Qualitative Content Analysis: A Focus on Trustworthiness

Abstract

Qualitative content analysis is commonly used for analyzing qualitative data. However, few articles have examined the trustworthiness of its use in nursing science studies. The trustworthiness of qualitative content analysis is often presented by using terms such as credibility, dependability, conformability, transferability, and authenticity. This article focuses on trustworthiness based on a review of previous studies, our own experiences, and methodological textbooks. Trustworthiness was described for the main qualitative content analysis phases from data collection to reporting of the results. We concluded that it is important to scrutinize the trustworthiness of every phase of the analysis process, including the preparation, organization, and reporting of results. Together, these phases should give a reader a clear indication of the overall trustworthiness of the study. Based on our findings, we compiled a checklist for researchers attempting to improve the trustworthiness of a content analysis study. The discussion in this article helps to clarify how content analysis should be reported in a valid and understandable manner, which would be of particular benefit to reviewers of scientific articles. Furthermore, we discuss that it is often difficult to evaluate the trustworthiness of qualitative content analysis studies because of defective data collection method description and/or analysis description.

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DOI: 10.1177/2158244014522633
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Article
Although qualitative content analysis is commonly used in
nursing science research, the trustworthiness of its use has
not yet been systematically evaluated. There is an ongoing
demand for effective and straightforward strategies for eval-
uating content analysis studies. A more focused discussion
about the quality of qualitative content analysis findings is
also needed, particularly as several articles have been pub-
lished on the validity and reliability of quantitative content
analysis (Neuendorf, 2011; Potter & Levine-Donnerstein,
1999; Rourke & Anderson, 2004) than qualitative content
analysis. Whereas many standardized procedures are avail-
able for performing quantitative content analysis (Baxter,
2009), this is not the case for qualitative content analysis.
Qualitative content analysis is one of the several qualita-
tive methods currently available for analyzing data and inter-
preting its meaning (Schreier, 2012). As a research method,
it represents a systematic and objective means of describing
and quantifying phenomena (Downe-Wamboldt, 1992;
Schreier, 2012). A prerequisite for successful content analy-
sis is that data can be reduced to concepts that describe the
research phenomenon (Cavanagh, 1997; Elo & Kyngäs,
2008; Hsieh & Shannon, 2005) by creating categories, con-
cepts, a model, conceptual system, or conceptual map (Elo &
Kyngäs, 2008; Morgan, 1993; Weber, 1990). The research
question specifies what to analyze and what to create (Elo &
Kyngäs, 2008; Schreier, 2012). In qualitative content analy-
sis, the abstraction process is the stage during which con-
cepts are created. Usually, some aspects of the process can be
readily described, but it also partially depends on the
researcher’s insight or intuitive action, which may be very
difficult to describe to others (Elo & Kyngäs, 2008;
Graneheim & Lundman, 2004). From the perspective of
validity, it is important to report how the results were cre-
ated. Readers should be able to clearly follow the analysis
and resulting conclusions (Schreier, 2012).
Qualitative content analysis can be used in either an
inductive or a deductive way. Both inductive and deductive
content analysis processes involve three main phases: prepa-
ration, organization, and reporting of results. The preparation
522633SGOXXX10.1177/2158244014522633SAGE OpenElo et al.
research-article2014
1University of Oulu, Finland
2Medical Research Center, Oulu University Hospital, Finland
3National Institute of Health and Welfare, Oulu, Finland
Corresponding Author:
Satu Elo, Senior University Lecturer, Institute of Health Sciences, Medical
Research Center Oulu, Oulu University Hospital and University of Oulu,
Box 5000, 90014, Finland.
Email: satu.elo@oulu.fi
Qualitative Content Analysis: A Focus on
Trustworthiness
Satu Elo1, Maria Kääriäinen1,2, Outi Kanste3, Tarja Pölkki1, Kati
Utriainen1, and Helvi Kyngäs1,2
Abstract
Qualitative content analysis is commonly used for analyzing qualitative data. However, few articles have examined the
trustworthiness of its use in nursing science studies. The trustworthiness of qualitative content analysis is often presented by
using terms such as credibility, dependability, conformability, transferability, and authenticity. This article focuses on trustworthiness
based on a review of previous studies, our own experiences, and methodological textbooks. Trustworthiness was described
for the main qualitative content analysis phases from data collection to reporting of the results. We concluded that it is
important to scrutinize the trustworthiness of every phase of the analysis process, including the preparation, organization,
and reporting of results. Together, these phases should give a reader a clear indication of the overall trustworthiness of
the study. Based on our findings, we compiled a checklist for researchers attempting to improve the trustworthiness of a
content analysis study. The discussion in this article helps to clarify how content analysis should be reported in a valid and
understandable manner, which would be of particular benefit to reviewers of scientific articles. Furthermore, we discuss that
it is often difficult to evaluate the trustworthiness of qualitative content analysis studies because of defective data collection
method description and/or analysis description.
Keywords
analysis method, nursing methodology research, qualitative content analysis, qualitative research, rigor, trustworthiness,
validity

2 SAGE Open
phase consists of collecting suitable data for content analy-
sis, making sense of the data, and selecting the unit of analy-
sis. In the inductive approach, the organization phase
includes open coding, creating categories, and abstraction
(Elo & Kyngäs, 2008). In deductive content analysis, the
organization phase involves categorization matrix develop-
ment, whereby all the data are reviewed for content and
coded for correspondence to or exemplification of the identi-
fied categories (Polit & Beck, 2012). The categorization
matrix can be regarded as valid if the categories adequately
represent the concepts, and from the viewpoint of validity,
the categorization matrix accurately captures what was
intended (Schreier, 2012). In the reporting phase, results are
described by the content of the categories describing the phe-
nomenon using a selected approach (either deductive or
inductive).
There has been much debate about the most appropriate
terms (rigor, validity, reliability, trustworthiness) for assess-
ing qualitative research validity (Koch & Harrington, 1998).
Criteria for reliability and validity are used in both quantita-
tive and qualitative studies when assessing the credibility
(Emden & Sandelowski, 1999; Koch & Harrington, 1998;
Ryan-Nicholls & Will, 2009). Such terms are mainly rooted
in a positivist conception of research. According to Schreier
(2012), there is no clear dividing line between qualitative
and quantitative content analysis, and similar terms and cri-
teria for reliability and validity are often used. Researchers
have mainly used qualitative criteria when evaluating aspects
of validity in content analysis (Kyngäs et al., 2011). The
most widely used criteria for evaluating qualitative content
analysis are those developed by Lincoln and Guba (1985).
They used the term trustworthiness. The aim of trustworthi-
ness in a qualitative inquiry is to support the argument that
the inquiry’s findings are “worth paying attention to”
(Lincoln & Guba, 1985). This is especially important when
using inductive content analysis as categories are created
from the raw data without a theory-based categorization
matrix. Thus, we decided to use such traditional qualitative
research terms when identifying factors affecting the trust-
worthiness of data collection, analysis, and presentation of
the results of content analysis.
Several other trustworthiness evaluation criteria have
been proposed for qualitative studies (Emden, Hancock,
Schubert, & Darbyshire, 2001; Lincoln & Guba, 1985;
Neuendorf, 2002; Polit & Beck, 2012; Schreier, 2012).
However, a common feature of these criteria is that they
aspire to support the trustworthiness by reporting the pro-
cess of content analysis accurately. Lincoln and Guba (1985)
have proposed four alternatives for assessing the trustwor-
thiness of qualitative research, that is, credibility, depend-
ability, conformability, and transferability. In 1994, the
authors added a fifth criterion referred to as authenticity.
From the perspective of establishing credibility, researchers
must ensure that those participating in research are
identified and described accurately. Dependability refers to
the stability of data over time and under different condi-
tions. Conformability refers to the objectivity, that is, the
potential for congruence between two or more independent
people about the data’s accuracy, relevance, or meaning.
Transferability refers to the potential for extrapolation. It
relies on the reasoning that findings can be generalized or
transferred to other settings or groups. The last criterion,
authenticity, refers to the extent to which researchers, fairly
and faithfully, show a range of realities (Lincoln & Guba,
1985; Polit & Beck, 2012)
Researchers often struggle with problems that compro-
mise the trustworthiness of qualitative research findings (de
Casterlé, Gastmans, Bryon, & Denier, 2012). The aim of the
study described in this article was to describe trustworthiness
based on the main qualitative content analysis phases, and to
compile a checklist for evaluating trustworthiness of content
analysis study. The primary research question was, “What is
essential for researchers attempting to improve the trustwor-
thiness of a content analysis study in each phase?” The
knowledge presented was identified from a narrative litera-
ture review of earlier studies, our own experiences, and
methodological textbooks. A combined search of Medline
(Ovid) and CINAHL (EBSCO) was conducted, using the fol-
lowing key words: trustworthiness, rigor OR validity, AND
qualitative content analysis. The following were used as
inclusion criteria: methodological articles focused on quali-
tative content analysis in the area of health sciences pub-
lished in English and with no restrictions on year. The search
identified 12 methodological content analysis articles from
databases and reference list checks (Cavanagh, 1997;
Downe-Wamboldt, 1992; Elo & Kyngäs, 2008; Graneheim
& Lundman, 2004; Guthrie, Yongvanich, & Ricceri, 2004;
Harwood & Garry, 2003; Holdford, 2008; Hsieh & Shannon,
2005; Morgan, 1993; Potter & Levine-Donnerstein, 1999;
Rourke & Anderson, 2004; Vaismoradi, Bondas, & Turunen,
2013). The reference list of selected papers was also checked,
and qualitative research methodology textbooks were used
when writing the synthesis of the review. The discussion in
this article helps to clarify how content analysis should be
reported in a valid and understandable manner, which, we
expect, will be of particular benefit to reviewers of scientific
articles.
Trustworthiness in the Preparation
Phase in Content Analysis Study
Based on the results of the literature search, the main trust-
worthiness issues in the preparation phases were identified
as trustworthiness of the data collection method, sampling
strategy, and the selection of a suitable unit of analysis.
Based on the findings, we have compiled a checklist for
researchers attempting to improve the trustworthiness of a
content analysis study in each phase (Table 1).

Elo et al. 3
Data Collection Method
Demonstration of the trustworthiness of data collection is
one aspect that supports a researcher’s ultimate argument
concerning the trustworthiness of a study (Rourke &
Anderson, 2004). Selection of the most appropriate method
of data collection is essential for ensuring the credibility of
content analysis (Graneheim & Lundman, 2004). Credibility
deals with the focus of the research and refers to the confi-
dence in how well the data address the intended focus (Polit
& Beck, 2012). Thus, the researcher should put a lot of
thought into how to collect the most suitable data for content
analysis. The strategy to ensure trustworthiness of content
analysis starts by choosing the best data collection method to
answer the research questions of interest. In most studies
where content analysis is used, the collected data are unstruc-
tured (Elo & Kyngäs, 2008; Neuendorf, 2002; Sandelowski,
1995b), gathered by methods such as interviews, observa-
tions, diaries, other written documents, or a combination of
different methods. However, depending on the aim of the
study, the collected data may be open and semi-structured. If
inductive content analysis is used, it is important that the data
are as unstructured as possible (Dey, 1993; Neuendorf,
2002).
From the perspective of trustworthiness, a key question is,
“What is the relationship between prefiguration and the data
collection method, that is, should the researcher use descrip-
tive or semi-structured questions?” Nowadays, qualitative
content analysis is most often applied to verbal data such as
interview transcripts (Schreier, 2012). With descriptive data
Table 1. Checklist for Researchers Attempting to Improve the Trustworthiness of a Content Analysis Study.
Phase of the content analysis
study Questions to check
Preparation phase Data collection method
How do I collect the most suitable data for my content analysis?
Is this method the best available to answer the target research question?
Should I use either descriptive or semi-structured questions?
Self-awareness: what are my skills as a researcher?
How do I pre-test my data collection method?
Sampling strategy
What is the best sampling method for my study?
Who are the best informants for my study?
What criteria should be used to select the participants?
Is my sample appropriate?
Is my data well saturated?
Selecting the unit of analysis
What is the unit of analysis?
Is the unit of analysis too narrow or too broad?
Organization phase Categorization and abstraction
How should the concepts or categories be created?
Is there still too many concepts?
Is there any overlap between categories?
Interpretation
What is the degree of interpretation in the analysis?
How do I ensure that the data accurately represent the information that the participants provided?
Representativeness
How to I check the trustworthiness of the analysis process?
How do I check the representativeness of the data as a whole?
Reporting phase Reporting results
Are the results reported systematically and logically?
How are connections between the data and results reported?
Is the content and structure of concepts presented in a clear and understandable way?
Can the reader evaluate the transferability of the results (are the data, sampling method, and
participants described in a detailed manner)?
Are quotations used systematically?
How well do the categories cover the data?
Are there similarities within and differences between categories?
Is scientific language used to convey the results?
Reporting analysis process
Is there a full description of the analysis process?
Is the trustworthiness of the content analysis discussed based on some criteria?

4 SAGE Open
collection, it can often be challenging to control the diversity of
experiences and prevent interviewer bias and the privileging of
one type of information or analytical perspective (Warr &
Pyett, 1999). For example, when using a descriptive question
such as “Could you please tell me, how do you take care of
yourself?” the researcher has to consider the aim of data collec-
tion and try to extract data for that purpose. However, if the
researcher opts for a semi-structured data collection method,
they should be careful not to steer the participant’s answers too
much to obtain inductive data. It may be useful for the inter-
view questions to be developed in association with a “critical
reference group” (Pyett, 2003). Critical reference groups are
used in participatory action research and is a generic term for
those the research and evaluation is intended primarily to ben-
efit (Wadsworth, 1998). Subjecting the interview questions to
evaluation by this kind of group may help to construct under-
standable questions that make better sense of the studied phe-
nomenon by asking the “right questions in the right way.”
From the viewpoint of credibility, self-awareness of the
researcher is essential (Koch, 1994). Pre-interviews may
help to determine whether the interview questions are suit-
able for obtaining rich data that answer the proposed research
questions. Interview tapes, videos, and transcribed text
should be examined carefully to critically assess the research-
er’s own actions For instance, questions should be asked
such as “Did I manipulate or lead the participant?” and “Did
I ask too broad or structured questions?” Such evaluation
should not only begin at the start of the study but also be sup-
ported by continuous reflection to ensure the trustworthiness
of content analysis.
To manage the data, pre-testing of the analysis method is
as important in qualitative as in quantitative research. When
using a deductive content analysis approach, the categoriza-
tion matrix also needs to be pretested in a pilot phase
(Schreier, 2012). This is essential, especially when two or
more researchers are involved in the coding. In trial coding,
researchers independently try out the coding of the newly
developed matrix (Schreier, 2012) and then discuss any
apparent difficulties in using the matrix (Kyngäs et. al.,
2011) and the units of coding they have interpreted differ-
ently (Schreier, 2012). Based on their discussion, the catego-
rization matrix is modified, if needed.
Sampling Strategy
From the viewpoint of sampling strategy, it is essential to ask
questions such as the following: What is the best sampling
method for my study? Who are the best informants for my
study and what criteria to use for selecting the participants?
Is my sample appropriate? Are my data well saturated?
Thoroughness as a criterion of validity refers to the adequacy
of the data and also depends on sound sampling and satura-
tion (Whittemore, Chase, & Mandle, 2001). It is important to
consider the sampling method used in qualitative studies
(Creswell, 2013). Based on our research, the sampling
method is rarely mentioned in qualitative content analysis
studies (Kyngäs et. al., 2011). In qualitative research, the
sampling strategy is usually chosen based on the methodol-
ogy and topic, and not by the need for generalizability of the
findings (Higginbottom, 2004). Types of qualitative sam-
pling include convenience, purposive, theoretical, selective,
within-case and snowball sampling (Creswell, 2013;
Higginbottom, 2004; Polit & Beck, 2012). However, the
sample must be appropriate and comprise participants who
best represent or have knowledge of the research topic.
The most commonly used method in content analysis
studies is purposive sampling (Kyngäs, Elo, Pölkki,
Kääriäinen, & Kanste, 2011): purposive sampling is suitable
for qualitative studies where the researcher is interested in
informants who have the best knowledge concerning the
research topic. When using purposeful sampling, decisions
need to be made about who or what is sampled, what form
the sampling should take, and how many people or sites need
to be sampled (Creswell, 2013). However, a disadvantage of
purposive sampling is that it can be difficult for the reader to
judge the trustworthiness of sampling if full details are not
provided. The researcher needs to determine which type of
purposeful sampling would be best to use (Creswell, 2013),
and a brief description of the sampling method should be
provided.
Dependability refers to the stability of data over time and
under different conditions. Therefore, it is important to state
the principles and criteria used to select participants and
detail the participants’ main characteristics so that the trans-
ferability of the results to other contexts can be assessed
(e.g., see Moretti et al., 2011). The main question is then,
“Would the findings of an inquiry be repeated if it were rep-
licated with the same or similar participants in the same con-
text (Lincoln & Guba, 1985; Polit & Beck, 2012)?”
According to Lincoln and Guba’s (1985) criteria for estab-
lishing credibility, researchers must ensure that those partici-
pating in research are identified and described accurately. To
gather credible data, different sampling methods may be
required in different studies.
Selection of the most appropriate sample size is important
for ensuring the credibility of content analysis study
(Graneheim & Lundman, 2004). Information on the sample
size is essential when evaluating whether the sample is
appropriate. There is no commonly accepted sample size for
qualitative studies because the optimal sample depends on
the purpose of the study, research questions, and richness of
the data. In qualitative content analysis, the homogeneity of
the study participants or differences expected between
groups are evaluated (Burmeister, 2012; Sandelowski,
1995a). For example, a study on the well-being and the sup-
portive physical environment characteristics of home-dwell-
ing elderly is likely to generate fairly heterogeneous data and
may need more participants than if restrictions are applied,
for example, studying only elderly aged above 85 years or
those living in rural areas.
It has been suggested that saturation of data may indicate
the optimal sample size (Guthrie et al., 2004; Sandelowski,

Elo et al. 5
1995a). By definition, saturated data ensure replication in
categories, which in turn verifies and ensures comprehension
and completeness (Morse, Barrett, Mayan, Olson, & Spiers,
2002). If the saturation of data is incomplete, it may cause
problems in data analysis and prevent items being linked
together (Cavanagh, 1997). Well-saturated data facilitates its
categorization and abstraction. It is easier to recognize when
saturation is achieved if data are at least preliminarily col-
lected and analyzed at the same time (Guthrie et al., 2004;
Sandelowski, 1995a, 2001). It is common that all data are
first collected and then analyzed later. We recommend that
preliminary analysis should start, for example, after a few
interviews. When saturation is not achieved, it is often diffi-
cult to group the data and create concepts (Elo & Kyngäs,
2008; Guthrie et al., 2004; Harwood & Garry, 2003), pre-
venting a complete analysis and generating simplified results
(Harwood & Garry, 2003; Weber, 1990).
Selection of a Suitable Unit of Analysis
The success of data collection should be assessed in relation
to the specific research questions and study aim. The prepa-
ration phase also involves the selection of a suitable unit of
analysis, which is also important for ensuring the credibility
of content analysis. The meaning unit can, for example, be a
letter, word, sentence portion of pages, or words (Robson,
1993). Too broad a unit of analysis will be difficult to man-
age and may have various meanings. Too narrow a meaning
unit may result in fragmentation. The most suitable unit of
analysis will be sufficiently large to be considered as a whole
but small enough to be a relevant meaning unit during the
analysis process. It is important to fully describe the meaning
unit when reporting the analysis process so that readers can
evaluate the trustworthiness of the analysis (Graneheim &
Lundman, 2004). However, in previous scientific articles,
the unit of analysis has often been inadequately described,
making it difficult to evaluate how successful was the mean-
ing unit used (Kyngäs et al., 2011).
Trustworthiness of Organization Phase
in Content Analysis Study
According to Moretti et al. (2011), the advantage of qualita-
tive research is the richness of the collected data and such
data need to be interpreted and coded in a valid and reliable
way. In the following sections, we discuss trustworthiness
issues associated with the organization phase. In this phase,
it is essential to consider whether the categories are well cre-
ated, what the level of interpretation is, and how to check the
trustworthiness of the analysis.
As part of the organization phase, an explanation of how
the concepts or categories are created should be provided to
indicate the trustworthiness of study. Describing the con-
cepts and how they have been created can often be challeng-
ing, which may hinder a complete analysis, particularly if the
researcher has not abstracted the data, or too many different
types of items have been grouped together (Dey, 1993;
Hickey & Kipping, 1996). In addition, a large number of
concepts usually indicates that the researcher has been unable
to group the data, that is, the abstraction process is incom-
plete, and categories may also overlap (Kyngäs et al., 2011).
In this case, the researcher must continue the grouping to
identify any similarities within and differences between
categories.
According to Graneheim and Lundman (2004), an essen-
tial consideration when discussing the trustworthiness of
findings from a qualitative content analysis is that there is
always some degree of interpretation when approaching a
text. All researchers have to consider how to confirm the
credibility and conformability of the organization phase.
Conformability of findings means that the data accurately
represent the information that the participants provided and
the interpretations of those data are not invented by the
inquirer (Polit & Beck, 2012). This is particularly important
if the researcher decides to analyze the latent content (notic-
ing silence, sighs, laughter, posture etc.) in addition to mani-
fest content (Catanzaro, 1988; Robson, 1993) as it may result
in over interpretation (Elo & Kyngäs, 2008). It is recom-
mended that the analysis be performed by more than one per-
son to increase the comprehensivity and provide sound
interpretation of the data (Burla et al., 2008; Schreier, 2012).
However, high intercoder reliability (ICR) is required when
more than one coder is involved in deductive data analysis
(Vaismoradi et al., 2013). Burla, Knierim, Barth, Duetz, and
Abel (2008) have demonstrated how ICR assessment can be
used to improve coding in qualitative content analysis. This
is useful when using deductive content analysis, which is
based on a categorization matrix or coding scheme.
However, there are no published recommendations on
how the trustworthiness should be checked if the inductive
content analysis is conducted by two or more researcher. Our
suggestion is that one researcher is responsible for the analy-
sis and others carefully follow-up on the whole analysis pro-
cess and categorization. All the researchers should
subsequently get together and discuss any divergent opinions
concerning the categorization, like in the pilot phase men-
tioned earlier. For example, in one of our studies, two
research team members checked the adequacy of the analysis
and asked for possible complements (Kyngäs et al., 2011).
One study (Kyngäs et al., 2011) has suggested that data
are most often analyzed by one researcher, especially when
using inductive content analysis. In such a case, the credibil-
ity of the analysis can be confirmed by checking for the rep-
resentativeness of the data as a whole (Thomas & Magilvy,
2011). According to Pyett (2003), a good qualitative
researcher cannot avoid the time-consuming work of return-
ing again and again to the data, to check whether the inter-
pretation is true to the data and the features identified are
corroborated by other interviews. Face validity has also been
used to estimate the trustworthiness of studies (Cavanagh,

6 SAGE Open
1997; Downe-Wamboldt, 1992; Hickey & Kipping, 1996).
In this case, the results are presented to people familiar with
the research topic, who then evaluate whether the results
match reality. If the deductive approach is used, double-cod-
ing often helps to assess the quality of categorization matrix.
According to Schreier (2012), if the code definitions are
clear and subcategories do not overlap, then two rounds of
independence coding should produce approximately the
same results.
The value of dialogue among co-researchers has often
been highlighted and it has been suggested that the partici-
pant’s recognition of the findings can also be used to indicate
the credibility or conformability (Graneheim & Lundman,
2004; Saldaña, 2011). However, it has been recommended
that this be undertaken with caution (Ryan-Nicholls & Will,
2009). Some studies have used member checks, whereby
participants check the research findings to make sure that
they are true to their experiences (Holloway & Wheeler,
2010; Koch, 1994; Saldaña, 2011; Thomas & Magilvy,
2011). Although Lincoln and Guba (1985) have described
member checks as a continuous process during data analysis
(e.g., by asking participants about hypothetical situations), it
has largely been interpreted and used by researchers for veri-
fication of the overall results with participants. Although it
may seem attractive to return the results to the original par-
ticipants for verification, it is not an established verification
strategy. Several methodologists have warned against basing
verification on whether readers, participants, or potential
users of the research judge the analysis to be correct, stating
that it is actually more often a threat to validity (Morse et al.,
2002). Pyett (2003) has argued that the study participants do
not always understand their own actions and motives,
whereas researchers have more capacity and academic obli-
gation to apply critical understanding to accounts.
Reporting Phase From the Viewpoint of
Content Analysis Trustworthiness
Writing makes something disappear and then reappear in
words. This is not always easy to achieve with rich data sets,
as encountered in nursing science. The problem with writing
is that phenomena that may escape all representation need to
be accurately represented in words (van Manen, 2006)
According to Holdford (2008), the analysis and reporting
component of content analysis should aim to make sense of
the findings for readers in a meaningful and useful way.
However, little attention has been paid to the most important
element of qualitative studies: the presentation of findings in
the reports (Sandelowski & Leeman, 2011). In the next sec-
tions, we discuss trustworthiness issues associated with the
reporting results, methods, and analysis process.
Reporting Results
Reporting results of content analysis is particularly linked to
transferability, conformability, and credibility. Results
should be reported systematically and carefully, with particu-
lar attention paid to how connections between the data and
results are reported. However, the reporting of results sys-
tematically can often be challenging (Kyngäs et al., 2011).
Problems with reporting results can be a consequence of
unsuccessful analysis (Dey, 1993; Elo & Kyngäs, 2008) or
difficulties in describing the process of abstraction because it
in part depends on the researcher’s insight or intuitive action,
which may be difficult to describe to others (Elo & Kyngäs,
2008; Graneheim & Lundman, 2004).
The content and structure of concepts created by content
analysis should be presented in a clear and understandable
way. It is often useful to provide a figure to give an overview
of the whole result. The aim of the study dictates what
research phenomena are conceptualized through the analysis
process. However, conception may have different objectives.
For example, the aim of the study may be merely to identify
concepts. In contrast, if the aim is to construct a model, the
results should be presented as a model outlining the con-
cepts, their hierarchy, and possible connections. Content
analysis per se does not include a technique to connect con-
cepts (Elo & Kyngäs, 2008; Harwood & Garry, 2003). The
main consideration is to ensure that the structure of results is
equivalent and answers the aim and research questions.
From the perspective of trustworthiness, the main ques-
tion is, “How can the reader evaluate the transferability of
the results?” Transferability refers to the extent to which the
findings can be transferred to other settings or groups. (Koch,
1994; Polit & Beck, 2012). Authors may offer suggestions
about transferability, but it is ultimately down to the reader’s
judgment as to whether or not the reported results are trans-
ferable to another context (Graneheim & Lundman, 2004).
Again, this highlights the importance of ensuring high qual-
ity results and reporting of the analysis process. It is also
valuable to give clear descriptions of the culture, context,
selection, and characteristics of participants. Trustworthiness
is increased if the results are presented in a way that allows
the reader to look for alternative interpretations (Graneheim
& Lundman, 2004). We fully agree with van Manen (2006)
that qualitative methods require sensitive interpretive skills
and creative talents from the researcher. Thus, scientific writ-
ing is a skill that needs to be enhanced by writing and com-
paring others’ analysis results.
It has been argued that the use of quotations is necessary
to indicate the trustworthiness of results (Polit & Beck, 2012;
Sandelowski, 1995a). Conformability refers to objectivity
and implies that the data accurately represent the information
that the participants provided and interpretations of those
data are not invented by the inquirer. The findings must
reflect the participants’ voice and conditions of the inquiry,
and not the researcher’s biases, motivations, or perspectives
(Lincoln & Guba, 1985; Polit & Beck, 2012) This is one rea-
son why authors often present representative quotations from
transcribed text (Graneheim & Lundman, 2004), particularly
to show a connection between the data and results. For exam-
ple, each main concept should be linked to the data by a

Elo et al. 7
quotation. Examples of quotations from as many participants
as possible help confirm the connection between the results
and data as well as the richness of data. However, the sys-
tematic use of quotations needs careful attention. Ideally,
quotations should be selected that are at least connected to all
main concepts and widely representative of the sample.
However, there is a risk that quotations may be overused,
thus weakening the analysis (Downe-Wamboldt, 1992;
Graneheim & Lundman, 2004, Kyngäs et. al., 2011). For
example, if quotations are overused in the Results section,
the results of the analysis may be unclear.
According to Hsieh and Shannon (2005), an important
problem is failure to develop a complete understanding of
the context, resulting in failure to identify the key categories.
In such a case, findings do not accurately represent the data.
To ensure the trustworthiness and especially credibility of
the results, it is important to evaluate how well categories
cover the data and identify whether there are similarities
within and differences between categories. In addition, fail-
ure to complete the analysis abstraction process may mean
that concepts are presented as results that are not mutually
exclusive, leading to oversimplistic conclusions (Harwood
& Garry, 2003; Weber, 1990). An incomplete analysis may
involve the use of everyday expressions or repetition of
respondents’ statements and/or their opinions rather than
reporting the results of the analysis (Kyngäs et al., 2011).
Reporting the Analysis Process
Without a full description of the analysis and logical use of
concepts, it is impossible to evaluate how the results have
been created and their trustworthiness (Guthrie et al., 2004).
An accurate description of the analysis and the relationship
between the results and original data allow readers to draw
their own conclusions regarding the trustworthiness of the
results. In nursing science, the number of methods concern-
ing content analysis published in books and scientific articles
has increased considerably over the last decade (Elo &
Kyngäs, 2008; Harwood & Garry, 2003; Hsieh & Shannon,
2005; Neuendorf, 2002; Schreier, 2012). This may have led
to improvements in the quality of reports on the process of
content analysis. More attention is now paid to descriptions
of the analysis, results, and how to evaluate the trustworthi-
ness of studies. Consequently, this makes it easier for readers
to evaluate the trustworthiness of studies.
The dependability of a study is high if another researcher
can readily follow the decision trail used by the initial
researcher (Thomas & Magilvy, 2011). Whittemore et al.
(2001) have argued that vividness involves the presentation
of rich, vivid, faithful, and artful descriptions that highlight
the salient themes in the data. The analysis process should be
reported in an adequate manner regardless of the methods
used to present the findings (see Moretti et al., 2011). Steps
should be taken to demonstrate credibility in research reports
to ensure the trustworthiness of the content analysis.
Monograph research reports facilitate detailed descriptions
of the analysis process and the use of figures, tables, and
attachments to explain the categorization process. Based on
our experiences, evaluation of the trustworthiness of results
as a reader can often be difficult because of insufficient
description of the analysis process (Kyngäs et. al., 2011).
Journal articles generally focus on the results rather than
describing the content analysis process. All too often, the use
of qualitative content analysis is only briefly mentioned in
the methodology section, making it hard for readers to evalu-
ate the process. A key question is, “In what detail should
trustworthiness be presented in scientific articles?”—partic-
ularly as word limits often apply.
The fact that pictures may convey results more clearly
than words should be borne in mind when reporting content
analysis findings. The use of figures can be highly effective
when reporting content analysis findings, especially when
explaining the purpose and process of the analysis and struc-
ture of concepts. Very often, these aspects can be shown in
the same figure, for example, a diagram that illustrates the
hierarchy of concepts or categories may also give an insight
into the analysis process (see, for example, Timlin, Riala, &
Kyngäs, 2013). After reporting the results, a discussion of
the trustworthiness of the analysis should be provided. It
should be based on a defined set of criteria that are followed
logically for each qualitative content analysis phase.
Discussion
The main purpose of this article was to discuss and highlight
factors affecting trustworthiness of qualitative content analy-
sis studies. The literature review used here was not a system-
atic review, so there are some limitations. First, we recognize
that this is not a full description of trustworthiness and some
points may be missing. For example, the language restric-
tions may have influenced the findings; research studies in
other languages might have added new information to our
description. Further studies are needed to systematically
evaluate the reporting of content analysis in scientific jour-
nals, that is, to examine what researchers have emphasized
when reporting the trustworthiness of their qualitative con-
tent analysis study, and how criteria of trustworthiness have
been interpreted by those studies. This may help to develop a
more complete description of trustworthiness in qualitative
content analysis. However, the present methodological arti-
cle was written by several authors who have extensive expe-
rience in using the content analysis method. In addition, the
authors’ experience as researchers, teachers, and supervisors
of master’s and doctoral students lends weight to our
discussion.
Holloway and Wheeler (2010) have stated that research-
ers often have difficulty in agreeing on how to judge the
trustworthiness of their qualitative study. The aim of this
article was to identify factors affecting qualitative content
analysis trustworthiness from the viewpoint of data collec-
tion and reporting of results. Qualitative researchers are
advised to be systematic and well organized to enhance the

8 SAGE Open
trustworthiness of their study (Saldaña, 2011). According to
Schreier (2012), content analysis is systematic because all
relevant material is taken into account, a sequence of steps is
followed during the analysis, and the researcher has to check
the coding for consistency. The information presented here
raises important issues about the use and development of
content analysis. If the method is thoroughly documented for
all phases of the process (preparation, organization, and
reporting), all aspects of the trustworthiness criteria are
increased.
Before choosing an analysis method, the researcher
should select the most suitable method for answering the tar-
get research question and consider whether the data richness
is sufficient for using content analysis. Prior to using the
method, the researcher should ask the question, “Is this
method the best available to answer the target research ques-
tion?” No analysis method is without drawbacks, but each
may be good for a certain purpose. It is essential for research-
ers to delineate the approach they are going to use to perform
content analysis before beginning the data analysis because
the use of a robust analytic procedure will increase the trust-
worthiness of the study (Hsieh & Shannon, 2005).
Qualitative content analysis is a popular method for ana-
lyzing written material. This means that results spanning a
wide range of qualities have been obtained using the method.
Content analysis is a methodology that requires researchers
who use it to make a strong case for the trustworthiness of
their data (Potter & Levine-Donnerstein, 1999; Sandelowski,
1995a). Every finding should be as trustworthy as possible,
and the study must be evaluated in relation to the procedures
used to generate the findings (Graneheim & Lundman,
2004). In many studies, content analysis has been used to
analyze answers to open-ended questions in questionnaires
(Kyngäs et al., 2011). However, such answers are often so
brief that it is difficult to use content analysis effectively;
reduction, grouping, and abstraction require rich data. In
addition, trustworthiness has often been difficult to evaluate
because articles have mainly focused on reporting the analy-
sis of quantitative rather than qualitative data obtained in the
study. Whether this affects the trustworthiness of the results
can only be speculated upon. However, if researchers use
content analysis to analyze answers to open-ended questions,
they should provide an adequate description so that readers
are able to readily evaluate its trustworthiness. Content anal-
ysis has also been commonly used in quantitative studies to
analyze answers to open-ended questions.
There is a need for a self-criticism and good analysis
skills when conducting qualitative content analysis. Any
qualitative analysis should include continuous reflection
and self-criticism by the researcher (Pyett, 2003; Thomas
& Magilvy, 2011) from the beginning of the study. The
researcher’s individual attributes and perspectives can
have an important influence on the analysis process
(Whittemore et al., 2001). It is possible to obtain simplistic
results using any method even when analysis skills are
lacking (Weber, 1990). According to Neuendorf (2002),
the content analysis method can be as easy or as difficult as
the researcher allows. Many researchers still perceive it as
a simple method, and hence, it is widely used. However,
inexperienced researchers may be unable to perform an
accurate analysis because they do not have the knowledge
and skills required. This can affect the authenticity (Lincoln
& Guba, 1985; Whittemore et al., 2001) of the study, which
refers to the extent to which researchers fairly and faith-
fully show a range of realities. A simplified result may be
obtained if the researcher is unable to use and report the
results correctly.
Furthermore, the reporting of the content analysis process
should be based on self-critical thinking at each phase of the
analysis. Whittemore et al. (2001) have argued that integrity
is demonstrated by ongoing self-reflection and self-scrutiny
to ensure that interpretations are valid and grounded in the
data. Not only should a sufficient description of the analysis
be provided to help validate the data, but the researcher
should also openly discuss the limitations of the study. We
agree with Creswell’s (2013) comment that validation in a
qualitative study is an attempt to assess the accuracy of the
findings, as best described by the researcher and the partici-
pants. This means that any report of research is a representa-
tion by the author. Discussion of the trustworthiness of a
study should be based on a defined set of criteria that are
followed logically. Although many criteria have been pro-
posed to evaluate the trustworthiness of qualitative studies,
they have rarely been followed. It is recommended that
authors clearly define their validation terms (see example
from Tucker, van Zandvoort, Burke, & Irwin, 2011) because
there are many types of qualitative validation terms in use,
for example, trustworthiness, verification, and authenticity
(Creswell, 2013).
Conclusion
The trustworthiness of content analysis results depends on
the availability of rich, appropriate, and well-saturated data.
Therefore, data collection, analysis, and result reporting go
hand in hand. Improving the trustworthiness of content anal-
ysis begins with thorough preparation prior to the study and
requires advanced skills in data gathering, content analysis,
trustworthiness discussion, and result reporting. The trust-
worthiness of data collection can be verified by providing
precise details of the sampling method and participants’
descriptions. Here, we showed how content analysis can be
reported in a valid and understandable manner, which we
anticipate will be of benefit to both writers and reviewers of
scientific articles. As important qualitative research results
are often reported as monograph reports, there is a need for
further study to analyze published articles where content
analysis is used. This may produce further information that
helps content analysis writers present their studies in a more
effective way.

Elo et al. 9
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.
Funding
The author(s) received no financial support for the research and/or
authorship of this article.
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Author Biographies
Satu Elo, PhD, is a Senior university lecturer in University of Oulu,
Institute of Health Sciences. She is the second chairman of Finnish
Research Society of Nursing Science. Her research and teaching
area are both focusing on elderly care environments, and research
methods especially from the viewpoint of theory development.
Maria Kääriäinen is Professor in University of Oulu, Institute of
Health Sciences. She is in charge of the Teacher Education Program
in Health Sciences. Her research work has focused on two fields: 1)
Health promotive counselling of chronically ill patients, and people
with overweight, and 2) Effectiveness of education on the compe-
tence of nursing staff, students and teachers.
Outi Kanste (PhD) is a senior researcher at the National Institute
for Health and Welfare in Finland. She has also worked at the
University of Oulu and development projects of social and health
services in municipalities. Her research interests are nursing leader-
ship and management as well as service system and integration par-
ticularly in services for children, youth and families.
Tarja Pölkki, PhD, is an Adjunct Professor, senior researcher and
lecturer in the Institute of Health Sciences, University of Oulu. Her
research interests concern the methodological issues in nursing sci-
ence, and the well-being of children and their families focusing on
the aspects of pain assessment and non-pharmacological interven-
tions, and promoting of child-and family-centeredness in nursing.
Kati Utriainen, PhD, is a Coordinator in University of Oulu,
Institute of Health Sciences, Finland. She is active in conducting
and developing web-based learning of physicians specializing in
occupational health care and education of their trainer doctors. She
also works as an occupational health nurse in the occupational
health centre of the City of Oulu.
Helvi Kyngäs is Professor in University of Oulu, Institute of Health
Sciences. She is also head on Nursing Science studies and head of
PhD studies in Health Sciences. She is also working a Part-time
Chief Nursing Officer at Northern Ostrobothnia Hospital.