how to conduct a deductive content analysis in counseling research

The Application of Content Analysis in Nursing Science Research pp 23–30 Cite as

Deductive Content Analysis

• Helvi Kyngäs 4 &
• Pirjo Kaakinen 4  
• First Online: 01 November 2019

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This chapter describes deductive content analysis and how it can be applied to the field of nursing science. Deductive content analysis is not commonly used, but is nevertheless beneficial for testing concepts, categories, theories or any conceptual structure in a new context. Deductive content analysis is similar to inductive content analysis in that it is applied in qualitative research and the data collection method aims to reach data saturation. The main difference between the two analytical techniques is that research in which deductive content analysis is applied usually has prior theoretical knowledge as the starting point. As such, the research questions are influenced by prior knowledge, and hence, affect the data collection stage. Another difference between inductive and deductive content analysis is that deductive content analysis is guided by a half-structured or structured analysis matrix. As in inductive content analysis, the reporting of results should be structured according to the identified concepts, categories and/or themes.

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Elo S, Kääriäinen M, Kanste O, Pölkki T, Utriainen K, Kyngäs H. Qualitative content analysis. A focus on trustworthiness. SAGE Open. 2014;4:1–10. https://doi.org/10.1177/2158244014522633 .

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Kyngäs, H., Kaakinen, P. (2020). Deductive Content Analysis. In: Kyngäs, H., Mikkonen, K., Kääriäinen, M. (eds) The Application of Content Analysis in Nursing Science Research. Springer, Cham. https://doi.org/10.1007/978-3-030-30199-6_3

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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
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• Cognitive bias
• Conformity bias
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• Availability heuristic
• Attrition bias
• Social desirability bias

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How to Conduct a Deductive Content Analysis in Counseling Research

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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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How to Conduct a Deductive Content Analysis in Counseling Research

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• https://doi.org/10.1080/21501378.2020.1846992

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Content analysis is a flexible methodology that allows researchers to examine trends in communication, such as journal articles, written narratives, personal journals, and videos, to name a few. In this article, we describe a deductive approach to content analysis methodology that follows an a priori design and allows for descriptive and inferential analysis of communication in counseling outcome research. We review four replicable steps designed to maximize validity and generalizability: unitizing data, sampling units, recording categories, and reducing units into interpretable categories. Within these four steps, we discuss identifying units for analysis, sampling strategies and sample sizes, constructing a coding team, developing codebooks and coding sheets, conducting pilot tests, tracking interrater reliability, reaching consensus, and writing up findings. We also present future applications for content analysis in counseling research, including diverse sources of data (e.g., case notes, counseling videos) and integration of inferential statistical testing into the method.

• Content analysis
• outcome research

Additional information

Notes on contributors, w. bradley mckibben.

W. Bradley McKibben is an Assistant Professor in the Department of Counseling at Nova Southeastern University.

Rochelle Cade

Rochelle Cade is an Associate Professor in the Department of Human Services and Educational Leadership at Stephen F. Austin State University.

Lucy L. Purgason

Lucy L. Purgason is an Assistant Professor at the Department of Human Development and Psychological Counseling at Appalachian State University.

Edward Wahesh

Edward Wahesh is an Associate Professor in the Department of Education and Counseling at Villanova University.

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Navigating Inductive Content Analysis in Qualitative Research

Which qualitative content analysis methods should you choose to make sense of your data? What best practices should you adopt for each method? In the latest episode of Between the Data. Dr. Stacy Penna talked to Dr. Danya Vears , Team Leader and Principal Research Fellow at the Murdoch Children’s Research Institute (MCRI) in Melbourne, Australia, about a qualitative research method she has been working with since her days as a student in biomedical ethics: inductive content analysis (ICA).

In the episode, Dr. Vears described how ICA works, discussed specific research contexts where ICA can be especially useful, and gave a step-by-step overview of the ICA process. This article offers some highlights from the episode.

What is Inductive Content Analysis, and How Does It Compare to Other Methodologies?

ICA is a qualitative content analysis method that produces a thematic summary of multiple texts by deriving categories and codes from the data. This makes it different from deductive content analysis in which a researcher develops a codebook based on a literature review , theoretical framework, or other preliminary work and then codes their data according to that predetermined list.

ICA also differs from thematic analysis , which is a close-grained, line-by-line coding process that results in a theoretical interpretation of the data. ICA begins at the higher category level, then builds out subcategories, ultimately resulting in a conceptual map.

An Often-Misunderstood Qualitative Content Analysis Method

Dr. Vears’s interest in ICA began while she was a PhD student. She and her supervisor, Professor Lynn Gillam, decided that ICA would be a good match for Vears’s research into the biomedical ethics of genetic carrier testing in children. ICA lends itself to both exploratory analysis and to research that aims to develop practical recommendations for improving services or policies.

However, the literature around ICA as a practice seemed muddled to Vears. “I started doing some research around how to do [ICA], and just found it was a bit of a mess,” she told Dr. Penna. Her investigations revealed that past researchers hadn’t settled on steps for the process — sometimes, researchers would draw in quantitative methods like counting as part of ICA. There was even a lack of agreement on what ICA should be called.

In preparing to complete her dissertation, Vears had found a new parallel interest: contributing to a better understanding of ICA as a methodology. “I just felt like we needed to do something to help junior researchers to figure out how to do this better.”

An Overview of the Inductive Content Analysis Process

Dr. Vears walked Penna through the basics of ICA. The first step, she explained, is to read and familiarize yourself with the texts you will analyze. “It’s particularly important if you haven’t actually collected that data yourself,” she notes. She says she often cautions researchers who are new to ICA to stay close to the data and not fall into the trap of over-extrapolating or interpreting themes as they go.

Next is to conduct a first round of coding that identifies high-level categories, or “big-picture meaning units” as Dr. Vears puts it. In a project based on interviews, for example, these categories will often relate

to the topics of each question. The coded sections in this initial round will usually be larger pieces of text — whole paragraphs or more.

The next step is to carry out a second round of coding that breaks each category down into even further fine-grain topics. If necessary, researchers can complete additional rounds of coding after this. The final step is to produce a map of the categories and concepts from across all the texts — one that can be used to draw conclusions about the whole body of research.

Dr. Vears likened the ICA process to drawing a road map: first you draw in the main roads, then the secondary roads, and finally the side streets.

Developing an ICA Map with Qualitative Research Software

Another advantage of ICA, according to Vears, is that it doesn’t necessarily require a particular technology. “You can do it on paper that you print out if you want to,” she says. But she also notes that she and her students have found NVivo to be a useful tool for ICA and for qualitative content analysis in general.

NVivo accommodates multiple rounds of coding well and makes developing categories and subcategories a simpler, more intuitive process. Plus, NVivo makes it possible to create visual representations of code hierarchies that can be used for collaboration across research teams or for presentations of data. One option with NVivo is to use the autocoding feature during the first pass of your coding which automatically detects and codes themes – saving you significant time.

Spreading the Word about ICA

Dr. Vears has become the go-to expert on using ICA in qualitative research at MCRI. She’s helped students and colleagues understand its applications in their research and conducted seminars on best practices. In 2022, she and Dr. Gillam published a paper, “Inductive Content Analysis: A Guide for Beginning Qualitative Researchers” .

She’s had a good response to the paper, with researchers from as far afield as Portugal asking her for more advice on ICA. Now, Dr. Vears is considering developing additional training resources to help others benefit from the methodology she’d struggled to find a good definition for as a PhD student — a methodology that has been so useful throughout her career.

Want to hear more about Dr. Vears’s thoughts on inductive content analysis and some specific examples of how she’s used it in her work at MCRI? Learn more about this research: listen to the full podcast episode here .

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