , 47 , 49 , 52 , 53 ]. The attainment of prior success and one’s own perception of a positive psychological state are, according to Bandura [ 59 ], suggested to increase self-efficacy and are therefore important for behavioural change maintenance. This is in line with Rothman [ 60 ], who emphasizes that the individual’s decision to maintain a behaviour change is dependent on their perceived satisfaction with the received outcomes.

Strategies and coping mechanisms for maintaining lifestyle change

The process of making plans and setting goals, knowledge and skills and the formation of habits, are important aspects in the process of identifying strategies and coping mechanisms to maintain lifestyle changes [ 16 ]. These aspects are discussed mainly at the intrapersonal level but they cannot be understood isolated from social, environmental, and societal influences.

According to Hennessy et al. [ 58 ], setting goals initiates self-regulation and acts as a key mechanism for behaviour change. Self-regulation refers to any effort to actively control unwanted behaviour by inhibiting dominant and automatic behaviours, such as urges, emotions, or desires, and replacing them with goal-directed responses [ 15 ]. A systematic review by Leman et al. [ 61 ] found that people require self-efficacy and self-regulation to motivate their consistent performance of healthy behaviour.

Several participants in the included studies experienced a gap between their behavioural intentions and actual behaviour change, which then amplified their feelings of self-blame, guilt, and shame, especially when in terms of dietary changes [ 34 , 35 , 48 , 52 , 53 ]. This can cause dissatisfaction and lead individuals to either expend greater effort toward achieving the lifestyle change goals or disengage from these goals [ 15 ]. This underlines the importance of setting attainable, personal, relevant, and intrinsically motivated goals.

In two of the included studies, self-compassion was put forward as a strategy for making plans and setting goals [ 48 , 49 ]. According to Neff [ 62 ], self-compassion entails three main overlapping and interacting components: self-kindness versus self-judgement, common humanity versus isolation, and mindfulness versus over-identification. Interestingly, in a recent meta-analysis by Liao et al. [ 63 ], a positive association was found between self-compassion and self-efficacy, indicating that self-compassion may play a role in protecting one’s self-efficacy when experiencing failures [ 63 ].

A Finnish study of adults with increased risk of type 2 diabetes found that eating competence is associated with a healthy diet and could therefore, in the long term, support the prevention of type 2 diabetes [ 64 ]. Supporting autonomy and confidence is central in facilitating competence [ 16 ] and health care providers therefore play an important role when giving information and guidance. According to Gardner et al. [ 65 ], habit formation takes place after a period of the successful self-regulation of a new behaviour, and this is considered to play a fundamental role in generating health behaviour. Once a new behaviour has become a habit, it requires less effort, and the level of required self-regulation is reduced [ 15 ]. Gardner et al. [ 65 ] stated that habits persist even when conscious motivation decreases, and, therefore, habit formation should be encouraged in interventions to promote long-term maintenance.

The importance of supportive relations and environments in initiating and maintaining lifestyle change

Within the ecological framework supportive relations and environments were identified at the interpersonal level, the environmental level and the policy level, affecting the motivation for initiating and maintaining lifestyle change for individuals with prediabetes.

At the interpersonal level of the ecological framework, supportive relations and social influence can be found in formal and informal social networks [ 21 ]. In line with the ecological perspective, Barry et al. [ 66 ] highlighted the importance of socio-cultural influences in diabetes prevention policies. When addressing barriers and facilitators for lifestyle change, we must consider the impact of social norms and cultural aspects within families and communities and consider how health behaviours are shaped within different contexts [ 67 ]. Considering this, lifestyle intervention programs and health care communication aiming to facilitate lifestyle change in people with prediabetes, should include and involve the families or other significant persons in the whole process. This could enhance the individuals’ perceived sense of relatedness in the lifestyle change process, which is important in maintaining a new behaviour [ 16 ]. In line with our findings, peer support can enhance the internalization and maintenance of lifestyle change through perceived relatedness, connection, and trust [ 16 ].

A systematic review and meta-analysis that investigated the best method to improve self-efficacy to promote lifestyle and recreational physical activity in healthy adults [ 68 ], found that interventions that included feedback on their past performance or others’ performance (comparative feedback) produced the highest levels of self-efficacy.

Lifestyle intervention programmes are not necessarily suitable for all individuals with prediabetes. This can be due to different life phases, family settings or personal preferences; or practical or logistical barriers, such as care responsibility, work, or geographical distance. In one study offering an online-modality lifestyle intervention programme, participants highlighted the logistical benefits of the flexibility and convenience of a digital follow-up [ 37 ]. There is promising evidence regarding the efficacy of diabetes prevention eHealth interventions [ 69 ], and the integration of specific behaviour change techniques and digital features may optimise digital diabetes prevention interventions achieving clinically significant weight loss in individuals with prediabetes [ 70 ]. At the same time our findings described that the use of technological devices and digital follow-up was motivating and inspiring [ 37 , 41 , 49 , 53 ] and this further supports the potential of acceptance and increased use of digital eHealth interventions in the prevention of type 2 diabetes.

In line with the ecological model and our findings, barriers and facilitators to promote healthy diet and physical activity in our external environment are to a great extent beyond the control of the individual. McLeroy [ 21 ] referred to “the ideology of individual responsibility” and how this may inhibit our understanding of the potential environmental assault on health and the opportunities for healthy behaviours. According to the review by Barry et al. [ 66 ], watchfulness should be put towards a biomedical approach where prediabetes is recognized as a reversible state of abnormal glucose metabolism that can be reversed solely by altering the individual patient’s lifestyle. This may lead to an overemphasis on the individual’s responsibility for lifestyle change, resulting in the creation of policy neglecting the complex sociocultural environment affecting health and illness. Therefore, identifying behaviour change and maintenance strategies that are tailored for individuals with prediabetes in their socio-cultural environment, is of great importance for the individual having prediabetes as well as for the society in order to reduce their risk of progression to type 2 diabetes [ 71 ].

At the public policy level, there are a range of incentives policy makers can use to influence health behaviour for the population and the individuals at risk for type 2 diabetes, including legislation, information campaigns and price signals [ 72 ]. A systematic review and meta-analysis has shown that the risk of being diagnosed with type 2 diabetes is associated with low socio-economic status [ 73 ]. Moreover, individuals of a lower level of socioeconomic status are more often exposed to negative lifestyle habits, such as smoking, physical inactivity, obesity, and low fruit and vegetable consumption [ 74 ]. Thus, a central challenge when implementing lifestyle interventions in practice is reaching people with prediabetes across social groups and socio-economic positions to avoid reinforcing health inequalities.

Strengths and limitations of the meta-synthesis

To the best of our knowledge, this is the first review to explore qualitative research on the facilitators and barriers of lifestyle changes and lifestyle change maintenance among people with prediabetes. The application of a rigorous and systematic meta-synthesis technique with a transparent analytical procedure strengthens our paper. Synthesizing qualitative research is viewed as essential in achieving the goal of evidence-based practice and mainly features the use of the best available evidence as the foundation for this practice [ 75 ]. Another strength is that the included studies represent findings from several different countries with variously structured health systems. Despite this heterogeneity, we were able to identify many common themes, thus indicating how heterogeneity can be a strength rather than a limitation in a meta-synthesis [ 76 ].

A limitation to the meta-synthesis could be that the included articles were restricted to the English language, similar potential studies reported in other languages were consequently not retrieved nor appraised. Our included studies had no publication year limit, the oldest studies were conducted in 2008. However, in qualitative research one may argue that people’s experiences and perceptions on a specific topic are affected by context and the aspect of time to varying degrees. A meta-synthesis is a new and more comprehensive interpretation of already interpreted qualitative data from the primary studies [ 76 ], hence we did not use the raw data from the primary studies.

Practical implications

The findings of these meta-synthesis might inform people with prediabetes, healthcare professionals and policy makers, in terms of the need for psychological, social, and environmental support for this population. More qualitative research is needed in this field to explore the reasons behind unhealthy behaviour and consider the complex interplay between all ecological levels influencing health behaviour. The translation of lifestyle intervention programs into practice seems to be limited since rates of type 2 diabetes are set to rise further. Considering this, it would be useful to pay more attention to the importance of the communication of risk and how people perceive risk and understand the diagnosis of prediabetes. This might provide insight into why people engage (or not) in lifestyle intervention programs for diabetes prevention. Lifestyle interventions in general seem to appeal more to those with greater resources and who can apply the appropriate information to improve health [ 77 ], therefore there is also a need for studies focusing on the effect of interventions for different groups in terms of socioeconomical status, culture, gender, and level of knowledge regarding prediabetes.

This meta-synthesis offers important insights into evidence relevant to understanding the complexity and challenges of lifestyle change among people with prediabetes. Awareness of prediabetes and the perception of its related risks affects the motivation for lifestyle change; but this does not automatically lead to lifestyle changes. Facilitators and barriers for lifestyle change in people at risk for type 2 diabetes are found to be in a complex interplay within multiple levels of an ecological framework. Our findings illustrate how internal motivation and successful self-regulation facilitate lifestyle change and maintenance at the intrapersonal level. At the interpersonal level, social influence and support from family, peers, and health professionals comprise important facilitators; however, family and social norms can also represent barriers to change. Lifestyle intervention programs are important supportive contexts for lifestyle change, enhancing autonomy, competence and relatedness. Moreover, technological devices for monitoring lifestyle change could provide support for those not participating in a lifestyle intervention programme. The environmental and policy levels set the foundations for the availability of health promoting options and plays a crucial role in shaping the conditions for successful lifestyle change. A purely individual approach is far from sufficient in combating the rising global epidemic of type 2 diabetes. A great responsibility lies on health authorities and policymakers to create health-promoting environments.

Availability of data and materials

All data generated or analysed during this study are included in this published article. The data presented in our review are retrieved from the published papers of the included studies.

Abbreviations

Impaired fasting glucose

Impaired glucose tolerance

Fasting plasma glucose

Fasting blood glucose

Oral glucose tolerance test

Body mass index

Type 2 diabetes

Diabetes prevention program

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The authors would like to thank health research librarian Malene W. Gundersen for her support and guidance regarding the literature search.

This project was made possible as a part of a research-funded PhD being undertaken by GS, through internal distribution of PhD fellowship at OsloMet-Oslo Metropolitan University, Faculty of Health Sciences. No external funding was obtained for this study.

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Synthesis, structural analysis, and gas-adsorption properties of a dibenzothiophene-based hydroxamate/zinc metal-organic framework

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Koh Sugamata, Sho Kobayashi, Akihiro Shirai, Natsuki Amanokura, Mao Minoura, Synthesis, structural analysis, and gas-adsorption properties of a dibenzothiophene-based hydroxamate/zinc metal-organic framework, Bulletin of the Chemical Society of Japan , Volume 97, Issue 3, March 2024, uoae017, https://doi.org/10.1093/bulcsj/uoae017

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We report the mixed-ligand synthesis of a novel hydroxamate/zinc metal-organic framework (MOF) with a dibenzothiophene scaffold. The reaction of dibenzothiophene-3,7-dicarbohydroxamic acid, isonicotinic acid, and zinc nitrate under solvothermal conditions afforded a porous hydroxamate/zinc MOF. The structure and gas-adsorption properties toward N 2 , H 2 , CO 2 , and CH 4 of the hydroxamate/zinc MOF were investigated.

The dibenzothiophene-based hydroxamate/zinc MOF was newly synthesized using a mixed ligand synthetic method. Single-crystal X-ray crystallographic analysis and gas-adsorption measurements revealed that the MOF showed permanent porosity and good gas storage capacity.

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Deep learning based synthesis of MRI, CT and PET: Review and analysis

Affiliations.

• 1 Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia. Electronic address: [email protected].
• 2 Monash Biomedical Imaging, Clayton VIC 3800, Australia.
• 3 Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia.
• 4 Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom.
• 5 Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
• 6 Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia.
• PMID: 38052145
• DOI: 10.1016/j.media.2023.103046

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.

Keywords: Generative deep-learning models; Medical image synthesis; Pseudo-CT; Synthetic MR; Synthetic PET.

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

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• Deep Learning*
• Image Processing, Computer-Assisted / methods
• Magnetic Resonance Imaging
• Positron-Emission Tomography
• Tomography, X-Ray Computed

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• Published: 09 April 2024

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

• Jiadong Chen   ORCID: orcid.org/0009-0004-7603-8838 1   na1 ,
• Samuel R. Cross 2   na1 ,
• Lincoln J. Miara   ORCID: orcid.org/0000-0002-2561-8216 2 ,
• Jeong-Ju Cho 2 ,
• Yan Wang   ORCID: orcid.org/0000-0002-8648-2172 2 &
• Wenhao Sun   ORCID: orcid.org/0000-0002-8416-455X 1  

Nature Synthesis ( 2024 ) Cite this article

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Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

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Autonomous and dynamic precursor selection for solid-state materials synthesis

Nathan J. Szymanski, Pragnay Nevatia, … Gerbrand Ceder

An autonomous laboratory for the accelerated synthesis of novel materials

Nathan J. Szymanski, Bernardus Rendy, … Gerbrand Ceder

Combinatorial synthesis for AI-driven materials discovery

John M. Gregoire, Lan Zhou & Joel A. Haber

There is currently a poor scientific understanding of how to design effective and efficient synthesis recipes to target inorganic materials 1 , 2 , 3 . As a result, synthesis often becomes a bottleneck in the scalable manufacturing of functional materials 4 , as well as in the laboratory realization of computationally predicted materials 5 , 6 . Density functional theory (DFT)-calculated thermodynamic stability or metastability can often estimate the synthesizability of materials 7 , 8 , 9 , but finding an optimal synthesis recipe—including temperatures, times and precursors—still requires extensive trial-and-error experimentation. The recent emergence of robotic laboratories 10 , 11 , 12 , 13 presents an exciting opportunity for high-throughput experiments and sequential learning algorithms to autonomously optimize materials synthesis recipes 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 . However, there remains a poor fundamental understanding of how changing a synthesis recipe affects the underlying thermodynamics and kinetics of a solid-state reaction. Without this scientific foundation, it is difficult to build physics-informed synthesis-planning algorithms to guide robotic laboratories 13 , 23 , meaning that parameter optimization via high-throughput experiments can end up being unnecessarily resource intensive and wasteful.

Multicomponent oxides represent an important and challenging space for targeted synthesis. These high-component materials are key to various device technologies—including battery cathodes (Li(Co,Mn,Ni)O 2 ), oxygen evolution catalysts (Bi 2 Sr 2 Ca n −1 Cu n O 2 n +4+ x ), high-temperature superconductors (HgBa 2 Ca 2 Cu 3 O 8 ) and solid-oxide fuel cells (La 3 SrCr 2 Mn 2 O 12 ) 24 . Multicomponent oxides are usually synthesized by solid-state reactions, which involves combining and firing the constituent binary oxide precursors in a furnace. However, this often yields impurity by-product phases arising from incomplete solid-state reactions. From a phase diagram perspective, precursors start at the corners of a phase diagram and combine together towards a target phase in the interior of the phase diagram. If the phase diagram is complicated, with many competing phases between the precursors and the target, undesired phases may form, consuming thermodynamic driving force and kinetically trapping the reaction in an incomplete non-equilibrium state.

High-component oxides reside in high-dimensional phase diagrams and can be synthesized from many possible precursor combinations. Here we present a thermodynamic strategy to navigate these multidimensional phase diagrams, where the primary objective is to identify precursor compositions that circumvent kinetically competitive by-products, while maximizing the thermodynamic driving force for fast reaction kinetics. We test our principles of precursor selection using a robotic inorganic materials synthesis laboratory, which automates many tedious aspects of the inorganic materials synthesis workflow such as powder precursor preparation, ball milling, oven firing and X-ray characterization of reaction products. With our robotic platform, a single human experimentalist can conduct powder inorganic materials synthesis in both a high-throughput and reproducible manner. Using a diverse target set of 35 quaternary Li-, Na- and K-based oxides, phosphates and borates, which are relevant chemistries for intercalation battery cathodes 25 , 26 and solid-state electrolytes 27 , we show that precursors identified by our thermodynamic strategy frequently outperform traditional precursors in synthesizing high-purity multicomponent oxides. Our work demonstrates the utility of robotic laboratories not only for automated materials synthesis and manufacturing, but also as a platform for large-scale hypothesis validation over a broad and diverse chemical space.

Principles of precursor selection

Recently, we showed that solid-state reactions between three or more precursors initiate at the interfaces between only two precursors at a time 28 . The first pair of precursors to react will usually form an intermediate by-product, which can consume much of the total reaction energy and leave insufficient driving force to complete a reaction 29 . Figure 1 illustrates this multistep reaction progression for an example target compound, LiBaBO 3 , whose simple oxide precursors are B 2 O 3 , BaO and Li 2 CO 3 . Because Li 2 CO 3 decomposes to Li 2 O upon heating, we can examine the competing chemical reactions 30 geometrically on a pseudo-ternary Li 2 O–B 2 O 3 –BaO convex hull. Although the overall reaction energy for Li 2 O + BaO + B 2 O 3  → LiBaBO 3 is large at Δ E  = −336 meV per atom, there are many low-energy ternary phases along the binary slices Li 2 O–B 2 O 3 (Fig. 1b , blue) and BaO–B 2 O 3 (Fig. 1b , green). In the initial pairwise reactions between Li 2 O + BaO + B 2 O 3 , we anticipate that stable ternary Li–B–O and Ba–B–O oxides, such as Li 3 BO 3 , Ba 3 (BO 3 ) 2 or others, will form rapidly due to large thermodynamic driving forces of Δ E  ≈ −300 meV per atom. Should these low-energy intermediates form, the ensuing reaction energies to the target product become miniscule, for example, Li 3 BO 3  + Ba 3 (BO 3 ) 2  → LiBaBO 3 has only Δ E  = −22 meV per atom (Fig. 1e , orange). (Note: one advantage of analysing reactions on convex hulls is that stoichiometric reactions are automatically balanced by the barycentric coordinates of the product relative to its precursors. For brevity, we do not balance reactions explicitly in this manuscript, but we do emphasize that all reaction energies are normalized per atom of product phase.)

a – e , The traditional reaction. f – h , The predicted reaction. Schematic of the pairwise reactions process for traditional recipes ( a ) and our predicted recipes ( f ), showing the phase evolution from precursors to the target. In pseudo-ternary Li 2 O–B 2 O 3 –BaO convex hulls, reaction convex hulls between precursor pairs are illustrated by coloured slices, for B 2 O 3 |Li 2 O (blue) and B 2 O 3 |BaO (green) ( b ), Li 3 BO 3 |Ba 3 (BO 3 ) 2 (orange) ( d ), LiBO 2 |BaO (purple) ( g ). The corresponding two-dimensional slices of the binary reaction convex hulls are B 2 O 3 |Li 2 O (blue) and B 2 O 3 |BaO (green) ( c ), Li 3 BO 3 |Ba 3 (BO 3 ) 2 (orange) ( e ), LiBO 2 |BaO (purple) ( h ), where grey arrows show the reaction energy of the corresponding reaction. i , Free energy change in a reaction progress, where a relatively high-energy intermediate state saves more energy for the final step in forming the target. j , X-ray diffraction of the solid-state synthesis of LiBaBO 3 , where red and blue curves are raw X-ray diffraction data for traditional and predicted precursors, respectively, and the black curve is the fit produced by the Rietveld refinement.

Source data

Instead of allowing the reactions to proceed between the three precursors all at once, we suggest initially synthesizing LiBO 2 , which can serve as a high-energy starting precursor for the reaction. Figure 1g (purple) shows that LiBaBO 3 can be formed directly in the pairwise reaction LiBO 2  + BaO → LiBaBO 3 with a substantial reaction energy of Δ E  = −192 meV per atom. Moreover, along this reaction isopleth, there is a low likelihood of forming impurity phases, as the competing kink of Li 6 B 4 O 9  + Ba 2 Li(BO 2 ) 5 has relatively small formation energy (Δ E  = −55 meV per atom) compared to LiBaBO 3 . Finally, the inverse hull energy of LiBaBO 3 , which we define as the energy below the neighbouring stable phases on the convex hull 31 , is substantial at Δ E inv  = −153 meV per atom, suggesting that the selectivity of the target LiBaBO 3 phase should be much greater than any potential impurity by-products along the LiBO 2 –BaO slice.

Figure 1i compares the energy progression between these two precursor pathways. Although both pathways share the same total reaction energy, synthesizing LiBaBO 3 from three precursors is likely to first produce low-energy ternary oxide intermediates (Fig. 1a ), leaving little reaction energy to drive the reaction kinetics to the target phase 28 . By first synthesizing a high-energy intermediate (LiBO 2 ), we retain a large fraction of overall reaction energy for the last step of the reaction, promoting the rapid and efficient synthesis of the target phase. We confirm this hypothesis experimentally (Fig. 1j ), where we find that solid-state synthesis of LiBaBO 3 from the traditional precursors Li 2 CO 3 , B 2 O 3 and BaO does not result in strong X-ray diffraction signals of the target phase, whereas LiBO 2  + BaO produces LiBaBO 3 with high phase purity ( Methods ).

From this instructive LiBaBO 3 example, we propose five principles to select effective precursors from a multicomponent convex hull. (1) Reactions should initiate between only two precursors if possible, minimizing the chances of simultaneous pairwise reactions between three or more precursors. (2) Precursors should be relatively high energy (unstable), maximizing the thermodynamic driving force and thereby the reaction kinetics to the target phase. (3) The target material should be the deepest point in the reaction convex hull, such that the thermodynamic driving force for nucleating the target phase is greater than all its competing phases. (4) The composition slice formed between the two precursors should intersect as few other competing phases as possible, minimizing the opportunity to form undesired reaction by-products. (5) If by-product phases are unavoidable, the target phase should have a relatively large inverse hull energy—in other words, the target phase should be substantially lower in energy than its neighbouring stable phases in composition space.

When there were multiple precursor pairs that could be used to synthesize the target compound, we ranked the ‘best’ precursor pair by first prioritizing principle 3, where the target compound was at the deepest point of the convex hull. This ensures that the thermodynamic driving force for nucleation of the target compound is greater than the driving forces to all other competing phases. We next prioritized principle 5, where the target compound has the largest inverse hull energy. A reaction having a large inverse hull energy supersedes principle 2, as a large reaction driving force is not a sufficient criterion for synthesis, for example, in Fig. 2b , where the magnitude of the driving force of Li 2 O + Zn 2 P 2 O 7  → LiZnPO 4 is large but selectivity may be weak compared to ZnO + Li 3 PO 4 . A large inverse hull energy also supersedes principle 4, as a large inverse hull energy means that, even if intermediate phases form, there would still be a large driving force for a secondary reaction to form the target compound.

a , c , e , The blue, red and purple slice planes correspond to Zn 2 P 2 O 7  + Li 2 O ( a ), Zn 3 (PO 4 ) 2  + Li 3 PO 4 ( c ) and LiPO 3  + ZnO ( e ) binary reaction convex hulls, respectively. b , d , f , The corresponding two-dimensional slices of reaction convex hulls are shown for Zn 2 P 2 O 7  + Li 2 O ( b ), Zn 3 (PO 4 ) 2  + Li 3 PO 4 ( d ) and LiPO 3  + ZnO ( f ).

In Fig. 2 , we interpret these precursor design principles for an example LiZnPO 4 target in the pseudo-ternary Li 2 O–P 2 O 5 –ZnO phase diagram. If we first synthesize Zn 2 P 2 O 7 to combine with Li 2 O (Fig. 2a,b , blue), the deepest point in the reaction convex hull is not LiZnPO 4 but rather ZnO + Li 3 PO 4 , suggesting a kinetic propensity to form these undesired by-products. If we start from Zn 3 (PO 4 ) 2  + Li 3 PO 4 (Fig. 2c,d , orange), LiZnPO 4 is located at the deepest point along the convex hull; however, Li 3 PO 4 is a low-energy starting precursor, meaning that there is a small driving force (Δ E  = −40 meV per atom) left to form LiZnPO 4 , which probably leads to slow reaction kinetics. We suggest that LiPO 3  + ZnO (Fig. 2e,f , purple) are the ideal precursors for LiZnPO 4 . LiPO 3 has a relatively high energy along the Li 2 O–P 2 O 5 binary hull, resulting in a large driving force to the target phase of Δ E  = −106 meV per atom. Additionally, there are no competing phases along the LiPO 3  + ZnO slice, minimizing the possibility of impurity by-product phases.

In Supplementary Note 1.2 , we further interpret our precursor selection principles from the dual perspective of chemical potential diagrams, and interpret the inverse hull energy with respect to the ‘chemical potential distance’, as proposed by Todd et al. 32 . Here, we chose a convex hull approach, since it graphically constrains stoichiometrically balanced pairwise reactions better than chemical potential diagrams. Additionally, in Supplementary Note 1.3 , we show that our predicted precursors generally differ from those predicted by the algorithms of McDermott et al. 30 and Aykol et al. 33 . Although all our works share the same goal of predicting inorganic synthesis recipes, the five principles that guide our precursor selection algorithm are based on our recent insights into the importance of pairwise reactions 27 , 28 , which was not considered in the PIRO algorithm by Aykol et al. PIRO therefore predicts the optimal precursors for BaLiBO 3 to be \(\frac12\) Ba +  \(\frac12\) Ba(BO 2 ) 2  + Li +  \(\frac12\) O 2  → BaLiBO 3 , which probably proceeds through intermediates in this multiprecursor reaction. Our approach of maximizing driving force also differs slightly from the cost function of McDermott et al., whose ideal predicted reaction is \(\frac13\) Ba 3 (BO 3 ) 2  +  \(\frac13\) Li 3 BO 3  → BaLiBO 3 , which, as discussed earlier, has a small driving force. As deeper fundamental understanding of solid-state reactions is achieved, we anticipate that new principles will need to be developed and included in our algorithms for the overarching ambition of predictive solid-state synthesis.

Validation with a robotic ceramic synthesis laboratory

To test our precursor selection hypotheses, we designed a large-scale experimental validation effort based on quaternary Li-, Na- and K-based oxides, phosphates and borates, which are representative chemistries for intercalation battery materials 24 , 26 . We surveyed the Materials Project 34 for all known quaternary compounds in this space, and then we used our selection principles to predict optimal precursors from the DFT-calculated convex hulls. The algorithms to identify these precursors are detailed in Supplementary Note 1 . We also determine the traditional precursors for these reactions, which we previously text-mined from the solid-state synthesis literature 35 . A full list of 3,104 reactions in this space is provided in Supplementary Data 1 . To efficiently maximize the coverage of our experimental validation, we Pareto-optimized our reaction list to select the fewest number of precursors that maximize the number of candidate reactions, resulting in 28 unique precursors for 35 target materials that span 27 elements.

We then compare the phase purity of target materials synthesized from our predicted precursors versus that from traditional precursors. We perform this large-scale validation effort using a robotic inorganic materials synthesis laboratory named ASTRAL (Automated Synthesis Testing and Research Augmentation Lab), located at the Samsung Advanced Institute of Technology, Cambridge, Massachusetts. As shown in Fig. 3 , ASTRAL uses a robotic arm to automate sample handling throughout a full ceramic synthesis workflow—from powder precursor preparation to ball milling, to oven firing and to X-ray characterization of reaction products. Videos of the robotic laboratory in action can be viewed in Supplementary Videos 1 – 4 . Three trays of 24 samples can pass sequentially through the ASTRAL workflow every 72 hours. The throughput of ASTRAL is bottlenecked by powder dispensing and processing, as each 24-sample tray is prepared serially, whereas the firing and characterization steps can, in principle, be run in parallel.

a , A robot-enabled inorganic materials synthesis workflow—from powder precursor preparation to ball milling, to oven firing and to X-ray diffraction (XRD) characterization of reaction products. b , Photograph of the ASTRAL laboratory. c , Robotic chemists enable large-scale exploration of synthesis hypotheses over a broad chemical space, which normally would have to be undertaken by multiple experimentalist groups. d , Human experimentalists have a trade-off between throughput and reproducibility, whereas robotic chemists can achieve both high reproducibility and throughput simultaneously.

ASTRAL automates inorganic materials synthesis from powder precursors, as opposed to previous robotic laboratories that rely on solution-based precursors 15 , 16 , 17 , 36 , inkjet printing 18 or combinatorial thin-film deposition 14 , 19 . Although it is easier to dose precursor concentrations using these other methods, the resulting products are typically only produced at milligram scale. Powder synthesis, on the other hand, can yield grams of material, which is needed to create ceramic pellets or electrodes for functional property characterization. Moreover, high-temperature powder synthesis is the primary synthesis method of ceramic oxides, so recipes determined from ASTRAL can be upscaled for industrial manufacturing. We overcame major practical challenges in powder precursor processing, which arise primarily from flowability differences between different powders due to varying particle sizes, hardness, hygroscopicity and compaction. In Supplementary Table 1 , we summarize the challenges in working with powder precursors, as well as our solutions to these challenges. The essential task is to identify the best dosing head for each precursor, as detailed in Supplementary Table 2 for the precursors used here.

In total, we conducted 224 synthesis reactions over 35 target materials, calcined at temperatures from 600 to 1,000 °C. Each reaction was conducted for 8 hours, and then impurity by-products were assessed, without regrinding or reannealing our samples. We deliberately chose these relatively short reaction times to evaluate the intrinsic reactivity of the two competing sets of precursors.

For a target space this diverse, traditional validation of our precursor selection principles would probably have required an extensive experimental effort, consisting of multiple human experimentalists working over many years. Once the robotic laboratory is set up, we can comprehensively survey this broad crystal chemistry space in a single experimental campaign (Fig. 3c ). Moreover, a large-scale human effort will inevitably require trade-offs between throughput and reproducibility. Meanwhile, a robotic laboratory produces single-source experimental data with high reproducibility, meaning we can systematically compare synthesis results while minimizing human variability and error (Fig. 3d ). Altogether, the robotic laboratory offers a new platform for data-driven empirical synthesis science, where hypotheses can be investigated rapidly, reproducibly and comprehensively over diverse crystal chemistries.

Results and discussion

For the 35 materials selected, Fig. 4a shows the relative yield of the target phase starting from computationally designed versus traditional precursors. Figure 4b shows the reaction temperatures attempted and Fig. 4c shows the relative performance of the predicted versus traditional precursors. A full list of targets, precursors and reaction results is given in Supplementary Table 3 . For 32 out of 35 compounds (91%), the predicted precursors successfully produced the target phase. In 15 targets, the predicted precursors achieved at least 20% higher phase purity than the traditional precursors (green), and 6 of these 15 target materials could ‘only’ be synthesized by the predicted precursors (dark green). For 16 reactions the precursors have similar target yields (light green), and only in 4 systems do the traditional precursors perform better than the predicted precursors (red). However, we note that even in these four systems, the predicted precursors also produce the target materials with moderate to high purities.

a , Table of the phase purity of 35 targets obtained from predicted precursors using the highest phase purity from various firing temperatures, compared to traditional precursors. Colour of ‘Precursor comparison’ column compares purity from predicted precursors versus traditional precursors, where green means predicted precursors achieve >10% better purity, light green means they have purities within ±10% and red means traditional precursors achieve >10% better purity. Targets with blue colour star are metastable materials. The same colour scheme is used in panels b – d . b , Heat map of phase purity of predicted precursors at different calcination temperatures. c , The target phase purity from predicted precursors versus traditional precursors. Phase purity methods in Supplementary Note 2.2.3 . d , Reaction energies and inverse hull energies for all targets. The marker shape corresponds to the best phase purity of predicted precursors, where diamonds are high purity, circles are moderate and low purity, and crosses with a red outline mean that both predicted precursors and traditional precursors failed. The red box represents the low thermodynamic driving force regions where kinetic process may be rate limiting. The dashed line represents when the inverse hull energy equals the reaction energy. Inset, convex hull illustrating the reaction energy and the inverse hull energy.

We also examined the robotic solid-state synthesis of four metastable compounds with mild energies above the convex hull 7 —LiNbWO 6 (10 meV per atom), LiZnBO 3 (8 meV per atom), KTiNbO 5 (1 meV per atom) and Li 3 Y 2 (BO 3 ) 3 (39 meV per atom), indicated by blue asterisks in Fig. 4 . These metastable compounds formed in our solid-state reactions, although generally with low phase purity. However, we still found that our predicted precursors would yield these target metastable phases with similar or better relative purity than when starting from traditional precursors (Supplementary Note 3.2 ). Recent work by Zeng et al. suggests that by tuning the thermodynamic driving forces from the precursors, it may be possible to selectively form desired stable or metastable phases on the basis of their calculated nucleation barriers 37 . Finally, in three systems, neither sets of precursors resulted in the target material, which for NaBSiO 4 was due to glass formation 38 , for Li 3 V 2 (PO 4 ) 3 a more reducing atmosphere was needed 39 and for NaBaBO 3 the published reaction temperature 40 was very precise at 790 °C, suggesting that perhaps a rounded number, such as 800 °C, may be too high. As discussed further in Supplementary Note 3.4 , these potential failure modes represent important considerations in future robotic laboratory design for solid-state synthesis.

Figure 4c shows that our predicted precursors tend to synthesize target materials with higher purity than traditional simple oxide precursors. Many of our predicted ternary oxide precursors are unusual, such as LiPO 3 , LiBO 2 and LiNbO 3 (see more in Supplementary Table 3 ), as these precursors do not appear from our previously text-mined database of 19,488 solid-state synthesis recipes 41 . Machine-learning algorithms for synthesis prediction trained on literature datasets would therefore be unlikely to predict our suggested precursors here. This highlights the limitations of machine-learning algorithms in predicting new opportunities in synthesis parameter space, outside the constraints of our anthropogenic biases in chemical reaction data 22 , 42 .

Our results show that the success of a reaction was not correlated to the crystal structure or chemistry of the target material; rather, it was primarily determined by the geometry of the underlying convex hull, as well as by the magnitude of the thermodynamic driving force. The success of our precursor selection principles is surprising, considering we evaluate precursor selection using only the DFT-calculated convex hull, which does not account for temperature-dependent effects, such as vibrational entropy or oxide decomposition, neglects kinetic considerations, such as diffusion rates and nucleation barriers 32 , and has known errors in DFT-calculated formation energies 43 .

Here we rationalize with order-of-magnitude energy arguments why, despite many simplifying assumptions, the DFT-calculated thermodynamic convex hull retains predictive power in identifying effective precursors. First, entropic contributions ( T Δ S ; T is temperature and S is entropy) can generally be neglected because the free energy change Δ G of an oxide synthesis reaction is usually dominated by the change in enthalpy Δ H contribution, rather than the T Δ S contribution. Supplementary Fig. 16 compiles a list of 100 experimental ternary oxide reaction energies, and shows that at 1,000 K the magnitude of |Δ G | for reactions is ~200 meV per atom, whereas the | T Δ S | contribution is only ~15 meV per atom. In 60% of the reactions, | T Δ S |/|Δ G | < 10%, except in cases where |Δ G | < 100 meV per atom, in which case T Δ S can be comparable in magnitude to Δ H . We validate these arguments in Supplementary Fig. 17 , showing that temperature-dependent free energies are negligibly different from reaction enthalpies 44 . The dominance of Δ H over T Δ S in oxide synthesis reactions is due to the irreversible exothermic nature of reactions of the form A + B → AB; as opposed to first-order phase transitions, such as melting, or polymorphic transformations, where Δ H  ≈  T Δ S . This assumption relies on both the reactants and products being solid phases—for reactions that evolve gases, the reaction entropy is approximately Δ S  = 1 eV per atom per 1,000 K; meaning that higher temperature largely favours the reaction direction with more moles of gas.

Second, ternary convex hulls are often skewed such that certain hull directions are much deeper than others, such as the Li 2 O–B 2 O 3 and BaO–B 2 O 3 directions illustrated on the Li 2 O–BaO–B 2 O 3 convex hull in Fig. 1 (more examples can be found in Supplementary Note 3 ). On a high-dimensional phase diagram, there are many combinations of precursor pairs that can slice through a target phase. Even an approximate convex hull, with systematic DFT formation energy errors of 25 meV per atom (refs. 8 , 42 ), can largely capture the relative depths of the convex hull in various compositional directions, as well as the complexity of the hull arising from competing phases. Importantly, DFT is well poised to capture the very stable phases, which are low-energy thermodynamic sinks to be avoided when designing the reaction isopleths between pairs of precursors.

Although we do not explicitly calculate kinetics here, the magnitude of the thermodynamic driving force is a good proxy for phase transformation kinetics, as Δ G reaction appears in the denominator of the classical nucleation barrier, as supersaturation in the JMAK theory of crystal growth and as d μ /d x in Fick’s first law of diffusion (where μ is chemical potential and x is distance) 45 . Because we aim to evaluate the relative reaction kinetics of different precursors, rather than absolute kinetics, we can usually compare thermodynamic driving forces between different precursor sets without explicitly calculating diffusion barriers 46 or surface energies for nucleation and growth analyses 47 , 48 .

However, there are limits to this assumption. Figure 4d shows the reaction energy and inverse hull energy for all 35 reactions using predicted precursors, among which three of the unsuccessful syntheses are marked with a cross and four red markers indicate conditions where the traditional precursors outperformed the predicted precursors. In cases where our predicted precursors were less successful (red box in Fig. 4d ), the reaction energy landscapes were shallow with Δ E reaction  > −70 meV per atom, and inverse hull energies of Δ E IH  > −50 meV per atom. Because these driving forces are of the order of k B T at solid-state synthesis temperatures (Boltzmann’s constant × ~1,000 K ≈ 100 meV per atom), unanticipated kinetic processes may become rate limiting and disqualify our thermodynamic driving force arguments. These counter examples provide valuable ‘failed synthesis’ results 49 to quantify bounds where our precursor selection principles offer less certainty of success, and can serve as soft cutoff energies for future algorithms for solid-state precursor prediction—although we note that many reactions within this energy cutoff can still be successful, as shown in our experiments.

Finally, additional opportunities to design large Δ G reaction include leveraging metathesis reactions 29 , 31 , for example, of the form 2NaCrS 2  + MgCl 2  → MgCr 2 S 4  + 2NaCl (ref. 50 ), where reactions can be thermodynamically driven by the formation of a stable salt by-product. Because there are a wide variety of opportunities to select potential by-product phases, metathesis reactions represent a rich design space to enhance the thermodynamics, and thereby the kinetics, of solid-state reactions.

Synthesis science is poorly understood, but new theories can be developed by examining falsifiable predictions through empirical validation. In this work, we hypothesized several principles to identify superior precursors for high-purity synthesis of multicomponent oxides. We argued that in high-dimensional phase diagrams with skewed energy landscapes, there is an opportunity to find precursors that are both high in energy and have compositions that circumvent low-energy, undesired kinetic by-products. Using a robotic synthesis laboratory, we validated this hypothesis over 35 target materials with diverse crystal chemistries, producing in this one study as many experimental results as a typical review paper might survey. This work highlights the potential of data-driven experimental synthesis science, where the high throughput and reproducibility of robotic laboratories enable a more comprehensive interrogation of synthesis science hypotheses. This exciting robotic platform can be directed to investigate further fundamental questions, such as the role of temperatures and reaction times in ceramic oxide synthesis. As we use these robotic laboratories to verify human-designed hypotheses, we will deepen our fundamental understanding of the interplay between thermodynamics and kinetics during materials formation. Simultaneously, this scientific understanding will drive the development of physically informed artificial intelligence synthesis-planning frameworks to enable truly autonomous materials processing and manufacturing.

DFT convex hulls for precursor identification

Material phases and formation energies were obtained from the Materials Project 34 using its REST API 51 (retrieved December 2020). Convex hulls were constructed from the phase diagram module in Pymatgen 52 , and reaction convex hulls were calculated from the interfacial reactions module 53 . Software for producing interactive reaction compound convex hulls can be found on GitHub at https://github.com/dd-debug/synthesis_planning_algorithm . Further details on the thermodynamic calculations and convex hull analysis are provided in Supplementary Note 1 .

Robotic laboratory

ASTRAL transports samples between stations using two robots, a seven-axis Panda robotic arm (Franka Emika) and a linear rail (Vention). By using the rail system to extend the range of the Panda arm, the system can perform precise laboratory manipulations over an area of 1.7 m × 4 m. Surrounding the central rail system are stations that perform specialized tasks for inorganic materials synthesis, such as dispensing solid powder precursor chemicals and liquid dispersants, a mechanical ball mill, furnace to calcine and react precursors and X-ray diffraction to characterize synthesis outcomes. Precursor powders are dispensed sequentially using a Quantos powder dispenser (Mettler Toledo), with sample vials and powder dosing heads exchanged using the robotic arm. Following powder dispensing, 1 ml of ethanol is dispensed into each vial using a Freedom EVO 150 liquid handling robot (Tecan Life Sciences), followed by rotary ball milling for 15 h at 100 r.p.m. to produce a uniform fine mixture of precursor powders. Alumina crucibles (Advalue Technology) are used to hold the mixed precursors. After ball milling, samples are heated to 80 °C for 2 h under vacuum to remove ethanol and then transferred to a furnace for calcination in air atmosphere for 8 h at temperatures from 600 to 1,000 °C. Powders are then characterized via powder X-ray diffraction (Rigaku Miniflex 600). Further details on the robotic infrastructure and synthesis procedures are provided in Supplementary Note 2 .

Automated X-ray diffraction refinement

Rietveld refinement of data was performed in the BGMN kernel 54 . The target structure is used as the sole input phase for the BMGN kernel, and the Rietveld refinement will split the X-ray diffraction signal into the target phase, background and residual. The background X-ray diffraction pattern was determined from empty sample holders. The fraction of the target phase was estimated by dividing the integrated intensity ( I ) of the target phase by the combined intensity of the target phase and residual phase, I target /( I target  +  I residual ). Values greater than 0.5 are considered high purity, between 0.2 and 0.5 are considered moderate purity and less than 0.2 is considered low purity. Further details on the automated X-ray diffraction refinement process are provided in Supplementary Note 2.2.3 .

Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information files. All thermodynamic data to reproduce our analyses can be freely obtained from the Materials Project database and its API, as discussed in Methods and Supplementary Note 1 . X-ray diffraction patterns for robotic laboratory synthesis results are all provided in the Supplementary Information . All experimental protocols regarding the construction and operation of the robotic laboratory are discussed in the Supplementary Information . Candidate reactions and their energies are available via figshare at https://doi.org/10.6084/m9.figshare.22671571 . Source data are provided with this paper.

Code availability

All code for evaluating precursors, as well as for producing interactive reaction compound convex hulls, can be found on GitHub at the following link: https://github.com/dd-debug/synthesis_planning_algorithm . The link includes a readme, demonstration file, installation guide, Python package requirements and instructions for use.

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Acknowledgements

This work was supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award no. DE-SC0021130. We thank J. Morgan for contributions to the development of Samsung ASTRAL. W.S. thanks S. Y. Chan for important discussions and support.

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These authors contributed equally: Jiadong Chen, Samuel R. Cross.

Authors and Affiliations

Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA

Jiadong Chen & Wenhao Sun

Advanced Materials Lab, Samsung Advanced Institute of Technology–America, Samsung Semiconductor Inc., Cambridge, MA, USA

Samuel R. Cross, Lincoln J. Miara, Jeong-Ju Cho & Yan Wang

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Contributions

J.C. and W.S. developed precursor selection principles, analysed candidate synthesis reactions and predicted optimal synthesis precursors for experimental validation. S.R.C., L.J.M. and Y.W. designed the traditional synthesis precursors. S.R.C., L.J.M., J.-J.C. and Y.W. built the ASTRAL automated laboratory. S.R.C. synthesized and characterized the quaternary materials. J.C., S.R.C. and W.S. conducted phase purity analysis. J.C., S.R.C., Y.W. and W.S. wrote the manuscript and Supplementary Information, with contributions and revisions from all authors. W.S., L.J.M. and Y.W. conceived and supervised all of the main aspects of the project.

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Correspondence to Yan Wang or Wenhao Sun .

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Nature Synthesis thanks Milad Abolhasani and Kedar Hippalgaonkar for their contribution to the peer review of this work. Primary Handling Editor: Alexandra Groves, in collaboration with the Nature Synthesis team.

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Supplementary information

Supplementary information.

Supplementary Notes 1–3, including Supplementary Figs. 1–52, Discussion and Tables 1–75.

Supplementary Data 1

Additional 3,104 predicted reactions.

Supplementary Video 1

ASTRAL dispensing power part 1.

Supplementary Video 2

ASTRAL dispensing power part 2.

Supplementary Video 3

ASTRAL furnace workflow.

Supplementary Video 4

ASTRAL XRD workflow.

Supplementary Data 2

Raw data for all X-ray diffraction figures in the Supplementary Information.

Source Data Fig. 1

Raw X-ray diffraction data for Fig. 1j.

Source Data Fig. 4

Statistical source data for Fig. 4c,d.

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Chen, J., Cross, S.R., Miara, L.J. et al. Navigating phase diagram complexity to guide robotic inorganic materials synthesis. Nat. Synth (2024). https://doi.org/10.1038/s44160-024-00502-y

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An analysis of iron ion occupation in barium hexaferrites prepared employing different synthesis techniques from magnetic and Mossbauer studies

• Published: 15 April 2024
• Volume 47 , article number  81 , ( 2024 )

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• Swathi Chanda 1 ,
• S Bharadwaj 2 ,
• V R Reddy 3 ,
• Kirana Kommuri 1 ,
• Adiraj Srinivas 4 &
• Y Kalyana Lakshmi   ORCID: orcid.org/0000-0002-0881-3810 1  

In the present investigation, the distribution of iron ions at octahedral and tetrahedral sites in BaFe 12 O 19 prepared by employing four different synthesis techniques, namely, solid-state reaction, oxalate precursor route, sol–gel and wet chemical methods, have been examined using Mossbauer studies and compared with magnetization data. It was observed that the iron ions distribute in different preferential order at various sites for hexaferrites prepared using different synthesis methods, which is confirmed by Mossbauer spectroscopy. Prepared samples were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and Field emission scanning electron microscopy. Rietveld refinement of all samples revealed an M-type hexagonal structure confirming P63/mmc space group along with a minor peak belonging to the α-Fe 2 O 3 phase, except for the sample synthesized by sol–gel route. A uniform spherical shape with a small grain size was observed in sol–gel prepared samples and the Williamson–Hall method was adopted to estimate the average crystallite size, which varies between 72 and 129 nm. The room temperature magnetization studies reveal that the sample synthesized via sol–gel route shows high coercivity and saturation magnetization values due to their smaller grain sizes. Mossbauer spectra of all BaFe 12 O 19 samples were fitted with five sexets assigned to the hexagonal crystal structure of 4f 2 , 4f 1 , 2a, 12k and 2b sites, where the variation in their relative areas confirms the redistribution of iron ions at these sites.

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Acknowledgements

YKL and SC acknowledge Dr. K. V. Siva Kumar (Rtd. Professor), Department of Physics, Sri Krishnadevaraya University, Anantapur, Andhra Pradesh, India, for his continuous support and encouragement in this work. We sincerely thank Prof. M Vithal, Department of Chemistry, for the discussion. We are also thankful to UGC-NRC, School of Physics, University of Hyderabad, Telangana, for providing the XRD and FESEM facilities.

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Department of Physics, University College of Science, Osmania University, Hyderabad, 500007, India

Swathi Chanda, Kirana Kommuri & Y Kalyana Lakshmi

Department of Physics, GITAM School of Science, GITAM (Deemed to be University), Hyderabad, 502329, India

S Bharadwaj

UGC-DAE Consortium for Scientific Research, Indore, Madhya Pradesh, 452001, India

Defence Metallurgical Research Laboratory, Hyderabad, Telangana, 500058, India

Adiraj Srinivas

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Correspondence to Y Kalyana Lakshmi .

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Chanda, S., Bharadwaj, S., Reddy, V.R. et al. An analysis of iron ion occupation in barium hexaferrites prepared employing different synthesis techniques from magnetic and Mossbauer studies. Bull Mater Sci 47 , 81 (2024). https://doi.org/10.1007/s12034-023-03139-3

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Received : 16 June 2023

Accepted : 08 October 2023

Published : 15 April 2024

DOI : https://doi.org/10.1007/s12034-023-03139-3

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Chemical Communications

Solvent-free synthesis and chiroptical properties of a c–n axially chiral cruciform dimer of benzo[ b ]phenoxazine.

A novel C–N axially chiral molecule composed of two tert-butyl-substituted benzo[ b ]phenoxazine ( BPO ) was synthesized via solvent-free reactions. The absolute configurations of the enantiomers were determined by X-ray single-crystal analysis. The enantiomers had a sufficiently high racemization barrier to ignore racemization at room temperature, and the solutions exhibited dual circularly polarized emissions stemming from fluorescence and phosphorescence.

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• Crystal structure data CIF (4787K)

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S. Ishikawa, D. Sakamaki, M. Gon, K. Tanaka and H. Fujiwara, Chem. Commun. , 2024, Accepted Manuscript , DOI: 10.1039/D4CC00977K

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