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 qualitative 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 methodologies among health science researchers because social problems lend themselves toward thoughtful exploration, such as when issues of interest are complex, have variables or concepts that are not easily measured, or involve listening to populations who have traditionally been silenced (Creswell, 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 problem at hand. Typically, qualitative textbooks present learners with five approaches for qualitative inquiry: narrative, phenomenological, grounded theory, case study, and ethnography. Yet eminent 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 standing with other qualitative methods, although it is one of the most frequently employed methodological approaches in the practice disciplines” (Sandelowski, 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.
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 phenomenology 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 likelihood 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 conceptual framework to guide their studies, but they are prepared to alter that framework as necessary during the course of the study (Sandelowski, 2010). These theories and frameworks serve as conceptual hooks upon which hang study procedures, analysis, and re-presentation. Findings are presented in straightforward language 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 orientations, (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 organized in a way that best fits the data (; Sandelowski, 2000, 2001, 2010).
Designing a Qualitative Descriptive Study
Methodology
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. Lincoln and Guba suggest that naturalistic inquiry deals with the concept of truth, whereby truth is “a systematic set of beliefs, together with their accompanying methods” (, 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 qualitative descriptive studies and serves as guide to the following discussion.
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 |
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a
Adapted from Jiggins Colorafi (2015).
b
Adapted from Evans, Belyea, Coon, and Ume (2012); Evans, Belyea, and Ume (2011)
Theoretical Framework
Theoretical frameworks serve as organizing structures for research design: sampling, data collection, analysis, and interpretation, including coding schemes, and formatting hypothesis for further testing (; ; Sandelowski, 2010). Such frameworks affect the way in which data are ultimately viewed; qualitative description supports and allows for the use of virtually any theory (Sandelowski, 2010). Creswell’s chapter on “Philosophical Assumptions and Interpretative Frameworks” (2013) is a useful 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 acceptability 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.
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 phenomenon 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 qualitative descriptive studies if they aim to more adequately or fully describe the participants or phenomenon of interest. Counting is conceptualized 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 simply 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 predictions, 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 forward 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; ; 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 provide a detailed description of the data analysis techniques used in qualitative descriptive methodology. 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 (; ; Miles et al., 2014) derived from the theoretical framework, literature, and the analysis of preliminary 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 analysis 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 of la familia” “is a result of” “cultural expectation.” Between the semantic relationship (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 culture who learn, share, and transmit this knowledge to others).
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. SabanaTI/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. SalTI, p. 9 |
| 1 B | 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 caregiving}, yes. SalTI, 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 … NevaTI, p. 4 |
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Note. SES = socioeconomic status.
Reading from the left in Table 2, codes were given a number and letter for use in marking sections of text. Next, the code name indicating a theme was entered in boldface type with a definition 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, forming ideas and recording reactions to the meaning of what is seen in the data. Marginal remarks often suggest new interpretations, 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 paragraphs 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 3, reading from left to right, the first column contains text that has been separated into meaning units by color. The second column 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 triggers 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 theory?”). Miles et al. (2014) provide many examples for creating, categorizing, and revising codes, including highlighting a technique used by Corbin and Strauss () 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 constructs. 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.
- 6
Analytic memos are defined by Miles et al. (2014, p. 95) as a “brief or extended narrative that documents the researcher’s reflections and thinking processes about the data.” Memos (see Figure 1 as an example) aided in data reduction by tying together different pieces of data into conceptual clusters. Memos were personal, methodological, or substantive in nature. These analytic memos were further analyzed by summarizing and creating additional analytic memos for groups of observations that contained similarities, effectively reducing the data collected through observation. Memoing was conducted throughout the analysis, beginning with data collection and continuing to the dissertation findings to chapter write-up.
- 7
Data displays (matrices), or visual representations containing concepts or variables were helpful in analyzing the data (Table 4). Data displays help the investigator draw conclusions through an iterative process whereby collected data are represented in data displays, thereby reducing data and conducting further analysis (Miles et al., 2014). Data displays are used extensively to categorize, organize, and analyze data. Such displays provide an opportunity to combine quantitative and qualitative findings, triangulating data collected by standardized measures, forms, observations, and interviews both 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 (; ; ; 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; ; Stake, 2010; Wolcott, 2009).
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 |
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Figure 1.
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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 |
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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 ().
Strategies for Ensuring Rigor of Findings
Many qualitative researchers do not provide enough information in their reports about the analytic strategies used to ensure verisimilitude or the “ring of truth” for the conclusions. Miles, Huberman, and Saldana (2014) outline 13 tactics for generating meaning from data and another 13 for testing or confirming findings. They also provide five standards for assessing the quality of conclusions. The techniques relied upon most heavily during 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, credibility, 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 (; Miles et al., 2014).
Objectivity
First, objectivity (confirmability) is conceptualized as relative neutrality and reasonable freedom from researcher bias and can be addressed by (a) describing the study’s methods and procedures 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 potential 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 collection worksheet. Quality control (Miles et al., 2014) can be fostered by:
deriving study procedures from clearly outlined research questions and conceptual theory, 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 characteristics of the sample population ();
demonstrating consistency in data collection for all participants (i.e., using the same investigator and preprinted worksheets, asking the same questions in the same order);
developing interview questions and observation 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; ; 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 collection should occur simultaneously, giving the investigator the opportunity to correct errors or make revisions.
Credibility
Third, credibility or verisimilitude (internal validity) is defined as the truth value of data: Do the findings of the study make sense (Miles et al., 2014, p. 312). Credibility in qualitative work promotes descriptive and evaluative understanding, which can be addressed by (a) providing context-rich “thick descriptions,” that is, the work of interpretation based on data (Sandelowski, 2004), (b) checking with other practitioners or researchers that the findings “ring true,” (c) providing a comprehensive account, (d) using triangulation strategies, (e) searching for negative evidence, and (f) linking findings to a theoretical framework.
Transferability
Fourth, transferability (external validity or “fittingness”) speaks to whether the findings of your study have larger import and application to other settings or studies. This includes a discussion of generalizability. 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 sample and setting sections, (c) using theoretical sampling, (d) presenting findings that are congruent 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 utilization, 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 product or environment.
Implications for Practice
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. 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.
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.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research assistance for data analysis and manuscript development 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 Gerontological Nursing Excellence at Arizona State University College of Nursing & Health Innovation.
Footnotes
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.
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