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10.1177/1049732305276687QUALITATIVE HEALTH RESEARCH / November 2005Hsieh, Shannon / PROBLEMS WITH INTERVIEWS
Three Approaches to
Qualitative Content Analysis
Hsiu-Fang Hsieh
Sarah E. Shannon
Content analysis is a widely used qualitative research technique. Rather than being a single
method, current applications of content analysis show three distinct approaches: conven
-
tional, directed, or summative. All three approaches are used to interpret meaning from the
content of text data and, hence, adhere to the naturalistic paradigm. The major differences
among the approaches are coding schemes, origins of codes, and threats to trustworthiness.
In conventional content analysis, coding categories are derived directly from the text data.
With a directed approach, analysis starts with a theory or relevant research findings as guid-
ance for initial codes. A summative content analysis involves counting and comparisons,
usually of keywords or content, followed by the interpretation of the underlying context. The
authors delineate analytic procedures specific to each approach and techniques addressing
trustworthiness with hypothetical examples drawn from the area of end-of-life care.
Keywords: content analysis; qualitative research; research methodology; end-of-life care
C
ontent analysis is a research method that has come into wide use in health
studies in recent years. A search of content analysis as a subject heading term in
the Cumulative Index to Nursing and Allied Health Literature produced more than
4,000 articles published between 1991 and 2002. The number of studies reporting
the use of content analysis grew from only 97 in 1991 to 332 in 1997 and 601 in 2002.
Researchers regard content analysis as a flexible method for analyzing text data
(Cavanagh, 1997). Content analysis describes a family of analytic approaches rang
-
ing from impressionistic, intuitive, interpretive analyses to systematic, strict textual
analyses (Rosengren, 1981). The specific type of content analysis approach chosen
by a researcher varies with the theoretical and substantive interests of the researcher
and the problem being studied (Weber, 1990). Although this flexibility has made
content analysis useful for a variety of researchers, the lack of a firm definition and
procedures has potentially limited the application of content analysis (Tesch, 1990).
The differentiation of content analysis is usually limited to classifying it as pri
-
marily a qualitative versus quantitative research method. Amore thorough analysis
of the ways in which qualitative content analysis can be used would potentially illu
-
minate key issues for researchers to consider in the design of studies purporting to
1277
AUTHORS’ NOTE: We wish to express our gratitude to Drs. Pamela L. Jordan, Carol J. Leppa, and
J. Randall Curtis for their feedback and support in writing this article.
QUALITATIVE HEALTH RESEARCH, Vol. 15 No. 9, November 2005 1277-1288
DOI: 10.1177/1049732305276687
© 2005 Sage Publications
use content analysis and the analytic procedures employed in such studies, thus
avoiding a muddling of methods (Morse, 1991).
Our purpose in this article is to present the breadth of approaches categorized
as qualitative content analysis. We have identified three distinct approaches: con
-
ventional, directed, and summative. All three approaches are used to interpret text
data from a predominately naturalistic paradigm. We begin with a brief review of
the history and definitions of content analysis. We then illustrate the three different
approaches to qualitative content analysis with hypothetical studies to explicate the
issues of study design and analytical procedures for each approach.
BACKGROUND ON THE
DEVELOPMENT OF CONTENT ANALYSIS
Content analysis has a long history in research, dating back to the 18th century in
Scandinavia (Rosengren, 1981). In the United States, content analysis was first used
as an analytic technique at the beginning of the 20th century (Barcus, 1959). Initially,
researchers used content analysis as either a qualitative or quantitative method in
their studies (Berelson, 1952). Later, content analysis was used primarily as a quan-
titative research method, with text data coded into explicit categories and then
described using statistics. This approach is sometimes referred to as quantitative
analysis of qualitative data (Morgan, 1993) and is not our primary focus in this arti-
cle. More recently, the potential of content analysis as a method of qualitative analy-
sis for health researchers has been recognized, leading to its increased application
and popularity (Nandy & Sarvela, 1997).
Qualitative content analysis is one of numerous research methods used to ana-
lyze text data. Other methods include ethnography, grounded theory, phenomenol-
ogy, and historical research. Research using qualitative content analysis focuses on
the characteristics of language as communication with attention to the content or
contextual meaning of the text (Budd, Thorp, & Donohew, 1967; Lindkvist, 1981;
McTavish & Pirro, 1990; Tesch, 1990). Text data might be in verbal, print, or elec
-
tronic form and might have been obtained from narrative responses, open-ended
survey questions, interviews, focus groups, observations, or print media such as
articles, books, or manuals (Kondracki & Wellman, 2002). Qualitative content anal
-
ysis goes beyond merely counting words to examining language intensely for the
purpose of classifying large amounts of text into an efficient number of categories
that represent similar meanings (Weber, 1990). These categories can represent either
explicit communication or inferred communication. The goal of content analysis
is “to provide knowledge and understanding of the phenomenon under study”
(Downe-Wamboldt, 1992, p. 314). In this article, qualitative content analysis is
defined as a research method for the subjective interpretation of the content of text
data through the systematic classification process of coding and identifying themes
or patterns.
To illustrate the possible applications of content analysis, we constructed hypo
-
thetical studies drawn from the area of end-of-life (EOL) research. Content analysis
has been a popular analytic method in studies related to EOL care, an area of
increasing emphasis as demonstrated by its inclusion as one of the five research
themes supported by the National Institutes of Health, National Institute of Nurs
-
1278 QUALITATIVE HEALTH RESEARCH / November 2005
ing Research (NINR) for 2003 (“Enhancing the End-of-Life Experience for Patients
and Their Families,” NINR, 2003).
CONVENTIONAL CONTENT ANALYSIS
Researcher X used a conventional approach to content analysis in her study
(Table 1). Conventional content analysis is generally used with a study design
whose aim is to describe a phenomenon, in this case the emotional reactions of hos
-
pice patients. This type of design is usually appropriate when existing theory or
research literature on a phenomenon is limited. Researchers avoid using precon
-
ceived categories (Kondracki & Wellman, 2002), instead allowing the categories
and names for categories to flow from the data. Researchers immerse themselves in
the data to allow new insights to emerge (Kondracki & Wellman, 2002), also
described as inductive category development (Mayring, 2000). Many qualitative
methods share this initial approach to study design and analysis.
If data are collected primarily through interviews, open-ended questions will
be used. Probes also tend to be open-ended or specific to the participant’s comments
rather than to a preexisting theory, such as “Can you tell me more about that?” Data
analysis starts with reading all data repeatedly to achieve immersion and obtain a
sense of the whole (Tesch, 1990) as one would read a novel. Then, data are read word
by word to derive codes (Miles & Huberman, 1994; Morgan, 1993; Morse & Field,
1995) by first highlighting the exact words from the text that appear to capture key
thoughts or concepts. Next, the researcher approaches the text by making notes of
his or her first impressions, thoughts, and initial analysis. As this process continues,
labels for codes emerge that are reflective of more than one key thought. These often
come directly from the text and are then become the initial coding scheme. Codes
then are sorted into categories based on how different codes are related and linked.
These emergent categories are used to organize and group codes into meaningful
clusters (Coffey & Atkinson, 1996; Patton, 2002). Ideally, the numbers of clusters are
between 10 and 15 to keep clusters broad enough to sort a large number of codes
(Morse & Field, 1995).
Depending on the relationships between subcategories, researchers can com
-
bine or organize this larger number of subcategories into a smaller number of cate
-
gories. Atree diagram can be developed to help in organizing these categories into a
hierarchical structure (Morse & Field, 1995). Next, definitions for each category,
subcategory, and code are developed. To prepare for reporting the findings, ex
-
emplars for each code and category are identified from the data. Depending on the
purpose of the study, researchers might decide to identify the relationship between
categories and subcategories further based on their concurrence, antecedents, or
consequences (Morse & Field, 1995).
With a conventional approach to content analysis, relevant theories or other
research findings are addressed in the discussion section of the study. In Researcher
X’s study, she might compare and contrast her findings to Kübler-Ross’s (1969) the
-
ory. The discussion would include a summary of how the findings from her study
contribute to knowledge in the area of interest and suggestions for practice, teach
-
ing, and future research.
The advantage of the conventional approach to content analysis is gaining
direct information from study participants without imposing preconceived catego
-
Hsieh, Shannon / PROBLEMS WITH INTERVIEWS 1279
ries or theoretical perspectives. Researcher X’s study depicts a research question
appropriate for this approach. Knowledge generated from her content analysis is
based on participants’ unique perspectives and grounded in the actual data. Her
sampling technique was designed to maximize diversity of emotional reactions,
and the analysis techniques were structured to capture that complexity.
One challenge of this type of analysis is failing to develop a complete under
-
standing of the context, thus failing to identify key categories. This can result in
findings that do not accurately represent the data. Lincoln and Guba (1985) de
-
scribed this as credibility within the naturalistic paradigm of trustworthiness or
internal validity within a paradigm of reliability and validity. Credibility can be
established through activities such as peer debriefing, prolonged engagement, per
-
sistent observation, triangulation, negative case analysis, referential adequacy, and
member checks (Lincoln & Guba, 1985; Manning, 1997).
Another challenge of the conventional approach to content analysis is that it
can easily be confused with other qualitative methods such as grounded theory
1280 QUALITATIVE HEALTH RESEARCH / November 2005
TABLE 1:
Hypothetical Research Study Using a Conventional Approach to Content Analysis—
Researcher X’s Study
Little is known about the emotional reactions of terminally ill patients who are receiving hospice
care, possibly because of their reluctance to discuss death issues (Wilson & Fletcher, 2002). Some
patients might feel relieved to have active therapy end, whereas others might feel afraid or even
abandoned. Researcher X wanted to learn more about the emotional experiences of hospice patients
to be able to address their needs more effectively. Because there was no existing theory to serve as a
framework for her study, her research question was “What are the emotional reactions of terminally
ill patients who are receiving hospice care?”
Based on her clinical experience, Researcher X suspected that the emotional reactions of patients
who were new to hospice care differed from those who had been in hospice care for a longer period.
She also suspected that those receiving home hospice care had different experiences from those
receiving in-patient hospice care. Researcher X therefore decided to use a stratified sampling tech
-
nique to ensure heterogeneity of the sample. The target sample size was 10 home hospice patients
and 10 inpatient hospice patients, with 5 from each group being recruited within 48 hours of enroll
-
ment into hospice and 5 recruited 7 to 10 days following enrollment. In addition, the sample would
include both men and women and both older and middle-aged people.
Prior to recruitment and data collection, the research procedures were approved for use with
human subjects. Informed consent was obtained from all participants. Researcher X collected data
through individual interviews using open-ended questions such as “What has it been like to be in
hospice care?” followed by specific probes. All interviews were audiotape-recorded and tran
-
scribed verbatim.
Researcher X used content analysis to analyze the data. She began by reading each transcript from
beginning to end, as one would read a novel. Then, she read each transcript carefully, highlighting
text that appeared to describe an emotional reaction and writing in the margin of the text a keyword
or phrase that seemed to capture the emotional reaction, using the participant’s words. As she
worked through the transcript, she attempted to limit these developing codes as much as possible.
After open coding of three to four transcripts, Researcher X decided on preliminary codes. She then
coded the remaining transcripts (and recoded the original ones) using these codes and adding new
codes when she encountered data that did not fit into an existing code.
Once all transcripts had been coded, Researcher X examined all data within a particular code. Some
codes were combined during this process, whereas others were split into subcategories. Finally, she
examined the final codes to organize them into a hierarchical structure if possible.
In the findings, the emotional responses of hospice patients were described using the identified
codes and hierarchical structure. In discussion of the findings, the results from this content analysis
were compared and contrasted with Kübler-Ross’s (1969) model to highlight similarities and
differences.
method (GTM) or phenomenology. These methods share a similar initial analytical
approach but go beyond content analysis to develop theory or a nuanced under
-
standing of the lived experience. The conventional approach to content analysis is
limited in both theory development and description of the lived experience, be
-
cause both sampling and analysis procedures make the theoretical relationship
between concepts difficult to infer from findings. At most, the result of a conven
-
tional content analysis is concept development or model building (Lindkvist, 1981).
For example, Researcher X might find that patients who are new to hospice care
express worry about how their social obligations will be met (such as finding care
for a pet), whereas patients who have been in hospice for long periods might
express more anticipatory grief. Researcher X might compare her findings to those
of Kübler-Ross (1969) and conclude that an additional emotional reaction to enter
-
ing hospice care is the process of “tying up loose ends,” which she might define as
making both financial and social arrangements.
DIRECTED CONTENT ANALYSIS
Sometimes, existing theory or prior research exists about a phenomenon that is
incomplete or would benefit from further description. The qualitative researcher
might choose to use a directed approach to content analysis, as Researcher Y did
(Table 2). Potter and Levine-Donnerstein (1999) might categorize this as a deductive
use of theory based on their distinctions on the role of theory. However the key ten-
ets of the naturalistic paradigm form the foundation of Researcher Y’s general
approach to the study design and analysis. The goal of a directed approach to con-
tent analysis is to validate or extend conceptually a theoretical framework or theory.
Existing theory or research can help focus the research question. It can provide pre-
dictions about the variables of interest or about the relationships among variables,
thus helping to determine the initial coding scheme or relationships between codes.
This has been referred to as deductive category application (Mayring, 2000).
Content analysis using a directed approach is guided by a more structured pro
-
cess than in a conventional approach (Hickey & Kipping, 1996). Using existing the
-
ory or prior research, researchers begin by identifying key concepts or variables as
initial coding categories (Potter & Levine-Donnerstein, 1999). Next, operational
definitions for each category are determined using the theory. In Researcher Y’s
study, Kübler-Ross’s (1969) five stages of grief served as an initial framework to
identify emotional stages of terminally ill patients.
If data are collected primarily through interviews, an open-ended question
might be used, followed by targeted questions about the predetermined categories.
After an open-ended question, Researcher Y used probes specifically to explore par
-
ticipants’ experiences of denial, anger, bargaining, depression, and acceptance.
Coding can begin with one of two strategies, depending on the research question. If
the goal of the research is to identify and categorize all instances of a particular phe
-
nomenon, such as emotional reactions, then it might be helpful to read the transcript
and highlight all text that on first impression appears to represent an emotional
reaction. The next step in analysis would be to code all highlighted passages using
the predetermined codes. Any text that could not be categorized with the initial
coding scheme would be given a new code.
Hsieh, Shannon / PROBLEMS WITH INTERVIEWS 1281
The second strategy that can be used in directed content analysis is to begin cod
-
ing immediately with the predetermined codes. Data that cannot be coded are iden
-
tified and analyzed later to determine if they represent a new category or a subcate
-
gory of an existing code. The choice of which of these approaches to use depends on
the data and the researcher’s goals. If the researcher wants to be sure to capture all
possible occurrences of a phenomenon, such as an emotional reaction, highlighting
identified text without coding might increase trustworthiness. If the researcher
feels confident that initial coding will not bias the identification of relevant text,
then coding can begin immediately. Depending on the type and breadth of a cate
-
gory, researchers might need to identify subcategories with subsequent analysis.
For example, Researcher Y might decide to separate anger into subcategories de
-
pending on whom the anger was directed toward.
The findings from a directed content analysis offer supporting and nonsup
-
porting evidence for a theory. This evidence can be presented by showing codes
with exemplars and by offering descriptive evidence. Because the study design and
analysis are unlikely to result in coded data that can be compared meaningfully
using statistical tests of difference, the use of rank order comparisons of frequency
1282 QUALITATIVE HEALTH RESEARCH / November 2005
TABLE 2:
Hypothetical Research Study Using a Directed Approach to Content Analysis—
Researcher Y’s Study
Despite their wide acceptance and popularity, Kübler-Ross’s (1969) five stages of grief (denial,
anger, bargaining, depression, and acceptance) have not been confirmed through other research. In
taking care of terminally ill patients, Researcher Y wondered how well Kübler-Ross’s theory
described his patients’ experiences with imminent death. His research question was “How well does
Kübler-Ross’s model describe the emotional passages or journeys of patients who have been diag
-
nosed with a terminal illness?”
Researcher Y designed a sampling plan to maximize the chance of recruiting participants at differ
-
ent stages. All participants were diagnosed with a terminal illness, but one third were recruited while
receiving “last chance” forms of curative therapy, one third after they refused further curative ther
-
apy but were not enrolled in hospice care, and one third who were contemplating (or had recently
made) the decision to enter hospice care. In addition, the sample was recruited for gender balance
and diagnostic diversity, specifically both oncology and non-oncology diagnoses. The target sample
size was 18 to 21 participants. Interviews were conducted with individuals using open-ended
questions, such as “What has your emotional journey been since being diagnosed with this illness?”
Specific probes were developed based on Kübler-Ross’s model, such as Have you felt angry since
your diagnosis? After institutional review board approval, informed consent from all participants
was obtained. All interviews were audiotape-recorded and transcribed verbatim.
Researcher Y developed operational definitions of the five emotional responses (anger, bargaining,
etc.) identified in Kübler-Ross’s model. He then reviewed all transcripts carefully, highlighting all text
that appeared to describe an emotional response. All highlighted text was coded using the predeter-
mined categories wherever possible. Text that could not be coded into one of these categories was
coded with another label that captured the essence of the emotion. After coding, Researcher Y exam-
ined the data for each category to determine whether subcategories were needed for a category (e.g.,
anger toward self, anger toward doctors, anger toward spiritual being). Data that could not be coded
into one of the five categories derived from the theory were reexamined to describe different
emotional reactions. Finally, Researcher Y compared the extent to which the data were supportive of
Kübler-Ross’s theory versus how much represented different emotional responses. The report of
study findings described the incidence of codes representing the emotional stages suggested by
Kübler-Ross with those that represented different emotional responses by comparing the rank order
of all codes. In the discussion section, Researcher Y summarized how the study validated Kübler-
Ross’s model and what new perspectives were added.
of codes can be used (Curtis et al., 2001). Researcher Y might choose to describe his
study findings by reporting the incidence of codes that represented the five main
categories derived from Kübler-Ross (1969) and the incidence of newly identified
emotional reactions. He also could descriptively report the percent of supporting
versus nonsupporting codes for each participant and for the total sample.
The theory or prior research used will guide the discussion of findings. Newly
identified categories either offer a contradictory view of the phenomenon or might
further refine, extend, and enrich the theory. In Researcher Y’s study, the discussion
might focus on the extent to which participants’ emotional journeys paralleled
Kübler-Ross’s (1969) model and the newly identified emotional reactions or stages
that were experienced by participants in the study.
The main strength of a directed approach to content analysis is that existing
theory can be supported and extended. In addition, as research in an area grows,
a directed approach makes explicit the reality that researchers are unlikely to be
working from the naive perspective that is often viewed as the hallmark of natural
-
istic designs.
The directed approach does present challenges to the naturalistic paradigm.
Using theory has some inherent limitations in that researchers approach the data
with an informed but, nonetheless, strong bias. Hence, researchers might be more
likely to find evidence that is supportive rather than nonsupportive of a theory. Sec-
ond, in answering the probe questions, some participants might get cues to answer
in a certain way or agree with the questions to please researchers. In Researcher Y’s
study, some patients might agree with the suggested emotional stages even though
they did not experience the emotion. Third, an overemphasis on the theory can
blind researchers to contextual aspects of the phenomenon. In Researcher Y’s study,
the emphasis on Kübler-Ross’s (1969) stages of emotional response to loss might
have clouded his ability to recognize contextual features that influence emotions.
For example, the cross-sectional design of the study might have overemphasized
current emotional reactions. These limitations are related to neutrality or confirm
-
ability of trustworthiness as the parallel concept to objectivity (Lincoln & Guba,
1985). To achieve neutral or unbiased results, an audit trail and audit process can be
used. In Researcher Y’s study, the vague terminology used in Kübler-Ross’s de
-
scription of the model would be a challenge for the researcher in creating useful
operational definitions. Having an auditor review and examine these definitions
before the study could greatly increase the accuracy of predetermined categories.
SUMMATIVE CONTENT ANALYSIS
Typically, a study using a summative approach to qualitative content analysis starts
with identifying and quantifying certain words or content in text with the purpose
of understanding the contextual use of the words or content (Table 3). This quantifi
-
cation is an attempt not to infer meaning but, rather, to explore usage. Analyzing for
the appearance of a particular word or content in textual material is referred to
as manifest content analysis (Potter & Levine-Donnerstein, 1999). If the analysis
stopped at this point, the analysis would be quantitative, focusing on counting the
frequency of specific words or content (Kondracki & Wellman, 2002). A summative
approach to qualitative content analysis goes beyond mere word counts to include
latent content analysis. Latent content analysis refers to the process of interpretation
Hsieh, Shannon / PROBLEMS WITH INTERVIEWS 1283
of content (Holsti, 1969). In this analysis, the focus is on discovering underlying
meanings of the words or the content (Babbie, 1992; Catanzaro, 1988; Morse & Field,
1995). In Researcher Z’s study, the initial part of the analysis technique, to count the
frequency of death, die, and dying is more accurately viewed as a quantitative
approach. However, Researcher Z went on to identify alternative terms for death
and to examine the contexts within which direct versus euphemistic terms were
used. Hence, Researcher Z used a summative approach to qualitative content
analysis.
Researchers report using content analysis from this approach in studies that
analyze manuscript types in a particular journal or specific content in textbooks.
Examples include studies examining content related to EOL care in medical text
-
books (Rabow, Hardie, Fair, & McPhee, 2000), EOL care in critical care nursing
textbooks (Kirchhoff, Beckstrand, & Anumandla, 2003), palliative care in nurs
-
ing textbooks (Ferrell, Virani, Grant, & Juarez, 2000), death and bereavement in
nursing textbooks (Ferrell, Virani, Grant, & Borneman, 1999), and spirituality in
1284 QUALITATIVE HEALTH RESEARCH / November 2005
TABLE 3:
Hypothetical Research Study Using a Summative Approach to Content Analysis—
Researcher Z’s Study
Talking about death has virtually been banished from our language (Callahan, 1995). Use of the
terms die, dying, and death remain taboo in U.S. society in favor of euphemisms such as passing, going
to a better place, and so on. A failure to use explicit terms might hinder the effectiveness of communi
-
cation between physicians and patients (Levy, 2001; Vincent, 1997). Recognizing this problem,
Researcher Z wanted to know how often health care providers, patients, or family members used
explicit terms versus euphemisms. Under what circumstances are these explicit terms used? Her
research question was How are the terms die, dying, and death used in clinician-patient communica
-
tion when discussing hospice care, and what alternative terms are used?
Researcher Z designed a sampling plan to maximize the diversity of the sample around
demographic characteristics of both the clinician and the patient/family. Patient characteristics
included gender, age, diagnosis, and ethnic background. Clinician characteristics included gender,
discipline, and area of specialization. Two types of communication events with patients who had
received a terminal diagnosis were sampled. One was discharge teaching for hospitalized patients
who were being transferred to home hospice, inpatient hospice, or skilled nursing facilities for end-
of-life (EOL) care. The other communication event was clinician-patient/family conferences in out- or
inpatient settings to plan EOL care. Fifty separate communication events were sampled for 50 differ
-
ent clinicians and patient/family pairs. The research proposal was approved by institutional review
boards before data collection. Informed consent was obtained from each participant. All clinician-
patient conversations were audiotape-recorded and transcribed verbatim.
Data analysis started with computer-assisted searches for occurrences of the terms die, death, and
dying in the transcripts. Word frequency counts for each of the three death-related terms in a
transcript were calculated and compared to the total length of the communication event. Researcher Z
also coded the identity of the speaker, such as physician, nurse, patient, or family member. Frequency
counts by type of speaker were calculated and compared to the total number of terms coded.
Next, Researcher Z tried to identify alternative terms or expressions used instead of death, die, or
dying. Occurrences of these terms were counted both as a total number and for each alternative term.
Frequencies of euphemisms versus direct terms were compared for type of speaker, demographic
characteristics of clinician, and demographic characteristics of patient within each communication
event and across the total sample.
The major study findings described the occurrences of the three explicit terms used in clinician-
patient communication as compared to euphemistic terms. Comparisons across type of speaker and
characteristics of clinicians and patients were made. The discussion of this study focused on
exploring possible explanations for differences in the use of explicit versus euphemistic terms
when discussing EOL care for different groups and in different situations.
nursing textbooks (McEwen, 2004). These researchers started with counting the
pages that covered specific topics followed by descriptions and interpretations of
the content, including evaluating the quality of the content. Others have compared
the results of a content analysis with other data collected within the same research
project, such as comparing preferences for various types of television programming
with socioeconomic indicators of participants (Krippendorff, 1980).
In a summative approach to qualitative content analysis, data analysis begins
with searches for occurrences of the identified words by hand or by computer. Word
frequency counts for each identified term are calculated, with source or speaker also
identified. Researcher Z wanted to know the frequency of words that were used to
refer to death but also to understand the underlying contexts for the use of explicit
versus euphemistic terms. He or she illuminated the context of euphemistic versus
explicit terms by reporting how their usage differed by variables such as the speaker
(patient versus clinician), the clinician’s specialization, and the age of the patient.
Counting is used to identify patterns in the data and to contextualize the codes
(Morgan, 1993). It allows for interpretation of the context associated with the use of
the word or phrase. Researchers try to explore word usage or discover the range of
meanings that a word can have in normal use.
A summative approach to qualitative content analysis has certain advantages.
It is an unobtrusive and nonreactive way to study the phenomenon of interest
(Babbie, 1992). It can provide basic insights into how words are actually used. How-
ever, the findings from this approach are limited by their inattention to the broader
meanings present in the data. As evidence of trustworthiness, this type of study
relies on credibility. Amechanism to demonstrate credibility or internal consistency
is to show that the textual evidence is consistent with the interpretation (Weber,
1990). For Researcher Z’s study, validation by content experts on what terms are
used to replace the death terms would be essential. Alternatively, researchers can
check with their participants as to their intended meaning through the process of
member check (Lincoln & Guba, 1985).
SUMMARY OF KEY ASPECTS
All approaches to qualitative content analysis require a similar analytical process of
seven classic steps, including formulating the research questions to be answered,
selecting the sample to be analyzed, defining the categories to be applied, outlining
the coding process and the coder training, implementing the coding process, deter
-
mining trustworthiness, and analyzing the results of the coding process (Kaid,
1989). We have outlined how this process differs depending on the specific content
analysis approach used. The success of a content analysis depends greatly on the
coding process. The basic coding process in content analysis is to organize large
quantities of text into much fewer content categories (Weber, 1990). Categories are
patterns or themes that are directly expressed in the text or are derived from them
through analysis. Then, relationships among categories are identified. In the coding
process, researchers using content analysis create or develop a coding scheme to
guide coders to make decisions in the analysis of content. A coding scheme is a
translation device that organizes data into categories (Poole & Folger, 1981). A cod
-
ing scheme includes the process and rules of data analysis that are systematic, logi
-
Hsieh, Shannon / PROBLEMS WITH INTERVIEWS 1285
cal, and scientific. The development of a good coding scheme is central to trust
-
worthiness in research using content analysis (Folger, Hewes, & Poole, 1984).
Key differences among conventional, directed, and summative approaches to
content analysis center on how initial codes are developed. In a conventional con-
tent analysis, categories are derived from data during data analysis. The researcher
is usually able to gain a richer understanding of a phenomenon with this approach.
With a directed content analysis, the researcher uses existing theory or prior re-
search to develop the initial coding scheme prior to beginning to analyze the data
(Kyngas & Vanhanen, 1999). As analysis proceeds, additional codes are developed,
and the initial coding scheme is revised and refined. Researchers employing a
directed approach can efficiently extend or refine existing theory. The summative
approach to content analysis is fundamentally different from the prior two ap-
proaches. Rather than analyzing the data as a whole, the text is often approached as
single words or in relation to particular content. An analysis of the patterns leads to
an interpretation of the contextual meaning of specific terms or content (Table 4).
CONCLUSIONS
Different research purposes require different research designs and analysis tech
-
niques (Knafl & Howard, 1984). The question of whether a study needs to use a con
-
ventional, directed, or summative approach to content analysis can be answered by
matching the specific research purpose and the state of science in the area of interest
with the appropriate analysis technique.
It is important for health researchers to delineate the specific approach to con
-
tent analysis they are going to use in their studies before beginning data analysis.
Creating and adhering to an analytic procedure or a coding scheme will increase
trustworthiness or validity of the study. Careful description of the type of approach
to content analysis used can provide a universal language for health researchers
and strengthen the method’s scientific base. Examples used in this article were
drawn from the area of research on end of life, but the content analysis techniques
described could be used in a broad range of studies. Content analysis offers re
-
searchers a flexible, pragmatic method for developing and extending knowledge of
the human experience of health and illness.
1286 QUALITATIVE HEALTH RESEARCH / November 2005
TABLE 4:
Major Coding Differences Among Three Approaches to Content Analysis
Timing of Defining Source of
Type of Content Analysis Study Starts With Codes or Keywords Codes or Keywords
Conventional content
analysis
Observation Codes are defined dur
-
ing data analysis
Codes are derived from
data
Directed content
analysis
Theory Codes are defined be
-
fore and during data
analysis
Codes are derived from
theory or relevant
research findings
Summative content
analysis
Keywords Keywords are identified
before and during data
analysis
Keywords are derived
from interest of re
-
searchers or review of
literature
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Hsiu-Fang Hsieh, Ph.D., is an assistant professor at Fooyin University, Kaohsiung Hsien, Taiwan.
Sarah E. Shannon, Ph.D., is an associate professor at the University of Washington, Seattle.
1288 QUALITATIVE HEALTH RESEARCH / November 2005
