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6 tools used to identify themes in qualitative research.

By Dani Babb, PhD

Qualitative dissertation research involves the collection and analysis of non-numerical data, such as interviews, observations, and open-ended survey responses. One of the key steps in qualitative research is to identify themes that emerge from the data you have collected. There are many different tools that researchers can use to analyze themes in qualitative research. In this blog, we'll explore some of the most popular and effective tools.

• Coding: Coding is a widely used method for identifying themes in qualitative research. Researchers review the data collected and identify specific words or phrases that are relevant to the research question. Each word or phrase is then assigned a code, and these codes are grouped together to form themes. Coding can be done manually, or researchers can use specialized software, such as NVivo or ATLAS.ti, to help with the process.
• Content analysis: Content analysis is another common method for analyzing themes in qualitative research. In this approach, researchers analyze the content of the data, looking for patterns and themes. This can involve identifying keywords and phrases that are used frequently, or analyzing the structure of the data to identify common themes. Content analysis can be done manually, or researchers can use specialized software, such as QDA Miner or MAXQDA, to help with the process.
• Grounded theory: Grounded theory is a method for developing theories based on the data collected in qualitative research. In this approach, researchers identify concepts and relationships between them that emerge from the data. These concepts are then used to develop a theory or model that explains the phenomenon being studied. This approach requires careful analysis and interpretation of the data and can be time-consuming, but can be very powerful in generating new insights.
• Thematic analysis: Thematic analysis is a flexible method for analyzing themes in qualitative research. In this approach, researchers review the data collected and identify patterns and themes. These themes are then organized into a hierarchical structure, with overarching themes and sub-themes. Thematic analysis can be done manually or using specialized software, such as Dedoose or Quirkos.
• Narrative analysis: Narrative analysis is a method for analyzing the stories or narratives told by participants in qualitative research. Researchers look for recurring themes and patterns in the stories, and analyze the structure and content of the narratives to identify key themes. Narrative analysis can be done manually or using specialized software, such as Nvivo or MAXQDA.
• Discourse analysis: Discourse analysis is a method for analyzing the use of language in qualitative data. Researchers look at how language is used to construct meaning, identify power dynamics, and reinforce or challenge social norms. This approach can be particularly useful in analyzing data related to social justice issues, such as race or gender. Discourse analysis can be done manually or using specialized software, such as Linguistic Inquiry and Word Count (LIWC).

There are many different tools that researchers can use to analyze themes in qualitative dissertation research. The most appropriate tool will depend on the research question, the data collected, and the skills and expertise of the research team. By carefully analyzing the data and identifying key themes, researchers can develop new insights and advance our understanding of complex phenomena.

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Topical themes and thematic progression: the “picture” of research articles

Leong Ping Alvin lectures at the Language and Communication Centre, Nanyang Technological University, Singapore. He obtained his PhD degree from the National University of Singapore in 2001 under a research scholarship. His book-length publications include Transforming Literacies and Language (co-editor; Continuum, 2011) and Theme and Rheme (Peter Lang, 2004). His research interests are in systemic-functional grammar, discourse analysis, and literacy studies.

Although much has been written about the features of academic writing, there is a lack of research attention on macro issues related to the development of ideas, particularly in the writing of research articles. A concept that is useful in investigating such issues is the Hallidayan notion of theme. However, the thematic structure of research articles has received only modest attention over the years. It is also rare for thematic diagrams to be used even though they can be helpful in clarifying the thematic structure of the text. In this exploratory study, the patterning of topical themes in research articles was investigated using a diagrammatic approach. Twenty biology-related research articles were divided into t-units and analyzed for topical themes. Thematic diagrams were generated for all the articles. The diagrams revealed a progressive thematic pattern in the introduction sections of all the articles. At the whole-text level, an anchored-development pattern was observed in the majority of the articles. These findings suggest that research articles at the macro level share similarities in their thematic structure. They also shed light on how authors achieve focus in the writing through the systematic use of clause-initial elements.

About the author

Appendix: articles analyzed in the study, articles from database.

Hishigaki, Haretsugu & Satoru Kuhara. 2011. hERGAPDbase: A database documenting hERG channel inhibitory potentials and APD-prolongation activities of chemical compounds. Database 2011. doi: 10.1093/database/bar017.

Green, Jason M., Jaturon Harnsomburana, Mary L. Schaeffer, Carolyn J. Lawrence & Chi-Ren Shyu. 2011. Multi-source and ontology-based retrieval engine for maize mutant phenotypes. Database 2011. doi: 10.1093/database/bar012.

Harper, Lisa C., Mary L. Schaeffer, Jordan Thistle, Jack M. Gardiner, Carson M. Andorf, Darwin A. Campbell, Ethalinda K. S. Cannon, Bremen L. Braun, Scott M. Birkett, Carolyn J. Lawrence & Taner Z. Sen. 2011. The MaizeGDB genome browser tutorial: One example of database outreach to biologists via video. Database 2011. doi: 10.1093/database/bar016.

Visendi, Paul, Wanjiku Ng’ang’a, Wallace Bulimo, Richard Bishop, James Ochanda & Etienne P. de Villiers. 2011. TparvaDB: A database to support Theileria parva vaccine development. Database 2011. doi: 10.1093/database/bar015.

Williams, G. W., P. A. Davis, A. S. Rogers, T. Bieri, P. Ozersky & J. Spieth. 2011. Methods and strategies for gene structure curation in WormBase. Database 2011. doi: 10.1093/database/baq039.

Blankenberg, Daniel, Nathan Coraor, Gregory Von Kuster, James Taylor & Anton Nekrutenko. 2011. Integrating diverse databases into an unified analysis framework: A Galaxy approach. Database 2011. doi: 10.1093/database/bar011.

Vellozo, Augusto F., Amélie S. Véron, Patrice Baa-Puyoulet, Jaime Huerta-Cepas, Ludovic Cottret, Gérard Febvay, Federica Calevro, Yvan Rahbé, Angela E. Douglas, Toni Gabaldón, Marie-France Sagot, Hubert Charles & Stefano Colella. 2011. CycADS: An annotation database system to ease the development and update of BioCyc databases. Database 2011. doi: 10.1093/database/bar008.

Magrane, Michele & UniProt Consortium. 2011. UniProt Knowledgebase: A hub of integrated protein data. Database 2011. doi: 10.1093/database/bar009.

Costanzo, Maria C., Julie Park, Rama Balakrishnan, J. Michael Cherry & Eurie L. Hong. 2011. Using computational predictions to improve literature-based Gene Ontology annotations: A feasibility study. Database 2011. doi: 10.1093/database/bar004.

Bluhm, Wolfgang F., Bojan Beran, Chunxiao Bi, Dimitris Dimitropoulos, Andreas Prlić, Gregory B. Quinn, Peter W. Rose, Chaitali Shah, Jasmine Young, Benjamin Yukich, Helen M. Berman & Philip E. Bourne. 2011. Quality assurance for the query and distribution systems of the RCSB protein data bank. Database 2011. doi: 10.1093/database/bar003.

Articles from DNA Research

Chi, Yunhua, Yansong Cheng, Jeevanandam Vanitha, Nadimuthu Kumar, Rengasamy Ramamoorthy, Srinivasan Ramachandran & Shu-Ye Jiang. 2011. Expansion mechanisms and functional divergence of the glutathione s-transferase family in sorghum and other higher plants. DNA Research 18(1). 1–16.

Le, Dung Tien, Rie Nishiyama, Yasuko Watanabe, Keiichi Mochida, Kazuko Yamaguchi-Shinozaki, Kazuo Shinozaki & Lam-Son Phan Tran. 2011. Genome-wide expression profiling of soybean two-component system genes in soybean root and shoot tissues under dehydration stress. DNA Research 18(1). 17–29.

Kenny, Elaine M., Paul Cormican, William P. Gilks, Amy S. Gates, Colm T. O’Dushlaine, Carlos Pinto, Aiden P. Corvin, Michael Gill & Derek W. Morris. 2011. Multiplex target enrichment using DNA indexing for ultra-high throughput SNP detection. DNA Research 18(1). 31–38.

Satbhai, Santosh B., Takafumi Yamashino, Ryo Okada, Yuji Nomoto, Takeshi Mizuno, Yuki Tezuka, Tomonori Itoh, Mitsuru Tomita, Susumu Otsuki & Setsuyuki Aoki. 2011. Pseudo-response regulator (PRR) homologues of the moss physcomitrella patens: Insights into the evolution of the PRR family in land plants. DNA Research 18(1). 39–52.

Garg, Rohini, Ravi K. Patel, Akhilesh K. Tyagi & Mukesh Jain. 2011. De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Research 18(1). 53–63.

Sato, Shusei, Hideki Hirakawa, Sachiko Isobe, Eigo Fukai, Akiko Watanabe, Midori Kato, Kumiko Kawashima, Chiharu Minami, Akiko Muraki, Naomi Nakazaki, Chika Takahashi, Shinobu Nakayama, Yoshie Kishida, Mitsuyo Kohara, Manabu Yamada, Hisano Tsuruoka, Shigemi Sasamoto, Satoshi Tabata, Tomoyuki Aizu, Atsushi Toyoda, Tadasu Shin-i, Yohei Minakuchi, Yuji Kohara, Asao Fujiyama, Suguru Tsuchimoto, Shin’ichiro Kajiyama, Eri Makigano, Nobuko Ohmido, Nakako Shibagaki, Joyce A. Cartagena, Naoki Wada, Tsutomu Kohinata, Alipour Atefeh, Shota Yuasa, Sachihiro Matsunaga & Kiichi Fukui. 2011. Sequence analysis of the genome of an oil-bearing tree, Jatropha Curcas L. DNA Research 18(1). 65–76.

Gourcilleau, Delphine, Catherine Lenne, Claudia Armenise, Bruno Moulia, Jean-Louis Julien, Gisèle Bronner & Nathalie Leblanc-Fournier. 2011. Phylogenetic study of plant q-type C2H2 zinc finger proteins and expression analysis of poplar genes in response to osmotic, cold, and mechanical stresses. DNA Research 18(2). 77–92.

Doorduin, Leonie, Barbara Gravendeel, Youri Lammers, Yavuz Ariyurek, Thomas Chin-A-Woeng & Klaas Vrieling. 2011. The complete chloroplast genome of 17 individuals of pest species Jacobaea vulgaris : SNPs, microsatellites and barcoding markers for population and phylogenetic studies. DNA Research 18(2). 93–105.

Sayama, Takashi, Tae-Young Hwang, Kunihiko Komatsu, Yoshitake Takada, Masakazu Takahashi, Shin Kato, Hiroko Sasama, Ayako Higashi, Yumi Nakamoto, Hideyuki Funatsuki & Masao Ishimoto. 2011. Development and application of a whole-genome simple sequence repeat panel for high-throughput genotyping in soybean. DNA Research 18(2). 107–115.

Lamprea-Burgunder, Estelle, Philipp Ludin & Pascal Mäser. 2011. Species-specific typing of DNA based on palindrome frequency patterns. DNA Research 18(2). 117–124.

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You are here, identifying themes to structure your literature review.

In order to develop the sections in your literature review you will need to be able to draw out the key themes from your reading. The following questions are designed to help you achieve this:

• What do I already know?
• What are the key concepts and definitions within this area of study?
• What are the political standpoints and historical context?
• What are the beliefs underpinning this area of study?
• Why is it important to study this research problem and how does it relate to the current national/international priorities within education?
• What are the key questions asked by other researchers in this area and how can these help me to develop my own research focus and questions?
• What research strategies and methods have been used by other researchers in this area of study?
• Can I learn from mistakes that have been made by other researchers?
• What are the key theoretical perspectives in this area?
• What are the key research findings?
• How do these existing ideas from theory and research link together?
• How can the literature help me to interpret my findings? E.g. Are there analytical frameworks that have been used by other researchers that I could use to analyse my findings?
• Are there any conflicting research findings or areas of controversy?
• Are there any gaps in the existing research?

(Adapted from Langdridge and Hagger-Johnson (2009); Bryman (2008); and Hart, cited in Punch, 2009)

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What Is the Theme of a Research Paper?

How to Write a Motif Paper

The term "theme" is a small word, but it can intimidate students when they see it on an assignment or test. To overcome the fear and develop confidence, especially with regard to research papers, understand what the word means and see the parallels with any work, including poems, essays, plays, novels and movies.

“Theme” Defined

A theme is a major and sometimes recurring idea, subject or topic that appears in a written work. A dominant theme usually reveals what the work is really about and can be helpful in forming insights and analysis. A theme can consist of one word, two words or more. For example, your teacher might ask you to explore the straightforward ideas of “anger” or “selfishness” or more complex themes of “emotional intelligence” or “conflicted emotions.” Either way, careful reading of the work is vital so that you can marshal examples of where the theme was apparent.

Examples in Research

Themes in research papers might require a little digging, but they are there. Sometimes they are easier to spot when several research papers on the same subject are compared or contrasted, for this is when such subtext emerges. For example, three research papers on the subject of avid TV viewing by teenagers might contain different themes, such as simpler ideas including “passivity” or "grades" or a more complex theme, such as “effects on familial relationships.”

Seize the Opportunity

Once you've identified the theme of a research paper or papers, seize the opportunity and analyze it. Say that you like the idea of exploring how avid TV viewing -- more than four hours per day -- affects teens' grades. Further, suppose that researchers are in general agreement about the correlation but cast a wide net in terms of how they define “passivity.” You might set up a thematic segue for a research paper by saying, “Researchers continue to debate how to define passivity in teens and reach across the spectrum to include the number of hours per day they spend in solitude, the number of people they count as close friends and their lack of interest in hobbies and extracurricular activities.” Then you would take each of these ideas and expound in greater detail.

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With education, health care and small business marketing as her core interests, M.T. Wroblewski has penned pieces for Woman's Day, Family Circle, Ladies Home Journal and many newspapers and magazines. She holds a master's degree in journalism from Northern Illinois University.

Helping Students Search for Themes in Literature

| By Don Boyden |

In the study of literature, knowing the underlying theme is essential to really understanding a work. Literature homework assignments frequently ask students to compare, contrast, and explicate the themes of a given work. Themes are one of those things that frequently get a “yes” answer to the universal question, “Is this going to be on the final?”

Analyzing Usage Logs Gives Insights to Student Search Practices

At Gale, we’re always investigating how we can better serve the needs of students who use our online products.  In support of that, I wanted to see what insights our usage analytics could provide on how students currently use our products to learn about the themes in literary works. This could in turn help focus our editorial and technical efforts to make their journey more successful.

Being a Systems Analyst, I started by going to the logs that capture the search terms people use in our products. I filtered the logs down to literature products and for the last 12 months (to accommodate cyclical assignments), then grabbed just the top 10,000 rows which represented over 1.5 million searches. Even at row 10,000, there were 23 people who submitted the same four-word query.

What the logs revealed is that there are as many different ways to search for themes as there are different themes—and they all get different results.

Students Try Many Different Approaches to find Themes

Some start out too broadly. There are thousands of searches, for example, on just the word “theme,” the search equivalent of a shot in the dark. The logs also show a block of searches on the words “themes literature,” which is a little better, but since these users were already in a literature product, the results were still going to be pretty broad.

Next, I saw many blocks of searches on specific themes. One of the most popular, used thousands of times, was the word “revenge.”  A search on “revenge” does retrieve good, solid articles where revenge is a central topic, but it also pulls thousands of articles where the word is simply mentioned in the article title or a few times in the text. Our relevance sort tries to overcome these tangential mentions by boosting articles with the word in the subject, title, or keywords, but the results can still be mixed.

They Don’t Look for Authors

I wondered what other patterns I might find for the theme searches students submit. For example, do students search for a theme combined with an author name? I looked for all occurrences of the names of the three top authors in the search logs and how often they were paired with a theme:

• Out of 739 searches that combined Shakespeare with a keyword, at best 328 of them paired his name with a theme (women, love, gender, feminism). There were thousands of searches that paired him with a specific work.
• Shelley got no pairings with a term that looked like a theme. Over 1,000 searches paired her name with just “Frankenstein.”
• Fitzgerald had no pairings with a theme, but 1,120 searches paired his name with a work.

So, students are usually not searching for themes in association with a particular author.

But They Love the Works

Next, I checked to see if students search for themes in association with a particular work. Three of the most popular works that are searched in our products are Frankenstein, Hamlet , and The Great Gatsby . I grouped all of those searches and looked for patterns.

Statistically, the most common theme-based search pattern in our literature products is the pairing of a theme with the title of a work.

• For The Great Gatsby , 11% of all queries that included the title of the work paired it with a theme in the search.
• For Frankenstein , 14% of searches paired the work and a theme.

For Hamlet , 25% of searches on the title included a theme.

A Solid Approach – with Limits

Having found the most common approach students took to find information on themes, I did a little digging to see how well it worked. There are several issues that affect the quality and quantity of results that a work/theme search will return.

Inconsistency in Phrasing of the Theme Affects Search Results

I compared a number of the endless lists of themes I found on the Web that were compiled by literary experts.  Some terms are on just about every list and are worded the same, such as  “American Dream” and “Coming of Age.” But, many themes are worded differently from list to list, such as “Madness / Insanity” and “Women / Female Roles / Feminism.” No lists contain the same number of themes. Even top 10 lists have little overlap.

Student’s search phrases exhibit the same inconsistencies as the expert’s, including all the popular variations.  Using similar search terms to get at the same theme, though, doesn’t always yield similar results.  For example, students searching in our literature products variously paired the themes of materialism, money, wealth, and possessions with The Great Gatsby , and the results were all quite different.

Little Things Matter in a Query

Building a query that will return exactly what you want is not always easy to do. Little things like including a “noise” word (e.g.—madness in Hamlet), or inadvertently using a “noise” word that is also a Boolean operator (e.g.—madness and Hamlet), or adding unnecessary punctuation (e.g.—“madness in Hamlet”) will produce different search results. In all likelihood, students will not realize how such subtle differences will affect their search results. These are counts from the above searches in Literature Resource Center:

• madness hamlet: 214 total results
• madness in hamlet: 110 total results
• madness and hamlet: 2833 total results
• “madness in hamlet”: 10 total results

How to Make Things Simpler

On the assumption that students should not have to become experts on search engine syntax or be expected to automatically know the favored wording for a particular theme, what can we do to make the search process simpler and search results more consistent? This is a question that involves many teams across Gale, and as we delve deeper into user behavior and challenges we are actively working to provide better results.

Themes, Motifs, and Topics

When I started working on this blog, I assumed that the study of themes in literature was the domain of  high school and college students. In doing research for the topic, though, one of the most helpful Web sites I found was actually created by a third grade teacher for her class!   Interestingly, in the comments section of the Web site, she was criticized by some of her peers for allegedly confusing themes with motifs and topics.  Hmm…what’s the difference between themes, motifs, and topics? Perhaps that’s fodder for a future blog.

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• Synthesizing Sources | Examples & Synthesis Matrix

Synthesizing Sources | Examples & Synthesis Matrix

Published on July 4, 2022 by Eoghan Ryan . Revised on May 31, 2023.

Synthesizing sources involves combining the work of other scholars to provide new insights. It’s a way of integrating sources that helps situate your work in relation to existing research.

Synthesizing sources involves more than just summarizing . You must emphasize how each source contributes to current debates, highlighting points of (dis)agreement and putting the sources in conversation with each other.

You might synthesize sources in your literature review to give an overview of the field or throughout your research paper when you want to position your work in relation to existing research.

Table of contents

Example of synthesizing sources, how to synthesize sources, synthesis matrix, other interesting articles, frequently asked questions about synthesizing sources.

Let’s take a look at an example where sources are not properly synthesized, and then see what can be done to improve it.

This paragraph provides no context for the information and does not explain the relationships between the sources described. It also doesn’t analyze the sources or consider gaps in existing research.

Research on the barriers to second language acquisition has primarily focused on age-related difficulties. Building on Lenneberg’s (1967) theory of a critical period of language acquisition, Johnson and Newport (1988) tested Lenneberg’s idea in the context of second language acquisition. Their research seemed to confirm that young learners acquire a second language more easily than older learners. Recent research has considered other potential barriers to language acquisition. Schepens, van Hout, and van der Slik (2022) have revealed that the difficulties of learning a second language at an older age are compounded by dissimilarity between a learner’s first language and the language they aim to acquire. Further research needs to be carried out to determine whether the difficulty faced by adult monoglot speakers is also faced by adults who acquired a second language during the “critical period.”

Prevent plagiarism. Run a free check.

To synthesize sources, group them around a specific theme or point of contention.

As you read sources, ask:

• What questions or ideas recur? Do the sources focus on the same points, or do they look at the issue from different angles?
• How does each source relate to others? Does it confirm or challenge the findings of past research?
• Where do the sources agree or disagree?

Once you have a clear idea of how each source positions itself, put them in conversation with each other. Analyze and interpret their points of agreement and disagreement. This displays the relationships among sources and creates a sense of coherence.

Consider both implicit and explicit (dis)agreements. Whether one source specifically refutes another or just happens to come to different conclusions without specifically engaging with it, you can mention it in your synthesis either way.

Synthesize your sources using:

• Topic sentences to introduce the relationship between the sources
• Signal phrases to attribute ideas to their authors
• Transition words and phrases to link together different ideas

To more easily determine the similarities and dissimilarities among your sources, you can create a visual representation of their main ideas with a synthesis matrix . This is a tool that you can use when researching and writing your paper, not a part of the final text.

In a synthesis matrix, each column represents one source, and each row represents a common theme or idea among the sources. In the relevant rows, fill in a short summary of how the source treats each theme or topic.

This helps you to clearly see the commonalities or points of divergence among your sources. You can then synthesize these sources in your work by explaining their relationship.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Is ChatGPT trustworthy?
• Using ChatGPT for your studies
• What is ChatGPT?
• Chicago style
• Paraphrasing

 Plagiarism

• Types of plagiarism
• Self-plagiarism
• Avoiding plagiarism
• Academic integrity
• Consequences of plagiarism
• Common knowledge

Synthesizing sources means comparing and contrasting the work of other scholars to provide new insights.

It involves analyzing and interpreting the points of agreement and disagreement among sources.

You might synthesize sources in your literature review to give an overview of the field of research or throughout your paper when you want to contribute something new to existing research.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

Topic sentences help keep your writing focused and guide the reader through your argument.

In an essay or paper , each paragraph should focus on a single idea. By stating the main idea in the topic sentence, you clarify what the paragraph is about for both yourself and your reader.

At college level, you must properly cite your sources in all essays , research papers , and other academic texts (except exams and in-class exercises).

Add a citation whenever you quote , paraphrase , or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text.

The exact format of your citations depends on which citation style you are instructed to use. The most common styles are APA , MLA , and Chicago .

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How to Identify the Theme in a Literary Work

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A theme is a central or underlying idea in literature , which may be stated directly or indirectly. All novels , stories, poems, and other literary works have at least one theme running through them. The writer may express insight about humanity or a worldview through a theme.

Subject Versus Theme

Don't confuse the subject of a work with its theme:

• The subject is a topic that acts as the foundation for a work of literature, such as marriage in 19th-century France.
• A  theme is an opinion the author expresses on the subject, for instance, the author's dissatisfaction with the narrow confines of French bourgeois marriage during that period.

Major and Minor Themes

There can be major and minor themes in works of literature:

• A major theme is an idea that a writer repeats in his work, making it the most significant idea in a literary work.
• A minor theme, on the other hand, refers to an idea that appears in a work briefly and that may or may not give way to another minor theme.

Read and Analyze the Work

Before you attempt to identify the theme of a work, you must have read the work, and you should understand at least the basics of the plot , characterizations, and other literary elements. Spend some time thinking about the main subjects covered in work. Common subjects include coming of age, death and mourning, racism, beauty, heartbreak and betrayal, loss of innocence, and power and corruption.

Next, consider what the author's view on these subjects might be. These views will point you toward the work's themes. Here's how to get started.

How to Identify Themes in a Published Work

• Note the plot of the work: Take a few moments to write down the main literary elements: plot, characterization, setting, tone, language style, etc. What were the conflicts in the work? What was the most important moment in the work? Does the author resolve the conflict? How did the work end?
• Identify the subject of the work: If you were to tell a friend what the work of literature was about, how would you describe that? What would you say is the topic?
• Who is the protagonist (the main character)?  How does he or she change? Does the protagonist affect other characters? How does this character relate to others?
• Assess the author's point of view : Finally, determine the author's view toward the characters and the choices they make. What might be the author's attitude toward the resolution of the main conflict? What message might the author be sending us? This message is the theme. You may find clues in the language used, in quotes from main characters, or in the final resolution of the conflicts.

Note that none of these elements (plot, subject, character, or point of view ) constitute a theme in and of itself. But identifying them is an important first step in identifying a work's major theme or themes.

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The Lesson Study Group

at Mills College

Develop a Research Theme

Access the teaching-learning plan, choose a topic.

10-30 minutes

• Develop or revisit your your long-term goals for student development
• Develop a draft of your “theory of action”—the approaches you will explore to build your long-term goals

Identify Your Long-Term Goals

The building blocks of your theme.

A research theme expresses the long-term goals of your work. If your team or school has already developed a research theme, revisit it now to refresh your memory about your long-term goals and ideas about how to get there.

Begin by having team members individually jot down qualities in response to the following prompt:

• Ideally, what qualities do we hope students will have when they graduate from our school? ( If we bumped into our students in 5-10 years, what qualities do we hope they would have?)

Now, again working individually, spend a few minutes jotting down a list of qualities in response to a second prompt:

• What are the current qualities of our students? (For example, what qualities of our students inspire us? Anything that concerns us?)

Again, share your individual lists and write all the qualities on a second list labeled “Current.”

Compare the two lists–ideal and current–and notice gaps that really speak to you as educators. Find one or two gaps where you would like to invest your time and energy.

Your research theme positively states the qualities you will work toward. Some examples follow.

• “For students to value friendship, develop their own perspectives and ways of thinking, and enjoy science.”
• “Develop social-emotional skills and…a deeper understanding of mathematics”
• “Across both math and language arts, develop our students’ abilities to use evidence and reasoning to support and critique arguments.”
• “…to take responsibility and initiative as learners.”

A lot of [U.S.] schools develop mission statements, but we don’t do anything with them. The mission statements get put in a drawer and then teachers become cynical…Lesson Study gives guts to a mission statement, makes it real, and brings it to life.

Develop a Theory of Action

Moving from the what to the how.

The second part of your research theme is a “theory of action”—how you will work toward your long-term goals and the specific research questions you will examine. What experiences in school help students move toward a goal such as “students have their own thoughts and can explain them logically?” Teachers addressing this research theme focused their initial theory of action on two classroom routines: students’ presentation of ideas at the board and their use of reflective journals. They actively tested strategies to improve these two classroom routines and posed questions about them. For example, they asked what the features are of effective student presentations and how teachers help students see the power of these strategies (such as using visual models). In order to strengthen the impact of reflective mathematics journals, teachers strategically selected several student journals from the prior day to be read aloud at the beginning of each mathematics lesson, which built students’ interest in each other’s ideas and helped them see the impact of well-explained ideas. The first part of your research theme—your overarching goal—is likely to stay the same for several years. The second part of your research theme—your theory of action—is likely to change as you incorporate effective ideas into your practice and go on to experiment with additional changes designed to achieve your long-term goals. For example, the group that experimented with changes to student presentations and reflective journals went on to experiment with routines for discussion and lesson summarization that further built students’ capacity “to have their own thoughts and explain them logically.”

Developing a Research Theme Presentation

Examples of Research Themes

• What is Lesson Study?
• Why Lesson Study?
• Teacher Learning
• Content Resources
• Teaching Through Problem-solving (TTP)
• School-wide Lesson Study
• U.S. Networks
• International Networks

Techniques to Identify Themes in Qualitative Data

Gery W. Ryan RAND 1700 Main Street P.O. Box 2138 Santa Monica, CA 90407-2138   H. Russell Bernard Department of Anthropology 1350 Turlington Hall University of Florida Gaineville, FL 32611 Key Words: Theme Identification, Exploratory Analysis, Open Coding, Text Analysis, Qualitative Research Methods

Theme identification is one of the most fundamental tasks in qualitative research. It also one of the most mysterious. Explicit descriptions of theme discovery are rarely described in articles and reports and if so are often regulated to appendices or footnotes. Techniques are shared among small groups of social scientists and are often impeded by disciplinary or epistemological boundaries. During the proposal-writing phase of a project, investigators struggle to clearly explain and justify plans for discovering themes. These issues are particularly cogent when funding reviewers are unfamiliar with qualitative traditions. In this article we have outlined a dozen techniques that social scientists have used to discover themes in texts. The techniques are drawn from across epistemological and disciplinary boundaries. They range from quick word counts to laborious, in-depth, line-by-line scrutiny. Some methods work well for short answers to open-ended questions while others are more appropriate for rich, complex narratives. Novices and non-native speakers may find some techniques easier than others. No single technique is does it all. To us, these techniques are simply tools to help us do better research.

Authors’ Statement

Gery W. Ryan is an Associate Behavioral Scientist at RAND in Santa Monica, California. H. Russell Bernard is professor of anthropology at the University of Florida. The research on which this article is based is part of a National Science Foundation Grant, on "Methods for Conducting Systematic Text Analysis" (SRB-9811166). We wish to thank Stephen Borgatti for his helpful suggestions and two anonymous reviewers for their invaluable comments on earlier drafts of this paper.

Introduction

At the heart of qualitative data analysis is the task of discovering themes. By themes, we mean abstract, often fuzzy, constructs which investigators identify before, during, and after data collection. Where do these themes come from?

They come from reviewing the literature, of course. Richer literatures produce more themes. They come from the characteristics of the phenomena being studied. And they come from already-agreed-upon professional definitions, from local common-sense constructs, and from researchers’ values, theoretical orientation, and personal experience with the subject matter (Bulmer 1979; Strauss 1987; Maxwell 1996).

Mostly, though, researchers who consider themselves part of the qualitative tradition in social science induce themes from texts. This is what grounded theorists call open coding , and what classic content analysts call qualitative analysis (Berleson 1952) or latent coding (Shapiro and Markoff 1997). There are many variations on these methods. Unfortunately, however, they are (a) scattered across journals and books that are read by disparate groups of specialists; and (b) often entangled in the epistemological wars that have divided the social sciences. Our goal in this paper is to cross these boundaries and lay out a variety of theme-dredging methods so that all researchers who deal with texts can use them to solve common research problems.

We outline here a dozen helpful techniques for discovering themes in texts. These techniques are based on: (1) an analysis of words (word repetitions, key-indigenous terms, and key-words-in contexts); (2) a careful reading of larger blocks of texts (compare and contrast, social science queries, and searching for missing information); (3) an intentional analysis of linguistic features (metaphors, transitions, connectors); and (4) the physical manipulation of texts (unmarked texts, pawing, and cut and sort procedures).

The list is by no means exhaustive. Social scientists are an enterprising lot. Over the last century they have invented solutions to all kinds of problems for managing and analyzing texts, and they will continue to do so. These bursts of methodological creativity, however, are commonly described perfunctorily, or are relegated to footnotes, and get little notice by colleagues across disciplines. The dozen methods we describe here come from across the social sciences and have been used by positivists and interpretivists alike.

1. Word repetitions

We begin with word-based techniques. Word repetitions, key-indigenous terms, and key-words-in-contexts (KWIC) all draw on a simple observation—if you want to understand what people are talking about, look at the words they use.

Words that occur a lot are often seen as being salient in the minds of respondents. D'Andrade notes that "perhaps the simplest and most direct indication of schematic organization in naturalistic discourse is the repetition of associative linkages" (1991:294). He observes that "indeed, anyone who has listened to long stretches of talk, whether generated by a friend, spouse, workmate, informant, or patient, knows how frequently people circle through the same network of ideas" (1991:287).

Word repetitions can be analyzed formally and informally. In the informal mode, investigators simply read the text and note words or synonyms that people use a lot. For example, while conducting multiple in-depth interviews with Tony, a retired blue collar worker in Connecticut, Claudia Strauss (1992) found that Tony repeatedly referred to ideas associated with greed, money, businessmen, siblings, and "being different." These repetitions indicated to Strauss that these ideas were important, recurring themes in Tony’s life. Strauss displayed the relationships among these ideas by writing the concepts on a page of paper and connecting them with lines and explanations. Computer programs such as ATLAS.ti and Nud*ist let you do this kind of connect-the-dots exercise by computer. 1

A more formal analysis of word frequencies can be done by generating a list of all the unique words in a text and counting the number of times each occurs. Computers can easily generate word-frequency lists from texts and are a quick and easy way to look for themes. Ryan and Weisner (1996) asked fathers and mothers of adolescents: "Describe your children. In your own words, just tell us about them." Ryan and Weisner produced a list of all the unique words in the set of responses and the number of times each word was used by mothers and by fathers. Mothers were more likely than fathers to use words like friends, creative, time, and honest; fathers were more likely than mothers to use words like school, good, lack, student, enjoys, independent, and extremely. Ryan and Weisner used this information as clues for themes that they would use later in actually coding the texts.

2. Indigenous categories

Another way to find themes is to look for local terms that may sound unfamiliar or are used in unfamiliar ways. Patton (1990:306, 393-400) refers to these as "indigenous categories" and contrasts them with "analyst-constructed typologies." Grounded theorist refer to the process of identifying local terms as in vivo coding (Strauss 1987:28-32, Strauss and Corbin 1990:61-74) .

Understanding indigenous categories and how they are organized has long been a goal of cognitive anthropologists. The basic idea in this area of research is that experience and expertise are often marked by specialized vocabulary. For example, Spradley (1972) recorded conversations among tramps at informal gatherings, meals, card games, and bull sessions. As the men talked to each other about their experiences, there were many references to making a flop .

Spradley combed through his recorded material and notes looking for verbatim statements made by informants about his topic. On analyzing the statements, he found that most of the statements could fit into subcategories such as kinds of flops , ways to make flops , ways to make your own flop , kinds of people who bother you when you flop , ways to make a bed , and kinds of beds . Spradley then returned to his informants and sought additional information from them on each of the subcategories. For other classic examples of coding for indigenous categories see Becker’s (1993) description of medical students use of the word crock , and Agar’s (1973) description of drug addicts’ understandings of what it means to shoot up .

3. Key-words-in-context (KWIC)

Key-words-in-context (KWIC) are closely associated with indigenous categories. KWIC is based on a simple observation: if you want to understand a concept, then look at how it is used. In this technique, researchers identify key words and then systematically search the corpus of text to find all instances of the word or phrase. Each time they find a word, they make a copy of it and its immediate context. Themes get identified by physically sorting the examples into piles of similar meaning.

The concept of deconstruction is an abstract and often incomprehensible term used by social scientists, literary critics and writers in the popular press. Jacques Derrida, who coined the term, refused to define it. To Derrida, the meaning of any text is inherently unstable and variable. Wiener (1997) was curious as to how the concept of deconstruction was used in the popular press. He used a text-based data set (such as Lexis/Nexis), to find instances of the word in popular publications. He found the term used in by everything from Entertainment Weekly to the American Banker . Wiener concludes that:

Most often writers use "deconstruction" as a fancy word for "analysis" or "explanation," or else as an upscale synonym for "destruction." But in some genres, like rock music writing, the term isn't negative at all; it has become a genuinely floating signifier, a verbal gesture that implies a kind of empty intellectual sophistication.

Word-based techniques are typically a fast and efficient ways to start looking for themes. We find that they are particularly useful at early stages of theme identification. These techniques are also easy for novice researchers to apply. Nothing, however, beats a careful scrutiny of the texts for finding themes that may be more subtle or that don’t get signified directly in the lexicon of the text. Scrutiny-based techniques are more time-intensive and require a lot of attention to details and nuances.

4. Compare and contrast

The compare and contrast approach is based on the idea that themes represent the ways in which texts are either similar or different from each other. Glazer and Strauss (1967:101_116) refer to this as the "constant comparison method." [For other good descriptions of the technique see Glazer (1978:56_72) and Strauss and Corbin (1990:84_95).] Typically, grounded theorists begin by conducting a careful line-by-line analysis. They read each line or sentence and ask themselves, "What is this about?" and "How does it differ from the preceding or following statements?" This kind of detailed work keeps the researcher focused on the data themselves rather than on theoretical flights of fancy (Charmaz 1990).

This approach is like interviewing the text and is remarkably similar to the ethnographic interviewing style that Spradley talks about using with his informants (1979:160_172). Researchers compare pairs of texts by asking "How is this text different from the preceding text?" and "What kinds of things are mentioned in both?" They ask hypothetical questions like "What if the informant who produced this text had been a woman instead of a man?" and "How similar is this text to my own experiences?" Bogdan and Biklen (1982:153) recommend reading through passages of text and asking "What does this remind me of?" Like a good journalist, investigators compare answers to questions across people, space, and time.

5. Social science queries

Besides identifying indigenous themes—themes that characterize the experience of informants—researchers are interested in understanding how textual data illuminate questions of importance to social science. Spradley (1979:199–201) suggested searching interviews for evidence of social conflict, cultural contradictions, informal methods of social control, things that people do in managing impersonal social relationships, methods by which people acquire and maintain achieved and ascribed status, and information about how people solve problems. Bogdan & Bilken (1982:156-162) suggested examining the setting and context, the perspectives of the informants, and informants’ ways of thinking about people, objects, processes, activities, events, and relationships. "Moving across substantive areas," says Charmaz, "fosters developing conceptual power, depth, and comprehensiveness" (1990:1163).

Strauss and Corbin (1990:158_175) urge investigators to be more sensitive to conditions, actions/interactions, and consequences of a phenomenon and to order these conditions and consequences into theories. To facilitate this, they offer a useful tool called the conditional matrix. The conditional matrix is a set of concentric circles, each level corresponding to a different unit of influence. At the center are actions and interactions. The inner rings represent individual and small group influences on these actions, and the outer rings represent international and national effects.

Querying the text as a social scientist is a powerful technique because investigators concentrate their efforts on searching for specific kinds of topics – any of which are likely to generate major social and cultural themes. By examining the data from a more theoretical perspective, however, researchers must be careful that they do not overfit the data – that is, find only that for which they are looking. There is a trade-off between bringing a lot of prior theorizing to the theme-identification effort and going at it fresh. Prior theorizing, as Charmaz says (1990), can inhibit the forming of fresh ideas and the making of surprising connections. Assiduous theory-avoidance brings the risk of not making the connection between data and important research questions. Novice researchers may be more comfortable with the tabula rasa approach. More seasoned researchers, who are more familiar with theory issues, may find the social science query approach more compatible with their interests.

6. Searching for missing information

The final scrutiny-based approach we describe works in reverse from typical theme identification techniques. Instead of identifying themes that emerge from the text, investigators search for themes that are missing in the text.

Much can be learned from a text by what is not mentioned. As early as 1959, propaganda analysts found that material not covered in political speeches were sometimes more predictive that material that was covered (George 1959). Sometimes silences indicate areas that people are unwilling or afraid to discuss. For instance, women with strong religious convictions may fail to mention abortion during discussions of birth control. In power-laden interviewers, silence may be tied to implicit or explicit domination (Gal 1991). In a study of birth planning in China, Greenhalgh (1994) surveyed 1,011ever-married women, gathered social and economic histories from 150 families. She conducted in-depth interviews with present and formal officials (known as cadres), and collected documentary evidence from local newspapers, journals and other sources. Greenhalgh notes that "Because I was largely constrained from asking direct questions about resistance, the informal record of field notes, interview transcripts, and questionnaire data contains few overt challenges to state policy (1994:9)." Greenhalgh concludes, however, that

I believe that in their conversations with us, both peasants and cadres made strategic use of silence to protest aspects of the policy they did not like. Cadres, for example were loathe to comment on birth-planning campaigns; peasant women were reluctant to talk about sterilization. These silences form one part of the unofficial record of birth planning in the villages. More explicit protests were registered in informal conversations. From these interactions emerged a sense of profound distress of villagers forced to choose between a resistance that was politically risky and a compliance that violated the norms of Chinese culture and of practical reason (1994:9).

Other times, absences may indicate primal assumptions made by respondents. Spradley (1987:314) noted that when people tell stories, they assume that their listeners share many assumptions about how the world works and so they leave out information that "everyone knows." He called this process abbreviating . Price (1987) takes this observation and builds on it. Thus, she looks for what is not said in order to identify underlying cultural assumptions. Price finds the missing pieces by trying to translate what people say in the stories into something that the general public would understand.

Of all the scrutiny-based techniques, searching for missing information is the most difficult. There are many reasons people do not mention topics. In addition to avoiding sensitive issues or assuming investigator already knows about the topic, people may not trust the interviewer, may not wish to speak when others are present, or may not understand the investigator’s questions. Distinguishing between when informants are unwilling to discuss topics and when they assume the investigator already knows about the topic requires a lot of familiarity with the subject matter.

In addition to word- and scrutiny-based techniques, researchers have used linguistic features such as metaphors, topical transitions, and keyword connectors to help identify themes.

7. Metaphors and analogies

Schema analysts suggest searching through text for metaphors, similes, and analogies (D’Andrade 1995, Quinn and Strauss 1997). The emphasis on metaphor owes much to the pioneering work by Lakoff and Johnson (1980) and the observation that people often represent their thoughts, behaviors, and experiences with analogies.

Naomi Quinn (1997) has analyzed hundreds of hours of interviews to discover concepts underlying American marriage and to show how these concepts are tied together. She began by looking at patterns of speech and at the repetition of key words and phrases, paying particular attention to informants' use of metaphors and the commonalities in their reasoning about marriage. Nan, one of her informants, says that "marriage is a manufactured product." This popular metaphor indicates that Nan sees marriages as something that has properties, like strength and staying power, and as something that requires work to produce. Some marriages are "put together well," while others "fall apart" like so many cars or toys or washing machines (Quinn 1987:174).

The object is to look for metaphors in rhetoric and deduce the schemas, or underlying principles, that might produce patterns in those metaphors. Quinn found that people talk about their surprise at the breakup of a marriage by saying that they thought the couple’s marriage was "like the Rock of Gibraltar" or that they thought the marriage had been "nailed in cement." People use these metaphors because they assume that their listeners know that cement and the Rock of Gibraltar are things that last forever.

But Quinn reasons that if schemas or scripts are what make it possible for people to fill in around the bare bones of a metaphor, then the metaphors must be surface phenomena and cannot themselves be the basis for shared understanding. Quinn found that the hundreds of metaphors in her corpus of texts fit into just eight linked classes that she calls: lastingness, sharedness, compatibility, mutual benefit, difficulty, effort, success (or failure), and risk of failure. For example, Quinn’s informants often compared marriages (their own and those of others) to manufactured and durable products ("it was put together pretty good") and to journeys ("we made it up as we went along; it was a sort of do-it-yourself project"). Quinn sees these metaphors, as well as references to marriage as "a lifetime proposition," as exemplars of the overall expectation of lastingness in marriage.

Other examples of the search for cultural schemas in texts include Holland’s (1985) study of the reasoning that Americans apply to interpersonal problems, Kempton’s (1987) study of ordinary Americans’ theories of home heat control, and Claudia Strauss’s (1997) study of what chemical plant workers and their neighbors think about the free enterprise system.

8. Transitions

Another linguistic approach is to look for naturally occurring shifts in thematic content. Linguistic forms of transition vary between oral and written texts. In written texts, new paragraphs are often used by authors to indicate either subtle or abrupt shifts in topics. In oral speech, pauses, change in tone, or particular phrases may indicate thematic transitions. Linguists who have worked with precisely recorded texts in Native American languages have noticed the recurrence of elements like "Now," "Then," "Now then," and "Now again." These often signal the separation of verses and "once such patterning has been discovered in cases with such markers, it can be discerned in cases without them" (Hymes 1977:439).

For example, Sherzer (1994) presents a detailed analysis of a two-hour performance by Chief Olopinikwa of a traditional San Blas Kuna chant. The chant was recorded in 1970. Like many linguistic anthropologists, Sherzer had taught an assistant, Alberto Campos, to use a phonetic transcription system. After the chant, Sherzer asked Campos, to transcribe and translate the tape. Campos put Kuna and Spanish on left- and right-facing pages (1994:907). By studying Campos’s translation against the original Kuna, Sherzer was able to pick out certain recurrent features. Campos left out the chanted utterances of the responding chief (usually something like "so it is"), which turned out to be markers for verse endings in the chant. Campos also left out so-called framing words and phrases (like "Thus" at the beginning of a verse and "it is said, so I pronounce" at the end of a verse). These contribute to the line and verse structure of the chant. Finally, "instead of transposing metaphors and other figurative and allusive language into Spanish" Campos "explains them in his translation" (Sherzer 1994:908). Researchers

In two-party and multiparty speech, transitions occur naturally. Conversation or discourse analysts closely examine linguistic features such as turn-taking and speaker interruptions to identify transitions in speech sequences. For a good overview, see Silverman (1993:114-143).

9. Connectors

A third linguistic approach is to look carefully at words and phrases that indicate relationships among things. For example, causal relationships are often indicated by such words and phrases as, because, since, and as a result. Words such as if or then , rather than, and instead of often signify conditional relationships. The phrase is a is often associated with taxonomic categories. Time-oriented relationships are expressed with words such as before, after, then, and next . Typically negative characteristics occur less often than positive characteristics. Simply searching for the words not , no , none , or the prefix non may be a quick way to identify themes. Investigator can discover themes by searching on such groups of word and looking to see what kinds of things the words connect.

What other kinds of relationships might be of interest to social scientists? Casagrande and Hale (1967) suggest looking for: attributes (e.g., X is Y), contingencies (e.g., if X, then Y), functions (e.g., X is a means of affecting Y), spatial orientations (e.g., X is close to Y), operational definitions (e.g., X is a tool for doing Y), examples (e.g., X is an instance of Y), comparisons (e.g., X resembles Y), class inclusions (X is a member of class Y), synonyms (e.g., X is equivalent to Y), antonyms (e.g., X is the negation of Y), provenience (e.g., X is the source of Y), and circularity (e.g., X is defined as X). [For lists of kinds of relationships that may be useful for identifying themes see Burton and Kirk (1980:271), Werner and Schoepfle (1987) and Lindsay and Norman (1972).]

Investigators often use the linguistic features described above unconsciously. Metaphors, transitions, and connectors are all part of a native speaker’s ability to grasp meaning in a text. By making these features more explicit, we sharpen our ability to find themes.

Finally, we turn to more tactile approaches for theme discovery. Each of the next three techniques requires some physical manipulation of the text itself.

10. Unmarked texts

One way to identify new themes is to examine any text that is not already associated with a theme (Ryan 1999). This technique requires multiple readings of a text. On the first reading, salient themes are clearly visible and can be quickly and readily marked with different colored pencils or highlighters. In the next stage, the search is for themes that remain unmarked. This tactic–marking obvious themes early and quickly—forces the search for new, and less obtrusive themes.

We highly recommend pawing through texts and marking them up with different colored highlighter pens. Sandelowski (1995a:373) observes that analysis of texts begins with proofreading the material and simply underlining key phrases "because they make some as yet inchoate sense." Bernard (2000) refers to this as the ocular scan method , otherwise known as eyeballing . In this method, you get a feel for the text by handling your data multiple times. [Bogdan and Biklen (1982:165) suggest reading over the text at least twice.] Researchers have been known to spread their texts out on the floor, tack bunches of them to a bulletin board, and sort them into different file folders. By living with the data, investigators can eventually perform the interocular percussion test—which is where you wait for patterns to hit you between the eyes.

This may not seem like a very scientific way to do things, but it is one of the best ways we know of to begin hunting for patterns in qualitative data. Once you have a feel for the themes and the relations among, then we see no reason to struggle bravely on without a computer. Of course, a computer is required from the onset if the project involves hundreds of interviews, or if it’s part of a multi-site, multi-investigator effort. Even then, there is no substitute for following hunches and intuitions in looking for themes to code in texts (Dey 1993).

12. Cutting and sorting

Cutting and sorting is a more formal way of pawing and a technique we both use quite a bit. It is particularly useful for identifying subthemes. The approach is based on a powerful trick most of us learned in kindergarten and requires paper and scissors. We first read through the text and identify quotes that seem somehow important. We cut out each quote (making sure to maintain some of the context in which it occurred) and paste the material on small index cards. On the back of each card, we then write down the quote’s reference—who said it and where it appeared in the text. Then we lay out the quotes randomly on a big table and sort them into piles of similar quotes. Then we name each pile. These are the themes. This can be done with tag and search software, but we find that nothing beats the ability to manually sort and group the cards.

There are many variations on this pile-sorting technique. The principle investigator on a large project might ask several team members to sort the quotes into named piles independently. This is likely to generate a longer list of possible themes than would be produced by a group discussion. In really large projects, pairs of coders could sort the quotes together and decide on the names for the piles. The pile-sorting exercise should be video- or audiotaped and investigators should pay close attention to discussions—between themselves and coders or between coders—about which quotes belong together and why. These conversations are about as close as we will ever get to witnessing the emergence of themes.

Barkin et al. (1999) interviewed clinicians, community leaders, and parents about what physicians could and did do to prevent violence among youth. These were long, complex interviews, so Barkin et al. broke the coding process into two steps. They started with three major themes that they developed from theory. The principle investigator went through the transcripts and cut out all the quotes that pertained to each of the major themes. Then four other coders independently sorted the quotes from each major theme into piles. Then, the pile sort data were analyzed with multidimensional scaling and cluster analysis to identify subthemes shared across coders. [See Patterson et al. (1993) for another example.]

Jehn and Doucet (1997) had short answers to open-ended questions. They found that several coders could easily sort these paragraph-length descriptions of inter and intra-ethnic conflict. Then, like Barkin et al., Jehn and Doucet then used multidimensional scaling and cluster analysis to identify subthemes of conflict.

Another advantage to the cutting and sorting technique is that the data can be used to systematically describe how such themes are distributed across informants. After the piles have been formed and themes have been named, simply turn over each quote and identify who mentioned each theme. (If the people sorting the quotes are unaware of who the quotes came from, this is an unbiased way of coding.)

The variety of methods available for coding texts raises some obvious questions:

(1) Which technique generates more themes?

Frankly, we don’t know. There are just too many factors that influence the number of themes that are generated, including the technique itself, who and how many people are looking for themes, and the kind and amount of texts being analyzed. If the goal is to generate as many themes as possible—which is often the case in initial exploratory phases of research—then more is better. This means using multiple techniques, investigators, and texts.

Nowhere is a multiple technique approach better exemplified than in the work of Jehn and Doucet (1996, 1997). Jehn and Doucet asked 76 U.S. managers who had worked in Sino_American joint ventures to describe recent interpersonal conflicts with business partners. Each person described a situation with a same_culture manager and a different_cultural manger. First they generated separate lists of words from the intercultural and intracultural conflict narratives. They asked 3 expatriate managers to act as judges and to identify all the words that were related to conflict. They settled on a list of 542 conflict words from the intercultural list and 242 words from the intracultural list.

Jehn and Doucet then asked the three judges to sort the words into piles or categories. The experts identified 15 subcategories for the intercultural data—things like conflict, expectations, rules, power, and volatile—and 15 categories for the intracultural data—things like conflict, needs, standards, power, contentious, and lose. Taking into consideration the total number of words in each corpus, conflict words were used more in intracultural interviews and resolution terms were more likely to be used in intercultural interviews.

Jehn and Doucet (1996, 1997) also used traditional content analysis on their data. The had two coders read the 152 conflict scenarios (76 intracultural and 76 intercultural) and evaluated (on a 5_point scale) each on 27 different themes they had identified from the literature. This produced two 76x27 scenario_by_theme profile matrices—one for the intracultural conflicts and one for the intercultural conflicts. The first three factors from the intercultural matrix reflect: (1) interpersonal animosity and hostility; (2) aggravation; and (3) the volatile nature of the conflict. The first two factors from the intracultural matrix reflect: (1) hatred and animosity with a volatile nature and (2) conflicts conducted calmly with little verbal intensity.

Finally, Jehn and Doucet identified the 30 intracultural and the 30 intercultural scenarios that they felt were the most clear and pithy. They recruited fifty more expatriate managers to assess the similarities (on a 5_point scale) of 60–120 randomly selected pairs of scenarios. When combined across informants, the managers judgments produced two aggregate, scenario_by_scenario, similarity matrices—one for the intracultural conflicts and one for the intercultural conflicts.

Multidimensional scaling of the intercultural similarity data identified four dimensions: (1) open versus resistant to change, (2) situational causes versus individual traits, (3) high_ versus low_resolution potential based on trust, and (4) high_ versus low_resolution potential based on patience. Scaling of the intracultural similarity data identified four different dimensions: (1) high versus low cooperation, (2) high versus low confrontation, (3) problem_solving versus accepting, and (4) resolved versus ongoing.

The work of Jehn and Doucet is impressive because the analysis of the data from these tasks produced different sets of themes. All three emically induced theme sets have some intuitive appeal and all three yield analytic results that are useful. They could have also used the techniques of grounded theory or schema analysis to discover even more themes.

(2) When are the various techniques most appropriate?

The choice of techniques depends minimally on the kind and amount of text, the experience of the researcher, and the goals of the project. Word-based techniques (e.g., word repetitions, indigenous categories, and KWIC) are probably the least labor intensive. Computer software such as Anthropac and Code-a-text have little trouble in generating frequency counts of key words. 2 A careful look at the frequency list and maybe some quick pile sorts are often enough to identify quite a few themes. Word-based techniques are also the most versatile. They can easily be used with complex texts such as the complete works of Shakespear or the Bible, as well as, with simple short answers to open-ended questions. They can also be used relatively easily by novice and expert investigators alike. Given their very nature, however, they are best used in combination with other approaches.

Scrutiny-based techniques (e.g., compare and contrast, querying the text, and examining absences) are most appropriate for rich textual accounts and tend to be overkill for analyzing short answer responses. Investigators who are just beginning to explore a new topical area might want to start with compare-and-contrast techniques before moving on to the more difficult tasks of querying the text or searching for missing information. We do not advise using the latter two techniques unless the investigator is fluent in the language in which the data are collected. If the primary goal of the this portion of the investigation is to discover as many themes as possible, then nothing beats using these techniques on a line-by-line basis.

Like scrutiny-based techniques, linguist-based approaches are better used on narrative style accounts rather than short answer responses. Looking for transitions is the easiest technique to use, especially if the texts are actually written by respondents themselves (rather than transcribed from tape recordings of verbal interviews). Searching for metaphors is also relatively easy once novices have been trained on what kind of things to look for in the texts. Looking for connecting words and phrases is best used as a secondary wave of finding themes, once the investigator has a more definite idea of what kinds of themes he or she finds most interesting.

In the early stages of exploration, nothing beats a thorough reading and pawing through of the data. This approach is the easiest for novice researchers to master and is particularly good for identifying major themes. As the exploration progresses, investigators often find themselves looking for subthemes within these major themes. The cutting and sorting techniques are most helpful here. Investigators can identify all text passages that are related to a major theme, cut them out, and sort them into subthematic categories. Likewise, if they are marking texts for each newly discovered theme, then they can apply the unmarked text technique as they go. We have seen these three techniques applied successfully to both rich narrative data as well as simple responses to open-ended questions.

An even more powerful strategy would be to combine multiple techniques in a sequential manner. For example, investigators might begin by pawing through the data to see what kinds of themes just stick out. As part of this process, they might want to make comparisons between paragraphs and across informants. A quick analysis of word repetitions would also be appropriate for identifying themes at such an early stage of the analysis. If key words or indigenous phrases are present, researchers might followed-up by conducting more focused KWIC analyses. If the project is examining issues of equality, investigators might also look for texts that are indicative of power differentials and access to resources. Texts representing major themes can be marked either on paper or by computer. Investigators can then search areas that are not already marked for additional themes or cut and sort marked texts into subthemes.

Researchers also might consider beginning by looking for identifying all metaphors and similes, marking them, cutting them out and sorting them into thematic categories. There is no single way to discover themes. In theme discovery, we assume that more is always better.

(3) When do you know when you’ve found all the themes?

There is no magic formula to answer this question. The problem is similar to asking members of a population to list all the illnesses they know. One can never be sure of the full range of illnesses without interviewing the entire population. This is true because there is always the possibility that the last person interviewed will mention a new disease. We can simplify the process considerably, however, if we are willing to miss rarely-mentioned illness. One strategy would be to interview people until some number of respondents in a row (say five or more) fail to mention any new illnesses.

In text analysis, grounded theorists refer to the point at which no new themes are being identified as theoretical saturation (Strauss and Corbin 1990:188). When and how theoretical saturation is reached, however, depends the number of texts and their complexity, as well as on investigator experience and fatigue, and the number of investigators examining the texts. Again, more is better. Investigators who have more experience finding themes are likely to reach saturation latter than novices. Wilson and Hutchinson warn against premature closure where the researcher "fails to move beyond the face value of the content in the narrative (1990:123)."

Theme identification is one of the most fundamental tasks in qualitative research. It also one of the most mysterious. Explicit descriptions of theme discovery are rarely described in articles and reports and if so are often regulated to appendices or footnotes. Techniques are shared among small groups of social scientists and are often impeded by disciplinary or epistemological boundaries. The lack of clear methodological descriptions is most evident during the grant-writing phase of research. Investigators (ourselves included) struggle to clearly explain and justify plans for discovering themes in the qualitative data. These issues are particularly cogent when funding reviewers are unfamiliar with qualitative traditions.

In this article we have outlined a dozen techniques that social scientists have used to discover themes in texts. The techniques are drawn from across epistemological and disciplinary boundaries. They range from quick word counts to laborious, in-depth, line-by-line scrutiny. Some work well for short answers to open-ended questions while others are more appropriate for rich, complex narratives. Novices and non-native speakers may find some techniques easier than others. No single technique is does it all. To us, these techniques are simply tools to help us do better research.

1 ATLAS.ti (Scientific Software Development) and Nud•ist (Qualitative Solutions & Research) are qualitative analysis packages distributed in the United States by SCOLARI, Sage Publications, Inc., 2455 Teller Road, Thousand Oaks, CA 91320. Tel: (805) 499 1325. Fax: (805) 499 0871. E_mail: [email protected]. Web: www.scolari.com.

2 Anthropac (Analytic Technologies) and Coda-A-Text (Cartwright) are software packages that have the capacity to convert free flowing texts into word-by-document matrices. Code-A-Text is distributed in the United States by SCOLARI, Sage Publications. Anthropac is created and distributed by Analytic Technologies, Inc., Analytic Technologies, Inc., 11 Ohlin Lane, Harvard, MA 01451. Tel: (978) 456_7372. Fax: (978) 456_7373. E_mail: [email protected]. Web: www.analytictech.com.

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This paper is in the following e-collection/theme issue:

Published on 17.4.2024 in Vol 10 (2024)

Preeclampsia Onset, Days to Delivery, and Autism Spectrum Disorders in Offspring: Clinical Birth Cohort Study

Authors of this article:

Original Paper

• Sarah Carter 1 , MA, PhD   ; 
• Jane C Lin 1 , MSc   ; 
• Ting Chow 1 , MPH   ; 
• Mayra P Martinez 1 , MPH   ; 
• Chunyuan Qiu 2 , MD   ; 
• R Klara Feldman 3 , MD   ; 
• Rob McConnell 4 , MD   ; 
• Anny H Xiang 1 , PhD  

1 Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States

2 Department of Anesthesiology and Perioperative Medicine, Baldwin Park Medical Center, Kaiser Permanente Southern California, Baldwin Park, CA, United States

3 Department of Obstetrics and Gynecology, Baldwin Park Medical Center, Kaiser Permanente Southern California, Baldwin Park, CA, United States

4 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States

Corresponding Author:

Anny H Xiang, PhD

Department of Research and Evaluation

Kaiser Permanente Southern California

100 S Los Robles Ave

Pasadena, CA, 91101

United States

Phone: 1 626 564 3966

Email: [email protected]

Background: Maternal preeclampsia is associated with a risk of autism spectrum disorders (ASD) in offspring. However, it is unknown whether the increased ASD risk associated with preeclampsia is due to preeclampsia onset or clinical management of preeclampsia after onset, as clinical expectant management of preeclampsia allows pregnant women with this complication to remain pregnant for potentially weeks depending on the onset and severity. Identifying the risk associated with preeclampsia onset and exposure provides evidence to support the care of high-risk pregnancies and reduce adverse effects on offspring.

Objective: This study aimed to fill the knowledge gap by assessing the ASD risk in children associated with the gestational age of preeclampsia onset and the number of days from preeclampsia onset to delivery.

Methods: This retrospective population-based clinical cohort study included 364,588 mother-child pairs of singleton births between 2001 and 2014 in a large integrated health care system in Southern California. Maternal social demographic and pregnancy health data, as well as ASD diagnosis in children by the age of 5 years, were extracted from electronic medical records. Cox regression models were used to assess hazard ratios (HRs) of ASD risk in children associated with gestational age of the first occurrence of preeclampsia and the number of days from first occurrence to delivery.

Results: Preeclampsia occurred in 16,205 (4.4%) out of 364,588 pregnancies; among the 16,205 pregnancies, 2727 (16.8%) first occurred at <34 weeks gestation, 4466 (27.6%) first occurred between 34 and 37 weeks, and 9012 (55.6%) first occurred at ≥37 weeks. Median days from preeclampsia onset to delivery were 4 (IQR 2,16) days, 1 (IQR 1,3) day, and 1 (IQR 0,1) day for those first occurring at <34, 34-37, and ≥37 weeks, respectively. Early preeclampsia onset was associated with greater ASD risk ( P =.003); HRs were 1.62 (95% CI 1.33-1.98), 1.43 (95% CI 1.20-1.69), and 1.23 (95% CI 1.08-1.41), respectively, for onset at <34, 34-37, and ≥37 weeks, relative to the unexposed group. Within the preeclampsia group, the number of days from preeclampsia onset to delivery was not associated with ASD risk in children; the HR was 0.995 (95% CI 0.986-1.004) after adjusting for gestational age of preeclampsia onset.

Conclusions: Preeclampsia during pregnancy was associated with ASD risk in children, and the risk was greater with earlier onset. However, the number of days from first preeclampsia onset to delivery was not associated with ASD risk in children. Our study suggests that ASD risk in children associated with preeclampsia is not increased by expectant management of preeclampsia in standard clinical practice. Our results emphasize the need to identify effective approaches to preventing the onset of preeclampsia, especially during early pregnancy. Further research is needed to confirm if this finding applies across different populations and clinical settings.

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by repetitive behaviors and difficulties in communication, social behavior, and sensory processing [ 1 ]. ASD is typically diagnosed in early childhood [ 2 ], with prevalence rising [ 3 ]. In 2020, it was estimated that 2.8% of children in the United States had an ASD diagnosis at 8 years old, an increase from 0.7% in 2000 and 2.3% in 2018, with rates varying by sex, race, and ethnicity [ 3 ]. ASD is characterized by a range of symptoms, behaviors, and severity; children with autistic disorders, Asperger syndrome, or pervasive developmental disorder not otherwise specified can be diagnosed with ASD [ 4 ]. The etiology of ASD is multifactorial, and genetic factors only explain a small proportion [ 5 ]. Early life exposures, including adverse maternal health conditions during pregnancy, such as obesity and diabetes, and environmental pollutants, such as near-roadway air pollution and particulate matter 2.5, are all shown to be associated with the development of ASD [ 5 - 9 ].

One common pregnancy complication is preeclampsia, a hypertensive condition defined by placental vascular deterioration and maternal liver or kidney dysfunction [ 10 ]. As preeclampsia is an inflammatory condition complicating 2% to 8% of pregnancies worldwide each year [ 11 ], it is important to understand its effect on offspring’s development and long-term health. Previous studies have reported associations between maternal hypertensive disorders and an increased risk of ASD in children [ 12 - 15 ]. Hypertensive disorders of pregnancy, which include preeclampsia, were shown to be associated with adverse childhood neurodevelopment [ 16 , 17 ] and a 2-fold increase in ASD risk in childhood [ 15 ]. A meta-analysis reported a higher risk of ASD in the offspring of mothers with preeclampsia than those with gestational hypertension [ 18 ]. Previous studies also reported that preeclampsia is associated with an increased risk of ASD [ 6 , 19 ], as well as with preterm birth, which is a risk factor for ASD [ 20 ]. However, it is unknown whether the increased ASD risk associated with preeclampsia is due to preeclampsia onset or clinical management of preeclampsia after the onset. Identifying the risk associated with preeclampsia onset and exposure is important to provide evidence supporting the care of high-risk pregnancies and to reduce adverse effects on offspring.

Evidence suggests that preeclampsia results from impaired vascularization of the placenta, with inadequate diastolic uterine arteries causing insufficient blood flow to the placenta and increased systemic maternal inflammation [ 21 ]. The primary treatment for preeclampsia is delivering the baby or managing the condition until the best time to deliver the baby [ 22 - 24 ]. Depending on the severity, health care providers will closely monitor symptoms, including blood pressure, platelet count, and fetal growth, as well as renal and hepatic function, and hypertensive medications can be added to control blood pressure [ 24 ]. Current preeclampsia treatment guidelines advise expectant management for preeclampsia symptoms until 34 or 37 weeks gestation [ 24 ], depending on the severity of symptoms, such that preeclampsia occurring early could be expected to continue for several weeks before delivery.

The purpose of this study is to fill the knowledge gap by examining whether the number of days from preeclampsia onset to delivery in clinical standard practice is associated with an increased risk of ASD in offspring, considering the gestational age of preeclampsia onset. This study will provide important information concerning clinical guidelines for the management of preeclampsia and the effort to minimize the impact on offspring. Data are derived from a large, representative clinical birth cohort with comprehensive electronic medical records (EMR).

Study Population

This population-based retrospective birth cohort study used mother-child pairs of singleton births at Kaiser Permanente Southern California (KPSC) hospitals between January 1, 2001, and December 31, 2014. Of 414,463 mother-child pairs, 49,875 were excluded for lack of Kaiser Permanente membership by the age of 1 year or death of the child by the age of 1 year. The final cohort for this study was 364,588 mother-child pairs. Children were followed through KPSC EMR from birth until age 5 years. The KPSC health care system includes a diverse population of 4.5 million members throughout Southern California, and member demographic data reflect that of local census tracts [ 25 ]. Maternal social demographic and pregnancy health data, as well as the child’s ASD diagnosis, were extracted from KPSC’s integrated EMR.

Outcome: ASD Diagnosis in Children at or Before the Age of 5 Years

The outcome of this study was whether a child had an ASD diagnosis before or at the age of 5 years and the age of the initial diagnosis in KPSC EMR. We chose the follow-up of children up to the age of 5 years because the majority of ASD cases were diagnosed by the age of 5 years, and prenatal exposure is likely to manifest its adverse effect on early developmental disorders. The diagnosis of ASD is recorded in EMR using the International Classification of Diseases ( ICD ) codes developed by the World Health Organization. KPSC transitioned from ICD-9 codes to ICD-10 on October 1, 2015. In EMR records dated before October 1, 2015, ASD diagnoses were identified by ICD-9 codes 299.0, 299.1, 299.8, and 299.9. After that date, ASD diagnosis was identified by ICD-10 codes F84.0, F84.3, F84.5, F84.8, and F84.9. The diagnostic codes included autistic disorders, Asperger syndrome, and pervasive developmental disorder not otherwise specified but excluded Rett syndrome or childhood disintegrative disorder. ASD diagnosis was determined if codes were present in the EMR at 2 or more separate health visits. This method was previously validated by an expert chart review with a positive predictive value of 88% [ 8 , 26 - 28 ].

Exposure: Maternal Preeclampsia During Pregnancy

Maternal preeclampsia during pregnancy was identified by ICD-9 codes 642.50-642.54, 642.60-642.64, and 642.40-642.44 and included eclampsia and hemolysis, elevated liver enzymes, low platelet count (HELLP) syndrome, a serious form of preeclampsia characterized by a low platelet count and elevated liver enzymes [ 29 ]. Preeclampsia is diagnosed when blood pressure measurements are >140 systolic or 90 diastolic and when a urine test reports >0.3 g of protein in 24 hours [ 24 , 30 ]. Severe cases involve blood pressure >160 systolic or 110 diastolic and 1 or more of the following: persistent headache, vision changes, thrombocytopenia (platelet count <100,000/mcL), impaired liver function, progressive renal insufficiency (serum creatinine >1.1 mg/dL or doubling of serum creatinine not explained by other known renal disease), and pulmonary edema [ 24 , 30 ]. The date of first occurrence (ie, onset defined for this study) of preeclampsia during pregnancy was extracted, and the corresponding gestational week of preeclampsia onset was calculated by subtracting the date of last menstrual period (LMP) from the date of first occurrence recorded in the EMR. Preeclampsia onset was categorized into the following three groups: (1) <34 weeks (diagnosis between 24 weeks 0 days and 33 weeks 6 days); (2) 34-37 weeks (34 weeks 0 days to 36 weeks 6 days); and (3) ≥37 weeks (≥37 weeks 0 days). This categorization reflected known gestational ages for fetal viability at birth and clinical management guidance for preeclampsia. The gestation of 24 weeks has been reported to be the lower boundary for infant survival [ 31 , 32 ], and expectant management guidelines aim to maintain pregnancies until 34 or 37 weeks, depending on the timing of preeclampsia onset and the severity of symptoms [ 30 ]. Expectant management duration was calculated as the number of days between the date of preeclampsia onset and the date of delivery.

Covariates selected to adjust for potential confounding were maternal age, self-reported race, ethnicity, and education; prepregnancy obesity, diabetes, and smoking during pregnancy; history of comorbidity (≥1 diagnosis of heart, lung, kidney, liver disease, or cancer); offspring sex; and census tract–level household income at child’s first birthday. These covariates were potential risk factors associated with child’s ASD risk, as shown in previous studies [ 21 , 24 ]. The birth year was also included as a covariate to account for trends of increasing ASD prevalence over the study period [ 8 ]. Maternal obesity was defined as a prepregnancy BMI ≥30 kg/m 2 . Maternal prepregnancy BMI was calculated using maternal height and weight recorded in EMR from the date closest to LMP, with a window of 6 months before and 3 months after LMP [ 33 ]. Diabetes during pregnancy included preexisting type 1 or type 2 diabetes and gestational diabetes mellitus diagnosed before 26 weeks, as these were previously associated with ASD risk in this study sample [ 26 , 27 ].

Statistical Analyses

Outcome variables were child’s ASD diagnosis by the age of 5 years and the age of ASD diagnosis. Exposure variables of interest were the gestational age of preeclampsia onset and days from preeclampsia onset to delivery. The gestational age of preeclampsia onset was analyzed as a continuous variable as well as a categorical variable categorized as <34 weeks, 34-37 weeks, and ≥37 weeks. Duration from preeclampsia onset to delivery was analyzed both as a continuous variable and a categorical variable, using the median number of days between onset and delivery for each onset group as the cutoff. Maternal and child characteristics by preeclampsia exposure status were reported as median and IQR for continuous variables and total number (n) and proportion (%) for categorical variables. Wilcoxon rank-sum tests and chi-square tests were used to assess differences in maternal and child characteristics between preeclampsia exposures.

Associations between preeclampsia exposure and child’s ASD risk were assessed using Cox regression models. Robust standard errors were used to correct for potential correlation between siblings born to the same mothers. Associations were quantified as hazard ratios (HRs) with 95% CI. We first assessed the risk of ASD associated with preeclampsia exposure (yes vs no), followed by assessing ASD risk associated with gestational age of onset within the preeclampsia group and comparing the risk of ASD in the <34 weeks, 34-37 weeks, and ≥37 weeks onset groups relative to the unexposed. We then examined ASD risk associated with duration from onset to delivery among those with preeclampsia exposure, adjusting for gestational weeks of the first occurrence of preeclampsia. All models adjusted for birth year, maternal age, self-reported race and ethnicity, educational qualifications, prepregnancy obesity, diabetes, smoking during pregnancy, census tract–level household income at child’s first birthday, and child’s sex. Birth year was modeled as a penalized spline to account for the nonlinear relationship between birth year and outcomes. We also assessed the roles of gestational age at delivery and birth weight as pathways to risk associated with early onset of preeclampsia and duration from onset to delivery by further adjusting these 2 variables.

Statistical significance was set at P <.05. All statistical analyses were performed in R software (version 3.6; R Foundation for Statistical Computing).

Ethical Considerations

This study was approved by KPSC Institutional Review Boards (review #12075), with individual participant consent waived. All data analyzed were deidentified. No compensation was offered to individual participants. There was no community involvement in the study.

Table 1 presents cohort characteristics by maternal preeclampsia status. Of the 364,588 children included in this study, 16,205 (4.4%) were exposed to preeclampsia in utero ( Table 1 ). The preeclampsia group had more nulliparous women than the non-preeclampsia group (8238/16,205, 50.8% vs 124,405/348,383, 35.7%). Maternal age at delivery, race, ethnicity, and educational qualifications, as well as census-tract household income and smoking behavior during pregnancy, did not differ between the 2 groups. Larger proportions of women with preeclampsia had diabetes preexisting or diagnosed at ≤26 weeks gestation (2194/16,204, 13.5% vs 20,147/348,383, 5.8%), obesity (4242/16,205, 26% vs 53,210/348,383, 15%), and histories of comorbidities (2694/16,205, 16.6% vs 45,291/348,383, 13%) than women who did not have preeclampsia. Sex was comparable among children of mothers with and without preeclampsia. Both median gestational age at delivery (38 weeks vs 39 weeks) and median birth weight (3005 g vs 3390 g) were lower in children of mothers with preeclampsia than in children of mothers without preeclampsia.

a Maternal diabetes includes preexisting type 1 diabetes (T1D) and type 2 diabetes (T1D) and gestational diabetes mellitus (GDM) diagnosed at ≤26 weeks (no preeclampsia—T1D: 574, 0.2%; T2D: 9150, 2.6%; GDM ≤26 weeks: 10,423, 3%; preeclampsia—T1D: 137, 0.8%; T2D: 1128, 7%; GDM ≤26 weeks: 929, 5.7%)

b Height and weight at each clinical visit were not recorded in Kaiser Permanente Southern California electronic medical records until late 2006; therefore, maternal obesity data were missing for children born between 2001 and 2006. Maternal prepregnancy BMI was categorized as obese (BMI ≥30 kg/m 2 ), nonobese (BMI <30 kg/m 2 ), and unknown (including mothers with unavailable BMI information). Maternal obesity proportion excludes women with missing BMI data (no preeclampsia: missing BMI sample size=135,139; preeclampsia=6229)

c Maternal comorbidity was defined as ≥1 diagnosis of heart, lung, kidney, liver disease, or cancer.

Of the 16,205 pregnancies with preeclampsia, 2727 (16.8%) first occurred at <34 weeks, 4466 (27.6%) first occurred between 34 and 37 weeks, and 9012 (55.6%) first occurred at ≥37 weeks. When assessing days from first occurrence to delivery, expectedly, mothers with onset at <34 weeks had the greatest number of days from first occurrence to delivery, with 31.6% (863/2727) having ≥10 days between onset to delivery in this group. For first onset at ≥37 weeks, 78.8% (7104/9012) delivered in ≤1 day. Median days from preeclampsia onset to delivery were 4 (IQR 2,16) days, 1 (IQR 1,3) day, and 1 (IQR 0,1) day for onset at <34, 34-37, and ≥37 weeks, respectively.

A total of 7194 (2.1%) of the 348,383 children were diagnosed with ASD by the age of 5 years: 2.9% (465/16,205) in the preeclampsia exposed and 1.9% (6729/348,383) in the unexposed groups, with an HR of 1.36 (95% CI 1.23-1.49) of ASD risk for exposed versus unexposed after adjusting for birth year, maternal age, race, ethnicity, education, history of comorbidity, prepregnancy obesity, diabetes, smoking during pregnancy, offspring sex, and census-tract household income ( Table 2 ). When considering preeclampsia exposure by gestational week of first onset, earlier preeclampsia onset was associated with greater ASD risk ( P =.003 for testing ASD association with gestational age of preeclampsia onset among the preeclampsia group). The HRs were 1.62 (95% CI 1.33-1.98), 1.43 (95% CI 1.20-1.69), and 1.23 (95% CI 1.08-1.41) for onset at <34, 34-37 weeks, and ≥37 weeks, respectively, relative to the unexposed group ( Table 2 ).

Among those exposed to preeclampsia, the number of days from first onset to delivery was not associated with ASD risk. When analyzed as a continuous variable adjusting for preeclampsia onset, the HR of ASD risk associated with days from onset to delivery was 0.995 (95% CI 0.986-1.004; Table 3 ). When analyzed as a categorical variable cut at median days stratified by gestational week of preeclampsia onset, the HR associated with days above the median relative to at or below the median days for each onset group were 1.16 (95% CI 0.74-1.82) for onset at <34 weeks; 1.11 (95% CI 0.74-1.65) for onset between 34 and 37 weeks; and 1.23 (95% CI 0.85-1.78) for onset at ≥37 weeks ( Table 3 ).

a Adjusted for gestational age at preeclampsia diagnosis in weeks, as a continuous variable.

b The hazard ratio represents the risk associated with days from diagnosis to delivery above the median relative to equal or below the median distribution within each group (<34 weeks: median 4, IQR 2.16 days); (34-37 weeks: median 1, IQR 1.3 day); (≥37 weeks: median 1, IQR 0.1 day).

Early preeclampsia diagnosis was positively correlated with early gestational age at delivery ( r =0.90) and lower birth weight ( r =0.70), but the number of days from onset to delivery was not correlated with gestational age at delivery ( r =–0.01) or birth weight ( r =–0.005). Further adjusting for gestational age at delivery and birth weight reduced HRs to 1.19 (95% CI 0.97-1.48) for onset at <34 weeks and 1.27 (1.07-1.50) for onset between 34 and 37 weeks but did not change the HR for preeclampsia onset ≥37 weeks (1.24, 95% CI 1.08-1.41). The number of days from onset to delivery remained unassociated with ASD risk in offspring after adjustment for gestational age at delivery and birth weight.

In this large sample and multiethnic clinical cohort study, children exposed to preeclampsia in utero were at higher risk of ASD than children who were not exposed, with a greater risk for children exposed to preeclampsia earlier in pregnancy. However, among children exposed to preeclampsia, there were no significant associations of risk of ASD with the number of days from first onset to delivery. This was analyzed both as a continuous variable adjusting for gestational age of preeclampsia first onset and as a categorical variable cut at the median number of days and stratified by gestational age of preeclampsia first occurrence. These results suggest that a child’s risk of ASD associated with preeclampsia is not increased by expectant management of preeclampsia in standard clinical practice.

To our knowledge, this study is the first to assess whether the reported ASD risk associated with maternal preeclampsia was due to preeclampsia onset or clinical management of preeclampsia during pregnancy. Current clinical management of preeclampsia advises maintenance of pregnancies until 34 or 37 weeks gestation, depending on the week of diagnosis and preeclampsia severity [ 24 ]. Expectant management of preeclampsia requires balancing maternal medical needs with intrauterine developmental requirements and reduction of infant comorbidities associated with preterm birth [ 34 , 35 ]. Planned preterm delivery of pregnancies complicated by preeclampsia improves maternal outcomes but increases the risk of neonatal admissions for prematurity [ 36 ]. Expectant management of moderate preeclampsia before 37 weeks gestation has been reported to extend pregnancies without increasing neonatal comorbidities; however, studies have reported the risk of stillbirth [ 37 ] and neonatal mortality [ 38 ] associated with expectant management for severe preeclampsia diagnosed before 34 weeks gestation. The results presented in our study demonstrate that early onset of preeclampsia is associated with a greater risk of ASD in offspring; however, clinical management for preeclampsia does not add additional risk, potentially eliminating one comorbidity of concern associated with preeclampsia exposure and expectant management.

Prenatal preeclampsia may affect child neurodevelopment by altering the maternal environment during fetal maturation. Preeclampsia has been observed to influence maternal immune activation [ 39 ], increasing circulation of maternal proinflammatory cytokines, and contributing to higher levels of oxidative stress, which have been associated with divergent neurodevelopment in children [ 40 ]. Studies have reported differences in the etiology of early and late-onset preeclampsia [ 41 , 42 ], as well as varying risks of adverse outcomes associated with the timing of exposure [ 43 , 44 ]. Early-onset preeclampsia may be driven by placental dysfunction [ 45 ], which occurs when a placental abnormality restricts blood flow, potentially due to suppression of estrogen-related receptor-gamma leading to vascular abnormalities [ 46 ]. Late-onset preeclampsia could be primarily a maternal hypertensive condition [ 45 ]. Placental inflammation and vascular dysfunction have been linked to an increased risk of ASD in children [ 47 , 48 ]. Therefore, differences in ASD risk by gestational week of onset may be explained by the influence of preeclampsia on specific stages of fetal neurodevelopment [ 19 ].

In our study, earlier onset of preeclampsia had larger HRs than onset in later pregnancy, which is consistent with results from a study of ASD risk and preeclampsia, which reported an increased risk of ASD in children exposed to preeclampsia earlier in pregnancy [ 19 ]. Adjustment for gestational age at delivery and birth weight attenuated the risk of ASD after early preeclampsia in our study, suggesting that there may be different mechanisms underlying the risk of ASD by the timing of preeclampsia exposure. Exposure to preeclampsia has been reported to be associated with an increased risk of ASD in offspring [ 49 ]. Preeclampsia is also associated with preterm birth, another risk factor for ASD [ 20 ]. Our results are consistent with previous results that prematurity and low birth weight are risk factors for ASD [ 6 , 20 ] and extend them to show that early preeclampsia is one of the root causes for prematurity, low birth weight, and associated ASD risk.

Our results concerning the increased risk of ASD associated with maternal preeclampsia during pregnancy are consistent with previous findings. However, the novel finding in our study is that the number of days between preeclampsia’s first occurrence and delivery was not associated with ASD risk in offspring after taking the gestational age of preeclampsia onset into account. Thus, the results of our study suggest that clinical expectant management of preeclampsia after diagnosis does not increase the risk of ASD in offspring. As this is the first study to report these results, future research is needed to determine whether this finding is consistent in other populations and to assess whether these associations vary by type of preeclampsia treatment or severity of illness. Our results provide evidence demonstrating the need to identify effective approaches to prevent the onset of preeclampsia, especially during early pregnancy, to mitigate risk not only to mothers but also to offspring.

Strengths and Limitations

A strength of this study is its large, clinical, and longitudinal birth cohort with characteristics reflecting census tract–level social and demographic information of Southern California. Comprehensive EMR data with detailed pregnancy history and dates allowed us to assess preeclampsia by gestational week of diagnosis and duration from diagnosis to delivery, and to adjust for relevant covariates. The continuity of care at KPSC minimizes the risk of ascertainment bias in exposures and outcomes. We think the assessment of expectant management of preeclampsia on ASD risk is novel.

This observational study has some limitations. The results presented here do not establish a causal link between preeclampsia exposure and ASD risk. The severity of preeclampsia was not explicitly considered due to the lack of a clear definition of severity in EMR. However, diagnosis to delivery periods allowed some inference about severity, as severe cases were likely to be delivered more quickly than moderate cases [ 24 ]. Genetic information was unavailable; therefore, we were unable to control for genetic contributions to ASD risk. There may be other perinatal risk factors or postnatal environmental exposures not adjusted for in these analyses.

In this population-based retrospective clinical birth cohort study, exposure to preeclampsia in utero was associated with an increased risk of ASD in offspring, with a greater risk for children of mothers with preeclampsia occurring earlier during pregnancy. However, among children of mothers with preeclampsia, the number of days between preeclampsia diagnosis and delivery was not associated with increased ASD risk. Our study suggests that clinical management of pregnancies with preeclampsia does not increase the risk of ASD in offspring. Future research into the prevention of preeclampsia is still needed.

Acknowledgments

The authors thank patients of Kaiser Permanente Southern California for helping us improve care using information collected through our integrated electronic health record systems.

This study was supported in part by the National Institutes of Health (R01ES029963) and by Kaiser Permanente Southern California Direct Community Benefit Funds. Funding agencies had no role in the study design, data analysis or interpretation, or manuscript preparation or approval.

Data Availability

The data set analyzed in this study is not publicly available as it was drawn from electronic medical records.

Authors' Contributions

AHX obtained funding, acquired data, was responsible for the study concept and design, analyzed and interpreted data, revised the manuscript for important intellectual content, was a guarantor of this work, and, as such, had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. SC was responsible for the study concept and design, analyzed and interpreted data, drafted the manuscript, revised the manuscript for important intellectual content, and was a guarantor of this work and, as such, had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. JCL, RKF, CQ, RM, and TC analyzed and interpreted data and revised the manuscript for important intellectual content. MPM acquired, analyzed, and interpreted data, and revised the manuscript for important intellectual content.

All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani, T Sanchez; submitted 20.03.23; peer-reviewed by T Ntalindwa, S Hewage; comments to author 22.11.23; revised version received 08.12.23; accepted 01.03.24; published 17.04.24.

©Sarah Carter, Jane C Lin, Ting Chow, Mayra P Martinez, Chunyuan Qiu, R Klara Feldman, Rob McConnell, Anny H Xiang. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 17.04.2024.

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

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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EPA scientists find common disinfectants harm brain cells

By Ellie Borst | 04/18/2024 01:30 PM EDT

More research is needed to determine what levels of exposure to the chemicals triggers risks, according to the scientists.

New research digs into neurological development risks posed by chemicals in widely used disinfectants. Marco Verch/Flickr

Chemicals commonly found in disinfectant cleaning products may disrupt brain development in children, according to new findings for a class of substances with limited information on health effects.

A new study , published in the peer-reviewed journal Nature Neuroscience from EPA and Case Western Reserve University scientists, screened more than 1,800 chemicals and found quaternary compounds were often toxic to cells in the nervous system. Those cells help with neuron activity, and disruption can lead to cognitive and motor disabilities.

Quaternary compounds are a class of chemicals that kill molds and viruses found in antibacterial soaps, cleansing wipes and disinfectant sprays, as well as hair conditioners and fabric softeners.

But what levels of exposure and if the products should be considered risky are still unknown, said Paul Tesar, one of the study’s authors and a professor of genetics and neurosciences at Case Western’s School of Medicine.

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Lincoln Center’s Summer Festival to Focus on Civic Bonds

The third edition of Summer for the City will feature hip-hop, comedy, classical music and more, under the motto “life, liberty and happiness.”

By Javier C. Hernández

Lincoln Center said on Wednesday that it would devote its summer festival to themes of community and civic participation, with a mix of hip-hop, comedy, dance, classical music and more under the motto “life, liberty and happiness.”

The festival, Summer for the City, will feature premieres of anthems about contemporary hopes and struggles. Classical music concerts will be more participatory than in the past; at one event, audience members will be asked to vote on the program. And civil rights will be prominent, with the New York premiere of an opera about Eric Garner , who died in 2014 at the hands of police officers on Staten Island.

“We know the performing arts have a role in strengthening our community and strengthening our civic bonds,” Shanta Thake, Lincoln Center’s chief artistic officer, said in an interview. “This is a time where we can really be together and celebrate the ideas and ideals that we all share.”

The third edition of the festival , which will run from June 12 to Aug. 10, is part of the center’s efforts to appeal to a younger, more diverse crowd, in part by promoting a broader array of genres, including pop music and social dance.

Under Henry Timms, Lincoln Center’s president and chief executive, the center has shifted its focus from classical music and international theater, prompting some criticism that it is not doing enough to promote traditional offerings. (Timms will depart his post in August; a search for his successor is in progress.)

After eliminating the Mostly Mozart Festival, a summer fixture since the 1970s, Lincoln Center renamed the Mostly Mozart Festival Orchestra, saying that it was time to reimagine the ensemble for a modern and more inclusive age. This season, the Festival Orchestra of Lincoln Center, as the ensemble is now called, will convene for the first time under the rising conductor Jonathon Heyward.

In July, the orchestra will give the North American premiere of Huang Ruo’s interactive “City of Floating Sounds.” In August, it will perform “He stretches out the north over the void and hangs the earth on nothing,” a world premiere by Hannah Kendall.

Summer for the City will open with a commission led by the director James Blaszko that features the drag performers Sapphira Cristál, a finalist on the current season of “RuPaul’s Drag Race,” and Monét X Change that will be “celebrating the fabulosity and queerness in opera,” according to a news release. The program will include works by Mozart and Mariah Carey.

Other highlights are a weeklong exploration of Indian culture in July, featuring the Ragamala Dance Company, DJ Rekha and others; and a comedy night that will include appearances by Aasif Mandvi and Hari Kondabolu.

The giant disco ball that has become a staple of the festival will once again hang over a dance floor built on Lincoln Center’s main plaza. The outdoor spaces, designed by the Broadway costume and set designer Clint Ramos, will this year evoke the “flora and fauna of the American prairie,” the center said.

Most of the more than 200 events will be free; tickets for some indoor performances will be sold at choose-what-you-pay prices, starting at $5.

The center said that about 380,000 people attended the festival last year, and that more than half identified as people of color; a third came from households with an annual income of less than $75,000; a quarter came from boroughs outside Manhattan.

“Live performance,” Thake said, “is one of the most important tools we have as a society to get people into conversation with their neighbors, with their neighborhood, with their sense of purpose and sense of belonging in a community.”

Javier C. Hernández is a culture reporter, covering the world of classical music and dance in New York City and beyond. He joined The Times in 2008 and previously worked as a correspondent in Beijing and New York. More about Javier C. Hernández

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