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