dissertation descriptive study

Child Care and Early Education Research Connections

Descriptive research studies.

Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?

Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from  randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.

A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using  descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.

Descriptive studies have an important role in early care and education research. Studies such as the  National Survey of Early Care and Education  and the  National Household Education Surveys Program  have greatly increased our knowledge of the supply of and demand for child care in the U.S. The  Head Start Family and Child Experiences Survey  and the  Early Childhood Longitudinal Study Program  have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.

Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.

Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).

Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.

Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."

Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.

Limitations:

Descriptive studies cannot be used to establish cause and effect relationships.

Respondents may not be truthful when answering survey questions or may give socially desirable responses.

The choice and wording of questions on a questionnaire may influence the descriptive findings.

Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.

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Characteristics of Qualitative Descriptive Studies: A Systematic Review

MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing

Justine S. Sefcik

MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing

Christine Bradway

PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing

Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing-related phenomena. However, limited discussions regarding QD are found in the existing literature. In this systematic review, we identified characteristics of methods and findings reported in research articles published in 2014 whose authors identified the work as QD. After searching and screening, data were extracted from the sample of 55 QD articles and examined to characterize research objectives, design justification, theoretical/philosophical frameworks, sampling and sample size, data collection and sources, data analysis, and presentation of findings. In this review, three primary findings were identified. First, despite inconsistencies, most articles included characteristics consistent with limited, available QD definitions and descriptions. Next, flexibility or variability of methods was common and desirable for obtaining rich data and achieving understanding of a phenomenon. Finally, justification for how a QD approach was chosen and why it would be an appropriate fit for a particular study was limited in the sample and, therefore, in need of increased attention. Based on these findings, recommendations include encouragement to researchers to provide as many details as possible regarding the methods of their QD study so that readers can determine whether the methods used were reasonable and effective in producing useful findings.

Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena ( Polit & Beck, 2009 , 2014 ). QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or experiences and gaining insights from informants regarding a poorly understood phenomenon. It is also the label of choice when a straight description of a phenomenon is desired or information is sought to develop and refine questionnaires or interventions ( Neergaard et al., 2009 ; Sullivan-Bolyai et al., 2005 ).

Despite many strengths and frequent citations of its use, limited discussions regarding QD are found in qualitative research textbooks and publications. To the best of our knowledge, only seven articles include specific guidance on how to design, implement, analyze, or report the results of a QD study ( Milne & Oberle, 2005 ; Neergaard, Olesen, Andersen, & Sondergaard, 2009 ; Sandelowski, 2000 , 2010 ; Sullivan-Bolyai, Bova, & Harper, 2005 ; Vaismoradi, Turunen, & Bondas, 2013 ; Willis, Sullivan-Bolyai, Knafl, & Zichi-Cohen, 2016 ). Furthermore, little is known about characteristics of QD as reported in journal-published, nursing-related, qualitative studies. Therefore, the purpose of this systematic review was to describe specific characteristics of methods and findings of studies reported in journal articles (published in 2014) self-labeled as QD. In this review, we did not have a goal to judge whether QD was done correctly but rather to report on the features of the methods and findings.

Features of QD

Several QD design features and techniques have been described in the literature. First, researchers generally draw from a naturalistic perspective and examine a phenomenon in its natural state ( Sandelowski, 2000 ). Second, QD has been described as less theoretical compared to other qualitative approaches ( Neergaard et al., 2009 ), facilitating flexibility in commitment to a theory or framework when designing and conducting a study ( Sandelowski, 2000 , 2010 ). For example, researchers may or may not decide to begin with a theory of the targeted phenomenon and do not need to stay committed to a theory or framework if their investigations take them down another path ( Sandelowski, 2010 ). Third, data collection strategies typically involve individual and/or focus group interviews with minimal to semi-structured interview guides ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fourth, researchers commonly employ purposeful sampling techniques such as maximum variation sampling which has been described as being useful for obtaining broad insights and rich information ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fifth, content analysis (and in many cases, supplemented by descriptive quantitative data to describe the study sample) is considered a primary strategy for data analysis ( Neergaard et al., 2009 ; Sandelowski, 2000 ). In some instances thematic analysis may also be used to analyze data; however, experts suggest care should be taken that this type of analysis is not confused with content analysis ( Vaismoradi et al., 2013 ). These data analysis approaches allow researchers to stay close to the data and as such, interpretation is of low-inference ( Neergaard et al., 2009 ), meaning that different researchers will agree more readily on the same findings even if they do not choose to present the findings in the same way ( Sandelowski, 2000 ). Finally, representation of study findings in published reports is expected to be straightforward, including comprehensive descriptive summaries and accurate details of the data collected, and presented in a way that makes sense to the reader ( Neergaard et al., 2009 ; Sandelowski, 2000 ).

It is also important to acknowledge that variations in methods or techniques may be appropriate across QD studies ( Sandelowski, 2010 ). For example, when consistent with the study goals, decisions may be made to use techniques from other qualitative traditions, such as employing a constant comparative analytic approach typically associated with grounded theory ( Sandelowski, 2000 ).

Search Strategy and Study Screening

The PubMed electronic database was searched for articles written in English and published from January 1, 2014 to December 31, 2014, using the terms, “qualitative descriptive study,” “qualitative descriptive design,” and “qualitative description,” combined with “nursing.” This specific publication year, “2014,” was chosen because it was the most recent full year at the time of beginning this systematic review. As we did not intend to identify trends in QD approaches over time, it seemed reasonable to focus on the nursing QD studies published in a certain year. The inclusion criterion for this review was data-based, nursing-related, research articles in which authors used the terms QD, qualitative descriptive study, or qualitative descriptive design in their titles or abstracts as well as in the main texts of the publication.

All articles yielded through an initial search in PubMed were exported into EndNote X7 ( Thomson Reuters, 2014 ), a reference management software, and duplicates were removed. Next, titles and abstracts were reviewed to determine if the publication met inclusion criteria; all articles meeting inclusion criteria were then read independently in full by two authors (HK and JS) to determine if the terms – QD or qualitative descriptive study/design – were clearly stated in the main texts. Any articles in which researchers did not specifically state these key terms in the main text were then excluded, even if the terms had been used in the study title or abstract. In one article, for example, although “qualitative descriptive study” was reported in the published abstract, the researchers reported a “qualitative exploratory design” in the main text of the article ( Sundqvist & Carlsson, 2014 ); therefore, this article was excluded from our review. Despite the possibility that there may be other QD studies published in 2014 that were not labeled as such, to facilitate our screening process we only included articles where the researchers clearly used our search terms for their approach. Finally, the two authors compared, discussed, and reconciled their lists of articles with a third author (CB).

Study Selection

Initially, although the year 2014 was specifically requested, 95 articles were identified (due to ahead of print/Epub) and exported into the EndNote program. Three duplicate publications were removed and the 20 articles with final publication dates of 2015 were also excluded. The remaining 72 articles were then screened by examining titles, abstracts, and full-texts. Based on our inclusion criteria, 15 (of 72) were then excluded because QD or QD design/study was not identified in the main text. We then re-examined the remaining 57 articles and excluded two additional articles that did not meet inclusion criteria (e.g., QD was only reported as an analytic approach in the data analysis section). The remaining 55 publications met inclusion criteria and comprised the sample for our systematic review (see Figure 1 ).

Flow Diagram of Study Selection

Of the 55 publications, 23 originated from North America (17 in the United States; 6 in Canada), 12 from Asia, 11 from Europe, 7 from Australia and New Zealand, and 2 from South America. Eleven studies were part of larger research projects and two of them were reported as part of larger mixed-methods studies. Four were described as a secondary analysis.

Quality Appraisal Process

Following the identification of the 55 publications, two authors (HK and JS) independently examined each article using the Critical Appraisal Skills Programme (CASP) qualitative checklist ( CASP, 2013 ). The CASP was chosen to determine the general adequacy (or rigor) of the qualitative studies included in this review as the CASP criteria are generic and intend to be applied to qualitative studies in general. In addition, the CASP was useful because we were able to examine the internal consistency between study aims and methods and between study aims and findings as well as the usefulness of findings ( CASP, 2013 ). The CASP consists of 10 main questions with several sub-questions to consider when making a decision about the main question ( CASP, 2013 ). The first two questions have reviewers examine the clarity of study aims and appropriateness of using qualitative research to achieve the aims. With the next eight questions, reviewers assess study design, sampling, data collection, and analysis as well as the clarity of the study’s results statement and the value of the research. We used the seven questions and 17 sub-questions related to methods and statement of findings to evaluate the articles. The results of this process are presented in Table 1 .

CASP Questions and Quality Appraisal Results (N = 55)

Note . The CASP questions are adapted from “10 questions to help you make sense of qualitative research,” by Critical Appraisal Skills Programme, 2013, retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . Its license can be found at http://creativecommons.org/licenses/by-nc-sa/3.0/

Once articles were assessed by the two authors independently, all three authors discussed and reconciled our assessment. No articles were excluded based on CASP results; rather, results were used to depict the general adequacy (or rigor) of all 55 articles meeting inclusion criteria for our systematic review. In addition, the CASP was included to enhance our examination of the relationship between the methods and the usefulness of the findings documented in each of the QD articles included in this review.

Process for Data Extraction and Analysis

To further assess each of the 55 articles, data were extracted on: (a) research objectives, (b) design justification, (c) theoretical or philosophical framework, (d) sampling and sample size, (e) data collection and data sources, (f) data analysis, and (g) presentation of findings (see Table 2 ). We discussed extracted data and identified common and unique features in the articles included in our systematic review. Findings are described in detail below and in Table 3 .

Elements for Data Extraction

Data Extraction and Analysis Results

Note . NR = not reported

Quality Appraisal Results

Justification for use of a QD design was evident in close to half (47.3%) of the 55 publications. While most researchers clearly described recruitment strategies (80%) and data collection methods (100%), justification for how the study setting was selected was only identified in 38.2% of the articles and almost 75% of the articles did not include any reason for the choice of data collection methods (e.g., focus-group interviews). In the vast majority (90.9%) of the articles, researchers did not explain their involvement and positionality during the process of recruitment and data collection or during data analysis (63.6%). Ethical standards were reported in greater than 89% of all articles and most articles included an in-depth description of data analysis (83.6%) and development of categories or themes (92.7%). Finally, all researchers clearly stated their findings in relation to research questions/objectives. Researchers of 83.3% of the articles discussed the credibility of their findings (see Table 1 ).

Research Objectives

In statements of study objectives and/or questions, the most frequently used verbs were “explore” ( n = 22) and “describe” ( n = 17). Researchers also used “identify” ( n = 3), “understand” ( n = 4), or “investigate” ( n = 2). Most articles focused on participants’ experiences related to certain phenomena ( n = 18), facilitators/challenges/factors/reasons ( n = 14), perceptions about specific care/nursing practice/interventions ( n = 11), and knowledge/attitudes/beliefs ( n = 3).

Design Justification

A total of 30 articles included references for QD. The most frequently cited references ( n = 23) were “Whatever happened to qualitative description?” ( Sandelowski, 2000 ) and “What’s in a name? Qualitative description revisited” ( Sandelowski, 2010 ). Other references cited included “Qualitative description – the poor cousin of health research?” ( Neergaard et al., 2009 ), “Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research” ( Pope & Mays, 1995 ), and general research textbooks ( Polit & Beck, 2004 , 2012 ).

In 26 articles (and not necessarily the same as those citing specific references to QD), researchers provided a rationale for selecting QD. Most researchers chose QD because this approach aims to produce a straight description and comprehensive summary of the phenomenon of interest using participants’ language and staying close to the data (or using low inference).

Authors of two articles distinctly stated a QD design, yet also acknowledged grounded-theory or phenomenological overtones by adopting some techniques from these qualitative traditions ( Michael, O'Callaghan, Baird, Hiscock, & Clayton, 2014 ; Peacock, Hammond-Collins, & Forbes, 2014 ). For example, Michael et al. (2014 , p. 1066) reported:

The research used a qualitative descriptive design with grounded theory overtones ( Sandelowski, 2000 ). We sought to provide a comprehensive summary of participants’ views through theoretical sampling; multiple data sources (focus groups [FGs] and interviews); inductive, cyclic, and constant comparative analysis; and condensation of data into thematic representations ( Corbin & Strauss, 1990 , 2008 ).

Authors of four additional articles included language suggestive of a grounded-theory or phenomenological tradition, e.g., by employing a constant comparison technique or translating themes stated in participants’ language into the primary language of the researchers during data analysis ( Asemani et al., 2014 ; Li, Lee, Chen, Jeng, & Chen, 2014 ; Ma, 2014 ; Soule, 2014 ). Additionally, Li et al. (2014) specifically reported use of a grounded-theory approach.

Theoretical or Philosophical Framework

In most (n = 48) articles, researchers did not specify any theoretical or philosophical framework. Of those articles in which a framework or philosophical stance was included, the authors of five articles described the framework as guiding the development of an interview guide ( Al-Zadjali, Keller, Larkey, & Evans, 2014 ; DeBruyn, Ochoa-Marin, & Semenic, 2014 ; Fantasia, Sutherland, Fontenot, & Ierardi, 2014 ; Ma, 2014 ; Wiens, Babenko-Mould, & Iwasiw, 2014 ). In two articles, data analysis was described as including key concepts of a framework being used as pre-determined codes or categories ( Al-Zadjali et al., 2014 ; Wiens et al., 2014 ). Oosterveld-Vlug et al. (2014) and Zhang, Shan, and Jiang (2014) discussed a conceptual model and underlying philosophy in detail in the background or discussion section, although the model and philosophy were not described as being used in developing interview questions or analyzing data.

Sampling and Sample Size

In 38 of the 55 articles, researchers reported ‘purposeful sampling’ or some derivation of purposeful sampling such as convenience ( n = 10), maximum variation ( n = 8), snowball ( n = 3), and theoretical sampling ( n = 1). In three instances ( Asemani et al., 2014 ; Chan & Lopez, 2014 ; Soule, 2014 ), multiple sampling strategies were described, for example, a combination of snowball, convenience, and maximum variation sampling. In articles where maximum variation sampling was employed, “variation” referred to seeking diversity in participants’ demographics ( n = 7; e.g., age, gender, and education level), while one article did not include details regarding how their maximum variation sampling strategy was operationalized ( Marcinowicz, Abramowicz, Zarzycka, Abramowicz, & Konstantynowicz, 2014 ). Authors of 17 articles did not specify their sampling techniques.

Sample sizes ranged from 8 to 1,932 with nine studies in the 8–10 participant range and 24 studies in the 11–20 participant range. The participant range of 21–30 and 31–50 was reported in eight articles each. Six studies included more than 50 participants. Two of these articles depicted quite large sample sizes (N=253, Hart & Mareno, 2014 ; N=1,932, Lyndon et al., 2014 ) and the authors of these articles described the use of survey instruments and analysis of responses to open-ended questions. This was in contrast to studies with smaller sample sizes where individual interviews and focus groups were more commonly employed.

Data Collection and Data Sources

In a majority of studies, researchers collected data through individual ( n = 39) and/or focus-group ( n = 14) interviews that were semistructured. Most researchers reported that interviews were audiotaped ( n = 51) and interview guides were described as the primary data collection tool in 29 of the 51 studies. In some cases, researchers also described additional data sources, for example, taking memos or field notes during participant observation sessions or as a way to reflect their thoughts about interviews ( n = 10). Written responses to open-ended questions in survey questionnaires were another type of data source in a small number of studies ( n = 4).

Data Analysis

The analysis strategy most commonly used in the QD studies included in this review was qualitative content analysis ( n = 30). Among the studies where this technique was used, most researchers described an inductive approach; researchers of two studies analyzed data both inductively and deductively. Thematic analysis was adopted in 14 studies and the constant comparison technique in 10 studies. In nine studies, researchers employed multiple techniques to analyze data including qualitative content analysis with constant comparison ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland, Christensen, Shone, Kearney, & Kitzman, 2014 ; Li et al., 2014 ) and thematic analysis with constant comparison ( Johansson, Hildingsson, & Fenwick, 2014 ; Oosterveld-Vlug et al., 2014 ). In addition, five teams conducted descriptive statistical analysis using both quantitative and qualitative data and counting the frequencies of codes/themes ( Ewens, Chapman, Tulloch, & Hendricks, 2014 ; Miller, 2014 ; Santos, Sandelowski, & Gualda, 2014 ; Villar, Celdran, Faba, & Serrat, 2014 ) or targeted events through video monitoring ( Martorella, Boitor, Michaud, & Gelinas, 2014 ). Tseng, Chen, and Wang (2014) cited Thorne, Reimer Kirkham, and O’Flynn-Magee (2004)’s interpretive description as the inductive analytic approach. In five out of 55 articles, researchers did not specifically name their analysis strategies, despite including descriptions about procedural aspects of data analysis. Researchers of 20 studies reported that data saturation for their themes was achieved.

Presentation of Findings

Researchers described participants’ experiences of health care, interventions, or illnesses in 18 articles and presented straightforward, focused, detailed descriptions of facilitators, challenges, factors, reasons, and causes in 15 articles. Participants’ perceptions of specific care, interventions, or programs were described in detail in 11 articles. All researchers presented their findings with extensive descriptions including themes or categories. In 25 of 55 articles, figures or tables were also presented to illustrate or summarize the findings. In addition, the authors of three articles summarized, organized, and described their data using key concepts of conceptual models ( Al-Zadjali et al., 2014 ; Oosterveld-Vlug et al., 2014 ; Wiens et al., 2014 ). Martorella et al. (2014) assessed acceptability and feasibility of hand massage therapy and arranged their findings in relation to pre-determined indicators of acceptability and feasibility. In one longitudinal QD study ( Kneck, Fagerberg, Eriksson, & Lundman, 2014 ), the researchers presented the findings as several key patterns of learning for persons living with diabetes; in another longitudinal QD study ( Stegenga & Macpherson, 2014 ), findings were presented as processes and themes regarding patients’ identity work across the cancer trajectory. In another two studies, the researchers described and compared themes or categories from two different perspectives, such as patients and nurses ( Canzan, Heilemann, Saiani, Mortari, & Ambrosi, 2014 ) or parents and children ( Marcinowicz et al., 2014 ). Additionally, Ma (2014) reported themes using both participants’ language and the researcher’s language.

In this systematic review, we examined and reported specific characteristics of methods and findings reported in journal articles self-identified as QD and published during one calendar year. To accomplish this we identified 55 articles that met inclusion criteria, performed a quality appraisal following CASP guidelines, and extracted and analyzed data focusing on QD features. In general, three primary findings emerged. First, despite inconsistencies, most QD publications had the characteristics that were originally observed by Sandelowski (2000) and summarized by other limited available QD literature. Next, there are no clear boundaries in methods used in the QD studies included in this review; in a number of studies, researchers adopted and combined techniques originating from other qualitative traditions to obtain rich data and increase their understanding of the phenomenon under investigation. Finally, justification for how QD was chosen and why it would be an appropriate fit for a particular study is an area in need of increased attention.

In general, the overall characteristics were consistent with design features of QD studies described in the literature ( Neergaard et al., 2009 ; Sandelowski, 2000 , 2010 ; Vaismoradi et al., 2013 ). For example, many authors reported that study objectives were to describe or explore participants’ experiences and factors related to certain phenomena, events, or interventions. In most cases, these authors cited Sandelowski (2000) as a reference for this particular characteristic. It was rare that theoretical or philosophical frameworks were identified, which also is consistent with descriptions of QD. In most studies, researchers used purposeful sampling and its derivative sampling techniques, collected data through interviews, and analyzed data using qualitative content analysis or thematic analysis. Moreover, all researchers presented focused or comprehensive, descriptive summaries of data including themes or categories answering their research questions. These characteristics do not indicate that there are correct ways to do QD studies; rather, they demonstrate how others designed and produced QD studies.

In several studies, researchers combined techniques that originated from other qualitative traditions for sampling, data collection, and analysis. This flexibility or variability, a key feature of recently published QD studies, may indicate that there are no clear boundaries in designing QD studies. Sandelowski (2010) articulated: “in the actual world of research practice, methods bleed into each other; they are so much messier than textbook depictions” (p. 81). Hammersley (2007) also observed:

“We are not so much faced with a set of clearly differentiated qualitative approaches as with a complex landscape of variable practice in which the inhabitants use a range of labels (‘ethnography’, ‘discourse analysis’, ‘life history work’, narrative study’, ……, and so on) in diverse and open-ended ways in order to characterize their orientation, and probably do this somewhat differently across audiences and occasions” (p. 293).

This concept of having no clear boundaries in methods when designing a QD study should enable researchers to obtain rich data and produce a comprehensive summary of data through various data collection and analysis approaches to answer their research questions. For example, using an ethnographical approach (e.g., participant observation) in data collection for a QD study may facilitate an in-depth description of participants’ nonverbal expressions and interactions with others and their environment as well as situations or events in which researchers are interested ( Kawulich, 2005 ). One example found in our review is that Adams et al. (2014) explored family members’ responses to nursing communication strategies for patients in intensive care units (ICUs). In this study, researchers conducted interviews with family members, observed interactions between healthcare providers, patients, and family members in ICUs, attended ICU rounds and family meetings, and took field notes about their observations and reflections. Accordingly, the variability in methods provided Adams and colleagues (2014) with many different aspects of data that were then used to complement participants’ interviews (i.e., data triangulation). Moreover, by using a constant comparison technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others looking for similarities and differences as well as reasoning why differences exist, to generate more general understanding of phenomena of interest ( Thorne, 2000 ). In fact, this constant comparison analysis is compatible with qualitative content analysis and thematic analysis and we found several examples of using this approach in studies we reviewed ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland et al., 2014 ; Johansson et al., 2014 ; Li et al., 2014 ; Oosterveld-Vlug et al., 2014 ).

However, this flexibility or variability in methods of QD studies may cause readers’ as well as researchers’ confusion in designing and often labeling qualitative studies ( Neergaard et al., 2009 ). Especially, it could be difficult for scholars unfamiliar with qualitative studies to differentiate QD studies with “hues, tones, and textures” of qualitative traditions ( Sandelowski, 2000 , p. 337) from grounded theory, phenomenological, and ethnographical research. In fact, the major difference is in the presentation of the findings (or outcomes of qualitative research) ( Neergaard et al., 2009 ; Sandelowski, 2000 ). The final products of grounded theory, phenomenological, and ethnographical research are a generation of a theory, a description of the meaning or essence of people’s lived experience, and an in-depth, narrative description about certain culture, respectively, through researchers’ intensive/deep interpretations, reflections, and/or transformation of data ( Streubert & Carpenter, 2011 ). In contrast, QD studies result in “a rich, straight description” of experiences, perceptions, or events using language from the collected data ( Neergaard et al., 2009 ) through low-inference (or data-near) interpretations during data analysis ( Sandelowski, 2000 , 2010 ). This feature is consistent with our finding regarding presentation of findings: in all QD articles included in this systematic review, the researchers presented focused or comprehensive, descriptive summaries to their research questions.

Finally, an explanation or justification of why a QD approach was chosen or appropriate for the study aims was not found in more than half of studies in the sample. While other qualitative approaches, including grounded theory, phenomenology, ethnography, and narrative analysis, are used to better understand people’s thoughts, behaviors, and situations regarding certain phenomena ( Sullivan-Bolyai et al., 2005 ), as noted above, the results will likely read differently than those for a QD study ( Carter & Little, 2007 ). Therefore, it is important that researchers accurately label and justify their choices of approach, particularly for studies focused on participants’ experiences, which could be addressed with other qualitative traditions. Justifying one’s research epistemology, methodology, and methods allows readers to evaluate these choices for internal consistency, provides context to assist in understanding the findings, and contributes to the transparency of choices, all of which enhance the rigor of the study ( Carter & Little, 2007 ; Wu, Thompson, Aroian, McQuaid, & Deatrick, 2016 ).

Use of the CASP tool drew our attention to the credibility and usefulness of the findings of the QD studies included in this review. Although justification for study design and methods was lacking in many articles, most authors reported techniques of recruitment, data collection, and analysis that appeared. Internal consistencies among study objectives, methods, and findings were achieved in most studies, increasing readers’ confidence that the findings of these studies are credible and useful in understanding under-explored phenomenon of interest.

In summary, our findings support the notion that many scholars employ QD and include a variety of commonly observed characteristics in their study design and subsequent publications. Based on our review, we found that QD as a scholarly approach allows flexibility as research questions and study findings emerge. We encourage authors to provide as many details as possible regarding how QD was chosen for a particular study as well as details regarding methods to facilitate readers’ understanding and evaluation of the study design and rigor. We acknowledge the challenge of strict word limitation with submissions to print journals; potential solutions include collaboration with journal editors and staff to consider creative use of charts or tables, or using more citations and less text in background sections so that methods sections are robust.

Limitations

Several limitations of this review deserve mention. First, only articles where researchers explicitly stated in the main body of the article that a QD design was employed were included. In contrast, articles labeled as QD in only the title or abstract, or without their research design named were not examined due to the lack of certainty that the researchers actually carried out a QD study. As a result, we may have excluded some studies where a QD design was followed. Second, only one database was searched and therefore we did not identify or describe potential studies following a QD approach that were published in non-PubMed databases. Third, our review is limited by reliance on what was included in the published version of a study. In some cases, this may have been a result of word limits or specific styles imposed by journals, or inconsistent reporting preferences of authors and may have limited our ability to appraise the general adequacy with the CASP tool and examine specific characteristics of these studies.

Conclusions

A systematic review was conducted by examining QD research articles focused on nursing-related phenomena and published in one calendar year. Current patterns include some characteristics of QD studies consistent with the previous observations described in the literature, a focus on the flexibility or variability of methods in QD studies, and a need for increased explanations of why QD was an appropriate label for a particular study. Based on these findings, recommendations include encouragement to authors to provide as many details as possible regarding the methods of their QD study. In this way, readers can thoroughly consider and examine if the methods used were effective and reasonable in producing credible and useful findings.

Acknowledgments

This work was supported in part by the John A. Hartford Foundation’s National Hartford Centers of Gerontological Nursing Excellence Award Program.

Hyejin Kim is a Ruth L. Kirschstein NRSA Predoctoral Fellow (F31NR015702) and 2013–2015 National Hartford Centers of Gerontological Nursing Excellence Patricia G. Archbold Scholar. Justine Sefcik is a Ruth L. Kirschstein Predoctoral Fellow (F31NR015693) through the National Institutes of Health, National Institute of Nursing Research.

Conflict of Interest Statement

The Authors declare that there is no conflict of interest.

Contributor Information

Hyejin Kim, MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing.

Justine S. Sefcik, MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing.

Christine Bradway, PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing.

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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• Developing the Quantitative Research Design
• Qualitative Descriptive Design

Overview of Descriptive Design

Sources of data.

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A descriptive design is a flexible, exploratory approach to qualitative research. Descriptive design is referred to in the literature by other labels including generic, general, basic, traditional, interpretive, and pragmatic. Descriptive design as an acceptable research design for dissertation and other robust scholarly research has received varying degrees of acceptance within the academic community. However, descriptive design has been gaining momentum since the early 2000’s as a suitable design for studies that do not fall into the more mainstream genres of qualitative research (ie. Case study, phenomenology, ethnography, narrative inquiry and grounded theory). In contrast to other qualitative designs, descriptive design is not aligned to specific methods (for example, bracketing in phenomenology, bounded systems in case study, or constant comparative analysis in grounded theory). Rather, descriptive design “borrows” methods appropriate to the proposed study from other designs. 

Arguments supporting the flexible nature of descriptive designs describe it as being preferable to forcing a research approach into a design that is not quite appropriate for the nature of the intended study. However, descriptive design has also been criticized for this mixing of methods as well as for the limited literature describing it. The descriptive design can be the foundation for a rigorous study within the ADE program. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose methods best suited to a practice-based research purpose.   

• Example Descriptive Design in an Applied Doctorate

Sources of Data in Descriptive Design

Because of the exploratory nature of descriptive design, the triangulation of multiple sources of data are often used for additional insight into the phenomenon. Sources of data that can be used in descriptive studies are similar to those that may be used in other qualitative designs and include interviews, focus groups, documents, artifacts, and observations.

The following video provides additional considerations for triangulation in qualitative designs including descriptive design: Triangulation: Pairing Thematic and Content Analysis

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Doctoral Dissertations and Projects

A qualitative descriptive study of therapists and their work with socially isolated female victims of sexual violence amid the covid-19 pandemic.

Iris Lazette Saldivar , Liberty University Follow

Helms School of Government

Doctor of Philosophy in Criminal Justice (PhD)

Vincent Giordano

therapists, sexual violence, pandemic, experiences, navigation

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Public Affairs, Public Policy and Public Administration

Recommended Citation

Saldivar, Iris Lazette, "A Qualitative Descriptive Study of Therapists and Their Work with Socially Isolated Female Victims of Sexual Violence amid the COVID-19 Pandemic" (2024). Doctoral Dissertations and Projects . 5722. https://digitalcommons.liberty.edu/doctoral/5722

The purpose of this qualitative descriptive study was to assess the experiences of therapists and the descriptions of their navigation processes while working with female victims of sexual violence who were socially isolated during the COVID-19 pandemic in the state of Texas. The theory that guided this study was the theoretical framework of loneliness, social isolation, and associated health outcomes. The research study focuses on two research questions that sought to examine the experiences and navigation of virtual treatment of therapists who provided counseling during the COVID-19 pandemic from March 2020 to March 2022. This study sought to find a straightforward description of an occurrence; the goal was to examine how therapists who work with victims of sexual violence describe their work with isolated female victims of sexual violence during the COVID-19 pandemic and how they navigated throughout. Data was collected via semi-structured interviews with 20 therapists. The setting of the interviews was completely virtual and conducted via Zoom. Findings suggest that therapists who treat socially isolated female victims of sexual violence during the COVID-19 pandemic will experience high levels of strain and will utilize effective problem-resolution solving to overcome barriers brought forth by the pandemic.

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• Published: 24 June 2024

Complex management and descriptive cost analysis of kidney transplant candidates: a descriptive cross-sectional study

• Guillermo Pedreira-Robles   ORCID: orcid.org/0000-0002-6180-4059 1 , 2 , 3 , 4 ,
• Paloma Garcimartín   ORCID: orcid.org/0000-0002-5758-6933 5 , 6 , 7 ,
• María José Pérez-Sáez   ORCID: orcid.org/0000-0002-8601-2699 1 , 8 ,
• Anna Bach-Pascual   ORCID: orcid.org/0000-0003-0571-6131 1 ,
• Marta Crespo   ORCID: orcid.org/0000-0002-1852-2259 1 , 8 &
• Victoria Morín-Fraile   ORCID: orcid.org/0000-0002-7800-8091 9  

BMC Health Services Research volume  24 , Article number:  763 ( 2024 ) Cite this article

Metrics details

The organisational care needs involved in accessing kidney transplant have not been described in the literature and therefore a detailed analysis thereof could help to establish a framework (including appropriate timing, investment, and costs) for the management of this population. The main objective of this study is to analyse the profile and care needs of kidney transplant candidates in a tertiary hospital and the direct costs of studying them.

A descriptive, cross-sectional study was conducted using data on a range of variables (sociodemographic and clinical characteristics, study duration, and investment in visits and supplementary tests) from 489 kidney transplant candidates evaluated in 2020.

The comorbidity index was high (> 4 in 64.3%), with a mean of 5.6 ± 2.4. Part of the study population had certain characteristics that could hinder their access a kidney transplant: physical dependence (9.4%), emotional distress (33.5%), non-adherent behaviours (25.2%), or language barriers (9.4%). The median study duration was 6.6[3.4;14] months. The ratio of required visits to patients was 5.97:1, meaning an investment of €237.10 per patient, and the ratio of supplementary tests to patients was 3.5:1, meaning an investment of €402.96 per patient.

Conclusions

The study population can be characterised as complex due to their profile and their investment in terms of time, visits, supplementary tests, and direct costs. Management based on our results involves designing work-adaptation strategies to the needs of the study population, which can lead to increased patient satisfaction, shorter waiting times, and reduced costs.

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The care of individuals with complex care needs has been extensively addressed in care management strategies, concluding that individuals who seek care from the health system and have certain characteristics that identify them as complex consume between 40 and 75% of available resources [ 1 , 2 ]. These studies on the general adult population over 65 years of age estimate the complexity prevalence to be around 3% to 5%, defining it as the difficulty in managing a person’s care and the need to implement specific individual plans due to the presence or concurrence of illnesses, as well as their way of using services or the characteristics of their environment [ 1 ].

Against this backdrop, chronic kidney disease is a public health problem with an increasing prevalence [ 3 , 4 ]. In addition, the most recent epidemiological studies conclude that individuals with chronic kidney disease are frequently part of the ageing population and have more comorbidities in addition to chronic kidney disease itself [ 4 , 5 , 6 ]. As a result, this population has generally been described in several ways: complex, comorbid, multi-pathological, fragile, etc. Due to their increased risk of complications [ 6 , 7 ] and also the increased health costs involved in their care [ 8 , 9 ], this group has unique characteristics that warrant specialised care. A thorough assessment of the kidney transplant candidate is therefore essential to reduce the risks associated with the transplant process [ 10 ]. Furthermore, this assessment may now require more investment, in all respects, given the ageing profile of the current population [ 9 , 11 ].

Despite these considerations, kidney transplant remains the best treatment option for the vast majority of individuals with chronic kidney disease in terms of quality of life, survival, and costs [ 12 , 13 , 14 ], with no upper age limit identified for kidney transplant [ 4 , 10 ]. There is, however, an estimated increased risk of complications and postoperative mortality in those who may meet criteria for frailty and comorbidity [ 6 , 15 ], which, as reported, are increasingly prevalent with age. If a living-donor kidney transplant is not an option, it is difficult to predict when the opportunity to receive a kidney transplant may come, with estimated waiting times, in our setting, at a median of 24 months from the start of dialysis [ 4 ], During this waiting period, close monitoring of the kidney transplant candidate by the professionals involved will be required, depending on the needs of the individual, to confirm that this treatment option is still feasible and safe [ 4 , 10 ].

The organisational care needs involved in accessing kidney transplant have not been described in the literature and therefore a detailed analysis thereof could help to establish a framework (including appropriate timing, investment, and costs) for the management of this complex population. The primary objective of this study is to analyse the profile of kidney transplant candidates and their care needs in a tertiary hospital as well as the direct costs of studying them in order to assess the complexity of managing this process. This analysis will allow us to propose more efficient strategies for kidney transplant candidates and for the health system as a whole.

A cross-sectional analysis was carried out on all kidney transplant candidates who were being monitored in the kidney transplant access consultation as of 31 December 2020. This research adheres to the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines for the reporting of observational studies [ 16 ].

The care process that includes kidney transplant access in our benchmark healthcare facility is made up of 3 monographic nursing, nephrology, and urology consultations. In line with the recommendations set out in the KDIGO guidelines [ 10 ], these unit specialists are directly involved in the assessment of kidney transplant candidates. In addition, there are monographic cardiology, pulmonology, and anaesthesiology consultations for referring and/or examining kidney transplant candidates that require specialised assessments, and other general consultations typical of a tertiary hospital, for professional opinion in relation to the case and according to the person’s needs.

The assessments made by the various professionals are accompanied by the necessary supplementary tests and examinations to weigh up the risks and benefits of the requested procedure. If these assessments are favourable, the person is deemed eligible for kidney transplant as a treatment option – without being able to predict exactly when the opportunity to receive such treatment will come, unless there is the option of a living-donor kidney transplant, which would allow the procedure to be scheduled. During the time on the kidney transplant waiting list, the kidney transplant candidate is assessed annually by the transplant facility team, so that this treatment option can continue to be considered as a viable and safe choice with the support of the teams from the benchmark healthcare facilities during follow-up. In addition, the necessary supplementary tests and examinations are updated periodically based on the service’s action protocol and the individual’s clinical progress and needs.

Candidates may arrive at the kidney transplant access pathway already having had the supplementary tests done at the healthcare facility of origin or they may need to undergo a full work-up at the referral transplant facility. At the time of this study analysis, regardless of the kidney transplant access model, kidney transplant candidates had the same number of visits, and the study duration was similar. The tests included in this study are those performed by the referral transplant facility and do not include other external tests that may be part of the kidney transplant candidate assessment process.

For the purpose of this article, an ‘uneventful study’ of a kidney transplant candidate has been defined as one requiring an initial assessment consultation, completion of the necessary supplementary tests, and a second assessment before a decision can be made to include the person in the kidney transplant programme. Due to various circumstances, cases where not all of the necessary information was available at the time of the second consultation were labelled as ‘study with incidents.’ Examining the causes of these incidents in relation to the care needs of the study population will enable us to gain a deeper understanding of the care pathways involved and make recommendations for improving their care.

In relation to costs, it is worth noting that, within the study context, costs are always funded by the public healthcare system, which is financed through direct taxes on the citizenry.

The study focuses on kidney transplant candidates who were being monitored in the kidney transplant access consultation as of 31 December 2020, thereby indicating that the costs pertain to the year 2020. Patients who had not completed the study as kidney transplant candidates or who had not attended the final consultation to be assessed for inclusion in the kidney transplant programme were excluded.

Data collection

The following variables were collected from the electronic health records at the referral facility:

Sociodemographic variables: sex; age; employment status; place of birth; place of residence; religion; and language barriers.

Clinical variables: renal replacement therapy; dependency in activities of daily living (Barthel index < 90); emotional distress during treatment as documented in the clinical record; non-adherent behaviours as documented in the clinical record; Charlson Comorbidity Index; high blood pressure; dyslipidaemia; diabetes mellitus; body mass index; physical activity of less than 30 min per day; tobacco use; chronic obstructive pulmonary disease and/or asthma; coronary artery disease.

Study duration and implications: study duration; reasons for incidents during the study.

Number of visits, types of visits (in-person or remote), and costs of the visits required in 2020 as part of the assessment.

Number, type, and direct costs of supplementary tests and examinations carried out in 2020.

These variables were agreed upon by the members of the research team and after a thorough review of the relevant literature published in current clinical practice guidelines [ 10 ]. The Charlson Comorbidity Index score has been shown to be linked to potential complications among kidney transplant candidates [ 17 ], as well as to having a body mass index score in the obese range [ 18 ]. Similarly, cardiovascular risk factors are highly prevalent in this population, meaning increased costs when studying kidney transplant candidates [ 19 , 20 ].

In the financial assessment, only variables within the analysis facility were quantified, without considering indirect costs or costs associated with other facilities. The cost of each activity analysed is based on the cost tables of the referral facility.

Data analysis

The results were recorded and analysed using a database created by the research team using SPSS© (version 26) from IBM (IBM Corporation). A descriptive analysis of the study variables was carried out: qualitative variables were expressed as absolute and relative frequencies, while quantitative variables were expressed as means and standard deviations in the case of a normal distribution and as medians and interquartile ranges in the case of a non-normal distribution. For the economic analysis, the ratio of visits or costs per patient was used.

The sample selection and care pathways are depicted in Fig.  1 . This flow diagram illustrates the participant inclusion process. In 2020, a total of 489 individuals were studied as candidates for kidney transplantation. This sample represents 85.5% of the individuals (recipient candidates and living kidney donors) assessed in the kidney transplant access consultations during the study period. Of these, 135 (27.6%) were incident patients in the evaluation process throughout the year, and 86 (63.7%) were placed on the waiting list for a kidney transplant. 354 (72.4%) candidates were either on the waiting list and under follow-up ( n  = 290; 81.9%) or temporarily excluded and under follow-up ( n  = 64; 18.1%). A total of 113 candidates underwent kidney transplantation during 2020.

Flow diagram showing the participant inclusion process. Note: KT, kidney transplant; KTc, kidney transplant candidate; SEs, supplementary examinations; NUR, Nursing; NEF, Nephrology; URO, Urology

Analysis of sociodemographic and clinical characteristics

Table 1 shows the sociodemographic and clinical characteristics of the sample analysed.

Part of the study population exhibited certain characteristics that may have implications for the outcome of the kidney transplant, such as: physical dependence (9.4%), emotional distress during treatment (33.5%), non-adherent behaviours during the study process that resulted in missed visits and/or missed supplementary tests (25.2%), and language barriers (9.4%).

The comorbidity index was high (> 4 in 64.3%), with a mean of 5.6 ± 2.4 (Table  1 ) and the study cardiovascular risk factors were prevalent in our sample (> 3 in 68.7%), with a mean of 3.4 ± 1.9 cardiovascular risk factors (Fig.  2 ).

Percentage of kidney transplant candidates studied with cardiovascular risk factors. Note: CVRFs included: high blood pressure; dyslipidaemia; diabetes mellitus; obesity; sedentary lifestyle; and smoking habit (see Table  1 )

In the patients classified as ‘uneventful’, the mean comorbidity index was 5 ± 2, while in the group of patients classified as ‘with incidents’ the mean comorbidity index was 6 ± 2.4.

Study duration and implications

Table 2 shows the study duration and its implications for the study of kidney transplant candidates.

Patients categorised as ‘uneventful’ accounted for 36.6% of the total, and 65.8% ( n  = 204) of the patients classified as ‘with incidents’ ( n  = 310; 63.4%) had a single cause for those incidents, while 34.2% ( n  = 106) had more than one cause. The main causes in the patients analysed were: the need to extend the assessment with cardiology tests and visits (16.9%); the need to extend the study with another medical or surgical speciality (14.1%); having a glomerular filtration above 15 ml/min (12.9%); and non-adherence as evidenced by unjustified missed visits and/or examinations (12.1%).

Visits made and costs

Table 3 presents the type, format, and financial costs of the 2,918 visits required in the study of the 489 kidney transplant candidates analysed. These visits account for 79.4% of the total annual activities carried out in the kidney transplant access consultations, with the remainder corresponding to the study of living kidney donor candidates. The ratio of required visits to patients was 5.97:1, representing an investment of €237.10 per patient.

Supplementary tests and examinations carried out and costs

Table 4 shows the type, format, and financial costs of the 1,735 supplementary tests and examinations necessary for studying the 489 kidney transplant candidates analysed. These supplementary tests and examinations account for 80.4% of the total annual activities carried out in the kidney transplant access consultations, with the remainder corresponding to the study of living kidney donor candidates. The ratio of required supplementary tests to patients was 3.5:1, representing an investment of €402.96 per patient.

This study is the first to report on the current profile of kidney transplant candidates in a tertiary hospital in order to assess the complexity of managing this group and the direct costs that can be incurred. Our analysis suggests that the current profile of kidney transplant candidates has changed over the past two decades, with an increase in the clinical and management complexity of this population. Our analysis has also revealed a great number of associated comorbidities and a high financial investment required for the current study of kidney transplant candidates.

In the study population, comorbidity (as measured with the Charlson comorbidity index) and associated cardiovascular risk factors are prevalent, which is similar to that reported in current registers in our setting [ 4 , 21 ] and in other international contexts [ 22 , 23 ]. However, the current profile differs substantially from that reported in the literature of the 1990s and 2000s [ 24 ]. Currently, individuals with high comorbidity indices are accepted as kidney transplant candidates due to the evidence-based benefits of this treatment option as compared with other therapeutic strategies [ 10 ]. This treatment option has been reported to improve the individual’s chance of survival [ 25 ] and quality of life [ 26 ], having a positive impact on the health system as a whole, [ 27 ] provided that these comorbidities are studied and monitored by the specialists in the interdisciplinary referral team [ 10 , 14 ]. It is therefore safe to say that the clinical profile of kidney transplant candidates today displays higher levels of comorbidity than in past decades, which can lead to greater complexity in their care.

We are aware that the current profile of kidney transplant candidates is also associated with an increased risk of cardiovascular events, malignancy, and/or potential complications compared to the general population [ 9 , 28 , 29 , 30 ], which can also determine whether the individual is unsuitable for a kidney transplant [ 27 ]. In our results, we find that more than 35% of the individuals studied de novo could not be included on the waiting list for a kidney transplant due to issues identified during the study or incompatibilities in accessing this treatment option. In addition, more than 15% remain off the waiting list more than one year after this initial study. These data suggest that, in general, the study population has complex characteristics relating to the management of their disease process.

Other characteristics found in our study have also been reported in the literature that individually assesses the complexity of managing the process and its relationship to socio-economic and cultural factors [ 31 ]. The literature explains that these are characteristics linked to socio-economic vulnerability, such as being a foreign national, language limitations, little social support, low income, and low level of education [ 31 , 32 , 33 ], which may delay or even prevent access to this treatment option.

Considering the profile of comorbidities and the socio-economic limitations of the population examined, our study reported more than 60% of kidney transplant access processes as having incidents. These cases with incidents take a long time (> 11 months) before they can access the kidney transplant programme due to the higher complexity of their process management. Most of the delays in the kidney transplant access process are caused by the need for further kidney transplant candidate assessment by another medical speciality and by non-adherent behaviours involving missed visits or supplementary tests. In the literature, non-adherence figures of 52% to 67% have been reported in the population who end up receiving kidney transplant [ 34 ], pointing to a significantly greater problem than that reported in our study.

Other causes of delay in the study, which make the management of the process even more complex, include missing visits or supplementary tests due to hospital admissions, detecting acute decompensated disease, and active abuse of illegal drugs. A study conducted with the same population reported that 38% of kidney transplant candidates were admitted to hospital at least once during their first year on the waiting list, rising to almost 50% in cases in which the individual met frailty criteria [ 35 ]. In addition, glomerular filtration levels far above 15 ml/min and the consequences of the COVID-19 pandemic, among others, call for a prior analysis of both the kidney transplant candidate’s situation and the hospital setting to avoid starting the study in circumstances that could prevent the kidney transplant candidate from being included in the kidney transplant programme. Taking into consideration the causes that may lead to study delays, we have detailed the benefits of having a kidney transplant access nurse to carry out a planning and optimisation analysis before and during the study process, as well as setting up a care plan to achieve the desired objectives before the surgical procedure takes place [ 36 ].

Despite the clinical and management complexity found in the study population, the care strategy to be followed is to study, and consequently minimise, the characteristics and risks through an interdisciplinary plan and thus continue to offer kidney transplant as the best treatment option for individuals with chronic kidney disease [ 10 , 12 , 13 , 14 , 17 , 35 ]. It is therefore essential to have management models in place that include effective strategies for establishing the candidate’s profile and determining differentiated strategies for the most complex cases – reducing waiting times and speeding up visits and supplementary examinations in the study of this population [ 36 ].

When considering the direct annual investment involved in a kidney transplant access programme in a tertiary hospital (in terms of tests, visits, and costs), we reported in a previous study conducted in the same context that, despite the greater clinical complexity of kidney transplant candidates, the study of kidney donor candidates requires twice the number of visits and supplementary tests in a shorter period of time [ 37 ]. A distinction must be drawn between clinical or physical complexity and the complexity involved in the care management of a healthcare process. In this case, it can be concluded that the study of kidney donor candidates is more complex to manage, despite the fact that the clinical complexity is lower [ 38 ].

Compared to previous studies, the overall cost of a kidney transplant is lower than that reported for dialysis therapies [ 14 , 39 ], which sufficiently justifies the reported investment and the strategies that could be implemented to increase the efficiency of this treatment option. In the study country (Spain), highly variable mean costs per disease have been reported, as costs increase with the number of co-occurring diseases. In general, an individual with one comorbidity amounts to a mean of €413 per year; an individual with five comorbidities, €2,413 per year; and an individual with ten comorbidities, €9,626 per year [ 40 ]. This study has only analysed the direct costs involved in accessing kidney transplant in a transplant facility, irrespective of previous studies carried out in the referral facility or the need to attend to other intercurrent care processes. A suitability study for a kidney transplant in our setting costs a mean of €640.06 per patient.

Taking the profile of kidney transplant candidates and their mean healthcare costs into account, it is safe to say that we are facing a care framework that is not just complex in terms of disease, but also in terms of management. The majority of the studies in this area of analysis conclude that heavy care workloads are one of the most important factors that most negatively impacts professionals and their role in relation to the individuals they care for [ 41 , 42 ]. The association between patient outcomes and staff outcomes has generated evidence in two respects: the needs of patients and the needs of professionals [ 42 ].

In relation to the needs of professionals, one of the main sources of professional stress is the high workload that can result from providing care directly to highly complex patients [ 43 ]. Professional stress is closely related to emotional exhaustion, depersonalisation, and low professional accomplishment, all of which can lead to burnout syndrome [ 44 ]. These heavy care workloads have a direct negative impact not only on the professionals, but also on the quality of the care they provide, which is poorer in settings with heavier workloads [ 45 ]. This takes us into the field of patients’ needs, which are closely related to their caregivers’ needs and are becoming ever more specific due to the aforementioned complexities. It has thus been established that adequate staffing is essential to high-quality care, satisfaction with the care received, and shorter waiting times in populations defined as complex [ 46 , 47 ].

Limitations of research

The primary limitation of this study lies in its planned single-centre design and the selective inclusion of variables, which solely address clinical and direct cost issues at a specific moment in the kidney transplant pathway. However, while this analysis can be replicated in other contexts to gather further evidence on the study objective, it's imperative to acknowledge the absence of indirect costs in our assessment, as well as the broader implications overlooked by focusing solely on this moment of the evaluation process. These omissions restrict our ability to comprehensively evaluate the economic impact on the healthcare system and the overall burden borne by kidney transplant candidates. By solely focusing on variables associated with direct costs within the transplant process at the referral facility, we provide clarity on this aspect but inadvertently overlook the broader financial implications and the comprehensive needs of the target population.

On the other hand, it is important to note that this study collects data from the year 2020, which was marked by the COVID-19 pandemic. During the months of lockdown and heightened severity (March and April), the kidney transplant access unit at the studied centre was closed, but all lost activity was promptly resumed thereafter.

Implications clinical practice

When comparing the results reported in this study with the reference literature, it seems vital that agreement is reached on the best organisational and management strategies to cater for this population with complex care needs. The involvement of nurses in providing access to kidney transplant has been reported to be beneficial in this regard [ 36 ], but is not yet widespread in our study context [ 38 ]. Therefore, it is necessary to continue to provide robust evidence of these benefits and to engage managers and policy makers to bring greater quality, effectiveness, and efficiency to the kidney transplant treatment option.

The first of these measures that we have adopted in our referral facility is an initial virtual appointment via a phone call to the kidney transplant candidate to review their clinical record, the evidence they provide, and their needs, to be able to plan the best organisational strategy for them and the people around them. This strategy aims to avoid initiating kidney transplant candidate evaluation at times when they may not be suitable and to prioritise the processes that will benefit from kidney transplant candidate evaluation being started. Clear examples include: avoiding starting a kidney transplant candidate study when the patient has very high glomerular filtration rates and slow clinical progress; in the context of a pandemic or other circumstances specific to the organisation of the facility; or in personal or organisational situations suggesting that it may not be the right time to initiate the study. Priority is thus given to evaluation of kidney transplant candidates who are eligible for a pre-emptive kidney transplant before starting renal replacement therapy, either because of the recipient’s own characteristics or because of the possibility of proceeding to a kidney transplant from a living donor. The implementation of this new model will be analysed to report on its efficacy and efficiency.

This study provides a detailed analysis of the profile of kidney transplant candidates and their care needs within a tertiary hospital, along with an examination of the direct costs associated with their evaluation. The study population can be characterised as complex due to their profile and their investment in terms of time, visits, supplementary tests, and direct costs. It illuminates the clinical complexity of this population and underscores the need for comprehensive interdisciplinary care planning to address their evolving needs. Management based on our results involves designing work-adaptation strategies tailored to the needs of the study population, which can lead to increased patient satisfaction, shorter waiting times, and reduced costs. Despite advances in the current management of this population, challenges persist, leading to delays or limitations in access to transplant programs and contributing to the complexity of treatment.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank all the nurses, nursing assistants, physicians, surgeons, psychologists, social workers, dietitians, and physiotherapists who actively participate in the assessment of KT candidates and in the improvement of the whole process. We would also like to thank the finance and documentation departments for their assistance in data collection. We acknowledge the support in the English translation of the manuscript by Alejandro García Aragón and Eleanor Staniforth.

This work was supported by the “Strategic Plan for Health Research and Innovation” from the Generalitat de Catalunya, grant number SLT017/20/000001, with a contribution of 57,239 euros. This funding source had no role in the design, execution, analyses, interpretation of the data, or decision to submit the results.

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Guillermo Pedreira-Robles, María José Pérez-Sáez, Anna Bach-Pascual & Marta Crespo

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Paloma Garcimartín

Research Group in Nursing Care, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain

Biomedical Network Research Center for Cardiovascular Diseases, (CIBERCV, Carlos III Health Institute), Madrid, Spain

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GPR, PGC, MJPS, and VMF made substantial contributions to the conception and design of this study. GPR and ABP participated in data collection and analysis. ABP and MCB were involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript. GPR takes responsibility of the paper as a whole.

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Correspondence to Paloma Garcimartín .

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This article is part of the principal investigator’s doctoral thesis entitled ‘The Advanced Practice Nurse in the construction of the kidney transplant candidate care map’, approved by the Clinical Research Ethics Committee (no. 2020/9418/I) at the Hospital del Mar Research Institute, with the principles set out in the Declaration of Helsinki. This project obtained informed consent from the participants.

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Pedreira-Robles, G., Garcimartín, P., Pérez-Sáez, M.J. et al. Complex management and descriptive cost analysis of kidney transplant candidates: a descriptive cross-sectional study. BMC Health Serv Res 24 , 763 (2024). https://doi.org/10.1186/s12913-024-11200-y

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Theses & Dissertations

Nurses' compliance with central line maintenance (handling) guidelines at a tertiary care setting in karachi, pakistan- a descriptive cross-sectional study.

Alina Amir Ali , Aga Khan University Follow

Date of Award

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Dissertation

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Master of Science in Nursing (MScN)

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Dr. Salma Rattani

School of Nursing and Midwifery, Pakistan

Background: Central venous catheters (CVCs) are integral to modern healthcare, facilitating critical treatments. However, the risk of complications, particularly infections, necessitates strict adherence to central line maintenance protocols. This study explores the compliance of nurses in executing these protocols. Purpose: In a tertiary care facility in Karachi, Pakistan, this study sought to evaluate nurses' adherence to each item on the central line management checklist as well as the checklist's total adherence in the Special Care Units. Method: A total of 119 registered nurses were assessed, using a quantitative structured observational design for central line handling in adult medical surgical special care unit at a tertiary care hospital. The study used Aga Khan University Hospital (AKUH) central line handling policy tool, the inter-rater reliability of which was tested and each step showed the Kappa value of 0.6 to 1.00. Ethical approval was obtained from AKUH ethical review committee (ERC), prior to the study. Data was analyzed using epi-data info version 7.2.4 and SPSS version 21. Finding(s): An overall compliance rate of central-line handling in special care units is assessed, revealed that 30% of respondents exhibit full compliance, 23% are compliant, 9% are partially compliant, and 38% are non-compliant. All the other practices collectively suggest a strong and significant association between the choice of duty shift (morning, evening, v night) and the actions taken. This data indicates that the duty shift may influence whether nurses perform these actions. Conclusion: The maintenance of central lines is a vital aspect of patient care in healthcare settings. Adherence to central line maintenance guidelines is essential to prevent infections and enhance patient safety. Study findings indicated that some nurses demonstrate full or partial compliance, while others exhibit non-compliance. By addressing areas of non-compliance and standardizing best practices, healthcare institutions can significantly reduce the risk of central line-associated infections and ultimately improve patient safety at their institutions.

Recommended Citation

Ali, A. A. (2023). Nurses' compliance with central line maintenance (Handling) guidelines at a tertiary care setting in Karachi, Pakistan- A descriptive cross-sectional study (Unpublished master's dissertation). Aga Khan University, Karachi, Pakistan.

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Matters of the heart and homeland

When Meaghan Arnold began a session in the lab, she usually donned a lab coat and gloves before checking the latest interactions in her yeast two-hybrid system. She was looking for a particular reaction between two proteins, WWP1 and WBP2, that simulated how they might interact in the human heart—could this never-before-proven interaction help explain the causes of heart failure and cancer?

For Carlos Sanchez-Julia , a day of field research started in the thick of Ecuador’s summer humidity. He and a local field guide set off on a motorcycle, navigating the roads of the Mache-Chindul Ecological Reserve, ready to ask the local farmers about cacao, the bean used to make chocolate: What variety they grew, how they cultivated it, when they harvested. How did their practices affect the land they farmed—land that many of them legally cannot own?

Arnold and Sanchez-Julia might have had different questions, methods and settings, but they now share a similarity: Their efforts earned them the 2024 William A. Mould Senior Thesis Award. Honoring the legacy of the late William A. Mould, inaugural dean of the Honors College, this award recognizes outstanding thesis projects with broad impacts in the students’ fields of study.

This year, the two winning theses demonstrate the different avenues that research projects can take, from the human heart to the heart of a forest, guided by curiosity, tenacity and a focus on important interactions.

Shining a light

Arnold, a biological sciences major, arrived on campus in the midst of the Covid-19 pandemic. Though she was eager to join a lab, the pandemic prevented her from doing so her first year, and her lab classes were online. Despite the virtual simulations of lab work, her curiosity sparked.

“I learned more about just how important the microscopic level of everything in biology is. You hear about diseases or anatomy, and stuff like that is very big picture,” says Arnold. “Learning that there were so many microscopic things going on that needed to be figured out, and that there were ways to study and understand these things was just so fascinating to me.”

Seeking in-person experience, she joined Jason Stewart’s lab her sophomore year and gained valuable insight into DNA repair and foundational lab skills. It was Stewart who recommended Arnold to Lydia Matesic, whose lab was studying WWP1, a protein that has been linked to heart failure.

Arnold took to the new lab instantly, enjoying the experiments and working with cells. “I also, very unexpectedly, found myself enjoying the intellectual aspect of it all, doing the problem-solving of ‘did this work?’” she says.

This interest, and Arnold’s rapid progress, was evident to her research mentor. “Never before have I seen a student come into a project and take such clear ownership of the direction and interpretation,” wrote Lydia Matesic about Arnold’s work. “In 19 short months, Meaghan has jumped into research that was already in progress in my lab...all the while [becoming] proficient in the foundational knowledge and technical skills needed to complete that project and to move forward in a new direction.”

The new direction? Enter the WBP2 protein.  According to Matesic, Arnold pointed out that both WWP1 and WBP2 had been linked to cancer. What if they were interacting in the heart, and what if those interactions led to cancer and heart failure? Before Arnold’s research, nothing was known about exactly how these proteins caused these diseases.

“Combined, these disorders account for the lion’s share of mortality worldwide,” wrote Matesic. “By shining a light on WWP1 and WBP2, Meaghan has opened the door to new avenues for drug design that could impact large numbers of individuals with cancer or heart failure worldwide.”

Arnold shone that light through two years of research. Her 65-page thesis, “Exploring the interaction between WWP1 and WBP2 in the heart,” chronicles her process for proving this phenomenon.  “My work,” she explains, “is really on understanding at the molecular level what is happening in order to cause these diseases to propagate.”

After months of painstaking work in yeast systems, and funded by Honors College and Magellan research grants, Arnold proved that WWP1 and WBP2 had a natural affinity to interact. The next step was to test the two proteins in human heart tissue, using immunofluorescent staining to detect the possible presence of WBP2 and its potential interactions with WWP1.

This time, the lights shone for Arnold. “Looking through the microscope and actually seeing my proteins in the human heart was pretty amazing,” she says.

It's an incredible breakthrough, and Arnold is eager to continue. “Based on my preliminary findings,” she says, “[the proteins’] location is at the membrane of a lot of cardiac myocytes, which is an area where a lot of signaling pathways start from, really primes them to cause a lot of these downstream mechanistic effects that cause those diseases to propagate.” From Arnold’s work, scientists can develop targeted therapeutics to treat the diseases caused by WWP1 and WBP2’s interactions.

Arnold hopes to share her findings with the rest of the scientific community through publishing a version of her thesis, and she will continue to impact an even broader community through her work as a physician. The Wilmington, Delaware, native will continue her education in the Palmetto State, attending the USC School of Medicine in Greenville.

Her passion for research burns as brightly as ever: “There could be a lot of benefit to having more interdisciplinary interactions between [projects]...and using that to work off of each other and better the understanding of both fields.”

Conservation and conversation

Early in his collegiate career, Sanchez-Julia wasn’t sure what social sciences research looked like. The international studies and economics double-major from Irmo, South Carolina, knew that he wanted to have a positive impact on the world, but wasn't sure how research fit into the plan.

“Through classes, we [were] always reading a bunch of really cool research papers and these different studies,” Sanchez-Julia says. “And I’m like, ‘Okay, that’s kind of cool. I guess I have to do this for Honors at some point. So what can I do?’”

The answer was hidden deep in the rainforests of Ecuador, and Sanchez-Julia's route to his research project was a multi-year expedition. During his sophomore year, one of his professors, David Kneas, researched in Ecuador. His interaction-centered experience fascinated Sanchez-Julia.

“I was like, ‘Oh my gosh, that sounds so fun,’” he reflects. “You just get to walk around and talk to people all day? That’s great.”

Later that semester, Sanchez-Julia's older sister, then a Ph.D. candidate in biology at Tulane University, detailed her recent trip to the Mache-Chindul Ecological Reserve in Ecuador. She connected her brother with a Tulane professor leading a field school that summer, and before he knew it, Sanchez-Julia was bound for Ecuador. 

There, he met a master’s student from Tulane, Liat Perlin, who would eventually serve as the second reader for his thesis. Perlin introduced Sanchez-Julia to the issues surrounding cacao farming on the preserve and the farmers’ lack of land rights, as well as the conservation NGO Fundación para la Conservación de los Andes Tropicales (FCAT). By the end of the summer, Sanchez-Julia’s head was “buzzing.”

“I [couldn’t] solve any of these big pressing issues that everybody’s talking about, and I was frustrated,” Sanchez-Julia recalls.

This frustration brought him back to Ecuador the following summer, funded by Honors and Magellan research grants. Sanchez-Julia had only 23 days to gather as much information as he could about how land tenure affected the farmers’ decisions about planting cacao: How and why did they choose between environmentally friendly and higher-producing, but more harmful, varieties.

Throughout, he was keenly aware that he was researching peoples’ homeland and livelihood. “I [wanted it] to be something that’s not just research for me and my sake, to check off a requirement,” he says. “It’s research relating to actual people and things going on in this area.”

This was no small feat. “Carlos maintained steadfast commitment to community engagement and respect in his fieldwork, ensuring his asking of questions was respectful and that all engagements with local people were approached with gratitude and reflexivity,” wrote Robert Kopack, Sanchez-Julia's thesis director. “Carlos faced every challenge that comes in the research process head on, always looking for ways to improve the document.”

His conscientiousness and passion for the project yielded important insights. In less than a month, Sanchez-Julia administered 28 surveys to cacao farmers, conducted two interviews with government officials and facilitated a focus group of farmers—all in Spanish—to learn more about the delicate balance between conservation, production and, for the farmers, survival.

Conversations and research culminated in a 74-page thesis, “The influence of neoliberal and ecoimperialist contexts on conservation: Insights from Ecuador’s Mache-Chindul Ecological Reserve.” By the end of the project, however, Sanchez-Julia was left with more questions; cacao and conservation are a complicated combination.

“I don’t think that conservation and people’s livelihoods necessarily have to be something that goes against each other,” says Sanchez-Julia. “But to do that, you need open dialogue and conversation.”

Sanchez-Julia hopes to continue that conversation in his master’s studies. He was awarded an Erasmus Mundus Joint Master’s Degree scholarship and will participate in the MERGED Global Environment and Development program at the University of Copenhagen and University of Warsaw. In the meantime, he’s in contact with a researcher from the University of Hawaii who hopes to utilize his research for a biology study.

And he’s still committed to making a positive impact: “The biggest part of this research and my whole experience was just talking to people and learning from them about how solutions that benefit everybody can come about. And all that is, is conversation...and just having a genuine interest and desire to learn from someone else—not learn about somebody else, but learn from them, and learn how you can help if you can.”

An exciting year for research

Two other theses were selected as finalists and recognized at the Honors College Revocation Ceremony on May 2:

“Phytoplankton biomass and community responses to additions of limiting nutrients in Lake Murray, SC” by Haley Durbin, marine science. She was nominated by Jay Pinckney, Ph.D., School of Earth, Ocean and Environment.

“Eat or be eaten: Taste, appetite and consumption as key ingredients in Quicksand and Bitter in the Mouth” by Reagan Green, English. She was nominated by Catherine Keyser, Ph.D., English Language and Literature. 

Challenge the conventional. Create the exceptional. No Limits.

Sacks Research Lab – Flavor, Food, Chemistry

Matthew defends ms, and ana gaby defends phd.

Matthew Sheehan successfully defended his MS thesis, which investigated corrosion in hard to hold alcoholic beverages in aluminum cans (sour beer, wines). Following an internship at Quintessa winery, he will start a new position as an R&D engineer with Mars, Inc.

And, Ana Gaby Ortiz Quezada defended her PhD thesis on deodorization of Concord juice using non-thermal techniques. She is remaining at Cornell, but in a new role as a Dairy Foods Extension Associate with Dr. Martin Wiedmann.

Congratulations!

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• Dissertation & Thesis Outline | Example & Free Templates

Dissertation & Thesis Outline | Example & Free Templates

Published on June 7, 2022 by Tegan George . Revised on November 21, 2023.

A thesis or dissertation outline is one of the most critical early steps in your writing process . It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to your field.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

In the final product, you can also provide a chapter outline for your readers. This is a short paragraph at the end of your introduction to inform readers about the organizational structure of your thesis or dissertation. This chapter outline is also known as a reading guide or summary outline.

Table of contents

How to outline your thesis or dissertation, dissertation and thesis outline templates, chapter outline example, sample sentences for your chapter outline, sample verbs for variation in your chapter outline, other interesting articles, frequently asked questions about thesis and dissertation outlines.

While there are some inter-institutional differences, many outlines proceed in a fairly similar fashion.

• Working Title
• “Elevator pitch” of your work (often written last).
• Introduce your area of study, sharing details about your research question, problem statement , and hypotheses . Situate your research within an existing paradigm or conceptual or theoretical framework .
• Subdivide as you see fit into main topics and sub-topics.
• Describe your research methods (e.g., your scope , population , and data collection ).
• Present your research findings and share about your data analysis methods.
• Answer the research question in a concise way.
• Interpret your findings, discuss potential limitations of your own research and speculate about future implications or related opportunities.

For a more detailed overview of chapters and other elements, be sure to check out our article on the structure of a dissertation or download our template .

To help you get started, we’ve created a full thesis or dissertation template in Word or Google Docs format. It’s easy adapt it to your own requirements.

 Download Word template    Download Google Docs template

It can be easy to fall into a pattern of overusing the same words or sentence constructions, which can make your work monotonous and repetitive for your readers. Consider utilizing some of the alternative constructions presented below.

Example 1: Passive construction

The passive voice is a common choice for outlines and overviews because the context makes it clear who is carrying out the action (e.g., you are conducting the research ). However, overuse of the passive voice can make your text vague and imprecise.

Example 2: IS-AV construction

You can also present your information using the “IS-AV” (inanimate subject with an active verb ) construction.

A chapter is an inanimate object, so it is not capable of taking an action itself (e.g., presenting or discussing). However, the meaning of the sentence is still easily understandable, so the IS-AV construction can be a good way to add variety to your text.

Example 3: The “I” construction

Another option is to use the “I” construction, which is often recommended by style manuals (e.g., APA Style and Chicago style ). However, depending on your field of study, this construction is not always considered professional or academic. Ask your supervisor if you’re not sure.

Example 4: Mix-and-match

To truly make the most of these options, consider mixing and matching the passive voice , IS-AV construction , and “I” construction .This can help the flow of your argument and improve the readability of your text.

As you draft the chapter outline, you may also find yourself frequently repeating the same words, such as “discuss,” “present,” “prove,” or “show.” Consider branching out to add richness and nuance to your writing. Here are some examples of synonyms you can use.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, November 21). Dissertation & Thesis Outline | Example & Free Templates. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/dissertation/dissertation-thesis-outline/

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Arts, Humanities, & Social Sciences

New dissertation grants expand global research support

The newly established penn global dissertation grants program provides as much as $8,000 in funding to each of 11 ph.d. candidates to enhance global components in their research..

Penn Global has announced the first recipients of the newly established Penn Global Dissertation Grants program , providing as much as $8,000 each in funding to 11 Ph.D. students across four schools

With dissertations ranging from examinations of artificial intelligence and computational immunology in Vietnam to a look at the intersection of women, food, and freedom in the Dominican Republic, the recipients will use the funds to enhance global components in their dissertation research.

A main priority for Penn Global under its third strategic framework is to develop initiatives that support graduate and professional research with global dimensions. Introducing the Penn Global Dissertation Grants, which will be offered annually, augments global opportunities for graduate and professional students by expanding the scope of Penn Global’s research support.

“This program is the best example of how our strategic plan is responsive to the needs of the University community,” says Amy Gadsden , associate vice provost for global initiatives. “When Vice Provost for Global Initiatives Zeke Emanuel and I met with stakeholders across campus last year to further refine the strategic plan, a discussion emerged among faculty about the need to address a growing gap in support for graduate and professional students pursuing international research, particularly in the humanities and social sciences. It was clear that what we needed was a funding mechanism to support international dissertation research. We ran with this idea and, in close partnership with the Office of the Vice Provost for Education, established the Penn Global Dissertation Grants.”

“It was important for our office to get behind this initiative,” says Karen Detlefsen, Vice Provost for Education. “It met a real need to provide more University support for graduate work that has potential to extend well beyond the boundaries of any discipline. The research many of our students are doing in a global context is inspiring.”

This program is intended to harness the power of Penn’s graduate student community to enhance the University’s global engagement.

“Graduate students are on the cutting edge of research, but finding support for global inquiry can be difficult,” Gadsden says. “This program ensures that Penn’s graduates students can stay on the cutting edge of their fields and do deeply immersive work to advance new knowledge.”

The program offers two tracks for Ph.D. students seeking to incorporate global dimensions into their work: global exploration and global enhancement. Global exploration applicants are still in the development stages of their dissertation and will use this program’s resources to explore and engage global components for their dissertation research. Global enhancement applicants already include a core global focus to their dissertation and will use this program’s resources to broaden and deepen their research, ideally leveraging their dissertation toward future career opportunities in global leadership.

In the School of Arts & Sciences , recipients are Juan Arboleda, a history Ph.D. student whose research focuses on Brazil and Colombia; Adwaita Banerjee, an anthropology Ph.D. student whose research focuses on India; Tayeba Batool, an anthropology Ph.D. student whose research focuses on Pakistan; Nursyazwani Binte Jamaludin, an anthropology Ph.D. student whose research focuses on Myanmar; Jalen Chang, a history of art Ph.D. student whose research focuses on France, Oceania, and New Caledonia; Bonnie Maldonado, a Ph.D. student in Africana studies whose research focuses on the Dominican Republic; Taylor Prescott, a history Ph.D. student whose research focuses on Sierra Leone; and Alexandra Zborovsky a history Ph.D. student whose research focuses on Russia, Ukraine, the Netherlands, and Israel.

In the Perelman School of Medicine , the recipient is Van Truong, whose focus is on genomics and computational biology in Vietnam. In the Annenberg School for Communication , the recipient is Adetobi Moses, whose research focuses on Ghana. In the Weitzman School of Design, the recipient is Hui Tian, whose research focuses on China.

The Penn Global Dissertation Grant program will also offer a platform for these students to share their work and research experience with the broader Penn community. One stipulation of the program is developing individualized post-award commitment plans. The inaugural cohort of awardees is currently finalizing their own plans, which may include Penn undergraduate mentorship programs, panel discussions on their research topics and outcomes, and other forms of publishable multimedia.

Currently enrolled Penn Ph.D. students from all Penn Schools and disciplines, including the humanities, social sciences, and STEM fields, may apply. Additional program information is available at https://global.upenn.edu/global-initiatives/penn-global-dissertation-grants .

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