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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo: http://www.qsrinternational.com/products.aspx

Atlas.ti: http://www.atlasti.com/webinars.html

R- RQDA package: http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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Methodology

Content Analysis | A Step-by-Step Guide with Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

Books, newspapers, and magazines
Speeches and interviews
Web content and social media posts
Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyse.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

Finding correlations and patterns in how concepts are communicated
Understanding the intentions of an individual, group, or institution
Identifying propaganda and bias in communication
Revealing differences in communication in different contexts
Analysing the consequences of communication content, such as the flow of information or audience responses

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Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context, and audience of the texts.

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

Definition:

Content analysis is a research method used to analyze and interpret the characteristics of various forms of communication, such as text, images, or audio. It involves systematically analyzing the content of these materials, identifying patterns, themes, and other relevant features, and drawing inferences or conclusions based on the findings.

Content analysis can be used to study a wide range of topics, including media coverage of social issues, political speeches, advertising messages, and online discussions, among others. It is often used in qualitative research and can be combined with other methods to provide a more comprehensive understanding of a particular phenomenon.

Types of Content Analysis

There are generally two types of content analysis:

Quantitative Content Analysis

This type of content analysis involves the systematic and objective counting and categorization of the content of a particular form of communication, such as text or video. The data obtained is then subjected to statistical analysis to identify patterns, trends, and relationships between different variables. Quantitative content analysis is often used to study media content, advertising, and political speeches.

Qualitative Content Analysis

This type of content analysis is concerned with the interpretation and understanding of the meaning and context of the content. It involves the systematic analysis of the content to identify themes, patterns, and other relevant features, and to interpret the underlying meanings and implications of these features. Qualitative content analysis is often used to study interviews, focus groups, and other forms of qualitative data, where the researcher is interested in understanding the subjective experiences and perceptions of the participants.

Methods of Content Analysis

There are several methods of content analysis, including:

Conceptual Analysis

This method involves analyzing the meanings of key concepts used in the content being analyzed. The researcher identifies key concepts and analyzes how they are used, defining them and categorizing them into broader themes.

Content Analysis by Frequency

This method involves counting and categorizing the frequency of specific words, phrases, or themes that appear in the content being analyzed. The researcher identifies relevant keywords or phrases and systematically counts their frequency.

Comparative Analysis

This method involves comparing the content of two or more sources to identify similarities, differences, and patterns. The researcher selects relevant sources, identifies key themes or concepts, and compares how they are represented in each source.

Discourse Analysis

This method involves analyzing the structure and language of the content being analyzed to identify how the content constructs and represents social reality. The researcher analyzes the language used and the underlying assumptions, beliefs, and values reflected in the content.

Narrative Analysis

This method involves analyzing the content as a narrative, identifying the plot, characters, and themes, and analyzing how they relate to the broader social context. The researcher identifies the underlying messages conveyed by the narrative and their implications for the broader social context.

Content Analysis Conducting Guide

Here is a basic guide to conducting a content analysis:

Define your research question or objective: Before starting your content analysis, you need to define your research question or objective clearly. This will help you to identify the content you need to analyze and the type of analysis you need to conduct.
Select your sample: Select a representative sample of the content you want to analyze. This may involve selecting a random sample, a purposive sample, or a convenience sample, depending on the research question and the availability of the content.
Develop a coding scheme: Develop a coding scheme or a set of categories to use for coding the content. The coding scheme should be based on your research question or objective and should be reliable, valid, and comprehensive.
Train coders: Train coders to use the coding scheme and ensure that they have a clear understanding of the coding categories and procedures. You may also need to establish inter-coder reliability to ensure that different coders are coding the content consistently.
Code the content: Code the content using the coding scheme. This may involve manually coding the content, using software, or a combination of both.
Analyze the data: Once the content is coded, analyze the data using appropriate statistical or qualitative methods, depending on the research question and the type of data.
Interpret the results: Interpret the results of the analysis in the context of your research question or objective. Draw conclusions based on the findings and relate them to the broader literature on the topic.
Report your findings: Report your findings in a clear and concise manner, including the research question, methodology, results, and conclusions. Provide details about the coding scheme, inter-coder reliability, and any limitations of the study.

Applications of Content Analysis

Content analysis has numerous applications across different fields, including:

Media Research: Content analysis is commonly used in media research to examine the representation of different groups, such as race, gender, and sexual orientation, in media content. It can also be used to study media framing, media bias, and media effects.
Political Communication : Content analysis can be used to study political communication, including political speeches, debates, and news coverage of political events. It can also be used to study political advertising and the impact of political communication on public opinion and voting behavior.
Marketing Research: Content analysis can be used to study advertising messages, consumer reviews, and social media posts related to products or services. It can provide insights into consumer preferences, attitudes, and behaviors.
Health Communication: Content analysis can be used to study health communication, including the representation of health issues in the media, the effectiveness of health campaigns, and the impact of health messages on behavior.
Education Research : Content analysis can be used to study educational materials, including textbooks, curricula, and instructional materials. It can provide insights into the representation of different topics, perspectives, and values.
Social Science Research: Content analysis can be used in a wide range of social science research, including studies of social media, online communities, and other forms of digital communication. It can also be used to study interviews, focus groups, and other qualitative data sources.

Examples of Content Analysis

Here are some examples of content analysis:

Media Representation of Race and Gender: A content analysis could be conducted to examine the representation of different races and genders in popular media, such as movies, TV shows, and news coverage.
Political Campaign Ads : A content analysis could be conducted to study political campaign ads and the themes and messages used by candidates.
Social Media Posts: A content analysis could be conducted to study social media posts related to a particular topic, such as the COVID-19 pandemic, to examine the attitudes and beliefs of social media users.
Instructional Materials: A content analysis could be conducted to study the representation of different topics and perspectives in educational materials, such as textbooks and curricula.
Product Reviews: A content analysis could be conducted to study product reviews on e-commerce websites, such as Amazon, to identify common themes and issues mentioned by consumers.
News Coverage of Health Issues: A content analysis could be conducted to study news coverage of health issues, such as vaccine hesitancy, to identify common themes and perspectives.
Online Communities: A content analysis could be conducted to study online communities, such as discussion forums or social media groups, to understand the language, attitudes, and beliefs of the community members.

Purpose of Content Analysis

The purpose of content analysis is to systematically analyze and interpret the content of various forms of communication, such as written, oral, or visual, to identify patterns, themes, and meanings. Content analysis is used to study communication in a wide range of fields, including media studies, political science, psychology, education, sociology, and marketing research. The primary goals of content analysis include:

Describing and summarizing communication: Content analysis can be used to describe and summarize the content of communication, such as the themes, topics, and messages conveyed in media content, political speeches, or social media posts.
Identifying patterns and trends: Content analysis can be used to identify patterns and trends in communication, such as changes over time, differences between groups, or common themes or motifs.
Exploring meanings and interpretations: Content analysis can be used to explore the meanings and interpretations of communication, such as the underlying values, beliefs, and assumptions that shape the content.
Testing hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the effects of media on attitudes and behaviors or the framing of political issues in the media.

When to use Content Analysis

Content analysis is a useful method when you want to analyze and interpret the content of various forms of communication, such as written, oral, or visual. Here are some specific situations where content analysis might be appropriate:

When you want to study media content: Content analysis is commonly used in media studies to analyze the content of TV shows, movies, news coverage, and other forms of media.
When you want to study political communication : Content analysis can be used to study political speeches, debates, news coverage, and advertising.
When you want to study consumer attitudes and behaviors: Content analysis can be used to analyze product reviews, social media posts, and other forms of consumer feedback.
When you want to study educational materials : Content analysis can be used to analyze textbooks, instructional materials, and curricula.
When you want to study online communities: Content analysis can be used to analyze discussion forums, social media groups, and other forms of online communication.
When you want to test hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the framing of political issues in the media or the effects of media on attitudes and behaviors.

Characteristics of Content Analysis

Content analysis has several key characteristics that make it a useful research method. These include:

Objectivity : Content analysis aims to be an objective method of research, meaning that the researcher does not introduce their own biases or interpretations into the analysis. This is achieved by using standardized and systematic coding procedures.
Systematic: Content analysis involves the use of a systematic approach to analyze and interpret the content of communication. This involves defining the research question, selecting the sample of content to analyze, developing a coding scheme, and analyzing the data.
Quantitative : Content analysis often involves counting and measuring the occurrence of specific themes or topics in the content, making it a quantitative research method. This allows for statistical analysis and generalization of findings.
Contextual : Content analysis considers the context in which the communication takes place, such as the time period, the audience, and the purpose of the communication.
Iterative : Content analysis is an iterative process, meaning that the researcher may refine the coding scheme and analysis as they analyze the data, to ensure that the findings are valid and reliable.
Reliability and validity : Content analysis aims to be a reliable and valid method of research, meaning that the findings are consistent and accurate. This is achieved through inter-coder reliability tests and other measures to ensure the quality of the data and analysis.

Advantages of Content Analysis

There are several advantages to using content analysis as a research method, including:

Objective and systematic : Content analysis aims to be an objective and systematic method of research, which reduces the likelihood of bias and subjectivity in the analysis.
Large sample size: Content analysis allows for the analysis of a large sample of data, which increases the statistical power of the analysis and the generalizability of the findings.
Non-intrusive: Content analysis does not require the researcher to interact with the participants or disrupt their natural behavior, making it a non-intrusive research method.
Accessible data: Content analysis can be used to analyze a wide range of data types, including written, oral, and visual communication, making it accessible to researchers across different fields.
Versatile : Content analysis can be used to study communication in a wide range of contexts and fields, including media studies, political science, psychology, education, sociology, and marketing research.
Cost-effective: Content analysis is a cost-effective research method, as it does not require expensive equipment or participant incentives.

Limitations of Content Analysis

While content analysis has many advantages, there are also some limitations to consider, including:

Limited contextual information: Content analysis is focused on the content of communication, which means that contextual information may be limited. This can make it difficult to fully understand the meaning behind the communication.
Limited ability to capture nonverbal communication : Content analysis is limited to analyzing the content of communication that can be captured in written or recorded form. It may miss out on nonverbal communication, such as body language or tone of voice.
Subjectivity in coding: While content analysis aims to be objective, there may be subjectivity in the coding process. Different coders may interpret the content differently, which can lead to inconsistent results.
Limited ability to establish causality: Content analysis is a correlational research method, meaning that it cannot establish causality between variables. It can only identify associations between variables.
Limited generalizability: Content analysis is limited to the data that is analyzed, which means that the findings may not be generalizable to other contexts or populations.
Time-consuming: Content analysis can be a time-consuming research method, especially when analyzing a large sample of data. This can be a disadvantage for researchers who need to complete their research in a short amount of time.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Chapter 17. Content Analysis

Introduction.

Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or Facebook. Really, almost anything can be the “content” to be analyzed. This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] Qualitative content analysis (sometimes referred to as QCA) is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest—in other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue. This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis. It is also a nice segue between our data collection methods (e.g., interviewing, observation) chapters and chapters 18 and 19, whose focus is on coding, the primary means of data analysis for most qualitative data. In many ways, the methods of content analysis are quite similar to the method of coding.

Although the body of material (“content”) to be collected and analyzed can be nearly anything, most qualitative content analysis is applied to forms of human communication (e.g., media posts, news stories, campaign speeches, advertising jingles). The point of the analysis is to understand this communication, to systematically and rigorously explore its meanings, assumptions, themes, and patterns. Historical and archival sources may be the subject of content analysis, but there are other ways to analyze (“code”) this data when not overly concerned with the communicative aspect (see chapters 18 and 19). This is why we tend to consider content analysis its own method of data collection as well as a method of data analysis. Still, many of the techniques you learn in this chapter will be helpful to any “coding” scheme you develop for other kinds of qualitative data. Just remember that content analysis is a particular form with distinct aims and goals and traditions.

An Overview of the Content Analysis Process

The first step: selecting content.

Figure 17.2 is a display of possible content for content analysis. The first step in content analysis is making smart decisions about what content you will want to analyze and to clearly connect this content to your research question or general focus of research. Why are you interested in the messages conveyed in this particular content? What will the identification of patterns here help you understand? Content analysis can be fun to do, but in order to make it research, you need to fit it into a research plan.

Figure 17.1. A Non-exhaustive List of "Content" for Content Analysis

To take one example, let us imagine you are interested in gender presentations in society and how presentations of gender have changed over time. There are various forms of content out there that might help you document changes. You could, for example, begin by creating a list of magazines that are coded as being for “women” (e.g., Women’s Daily Journal ) and magazines that are coded as being for “men” (e.g., Men’s Health ). You could then select a date range that is relevant to your research question (e.g., 1950s–1970s) and collect magazines from that era. You might create a “sample” by deciding to look at three issues for each year in the date range and a systematic plan for what to look at in those issues (e.g., advertisements? Cartoons? Titles of articles? Whole articles?). You are not just going to look at some magazines willy-nilly. That would not be systematic enough to allow anyone to replicate or check your findings later on. Once you have a clear plan of what content is of interest to you and what you will be looking at, you can begin, creating a record of everything you are including as your content. This might mean a list of each advertisement you look at or each title of stories in those magazines along with its publication date. You may decide to have multiple “content” in your research plan. For each content, you want a clear plan for collecting, sampling, and documenting.

The Second Step: Collecting and Storing

Once you have a plan, you are ready to collect your data. This may entail downloading from the internet, creating a Word document or PDF of each article or picture, and storing these in a folder designated by the source and date (e.g., “ Men’s Health advertisements, 1950s”). Sølvberg ( 2021 ), for example, collected posted job advertisements for three kinds of elite jobs (economic, cultural, professional) in Sweden. But collecting might also mean going out and taking photographs yourself, as in the case of graffiti, street signs, or even what people are wearing. Chaise LaDousa, an anthropologist and linguist, took photos of “house signs,” which are signs, often creative and sometimes offensive, hung by college students living in communal off-campus houses. These signs were a focal point of college culture, sending messages about the values of the students living in them. Some of the names will give you an idea: “Boot ’n Rally,” “The Plantation,” “Crib of the Rib.” The students might find these signs funny and benign, but LaDousa ( 2011 ) argued convincingly that they also reproduced racial and gender inequalities. The data here already existed—they were big signs on houses—but the researcher had to collect the data by taking photographs.

In some cases, your content will be in physical form but not amenable to photographing, as in the case of films or unwieldy physical artifacts you find in the archives (e.g., undigitized meeting minutes or scrapbooks). In this case, you need to create some kind of detailed log (fieldnotes even) of the content that you can reference. In the case of films, this might mean watching the film and writing down details for key scenes that become your data. [2] For scrapbooks, it might mean taking notes on what you are seeing, quoting key passages, describing colors or presentation style. As you might imagine, this can take a lot of time. Be sure you budget this time into your research plan.

Researcher Note

A note on data scraping : Data scraping, sometimes known as screen scraping or frame grabbing, is a way of extracting data generated by another program, as when a scraping tool grabs information from a website. This may help you collect data that is on the internet, but you need to be ethical in how to employ the scraper. A student once helped me scrape thousands of stories from the Time magazine archives at once (although it took several hours for the scraping process to complete). These stories were freely available, so the scraping process simply sped up the laborious process of copying each article of interest and saving it to my research folder. Scraping tools can sometimes be used to circumvent paywalls. Be careful here!

The Third Step: Analysis

There is often an assumption among novice researchers that once you have collected your data, you are ready to write about what you have found. Actually, you haven’t yet found anything, and if you try to write up your results, you will probably be staring sadly at a blank page. Between the collection and the writing comes the difficult task of systematically and repeatedly reviewing the data in search of patterns and themes that will help you interpret the data, particularly its communicative aspect (e.g., What is it that is being communicated here, with these “house signs” or in the pages of Men’s Health ?).

The first time you go through the data, keep an open mind on what you are seeing (or hearing), and take notes about your observations that link up to your research question. In the beginning, it can be difficult to know what is relevant and what is extraneous. Sometimes, your research question changes based on what emerges from the data. Use the first round of review to consider this possibility, but then commit yourself to following a particular focus or path. If you are looking at how gender gets made or re-created, don’t follow the white rabbit down a hole about environmental injustice unless you decide that this really should be the focus of your study or that issues of environmental injustice are linked to gender presentation. In the second round of review, be very clear about emerging themes and patterns. Create codes (more on these in chapters 18 and 19) that will help you simplify what you are noticing. For example, “men as outdoorsy” might be a common trope you see in advertisements. Whenever you see this, mark the passage or picture. In your third (or fourth or fifth) round of review, begin to link up the tropes you’ve identified, looking for particular patterns and assumptions. You’ve drilled down to the details, and now you are building back up to figure out what they all mean. Start thinking about theory—either theories you have read about and are using as a frame of your study (e.g., gender as performance theory) or theories you are building yourself, as in the Grounded Theory tradition. Once you have a good idea of what is being communicated and how, go back to the data at least one more time to look for disconfirming evidence. Maybe you thought “men as outdoorsy” was of importance, but when you look hard, you note that women are presented as outdoorsy just as often. You just hadn’t paid attention. It is very important, as any kind of researcher but particularly as a qualitative researcher, to test yourself and your emerging interpretations in this way.

The Fourth and Final Step: The Write-Up

Only after you have fully completed analysis, with its many rounds of review and analysis, will you be able to write about what you found. The interpretation exists not in the data but in your analysis of the data. Before writing your results, you will want to very clearly describe how you chose the data here and all the possible limitations of this data (e.g., historical-trace problem or power problem; see chapter 16). Acknowledge any limitations of your sample. Describe the audience for the content, and discuss the implications of this. Once you have done all of this, you can put forth your interpretation of the communication of the content, linking to theory where doing so would help your readers understand your findings and what they mean more generally for our understanding of how the social world works. [3]

Analyzing Content: Helpful Hints and Pointers

Although every data set is unique and each researcher will have a different and unique research question to address with that data set, there are some common practices and conventions. When reviewing your data, what do you look at exactly? How will you know if you have seen a pattern? How do you note or mark your data?

Let’s start with the last question first. If your data is stored digitally, there are various ways you can highlight or mark up passages. You can, of course, do this with literal highlighters, pens, and pencils if you have print copies. But there are also qualitative software programs to help you store the data, retrieve the data, and mark the data. This can simplify the process, although it cannot do the work of analysis for you.

Qualitative software can be very expensive, so the first thing to do is to find out if your institution (or program) has a universal license its students can use. If they do not, most programs have special student licenses that are less expensive. The two most used programs at this moment are probably ATLAS.ti and NVivo. Both can cost more than $500 [4] but provide everything you could possibly need for storing data, content analysis, and coding. They also have a lot of customer support, and you can find many official and unofficial tutorials on how to use the programs’ features on the web. Dedoose, created by academic researchers at UCLA, is a decent program that lacks many of the bells and whistles of the two big programs. Instead of paying all at once, you pay monthly, as you use the program. The monthly fee is relatively affordable (less than $15), so this might be a good option for a small project. HyperRESEARCH is another basic program created by academic researchers, and it is free for small projects (those that have limited cases and material to import). You can pay a monthly fee if your project expands past the free limits. I have personally used all four of these programs, and they each have their pluses and minuses.

Regardless of which program you choose, you should know that none of them will actually do the hard work of analysis for you. They are incredibly useful for helping you store and organize your data, and they provide abundant tools for marking, comparing, and coding your data so you can make sense of it. But making sense of it will always be your job alone.

So let’s say you have some software, and you have uploaded all of your content into the program: video clips, photographs, transcripts of news stories, articles from magazines, even digital copies of college scrapbooks. Now what do you do? What are you looking for? How do you see a pattern? The answers to these questions will depend partially on the particular research question you have, or at least the motivation behind your research. Let’s go back to the idea of looking at gender presentations in magazines from the 1950s to the 1970s. Here are some things you can look at and code in the content: (1) actions and behaviors, (2) events or conditions, (3) activities, (4) strategies and tactics, (5) states or general conditions, (6) meanings or symbols, (7) relationships/interactions, (8) consequences, and (9) settings. Table 17.1 lists these with examples from our gender presentation study.

Table 17.1. Examples of What to Note During Content Analysis

One thing to note about the examples in table 17.1: sometimes we note (mark, record, code) a single example, while other times, as in “settings,” we are recording a recurrent pattern. To help you spot patterns, it is useful to mark every setting, including a notation on gender. Using software can help you do this efficiently. You can then call up “setting by gender” and note this emerging pattern. There’s an element of counting here, which we normally think of as quantitative data analysis, but we are using the count to identify a pattern that will be used to help us interpret the communication. Content analyses often include counting as part of the interpretive (qualitative) process.

In your own study, you may not need or want to look at all of the elements listed in table 17.1. Even in our imagined example, some are more useful than others. For example, “strategies and tactics” is a bit of a stretch here. In studies that are looking specifically at, say, policy implementation or social movements, this category will prove much more salient.

Another way to think about “what to look at” is to consider aspects of your content in terms of units of analysis. You can drill down to the specific words used (e.g., the adjectives commonly used to describe “men” and “women” in your magazine sample) or move up to the more abstract level of concepts used (e.g., the idea that men are more rational than women). Counting for the purpose of identifying patterns is particularly useful here. How many times is that idea of women’s irrationality communicated? How is it is communicated (in comic strips, fictional stories, editorials, etc.)? Does the incidence of the concept change over time? Perhaps the “irrational woman” was everywhere in the 1950s, but by the 1970s, it is no longer showing up in stories and comics. By tracing its usage and prevalence over time, you might come up with a theory or story about gender presentation during the period. Table 17.2 provides more examples of using different units of analysis for this work along with suggestions for effective use.

Table 17.2. Examples of Unit of Analysis in Content Analysis

Every qualitative content analysis is unique in its particular focus and particular data used, so there is no single correct way to approach analysis. You should have a better idea, however, of what kinds of things to look for and what to look for. The next two chapters will take you further into the coding process, the primary analytical tool for qualitative research in general.

How to do a content analysis

What is content analysis?

Why would you use a content analysis, types of content analysis, conceptual content analysis, relational content analysis, reliability and validity, reliability, the advantages and disadvantages of content analysis, a step-by-step guide to conducting a content analysis, step 1: develop your research questions, step 2: choose the content you’ll analyze, step 3: identify your biases, step 4: define the units and categories of coding, step 5: develop a coding scheme, step 6: code the content, step 7: analyze the results, frequently asked questions about content analysis, related articles.

In research, content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. Simply put, content analysis is a research method that aims to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data , depending on the specific use case.

As such, some of the objectives of content analysis include:

Simplifying complex, unstructured content.
Identifying trends, patterns, and relationships in the content.
Determining the characteristics of the content.
Identifying the intentions of individuals through the analysis of the content.
Identifying the implied aspects in the content.

Typically, when doing a content analysis, you’ll gather data not only from written text sources like newspapers, books, journals, and magazines but also from a variety of other oral and visual sources of content like:

Voice recordings, speeches, and interviews.
Web content, blogs, and social media content.
Films, videos, and photographs.

One of content analysis’s distinguishing features is that you'll be able to gather data for research without physically gathering data from participants. In other words, when doing a content analysis, you don't need to interact with people directly.

The process of doing a content analysis usually involves categorizing or coding concepts, words, and themes within the content and analyzing the results. We’ll look at the process in more detail below.

Typically, you’ll use content analysis when you want to:

Identify the intentions, communication trends, or communication patterns of an individual, a group of people, or even an institution.
Analyze and describe the behavioral and attitudinal responses of individuals to communications.
Determine the emotional or psychological state of an individual or a group of people.
Analyze the international differences in communication content.
Analyzing audience responses to content.

Keep in mind, though, that these are just some examples of use cases where a content analysis might be appropriate and there are many others.

The key thing to remember is that content analysis will help you quantify the occurrence of specific words, phrases, themes, and concepts in content. Moreover, it can also be used when you want to make qualitative inferences out of the data by analyzing the semantic meanings and interrelationships between words, themes, and concepts.

In general, there are two types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions. With that in mind, let’s now look at these two types of content analysis in more detail.

With conceptual analysis, you’ll determine the existence of certain concepts within the content and identify their frequency. In other words, conceptual analysis involves the number of times a specific concept appears in the content.

Conceptual analysis is typically focused on explicit data, which means you’ll focus your analysis on a specific concept to identify its presence in the content and determine its frequency.

However, when conducting a content analysis, you can also use implicit data. This approach is more involved, complicated, and requires the use of a dictionary, contextual translation rules, or a combination of both.

No matter what type you use, conceptual analysis brings an element of quantitive analysis into a qualitative approach to research.

Relational content analysis takes conceptual analysis a step further. So, while the process starts in the same way by identifying concepts in content, it doesn’t focus on finding the frequency of these concepts, but rather on the relationships between the concepts, the context in which they appear in the content, and their interrelationships.

Before starting with a relational analysis, you’ll first need to decide on which subcategory of relational analysis you’ll use:

Affect extraction: With this relational content analysis approach, you’ll evaluate concepts based on their emotional attributes. You’ll typically assess these emotions on a rating scale with higher values assigned to positive emotions and lower values to negative ones. In turn, this allows you to capture the emotions of the writer or speaker at the time the content is created. The main difficulty with this approach is that emotions can differ over time and across populations.
Proximity analysis: With this approach, you’ll identify concepts as in conceptual analysis, but you’ll evaluate the way in which they occur together in the content. In other words, proximity analysis allows you to analyze the relationship between concepts and derive a concept matrix from which you’ll be able to develop meaning. Proximity analysis is typically used when you want to extract facts from the content rather than contextual, emotional, or cultural factors.
Cognitive mapping: Finally, cognitive mapping can be used with affect extraction or proximity analysis. It’s a visualization technique that allows you to create a model that represents the overall meaning of content and presents it as a graphic map of the relationships between concepts. As such, it’s also commonly used when analyzing the changes in meanings, definitions, and terms over time.

Now that we’ve seen what content analysis is and looked at the different types of content analysis, it’s important to understand how reliable it is as a research method . We’ll also look at what criteria impact the validity of a content analysis.

There are three criteria that determine the reliability of a content analysis:

Stability . Stability refers to the tendency of coders to consistently categorize or code the same data in the same way over time.
Reproducibility . This criterion refers to the tendency of coders to classify categories membership in the same way.
Accuracy . Accuracy refers to the extent to which the classification of content corresponds to a specific standard.

Keep in mind, though, that because you’ll need to code or categorize the concepts you’ll aim to identify and analyze manually, you’ll never be able to eliminate human error. However, you’ll be able to minimize it.

In turn, three criteria determine the validity of a content analysis:

Closeness of categories . This is achieved by using multiple classifiers to get an agreed-upon definition for a specific category by using either implicit variables or synonyms. In this way, the category can be broadened to include more relevant data.
Conclusions . Here, it’s crucial to decide what level of implication will be allowable. In other words, it’s important to consider whether the conclusions are valid based on the data or whether they can be explained using some other phenomena.
Generalizability of the results of the analysis to a theory . Generalizability comes down to how you determine your categories as mentioned above and how reliable those categories are. In turn, this relies on how accurately the categories are at measuring the concepts or ideas that you’re looking to measure.

Considering everything mentioned above, there are definite advantages and disadvantages when it comes to content analysis:

Let’s now look at the steps you’ll need to follow when doing a content analysis.

The first step will always be to formulate your research questions. This is simply because, without clear and defined research questions, you won’t know what question to answer and, by implication, won’t be able to code your concepts.

Based on your research questions, you’ll then need to decide what content you’ll analyze. Here, you’ll use three factors to find the right content:

The type of content . Here you’ll need to consider the various types of content you’ll use and their medium like, for example, blog posts, social media, newspapers, or online articles.
What criteria you’ll use for inclusion . Here you’ll decide what criteria you’ll use to include content. This can, for instance, be the mentioning of a certain event or advertising a specific product.
Your parameters . Here, you’ll decide what content you’ll include based on specified parameters in terms of date and location.

The next step is to consider your own pre-conception of the questions and identify your biases. This process is referred to as bracketing and allows you to be aware of your biases before you start your research with the result that they’ll be less likely to influence the analysis.

Your next step would be to define the units of meaning that you’ll code. This will, for example, be the number of times a concept appears in the content or the treatment of concept, words, or themes in the content. You’ll then need to define the set of categories you’ll use for coding which can be either objective or more conceptual.

Based on the above, you’ll then organize the units of meaning into your defined categories. Apart from this, your coding scheme will also determine how you’ll analyze the data.

The next step is to code the content. During this process, you’ll work through the content and record the data according to your coding scheme. It’s also here where conceptual and relational analysis starts to deviate in relation to the process you’ll need to follow.

As mentioned earlier, conceptual analysis aims to identify the number of times a specific concept, idea, word, or phrase appears in the content. So, here, you’ll need to decide what level of analysis you’ll implement.

In contrast, with relational analysis, you’ll need to decide what type of relational analysis you’ll use. So, you’ll need to determine whether you’ll use affect extraction, proximity analysis, cognitive mapping, or a combination of these approaches.

Once you’ve coded the data, you’ll be able to analyze it and draw conclusions from the data based on your research questions.

Content analysis offers an inexpensive and flexible way to identify trends and patterns in communication content. In addition, it’s unobtrusive which eliminates many ethical concerns and inaccuracies in research data. However, to be most effective, a content analysis must be planned and used carefully in order to ensure reliability and validity.

The two general types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions.

In qualitative research coding means categorizing concepts, words, and themes within your content to create a basis for analyzing the results. While coding, you work through the content and record the data according to your coding scheme.

Content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. The goal of a content analysis is to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data, depending on the specific use case.

Content analysis is a qualitative method of data analysis and can be used in many different fields. It is particularly popular in the social sciences.

It is possible to do qualitative analysis without coding, but content analysis as a method of qualitative analysis requires coding or categorizing data to then analyze it according to your coding scheme in the next step.

What Is Qualitative Content Analysis?

Qca explained simply (with examples).

By: Jenna Crosley (PhD). Reviewed by: Dr Eunice Rautenbach (DTech) | February 2021

If you’re in the process of preparing for your dissertation, thesis or research project, you’ve probably encountered the term “ qualitative content analysis ” – it’s quite a mouthful. If you’ve landed on this post, you’re probably a bit confused about it. Well, the good news is that you’ve come to the right place…

Overview: Qualitative Content Analysis

What (exactly) is qualitative content analysis
The two main types of content analysis
When to use content analysis
How to conduct content analysis (the process)
The advantages and disadvantages of content analysis

1. What is content analysis?

Content analysis is a qualitative analysis method that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants – this is called unobtrusive research.

In other words, with content analysis, you don’t necessarily need to interact with participants (although you can if necessary); you can simply analyse the data that they have already produced. With this type of analysis, you can analyse data such as text messages, books, Facebook posts, videos, and audio (just to mention a few).

The basics – explicit and implicit content

When working with content analysis, explicit and implicit content will play a role. Explicit data is transparent and easy to identify, while implicit data is that which requires some form of interpretation and is often of a subjective nature. Sounds a bit fluffy? Here’s an example:

Joe: Hi there, what can I help you with?

Lauren: I recently adopted a puppy and I’m worried that I’m not feeding him the right food. Could you please advise me on what I should be feeding?

Joe: Sure, just follow me and I’ll show you. Do you have any other pets?

Lauren: Only one, and it tweets a lot!

In this exchange, the explicit data indicates that Joe is helping Lauren to find the right puppy food. Lauren asks Joe whether she has any pets aside from her puppy. This data is explicit because it requires no interpretation.

On the other hand, implicit data , in this case, includes the fact that the speakers are in a pet store. This information is not clearly stated but can be inferred from the conversation, where Joe is helping Lauren to choose pet food. An additional piece of implicit data is that Lauren likely has some type of bird as a pet. This can be inferred from the way that Lauren states that her pet “tweets”.

As you can see, explicit and implicit data both play a role in human interaction and are an important part of your analysis. However, it’s important to differentiate between these two types of data when you’re undertaking content analysis. Interpreting implicit data can be rather subjective as conclusions are based on the researcher’s interpretation. This can introduce an element of bias , which risks skewing your results.

Explicit and implicit data both play an important role in your content analysis, but it’s important to differentiate between them.

2. The two types of content analysis

Now that you understand the difference between implicit and explicit data, let’s move on to the two general types of content analysis : conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.

Conceptual analysis focuses on the number of times a concept occurs in a set of data and is generally focused on explicit data. For example, if you were to have the following conversation:

Marie: She told me that she has three cats.

Jean: What are her cats’ names?

Marie: I think the first one is Bella, the second one is Mia, and… I can’t remember the third cat’s name.

In this data, you can see that the word “cat” has been used three times. Through conceptual content analysis, you can deduce that cats are the central topic of the conversation. You can also perform a frequency analysis , where you assess the term’s frequency in the data. For example, in the exchange above, the word “cat” makes up 9% of the data. In other words, conceptual analysis brings a little bit of quantitative analysis into your qualitative analysis.

As you can see, the above data is without interpretation and focuses on explicit data . Relational content analysis, on the other hand, takes a more holistic view by focusing more on implicit data in terms of context, surrounding words and relationships.

There are three types of relational analysis:

Affect extraction
Proximity analysis
Cognitive mapping

Affect extraction is when you assess concepts according to emotional attributes. These emotions are typically mapped on scales, such as a Likert scale or a rating scale ranging from 1 to 5, where 1 is “very sad” and 5 is “very happy”.

If participants are talking about their achievements, they are likely to be given a score of 4 or 5, depending on how good they feel about it. If a participant is describing a traumatic event, they are likely to have a much lower score, either 1 or 2.

Proximity analysis identifies explicit terms (such as those found in a conceptual analysis) and the patterns in terms of how they co-occur in a text. In other words, proximity analysis investigates the relationship between terms and aims to group these to extract themes and develop meaning.

Proximity analysis is typically utilised when you’re looking for hard facts rather than emotional, cultural, or contextual factors. For example, if you were to analyse a political speech, you may want to focus only on what has been said, rather than implications or hidden meanings. To do this, you would make use of explicit data, discounting any underlying meanings and implications of the speech.

Lastly, there’s cognitive mapping, which can be used in addition to, or along with, proximity analysis. Cognitive mapping involves taking different texts and comparing them in a visual format – i.e. a cognitive map. Typically, you’d use cognitive mapping in studies that assess changes in terms, definitions, and meanings over time. It can also serve as a way to visualise affect extraction or proximity analysis and is often presented in a form such as a graphic map.

To recap on the essentials, content analysis is a qualitative analysis method that focuses on recorded human artefacts . It involves both conceptual analysis (which is more numbers-based) and relational analysis (which focuses on the relationships between concepts and how they’re connected).

Need a helping hand?

3. When should you use content analysis?

Content analysis is a useful tool that provides insight into trends of communication . For example, you could use a discussion forum as the basis of your analysis and look at the types of things the members talk about as well as how they use language to express themselves. Content analysis is flexible in that it can be applied to the individual, group, and institutional level.

Content analysis is typically used in studies where the aim is to better understand factors such as behaviours, attitudes, values, emotions, and opinions . For example, you could use content analysis to investigate an issue in society, such as miscommunication between cultures. In this example, you could compare patterns of communication in participants from different cultures, which will allow you to create strategies for avoiding misunderstandings in intercultural interactions.

Another example could include conducting content analysis on a publication such as a book. Here you could gather data on the themes, topics, language use and opinions reflected in the text to draw conclusions regarding the political (such as conservative or liberal) leanings of the publication.

Content analysis is typically used in projects where the research aims involve getting a better understanding of factors such as behaviours, attitudes, values, emotions, and opinions.

4. How to conduct a qualitative content analysis

Conceptual and relational content analysis differ in terms of their exact process ; however, there are some similarities. Let’s have a look at these first – i.e., the generic process:

Recap on your research questions
Undertake bracketing to identify biases
Operationalise your variables and develop a coding scheme
Code the data and undertake your analysis

Step 1 – Recap on your research questions

It’s always useful to begin a project with research questions , or at least with an idea of what you are looking for. In fact, if you’ve spent time reading this blog, you’ll know that it’s useful to recap on your research questions, aims and objectives when undertaking pretty much any research activity. In the context of content analysis, it’s difficult to know what needs to be coded and what doesn’t, without a clear view of the research questions.

For example, if you were to code a conversation focused on basic issues of social justice, you may be met with a wide range of topics that may be irrelevant to your research. However, if you approach this data set with the specific intent of investigating opinions on gender issues, you will be able to focus on this topic alone, which would allow you to code only what you need to investigate.

With content analysis, it’s difficult to know what needs to be coded without a clear view of the research questions.

Step 2 – Reflect on your personal perspectives and biases

It’s vital that you reflect on your own pre-conception of the topic at hand and identify the biases that you might drag into your content analysis – this is called “ bracketing “. By identifying this upfront, you’ll be more aware of them and less likely to have them subconsciously influence your analysis.

For example, if you were to investigate how a community converses about unequal access to healthcare, it is important to assess your views to ensure that you don’t project these onto your understanding of the opinions put forth by the community. If you have access to medical aid, for instance, you should not allow this to interfere with your examination of unequal access.

You must reflect on the preconceptions and biases that you might drag into your content analysis - this is called "bracketing".

Step 3 – Operationalise your variables and develop a coding scheme

Next, you need to operationalise your variables . But what does that mean? Simply put, it means that you have to define each variable or construct . Give every item a clear definition – what does it mean (include) and what does it not mean (exclude). For example, if you were to investigate children’s views on healthy foods, you would first need to define what age group/range you’re looking at, and then also define what you mean by “healthy foods”.

In combination with the above, it is important to create a coding scheme , which will consist of information about your variables (how you defined each variable), as well as a process for analysing the data. For this, you would refer back to how you operationalised/defined your variables so that you know how to code your data.

For example, when coding, when should you code a food as “healthy”? What makes a food choice healthy? Is it the absence of sugar or saturated fat? Is it the presence of fibre and protein? It’s very important to have clearly defined variables to achieve consistent coding – without this, your analysis will get very muddy, very quickly.

When operationalising your variables, you must give every item a clear definition. In other words, what does it mean (include) and what does it not mean (exclude).

Step 4 – Code and analyse the data

The next step is to code the data. At this stage, there are some differences between conceptual and relational analysis.

As described earlier in this post, conceptual analysis looks at the existence and frequency of concepts, whereas a relational analysis looks at the relationships between concepts. For both types of analyses, it is important to pre-select a concept that you wish to assess in your data. Using the example of studying children’s views on healthy food, you could pre-select the concept of “healthy food” and assess the number of times the concept pops up in your data.

Here is where conceptual and relational analysis start to differ.

At this stage of conceptual analysis , it is necessary to decide on the level of analysis you’ll perform on your data, and whether this will exist on the word, phrase, sentence, or thematic level. For example, will you code the phrase “healthy food” on its own? Will you code each term relating to healthy food (e.g., broccoli, peaches, bananas, etc.) with the code “healthy food” or will these be coded individually? It is very important to establish this from the get-go to avoid inconsistencies that could result in you having to code your data all over again.

On the other hand, relational analysis looks at the type of analysis. So, will you use affect extraction? Proximity analysis? Cognitive mapping? A mix? It’s vital to determine the type of analysis before you begin to code your data so that you can maintain the reliability and validity of your research .

How to conduct conceptual analysis

First, let’s have a look at the process for conceptual analysis.

Once you’ve decided on your level of analysis, you need to establish how you will code your concepts, and how many of these you want to code. Here you can choose whether you want to code in a deductive or inductive manner. Just to recap, deductive coding is when you begin the coding process with a set of pre-determined codes, whereas inductive coding entails the codes emerging as you progress with the coding process. Here it is also important to decide what should be included and excluded from your analysis, and also what levels of implication you wish to include in your codes.

For example, if you have the concept of “tall”, can you include “up in the clouds”, derived from the sentence, “the giraffe’s head is up in the clouds” in the code, or should it be a separate code? In addition to this, you need to know what levels of words may be included in your codes or not. For example, if you say, “the panda is cute” and “look at the panda’s cuteness”, can “cute” and “cuteness” be included under the same code?

Once you’ve considered the above, it’s time to code the text . We’ve already published a detailed post about coding , so we won’t go into that process here. Once you’re done coding, you can move on to analysing your results. This is where you will aim to find generalisations in your data, and thus draw your conclusions .

How to conduct relational analysis

Now let’s return to relational analysis.

As mentioned, you want to look at the relationships between concepts . To do this, you’ll need to create categories by reducing your data (in other words, grouping similar concepts together) and then also code for words and/or patterns. These are both done with the aim of discovering whether these words exist, and if they do, what they mean.

Your next step is to assess your data and to code the relationships between your terms and meanings, so that you can move on to your final step, which is to sum up and analyse the data.

To recap, it’s important to start your analysis process by reviewing your research questions and identifying your biases . From there, you need to operationalise your variables, code your data and then analyse it.

5. What are the pros & cons of content analysis?

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

For example, with conceptual analysis, you can count the number of times that a term or a code appears in a dataset, which can be assessed from a quantitative standpoint. In addition to this, you can then use a qualitative approach to investigate the underlying meanings of these and relationships between them.

Content analysis is also unobtrusive and therefore poses fewer ethical issues than some other analysis methods. As the content you’ll analyse oftentimes already exists, you’ll analyse what has been produced previously, and so you won’t have to collect data directly from participants. When coded correctly, data is analysed in a very systematic and transparent manner, which means that issues of replicability (how possible it is to recreate research under the same conditions) are reduced greatly.

On the downside , qualitative research (in general, not just content analysis) is often critiqued for being too subjective and for not being scientifically rigorous enough. This is where reliability (how replicable a study is by other researchers) and validity (how suitable the research design is for the topic being investigated) come into play – if you take these into account, you’ll be on your way to achieving sound research results.

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

Recap: Qualitative content analysis

In this post, we’ve covered a lot of ground – click on any of the sections to recap:

If you have any questions about qualitative content analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your qualitative content analysis, be sure to book an initial consultation with one of our friendly Research Coaches.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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14 Comments

If I am having three pre-decided attributes for my research based on which a set of semi-structured questions where asked then should I conduct a conceptual content analysis or relational content analysis. please note that all three attributes are different like Agility, Resilience and AI.

Thank you very much. I really enjoyed every word.

please send me one/ two sample of content analysis

send me to any sample of qualitative content analysis as soon as possible

Many thanks for the brilliant explanation. Do you have a sample practical study of a foreign policy using content analysis?

1) It will be very much useful if a small but complete content analysis can be sent, from research question to coding and analysis. 2) Is there any software by which qualitative content analysis can be done?

Common software for qualitative analysis is nVivo, and quantitative analysis is IBM SPSS

Thank you. Can I have at least 2 copies of a sample analysis study as my reference?

Could you please send me some sample of textbook content analysis?

Can I send you my research topic, aims, objectives and questions to give me feedback on them?

please could you send me samples of content analysis?

Yes please send

really we enjoyed your knowledge thanks allot. from Ethiopia

can you please share some samples of content analysis(relational)? I am a bit confused about processing the analysis part

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1 Introduction

Published: November 2015
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This chapter offers an inclusive definition of content analysis. This helps in clarifying some key terms and concepts. Three approaches to content analysis are introduced and defined briefly: basic content analysis, interpretive content analysis, and qualitative content analysis. Long-standing differences between quantitative and qualitative approaches to content analysis that are still evident in contemporary published research are also touched on here. In addition, the chapter examines the origins, evolution, and conceptual foundations of content analysis, as well as the development of content analysis in the social work profession. Finally, the chapter offers illustrative examples of different approaches to content analysis to ground the discussion in examples of published research.

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What is content analysis?

Last updated

20 March 2023

Reviewed by

Miroslav Damyanov

When you're conducting qualitative research, you'll find yourself analyzing various texts. Perhaps you'll be evaluating transcripts from audio interviews you've conducted. Or you may find yourself assessing the results of a survey filled with open-ended questions.

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Content analysis is a research method used to identify the presence of various concepts, words, and themes in different texts. Two types of content analysis exist: conceptual analysis and relational analysis . In the former, researchers determine whether and how frequently certain concepts appear in a text. In relational analysis, researchers explore how different concepts are related to one another in a text.

Both types of content analysis require the researcher to code the text. Coding the text means breaking it down into different categories that allow it to be analyzed more easily.

What are some common uses of content analysis?

You can use content analysis to analyze many forms of text, including:

Interview and discussion transcripts

Newspaper articles and headline

Literary works

Historical documents

Government reports

Academic papers

Music lyrics

Researchers commonly use content analysis to draw insights and conclusions from literary works. Historians and biographers may apply this approach to letters, papers, and other historical documents to gain insight into the historical figures and periods they are writing about. Market researchers can also use it to evaluate brand performance and perception.

Some researchers have used content analysis to explore differences in decision-making and other cognitive processes. While researchers traditionally used this approach to explore human cognition, content analysis is also at the heart of machine learning approaches currently being used and developed by software and AI companies.

Conducting a conceptual analysis

Conceptual analysis is more commonly associated with content analysis than relational analysis.

In conceptual analysis, you're looking for the appearance and frequency of different concepts. Why? This information can help further your qualitative or quantitative analysis of a text. It's an inexpensive and easily understood research method that can help you draw inferences and conclusions about your research subject. And while it is a relatively straightforward analytical tool, it does consist of a multi-step process that you must closely follow to ensure the reliability and validity of your study.

When you're ready to conduct a conceptual analysis, refer to your research question and the text. Ask yourself what information likely found in the text is relevant to your question. You'll need to know this to determine how you'll code the text. Then follow these steps:

1. Determine whether you're looking for explicit terms or implicit terms.

Explicit terms are those that directly appear in the text, while implicit ones are those that the text implies or alludes to or that you can infer.

Coding for explicit terms is straightforward. For example, if you're looking to code a text for an author's explicit use of color, you'd simply code for every instance a color appears in the text. However, if you're coding for implicit terms, you'll need to determine and define how you're identifying the presence of the term first. Doing so involves a certain amount of subjectivity and may impinge upon the reliability and validity of your study .

2. Next, identify the level at which you'll conduct your analysis.

You can search for words, phrases, or sentences encapsulating your terms. You can also search for concepts and themes, but you'll need to define how you expect to identify them in the text. You must also define rules for how you'll code different terms to reduce ambiguity. For example, if, in an interview transcript, a person repeats a word one or more times in a row as a verbal tic, should you code it more than once? And what will you do with irrelevant data that appears in a term if you're coding for sentences?

Defining these rules upfront can help make your content analysis more efficient and your final analysis more reliable and valid.

3. You'll need to determine whether you're coding for a concept or theme's existence or frequency.

If you're coding for its existence, you’ll only count it once, at its first appearance, no matter how many times it subsequently appears. If you're searching for frequency, you'll count the number of its appearances in the text.

4. You'll also want to determine the number of terms you want to code for and how you may wish to categorize them.

For example, say you're conducting a content analysis of customer service call transcripts and looking for evidence of customer dissatisfaction with a product or service. You might create categories that refer to different elements with which customers might be dissatisfied, such as price, features, packaging, technical support, and so on. Then you might look for sentences that refer to those product elements according to each category in a negative light.

5. Next, you'll need to develop translation rules for your codes.

Those rules should be clear and consistent, allowing you to keep track of your data in an organized fashion.

6. After you've determined the terms for which you're searching, your categories, and translation rules, you're ready to code.

You can do so by hand or via software. Software is quite helpful when you have multiple texts. But it also becomes more vital for you to have developed clear codes, categories, and translation rules, especially if you're looking for implicit terms and concepts. Otherwise, your software-driven analysis may miss key instances of the terms you seek.

7. When you have your text coded, it's time to analyze it.

Look for trends and patterns in your results and use them to draw relevant conclusions about your research subject.

Conducting a relational analysis

In a relational analysis, you're examining the relationship between different terms that appear in your text(s). To do so requires you to code your texts in a similar fashion as in a relational analysis. However, depending on the type of relational analysis you're trying to conduct, you may need to follow slightly different rules.

Three types of relational analyses are commonly used: affect extraction , proximity analysis , and cognitive mapping .

Affect extraction

This type of relational analysis involves evaluating the different emotional concepts found in a specific text. While the insights from affect extraction can be invaluable, conducting it may prove difficult depending on the text. For example, if the text captures people's emotional states at different times and from different populations, you may find it difficult to compare them and draw appropriate inferences.

Proximity analysis

A relatively simpler analytical approach than affect extraction, proximity analysis assesses the co-occurrence of explicit concepts in a text. You can create what's known as a concept matrix, which is a group of interrelated co-occurring concepts. Concept matrices help evaluate and determine the overall meaning of a text or the identification of a secondary message or theme.

Cognitive mapping

You can use cognitive mapping as a way to visualize the results of either affect extraction or proximity analysis. This technique uses affect extraction or proximity analysis results to create a graphic map illustrating the relationship between co-occurring emotions or concepts.

To conduct a relational analysis, you must start by determining the type of analysis that best fits the study: affect extraction or proximity analysis.

Complete steps one through six as outlined above. When it comes to the seventh step, analyze the text according to the relational analysis type they've chosen. During this step, feel free to use cognitive mapping to help draw inferences and conclusions about the relationships between co-occurring emotions or concepts. And use other tools, such as mental modeling and decision mapping as necessary, to analyze the results.

The advantages of content analysis

Content analysis provides researchers with a robust and inexpensive method to qualitatively and quantitatively analyze a text. By coding the data, you can perform statistical analyses of the data to affirm and reinforce conclusions you may draw. And content analysis can provide helpful insights into language use, behavioral patterns, and historical or cultural conventions that can be valuable beyond the scope of the initial study.

When content analyses are applied to interview data, the approach provides a way to closely analyze data without needing interview-subject interaction, which can be helpful in certain contexts. For example, suppose you want to analyze the perceptions of a group of geographically diverse individuals. In this case, you can conduct a content analysis of existing interview transcripts rather than assuming the time and expense of conducting new interviews.

What is meant by content analysis?

Content analysis is a research method that helps a researcher explore the occurrence of and relationships between various words, phrases, themes, or concepts in a text or set of texts. The method allows researchers in different disciplines to conduct qualitative and quantitative analyses on a variety of texts.

Where is content analysis used?

Content analysis is used in multiple disciplines, as you can use it to evaluate a variety of texts. You can find applications in anthropology, communications, history, linguistics, literary studies, marketing, political science, psychology, and sociology, among other disciplines.

What are the two types of content analysis?

Content analysis may be either conceptual or relational. In a conceptual analysis, researchers examine a text for the presence and frequency of specific words, phrases, themes, and concepts. In a relational analysis, researchers draw inferences and conclusions about the nature of the relationships of co-occurring words, phrases, themes, and concepts in a text.

What's the difference between content analysis and thematic analysis?

Content analysis typically uses a descriptive approach to the data and may use either qualitative or quantitative analytical methods. By contrast, a thematic analysis only uses qualitative methods to explore frequently occurring themes in a text.

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Latest articles

Demystifying Content Analysis

A. j. kleinheksel.

a The Medical College of Georgia at Augusta University, Augusta, Georgia

Nicole Rockich-Winston

Huda tawfik.

b Central Michigan University, College of Medicine, Mt. Pleasant, Michigan

Tasha R. Wyatt

Objective. In the course of daily teaching responsibilities, pharmacy educators collect rich data that can provide valuable insight into student learning. This article describes the qualitative data analysis method of content analysis, which can be useful to pharmacy educators because of its application in the investigation of a wide variety of data sources, including textual, visual, and audio files.

Findings. Both manifest and latent content analysis approaches are described, with several examples used to illustrate the processes. This article also offers insights into the variety of relevant terms and visualizations found in the content analysis literature. Finally, common threats to the reliability and validity of content analysis are discussed, along with suitable strategies to mitigate these risks during analysis.

Summary. This review of content analysis as a qualitative data analysis method will provide clarity and actionable instruction for both novice and experienced pharmacy education researchers.

INTRODUCTION

The Academy’s growing interest in qualitative research indicates an important shift in the field’s scientific paradigm. Whereas health science researchers have historically looked to quantitative methods to answer their questions, this shift signals that a purely positivist, objective approach is no longer sufficient to answer pharmacy education’s research questions. Educators who want to study their teaching and students’ learning will find content analysis an easily accessible, robust method of qualitative data analysis that can yield rigorous results for both publication and the improvement of their educational practice. Content analysis is a method designed to identify and interpret meaning in recorded forms of communication by isolating small pieces of the data that represent salient concepts and then applying or creating a framework to organize the pieces in a way that can be used to describe or explain a phenomenon. 1 Content analysis is particularly useful in situations where there is a large amount of unanalyzed textual data, such as those many pharmacy educators have already collected as part of their teaching practice. Because of its accessibility, content analysis is also an appropriate qualitative method for pharmacy educators with limited experience in educational research. This article will introduce and illustrate the process of content analysis as a way to analyze existing data, but also as an approach that may lead pharmacy educators to ask new types of research questions.

Content analysis is a well-established data analysis method that has evolved in its treatment of textual data. Content analysis was originally introduced as a strictly quantitative method, recording counts to measure the observed frequency of pre-identified targets in consumer research. 1 However, as the naturalistic qualitative paradigm became more prevalent in social sciences research and researchers became increasingly interested in the way people behave in natural settings, the process of content analysis was adapted into a more interesting and meaningful approach. Content analysis has the potential to be a useful method in pharmacy education because it can help educational researchers develop a deeper understanding of a particular phenomenon by providing structure in a large amount of textual data through a systematic process of interpretation. It also offers potential value because it can help identify problematic areas in student understanding and guide the process of targeted teaching. Several research studies in pharmacy education have used the method of content analysis. 2-7 Two studies in particular offer noteworthy examples: Wallman and colleagues employed manifest content analysis to analyze semi-structured interviews in order to explore what students learn during experiential rotations, 7 while Moser and colleagues adopted latent content analysis to evaluate open-ended survey responses on student perceptions of learning communities. 6 To elaborate on these approaches further, we will describe the two types of qualitative content analysis, manifest and latent, and demonstrate the corresponding analytical processes using examples that illustrate their benefit.

Qualitative Content Analysis

Content analysis rests on the assumption that texts are a rich data source with great potential to reveal valuable information about particular phenomena. 8 It is the process of considering both the participant and context when sorting text into groups of related categories to identify similarities and differences, patterns, and associations, both on the surface and implied within. 9-11 The method is considered high-yield in educational research because it is versatile and can be applied in both qualitative and quantitative studies. 12 While it is important to note that content analysis has application in visual and auditory artifacts (eg, an image or song), for our purposes we will largely focus on the most common application, which is the analysis of textual or transcribed content (eg, open-ended survey responses, print media, interviews, recorded observations, etc). The terminology of content analysis can vary throughout quantitative and qualitative literature, which may lead to some confusion among both novice and experienced researchers. However, there are also several agreed-upon terms and phrases that span the literature, as found in Table 1 .

Terms and Definitions Used in Qualitative Content Analysis

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There is more often disagreement on terminology in the methodological approaches to content analysis, though the most common differentiation is between the two types of content: manifest and latent. In much of the literature, manifest content analysis is defined as describing what is occurring on the surface, what is and literally present, and as “staying close to the text.” 8,13 Manifest content analysis is concerned with data that are easily observable both to researchers and the coders who assist in their analyses, without the need to discern intent or identify deeper meaning. It is content that can be recognized and counted with little training. Early applications of manifest analysis focused on identifying easily observable targets within text (eg, the number of instances a certain word appears in newspaper articles), film (eg, the occupation of a character), or interpersonal interactions (eg, tracking the number of times a participant blinks during an interview). 14 This application, in which frequency counts are used to understand a phenomenon, reflects a surface-level analysis and assumes there is objective truth in the data that can be revealed with very little interpretation. The number of times a target (ie, code) appears within the text is used as a way to understand its prevalence. Quantitative content analysis is always describing a positivist manifest content analysis, in that the nature of truth is believed to be objective, observable, and measurable. Qualitative research, which favors the researcher’s interpretation of an individual’s experience, may also be used to analyze manifest content. However, the intent of the application is to describe a dynamic reality that cannot be separated from the lived experiences of the researcher. Although qualitative content analysis can be conducted whether knowledge is thought to be innate, acquired, or socially constructed, the purpose of qualitative manifest content analysis is to transcend simple word counts and delve into a deeper examination of the language in order to organize large amounts of text into categories that reflect a shared meaning. 15,16 The practical distinction between quantitative and qualitative manifest content analysis is the intention behind the analysis. The quantitative method seeks to generate a numerical value to either cite prevalence or use in statistical analyses, while the qualitative method seeks to identify a construct or concept within the text using specific words or phrases for substantiation, or to provide a more organized structure to the text being described.

Latent content analysis is most often defined as interpreting what is hidden deep within the text. In this method, the role of the researcher is to discover the implied meaning in participants’ experiences. 8,13 For example, in a transcribed exchange in an office setting, a participant might say to a coworker, “Yeah, here we are…another Monday. So exciting!” The researcher would apply context in order to discover the emotion being conveyed (ie, the implied meaning). In this example, the comment could be interpreted as genuine, it could be interpreted as a sarcastic comment made in an attempt at humor in order to develop or sustain social bonds with the coworker, or the context might imply that the sarcasm was meant to convey displeasure and end the interaction.

Latent content analysis acknowledges that the researcher is intimately involved in the analytical process and that the their role is to actively use mental schema, theories, and lenses to interpret and understand the data. 10 Whereas manifest analyses are typically conducted in a way that the researcher is thought to maintain distance and separation from the objects of study, latent analyses underscore the importance of the researcher co-creating meaning with the text. 17 Adding nuance to this type of content, Potter and Levine‐Donnerstein argue that within latent content analysis, there are two distinct types: latent pattern and latent projective . 14 Latent pattern content analysis seeks to establish a pattern of characteristics in the text itself, while latent projective content analysis leverages the researcher’s own interpretations of the meaning of the text. While both approaches rely on codes that emerge from the content using the coder’s own perspectives and mental schema, the distinction between these two types of analyses are in their foci. 14 Though we do not agree, some researchers believe that all qualitative content analysis is latent content analysis. 11 These disagreements typically occur where there are differences in intent and where there are areas of overlap in the results. For example, both qualitative manifest and latent pattern content analyses may identify patterns as a result of their application. Though in their research design, the researcher would have approached the content with different methodological approaches, with a manifest approach seeking only to describe what is observed, and the latent pattern approach seeking to discover an unseen pattern. At this point, these distinctions may seem too philosophical to serve a practical purpose, so we will attempt to clarify these concepts by presenting three types of analyses for illustrative purposes, beginning with a description of how codes are created and used.

Creating and Using Codes

Codes are the currency of content analysis. Researchers use codes to organize and understand their data. Through the coding process, pharmacy educators can systematically and rigorously categorize and interpret vast amounts of text for use in their educational practice or in publication. Codes themselves are short, descriptive labels that symbolically assign a summative or salient attribute to more than one unit of meaning identified in the text. 18 To create codes, a researcher must first become immersed in the data, which typically occurs when a researcher transcribes recorded data or conducts several readings of the text. This process allows the researcher to become familiar with the scope of the data, which spurs nascent ideas about potential concepts or constructs that may exist within it. If studying a phenomenon that has already been described through an existing framework, codes can be created a priori using theoretical frameworks or concepts identified in the literature. If there is no existing framework to apply, codes can emerge during the analytical process. However, emergent codes can also be created as addenda to a priori codes that were identified before the analysis begins if the a priori codes do not sufficiently capture the researcher’s area of interest.

The process of detecting emergent codes begins with identification of units of meaning. While there is no one way to decide what qualifies as a meaning unit, researchers typically define units of meaning differently depending on what kind of analysis is being conducted. As a general rule, when dialogue is being analyzed, such as interviews or focus groups, meaning units are identified as conversational turns, though a code can be as short as one or two words. In written text, such as student reflections or course evaluation data, the researcher must decide if the text should be divided into phrases or sentences, or remain as paragraphs. This decision is usually made based on how many different units of meaning are expressed in a block of text. For example, in a paragraph, if there are several thoughts or concepts being expressed, it is best to break up the paragraph into sentences. If one sentence contains multiple ideas of interest, making it difficult to separate one important thought or behavior from another, then the sentence can be divided into smaller units, such as phrases or sentence fragments. These phrases or sentence fragments are then coded as separate meaning units. Conversely, longer or more complex units of meaning should be condensed into shorter representations that still retain the original meaning in order to reduce the cognitive burden of the analytical process. This could entail removing verbal ticks (eg, “well, uhm…”) from transcribed data or simplifying a compound sentence. Condensation does not ascribe interpretation or implied meaning to a unit, but only shortens a meaning unit as much as possible while preserving the original meaning identified. 18 After condensation, a researcher can proceed to the creation of codes.

Many researchers begin their analyses with several general codes in mind that help guide their focus as defined by their research question, even in instances where the researcher has no a priori model or theory. For example, if a group of instructors are interested in examining recorded videos of their lectures to identify moments of student engagement, they may begin with using generally agreed upon concepts of engagement as codes, such as students “raising their hands,” “taking notes,” and “speaking in class.” However, as the instructors continue to watch their videos, they may notice other behaviors which were not initially anticipated. Perhaps students were seen creating flow charts based on information presented in class. Alternatively, perhaps instructors wanted to include moments when students posed questions to their peers without being prompted. In this case, the instructors would allow the codes of “creating graphic organizers” and “questioning peers” to emerge as additional ways to identify the behavior of student engagement.

Once a researcher has identified condensed units of meaning and labeled them with codes, the codes are then sorted into categories which can help provide more structure to the data. In the above example of recorded lectures, perhaps the category of “verbal behaviors” could be used to group the codes of “speaking in class” and “questioning peers.” For complex analyses, subcategories can also be used to better organize a large amount of codes, but solely at the discretion of the researcher. Two or more categories of codes are then used to identify or support a broader underlying meaning which develops into themes. Themes are most often employed in latent analyses; however, they are appropriate in manifest analyses as well. Themes describe behaviors, experiences, or emotions that occur throughout several categories. 18 Figure 1 illustrates this process. Using the same videotaped lecture example, the instructors might identify two themes of student engagement, “active engagement” and “passive engagement,” where active engagement is supported by the category of “verbal behavior” and also a category that includes the code of “raising their hands” (perhaps something along the lines of “pursuing engagement”), and the theme of “passive engagement” is supported by a category used to organize the behaviors of “taking notes” and “creating graphic organizers.”

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The Process of Qualitative Content Analysis

To more fully demonstrate the process of content analysis and the generation and use of codes, categories, and themes, we present and describe examples of both manifest and latent content analysis. Given that there are multiple ways to create and use codes, our examples illustrate both processes of creating and using a predetermined set of codes. Regardless of the kind of content analysis instructors want to conduct, the initial steps are the same. The instructor must analyze the data using codes as a sense-making process.

Manifest Content Analysis

The first form of analysis, manifest content analysis, examines text for elements that exist on the surface of the text, the meaning of which is taken at face value. Schools and colleges of pharmacy may benefit from conducting manifest content analyses at a programmatic level, including analysis of student evaluations to determine the value of certain courses, or analysis of recruitment materials for addressing issues of cultural humility in a uniform manner. Such uses for manifest content analysis may help administrators make more data-based decisions about students and courses. However, for our example of manifest content analysis, we illustrate the use of content analysis in informing instruction for a single pharmacy educator ( Figure 2 ).

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A Student’s Completed Beta-blocker Case with Codes in Underlined Bold Text

In the example, a pharmacology instructor is trying to assess students’ understanding of three concepts related to the beta-blocker class of drugs: indication of the drug, relevance of family history, and contraindications and precautions. To do so, the instructor asks the students to write a patient case in which beta-blockers are indicated. The instructor gives the students the following prompt: “Reverse-engineer a case in which beta-blockers would be prescribed to the patient. Include a history of the present illness, the patients’ medical, family, and social history, medications, allergies, and relevant lab tests.” Figure 2 is a hypothetical student’s completed assignment, in which they demonstrate their understanding of when and why a beta-blocker would be prescribed.

The student-generated cases are then treated as data and analyzed for the presence of the three previously identified indicators of understanding in order to help the instructor make decisions about where and how to focus future teaching efforts related to this drug class. Codes are created a priori out of the instructor’s interest in analyzing students’ understanding of the concepts related to beta-blocker prescriptions. A codebook ( Table 2 ) is created with the following columns: name of code, code description, and examples of the code. This codebook helps an individual researcher to approach their analysis systematically, but it can also facilitate coding by multiple coders who would apply the same rules outlined in the codebook to the coding process.

Example Code Book Created for Manifest Content Analysis

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Using multiple coders introduces complexity to the analysis process, but it is oftentimes the only practical way to analyze large amounts of data. To ensure that all coders are working in tandem, they must establish inter-rater reliability as part of their training process. This process requires that a single form of text be selected, such as one student evaluation. After reviewing the codebook and receiving instruction, everyone on the team individually codes the same piece of data. While calculating percentage agreement has sometimes been used to establish inter-rater reliability, most publication editors require more rigorous statistical analysis (eg, Krippendorf’s alpha, or Cohen’s kappa). 19 Detailed descriptions of these statistics fall outside the scope of this introduction, but it is important to note that the choice depends on the number of coders, the sample size, and the type of data to be analyzed.

Latent Content Analysis

Latent content analysis is another option for pharmacy educators, especially when there are theoretical frameworks or lenses the educator proposes to apply. Such frameworks describe and provide structure to complex concepts and may often be derived from relevant theories. Latent content analysis requires that the researcher is intimately involved in interpreting and finding meaning in the text because meaning is not readily apparent on the surface. 10 To illustrate a latent content analysis using a combination of a priori and emergent codes, we will use the example of a transcribed video excerpt from a student pharmacist interaction with a standardized patient. In this example, the goal is for first-year students to practice talking to a customer about an over-the-counter medication. The case is designed to simulate a customer at a pharmacy counter, who is seeking advice on a medication. The learning objectives for the pharmacist in-training are to assess the customer’s symptoms, determine if the customer can self-treat or if they need to seek out their primary care physician, and then prescribe a medication to alleviate the patient’s symptoms.

To begin, pharmacy educators conducting educational research should first identify what they are looking for in the video transcript. In this case, because the primary outcome for this exercise is aimed at assessing the “soft skills” of student pharmacists, codes are created using the counseling rubric created by Horton and colleagues. 20 Four a priori codes are developed using the literature: empathy, patient-friendly terms, politeness, and positive attitude. However, because the original four codes are inadequate to capture all areas representing the skills the instructor is looking for during the process of analysis, four additional codes are also created: active listening, confidence, follow-up, and patient at ease. Figure 3 presents the video transcript with each of the codes assigned to the meaning units in bolded parentheses.

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A Transcript of a Student’s (JR) Experience with a Standardized Patient (SP) in Which the Codes are Bolded in Parentheses

Following the initial coding using these eight codes, the codes are consolidated to create categories, which are depicted in the taxonomy in Figure 4 . Categories are relationships between codes that represent a higher level of abstraction in the data. 18 To reach conclusions and interpret the fundamental underlying meaning in the data, categories are then organized into themes ( Figure 1 ). Once the data are analyzed, the instructor can assign value to the student’s performance. In this case, the coding process determines that the exercise demonstrated both positive and negative elements of communication and professionalism. Under the category of professionalism, the student generally demonstrated politeness and a positive attitude toward the standardized patient, indicating to the reviewer that the theme of perceived professionalism was apparent during the encounter. However, there were several instances in which confidence and appropriate follow-up were absent. Thus, from a reviewer perspective, the student's performance could be perceived as indicating an opportunity to grow and improve as a future professional. Typically, there are multiple codes in a category and multiple categories in a theme. However, as seen in the example taxonomy, this is not always the case.

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Example of a Latent Content Analysis Taxonomy

If the educator is interested in conducting a latent projective analysis, after identifying the construct of “soft skills,” the researcher allows for each coder to apply their own mental schema as they look for positive and negative indicators of the non-technical skills they believe a student should develop. Mental schema are the cognitive structures that provide organization to knowledge, which in this case allows coders to categorize the data in ways that fit their existing understanding of the construct. The coders will use their own judgement to identify the codes they feel are relevant. The researcher could also choose to apply a theoretical lens to more effectively conceptualize the construct of “soft skills,” such as Rogers' humanism theory, and more specifically, concepts underlying his client-centered therapy. 21 The role of theory in both latent pattern and latent projective analyses is at the discretion of the researcher, and often is determined by what already exists in the literature related to the research question. Though, typically, in latent pattern analyses theory is used for deductive coding, and in latent projective analyses underdeveloped theory is used to first deduce codes and then for induction of the results to strengthen the theory applied. For our example, Rogers describes three salient qualities to develop and maintain a positive client-professional relationship: unconditional positive regard, genuineness, and empathetic understanding. 21 For the third element, specifically, the educator could look for units of meaning that imply empathy and active listening. For our video transcript analysis, this is evident when the student pharmacist demonstrated empathy by responding, "Yeah, I understand," when discussing aggravating factors for the patient's condition. The outcome for both latent pattern and latent projective content analysis is to discover the underlying meaning in a text, such as social rules or mental models. In this example, both pattern and projective approaches can discover interpreted aspects of a student’s abilities and mental models for constructs such as professionalism and empathy. The difference in the approaches is where the precedence lies: in the belief that a pattern is recognizable in the content, or in the mental schema and lived experiences of the coder(s). To better illustrate the differences in the processes of latent pattern and projective content analyses, Figure 5 presents a general outline of each method beginning with the creation of codes and concluding with the generation of themes.

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Flow Chart of the Stages of Latent Pattern and Latent Projective Content Analysis

How to Choose a Methodological Approach to Content Analysis

To determine which approach a researcher should take in their content analysis, two decisions need to be made. First, researchers must determine their goal for the analysis. Second, the researcher must decide where they believe meaning is located. 14 If meaning is located in the discrete elements of the content that are easily identified on the surface of the text, then manifest content analysis is appropriate. If meaning is located deep within the content and the researcher plans to discover context cues and make judgements about implied meaning, then latent content analysis should be applied. When designing the latent content analysis, a researcher then must also identify their focus. If the analysis is intended to identify a recognizable truth within the content by uncovering connections and characteristics that all coders should be able to discover, then latent pattern content analysis is appropriate. If, on the other hand, the researcher will rely heavily on the judgment of the coders and believes that interpretation of the content must leverage the mental schema of the coders to locate deeper meaning, then latent projective content analysis is the best choice.

To demonstrate how a researcher might choose a methodological approach, we have presented a third example of data in Figure 6 . In our two previous examples of content analysis, we used student data. However, faculty data can also be analyzed as part of educational research or for faculty members to improve their own teaching practices. Recall in the video data analyzed using latent content analysis, the student was tasked to identify a suitable over-the-counter medication for a patient complaining of heartburn symptoms. We have extended this example by including an interview with the pharmacy educator supervising the student who was videotaped. The goal of the interview is to evaluate the educator’s ability to assess the student’s performance with the standardized patient. Figure 6 is an excerpt of the interview between the course instructor and an instructional coach. In this conversation, the instructional coach is eliciting evidence to support the faculty member’s views, judgements, and rationale for the educator’s evaluation of the student’s performance.

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A Transcript of an Interview in Which the Interviewer (IN) Questions a Faculty Member (FM) Regarding Their Student’s Standardized Patient Experience

Manifest content analysis would be a valid choice for this data if the researcher was looking to identify evidence of the construct of “instructor priorities” and defined discrete codes that described aspects of performance such as “communication,” “referrals,” or “accurate information.” These codes could be easily identified on the surface of the transcribed interview by identifying keywords related to each code, such as “communicate,” “talk,” and “laugh,” for the code of “communication.” This would allow coders to identify evidence of the concept of “instructor priorities” by sorting through a potentially large amount of text with predetermined targets in mind.

To conduct a latent pattern analysis of this interview, researchers would first immerse themselves in the data to identify a theoretical framework or concepts that represent the area of interest so that coders could discover an emerging truth underneath the surface of the data. After immersion in the data, a researcher might believe it would be interesting to more closely examine the strategies the coach uses to establish rapport with the instructor as a way to better understand models of professional development. These strategies could not be easily identified in the transcripts if read literally, but by looking for connections within the text, codes related to instructional coaching tactics emerge. A latent pattern analysis would require that the researcher code the data in a way that looks for patterns, such as a code of “facilitating reflection,” that could be identified in open-ended questions and other units of meaning where the coder saw evidence of probing techniques, or a code of “establishing rapport” for which a coder could identify nonverbal cues such as “[IN leans forward in chair].”

Conducting latent projective content analysis might be useful if the researcher was interested in using a broader theoretical lens, such as Mezirow’s theory of transformative learning. 22 In this example, the faculty member is understood to have attempted to change a learner’s frame of reference by facilitating cognitive dissonance or a disorienting experience through a standardized patient simulation. To conduct a latent projective analysis, the researcher could analyze the faculty member’s interview using concepts found in this theory. This kind of analysis will help the researcher assess the level of change that the faculty member was able to perceive, or expected to witness, in their attempt to help their pharmacy students improve their interactions with patients. The units of meaning and subsequent codes would rely on the coders to apply their own knowledge of transformative learning because of the absence in the theory of concrete, context-specific behaviors to identify. For this analysis, the researcher would rely on their interpretations of what challenging educational situations look like, what constitutes cognitive dissonance, or what the faculty member is really expecting from his students’ performance. The subsequent analysis could provide evidence to support the use of such standardized patient encounters within the curriculum as a transformative learning experience and would also allow the educator to self-reflect on his ability to assess simulated activities.

OTHER ASPECTS TO CONSIDER

Navigating terminology.

Among the methodological approaches, there are other terms for content analysis that researchers may come across. Hsieh and Shannon 10 proposed three qualitative approaches to content analysis: conventional, directed, and summative. These categories were intended to explain the role of theory in the analysis process. In conventional content analysis, the researcher does not use preconceived categories because existing theory or literature are limited. In directed content analysis, the researcher attempts to further describe a phenomenon already addressed by theory, applying a deductive approach and using identified concepts or codes from exiting research to validate the theory. In summative content analysis, a descriptive approach is taken, identifying and quantifying words or content in order to describe their context. These three categories roughly map to the terms of latent projective, latent pattern, and manifest content analyses respectively, though not precisely enough to suggest that they are synonyms.

Graneheim and colleagues 9 reference the inductive, deductive, and abductive methods of interpretation of content analysis, which are data-driven, concept-driven, and fluid between both data and concepts, respectively. Where manifest content produces phenomenological descriptions most often (but not always) through deductive interpretation, and latent content analysis produces interpretations most often (but not always) through inductive or abductive interpretations. Erlingsson and Brysiewicz 23 refer to content analysis as a continuum, progressing as the researcher develops codes, then categories, and then themes. We present these alternative conceptualizations of content analysis to illustrate that the literature on content analysis, while incredibly useful, presents a multitude of interpretations of the method itself. However, these complexities should not dissuade readers from using content analysis. Identifying what you want to know (ie, your research question) will effectively direct you toward your methodological approach. That said, we have found the most helpful aid in learning content analysis is the application of the methods we have presented.

Ensuring Quality

The standards used to evaluate quantitative research are seldom used in qualitative research. The terms “reliability” and “validity” are typically not used because they reflect the positivist quantitative paradigm. In qualitative research, the preferred term is “trustworthiness,” which is comprised of the concepts of credibility, transferability, dependability, and confirmability, and researchers can take steps in their work to demonstrate that they are trustworthy. 24 Though establishing trustworthiness is outside the scope of this article, novice researchers should be familiar with the necessary steps before publishing their work. This suggestion includes exploration of the concept of saturation, the idea that researchers must demonstrate they have collected and analyzed enough data to warrant their conclusions, which has been a focus of recent debate in qualitative research. 25

There are several threats to the trustworthiness of content analysis in particular. 14 We will use the terms “reliability and validity” to describe these threats, as they are conceptualized this way in the formative literature, and it may be easier for researchers with a quantitative research background to recognize them. Though some of these threats may be particular to the type of data being analyzed, in general, there are risks specific to the different methods of content analysis. In manifest content analysis, reliability is necessary but not sufficient to establish validity. 14 Because there is little judgment required of the coders, lack of high inter-rater agreement among coders will render the data invalid. 14 Additionally, coder fatigue is a common threat to manifest content analysis because the coding is clerical and repetitive in nature.

For latent pattern content analysis, validity and reliability are inversely related. 14 Greater reliability is achieved through more detailed coding rules to improve consistency, but these rules may diminish the accessibility of the coding to consumers of the research. This is defined as low ecological validity. Higher ecological validity is achieved through greater reliance on coder judgment to increase the resonance of the results with the audience, yet this often decreases the inter-rater reliability. In latent projective content analysis, reliability and validity are equivalent. 14 Consistent interpretations among coders both establishes and validates the constructed norm; construction of an accurate norm is evidence of consistency. However, because of this equivalence, issues with low validity or low reliability cannot be isolated. A lack of consistency may result from coding rules, lack of a shared schema, or issues with a defined variable. Reasons for low validity cannot be isolated, but will always result in low consistency.

Any good analysis starts with a codebook and coder training. It is important for all coders to share the mental model of the skill, construct, or phenomenon being coded in the data. However, when conducting latent pattern or projective content analysis in particular, micro-level rules and definitions of codes increase the threat of ecological validity, so it is important to leave enough room in the codebook and during the training to allow for a shared mental schema to emerge in the larger group rather than being strictly directed by the lead researcher. Stability is another threat, which occurs when coders make different judgments as time passes. To reduce this risk, allowing for recoding at a later date can increase the consistency and stability of the codes. Reproducibility is not typically a goal of qualitative research, 15 but for content analysis, codes that are defined both prior to and during analysis should retain their meaning. Researchers can increase the reproducibility of their codebook by creating a detailed audit trail, including descriptions of the methods used to create and define the codes, materials used for the training of the coders, and steps taken to ensure inter-rater reliability.

In all forms of qualitative analysis, coder fatigue is a common threat to trustworthiness, even when the instructor is coding individually. Over time, the cases may start to look the same, making it difficult to refocus and look at each case with fresh eyes. To guard against this, coders should maintain a reflective journal and write analytical memos to help stay focused. Memos might include insights that the researcher has, such as patterns of misunderstanding, areas to focus on when considering re-teaching specific concepts, or specific conversations to have with students. Fatigue can also be mitigated by occasionally talking to participants (eg, meeting with students and listening for their rationale on why they included specific pieces of information in an assignment). These are just examples of potential exercises that can help coders mitigate cognitive fatigue. Most researchers develop their own ways to prevent the fatigue that can seep in after long hours of looking at data. But above all, a sufficient amount of time should be allowed for analysis, so that coders do not feel rushed, and regular breaks should be scheduled and enforced.

Qualitative content analysis is both accessible and high-yield for pharmacy educators and researchers. Though some of the methods may seem abstract or fluid, the nature of qualitative content analysis encompasses these concerns by providing a systematic approach to discover meaning in textual data, both on the surface and implied beneath it. As with most research methods, the surest path towards proficiency is through application and intentional, repeated practice. We encourage pharmacy educators to ask questions suited for qualitative research and to consider the use of content analysis as a qualitative research method for discovering meaning in their data.

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

Review Article
Published: 24 October 2022
Volume 29 , pages 86954–86993, ( 2022 )

Cite this article

Junpeng Huang ORCID: orcid.org/0000-0003-4785-7506 1 ,
Xiyong Wu 2 nAff1 ,
Sixiang Ling ORCID: orcid.org/0000-0001-9697-1212 1 , 2 ,
Xiaoning Li 3 ,
Yuxin Wu 1 ,
Lei Peng 1 &
Zhiyi He 1

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11 Citations

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To assess the status of hotspots and research trends on geographic information system (GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas Reuters Web of Science Core Collection database published during 2001–2020 by combining bibliometric and content analysis. The paper number, authors, institutions, corporations, publication sources, citations, and keywords are noted as sub/categories for the bibliometric analysis. Thematic LS data, including the study site, landslide inventory, conditioning factors, mapping unit, susceptibility models, and mode fit/prediction performance evaluation, are presented in the content analysis. Then, we reveal the advantages and limitations of the common approaches used in thematic LS data and summarise the development trends. The results indicate that the distribution of articles shows clear clusters of authors, institutions, and countries with high academic activity. The application of remote sensing technology for interpreting landslides provides a more convenient and efficient landslide inventory. In the landslide inventory, most of the sample strategies representing the landslides are point and polygon, and the most frequently used sample subdividing strategy is random sampling. The scale effects, lack of geographic consistency, and no standard are key problems in landslide conditioning factors. Feature selection is used to choose the factors that can improve the model’s accuracy. With advances in computing technology and artificial intelligence, LS models are changing from simple qualitative and statistical models to complex machine learning and hybrid models. Finally, five future research opportunities are revealed. This study will help investigators clarify the status of LS research and provide guidance for future research.

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Mapping landslides from space: A review

Integrated watershed management: evolution, development and emerging trends.

Geomatic tools used in the management of agricultural activities: a systematic review

Data availability.

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

Abdulwahid WM, Pradhan B (2017) Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14:1057–1076. https://doi.org/10.1007/s10346-016-0744-0

Article Google Scholar

Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006

Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75:553. https://doi.org/10.1007/s12665-015-5233-6

Akgun A, Erkan O (2016) Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey. Arabian J Geosci 9:165. https://doi.org/10.1007/s12517-015-2142-7

Akinci H, Kilicoglu C, Dogan S (2020) Random forest-based landslide susceptibility mapping in coastal regions of Artvin. Turkey ISPRS Int J Geo-Inf 9:553. https://doi.org/10.3390/ijgi9090553

Althuwaynee OF, Pradhan B, Park H-J, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36. https://doi.org/10.1016/j.catena.2013.10.011

Arabameri A, Pradhan B, Rezaei K, Lee C-W (2019) Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs. Remote Sens 11:999. https://doi.org/10.3390/rs11090999

Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010

Ba Q, Chen Y, Deng S, Yang J, Li H (2018) A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inf 11:373–388. https://doi.org/10.1007/s12145-018-0335-9

Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329. https://doi.org/10.1007/s11069-005-5182-6

Bordoni M, Galanti Y, Bartelletti C, Persichillo MG, Barsanti M, Giannecchini R, Avanzi GD, Cevasco A, Brandolini P, Galve JP, Meisina C (2020) The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models. Catena 193:104630. https://doi.org/10.1016/j.catena.2020.104630

Brabb EE (1984) Innovative approaches to landslide hazard mapping. Proceedings of the 4th International Symposium of Landslides, Toronto, Canada. pp 307–324

Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862. https://doi.org/10.5194/nhess-5-853-2005

Broeckx J, Vanmaercke M, Duchateau R, Poesen J (2018) A data-based landslide susceptibility map of Africa. Earth Sci Rev 185:102–121. https://doi.org/10.1016/j.earscirev.2018.05.002

Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436. https://doi.org/10.1007/s10346-014-0550-5

Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40. https://doi.org/10.1016/j.catena.2012.04.001

Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6

Bui DT, Shahabi H, Shirzadi A, Chapi K, Alizadeh M, Chen W, Mohammadi A, Bin Ahmad B, Panahi M, Hong H, Tian Y (2018) Landslide detection and susceptibility mapping by AIRSAR data using support vector machine and index of entropy models in Cameron Highlands. Malaysia Remote Sens 10:1527. https://doi.org/10.3390/rs10101527

Bui DT, Tsangaratos P, Nguyen VT, Liem NV, Trinh PT (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426. https://doi.org/10.1016/j.catena.2019.104426

Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci 75:238. https://doi.org/10.1007/s12665-015-5047-6

Camargo JMR, Silva MVB, Ferreira AV, Araujo TCM (2019) Marine geohazards: a bibliometric-based review. Geosciences 9:100. https://doi.org/10.3390/geosciences9020100

Canavesi V, Segoni S, Rosi A, Ting X, Nery T, Catani F, Casagli N (2020) Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: a case study in Rio de Janeiro (Brazil). Remote Sens 12:1826. https://doi.org/10.3390/rs12111826

Carvalho DV, Pereira EM, Cardoso JS (2019) Machine learning interpretability: a survey on methods and metrics. Electronics 8:832. https://doi.org/10.3390/electronics8080832

Chacon J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65:341–411. https://doi.org/10.1007/s10064-006-0064-z

Chang KT, Merghadi A, Yunus AP, Pham BT, Dou J (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9:12296. https://doi.org/10.1038/s41598-019-48773-2

Article CAS Google Scholar

Chang ZL, Du Z, Zhang F, Huang FM, Chen JW, Li WB, Guo ZZ (2020) Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models. Remote Sens 12:502. https://doi.org/10.3390/rs12030502

Chen CM (2017) Science mapping: a systematic review of the literature. J Data Info Sci 2:1–40. https://doi.org/10.1515/jdis-2017-0006

Chen W, Li X, Wang Y, Liu S (2013) Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China. Environ Earth Sci 70:673–685. https://doi.org/10.1007/s12665-012-2151-8

Chen W, Peng JB, Hong HY, Shahabi H, Pradhan B, Liu JZ, Zhu AX, Pei XJ, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135. https://doi.org/10.1016/j.scitotenv.2018.01.124

Chen W, Shahabi H, Zhang S, Khosravi K, Shirzadi A, Chapi K, Binh Thai P, Zhang T, Zhang L, Chai H, Ma J, Chen Y, Wang X, Li R, Bin Ahmad B (2018b) Landslide susceptibility modeling based on GIS and novel bagging-based kernel logistic regression. Applied Sciences-Basel 8:2540. https://doi.org/10.3390/app8122540

Chen W, Xie XS, Peng JB, Shahabi H, Hong HY, Dieu Tien B, Duan Z, Li SJ, Zhu AX (2018c) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164:135–149. https://doi.org/10.1016/j.catena.2018.01.012

Chen W, Chen YZ, Tsangaratos P, Ilia I, Wang XJ (2020) Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens 12:3854. https://doi.org/10.3390/rs12233854

Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011

Ciampalini A, Raspini F, Lagomarsino D, Catani F, Casagli N (2016) Landslide susceptibility map refinement using PSInSAR data. Remote Sens Environ 184:302–315. https://doi.org/10.1016/j.rse.2016.07.018

Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961. https://doi.org/10.1007/s00254-006-0264-7

Clerici A, Perego S, Tellini C, Vescovi P (2010) Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy). Nat Hazards 52:1–29. https://doi.org/10.1007/s11069-009-9349-4

Conoscenti C, Di Maggio C, Rotigliano E (2008) GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94:325–339. https://doi.org/10.1016/j.geomorph.2006.10.039

Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38:911–923. https://doi.org/10.1139/t01-021

Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391. https://doi.org/10.1007/s002540000163

Das I, Stein A, Kerle N, Dadhwal VK (2011) Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India. Landslides 8:293–308. https://doi.org/10.1007/s10346-011-0257-9

Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125. https://doi.org/10.1016/j.geomorph.2012.08.004

Dou J, Yunus AP, Bui DT, Sahana M, Chen C-W, Zhu Z, Wang W, Pham BT (2019) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11:638. https://doi.org/10.3390/rs11060638

Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu ZF, Chen CW, Han Z, Pham BT (2020) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17:641–658. https://doi.org/10.1007/s10346-019-01286-5

Dumperth C, Rohn J, Fleer A, Wang JG, Xiang W, Zimmermann K (2016) An easy approach to assess the susceptibility of a landslide by utilizing simple raster overlay methods: a case study on Huangtupo landslide (PR China). J Mt Sci 13:1701–1710. https://doi.org/10.1007/s11629-016-4011-6

Eker AM, Dikmen M, Cambazoglu S, Duzgun SHB, Akgun H (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartin, northern Turkey. Int J Geogr Inf Sci 29:132–158. https://doi.org/10.1080/13658816.2014.953164

Ercanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949–964. https://doi.org/10.1007/s12665-011-0912-4

Fan JL, Shen S, Wang JD, Wei SJ, Zhang X, Zhong P, Wang H (2020) Scientific and technological power and international cooperation in the field of natural hazards: a bibliometric analysis. Nat Hazards 102:807–827. https://doi.org/10.1007/s11069-020-03919-8

Federici PR, Puccinelli A, Cantarelli E, Casarosa N, Avanzi GDA, Falaschi F, Giannecchini R, Pochini A, Ribolini A, Bottai M, Salvati N, Testi C (2007) Multidisciplinary investigations in evaluating landslide susceptibility - an example in the Serchio River valley (Italy). Quat Int 171–72:52–63. https://doi.org/10.1016/j.quaint.2006.10.018

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ, Eng J-JTCL (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning Commentary. Eng Geol 102:99–111. https://doi.org/10.1016/j.enggeo.2008.03.014

Garfield E, Pudovkin AI, Istomin VS (2003) Why do we need algorithmic historiography? J Am Soc Inf Sci Tech 54:400–412. https://doi.org/10.1002/asi.10226

Ghasemain B, Asle DT, Pham BT, Avand M, Nguyen HD, Janizadeh S (2020) Shallow landslide susceptibility mapping: a comparison between classification and regression tree and reduced error pruning tree algorithms Vietnam J. Earth Sci 42:208–227. https://doi.org/10.15625/0866-7187/42/3/14952

Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386. https://doi.org/10.1016/j.geomorph.2011.03.001

Gorsevski PV, Brown MK, Panter K, Onasch CM, Simic A, Snyder J (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13:467–484. https://doi.org/10.1007/s10346-015-0587-0

Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216. https://doi.org/10.1016/s0169-555x(99)00078-1

Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. https://doi.org/10.1016/j.geomorph.2006.04.007

Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001

Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazards Earth Syst Sci 14:259–278. https://doi.org/10.5194/nhess-14-259-2014

Hess DM, Leshchinsky BA, Bunn M, Mason HB, Olsen MJ (2017) A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity. Landslides 14:1677–1697. https://doi.org/10.1007/s10346-017-0810-2

Hoang N, Mehrabi M, Kalantar B, Moayedi H, MaM A (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomat Nat Haz Risk 10:1667–1693. https://doi.org/10.1080/19475705.2019.1607782

Hong Y, Adler RF (2008) Predicting global landslide spatiotemporal distribution: Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates. Int J Sediment Res 23:249–257. https://doi.org/10.1016/s1001-6279(08)60022-0

Hong Y, Adler R, Huffman G (2007a) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43:245–256. https://doi.org/10.1007/s11069-006-9104-z

Hong Y, Adler RF, Huffman G (2007b) An experimental global prediction system for rainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEE Trans Geosci Remote Sens 45:1671–1680. https://doi.org/10.1109/tgrs.2006.888436

Hong HY, Pradhan B, Sameen MI, Chen W, Xu C (2017) Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China). Geomat Nat Haz Risk 8:1997–2022. https://doi.org/10.1080/19475705.2017.1403974

Hong HY, Liu JY, Bui DT, Pradhan B, Acharya TD, Binh Thai P, Zhu AX, Chen W, Bin Ahmad B (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena 163:399–413. https://doi.org/10.1016/j.catena.2018.01.005

Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. Catena 165:520–529. https://doi.org/10.1016/j.catena.2018.03.003

Huang HP, Yang KC, Lin BW (2013) Statistical evaluation of the effect of earthquake with other related factors on landslide susceptibility: using the watershed area of Shihmen reservoir in Taiwan as a case study. Environ Earth Sci 69:2151–2166. https://doi.org/10.1007/s12665-012-2044-x

Huang JP, Sun CW, Wu XY, Ling SX, Wang S, Deng R (2020a) Stability assessment of tunnel slopes along the Dujiangyan City to Siguniang Mountain Railway, China. Bull Eng Geol Environ 79:5309–5327. https://doi.org/10.1007/s10064-020-01913-9

Huang JX, Xie MW, Atkinson PM (2020b) Dynamic susceptibility mapping of slow-moving landslides using PSInSAR. Int J Remote Sens 41:7509–7529. https://doi.org/10.1080/01431161.2020.1760398

Huang FM, Tao SY, Chang ZL, Huang JS, Fan XM, Jiang SH, Li WB (2021) Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments. Landslides 18:3715–3731. https://doi.org/10.1007/s10346-021-01756-9

Huang JP, Ling SX, Wu XY, Deng R (2022a) GIS-based comparative study of the Bayesian network, decision table, radial basis function network and stochastic gradient descent for the spatial prediction of landslide susceptibility. Land 11:436. https://doi.org/10.3390/land11030436

Huang JP Ma N Ling SX, Wu XY (2022b) Comparing the prediction performance of logistic model tree with different ensemble techniques in susceptibility assessments of different landslide types. Geocarto Int 1-31. https://doi.org/10.1080/10106049.2022.2087751

Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. https://doi.org/10.1007/s10346-013-0436-y

Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, van Westen C, Balteanu D (2016) Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253:508–523. https://doi.org/10.1016/j.geomorph.2015.10.030

Jaafari A (2018) LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process. Environ Earth Sci 77:42. https://doi.org/10.1007/s12665-017-7207-3

Jaafari A, Najafi A, Rezaeian J, Sattarian A, Ghajar I (2015) Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy 47:198–208. https://doi.org/10.1016/j.landusepol.2015.04.010

Jaafari A, Panahi M, Pham BT, Shahabi H, Bui DT, Rezaie F, Lee S (2019) Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena 175:430–445. https://doi.org/10.1016/j.catena.2018.12.033

Jaboyedoff M, Oppikofer T, Abellan A, Derron MH, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Hazards 61:5–28. https://doi.org/10.1007/s11069-010-9634-2

Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013

Ji SP, Yu DW, Shen CY, Li WL, Xu Q (2020) Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides 17:1337–1352. https://doi.org/10.1007/s10346-020-01353-2

Jimenez-Peralvarez JD, El Hamdouni R, Palenzuela JA, Irigaray C, Chacon J (2017) Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain). Landslides 14:1975–1991. https://doi.org/10.1007/s10346-017-0851-6

Kakavas MP, Nikolakopoulos KG (2021) Digital elevation models of rockfalls and landslides: a review and meta-analysis. Geosciences 11:256. https://doi.org/10.3390/geosciences11060256

Kaminski M (2020) The impact of quality of digital elevation models on the result of landslide susceptibility modeling using the method of weights of evidence. Geosciences 10:488. https://doi.org/10.3390/geosciences10120488

Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101:631–642. https://doi.org/10.1016/j.geomorph.2008.03.003

Kanungo DP (2015) Landslide susceptibility zonation (LSZ) mapping — a review. Landslides 12:631–640. https://doi.org/10.1007/s10346-015-0586-1

Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408. https://doi.org/10.1016/j.cageo.2012.11.003

Kim J, Lee K, Jeong S, Kim G (2014) GIS-based prediction method of landslide susceptibility using a rainfall infiltration-groundwater flow model. Eng Geol 182:63–78. https://doi.org/10.1016/j.enggeo.2014.09.001

Kornejady A, Ownegh M, Bahremand A (2017) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 152:144–162. https://doi.org/10.1016/j.catena.2017.01.010

Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26:1477–1491. https://doi.org/10.1080/01431160412331331012

Lee S (2019) Current and future status of GIS-based landslide susceptibility mapping: a literature review. Korean J Remote Sens 35:179–193. https://doi.org/10.7780/kjrs.2019.35.1.12

Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113. https://doi.org/10.1007/s002540100310

Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672. https://doi.org/10.1007/s12040-006-0004-0

Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y

Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol 44:820–833. https://doi.org/10.1007/s00254-003-0825-y

Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38:199–220. https://doi.org/10.1007/s11004-005-9012-x

Lee S, Ryu J-H, Kim I-S (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338. https://doi.org/10.1007/s10346-007-0088-x

Lee J-H, Sameen MI, Pradhan B, Park H-J (2018) Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology 303:284–298. https://doi.org/10.1016/j.geomorph.2017.12.007

Lee S, Jang J, Kim Y, Cho N, Lee M-J (2020) Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method. Remote Sens 12:2663. https://doi.org/10.3390/rs12162663

Leydesdorff L, Rafols I (2012) Interactive overlays: a new method for generating global journal maps from Web-of-Science data. J Informetr 6:318–332. https://doi.org/10.1016/j.joi.2011.11.003

Li Y, Xu ZS, Wang XX, Wang XZ (2020) A bibliometric analysis on deep learning during 2007–2019. Int J Mach Learn Cybern 11:2807–2826. https://doi.org/10.1007/s13042-020-01152-0

Liang Z, Wang CM, Duan ZJ, Liu HL, Liu XY, Khan KUJ (2021) A hybrid model consisting of supervised and unsupervised learning for landslide susceptibility mapping. Remote Sens 13:1464. https://doi.org/10.3390/rs13081464

Lin L, Lin QG, Wang Y (2017) Landslide susceptibility mapping on a global scale using the method of logistic regression. Nat Hazards Earth Syst Sci 17:1411–1424. https://doi.org/10.5194/nhess-17-1411-2017

Ling SX, Chigira M (2020) Characteristics and triggers of earthquake induced landslides of pyroclastic fall deposits: an example from Hachinohe during the 1968 M7.9 tokachi-Oki earthquake, Japan. Eng Geol 264:105301. https://doi.org/10.1016/j.enggeo.2019.105301

Ling SX, Sun CW, Li XN, Ren Y, Xu JX, Huang T (2021) Characterizing the distribution pattern and geologic and geomorphic controls on earthquake-triggered landslide occurrence during the 2017 Ms 7.0 Jiuzhaigou earthquake, Sichuan. China Landslides 18:1275–1291. https://doi.org/10.1007/s10346-020-01549-6

Ling SX, Zhao SY, Huang JP, Zhang XT (2022) Landslide susceptibility assessment using statistical and machine learning techniques: a case study in the upper reaches of the Minjiang River, southwestern China. Front Earth Sci 10:986172. https://doi.org/10.3389/feart.2022.986172

Liu HH, Li XG, Meng T, Liu YY (2020) Susceptibility mapping of damming landslide based on slope unit using frequency ratio model. Arabian J Geosci 13:790. https://doi.org/10.1007/s12517-020-05689-w

Luo X, Lin F, Chen Y, Zhu S, Xu Z, Huo Z, Yu M, Peng J (2019) Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features. Sci Rep 9:15369. https://doi.org/10.1038/s41598-019-51941-z

Ma ZJ, Mei G, Piccialli F (2021) Machine learning for landslides prevention: a survey. Neural Comput Appl 33:10881–10907. https://doi.org/10.1007/s00521-020-05529-8

Mahalingam R, Olsen MJ (2016) Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping. Geomat Nat Haz Risk 7:1835–1855. https://doi.org/10.1080/19475705.2015.1115431

Mahalingam R, Olsen MJ, O’Banion MS (2016) Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study). Geomat Nat Haz Risk 7:1884–1907. https://doi.org/10.1080/19475705.2016.1172520

Mahdadi F, Boumezbeur A, Hadji R, Kanungo DP, Zahri F (2018) GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria. Arabian J Geosci 11:476. https://doi.org/10.1007/s12517-018-3770-5

Mancini F, Ceppi C, Ritrovato G (2010) GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Nat Hazards Earth Syst Sci 10:1851–1864. https://doi.org/10.5194/nhess-10-1851-2010

Maxwell AE, Sharma M, Kite JS, Donaldson KA, Thompson JA, Bell ML, Maynard SM (2020) Slope failure prediction using random forest machine learning and LiDAR in an eroded folded mountain belt. Remote Sens 12:486. https://doi.org/10.3390/rs12030486

Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225

Mohan A, Singh AK, Kumar B, Dwivedi R (2021) Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerging Telecommun Technol 32:e3998. https://doi.org/10.1002/ett.3998

Neuland H (1976) A prediction model of landslips. Catena 3:215–230. https://doi.org/10.1016/0341-8162(76)90011-4

Nhu VH, Shirzadi A, Shahabi H, Chen W, Clague JJ, Geertsema M, Jaafari A, Avand M, Miraki S, Asl DT, Pham BT, Bin Ahmad B, Lee S (2020) Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests 11:421. https://doi.org/10.3390/f11040421

Niu RQ, Wu XL, Yao DK, Peng L, Ai L, Peng JH (2014) Susceptibility assessment of landslides triggered by the Lushan earthquake, April 20, 2013, China. IEEE J Sel Top Appl Earth Obs Remote Sens 7:3979–3992. https://doi.org/10.1109/jstars.2014.2308553

Pandey VK, Pourghasemi HR, Sharma MC (2020) Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya. Geocarto Int 35:168–187. https://doi.org/10.1080/10106049.2018.1510038

Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. Springerplus 2:523. https://doi.org/10.1186/2193-1801-2-523

Pareek N, Pal S, Sharma ML, Arora MK (2013) Study of effect of seismic displacements on landslide susceptibility zonation (LSZ) in Garhwal Himalayan region of India using GIS and remote sensing techniques. Comput Geosci 61:50–63. https://doi.org/10.1016/j.cageo.2013.07.018

Park NW, Chi KH (2008) Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model. Int J Remote Sens 29:247–264. https://doi.org/10.1080/01431160701227661

Paulin GL, Bursik M, Lugo-Hubp J, Orozco JJZ (2010) Effect of pixel size on cartographic representation of shallow and deep-seated. landslide, and its collateral effects on the forecasting of landslides by SINMAP and Multiple Logistic Regression landslide models. Phys Chem Earth 35:137–148. https://doi.org/10.1016/j.pce.2010.04.008

Pham BT, Bui DT, Prakash I (2018) Bagging based support vector machines for spatial prediction of landslides. Environ Earth Sci 77:146. https://doi.org/10.1007/s12665-018-7268-y

Pham BT, Phong TV, Nguyen-Thoi T, Trinh PT, Tran QC, Ho LS, Singh SK, Duyen TTT, Nguyen LT, Le HQ, Le HV, Hanh NTB, Quoc NK, Prakash I (2020) GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv Space Res 66:1303–1320. https://doi.org/10.1016/j.asr.2020.05.016

Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? Catena 162:177–192. https://doi.org/10.1016/j.catena.2017.11.022

Pourghasemi HR, Yansari ZT, Panagos P, Pradhan B (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arabian J Geosci 11:193. https://doi.org/10.1007/s12517-018-3531-5

Pourghasemi HR, Kornejady A, Kerle N, Shabani F (2020) Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena 187:104364. https://doi.org/10.1016/j.catena.2019.104364

Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349. https://doi.org/10.1007/s12665-010-0705-1

Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023

Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16:107–126. https://doi.org/10.2113/gseegeosci.16.2.107

Pradhan AMS, Kim YT (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena 140:125–139. https://doi.org/10.1016/j.catena.2016.01.022

Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054. https://doi.org/10.1007/s12665-009-0245-8

Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ ModeLl Softw 25:747–759. https://doi.org/10.1016/j.envsoft.2009.10.016

Pradhan B, Singh RP, Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709. https://doi.org/10.1016/j.asr.2005.03.137

Pradhan B, Lee S, Mansor S, Buchroithner M, Jamaluddin N, Khujaimah Z (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. J Appl Remote Sens 2:023542. https://doi.org/10.1117/1.3026536

Qin SW, Lv JF, Cao C, Ma ZJ, Hu XY, Liu F, Qiao SS, Dou Q (2019) Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study. Geomat Nat Haz Risk 10:1648–1666. https://doi.org/10.1080/19475705.2019.1604572

Qin Z, Zhao Z, Xia L, Ohore OE (2022) Research trends and hotspots of aquatic biofilms in freshwater environment during the last three decades: a critical review and bibliometric analysis. Environ Sci Pollut Res 29:47915–47930. https://doi.org/10.1007/s11356-022-20238-6

Regmi NR, Giardino JR, McDonald EV, Vitek JD (2014) A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA. Landslides 11:247–262. https://doi.org/10.1007/s10346-012-0380-2

Reichenbach P, Busca C, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manage 54:1372–1384. https://doi.org/10.1007/s00267-014-0357-0

Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001

Rosi A, Tofani V, Tanteri L, Stefanelli CT, Agostini A, Catani F, Casagli N (2018) The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15:5–19. https://doi.org/10.1007/s10346-017-0861-4

Saadatkhah N, Kassim A, Lee LM (2015) Susceptibility assessment of shallow landslides in Hulu Kelang Area, Kuala Lumpur, Malaysia using analytical hierarchy process and frequency ratio. Geotech Geol Eng 33:43–57. https://doi.org/10.1007/s10706-014-9818-8

Sahin EK, Colkesen I, Kavzoglu T (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 35:341–363. https://doi.org/10.1080/10106049.2018.1516248

Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10:e0118432. https://doi.org/10.1371/journal.pone.0118432

Saleem N, Huq ME, Twumasi NYD, Javed A, Sajjad A (2019) Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility mapping and landslide risk assessment: a review. ISPRS Int J Geo-Inf 8:545. https://doi.org/10.3390/ijgi8120545

Sameen MI, Pradhan B, Bui DT, Alamri AM (2020a) Systematic sample subdividing strategy for training landslide susceptibility models. CATENA 187:104358. https://doi.org/10.1016/j.catena.2019.104358

Sameen MI, Pradhan B, Lee S (2020b) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA 186:104249. https://doi.org/10.1016/j.catena.2019.104249

Sassa K (2019) Registered speakers of the Fifth World Landslide Forum-Implementing and Monitoring the Sendai Landslide Partnerships 2015–2025-Voluntary contribution to the Sendai Framework 2015–2030 and the Agenda 2030-Sustainable Development Goals. Landslides 16:1423–1424. https://doi.org/10.1007/s10346-019-01213-8

Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens 6:9600–9652. https://doi.org/10.3390/rs6109600

Shahabi H, Ahmad BB, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arabian J Geosci 6:3885–3907. https://doi.org/10.1007/s12517-012-0650-2

Shahabi H, Khezri S, Bin Ahmad B, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70. https://doi.org/10.1016/j.catena.2013.11.014

Shano L, Raghuvanshi TK, Meten M (2020) Landslide susceptibility evaluation and hazard zonation techniques - a review. Geoenvironmental Disasters 7:18. https://doi.org/10.1186/s40677-020-00152-0

Sharma LP, Patel N, Ghose MK, Debnath P (2011) Landslide vulnerability assessment and zonation through ranking of causative parameters based on landslide density-derived statistical indicators. Geocarto Int 26:491–504. https://doi.org/10.1080/10106049.2011.598951

Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, Chen W, Khosravi K, Binh Thai P, Pradhan B, Ahmad A, Bin Ahmad B, Dieu Tien B (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors 18:3777. https://doi.org/10.3390/s18113777

Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Bin Ahmad B, Dieu Tien B (2019) Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. Catena 178:172–188. https://doi.org/10.1016/j.catena.2019.03.017

Shou K-J, Yang C-M (2015) Predictive analysis of landslide susceptibility under climate change conditions - a study on the Chingshui River Watershed of Taiwan. Eng Geol 192:46–62. https://doi.org/10.1016/j.enggeo.2015.03.012

Simon N, De Roiste M, Crozier M, Rafek AG (2017) Representing landslides as polygon (areal) or points? How different data types influence the accuracy of landslide susceptibility maps. Sains Malays 46:27–34. https://doi.org/10.17576/jsm-2017-4601-04

Stanley T, Kirschbaum DB (2017) A heuristic approach to global landslide susceptibility mapping. Nat Hazards 87:145–164. https://doi.org/10.1007/s11069-017-2757-y

Stehman SV, Hansen MC, Broich M, Potapov PV (2011) Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery. Remote Sens Environ 115:650–658. https://doi.org/10.1016/j.rse.2010.10.009

Sujatha ER, Kumaravel P, Rajamanickam VG (2012) Landslide susceptibility mapping using remotely sensed data through conditional probability analysis using seed cell and point sampling techniques. J Indian Soc Remote Sens 40:669–678. https://doi.org/10.1007/s12524-011-0192-1

Suzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679. https://doi.org/10.1007/s00254-003-0917-8

Tang YM, Feng F, Guo ZZ, Feng W, Li ZG, Wang JY, Sun QY, Ma HN, Li YN (2020) Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: a comparative study from the loess plateau area in Shanxi (China). J Cleaner Prod 277:124159. https://doi.org/10.1016/j.jclepro.2020.124159

Tanoli JI, Chen N, Regmi AD, Jun L (2017) Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arabian J Geosci 10:277. https://doi.org/10.1007/s12517-017-3026-9

Tao J, Qiu DY, Yang FQ, Duan ZP (2020) A bibliometric analysis of human reliability research. J Cleaner Prod 260:121041. https://doi.org/10.1016/j.jclepro.2020.121041

Vakhshoori V, Zare M (2018) Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? Geomat Nat Haz Risk 9:249–266. https://doi.org/10.1080/19475705.2018.1424043

van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3

van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010

Waltman L, van Eck NJ, Noyons ECM (2010) A unified approach to mapping and clustering of bibliometric networks. J Informetr 4:629–635. https://doi.org/10.1016/j.joi.2010.07.002

Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibililot map. Eng Geol 108:237–251. https://doi.org/10.1016/j.enggeo.2009.06.014

Wang HB, Liu GJ, Xu WY, Wang GH (2005) GIS-based landslide hazard assessment: an overview. Prog Phy Geog 29:548–567. https://doi.org/10.1191/0309133305pp462ra

Wang X, Zhang L, Wang S, Lari S (2014) Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides 11:399–409. https://doi.org/10.1007/s10346-013-0392-6

Wu XL, Chen XY, Zhan FB, Hong S (2015) Global research trends in landslides during 1991–2014: a bibliometric analysis. Landslides 12:1215–1226. https://doi.org/10.1007/s10346-015-0624-z

Wu YL, Ke YT, Chen Z, Liang SY, Zhao HL, Hong HY (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena 187:104396. https://doi.org/10.1016/j.catena.2019.104396

Xiao PF, Wu DD, Wang JQ (2022) Bibliometric analysis of global research on white rot fungi biotechnology for environmental application. Environ Sci Pollut Res 29:1491–1507. https://doi.org/10.1007/s11356-021-15787-1

Xie Z, Chen G, Meng X, Zhang Y, Qiao L, Tan L (2017) A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin. China Environ Earth Sci 76:313. https://doi.org/10.1007/s12665-017-6640-7

Xiong YB, Zhou Y, Wang FT, Wang SX, Wang JM, Ji JW, Wang ZQ (2021) Landslide susceptibility mapping using ant colony optimization strategy and deep belief network in Jiuzhaigou Region. IEEE J Sel Top Appl Earth Obs Remote Sens 14:11042–11057. https://doi.org/10.1109/Jstars.2021.3122825

Xu C, Dai FC, Xu XW, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80. https://doi.org/10.1016/j.geomorph.2011.12.040

Xu C, Xu XW, Yu GH (2013) Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu. China Earthquake Landslides 10:421–431. https://doi.org/10.1007/s10346-012-0340-x

Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41:201–226. https://doi.org/10.1007/s11069-006-9030-0

Yang JG, Cheng CX, Song CQ, Shen S, Ning LX (2019) Visual analysis of the evolution and focus in landslide research field. J Mt Sci 16:991–1004. https://doi.org/10.1007/s11629-018-5280-z

Yang YF, Chen GH, Reniers G, Goerlandt F (2020) A bibliometric analysis of process safety research in China: understanding safety research progress as a basis for making China’s chemical industry more sustainable. J Cleaner Prod 263:121433. https://doi.org/10.1016/j.jclepro.2020.121433

Yi YN, Zhang ZJ, Zhang WC, Xu Q, Deng C, Li QL (2019) GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China. Nat Hazards Earth Syst Sci 19:1973–1988. https://doi.org/10.5194/nhess-19-1973-2019

Yi YN, Zhang ZJ, Zhang WC, Jia HH, Zhang JQ (2020) Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. Catena 195:104851. https://doi.org/10.1016/j.catena.2020.104851

Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138. https://doi.org/10.1016/j.cageo.2008.08.007

Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60:505–519. https://doi.org/10.1007/s12665-009-0191-5

Yilmaz I, Keskin I (2009) GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ 68:459–471. https://doi.org/10.1007/s10064-009-0188-z

Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2015) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73:3745–3761. https://doi.org/10.1007/s12665-014-3661-3

Yu CL, Chen JP (2020) Application of a GIS-based slope unit method for landslide susceptibility mapping in Helong city: comparative assessment of ICM, AHP, and RF model. Symmetry 12:1848. https://doi.org/10.3390/sym12111848

Yu X, Wang Y, Niu R, Hu Y (2016) A combination of geographically weighted regression, particle swarm optimization and support vector machine for landslide susceptibility mapping: a case study at Wanzhou in the Three Gorges Area, China. Int J Environ Res Public Health 13:487. https://doi.org/10.3390/ijerph13050487

Zezere JL, Pereira S, Melo R, Oliveira SC, Garcia RAC (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267. https://doi.org/10.1016/j.scitotenv.2017.02.188

Zhang Y, Chen YP (2020) Research trends and areas of focus on the Chinese Loess Plateau: a bibliometric analysis during 1991–2018. CATENA 194:104798. https://doi.org/10.1016/j.catena.2020.104798

Zhao Y, Wang R, Jiang YJ, Liu HJ, Wei ZL (2019) GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China. Eng Geol 259:105147. https://doi.org/10.1016/j.enggeo.2019.105147

Zhou CB, Song WY (2021) Digitalization as a way forward: a bibliometric analysis of 20 Years of servitization research. J Cleaner Prod 300:126943. https://doi.org/10.1016/j.jclepro.2021.126943

Zhou C, Yin KL, Cao Y, Ahmed B, Li YY, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37. https://doi.org/10.1016/j.cageo.2017.11.019

Zhou XZ, Wen HJ, Li ZW, Zhang H, Zhang WG (2022) An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost. Geocarto Int 1–32. https://doi.org/10.1080/10106049.2022.2076928

Zhuang JQ, Peng JB, Wang GH, Iqbal J, Wang Y, Li W, Xu Q, Zhu XH (2017) Prediction of rainfall-induced shallow landslides in the Loess Plateau, Yan’an, China, using the TRIGRS model. Earth Surf Processes Landforms 42:915–927. https://doi.org/10.1002/esp.4050

Zou Y, Zheng C (2022) A scientometric analysis of predicting methods for identifying the environmental risks caused by landslides. Appl Sci 12:4333. https://doi.org/10.3390/app12094333

Zulhaidi H, Shafri M, Zahidi IM, Abu Bakar S (2010) Development of landslide susceptibility map utilizing remote sensing and Geographic Information Systems (GIS). Disaster Prev Manag 19:59–69. https://doi.org/10.1108/09653561011022144

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Acknowledgements

The authors thank editor-in-chief Dr. Philippe Garrigues, editorial assistants Fanny Creusot and Giulia Marinaccio, and three reviewers for their critical comments and valuable suggestions.

This work was supported by the National Natural Science Foundation of China (No. 41907228), Chengdu Science and Technology Program (2022-YF05-00340-SN), Sichuan Science and Technology Program, China (No. 2020YFS0297), and the Fundamental Research Funds for the Central Universities (No. 2682020CX11).

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Junpeng Huang, Sixiang Ling, Yuxin Wu, Lei Peng & Zhiyi He

Ministry of Education, Key Laboratory of High-Speed Railway Engineering, Southwest Jiaotong University, Chengdu, 610031, China

Xiyong Wu & Sixiang Ling

School of Emergency Management, Xihua University, Chengdu, 610039, China

Xiaoning Li

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Huang, J., Wu, X., Ling, S. et al. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020. Environ Sci Pollut Res 29 , 86954–86993 (2022). https://doi.org/10.1007/s11356-022-23732-z

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Published: 19 April 2024

A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact

Aklilu Endalamaw 1 , 2 ,
Resham B Khatri 1 , 3 ,
Tesfaye Setegn Mengistu 1 , 2 ,
Daniel Erku 1 , 4 , 5 ,
Eskinder Wolka 6 ,
Anteneh Zewdie 6 &
Yibeltal Assefa 1

BMC Health Services Research volume 24 , Article number: 487 ( 2024 ) Cite this article

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The growing adoption of continuous quality improvement (CQI) initiatives in healthcare has generated a surge in research interest to gain a deeper understanding of CQI. However, comprehensive evidence regarding the diverse facets of CQI in healthcare has been limited. Our review sought to comprehensively grasp the conceptualization and principles of CQI, explore existing models and tools, analyze barriers and facilitators, and investigate its overall impacts.

This qualitative scoping review was conducted using Arksey and O’Malley’s methodological framework. We searched articles in PubMed, Web of Science, Scopus, and EMBASE databases. In addition, we accessed articles from Google Scholar. We used mixed-method analysis, including qualitative content analysis and quantitative descriptive for quantitative findings to summarize findings and PRISMA extension for scoping reviews (PRISMA-ScR) framework to report the overall works.

A total of 87 articles, which covered 14 CQI models, were included in the review. While 19 tools were used for CQI models and initiatives, Plan-Do-Study/Check-Act cycle was the commonly employed model to understand the CQI implementation process. The main reported purposes of using CQI, as its positive impact, are to improve the structure of the health system (e.g., leadership, health workforce, health technology use, supplies, and costs), enhance healthcare delivery processes and outputs (e.g., care coordination and linkages, satisfaction, accessibility, continuity of care, safety, and efficiency), and improve treatment outcome (reduce morbidity and mortality). The implementation of CQI is not without challenges. There are cultural (i.e., resistance/reluctance to quality-focused culture and fear of blame or punishment), technical, structural (related to organizational structure, processes, and systems), and strategic (inadequate planning and inappropriate goals) related barriers that were commonly reported during the implementation of CQI.

Conclusions

Implementing CQI initiatives necessitates thoroughly comprehending key principles such as teamwork and timeline. To effectively address challenges, it’s crucial to identify obstacles and implement optimal interventions proactively. Healthcare professionals and leaders need to be mentally equipped and cognizant of the significant role CQI initiatives play in achieving purposes for quality of care.

Peer Review reports

Continuous quality improvement (CQI) initiative is a crucial initiative aimed at enhancing quality in the health system that has gradually been adopted in the healthcare industry. In the early 20th century, Shewhart laid the foundation for quality improvement by describing three essential steps for process improvement: specification, production, and inspection [ 1 , 2 ]. Then, Deming expanded Shewhart’s three-step model into ‘plan, do, study/check, and act’ (PDSA or PDCA) cycle, which was applied to management practices in Japan in the 1950s [ 3 ] and was gradually translated into the health system. In 1991, Kuperman applied a CQI approach to healthcare, comprising selecting a process to be improved, assembling a team of expert clinicians that understands the process and the outcomes, determining key steps in the process and expected outcomes, collecting data that measure the key process steps and outcomes, and providing data feedback to the practitioners [ 4 ]. These philosophies have served as the baseline for the foundation of principles for continuous improvement [ 5 ].

Continuous quality improvement fosters a culture of continuous learning, innovation, and improvement. It encourages proactive identification and resolution of problems, promotes employee engagement and empowerment, encourages trust and respect, and aims for better quality of care [ 6 , 7 ]. These characteristics drive the interaction of CQI with other quality improvement projects, such as quality assurance and total quality management [ 8 ]. Quality assurance primarily focuses on identifying deviations or errors through inspections, audits, and formal reviews, often settling for what is considered ‘good enough’, rather than pursuing the highest possible standards [ 9 , 10 ], while total quality management is implemented as the management philosophy and system to improve all aspects of an organization continuously [ 11 ].

Continuous quality improvement has been implemented to provide quality care. However, providing effective healthcare is a complicated and complex task in achieving the desired health outcomes and the overall well-being of individuals and populations. It necessitates tackling issues, including access, patient safety, medical advances, care coordination, patient-centered care, and quality monitoring [ 12 , 13 ], rooted long ago. It is assumed that the history of quality improvement in healthcare started in 1854 when Florence Nightingale introduced quality improvement documentation [ 14 ]. Over the passing decades, Donabedian introduced structure, processes, and outcomes as quality of care components in 1966 [ 15 ]. More comprehensively, the Institute of Medicine in the United States of America (USA) has identified effectiveness, efficiency, equity, patient-centredness, safety, and timeliness as the components of quality of care [ 16 ]. Moreover, quality of care has recently been considered an integral part of universal health coverage (UHC) [ 17 ], which requires initiatives to mobilise essential inputs [ 18 ].

While the overall objective of CQI in health system is to enhance the quality of care, it is important to note that the purposes and principles of CQI can vary across different contexts [ 19 , 20 ]. This variation has sparked growing research interest. For instance, a review of CQI approaches for capacity building addressed its role in health workforce development [ 21 ]. Another systematic review, based on random-controlled design studies, assessed the effectiveness of CQI using training as an intervention and the PDSA model [ 22 ]. As a research gap, the former review was not directly related to the comprehensive elements of quality of care, while the latter focused solely on the impact of training using the PDSA model, among other potential models. Additionally, a review conducted in 2015 aimed to identify barriers and facilitators of CQI in Canadian contexts [ 23 ]. However, all these reviews presented different perspectives and investigated distinct outcomes. This suggests that there is still much to explore in terms of comprehensively understanding the various aspects of CQI initiatives in healthcare.

As a result, we conducted a scoping review to address several aspects of CQI. Scoping reviews serve as a valuable tool for systematically mapping the existing literature on a specific topic. They are instrumental when dealing with heterogeneous or complex bodies of research. Scoping reviews provide a comprehensive overview by summarizing and disseminating findings across multiple studies, even when evidence varies significantly [ 24 ]. In our specific scoping review, we included various types of literature, including systematic reviews, to enhance our understanding of CQI.

This scoping review examined how CQI is conceptualized and measured and investigated models and tools for its application while identifying implementation challenges and facilitators. It also analyzed the purposes and impact of CQI on the health systems, providing valuable insights for enhancing healthcare quality.

Protocol registration and results reporting

Protocol registration for this scoping review was not conducted. Arksey and O’Malley’s methodological framework was utilized to conduct this scoping review [ 25 ]. The scoping review procedures start by defining the research questions, identifying relevant literature, selecting articles, extracting data, and summarizing the results. The review findings are reported using the PRISMA extension for a scoping review (PRISMA-ScR) [ 26 ]. McGowan and colleagues also advised researchers to report findings from scoping reviews using PRISMA-ScR [ 27 ].

Defining the research problems

This review aims to comprehensively explore the conceptualization, models, tools, barriers, facilitators, and impacts of CQI within the healthcare system worldwide. Specifically, we address the following research questions: (1) How has CQI been defined across various contexts? (2) What are the diverse approaches to implementing CQI in healthcare settings? (3) Which tools are commonly employed for CQI implementation ? (4) What barriers hinder and facilitators support successful CQI initiatives? and (5) What effects CQI initiatives have on the overall care quality?

Information source and search strategy

We conducted the search in PubMed, Web of Science, Scopus, and EMBASE databases, and the Google Scholar search engine. The search terms were selected based on three main distinct concepts. One group was CQI-related terms. The second group included terms related to the purpose for which CQI has been implemented, and the third group included processes and impact. These terms were selected based on the Donabedian framework of structure, process, and outcome [ 28 ]. Additionally, the detailed keywords were recruited from the primary health framework, which has described lists of dimensions under process, output, outcome, and health system goals of any intervention for health [ 29 ]. The detailed search strategy is presented in the Supplementary file 1 (Search strategy). The search for articles was initiated on August 12, 2023, and the last search was conducted on September 01, 2023.

Eligibility criteria and article selection

Based on the scoping review’s population, concept, and context frameworks [ 30 ], the population included any patients or clients. Additionally, the concepts explored in the review encompassed definitions, implementation, models, tools, barriers, facilitators, and impacts of CQI. Furthermore, the review considered contexts at any level of health systems. We included articles if they reported results of qualitative or quantitative empirical study, case studies, analytic or descriptive synthesis, any review, and other written documents, were published in peer-reviewed journals, and were designed to address at least one of the identified research questions or one of the identified implementation outcomes or their synonymous taxonomy as described in the search strategy. Based on additional contexts, we included articles published in English without geographic and time limitations. We excluded articles with abstracts only, conference abstracts, letters to editors, commentators, and corrections.

We exported all citations to EndNote x20 to remove duplicates and screen relevant articles. The article selection process includes automatic duplicate removal by using EndNote x20, unmatched title and abstract removal, citation and abstract-only materials removal, and full-text assessment. The article selection process was mainly conducted by the first author (AE) and reported to the team during the weekly meetings. The first author encountered papers that caused confusion regarding whether to include or exclude them and discussed them with the last author (YA). Then, decisions were ultimately made. Whenever disagreements happened, they were resolved by discussion and reconsideration of the review questions in relation to the written documents of the article. Further statistical analysis, such as calculating Kappa, was not performed to determine article inclusion or exclusion.

Data extraction and data items

We extracted first author, publication year, country, settings, health problem, the purpose of the study, study design, types of intervention if applicable, CQI approaches/steps if applicable, CQI tools and procedures if applicable, and main findings using a customized Microsoft Excel form.

Summarizing and reporting the results

The main findings were summarized and described based on the main themes, including concepts under conceptualizing, principles, teams, timelines, models, tools, barriers, facilitators, and impacts of CQI. Results-based convergent synthesis, achieved through mixed-method analysis, involved content analysis to identify the thematic presentation of findings. Additionally, a narrative description was used for quantitative findings, aligning them with the appropriate theme. The authors meticulously reviewed the primary findings from each included material and contextualized these findings concerning the main themes1. This approach provides a comprehensive understanding of complex interventions and health systems, acknowledging quantitative and qualitative evidence.

Search results

A total of 11,251 documents were identified from various databases: SCOPUS ( n = 4,339), PubMed ( n = 2,893), Web of Science ( n = 225), EMBASE ( n = 3,651), and Google Scholar ( n = 143). After removing duplicates ( n = 5,061), 6,190 articles were evaluated by title and abstract. Subsequently, 208 articles were assessed for full-text eligibility. Following the eligibility criteria, 121 articles were excluded, leaving 87 included in the current review (Fig. 1 ).

Article selection process

Operationalizing continuous quality improvement

Continuous Quality Improvement (CQI) is operationalized as a cyclic process that requires commitment to implementation, teamwork, time allocation, and celebrating successes and failures.

CQI is a cyclic ongoing process that is followed reflexive, analytical and iterative steps, including identifying gaps, generating data, developing and implementing action plans, evaluating performance, providing feedback to implementers and leaders, and proposing necessary adjustments [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].

CQI requires committing to the philosophy, involving continuous improvement [ 19 , 38 ], establishing a mission statement [ 37 ], and understanding quality definition [ 19 ].

CQI involves a wide range of patient-oriented measures and performance indicators, specifically satisfying internal and external customers, developing quality assurance, adopting common quality measures, and selecting process measures [ 8 , 19 , 35 , 36 , 37 , 39 , 40 ].

CQI requires celebrating success and failure without personalization, leading each team member to develop error-free attitudes [ 19 ]. Success and failure are related to underlying organizational processes and systems as causes of failure rather than blaming individuals [ 8 ] because CQI is process-focused based on collaborative, data-driven, responsive, rigorous and problem-solving statistical analysis [ 8 , 19 , 38 ]. Furthermore, a gap or failure opens another opportunity for establishing a data-driven learning organization [ 41 ].

CQI cannot be implemented without a CQI team [ 8 , 19 , 37 , 39 , 42 , 43 , 44 , 45 , 46 ]. A CQI team comprises individuals from various disciplines, often comprising a team leader, a subject matter expert (physician or other healthcare provider), a data analyst, a facilitator, frontline staff, and stakeholders [ 39 , 43 , 47 , 48 , 49 ]. It is also important to note that inviting stakeholders or partners as part of the CQI support intervention is crucial [ 19 , 38 , 48 ].

The timeline is another distinct feature of CQI because the results of CQI vary based on the implementation duration of each cycle [ 35 ]. There is no specific time limit for CQI implementation, although there is a general consensus that a cycle of CQI should be relatively short [ 35 ]. For instance, a CQI implementation took 2 months [ 42 ], 4 months [ 50 ], 9 months [ 51 , 52 ], 12 months [ 53 , 54 , 55 ], and one year and 5 months [ 49 ] duration to achieve the desired positive outcome, while bi-weekly [ 47 ] and monthly data reviews and analyses [ 44 , 48 , 56 ], and activities over 3 months [ 57 ] have also resulted in a positive outcome.

Continuous quality improvement models and tools

There have been several models are utilized. The Plan-Do-Study/Check-Act cycle is a stepwise process involving project initiation, situation analysis, root cause identification, solution generation and selection, implementation, result evaluation, standardization, and future planning [ 7 , 36 , 37 , 45 , 47 , 48 , 49 , 50 , 51 , 53 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. The FOCUS-PDCA cycle enhances the PDCA process by adding steps to find and improve a process (F), organize a knowledgeable team (O), clarify the process (C), understand variations (U), and select improvements (S) [ 55 , 71 , 72 , 73 ]. The FADE cycle involves identifying a problem (Focus), understanding it through data analysis (Analyze), devising solutions (Develop), and implementing the plan (Execute) [ 74 ]. The Logic Framework involves brainstorming to identify improvement areas, conducting root cause analysis to develop a problem tree, logically reasoning to create an objective tree, formulating the framework, and executing improvement projects [ 75 ]. Breakthrough series approach requires CQI teams to meet in quarterly collaborative learning sessions, share learning experiences, and continue discussion by telephone and cross-site visits to strengthen learning and idea exchange [ 47 ]. Another CQI model is the Lean approach, which has been conducted with Kaizen principles [ 52 ], 5 S principles, and the Six Sigma model. The 5 S (Sort, Set/Straighten, Shine, Standardize, Sustain) systematically organises and improves the workplace, focusing on sorting, setting order, shining, standardizing, and sustaining the improvement [ 54 , 76 ]. Kaizen principles guide CQI by advocating for continuous improvement, valuing all ideas, solving problems, focusing on practical, low-cost improvements, using data to drive change, acknowledging process defects, reducing variability and waste, recognizing every interaction as a customer-supplier relationship, empowering workers, responding to all ideas, and maintaining a disciplined workplace [ 77 ]. Lean Six Sigma, a CQI model, applies the DMAIC methodology, which involves defining (D) and measuring the problem (M), analyzing root causes (A), improving by finding solutions (I), and controlling by assessing process stability (C) [ 78 , 79 ]. The 5 C-cyclic model (consultation, collection, consideration, collaboration, and celebration), the first CQI framework for volunteer dental services in Aboriginal communities, ensures quality care based on community needs [ 80 ]. One study used meetings involving activities such as reviewing objectives, assigning roles, discussing the agenda, completing tasks, retaining key outputs, planning future steps, and evaluating the meeting’s effectiveness [ 81 ].

Various tools are involved in the implementation or evaluation of CQI initiatives: checklists [ 53 , 82 ], flowcharts [ 81 , 82 , 83 ], cause-and-effect diagrams (fishbone or Ishikawa diagrams) [ 60 , 62 , 79 , 81 , 82 ], fuzzy Pareto diagram [ 82 ], process maps [ 60 ], time series charts [ 48 ], why-why analysis [ 79 ], affinity diagrams and multivoting [ 81 ], and run chart [ 47 , 48 , 51 , 60 , 84 ], and others mentioned in the table (Table 1 ).

Barriers and facilitators of continuous quality improvement implementation

Implementing CQI initiatives is determined by various barriers and facilitators, which can be thematized into four dimensions. These dimensions are cultural, technical, structural, and strategic dimensions.

Continuous quality improvement initiatives face various cultural, strategic, technical, and structural barriers. Cultural dimension barriers involve resistance to change (e.g., not accepting online technology), lack of quality-focused culture, staff reporting apprehensiveness, and fear of blame or punishment [ 36 , 41 , 85 , 86 ]. The technical dimension barriers of CQI can include various factors that hinder the effective implementation and execution of CQI processes [ 36 , 86 , 87 , 88 , 89 ]. Structural dimension barriers of CQI arise from the organization structure, process, and systems that can impede the effective implementation and sustainability of CQI [ 36 , 85 , 86 , 87 , 88 ]. Strategic dimension barriers are, for example, the inability to select proper CQI goals and failure to integrate CQI into organizational planning and goals [ 36 , 85 , 86 , 87 , 88 , 90 ].

Facilitators are also grouped to cultural, structural, technical, and strategic dimensions to provide solutions to CQI barriers. Cultural challenges were addressed by developing a group culture to CQI and other rewards [ 39 , 41 , 80 , 85 , 86 , 87 , 90 , 91 , 92 ]. Technical facilitators are pivotal to improving technical barriers [ 39 , 42 , 53 , 69 , 86 , 90 , 91 ]. Structural-related facilitators are related to improving communication, infrastructure, and systems [ 86 , 92 , 93 ]. Strategic dimension facilitators include strengthening leadership and improving decision-making skills [ 43 , 53 , 67 , 86 , 87 , 92 , 94 , 95 ] (Table 2 ).

Impact of continuous quality improvement

Continuous quality improvement initiatives can significantly impact the quality of healthcare in a wide range of health areas, focusing on improving structure, the health service delivery process and improving client wellbeing and reducing mortality.

Structure components

These are health leadership, financing, workforce, technology, and equipment and supplies. CQI has improved planning, monitoring and evaluation [ 48 , 53 ], and leadership and planning [ 48 ], indicating improvement in leadership perspectives. Implementing CQI in primary health care (PHC) settings has shown potential for maintaining or reducing operation costs [ 67 ]. Findings from another study indicate that the costs associated with implementing CQI interventions per facility ranged from approximately $2,000 to $10,500 per year, with an average cost of approximately $10 to $60 per admitted client [ 57 ]. However, based on model predictions, the average cost savings after implementing CQI were estimated to be $5430 [ 31 ]. CQI can also be applied to health workforce development [ 32 ]. CQI in the institutional system improved medical education [ 66 , 96 , 97 ], human resources management [ 53 ], motivated staffs [ 76 ], and increased staff health awareness [ 69 ], while concerns raised about CQI impartiality, independence, and public accountability [ 96 ]. Regarding health technology, CQI also improved registration and documentation [ 48 , 53 , 98 ]. Furthermore, the CQI initiatives increased cleanliness [ 54 ] and improved logistics, supplies, and equipment [ 48 , 53 , 68 ].

Process and output components

The process component focuses on the activities and actions involved in delivering healthcare services.

Service delivery

CQI interventions improved service delivery [ 53 , 56 , 99 ], particularly a significant 18% increase in the overall quality of service performance [ 48 ], improved patient counselling, adherence to appropriate procedures, and infection prevention [ 48 , 68 ], and optimised workflow [ 52 ].

Coordination and collaboration

CQI initiatives improved coordination and collaboration through collecting and analysing data, onsite technical support, training, supportive supervision [ 53 ] and facilitating linkages between work processes and a quality control group [ 65 ].

Patient satisfaction

The CQI initiatives increased patient satisfaction and improved quality of life by optimizing care quality management, improving the quality of clinical nursing, reducing nursing defects and enhancing the wellbeing of clients [ 54 , 76 , 100 ], although CQI was not associated with changes in adolescent and young adults’ satisfaction [ 51 ].

CQI initiatives reduced medication error reports from 16 to 6 [ 101 ], and it significantly reduced the administration of inappropriate prophylactic antibiotics [ 44 ], decreased errors in inpatient care [ 52 ], decreased the overall episiotomy rate from 44.5 to 33.3% [ 83 ], reduced the overall incidence of unplanned endotracheal extubation [ 102 ], improving appropriate use of computed tomography angiography [ 103 ], and appropriate diagnosis and treatment selection [ 47 ].

Continuity of care

CQI initiatives effectively improve continuity of care by improving client and physician interaction. For instance, provider continuity levels showed a 64% increase [ 55 ]. Modifying electronic medical record templates, scheduling, staff and parental education, standardization of work processes, and birth to 1-year age-specific incentives in post-natal follow-up care increased continuity of care to 74% in 2018 compared to baseline 13% in 2012 [ 84 ].

The CQI initiative yielded enhanced efficiency in the cardiac catheterization laboratory, as evidenced by improved punctuality in procedure starts and increased efficiency in manual sheath-pulls inside [ 78 ].

Accessibility

CQI initiatives were effective in improving accessibility in terms of increasing service coverage and utilization rate. For instance, screening for cigarettes, nutrition counselling, folate prescription, maternal care, immunization coverage [ 53 , 81 , 104 , 105 ], reducing the percentage of non-attending patients to surgery to 0.9% from the baseline 3.9% [ 43 ], increasing Chlamydia screening rates from 29 to 60% [ 45 ], increasing HIV care continuum coverage [ 51 , 59 , 60 ], increasing in the uptake of postpartum long-acting reversible contraceptive use from 6.9% at the baseline to 25.4% [ 42 ], increasing post-caesarean section prophylaxis from 36 to 89% [ 62 ], a 31% increase of kangaroo care practice [ 50 ], and increased follow-up [ 65 ]. Similarly, the QI intervention increased the quality of antenatal care by 29.3%, correct partograph use by 51.7%, and correct active third-stage labour management, a 19.6% improvement from the baseline, but not significantly associated with improvement in contraceptive service uptake [ 61 ].

Timely access

CQI interventions improved the time care provision [ 52 ], and reduced waiting time [ 62 , 74 , 76 , 106 ]. For instance, the discharge process waiting time in the emergency department decreased from 76 min to 22 min [ 79 ]. It also reduced mean postprocedural length of stay from 2.8 days to 2.0 days [ 31 ].

Acceptability

Acceptability of CQI by healthcare providers was satisfactory. For instance, 88% of the faculty, 64% of the residents, and 82% of the staff believed CQI to be useful in the healthcare clinic [ 107 ].

Outcome components

Morbidity and mortality.

CQI efforts have demonstrated better management outcomes among diabetic patients [ 40 ], patients with oral mucositis [ 71 ], and anaemic patients [ 72 ]. It has also reduced infection rate in post-caesarean Sect. [ 62 ], reduced post-peritoneal dialysis peritonitis [ 49 , 108 ], and prevented pressure ulcers [ 70 ]. It is explained by peritonitis incidence from once every 40.1 patient months at baseline to once every 70.8 patient months after CQI [ 49 ] and a 63% reduction in pressure ulcer prevalence within 2 years from 2008 to 2010 [ 70 ]. Furthermore, CQI initiatives significantly reduced in-hospital deaths [ 31 ] and increased patient survival rates [ 108 ]. Figure 2 displays the overall process of the CQI implementations.

The overall mechanisms of continuous quality improvement implementation

In this review, we examined the fundamental concepts and principles underlying CQI, the factors that either hinder or assist in its successful application and implementation, and the purpose of CQI in enhancing quality of care across various health issues.

Our findings have brought attention to the application and implementation of CQI, emphasizing its underlying concepts and principles, as evident in the existing literature [ 31 , 32 , 33 , 34 , 35 , 36 , 39 , 40 , 43 , 45 , 46 ]. Continuous quality improvement has shared with the principles of continuous improvement, such as a customer-driven focus, effective leadership, active participation of individuals, a process-oriented approach, systematic implementation, emphasis on design improvement and prevention, evidence-based decision-making, and fostering partnership [ 5 ]. Moreover, Deming’s 14 principles laid the foundation for CQI principles [ 109 ]. These principles have been adapted and put into practice in various ways: ten [ 19 ] and five [ 38 ] principles in hospitals, five principles for capacity building [ 38 ], and two principles for medication error prevention [ 41 ]. As a principle, the application of CQI can be process-focused [ 8 , 19 ] or impact-focused [ 38 ]. Impact-focused CQI focuses on achieving specific outcomes or impacts, whereas process-focused CQI prioritizes and improves the underlying processes and systems. These principles complement each other and can be utilized based on the objectives of quality improvement initiatives in healthcare settings. Overall, CQI is an ongoing educational process that requires top management’s involvement, demands coordination across departments, encourages the incorporation of views beyond clinical area, and provides non-judgemental evidence based on objective data [ 110 ].

The current review recognized that it was not easy to implement CQI. It requires reasonable utilization of various models and tools. The application of each tool can be varied based on the studied health problem and the purpose of CQI initiative [ 111 ], varied in context, content, structure, and usability [ 112 ]. Additionally, overcoming the cultural, technical, structural, and strategic-related barriers. These barriers have emerged from clinical staff, managers, and health systems perspectives. Of the cultural obstacles, staff non-involvement, resistance to change, and reluctance to report error were staff-related. In contrast, others, such as the absence of celebration for success and hierarchical and rational culture, may require staff and manager involvement. Staff members may exhibit reluctance in reporting errors due to various cultural factors, including lack of trust, hierarchical structures, fear of retribution, and a blame-oriented culture. These challenges pose obstacles to implementing standardized CQI practices, as observed, for instance, in community pharmacy settings [ 85 ]. The hierarchical culture, characterized by clearly defined levels of power, authority, and decision-making, posed challenges to implementing CQI initiatives in public health [ 41 , 86 ]. Although rational culture, a type of organizational culture, emphasizes logical thinking and rational decision-making, it can also create challenges for CQI implementation [ 41 , 86 ] because hierarchical and rational cultures, which emphasize bureaucratic norms and narrow definitions of achievement, were found to act as barriers to the implementation of CQI [ 86 ]. These could be solved by developing a shared mindset and collective commitment, establishing a shared purpose, developing group norms, and cultivating psychological preparedness among staff, managers, and clients to implement and sustain CQI initiatives. Furthermore, reversing cultural-related barriers necessitates cultural-related solutions: development of a culture and group culture to CQI [ 41 , 86 ], positive comprehensive perception [ 91 ], commitment [ 85 ], involving patients, families, leaders, and staff [ 39 , 92 ], collaborating for a common goal [ 80 , 86 ], effective teamwork [ 86 , 87 ], and rewarding and celebrating successes [ 80 , 90 ].

The technical dimension barriers of CQI can include inadequate capitalization of a project and insufficient support for CQI facilitators and data entry managers [ 36 ], immature electronic medical records or poor information systems [ 36 , 86 ], and the lack of training and skills [ 86 , 87 , 88 ]. These challenges may cause the CQI team to rely on outdated information and technologies. The presence of barriers on the technical dimension may challenge the solid foundation of CQI expertise among staff, the ability to recognize opportunities for improvement, a comprehensive understanding of how services are produced and delivered, and routine use of expertise in daily work. Addressing these technical barriers requires knowledge creation activities (training, seminar, and education) [ 39 , 42 , 53 , 69 , 86 , 90 , 91 ], availability of quality data [ 86 ], reliable information [ 92 ], and a manual-online hybrid reporting system [ 85 ].

Structural dimension barriers of CQI include inadequate communication channels and lack of standardized process, specifically weak physician-to-physician synergies [ 36 ], lack of mechanisms for disseminating knowledge and limited use of communication mechanisms [ 86 ]. Lack of communication mechanism endangers sharing ideas and feedback among CQI teams, leading to misunderstandings, limited participation and misinterpretations, and a lack of learning [ 113 ]. Knowledge translation facilitates the co-production of research, subsequent diffusion of knowledge, and the developing stakeholder’s capacity and skills [ 114 ]. Thus, the absence of a knowledge translation mechanism may cause missed opportunities for learning, inefficient problem-solving, and limited creativity. To overcome these challenges, organizations should establish effective communication and information systems [ 86 , 93 ] and learning systems [ 92 ]. Though CQI and knowledge translation have interacted with each other, it is essential to recognize that they are distinct. CQI focuses on process improvement within health care systems, aiming to optimize existing processes, reduce errors, and enhance efficiency.

In contrast, knowledge translation bridges the gap between research evidence and clinical practice, translating research findings into actionable knowledge for practitioners. While both CQI and knowledge translation aim to enhance health care quality and patient outcomes, they employ different strategies: CQI utilizes tools like Plan-Do-Study-Act cycles and statistical process control, while knowledge translation involves knowledge synthesis and dissemination. Additionally, knowledge translation can also serve as a strategy to enhance CQI. Both concepts share the same principle: continuous improvement is essential for both. Therefore, effective strategies on the structural dimension may build efficient and effective steering councils, information systems, and structures to diffuse learning throughout the organization.

Strategic factors, such as goals, planning, funds, and resources, determine the overall purpose of CQI initiatives. Specific barriers were improper goals and poor planning [ 36 , 86 , 88 ], fragmentation of quality assurance policies [ 87 ], inadequate reinforcement to staff [ 36 , 90 ], time constraints [ 85 , 86 ], resource inadequacy [ 86 ], and work overload [ 86 ]. These barriers can be addressed through strengthening leadership [ 86 , 87 ], CQI-based mentoring [ 94 ], periodic monitoring, supportive supervision and coaching [ 43 , 53 , 87 , 92 , 95 ], participation, empowerment, and accountability [ 67 ], involving all stakeholders in decision-making [ 86 , 87 ], a provider-payer partnership [ 64 ], and compensating staff for after-hours meetings on CQI [ 85 ]. The strategic dimension, characterized by a strategic plan and integrated CQI efforts, is devoted to processes that are central to achieving strategic priorities. Roles and responsibilities are defined in terms of integrated strategic and quality-related goals [ 115 ].

The utmost goal of CQI has been to improve the quality of care, which is usually revealed by structure, process, and outcome. After resolving challenges and effectively using tools and running models, the goal of CQI reflects the ultimate reason and purpose of its implementation. First, effectively implemented CQI initiatives can improve leadership, health financing, health workforce development, health information technology, and availability of supplies as the building blocks of a health system [ 31 , 48 , 53 , 68 , 98 ]. Second, effectively implemented CQI initiatives improved care delivery process (counselling, adherence with standards, coordination, collaboration, and linkages) [ 48 , 53 , 65 , 68 ]. Third, the CQI can improve outputs of healthcare delivery, such as satisfaction, accessibility (timely access, utilization), continuity of care, safety, efficiency, and acceptability [ 52 , 54 , 55 , 76 , 78 ]. Finally, the effectiveness of the CQI initiatives has been tested in enhancing responses related to key aspects of the HIV response, maternal and child health, non-communicable disease control, and others (e.g., surgery and peritonitis). However, it is worth noting that CQI initiative has not always been effective. For instance, CQI using a two- to nine-times audit cycle model through systems assessment tools did not bring significant change to increase syphilis testing performance [ 116 ]. This study was conducted within the context of Aboriginal and Torres Strait Islander people’s primary health care settings. Notably, ‘the clinics may not have consistently prioritized syphilis testing performance in their improvement strategies, as facilitated by the CQI program’ [ 116 ]. Additionally, by applying CQI-based mentoring, uptake of facility-based interventions was not significantly improved, though it was effective in increasing community health worker visits during pregnancy and the postnatal period, knowledge about maternal and child health and exclusive breastfeeding practice, and HIV disclosure status [ 117 ]. The study conducted in South Africa revealed no significant association between the coverage of facility-based interventions and Continuous Quality Improvement (CQI) implementation. This lack of association was attributed to the already high antenatal and postnatal attendance rates in both control and intervention groups at baseline, leaving little room for improvement. Additionally, the coverage of HIV interventions remained consistently high throughout the study period [ 117 ].

Regarding health care and policy implications, CQI has played a vital role in advancing PHC and fostering the realization of UHC goals worldwide. The indicators found in Donabedian’s framework that are positively influenced by CQI efforts are comparable to those included in the PHC performance initiative’s conceptual framework [ 29 , 118 , 119 ]. It is clearly explained that PHC serves as the roadmap to realizing the vision of UHC [ 120 , 121 ]. Given these circumstances, implementing CQI can contribute to the achievement of PHC principles and the objectives of UHC. For instance, by implementing CQI methods, countries have enhanced the accessibility, affordability, and quality of PHC services, leading to better health outcomes for their populations. CQI has facilitated identifying and resolving healthcare gaps and inefficiencies, enabling countries to optimize resource allocation and deliver more effective and patient-centered care. However, it is crucial to recognize that the successful implementation of Continuous Quality Improvement (CQI) necessitates optimizing the duration of each cycle, understanding challenges and barriers that extend beyond the health system and settings, and acknowledging that its effectiveness may be compromised if these challenges are not adequately addressed.

Despite abundant literature, there are still gaps regarding the relationship between CQI and other dimensions within the healthcare system. No studies have examined the impact of CQI initiatives on catastrophic health expenditure, effective service coverage, patient-centredness, comprehensiveness, equity, health security, and responsiveness.

Limitations

In conducting this review, it has some limitations to consider. Firstly, only articles published in English were included, which may introduce the exclusion of relevant non-English articles. Additionally, as this review follows a scoping methodology, the focus is on synthesising available evidence rather than critically evaluating or scoring the quality of the included articles.

Continuous quality improvement is investigated as a continuous and ongoing intervention, where the implementation time can vary across different cycles. The CQI team and implementation timelines were critical elements of CQI in different models. Among the commonly used approaches, the PDSA or PDCA is frequently employed. In most CQI models, a wide range of tools, nineteen tools, are commonly utilized to support the improvement process. Cultural, technical, structural, and strategic barriers and facilitators are significant in implementing CQI initiatives. Implementing the CQI initiative aims to improve health system blocks, enhance health service delivery process and output, and ultimately prevent morbidity and reduce mortality. For future researchers, considering that CQI is context-dependent approach, conducting scale-up implementation research about catastrophic health expenditure, effective service coverage, patient-centredness, comprehensiveness, equity, health security, and responsiveness across various settings and health issues would be valuable.

Availability of data and materials

The data used and/or analyzed during the current study are available in this manuscript and/or the supplementary file.

Shewhart WA, Deming WE. Memoriam: Walter A. Shewhart, 1891–1967. Am Stat. 1967;21(2):39–40.

Article Google Scholar

Shewhart WA. Statistical method from the viewpoint of quality control. New York: Dover; 1986. ISBN 978-0486652320. OCLC 13822053. Reprint. Originally published: Washington, DC: Graduate School of the Department of Agriculture, 1939.

Moen R, editor Foundation and History of the PDSA Cycle. Asian network for quality conference Tokyo. https://www.deming.org/sites/default/files/pdf/2015/PDSA_History_Ron_MoenPdf . 2009.

Kuperman G, James B, Jacobsen J, Gardner RM. Continuous quality improvement applied to medical care: experiences at LDS hospital. Med Decis Making. 1991;11(4suppl):S60–65.

Article CAS PubMed Google Scholar

Singh J, Singh H. Continuous improvement philosophy–literature review and directions. Benchmarking: An International Journal. 2015;22(1):75–119.

Goldstone J. Presidential address: Sony, Porsche, and vascular surgery in the 21st century. J Vasc Surg. 1997;25(2):201–10.

Radawski D. Continuous quality improvement: origins, concepts, problems, and applications. J Physician Assistant Educ. 1999;10(1):12–6.

Shortell SM, O’Brien JL, Carman JM, Foster RW, Hughes E, Boerstler H, et al. Assessing the impact of continuous quality improvement/total quality management: concept versus implementation. Health Serv Res. 1995;30(2):377.

CAS PubMed PubMed Central Google Scholar

Lohr K. Quality of health care: an introduction to critical definitions, concepts, principles, and practicalities. Striving for quality in health care. 1991.

Berwick DM. The clinical process and the quality process. Qual Manage Healthc. 1992;1(1):1–8.

Article CAS Google Scholar

Gift B. On the road to TQM. Food Manage. 1992;27(4):88–9.

CAS PubMed Google Scholar

Greiner A, Knebel E. The core competencies needed for health care professionals. health professions education: A bridge to quality. 2003:45–73.

McCalman J, Bailie R, Bainbridge R, McPhail-Bell K, Percival N, Askew D et al. Continuous quality improvement and comprehensive primary health care: a systems framework to improve service quality and health outcomes. Front Public Health. 2018:6 (76):1–6.

Sheingold BH, Hahn JA. The history of healthcare quality: the first 100 years 1860–1960. Int J Afr Nurs Sci. 2014;1:18–22.

Google Scholar

Donabedian A. Evaluating the quality of medical care. Milbank Q. 1966;44(3):166–206.

Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Academies Press (US). 2001. 2, Improving the 21st-century Health Care System. Available from: https://www.ncbi.nlm.nih.gov/books/NBK222265/ .

Rubinstein A, Barani M, Lopez AS. Quality first for effective universal health coverage in low-income and middle-income countries. Lancet Global Health. 2018;6(11):e1142–1143.

Article PubMed Google Scholar

Agency for Healthcare Reserach and Quality. Quality Improvement and monitoring at your fingertips USA,: Agency for Healthcare Reserach and Quality. 2022. Available from: https://qualityindicators.ahrq.gov/ .

Anderson CA, Cassidy B, Rivenburgh P. Implementing continuous quality improvement (CQI) in hospitals: lessons learned from the International Quality Study. Qual Assur Health Care. 1991;3(3):141–6.

Gardner K, Mazza D. Quality in general practice - definitions and frameworks. Aust Fam Physician. 2012;41(3):151–4.

PubMed Google Scholar

Loper AC, Jensen TM, Farley AB, Morgan JD, Metz AJ. A systematic review of approaches for continuous quality improvement capacity-building. J Public Health Manage Pract. 2022;28(2):E354.

Hill JE, Stephani A-M, Sapple P, Clegg AJ. The effectiveness of continuous quality improvement for developing professional practice and improving health care outcomes: a systematic review. Implement Sci. 2020;15(1):1–14.

Candas B, Jobin G, Dubé C, Tousignant M, Abdeljelil AB, Grenier S, et al. Barriers and facilitators to implementing continuous quality improvement programs in colonoscopy services: a mixed methods systematic review. Endoscopy Int Open. 2016;4(02):E118–133.

Peters MD, Marnie C, Colquhoun H, Garritty CM, Hempel S, Horsley T, et al. Scoping reviews: reinforcing and advancing the methodology and application. Syst Reviews. 2021;10(1):1–6.

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

McGowan J, Straus S, Moher D, Langlois EV, O’Brien KK, Horsley T, et al. Reporting scoping reviews—PRISMA ScR extension. J Clin Epidemiol. 2020;123:177–9.

Donabedian A. Explorations in quality assessment and monitoring: the definition of quality and approaches to its assessment. Health Administration Press, Ann Arbor. 1980;1.

World Health Organization. Operational framework for primary health care: transforming vision into action. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF); 2020 [updated 14 December 2020; cited 2023 Nov Oct 17]. Available from: https://www.who.int/publications/i/item/9789240017832 .

The Joanna Briggs Institute. The Joanna Briggs Institute Reviewers’ Manual :2014 edition. Australia: The Joanna Briggs Institute. 2014:88–91.

Rihal CS, Kamath CC, Holmes DR Jr, Reller MK, Anderson SS, McMurtry EK, et al. Economic and clinical outcomes of a physician-led continuous quality improvement intervention in the delivery of percutaneous coronary intervention. Am J Manag Care. 2006;12(8):445–52.

Ade-Oshifogun JB, Dufelmeier T. Prevention and Management of Do not return notices: a quality improvement process for Supplemental staffing nursing agencies. Nurs Forum. 2012;47(2):106–12.

Rubenstein L, Khodyakov D, Hempel S, Danz M, Salem-Schatz S, Foy R, et al. How can we recognize continuous quality improvement? Int J Qual Health Care. 2014;26(1):6–15.

O’Neill SM, Hempel S, Lim YW, Danz MS, Foy R, Suttorp MJ, et al. Identifying continuous quality improvement publications: what makes an improvement intervention ‘CQI’? BMJ Qual Saf. 2011;20(12):1011–9.

Article PubMed PubMed Central Google Scholar

Sibthorpe B, Gardner K, McAullay D. Furthering the quality agenda in Aboriginal community controlled health services: understanding the relationship between accreditation, continuous quality improvement and national key performance indicator reporting. Aust J Prim Health. 2016;22(4):270–5.

Bennett CL, Crane JM. Quality improvement efforts in oncology: are we ready to begin? Cancer Invest. 2001;19(1):86–95.

VanValkenburgh DA. Implementing continuous quality improvement at the facility level. Adv Ren Replace Ther. 2001;8(2):104–13.

Loper AC, Jensen TM, Farley AB, Morgan JD, Metz AJ. A systematic review of approaches for continuous quality improvement capacity-building. J Public Health Manage Practice. 2022;28(2):E354–361.

Ryan M. Achieving and sustaining quality in healthcare. Front Health Serv Manag. 2004;20(3):3–11.

Nicolucci A, Allotta G, Allegra G, Cordaro G, D’Agati F, Di Benedetto A, et al. Five-year impact of a continuous quality improvement effort implemented by a network of diabetes outpatient clinics. Diabetes Care. 2008;31(1):57–62.

Wakefield BJ, Blegen MA, Uden-Holman T, Vaughn T, Chrischilles E, Wakefield DS. Organizational culture, continuous quality improvement, and medication administration error reporting. Am J Med Qual. 2001;16(4):128–34.

Sori DA, Debelew GT, Degefa LS, Asefa Z. Continuous quality improvement strategy for increasing immediate postpartum long-acting reversible contraceptive use at Jimma University Medical Center, Jimma, Ethiopia. BMJ Open Qual. 2023;12(1):e002051.

Roche B, Robin C, Deleaval PJ, Marti MC. Continuous quality improvement in ambulatory surgery: the non-attending patient. Ambul Surg. 1998;6(2):97–100.

O’Connor JB, Sondhi SS, Mullen KD, McCullough AJ. A continuous quality improvement initiative reduces inappropriate prescribing of prophylactic antibiotics for endoscopic procedures. Am J Gastroenterol. 1999;94(8):2115–21.

Ursu A, Greenberg G, McKee M. Continuous quality improvement methodology: a case study on multidisciplinary collaboration to improve chlamydia screening. Fam Med Community Health. 2019;7(2):e000085.

Quick B, Nordstrom S, Johnson K. Using continuous quality improvement to implement evidence-based medicine. Lippincotts Case Manag. 2006;11(6):305–15 ( quiz 16 – 7 ).

Oyeledun B, Phillips A, Oronsaye F, Alo OD, Shaffer N, Osibo B, et al. The effect of a continuous quality improvement intervention on retention-in-care at 6 months postpartum in a PMTCT Program in Northern Nigeria: results of a cluster randomized controlled study. J Acquir Immune Defic Syndr. 2017;75(Suppl 2):S156–164.

Nyengerai T, Phohole M, Iqaba N, Kinge CW, Gori E, Moyo K, et al. Quality of service and continuous quality improvement in voluntary medical male circumcision programme across four provinces in South Africa: longitudinal and cross-sectional programme data. PLoS ONE. 2021;16(8):e0254850.

Article CAS PubMed PubMed Central Google Scholar

Wang J, Zhang H, Liu J, Zhang K, Yi B, Liu Y, et al. Implementation of a continuous quality improvement program reduces the occurrence of peritonitis in PD. Ren Fail. 2014;36(7):1029–32.

Stikes R, Barbier D. Applying the plan-do-study-act model to increase the use of kangaroo care. J Nurs Manag. 2013;21(1):70–8.

Wagner AD, Mugo C, Bluemer-Miroite S, Mutiti PM, Wamalwa DC, Bukusi D, et al. Continuous quality improvement intervention for adolescent and young adult HIV testing services in Kenya improves HIV knowledge. AIDS. 2017;31(Suppl 3):S243–252.

Le RD, Melanson SE, Santos KS, Paredes JD, Baum JM, Goonan EM, et al. Using lean principles to optimise inpatient phlebotomy services. J Clin Pathol. 2014;67(8):724–30.

Manyazewal T, Mekonnen A, Demelew T, Mengestu S, Abdu Y, Mammo D, et al. Improving immunization capacity in Ethiopia through continuous quality improvement interventions: a prospective quasi-experimental study. Infect Dis Poverty. 2018;7:7.

Kamiya Y, Ishijma H, Hagiwara A, Takahashi S, Ngonyani HAM, Samky E. Evaluating the impact of continuous quality improvement methods at hospitals in Tanzania: a cluster-randomized trial. Int J Qual Health Care. 2017;29(1):32–9.

Kibbe DC, Bentz E, McLaughlin CP. Continuous quality improvement for continuity of care. J Fam Pract. 1993;36(3):304–8.

Adrawa N, Ongiro S, Lotee K, Seret J, Adeke M, Izudi J. Use of a context-specific package to increase sputum smear monitoring among people with pulmonary tuberculosis in Uganda: a quality improvement study. BMJ Open Qual. 2023;12(3):1–6.

Hunt P, Hunter SB, Levan D. Continuous quality improvement in substance abuse treatment facilities: how much does it cost? J Subst Abuse Treat. 2017;77:133–40.

Azadeh A, Ameli M, Alisoltani N, Motevali Haghighi S. A unique fuzzy multi-control approach for continuous quality improvement in a radio therapy department. Qual Quantity. 2016;50(6):2469–93.

Memiah P, Tlale J, Shimabale M, Nzyoka S, Komba P, Sebeza J, et al. Continuous quality improvement (CQI) institutionalization to reach 95:95:95 HIV targets: a multicountry experience from the Global South. BMC Health Serv Res. 2021;21(1):711.

Yapa HM, De Neve JW, Chetty T, Herbst C, Post FA, Jiamsakul A, et al. The impact of continuous quality improvement on coverage of antenatal HIV care tests in rural South Africa: results of a stepped-wedge cluster-randomised controlled implementation trial. PLoS Med. 2020;17(10):e1003150.

Dadi TL, Abebo TA, Yeshitla A, Abera Y, Tadesse D, Tsegaye S, et al. Impact of quality improvement interventions on facility readiness, quality and uptake of maternal and child health services in developing regions of Ethiopia: a secondary analysis of programme data. BMJ Open Qual. 2023;12(4):e002140.

Weinberg M, Fuentes JM, Ruiz AI, Lozano FW, Angel E, Gaitan H, et al. Reducing infections among women undergoing cesarean section in Colombia by means of continuous quality improvement methods. Arch Intern Med. 2001;161(19):2357–65.

Andreoni V, Bilak Y, Bukumira M, Halfer D, Lynch-Stapleton P, Perez C. Project management: putting continuous quality improvement theory into practice. J Nurs Care Qual. 1995;9(3):29–37.

Balfour ME, Zinn TE, Cason K, Fox J, Morales M, Berdeja C, et al. Provider-payer partnerships as an engine for continuous quality improvement. Psychiatric Serv. 2018;69(6):623–5.

Agurto I, Sandoval J, De La Rosa M, Guardado ME. Improving cervical cancer prevention in a developing country. Int J Qual Health Care. 2006;18(2):81–6.

Anderson CI, Basson MD, Ali M, Davis AT, Osmer RL, McLeod MK, et al. Comprehensive multicenter graduate surgical education initiative incorporating entrustable professional activities, continuous quality improvement cycles, and a web-based platform to enhance teaching and learning. J Am Coll Surg. 2018;227(1):64–76.

Benjamin S, Seaman M. Applying continuous quality improvement and human performance technology to primary health care in Bahrain. Health Care Superv. 1998;17(1):62–71.

Byabagambi J, Marks P, Megere H, Karamagi E, Byakika S, Opio A, et al. Improving the quality of voluntary medical male circumcision through use of the continuous quality improvement approach: a pilot in 30 PEPFAR-Supported sites in Uganda. PLoS ONE. 2015;10(7):e0133369.

Hogg S, Roe Y, Mills R. Implementing evidence-based continuous quality improvement strategies in an urban Aboriginal Community Controlled Health Service in South East Queensland: a best practice implementation pilot. JBI Database Syst Rev Implement Rep. 2017;15(1):178–87.

Hopper MB, Morgan S. Continuous quality improvement initiative for pressure ulcer prevention. J Wound Ostomy Cont Nurs. 2014;41(2):178–80.

Ji J, Jiang DD, Xu Z, Yang YQ, Qian KY, Zhang MX. Continuous quality improvement of nutrition management during radiotherapy in patients with nasopharyngeal carcinoma. Nurs Open. 2021;8(6):3261–70.

Chen M, Deng JH, Zhou FD, Wang M, Wang HY. Improving the management of anemia in hemodialysis patients by implementing the continuous quality improvement program. Blood Purif. 2006;24(3):282–6.

Reeves S, Matney K, Crane V. Continuous quality improvement as an ideal in hospital practice. Health Care Superv. 1995;13(4):1–12.

Barton AJ, Danek G, Johns P, Coons M. Improving patient outcomes through CQI: vascular access planning. J Nurs Care Qual. 1998;13(2):77–85.

Buttigieg SC, Gauci D, Dey P. Continuous quality improvement in a Maltese hospital using logical framework analysis. J Health Organ Manag. 2016;30(7):1026–46.

Take N, Byakika S, Tasei H, Yoshikawa T. The effect of 5S-continuous quality improvement-total quality management approach on staff motivation, patients’ waiting time and patient satisfaction with services at hospitals in Uganda. J Public Health Afr. 2015;6(1):486.

PubMed PubMed Central Google Scholar

Jacobson GH, McCoin NS, Lescallette R, Russ S, Slovis CM. Kaizen: a method of process improvement in the emergency department. Acad Emerg Med. 2009;16(12):1341–9.

Agarwal S, Gallo J, Parashar A, Agarwal K, Ellis S, Khot U, et al. Impact of lean six sigma process improvement methodology on cardiac catheterization laboratory efficiency. Catheter Cardiovasc Interv. 2015;85:S119.

Rahul G, Samanta AK, Varaprasad G A Lean Six Sigma approach to reduce overcrowding of patients and improving the discharge process in a super-specialty hospital. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN) 2020 July 3 (pp. 1-6). IEEE

Patel J, Nattabi B, Long R, Durey A, Naoum S, Kruger E, et al. The 5 C model: A proposed continuous quality improvement framework for volunteer dental services in remote Australian Aboriginal communities. Community Dent Oral Epidemiol. 2023;51(6):1150–8.

Van Acker B, McIntosh G, Gudes M. Continuous quality improvement techniques enhance HMO members’ immunization rates. J Healthc Qual. 1998;20(2):36–41.

Horine PD, Pohjala ED, Luecke RW. Healthcare financial managers and CQI. Healthc Financ Manage. 1993;47(9):34.

Reynolds JL. Reducing the frequency of episiotomies through a continuous quality improvement program. CMAJ. 1995;153(3):275–82.

Bunik M, Galloway K, Maughlin M, Hyman D. First five quality improvement program increases adherence and continuity with well-child care. Pediatr Qual Saf. 2021;6(6):e484.

Boyle TA, MacKinnon NJ, Mahaffey T, Duggan K, Dow N. Challenges of standardized continuous quality improvement programs in community pharmacies: the case of SafetyNET-Rx. Res Social Adm Pharm. 2012;8(6):499–508.

Price A, Schwartz R, Cohen J, Manson H, Scott F. Assessing continuous quality improvement in public health: adapting lessons from healthcare. Healthc Policy. 2017;12(3):34–49.

Gage AD, Gotsadze T, Seid E, Mutasa R, Friedman J. The influence of continuous quality improvement on healthcare quality: a mixed-methods study from Zimbabwe. Soc Sci Med. 2022;298:114831.

Chan YC, Ho SJ. Continuous quality improvement: a survey of American and Canadian healthcare executives. Hosp Health Serv Adm. 1997;42(4):525–44.

Balas EA, Puryear J, Mitchell JA, Barter B. How to structure clinical practice guidelines for continuous quality improvement? J Med Syst. 1994;18(5):289–97.

ElChamaa R, Seely AJE, Jeong D, Kitto S. Barriers and facilitators to the implementation and adoption of a continuous quality improvement program in surgery: a case study. J Contin Educ Health Prof. 2022;42(4):227–35.

Candas B, Jobin G, Dubé C, Tousignant M, Abdeljelil A, Grenier S, et al. Barriers and facilitators to implementing continuous quality improvement programs in colonoscopy services: a mixed methods systematic review. Endoscopy Int Open. 2016;4(2):E118–133.

Brandrud AS, Schreiner A, Hjortdahl P, Helljesen GS, Nyen B, Nelson EC. Three success factors for continual improvement in healthcare: an analysis of the reports of improvement team members. BMJ Qual Saf. 2011;20(3):251–9.

Lee S, Choi KS, Kang HY, Cho W, Chae YM. Assessing the factors influencing continuous quality improvement implementation: experience in Korean hospitals. Int J Qual Health Care. 2002;14(5):383–91.

Horwood C, Butler L, Barker P, Phakathi S, Haskins L, Grant M, et al. A continuous quality improvement intervention to improve the effectiveness of community health workers providing care to mothers and children: a cluster randomised controlled trial in South Africa. Hum Resour Health. 2017;15(1):39.

Hyrkäs K, Lehti K. Continuous quality improvement through team supervision supported by continuous self-monitoring of work and systematic patient feedback. J Nurs Manag. 2003;11(3):177–88.

Akdemir N, Peterson LN, Campbell CM, Scheele F. Evaluation of continuous quality improvement in accreditation for medical education. BMC Med Educ. 2020;20(Suppl 1):308.

Barzansky B, Hunt D, Moineau G, Ahn D, Lai CW, Humphrey H, et al. Continuous quality improvement in an accreditation system for undergraduate medical education: benefits and challenges. Med Teach. 2015;37(11):1032–8.

Gaylis F, Nasseri R, Salmasi A, Anderson C, Mohedin S, Prime R, et al. Implementing continuous quality improvement in an integrated community urology practice: lessons learned. Urology. 2021;153:139–46.

Gaga S, Mqoqi N, Chimatira R, Moko S, Igumbor JO. Continuous quality improvement in HIV and TB services at selected healthcare facilities in South Africa. South Afr J HIV Med. 2021;22(1):1202.

Wang F, Yao D. Application effect of continuous quality improvement measures on patient satisfaction and quality of life in gynecological nursing. Am J Transl Res. 2021;13(6):6391–8.

Lee SB, Lee LL, Yeung RS, Chan J. A continuous quality improvement project to reduce medication error in the emergency department. World J Emerg Med. 2013;4(3):179–82.

Chiang AA, Lee KC, Lee JC, Wei CH. Effectiveness of a continuous quality improvement program aiming to reduce unplanned extubation: a prospective study. Intensive Care Med. 1996;22(11):1269–71.

Chinnaiyan K, Al-Mallah M, Goraya T, Patel S, Kazerooni E, Poopat C, et al. Impact of a continuous quality improvement initiative on appropriate use of coronary CT angiography: results from a multicenter, statewide registry, the advanced cardiovascular imaging consortium (ACIC). J Cardiovasc Comput Tomogr. 2011;5(4):S29–30.

Gibson-Helm M, Rumbold A, Teede H, Ranasinha S, Bailie R, Boyle J. A continuous quality improvement initiative: improving the provision of pregnancy care for Aboriginal and Torres Strait Islander women. BJOG: Int J Obstet Gynecol. 2015;122:400–1.

Bennett IM, Coco A, Anderson J, Horst M, Gambler AS, Barr WB, et al. Improving maternal care with a continuous quality improvement strategy: a report from the interventions to minimize preterm and low birth weight infants through continuous improvement techniques (IMPLICIT) network. J Am Board Fam Med. 2009;22(4):380–6.

Krall SP, Iv CLR, Donahue L. Effect of continuous quality improvement methods on reducing triage to thrombolytic interval for Acute myocardial infarction. Acad Emerg Med. 1995;2(7):603–9.

Swanson TK, Eilers GM. Physician and staff acceptance of continuous quality improvement. Fam Med. 1994;26(9):583–6.

Yu Y, Zhou Y, Wang H, Zhou T, Li Q, Li T, et al. Impact of continuous quality improvement initiatives on clinical outcomes in peritoneal dialysis. Perit Dial Int. 2014;34(Suppl 2):S43–48.

Schiff GD, Goldfield NI. Deming meets Braverman: toward a progressive analysis of the continuous quality improvement paradigm. Int J Health Serv. 1994;24(4):655–73.

American Hospital Association Division of Quality Resources Chicago, IL: The role of hospital leadership in the continuous improvement of patient care quality. American Hospital Association. J Healthc Qual. 1992;14(5):8–14,22.

Scriven M. The Logic and Methodology of checklists [dissertation]. Western Michigan University; 2000.

Hales B, Terblanche M, Fowler R, Sibbald W. Development of medical checklists for improved quality of patient care. Int J Qual Health Care. 2008;20(1):22–30.

Vermeir P, Vandijck D, Degroote S, Peleman R, Verhaeghe R, Mortier E, et al. Communication in healthcare: a narrative review of the literature and practical recommendations. Int J Clin Pract. 2015;69(11):1257–67.

Eljiz K, Greenfield D, Hogden A, Taylor R, Siddiqui N, Agaliotis M, et al. Improving knowledge translation for increased engagement and impact in healthcare. BMJ open Qual. 2020;9(3):e000983.

O’Brien JL, Shortell SM, Hughes EF, Foster RW, Carman JM, Boerstler H, et al. An integrative model for organization-wide quality improvement: lessons from the field. Qual Manage Healthc. 1995;3(4):19–30.

Adily A, Girgis S, D’Este C, Matthews V, Ward JE. Syphilis testing performance in Aboriginal primary health care: exploring impact of continuous quality improvement over time. Aust J Prim Health. 2020;26(2):178–83.

Veillard J, Cowling K, Bitton A, Ratcliffe H, Kimball M, Barkley S, et al. Better measurement for performance improvement in low- and middle-income countries: the primary Health Care Performance Initiative (PHCPI) experience of conceptual framework development and indicator selection. Milbank Q. 2017;95(4):836–83.

Barbazza E, Kringos D, Kruse I, Klazinga NS, Tello JE. Creating performance intelligence for primary health care strengthening in Europe. BMC Health Serv Res. 2019;19(1):1006.

Assefa Y, Hill PS, Gilks CF, Admassu M, Tesfaye D, Van Damme W. Primary health care contributions to universal health coverage. Ethiopia Bull World Health Organ. 2020;98(12):894.

Van Weel C, Kidd MR. Why strengthening primary health care is essential to achieving universal health coverage. CMAJ. 2018;190(15):E463–466.

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Endalamaw, A., Khatri, R.B., Mengistu, T.S. et al. A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Serv Res 24 , 487 (2024). https://doi.org/10.1186/s12913-024-10828-0

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Advances in Infrared Lasers and Their Applications

Fine Grained Analysis Method for Unmanned Aerial Vehicle Measurement Based on Laser-based Light Scattering Particle Sensing Provisionally Accepted

1 Qinhuangdao Tianda Environmental Research Protection Institute Co., LTD, China

The final, formatted version of the article will be published soon.

As an effective particle measurement method, laser-based particle sensors combined with unmanned aerial vehicles (UAVs) can be used for measuring air quality in near ground space. In order to assess the air quality between flight trajectories, a new fine-grained analysis method, Co-KNN-DNN is proposed to present the continuous distribution of air quality in more detail. First of all, the overall scheme was designed, M30T UAV was selected to carry the portable air quality monitoring equipment, with laser-based laser particulate matter sensor and Mini2, to collect AQI and related attributes of the near-ground layer in the selected research area, to do the necessary processing of the collected data, to build a data set suitable for model input, etc., to train and optimize the model, and to carry out practical application of the model. Based on the spatial dimension-based air quality finegrained analysis model Co-KNN-DNN, three experiments were conducted at different altitudes within the study area. 290 samples from each altitude data set were randomly selected to form the initial marker sample set, and 200 samples from each altitude were randomly selected as the test sample set. The remaining samples were unlabeled sample sets. The experimental results show that the average R-squared value can reach 0.99. The effectiveness and practicability of the Co-KNN-DNN model were verified by application research.

Keywords: air quality fine-grained, Sniffer4D Mini2, M30T UAV, Laser Particulate Matter Sensor, Co-KNN-DNN

Received: 06 Apr 2024; Accepted: 26 Apr 2024.

Copyright: © 2024 Jia, Song and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Xutao Jia, Qinhuangdao Tianda Environmental Research Protection Institute Co., LTD, Qinhuangdao, China

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Content Analysis | A Step-by-Step Guide with Examples

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Step 1: Select the content you will analyse

Step 2: Define the units and categories of analysis

Step 3: Develop a set of rules for coding

Step 4: Code the text according to the rules

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Content Analysis – Methods, Types and Examples

Content Analysis

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Methods of Content Analysis

Conceptual Analysis

Content Analysis by Frequency

Comparative Analysis

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Content Analysis Conducting Guide

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An Overview of the Content Analysis Process

The Second Step: Collecting and Storing

The Third Step: Analysis

The Fourth and Final Step: The Write-Up

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The basics – explicit and implicit content

2. The two types of content analysis

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3. When should you use content analysis?

4. How to conduct a qualitative content analysis

Step 1 – Recap on your research questions

Step 2 – Reflect on your personal perspectives and biases

Step 3 – Operationalise your variables and develop a coding scheme

Step 4 – Code and analyse the data

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5. What are the pros & cons of content analysis?

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