WEEK 2 DQ 1 AND 2
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QualitativeDescriptive.pdf
Methods
Qualitative Descriptive Methods in Health Science Research
Karen Jiggins Colorafi, PhD, MBA, RN1, and Bronwynne Evans, PhD, RN, FNGNA, ANEF, FAAN1
Abstract Objective: The purpose of this methodology paper is to describe an approach to qualitative design known as qualitative descriptive that is well suited to junior health sciences researchers because it can be used with a variety of theoretical approaches, sampling techniques, and data collection strategies. Background: It is often difficult for junior qualitative researchers to pull together the tools and resources they need to embark on a high-quality qualitative research study and to manage the volumes of data they collect during qualitative studies. This paper seeks to pull together much needed resources and provide an overview of methods. Methods: A step-by-step guide to planning a qua- litative descriptive study and analyzing the data is provided, utilizing exemplars from the authors’ research. Results: This paper presents steps to conducting a qualitative descriptive study under the following headings: describing the qualitative descriptive approach, designing a qualitative descriptive study, steps to data analysis, and ensuring rigor of findings. Conclusions: The qualitative descriptive approach results in a summary in everyday, factual language that facilitates understanding of a selected phenomenon across disciplines of health science researchers.
Keywords qualitative descriptive, qualitative methodology, rigor, qualitative design, qualitative analysis
There is an explosion in qualitative methodolo-
gies among health science researchers because
social problems lend themselves toward thought-
ful exploration, such as when issues of interest are
complex, have variables or concepts that are not
easily measured, or involve listening to popula-
tions who have traditionally been silenced (Cres-
well, 2013). Creswell (2013, p. 48) suggests
qualitative research is preferred when health
science researchers seek to (a) share individual
stories, (b) write in a literary, flexible style, (c)
understand the context or setting of issues, (d)
explain mechanisms or linkages in causal theories,
(e) develop theories, and (f) when traditional
quantitative statistical analyses do not fit the prob-
lem at hand. Typically, qualitative textbooks pres-
ent learners with five approaches for qualitative
inquiry: narrative, phenomenological, grounded
theory, case study, and ethnography. Yet eminent
1 College of Nursing & Health Innovation, Arizona State
University, Phoenix, AZ, USA
Corresponding Author:
Karen Jiggins Colorafi, PhD, MBA, RN, College of Nursing &
Health Innovation, Arizona State University, 550N. 3rd
Street, Phoenix, AZ 85004, USA.
Email: [email protected]
Health Environments Research & Design Journal
2016, Vol. 9(4) 16-25 ª The Author(s) 2016
Reprints and permission: sagepub.com/journalsPermissions.nav
DOI: 10.1177/1937586715614171 herd.sagepub.com
researcher Margarete Sandelowski argues that in
‘‘the now vast qualitative methods literature, there
is no comprehensive description of qualitative
description as a distinctive method of equal stand-
ing with other qualitative methods, although it is
one of the most frequently employed methodologi-
cal approaches in the practice disciplines’’ (Sande-
lowski, 2000). Qualitative description is especially
amenable to health environments research because
it provides factual responses to questions about
how people feel about a particular space, what
reasons they have for using features of the space,
who is using particular services or functions of
a space, and the factors that facilitate or hinder use.
Qualitative description is especially
amenable to health environments research
because it provides factual responses to
questions about how people feel about a
particular space, what reasons they have
for using features of the space, who is
using particular services or functions of
a space, and the factors that facilitate
or hinder use.
The purpose of this methodology article is
to define and outline qualitative description for
health science researchers, providing a starter
guide containing important primary sources for
those who wish to become better acquainted with
this methodological approach.
Describing the Qualitative Descriptive Approach
In two seminal articles, Sandelowski promotes
the mainstream use of qualitative description
(Sandelowski, 2000, 2010) as a well-developed
but unacknowledged method which provides a
‘‘comprehensive summary of an event in the
every day terms of those events’’ (Sandelowski,
2000, p. 336). Such studies are characterized
by lower levels of interpretation than are high-
inference qualitative approaches such as phe-
nomenology or grounded theory and require
a less ‘‘conceptual or otherwise highly abstract
rendering of data’’ (Sandelowski, 2000, p.
335). Researchers using qualitative description
‘‘stay closer to their data and to the surface of
words and events’’ (Sandelowski, 2000, p. 336)
than many other methodological approaches.
Qualitative descriptive studies focus on low-
inference description, which increases the likeli-
hood of agreement among multiple researchers.
The difference between high and low inference
approaches is not one of rigor but refers to the
amount of logical reasoning required to move from
a data-based premise to a conclusion. Researchers
who use qualitative description may choose to use
the lens of an associated interpretive theory or con-
ceptual framework to guide their studies, but
they are prepared to alter that framework as nec-
essary during the course of the study (Sande-
lowski, 2010). These theories and frameworks
serve as conceptual hooks upon which hang
study procedures, analysis, and re-presentation.
Findings are presented in straightforward lan-
guage that clearly describes the phenomena of
interest.
Other cardinal features of the qualitative
descriptive approach include (a) a broad range
of choices for theoretical or philosophical orien-
tations, (b) the use of virtually any purposive
sampling technique (e.g., maximum variation,
homogenous, typical case, criterion), (c) the use
of observations, document review, or minimally
to moderately structured interview or focus group
questions, (d) content analysis and descriptive
statistical analysis as data analysis techniques,
and (e) the provision of a descriptive summary
of the informational contents of the data orga-
nized in a way that best fits the data (Neergaard,
Olesen, Andersen, & Sondergaard, 2009; Sande-
lowski, 2000, 2001, 2010).
Designing a Qualitative Descriptive Study
Methodology
Unlike traditional qualitative methodologies such
as grounded theory, which are built upon a partic-
ular, prescribed constellation of procedures and
techniques, qualitative description is grounded
in the general principles of naturalistic inquiry.
Lincoln and Guba suggest that naturalistic
inquiry deals with the concept of truth, whereby
Jiggins Colorafi and Evans 17
truth is ‘‘a systematic set of beliefs, together with
their accompanying methods’’ (Lincoln & Guba,
1985, p. 16). Using an often eclectic compilation
of sampling, data collection, and data analysis
techniques, the researcher studies something in its
natural state and does not attempt to manipulate
or interfere with the ordinary unfolding of events.
Taken together, these practices lead to ‘‘true
understanding’’ or ‘‘ultimate truth.’’ Table 1
describes design elements in two exemplar quali-
tative descriptive studies and serves as guide to
the following discussion.
Unlike traditional qualitative
methodologies such as grounded theory,
which are built upon a particular,
prescribed constellation of procedures
and techniques, qualitative description is
grounded in the general principles of
naturalistic inquiry.
Theoretical Framework
Theoretical frameworks serve as organizing
structures for research design: sampling, data col-
lection, analysis, and interpretation, including
coding schemes, and formatting hypothesis
for further testing (Evans, Coon, & Ume, 2011;
Miles, Huberman, & Saldana, 2014; Sande-
lowski, 2010). Such frameworks affect the way
in which data are ultimately viewed; qualitative
description supports and allows for the use of vir-
tually any theory (Sandelowski, 2010). Cres-
well’s chapter on ‘‘Philosophical Assumptions
and Interpretative Frameworks’’ (2013) is a use-
ful place to gain understanding about how to
embed a theory into a study.
Sampling
Sampling choices place a boundary around the
conclusions you can draw from your qualitative
study and influence the confidence you and others
place in them (Miles et al., 2014). A hallmark of
the qualitative descriptive approach is the accept-
ability of virtually any sampling technique (e.g.,
maximum variation where you aim to collect as
many different cases as possible or homogenous
whereby participants are mostly the same). See
Miles, Huberman, and Saldana’s (2014, p. 30)
‘‘Bounding the Collection of Data’’ discussion
to select an appropriate and congruent purposive
sampling strategy for your qualitative study.
Data Collection
In qualitative descriptive studies, data collection
attempts to discover ‘‘the who, what and where
of events’’ or experiences (Sandelowski, 2000,
p.339). This includes, but is not limited to focus
groups, individual interviews, observation, and
the examination of documents or artifacts.
Table 1. Example of Study Design Elements for Two Studies.
Design Element Patient engagement with the plan of carea Mexican American caregiversb
Theory Individual and family self-management theory Life course perspective Sampling strategy Multiple case purposive sampling Stratified purposeful sampling Data collection 40 Observations with semistructured
interviews/standardized instruments at clinical encounter
6 Semistructured interviews/standardized instruments at 10-week intervals for 15 months
Data analysis Directed content analysis, descriptive statistics
Conventional content analysis, descriptive and inferential statistics
Data re-presentation
Ideas derived from interviews and observations lead to the creation of recommendations, written in the voice of the patient, and presented according to the theoretical framework
Several data cuts and secondary analyses using verbatim data, its relationship with the theoretical framework, and a primarily qualitative format
aAdapted from Jiggins Colorafi (2015). bAdapted from Evans, Belyea, Coon, and Ume (2012); Evans, Belyea, and Ume (2011)
18 Health Environments Research & Design Journal 9(4)
Data Analysis
Content analysis refers to a technique commonly
used in qualitative research to analyze words or
phrases in text documents. Hsieh and Shannon
(2005) present three types of content analysis,
any of which could be used in a qualitative
descriptive study. Conventional content analysis
is used in studies that aim to describe a phenom-
enon where exiting research and theory are
limited. Data are collected from open-ended
questions, read word for word, and then coded.
Notes are made and codes are categorized.
Directed content analysis is used in studies where
existing theory or research exists: it can be used to
further describe phenomena that are incomplete
or would benefit from further description. Initial
codes are created from theory or research and
applied to data and unlabeled portions of text are
given new codes. Summative content analysis is
used to quantify and interpret words in context,
exploring their usage. Data sources are typically
seminal texts or electronic word searches.
Quantitative data can be included in qualita-
tive descriptive studies if they aim to more
adequately or fully describe the participants or
phenomenon of interest. Counting is conceptua-
lized as a ‘‘means to and end, not the end itself’’
by Sandelowski (2000, p. 338) who emphasizes
that careful descriptive statistical analysis is an
effort to understand the content of data, not sim-
ply the means and frequencies, and results in
a highly nuanced description of the patterns
or regularities of the phenomenon of interest
(Sandelowski, 2000, 2010). The use of validated
measures can assist with generating dependable
and meaningful findings, especially when the
instrument (e.g., survey, questionnaire, or list
of questions) used in your study has been used
in others, helping to build theory, improve pre-
dictions, or make recommendations (Miles
et al., 2014).
Data Re-Presentation
In clear and simple terms, the ‘‘expected outcome
of qualitative descriptive studies is a straight for-
ward descriptive summary of the informational
contents of data organized in a way that best
fits the data’’ (Sandelowski, 2000, p. 339). Data
re-presentation techniques allow for tremendous
creativity and variation among researchers and
studies. Several good resources are provided to
spur imagination (Miles et al., 2014; Munhall &
Chenail, 2008; Wolcott, 2009).
Steps to Data Analysis
It is often difficult for junior health science
researchers to know what to do with the volumes
of data collected during a qualitative study and
formal course work in traditional qualitative
methods courses are typically sparse regarding
the specifics of data management. It is for those
reasons that this section of our article will pro-
vide a detailed description of the data analysis
techniques used in qualitative descriptive metho-
dology. The following steps are case examples of
a study undertaken by one author (K.J.C.) after
completing a data management course offered
by another author (B.E.). Examples are offered
from the two studies noted in Table 1. It is
offered in list format for general readability, but
the qualitative researcher should recognize that
qualitative analyses are iterative and recursive
by nature.
1. Prior to initiating data collection, a coding
manual containing a beginning list of codes
(Fonteyn, Vettese, Lancaster, & Bauer-Wu,
2008; Hsieh & Shannon, 2005; Miles et al.,
2014) derived from the theoretical frame-
work, literature, and the analysis of pre-
liminary data, was developed. Codes are
action-oriented words or labels assigned
to designated portions (chunks or meaning
units) of text reflecting themes or topics
that occur with regularity (Miles et al.,
2014, p. 71). In the coding manual (see
example in Table 2), themes which were
conceptually similar were grouped together
using an ethnographic technique of domain
analysis (Spradley, 1980). A domain analy-
sis contains a series of themes, a semantic
relationship such as ‘‘is a component of’’
or ‘‘is a type of,’’ and the name of the
domain. It is read from the bottom up,
hence, ‘‘Acknowledging the importance
Jiggins Colorafi and Evans 19
of la familia’’ ‘‘is a result of’’ ‘‘cultural
expectation.’’ Between the semantic rela-
tionship (is a result of) and the domain
name, we inserted a definition of the
domain itself (values, beliefs, and activities
seen as normative by members of the cul-
ture who learn, share, and transmit this
knowledge to others).
Reading from the left in Table 2, codes were
given a number and letter for use in marking sec-
tions of text. Next, the code name indicating a
theme was entered in boldface type with a defini-
tion in the code immediately under it. The second
column provided an exemplar of each code, along
with a notation indicating where it was found in
the data, so that coders could recognize instances
of that particular code when they saw them.
The coding manual was tested against data
gathered in a preliminary study and was revised
as codes found to overlap or be missing entirely.
We continued to revise it iteratively during the
study as data collection and analysis proceeded
and then used it to recode previously coded data.
Using this procedure, it was used to revisit the
data several times.
2. Each transcribed document was formatted
with wide right margins that allowed the
investigator to apply codes and generate
marginal remarks by hand. Marginal
remarks are handwritten comments entered
by the investigator. They represent an
attempt to stay ‘‘alert’’ about analysis, form-
ing ideas and recording reactions to the
meaning of what is seen in the data. Mar-
ginal remarks often suggest new interpreta-
tions, leads, and connections or distinctions
with other parts of the data (Miles et al.,
2014). Such remarks are preanalytic and add
meaning and clarity to transcripts.
3. The investigator took sentences or para-
graphs in the transcripts and divided them
into meaning units, which are segments of
text that contain a single idea (Table 3).
One or more codes were applied to each
meaning unit during first-level coding,
which is highly descriptive in nature. In
Table 2. Example of a Coding Manual.
1. Cultural expectation (values, beliefs, and activities seen as normative by members of the culture who learn, share, and transmit this knowledge to others) ^ is a result of ^
1A Acknowledging the importance of la familia: Expressing strong support and intergenerational reliance (family is main source of social interaction; transcends SES or gender)
We were raised to take care of la familia. . . . We don’t put them in a nursing home facility. Like a lot of my gringo friends have done that. It’s so sad. I couldn’t live if I did that. It’s not in me. SabanaT1/2, p. 5 Her mother took care of her grandmother, and my mother took care of my grandmother and both took care of her mother, both had some help taking care of my dad when he was sick, and I know that it was inbred in me, not really inbred, but something I saw; you follow suit by example. SalT1, p. 9
1B Reciprocating for past care: Feeling strong familial and moral obligation to unconditionally help and care for elders who cared for you
When you were little, your parents changed your diapers. Now that they are older it’s up to you take care of them, Honor Your Father and Mother by taking care of them, now that they need from you because you needed from them when you were growing up. CalandriaT1, p. 10
1C Living out the precepts of marianismo: Acting with saintliness and goodness of Virgin Mary; a sense of nobility and dignity; self-sacrifice, faithfulness, and subordination to husband (father, brothers)
My wife fell right in along beside me [for caregivingg, yes. SalT1, p. 8 This is the mother of my husband, and the grandmother of my children. So this is the message that I give. Because it is the saddest thing for a person to become a senior and find themselves forgotten, abandoned, uncared for, hungry, dirty, exiled. This is most grievous . . . NevaT1, p. 4
Note. SES ¼ socioeconomic status.
20 Health Environments Research & Design Journal 9(4)
Table 3, reading from left to right, the first
column contains text that has been separated
into meaning units by color. The second col-
umn lists codes that were applied to each
meaning unit, also color coded for clarity.
First-level codes are in gerund form: a verb
with an ‘‘ing’’ ending that denotes action.
Gerunds are used to help the researcher
focus on participant behaviors and actions
in the transcript. Table 3 is an example of
first-level or coarse coding (applying fewer
codes to bigger ‘‘chunks’’ of material).
Alternatively, individual researchers may
choose to code finely (applying more codes
to smaller ‘‘chunks’’ of material). Coding is
a form of analysis; they ‘‘are prompts or trig-
gers for deeper reflection’’ (Miles et al.,
2014, p. 73). Because coding is a way to
condense data, the researcher may choose
to put ‘‘chunks’’ of coded material in large
or small groupings, effectively slicing the
data in a fine or coarse manner.
4. Conceptually similar codes were organized
into categories (coding groups of coded
themes that were increasingly abstract)
through revisiting the theory framing the
study (asking, ‘‘does this system of coding
make sense according to the chosen the-
ory?’’). Miles et al. (2014) provide many
examples for creating, categorizing, and
revising codes, including highlighting a
technique used by Corbin and Strauss (Cor-
bin & Strauss, 2015) that includes growing
a list of codes and then applying a slightly
more abstract label to the code, creating
new categories of codes with each revision.
This is often referred to as second-level or
pattern coding, a way of grouping data into
a smaller number of sets, themes, or con-
structs. During the analysis of data, patterns
were generated and the researcher spent
significant amounts of time with different
categorizations, asking questions, checking
relationships, and generally resisting the
urge to be ‘‘locked too quickly into naming
a pattern’’ (Miles et al., 2014, p. 69).
5. During this phase of analysis, pattern
codes were revised and redefined in the
coding manual and exemplars were used
to clarify the understanding of each code.
Miles et al. (2014) suggest that software
can be helpful during this categorization
(counting) step, so lists of observed
engagement behaviors were also recorded
in Dedoose software (Dedoose, 2015) by
code so that frequencies could be captured
and analyzed. Despite the assistance of
Dedoose, the researcher found that hand
sorting codes into themes and categories
was best done on paper.
Table 3. Level 1 Coding With Meaning Units.
Original text (meaning unit highlighted in relation to applied code) Code(s) applied to meaning unit
I try to eat well. My wife seems to do a good job with that stuff and everything. I am fairly active around the house and stuff
Eating well Remaining active
I’ve recently become semi-retired, so even though retirement means like relaxation, it really hasn’t. It has just given me more work to do around the house and stuff, and again, having children of my own, basically, I not only have a honey-do list from wife, I have a honey-do list for my two charming daughters
Becoming retired Working around the house Having multiple honey-do lists
Again too, I’d like to be around as long as possible. I enjoy life. I try to enjoy it to the fullest. I’d like to be—I want to live life. I don’t want survive, I guess is what I’d say. I’ve seen too many instances of this. My mother-in-law is a prime example. She is in an assisted-living facility, and I really think she’s just about, I don’t want to say given up and stuff, but she’s not living. She is surviving. I think that’s sad. I really do. I think you are going to get out of life what you put into life. I think if she would put a little more effort into life, her life would be a lot more fulfilling and rewarding to her and basically to people around her
Trying to enjoy life vs. surviving life
Being sad when people give up
Getting out what you put in
Jiggins Colorafi and Evans 21
6. Analytic memos are defined by Miles et al.
(2014, p. 95) as a ‘‘brief or extended narra-
tive that documents the researcher’s reflec-
tions and thinking processes about the
data.’’ Memos (see Figure 1 as an exam-
ple) aided in data reduction by tying
together different pieces of data into con-
ceptual clusters. Memos were personal,
methodological, or substantive in nature.
These analytic memos were further ana-
lyzed by summarizing and creating addi-
tional analytic memos for groups of
observations that contained similarities,
effectively reducing the data collected
through observation. Memoing was con-
ducted throughout the analysis, beginning
with data collection and continuing to the
dissertation findings to chapter write-up.
7. Data displays (matrices), or visual represen-
tations containing concepts or variables were
helpful in analyzing the data (Table 4). Data
displays help the investigator draw conclu-
sions through an iterative process whereby
collected data are represented in data dis-
plays, thereby reducing data and conducting
further analysis (Miles et al., 2014). Data dis-
plays are used extensively to categorize,
organize, and analyze data. Such displays
provide an opportunity to combine quantita-
tive and qualitative findings, triangulating
data collected by standardized measures,
forms, observations, and interviews both
We have clear data saturation at 60 interviews and can now proceed to validate the code book. This will require, however, that all previously coded interviews be recoded with the finalized codebook, which is labor-intensive for research technicians/cultural brokers and may put us behind with data analysis. Nevertheless, validating at 60 interviews is very conservative and will ensure, as far as possible, that all relevant codes/themes are captured.
The one exception to this is the domain of adaptive strategies, which is large and growing daily. Family caregivers seem to have an unendingly creative, adaptive catalog of ways with which to manage transitions and turning points in the caregiving trajectory. For example, when one older family member with dementia wandered at night through the dining room door into the kitchen, turning on the stove, it created a safety issue. Family reported that they moved a large china cabinet across the door from the dining room into the kitchen, blocking the doorway and requiring that a second door at the other end of the room be used for kitchen access. The wanderer could not process how to use this alternate route and so the arrangement successfully deflected the night-time kitchen visits. I believe that this compendium of creative, day-by-day strategies to manage transitions in care will continue to grow throughout the duration of the study. The compendium is a tribute to the commitment, intelligence, and resilience of caregivers but it also means that we will never achieve data saturation in this domain. Rather than attempting to code each individually, ad infinitum, we will maintain a list of strategies with associated pseudonyms, interview numbers, and page numbers that can get us back into the data for details on each occurrence.
Figure 1. Example of an analytic memo used in qualitative description analysis.
Table 4. Data Matrix.
Case CLOX-CG CLOX-CR CG Vigilance Scale CG Strain CG Gain
1 5 (High) 1 (Low) 20 hr/wk (Moderate) Moderate: fatigue and moderate anxiety
Moderate: Giving back to mom
2 3 (Moderate) 1 (Low) 30 hr/wk (High) High: debilitating fatigue, high anxiety, feels depressed, and sleeplessness
Low: Unable to see positive aspects
Note. The CLOX is an executive clock drawing task that tests cognition and was used in this study with the caregiver (CG) and the care recipient (CR). The CG Strain and the CG Gain scores were derived by the researcher through a qualitative content analysis (Evans, Coon, & Belyea, 2006).
22 Health Environments Research & Design Journal 9(4)
within case and cross case. Triangulation
refers to the use of more than one approach
for investigating the research question in
order to enhance confidence in the findings
(Creswell & Plano-Clark, 2007; Denzin &
Lincoln, 1994; Denzin, Lincoln, & Giardina,
2006; Sandelowski, 2001).
8. Finally, the data are re-presented in a creative
but rigorous way that are judged to best fit the
findings (Miles et al., 2014; Sandelowski &
Leeman, 2012; Stake, 2010; Wolcott, 2009).
Strategies for Ensuring Rigor of Findings
Many qualitative researchers do not provide
enough information in their reports about the analy-
tic strategies used to ensure verisimilitude or the
‘‘ring of truth’’ for the conclusions. Miles, Huber-
man, and Saldana (2014) outline 13 tactics for gen-
erating meaning from data and another 13 for
testing or confirming findings. They also provide
five standards for assessing the quality of conclu-
sions. The techniques relied upon most heavily dur-
ing a qualitative descriptive study ought to be
addressed within the research report. It is important
to establish ‘‘trustworthiness’’ and ‘‘authenticity’’
in qualitative research that are similar to the terms
validity and reliability in quantitative research. The
five standards (objectivity, dependability, credibil-
ity, transferability, and application) typically used
in qualitative descriptive studies to assess quality
and legitimacy (trustworthiness and authenticity)
of the conclusions are discussed in the next sections
(Lincoln & Guba, 1985; Miles et al., 2014).
Objectivity
First, objectivity (confirmability) is conceptua-
lized as relative neutrality and reasonable free-
dom from researcher bias and can be addressed
by (a) describing the study’s methods and proce-
dures in explicit detail, (b) sharing the sequence
of data collection, analysis, and presentation
methods to create an audit trail, (c) being aware
of and reporting personal assumptions and poten-
tial bias, (d) retaining study data and making it
available to collaborators for evaluation.
Dependability
Second, dependability (reliability or auditability)
can be fostered by consistency in procedures
across participants over time through various
methods, including the use of semistructured
interview questions and an observation data col-
lection worksheet. Quality control (Miles et al.,
2014) can be fostered by:
� deriving study procedures from clearly out-
lined research questions and conceptual the-
ory, so that data analysis could be linked
back to theoretical constructs;
� clearly describing the investigator’s role
and status at the research site;
� demonstrating parallelism in findings across
sources (i.e., interview vs. observation, etc.);
� triangulation through the use of observations,
interviews, and standardized measures to
more adequately describe various character-
istics of the sample population (Denzin &
Lincoln, 1994);
� demonstrating consistency in data collec-
tion for all participants (i.e., using the same
investigator and preprinted worksheets, ask-
ing the same questions in the same order);
� developing interview questions and obser-
vation techniques based on theory, revised,
and tested during preliminary work;
� developing a coding manual a priori to guide
data analysis, containing a ‘‘start list’’ of codes
derived from the theoretical framework and
relevant literature (Fonteyn et al., 2008; Hsieh
& Shannon, 2005; Miles et al., 2014); and
� developing a monitoring plan (fidelity) to
ensure that junior researchers, especially
do not go ‘‘beyond the data’’ (Sandelowski,
2000) in interpretation. In keeping with the
qualitative tradition, data analysis and col-
lection should occur simultaneously, giving
the investigator the opportunity to correct
errors or make revisions.
Credibility
Third, credibility or verisimilitude (internal valid-
ity) is defined as the truth value of data: Do the find-
ings of the study make sense (Miles et al., 2014, p.
312). Credibility in qualitative work promotes
Jiggins Colorafi and Evans 23
descriptive and evaluative understanding, which
can be addressed by (a) providing context-rich
‘‘thick descriptions,’’ that is, the work of interpreta-
tion based on data (Sandelowski, 2004), (b) check-
ing with other practitioners or researchers that the
findings ‘‘ring true,’’ (c) providing a comprehen-
sive account, (d) using triangulation strategies, (e)
searching for negative evidence, and (f) linking
findings to a theoretical framework.
Transferability
Fourth, transferability (external validity or ‘‘fitting-
ness’’) speaks to whether the findings of your study
have larger import and application to other settings
or studies. This includes a discussion of general-
izability. Sample to population generalizability is
important to quantitative researchers and less
helpful to qualitative researchers who seek more
of an analytic or case-to-case transfer (Miles
et al., 2014). Nonetheless, transferability can be
aided by (a) describing the characteristics of the
participants fully so that comparisons with other
groups may be made, (b) adequately describing
potential threats to generalizability through sam-
ple and setting sections, (c) using theoretical
sampling, (d) presenting findings that are congru-
ent with theory, and (e) suggesting ways that
findings from your study could be tested further
by other researchers.
Application
Finally, Miles et al. (2014) speak to the utiliza-
tion, application, or action orientation of the data.
‘‘Even if we know that a study’s findings are
valid and transferable,’’ they write, ‘‘we still need
to know what the study does for its participants
and its consumers’’ (Miles et al., 2014, p. 314).
To address application, findings of qualitative
descriptive studies are typically made accessible
to potential consumers of information through the
publication of manuscripts, poster presentations,
and summary reports written for consumers. In
addition, qualitative descriptive study findings
may stimulate further research, promote policy
discussions, or suggest actual changes to a prod-
uct or environment.
Implications for Practice
The qualitative description clarified and advocated
by Sandelowski (2000, 2010) is an excellent meth-
odological choice for the healthcare environments
designer, practitioner, or health sciences researcher
because it provides rich descriptive content from
the subjects’ perspective. Qualitative description
allows the investigator to select from any number
of theoretical frameworks, sampling strategies, and
data collection techniques. The various content
analysis strategies described in this paper serve
to introduce the investigator to methods for data
analysis that promote staying ‘‘close’’ to the data,
thereby avoiding high-inference techniques likely
challenging to the novice investigator. Finally, the
devotion to thick description (interpretation based
on data) and flexibility in the re-presentation of
study findings is likely to produce meaningful
information to designers and healthcare leaders.
The practical, step-by-step nature of this article
should serve as a starting guide to researchers
interested in this technique as a way to answer
their own burning questions.
The qualitative description clarified and
advocated by Sandelowski (2000, 2010) is
an excellent methodological choice for
the healthcare environments designer,
practitioner, or health sciences researcher
because it provides rich descriptive content
from the subjects’ perspective.
Acknowledgments
The author would like to recognize the other
members of her dissertation committee for their
contributions to the study: Gerri Lamb, Karen
Dorman Marek, and Robert Greenes.
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) disclosed receipt of the following
financial support for the research, authorship,
and/or publication of this article: Research
24 Health Environments Research & Design Journal 9(4)
assistance for data analysis and manuscript devel-
opment was supported by training funds from the
National Institutes of Health/ National Institute
on Nursing Research (NIH/ NINR), award T32
1T32NR012718-01 Transdisciplinary Training in
Health Disparities Science (C. Keller, P.I.). The
content is solely the responsibility of the authors
and does not necessarily represent the official
views of the NIH or the NINR. This research was
supported through the Hartford Center of Geronto-
logical Nursing Excellence at Arizona State Uni-
versity College of Nursing & Health Innovation.
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