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Qualitative Content Analysis in Practice

• Margrit Schreier - Jacobs University Bremen, Germany
• Description

In one of the first to focus on qualitative content analysis, Margrit Schreier takes students step-by step through:

- creating a coding frame

- segmenting the material

- trying out the coding frame

- evaluating the trial coding

- carrying out the main coding

- what comes after qualitative content analysis

- making use of software when conducting qualitative content analysis.

Each part of the process is described in detail and research examples are provided to illustrate each step. Frequently asked questions are answered, the most important points are summarized, and end of chapter questions provide an opportunity to revise these points. After reading the book, students are fully equiped to conduct their own qualitative content analysis.

This book provides a well written, clear and detailed account of QCA, highlighting the value of this research method for the analysis of social, political and psychological phenomena Tereza Capelos University of Surrey

Schreier writes clearly and with authority, positioning QCA in relation to other qualitative research methods and emphasising the hands-on aspects of the analysis process. She offers numerous illuminating examples and helpful pedagogical tools for the reader. This book will thus be most welcomed by students at different levels as well as by researchers.

Ulla Hällgren Graneheim Umeå University, Sweden

This book has been written for students but would be of value to anyone considering using the analysis method to help them reduce and make sense of a large volume of textual data. [...]The content is detailed and presented in text-book style with key points, definitions and beginners’ mistakes scattered throughout and frequently asked questions and end of chapter questions. These break up the text but also help when skimming. In addition, and what I found particularly valuable, was the liberal use of examples drawn from published papers. These really help to clarify and bring to life the issues raised.

Schreier provides several helpful educational tools, such as mid-chapter definitions, summaries and key-points....this is an excellent introductory or reference book for all students of content analysis

This book makes a valuable supplementary reading text about applied content analysis for my course in ethnography.

I will use this as a recommended text, and not adopt as a main or compulsory text. This is so given the book's particular focus on QCA, and the more complex treatment of research issues in general, which will be of use to a limited number of students but will be a good resource potentially for some select few taking my spring 2014 senior thesis class and NOT the fall basic / intro research methods class. Thank you.

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End of Chapter Questions

Introduction

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• v.23(1); 2018 Feb

Directed qualitative content analysis: the description and elaboration of its underpinning methods and data analysis process

Qualitative content analysis consists of conventional, directed and summative approaches for data analysis. They are used for provision of descriptive knowledge and understandings of the phenomenon under study. However, the method underpinning directed qualitative content analysis is insufficiently delineated in international literature. This paper aims to describe and integrate the process of data analysis in directed qualitative content analysis. Various international databases were used to retrieve articles related to directed qualitative content analysis. A review of literature led to the integration and elaboration of a stepwise method of data analysis for directed qualitative content analysis. The proposed 16-step method of data analysis in this paper is a detailed description of analytical steps to be taken in directed qualitative content analysis that covers the current gap of knowledge in international literature regarding the practical process of qualitative data analysis. An example of “the resuscitation team members' motivation for cardiopulmonary resuscitation” based on Victor Vroom's expectancy theory is also presented. The directed qualitative content analysis method proposed in this paper is a reliable, transparent, and comprehensive method for qualitative researchers. It can increase the rigour of qualitative data analysis, make the comparison of the findings of different studies possible and yield practical results.

Introduction

Qualitative content analysis (QCA) is a research approach for the description and interpretation of textual data using the systematic process of coding. The final product of data analysis is the identification of categories, themes and patterns ( Elo and Kyngäs, 2008 ; Hsieh and Shannon, 2005 ; Zhang and Wildemuth, 2009 ). Researchers in the field of healthcare commonly use QCA for data analysis ( Berelson, 1952 ). QCA has been described and used in the first half of the 20th century ( Schreier, 2014 ). The focus of QCA is the development of knowledge and understanding of the study phenomenon. QCA, as the application of language and contextual clues for making meanings in the communication process, requires a close review of the content gleaned from conducting interviews or observations ( Downe-Wamboldt, 1992 ; Hsieh and Shannon, 2005 ).

QCA is classified into conventional (inductive), directed (deductive) and summative methods ( Hsieh and Shannon, 2005 ; Mayring, 2000 , 2014 ). Inductive QCA, as the most popular approach in data analysis, helps with the development of theories, schematic models or conceptual frameworks ( Elo and Kyngäs, 2008 ; Graneheim and Lundman, 2004 ; Vaismoradi et al., 2013 , 2016 ), which should be refined, tested or further developed by using directed QCA ( Elo and Kyngäs, 2008 ). Directed QCA is a common method of data analysis in healthcare research ( Elo and Kyngäs, 2008 ), but insufficient knowledege is available about how this method is applied ( Elo and Kyngäs, 2008 ; Hsieh and Shannon, 2005 ). This may hamper the use of directed QCA by novice qualitative researchers and account for a low application of this method compared with the inductive method ( Elo and Kyngäs, 2008 ; Mayring, 2000 ). Therefore, this paper aims to describe and integrate methods applied in directed QCA.

International databases such as PubMed (including Medline), Scopus, Web of Science and ScienceDirect were searched for retrieval of papers related to QCA and directed QCA. Use of keywords such as ‘directed content analysis’, ‘deductive content analysis’ and ‘qualitative content analysis’ led to 13,738 potentially eligible papers. Applying inclusion criteria such as ‘focused on directed qualitative content analysis’ and ‘published in peer-reviewed journals’; and removal of duplicates resulted in 30 papers. However, only two of these papers dealt with the description of directed QCA in terms of the methodological process. Ancestry and manual searches within these 30 papers revealed the pioneers of the description of this method in international literature. A further search for papers published by the method's pioneers led to four more papers and one monograph dealing with directed QCA ( Figure 1 ).

The search strategy for the identification of papers.

Finally, the authors of this paper integrated and elaborated a comprehensive and stepwise method of directed QCA based on the commonalities of methods discussed in the included papers. Also, the experiences of the current authors in the field of qualitative research were incorporated into the suggested stepwise method of data analysis for directed QCA ( Table 1 ).

The suggested steps for directed content analysis.

While the included papers about directed QCA were the most cited ones in international literature, none of them provided sufficient detail with regard to how to conduct the data analysis process. This might hamper the use of this method by novice qualitative researchers and hinder its application by nurse researchers compared with inductive QCA. As it can be seen in Figure 1 , the search resulted in 5 articles that explain DCA method. The following is description of the articles, along with their strengths and weaknesses. Authors used the strengths in their suggested method as mentioned in Table 1 .

The methods suggested for directed QCA in the international literature

The method suggested by hsieh and shannon (2005).

Hsieh and Shannon (2005) developed two strategies for conducting directed QCA. The first strategy consists of reading textual data and highlighting those parts of the text that, on first impression, appeared to be related to the predetermined codes dictated by a theory or prior research findings. Next, the highlighted texts would be coded using the predetermined codes.

As for the second strategy, the only difference lay in starting the coding process without primarily highlighting the text. In both analysis strategies, the qualitative researcher should return to the text and perform reanalysis after the initial coding process ( Hsieh and Shannon, 2005 ). However, the current authors believe that this second strategy provides an opportunity for recognising missing texts related to the predetermined codes and also newly emerged ones. It also enhances the trustworthiness of findings.

As an important part of the method suggested by Hsieh and Shannon (2005) , the term ‘code’ was used for the different levels of abstraction, but a more precise definition of this term seems to be crucial. For instance, they stated that ‘data that cannot be coded are identified and analyzed later to determine if they represent a new category or a subcategory of an existing code’ (2005: 1282).

It seems that the first ‘code’ in the above sentence indicates the lowest level of abstraction that could be achieved instantly from raw data. However, the ‘code’ at the end of the sentence refers to a higher level of abstraction, because it denotes to a category or subcategory.

Furthermore, the interchangeable and inconsistent use of the words ‘predetermined code’ and ‘category’ could be confusing to novice qualitative researchers. Moreover, Hsieh and Shannon (2005) did not specify exactly which parts of the text, whether highlighted, coded or the whole text, should be considered during the reanalysis of the text after initial coding process. Such a lack of specification runs the risk of missing the content during the initial coding process, especially if the second review of the text is restricted to highlighted sections. One final important omission in this method is the lack of an explicit description of the process through which new codes emerge during the reanalysis of the text. Such a clarification is crucial, because the detection of subtle links between newly emerging codes and the predetermined ones is not straightforward.

The method suggested by Elo and Kyngäs (2008)

Elo and Kyngäs (2008) suggested ‘structured’ and ‘unconstrained’ methods or paths for directed QCA. Accordingly, after determining the ‘categorisation matrix’ as the framework for data collection and analysis during the study process, the whole content would be reviewed and coded. The use of the unconstrained matrix allows the development of some categories inductively by using the steps of ‘grouping’, ‘categorisation’ and ‘abstraction’. The use of a structured method requires a structured matrix upon which data are strictly coded. Hypotheses suggested by previous studies often are tested using this method ( Elo and Kyngäs, 2008 ).

The current authors believe that the label of ‘data gathering by the content’ (p. 110) in the unconstrained matrix path can be misleading. It refers to the data coding step rather than data collection. Also, in the description of the structured path there is an obvious discrepancy with regard to the selection of the portions of the content that fit or do not fit the matrix: ‘… if the matrix is structured, only aspects that fit the matrix of analysis are chosen from the data …’; ‘… when using a structured matrix of analysis, it is possible to choose either only the aspects from the data that fit the categorization frame or, alternatively, to choose those that do not’ ( Elo and Kyngäs, 2008 : 111–112).

Figure 1 in Elo and Kyngäs's paper ( 2008 : 110) clearly distinguished between the structured and unconstrained paths. On the other hand, the first sentence in the above quotation clearly explained the use of the structured matrix, but it was not clear whether the second sentence referred to the use of the structured or unconstrained matrix.

The method suggested by Zhang and Wildemuth (2009)

Considering the method suggested by Hsieh and Shannon (2005) , Zhang and Wildemuth (2009) suggested an eight-step method as follows: (1) preparation of data, (2) definition of the unit of analysis, (3) development of categories and the coding scheme, (4) testing the coding scheme in a text sample, (5) coding the whole text, (6) assessment of the coding's consistency, (7) drawing conclusions from the coded data, and (8) reporting the methods and findings ( Zhang and Wildemuth, 2009 ). Only in the third step of this method, the description of the process of category development, did Zhang and Wildemuth (2009) briefly make a distinction between the inductive versus deductive content analysis methods. On first impression, the only difference between the two approaches seems to be the origin from which categories are developed. In addition, the process of connecting the preliminary codes extracted from raw data with predetermined categories is described. Furthermore, it is not clear whether this linking should be established from categories to primary codes, or vice versa.

The method suggested by Mayring ( 2000 , 2014 )

Mayring ( 2000 , 2014 ) suggested a seven-step method for directed QCA that distinctively differentiated between inductive and deductive methods as follows: (1) determination of the research question and theoretical background, (2) definition of the category system such as main categories and subcategories based on the previous theory and research, (3) establishing a guideline for coding, considering definitions, anchor examples and coding rules, (5) reading the whole text, determining preliminary codes, adding anchor examples and coding rules, (5) revision of the category and coding guideline after working through 10–50% of the data, (6) reworking data if needed, or listing the final category, and (7) analysing and interpreting based on the category frequencies and contingencies.

Mayring suggested that coding rules should be defined to distinctly assign the parts of the text to a particular category. Furthermore, indicating which concrete part of the text serves as typical examples, also known as ‘anchor samples’, and belongs to a particular category was recommended for describing each category ( Mayring, 2000 , 2014 ). The current authors believe that these suggestions help clarify directed QCA and enhance its trustworthiness.

But when the term ‘preliminary coding’ was used, Mayring ( 2000 , 2014 ) did not clearly clarify whether these codes are inductively or deductively created. In addition, Mayring was inclined to apply the quantitative approach implicitly in steps 5 and 7, which is incongruent with the qualitative paradigm. Furthermore, nothing was stated about the possibility of the development of new categories from the textual material: ‘… theoretical considerations can lead to a further categories or rephrasing of categories from previous studies, but the categories are not developed out of the text material like in inductive category formation …’ ( Mayring, 2014 : 97).

Integration and clarification of methods for directed QCA

Directed QCA took different paths when the categorisation matrix contained concepts with higher-level versus lower-level abstractions. In matrices with low abstraction levels, linking raw data to predetermined categories was not difficult, and suggested methods in international nursing literature seem appropriate and helpful. For instance, Elo and Kyngäs (2008) introduced ‘mental well-being threats’ based on the categories of ‘dependence’, ‘worries’, ‘sadness’ and ‘guilt’. Hsieh and Shannon (2005) developed the categories of ‘denial’, ‘anger’, ‘bargaining’, ‘depression’ and ‘acceptance’ when elucidating the stages of grief. Therefore, the low-level abstractions easily could link raw data to categories. The predicament of directed QCA began when the categorisation matrix contained the concepts with high levels of abstraction. The gap regarding how to connect the highly abstracted categories to the raw data should be bridged by using a transparent and comprehensive analysis strategy. Therefore, the authors of this paper integrated the methods of directed QCA outlined in the international literature and elaborated them using the phases of ‘preparation’, ‘organization’ and ‘reporting’ proposed by Elo and Kyngäs (2008) . Also, the experiences of the current authors in the field of qualitative research were incorporated into their suggested stepwise method of data analysis. The method was presented using the example of the “team members’ motivation for cardiopulmonary resuscitation (CPR)” based on Victor Vroom's expectancy theory ( Assarroudi et al., 2017 ). In this example, interview transcriptions were considered as the unit of analysis, because interviews are the most common method of data collection in qualitative studies ( Gill et al., 2008 ).

Suggested method of directed QCA by the authors of this paper

This method consists of 16 steps and three phases, described below: preparation phase (steps 1–7), organisation phase (steps 8–15), and reporting phase (step 16).

The preparation phase:

• The acquisition of general skills . In the first step, qualitative researchers should develop skills including self-critical thinking, analytical abilities, continuous self-reflection, sensitive interpretive skills, creative thinking, scientific writing, data gathering and self-scrutiny ( Elo et al., 2014 ). Furthermore, they should attain sufficient scientific and content-based mastery of the method chosen for directed QCA. In the proposed example, qualitative researchers can achieve this mastery through conducting investigations in original sources related to Victor Vroom's expectancy theory. Main categories pertaining to Victor Vroom's expectancy theory were ‘expectancy’, ‘instrumentality’ and ‘valence’. This theory defined ‘expectancy’ as the perceived probability that efforts could lead to good performance. ‘Instrumentality’ was the perceived probability that good performance led to desired outcomes. ‘Valence’ was the value that the individual personally placed on outcomes ( Vroom, 1964 , 2005 ).
• Selection of the appropriate sampling strategy . Qualitative researchers need to select the proper sampling strategies that facilitate an access to key informants on the study phenomenon ( Elo et al., 2014 ). Sampling methods such as purposive, snowball and convenience methods ( Coyne, 1997 ) can be used with the consideration of maximum variations in terms of socio-demographic and phenomenal characteristics ( Sandelowski, 1995 ). The sampling process ends when information ‘redundancy’ or ‘saturation’ is reached. In other words, it ends when all aspects of the phenomenon under study are explored in detail and no additional data are revealed in subsequent interviews ( Cleary et al., 2014 ). In line with this example, nurses and physicians who are the members of the CPR team should be selected, given diversity in variables including age, gender, the duration of work, number of CPR procedures, CPR in different patient groups and motivation levels for CPR.
• Deciding on the analysis of manifest and/or latent content . Qualitative researchers decide whether the manifest and/or latent contents should be considered for analysis based on the study's aim. The manifest content is limited to the transcribed interview text, but latent content includes both the researchers' interpretations of available text, and participants' silences, pauses, sighs, laughter, posture, etc. ( Elo and Kyngäs, 2008 ). Both types of content are recommended to be considered for data analysis, because a deep understanding of data is preferred for directed QCA ( Thomas and Magilvy, 2011 ).
• Developing an interview guide . The interview guide contains open-ended questions based on the study's aims, followed by directed questions about main categories extracted from the existing theory or previous research ( Hsieh and Shannon, 2005 ). Directed questions guide how to conduct interviews when using directed or conventional methods. The following open-ended and directed questions were used in this example: An open-ended question was ‘What is in your mind when you are called for performing CPR?’ The directed question for the main category of ‘expectancy’ could be ‘How does the expectancy of the successful CPR procedure motivate you to resuscitate patients?’
• Conducting and transcribing interviews . An interview guide is used to conduct interviews for directed QCA. After each interview session, the entire interview is transcribed verbatim immediately ( Poland, 1995 ) and with utmost care ( Seidman, 2013 ). Two recorders should be used to ensure data backup ( DiCicco-Bloom and Crabtree, 2006 ). (For more details concerning skills required for conducting successful qualitative interviews, see Edenborough, 2002 ; Kramer, 2011 ; Schostak, 2005 ; Seidman, 2013 ).
• Specifying the unit of analysis . The unit of analysis may include the person, a program, an organisation, a class, community, a state, a country, an interview, or a diary written by the researchers ( Graneheim and Lundman, 2004 ). The transcriptions of interviews are usually considered units of analysis when data are collected using interviews. In this example, interview transcriptions and filed notes are considered as the units of analysis.
• Immersion in data . The transcribed interviews are read and reviewed several times with the consideration of the following questions: ‘Who is telling?’, ‘Where is this happening?’, ‘When did it happen?’, ‘What is happening?’, and ‘Why?’ ( Elo and Kyngäs, 2008 ). These questions help researchers get immersed in data and become able to extract related meanings ( Elo and Kyngäs, 2008 ; Elo et al., 2014 ).

The organisation phase:

The categorisation matrix of the team members' motivation for CPR.

CPR: cardiopulmonary resuscitation.

• Theoretical definition of the main categories and subcategories . Derived from the existing theory or previous research, the theoretical definitions of categories should be accurate and objective ( Mayring, 2000 , 2014 ). As for this example, ‘expectancy’ as a main category could be defined as the “subjective probability that the efforts by an individual led to an acceptable level of performance (effort–performance association) or to the desired outcome (effort–outcome association)” ( Van Eerde and Thierry, 1996 ; Vroom, 1964 ).
• – Expectancy in the CPR was a subjective probability formed in the rescuer's mind.
• – This subjective probability should be related to the association between the effort–performance or effort–outcome relationship perceived by the rescuer.
• The pre-testing of the categorisation matrix . The categorisation matrix should be tested using a pilot study. This is an essential step, particularly if more than one researcher is involved in the coding process. In this step, qualitative researchers should independently and tentatively encode the text, and discuss the difficulties in the use of the categorisation matrix and differences in the interpretations of the unit of analysis. The categorisation matrix may be further modified as a result of such discussions ( Elo et al., 2014 ). This also can increase inter-coder reliability ( Vaismoradi et al., 2013 ) and the trustworthiness of the study.
• Choosing and specifying the anchor samples for each main category . An anchor sample is an explicit and concise exemplification, or the identifier of a main category, selected from meaning units ( Mayring, 2014 ). An anchor sample for ‘expectancy’ as the main category of this example could be as follows: ‘… the patient with advanced metastatic cancer who requires CPR … I do not envision a successful resuscitation for him.’

An example of steps taken for the abstraction of the phenomenon of expectancy (main category).

CPR: cardiopulmonary resuscitation

• The inductive abstraction of main categories from preliminary codes . Preliminary codes are grouped and categorised according to their meanings, similarities and differences. The products of this categorisation process are known as ‘generic categories’ ( Elo and Kyngäs, 2008 ) ( Table 3 ).
• The establishment of links between generic categories and main categories . The constant comparison of generic categories and main categories results in the development of a conceptual and logical link between generic and main categories, nesting generic categories into the pre-existing main categories and creating new main categories. The constant comparison technique is applied to data analysis throughout the study ( Zhang and Wildemuth, 2009 ) ( Table 3 ).

The reporting phase:

• Reporting all steps of directed QCA and findings . This includes a detailed description of the data analysis process and the enumeration of findings ( Elo and Kyngäs, 2008 ). Findings should be systematically presented in such a way that the association between the raw data and the categorisation matrix is clearly shown and easily followed. Detailed descriptions of the sampling process, data collection, analysis methods and participants' characteristics should be presented. The trustworthiness criteria adopted along with the steps taken to fulfil them should also be outlined. Elo et al. (2014) developed a comprehensive and specific checklist for reporting QCA studies.

Trustworthiness

Multiple terms are used in the international literature regarding the validation of qualitative studies ( Creswell, 2013 ). The terms ‘validity’, ‘reliability’, and ‘generalizability’ in quantitative studies are equivalent to ‘credibility’, ‘dependability’, and ‘transferability’ in qualitative studies, respectively ( Polit and Beck, 2013 ). These terms, along with the additional concept of confirmability, were introduced by Lincoln and Guba (1985) . Polit and Beck added the term ‘authenticity’ to the list. Collectively, they are the different aspects of trustworthiness in all types of qualitative studies ( Polit and Beck, 2013 ).

To ehnance the trustworthiness of the directed QCA study, researchers should thoroughly delineate the three phases of ‘preparation’, ‘organization’, and ‘reporting’ ( Elo et al., 2014 ). Such phases are needed to show in detail how categories are developed from data ( Elo and Kyngäs, 2008 ; Graneheim and Lundman, 2004 ; Vaismoradi et al., 2016 ). To accomplish this, appendices, tables and figures may be used to depict the reduction process ( Elo and Kyngäs, 2008 ; Elo et al., 2014 ). Furthermore, an honest account of different realities during data analysis should be provided ( Polit and Beck, 2013 ). The authors of this paper believe that adopting this 16-step method can enhance the trustworthiness of directed QCA.

Directed QCA is used to validate, refine and/or extend a theory or theoretical framework in a new context ( Elo and Kyngäs, 2008 ; Hsieh and Shannon, 2005 ). The purpose of this paper is to provide a comprehensive, systematic, yet simple and applicable method for directed QCA to facilitate its use by novice qualitative researchers.

Despite the current misconceptions regarding the simplicity of QCA and directed QCA, knowledge development is required for conducting them ( Elo and Kyngäs, 2008 ). Directed QCA is often performed on a considerable amount of textual data ( Pope et al., 2000 ). Nevertheless, few studies have discussed the multiple steps need to be taken to conduct it. In this paper, we have integrated and elaborated the essential steps pointed to by international qualitative researchers on directed QCA such as ‘preliminary coding’, ‘theoretical definition’ ( Mayring, 2000 , 2014 ), ‘coding rule’, ‘anchor sample’ ( Mayring, 2014 ), ‘inductive analysis in directed qualitative content analysis’ ( Elo and Kyngäs, 2008 ), and ‘pretesting the categorization matrix’ ( Elo et al., 2014 ). Moreover, the authors have added a detailed discussion regarding ‘the use of inductive abstraction’ and ‘linking between generic categories and main categories’.

The importance of directed QCA is increased due to the development of knowledge and theories derived from QCA using the inductive approach, and the growing need to test the theories. Directed QCA proposed in this paper, is a reliable, transparent and comprehensive method that may increase the rigour of data analysis, allow the comparison of the findings of different studies, and yield practical results.

Abdolghader Assarroudi (PhD, MScN, BScN) is Assistant Professor in Nursing, Department of Medical‐Surgical Nursing, School of Nursing and Midwifery, Sabzevar University of Medical Sciences, Sabzevar, Iran. His main areas of research interest are qualitative research, instrument development study and cardiopulmonary resuscitation.

Fatemeh Heshmati Nabavi (PhD, MScN, BScN) is Assistant Professor in nursing, Department of Nursing Management, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran. Her main areas of research interest are medical education, nursing management and qualitative study.

Mohammad Reza Armat (MScN, BScN) graduated from the Mashhad University of Medical Sciences in 1991 with a Bachelor of Science degree in nursing. He completed his Master of Science degree in nursing at Tarbiat Modarres University in 1995. He is an instructor in North Khorasan University of Medical Sciences, Bojnourd, Iran. Currently, he is a PhD candidate in nursing at the Mashhad School of Nursing and Midwifery, Mashhad University of Medical Sciences, Iran.

Abbas Ebadi (PhD, MScN, BScN) is professor in nursing, Behavioral Sciences Research Centre, School of Nursing, Baqiyatallah University of Medical Sciences, Tehran, Iran. His main areas of research interest are instrument development and qualitative study.

Mojtaba Vaismoradi (PhD, MScN, BScN) is a doctoral nurse researcher at the Faculty of Nursing and Health Sciences, Nord University, Bodø, Norway. He works in Nord’s research group ‘Healthcare Leadership’ under the supervision of Prof. Terese Bondas. For now, this team has focused on conducting meta‐synthesis studies with the collaboration of international qualitative research experts. His main areas of research interests are patient safety, elderly care and methodological issues in qualitative descriptive approaches. Mojtaba is the associate editor of BMC Nursing and journal SAGE Open in the UK.

Key points for policy, practice and/or research

• In this paper, essential steps pointed to by international qualitative researchers in the field of directed qualitative content analysis were described and integrated.
• A detailed discussion regarding the use of inductive abstraction, and linking between generic categories and main categories, was presented.
• A 16-step method of directed qualitative content analysis proposed in this paper is a reliable, transparent, comprehensive, systematic, yet simple and applicable method. It can increase the rigour of data analysis and facilitate its use by novice qualitative researchers.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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• Content Analysis | Guide, Methods & Examples

Content Analysis | Guide, Methods & Examples

Published on July 18, 2019 by Amy Luo . Revised on June 22, 2023.

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 categorize or “code” words, themes, and concepts within the texts and then analyze the results.

Table of contents

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

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 analyze.

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

Quantitative content analysis example

To research the importance of employment issues in political campaigns, you could analyze campaign speeches for the frequency of terms such as unemployment , jobs , and work  and use statistical analysis to find differences over time or between candidates.

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

Qualitative content analysis example

To gain a more qualitative understanding of employment issues in political campaigns, you could locate the word unemployment in speeches, identify what other words or phrases appear next to it (such as economy,   inequality or  laziness ), and analyze the meanings of these relationships to better understand the intentions and targets of different campaigns.

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
• Analyzing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyze 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, leading to various types of research bias and cognitive bias .

• 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 .

Example research question for content analysis

Is there a difference in how the US media represents younger politicians compared to older ones in terms of trustworthiness?

Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. 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 inclusion and exclusion criteria (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 amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample .

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze 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 ).

Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on age and the concept of trustworthiness. To get more detailed data, you also code for other categories such as their political party and the marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing 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.

In considering the category “younger politician,” you decide which titles will be coded with this category ( senator, governor, counselor, mayor ). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable ) will be coded in this category.

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 categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze 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.

Let’s say the results reveal that words and phrases related to trustworthiness appeared in the same sentence as an older politician more frequently than they did in the same sentence as a younger politician. From these results, you conclude that national newspapers present older politicians as more trustworthy than younger politicians, and infer that this might have an effect on readers’ perceptions of younger people in politics.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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21 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

For qualitative studies, can the findings be structured according to the Research questions? Thank you.

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Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study

Affiliation.

• 1 College of Human and Health Sciences, Swansea University, Swansea, UK; Faculty of Professional Studies, University of Nordland, Bodø, Norway.
• PMID: 23480423
• DOI: 10.1111/nhs.12048

Qualitative content analysis and thematic analysis are two commonly used approaches in data analysis of nursing research, but boundaries between the two have not been clearly specified. In other words, they are being used interchangeably and it seems difficult for the researcher to choose between them. In this respect, this paper describes and discusses the boundaries between qualitative content analysis and thematic analysis and presents implications to improve the consistency between the purpose of related studies and the method of data analyses. This is a discussion paper, comprising an analytical overview and discussion of the definitions, aims, philosophical background, data gathering, and analysis of content analysis and thematic analysis, and addressing their methodological subtleties. It is concluded that in spite of many similarities between the approaches, including cutting across data and searching for patterns and themes, their main difference lies in the opportunity for quantification of data. It means that measuring the frequency of different categories and themes is possible in content analysis with caution as a proxy for significance.

Keywords: content analysis; nursing; qualitative descriptive research; thematic analysis.

© 2013 Wiley Publishing Asia Pty Ltd.

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Using Taguette Software for Qualitative Content Analysis of Black Lives Matter Documents and Media Articles

• Todd Lu + −

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Learning goals and assessments.

Learning Goal(s):

• Students will learn methodological practice of process coding using a free and open-source qualitative content analysis software.
• Students will develop critical thinking by leveraging evidence generated from texts to come to conclusions on differences between movements and media content.
• Students will develop hands-on experience coding texts that will expose them to themes of activism, racism, and intersectional perspectives.

Goal Assessment(s):

• The instructor can assess students based on their active in-class participation using Taguette software to code documents during the class period.
• The instructor can assess students by requiring students to export coded documents in Word or PDF files, which is a feature enabled in Taguette.
• The instructor can assess students based on written reflection notes from small group discussion handouts and in-class report backs.

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